CN114296410A - Self-adaptive multi-source slowly-varying quantity selection and control method - Google Patents
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Abstract
A self-adaptive multi-source slowly-varying quantity selection and control method comprises the following steps: initializing a centralized control system, and detecting whether the centralized control system normally operates; step 2: collecting signals of a centralized control system; and step 3: sorting the priorities of the signals, and assigning the data with the highest priority to the main signal data M; and 4, step 4: detecting whether the validity of the main signal data M is normal or not, wherein the criterion is as follows: if the signal data is in the range and the data channel quality is normal, executing step 5; otherwise, the use priority of the signal data is set to the last bit, the signal is blocked, the signal is marked to be untrustworthy, a warning is given, and the steps of 3 and the like are skipped to be executed.
Description
Technical Field
The invention belongs to the technical field of industrial centralized control, and particularly relates to a self-adaptive multi-source slowly-varying quantity selection and control method.
Background
Remote centralized control is the mainstream control means in the industrial control field, higher requirements are provided for the reliability of key analog quantity data related to safe production and economic benefits, particularly slowly-changing analog quantity data during acquisition, and the centralized control system can still screen and adopt the most appropriate data when the problem of source of degraded data is not manually processed for a long period, so that the maintenance cost is reduced, the economic benefit is improved, and the method becomes the direction of continuous efforts in the field of centralized control.
The method is deeply researched aiming at the authenticity problem of the slowly-varying analog quantity data acquired by the centralized control system, starts from aspects of multi-target selection, effectiveness judgment, sliding mean calculation and the like, has certain self-learning capacity, and carries out deeper discrimination on the authenticity and the effectiveness of the data, so that the reliability of the data acquired by the centralized control system can be obviously improved, and the stable operation of a production system is ensured.
Disclosure of Invention
The invention aims to provide a selection control method of slowly changing analog quantity for a user under the condition that the slow degradation of the data source cannot be solved, and the quality regulation problems that the slow degradation of the data cannot be known and the like are solved by screening and adopting the most appropriate data through the self learning capacity.
A self-adaptive multi-source slowly-varying quantity selection and control method comprises the following steps:
step 1: initializing a centralized control system, and detecting whether the centralized control system normally operates;
step 2: collecting signals of a centralized control system;
and step 3: sorting the priorities of the signals, and assigning the data with the highest priority to the main signal data M;
and 4, step 4: detecting whether the validity of the main signal data M is normal or not, wherein the criterion is as follows: if the signal data is in the range and the quality of the data channel (namely, whether the current or the voltage acquired by the PLC acquisition module exceeds the parameter limit value set by the module) is normal, executing the step 5; otherwise, the using priority of the signal data is set at the last bit, the signal is blocked, the signal is marked to be untrustworthy, a warning is sent out, and the step 3 is skipped to be executed;
and 5: resetting the sliding period T and the filtering value P to the initial state, and executing the step 6 if the sliding period T and the filtering value P meet the initial state;
step 6: performing median sliding mean calculation on the signal data M to obtain a data value Y in a unit periodn；
And 7: signal data YnAnd the maximum value Y after filtrationmaxOr minimum value YminComparing if the power ratio is Ymax-YnAlpha (alpha is constant) and Y are not more thann-YminAnd (c) if the power ratio is less than or equal to alpha (alpha is a constant), executing the step 8, otherwise, incrementally increasing the filtering value P, and then, skipping to execute the step 6, wherein the incremental step is lambda (lambda is a constant);
and 8: detecting the execution times beta of lambda, if beta is less than or equal to gamma (gamma is a constant), executing the step 9, otherwise, increasing the sliding period T incrementally, and the increment step length is mu (mu is a constant), resetting beta, and then jumping to execute the step 6;
and step 9: if the sliding period T is not equal to T + mu and beta is not less than 1, the using priority of the signal data is set at the last bit, the signal is locked, the signal is marked to be untrustworthy and a warning is sent out, then the step 3 is executed, and if not, the step 10 is executed;
step 10: obtaining the data value Y calculated in the last sliding periodn-1And judging the signal data YnIf the data is reasonable, executing the step 11, otherwise, setting the use priority of the signal data to the last bit, locking the signal, marking the signal as untrustworthy, giving out a warning, and then jumping to execute the step 3;
step 11: every specified time interval, the latest data value Y is addednAnd a manual set value YopComparing and judging the latest data value YnIf it is reasonable, executing step 12, otherwise, manually setting the value YopAssign to data YnAnd a warning is given;
step 12: using signal data YnAs end-use data.
In step 6, the signal data M is subjected to median sliding average calculation, that is, a sliding period T of a certain time is set, a number is taken every time T, n +2(n +2 ═ T/T) data values which are the latest in the period are sorted according to the magnitude, P maximum values and P minimum values are filtered, the remaining data are subjected to average calculation,
obtaining the data value Y in the unit periodn。
In step 10, the last slip is acquiredPeriodically calculated data value Yn-1If/then Yn-Yn-1The specific power is less than or equal to epsilon (epsilon is constant), in this case YnAnd (5) executing step 11 if the numerical value is reasonable.
In step 11, every m slip periods TmThe latest data value YnAnd a manual set value YopComparison if/rate Yn-YopThe specific power is less than or equal to eta (eta is constant), in this case YnThe numerical value is reasonable, step 12 is executed, otherwise, the value Y is manually setopAssign to data YnAnd a warning is issued.
In step 1, initializing the centralized control system, and detecting whether the centralized control system normally operates, wherein the criterion is as follows: the centralized control system self-checks the normal system word as the target value, otherwise waits for meeting the criterion condition.
In step 3, the priority of the digital signals obtained in step 2 is sorted, the sorting order of the priority is obtained under the condition that all data are not locked, and then the data with the highest priority is assigned to the main data.
Compared with the prior art, the invention has the following technical effects:
1) the invention adopts a self-adaptive median sliding mean value calculation method, can completely solve the influence of accidental jump and slow deterioration on data authenticity to a certain extent, and can solve the problem of small error accumulated deviation by a timing reset intervention method, thereby improving the reliability and stability of data.
2) According to the invention, through timely judging, comparing, filtering and screening of data, the problem of unreliable data caused by slow degradation of data source can be solved to a certain extent, and through self learning capacity, the reliability of data is enhanced, so that the maintenance period and cost of data source are reduced, and the economic benefit is improved.
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The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a flow chart of the present invention.
Detailed Description
As shown in fig. 1, a self-adaptive multi-source slowly-varying quantity selection and control method includes the following steps:
step 1: initializing a centralized control system (including cold/hot start), and detecting whether the centralized control system normally operates or not, wherein the criterion is as follows: the PLC self-checks that the normal system word is 1, otherwise waits for meeting the criterion condition; the centralized control system can select a PLC centralized control system;
step 2: analog quantity signal I collected by analog quantity collection module of centralized control system1Signal I2Signal I3Waiting for several data, and performing A/D conversion to obtain M1、M2、M3A plurality of digital signals;
and step 3: sorting the use priority of a plurality of digital signals (signal sources) obtained in the step 2, wherein the priority is M under the condition that all data are not locked1≥M2≥M3Assigning the data with the highest priority to the main data M;
and 4, step 4: detecting whether the validity of the current main signal data M is normal or not, wherein the criterion is as follows: if the data is in the range, and the quality of the data channel (namely whether the current or the voltage acquired by the PLC acquisition module is judged to exceed the parameter limit value set by the module) is normal, if the data meets the requirement, the step 5 is executed, otherwise, the use priority of the signal data is set to the last position, the signal is locked, the signal is marked to be unreliable, a warning is sent, and then the step 3 is executed;
and 5: resetting the sliding period T and the filtering value P to the initial state, and executing the step 6 if the sliding period T and the filtering value P meet the initial state;
step 6: performing median sliding average calculation on the signal data M, namely setting a sliding period T of a certain time, taking a number at intervals of T, sorting the latest n +2(n +2 is T/T) data values in the period according to the magnitude of the data values, filtering P maximum values and P minimum values, performing average calculation on the rest data,
where n is the number of signal data to obtain the data value Y in the unit periodn；
And 7: signal data YnAnd maximum value after filtrationYmaxOr minimum value YminComparing if the power ratio is Ymax-YnAlpha (alpha is constant) and Y are not more thann-YminAnd (c) if the power ratio is less than or equal to alpha (alpha is a constant), executing the step 8, otherwise, incrementally increasing the filtering value P, and then, skipping to execute the step 6, wherein the incremental step is lambda (lambda is a constant);
and 8: detecting the execution times beta of lambda, if beta is less than or equal to gamma (gamma is a constant), executing the step 9, otherwise, increasing the sliding period T incrementally, and the increment step length is mu (mu is a constant), resetting beta, and then jumping to execute the step 6;
and step 9: if the sliding period T is not equal to T + mu and beta is not less than 1, the using priority of the signal data is set at the last bit, the signal is locked, the signal is marked to be untrustworthy and a warning is sent out, then the step 3 is executed, and if not, the step 10 is executed;
step 10: obtaining the data value Y calculated in the last sliding periodn-1If/then Yn-Yn-1The specific power is less than or equal to epsilon (epsilon is constant), in this case YnIf the numerical value is reasonable, executing step 11, otherwise, setting the use priority of the signal data to the last bit, locking the signal, marking the signal as untrustworthy, giving out a warning, and then jumping to execute step 3;
step 11: every m slip periods TmThe latest data value YnAnd a manual set value YopComparison if/rate Yn-YopThe specific power is less than or equal to eta (eta is constant), in this case YnThe numerical value is reasonable, step 12 is executed, otherwise, the value Y is manually setopAssign to data YnAnd a warning is given;
step 12: using signal data YnAs end-use data.
Example (b): in order to verify the feasibility of the method, the water level data of the reservoir of a certain hydropower station is analyzed, and the actual water level of the reservoir is constant to 100.0m within a certain time. The effective interval of the main water level M is between 80.0M and 120.0M, and the current M is1＝78.0m，M2＝100.0m，M3101.0M, due to water level M1When the effective range is exceeded, the lock can not be reliably sent out, so that M is preferentially adopted2Is the main water level. Setting the time interval T to 10s, the sliding period T to 100s, and the edge removal data P to 1M 2, slip period Tm200s, 0.2m, 1 times gamma, 1 times lambda, 20s, 0.5m epsilon, 0.3m eta, Yop＝100.0m。
1、M2The sampling data of 120s is 100.0m, 101.2m, 99.5m, 100.5m, 100.4m, 99.9m, 100.2m, 100.4m, 100.7m, 99.8m, 100.0m and 100.0m, and the water level of unit period is 2Yn＝100.2m，︳Ymax-Yn︳＝0.5m＞α，︳Yn-YminWhen the concentration is 0.4m & gt alpha, the average value calculation is needed again, and in this case, P & ltlambda & gt +1 & ltlambda & gt 2 & gt;
2. after the second average calculation, the current/data rate Ymax-Yn︳＝0.3m＞α，︳Yn-YminThe third average value calculation is needed when the concentration is 0.3m & gt alpha, and in this case, P & ltlambda & gt +1 & ltlambda & gt is 3;
3. after the third mean value calculation, the current rate Ymax-Yn︳＝0.2m≤α，︳Yn-YminThe fourth average calculation is carried out when the concentration is equal to or less than 0.2m and is equal to or less than 2 and is equal to or more than gamma, and the T is equal to 100s and the mu is equal to or more than 120 s;
4. after the fourth mean calculation, the current/data rate Ymax-Yn︳＝0.5m＞α，︳Yn-YminWater level M is 0.4M > α, β is 1, T is 120s2The lock is not reliable and sends out a warning;
M3the sampling data of 120s is 100.2m, 100.1m, 100.0m, 99.9m, 99.8m, 99.7m and 99.7m, the water level of unit period is 3Yn-1＝99.9m，︳Ymax-Yn︳＝0.2m≤α，︳Yn-YminThe concentration is not more than 0.1m and not more than alpha; water level per unit period 3Yn＝99.8m，︳Ymax-Yn︳＝0.2m≤α，︳Yn-YminThe concentration is not more than 0.2m and not more than alpha, and then Y is mixedn-Yn-1If the oil/fuel ratio is 0.5m or less, then 3Y is usednAs water level data for final use. We are from M3Variation of the sampled data is readily apparent as M3Has a deterioration tendency when the speed is higher than the speed of the input/outputn-YopWhen the fuel is higher than eta, the main water level YnIs constantly equal to Yop＝100.0m。
Claims (4)
1. A self-adaptive multi-source slowly-varying quantity selection and control method is characterized by comprising the following steps:
step 1: initializing a centralized control system, and detecting whether the centralized control system normally operates;
step 2: collecting signals of a centralized control system;
and step 3: sorting the priorities of the signals, and assigning the data with the highest priority to the main signal data M;
and 4, step 4: detecting whether the validity of the main signal data M is normal or not, wherein the criterion is as follows: if the signal data is in the range and the data channel quality is normal, executing step 5; otherwise, the using priority of the signal data is set at the last bit, the signal is blocked, the signal is marked to be untrustworthy, a warning is sent out, and the step 3 is skipped to be executed;
and 5: resetting the sliding period T and the filtering value P to the initial state, and executing the step 6 if the sliding period T and the filtering value P meet the initial state;
step 6: performing median sliding mean calculation on the signal data M to obtain a data value Y in a unit periodn；
And 7: signal data YnAnd the maximum value Y after filtrationmaxOr minimum value YminComparing if the power ratio is Ymax-YnAlpha (alpha is constant) and Y are not more thann-YminAnd (c) if the power ratio is less than or equal to alpha (alpha is a constant), executing the step 8, otherwise, incrementally increasing the filtering value P, and then, skipping to execute the step 6, wherein the incremental step is lambda (lambda is a constant);
and 8: detecting the execution times beta of lambda, if beta is less than or equal to gamma (gamma is a constant), executing the step 9, otherwise, increasing the sliding period T incrementally, and the increment step length is mu (mu is a constant), resetting beta, and then jumping to execute the step 6;
and step 9: if the sliding period T is not equal to T + mu and beta is not less than 1, the using priority of the signal data is set at the last bit, the signal is locked, the signal is marked to be untrustworthy and a warning is sent out, then the step 3 is executed, and if not, the step 10 is executed;
step 10: obtaining the data value Y calculated in the last sliding periodn-1And judging the signalData YnIf the data is reasonable, executing the step 11, otherwise, setting the use priority of the signal data to the last bit, locking the signal, marking the signal as untrustworthy, giving out a warning, and then jumping to execute the step 3;
step 11: every specified time interval, the latest data value Y is addednAnd a manual set value YopComparing and judging the latest data value YnIf it is reasonable, executing step 12, otherwise, manually setting the value YopAssign to data YnAnd a warning is given;
step 12: using signal data YnAs end-use data.
2. The method of claim 1, wherein: in step 6, the signal data M is subjected to median sliding average calculation, that is, a sliding period T of a certain time is set, a number is taken every time T, n +2(n +2 ═ T/T) data values which are the latest in the period are sorted according to the magnitude, P maximum values and P minimum values are filtered, the remaining data are subjected to average calculation,
obtaining the data value Y in the unit periodn。
3. Method according to claim 1, characterized in that in step 10, the data value Y calculated in the last slip period is obtainedn-1If/then Yn-Yn-1The specific power is less than or equal to epsilon (epsilon is constant), in this case YnAnd (5) executing step 11 if the numerical value is reasonable.
4. Method according to claim 1, characterized in that in step 11 every m slip periods TmThe latest data value YnAnd a manual set value YopComparison if/rate Yn-YopThe specific power is less than or equal to eta (eta is constant), in this case YnThe numerical value is reasonable, step 12 is executed, otherwise, the value Y is manually setopAssign to data YnAnd send out alarmAnd (6) informing.
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