CN115962551B - Intelligent air conditioner control system and method for building automatic control - Google Patents
Intelligent air conditioner control system and method for building automatic control Download PDFInfo
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Abstract
The invention relates to the field of self-adaptive control systems, and provides an intelligent air conditioner control system and method for building automatic control, wherein the intelligent air conditioner control system comprises the following steps: collecting current day data and historical data; performing time interval division on the historical data and the current day data to obtain the current day data and a plurality of reference data of each time interval; segmenting the current day data and the reference data according to the data change, and acquiring the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging period; acquiring short-term distribution parameters of each segment according to the long-term distribution parameters of each segment according to the comprehensive similarity degree, and acquiring correction characteristic parameters of each segment to acquire a temperature predicted value; and setting a fuzzy rule according to the temperature predicted value and the temperature data at the current moment, so as to realize the accurate control of the air conditioning system. The invention aims to solve the problem of poor control effect of an air conditioning system caused by the hysteresis of temperature change and the interference of people flow factor.
Description
Technical Field
The invention relates to the field of self-adaptive control systems, in particular to an intelligent air conditioner control system and method for building automatic control.
Background
Along with the continuous development of economy and the improvement of the living standard of people, the intelligent building industry is rapidly developed, and is a combination of the building industry, the automation industry, the computer industry and the network technology industry; the Building Automation System (BAS) is a main functional subsystem and comprises an air conditioning system, a ventilation system, a water supply and drainage system, a lighting system and the like, wherein the air conditioning system can cause unbalanced power supply and demand due to the influence on energy consumption, so that the air conditioning system in the building automation system is intelligently controlled, and the energy consumption problem can be greatly relieved.
In the prior art, a fuzzy PID control air conditioning system is generally adopted, fuzzy PID control combines a fuzzy theory and a PID control algorithm, and PID parameters are set by using a fuzzy rule; the indoor temperature change has time hysteresis characteristics, and is interfered by factors such as people flow, so that the air conditioning system is poor in effect in the control process; if the PID parameters are obtained by using a traditional fuzzy PID control algorithm so as to obtain the control quantity of the air conditioner parameters, the control quantity value of the error at the current moment can be obtained due to the hysteresis of temperature change and corresponding people flow change; therefore, the change characteristics of temperature data and corresponding people flow change characteristics in the time period to which the current moment belongs are required to be quantized, the characteristics of the current moment are replaced by the characteristics of the current time period in the input data of the traditional fuzzy PID, and a data prediction algorithm is combined, so that a more accurate predicted value of the current time period is obtained, and the accurate control of an air conditioning system is realized.
Disclosure of Invention
The invention provides an intelligent air conditioner control system and method for building automatic control, which are used for solving the problem of poor control effect of an air conditioner system caused by hysteresis of the existing temperature change and interference of people flow factor, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides an intelligent air conditioner control method for building automatic control, the method including the steps of:
acquiring current day temperature data and current day people flow data in current day data, and acquiring historical temperature data and historical people flow data in a plurality of days of historical data;
performing time interval division on the historical data and the current day data to obtain current day data and a plurality of reference data of each time interval in the current day data;
acquiring the current day temperature data in the current day data of each period and the reference temperature data in the reference data, acquiring the segmentation time in the current day temperature data of each period and the segmentation time in the reference temperature data according to the current day temperature data or the data change of adjacent time in the reference temperature data, segmenting the current day data according to the segmentation time in the current day temperature data of each period to obtain segmented current day data, and segmenting the reference data according to the segmentation time in each reference temperature data of each period to obtain segmented reference data;
Acquiring the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period according to the similarity of the current day data of each segment and the reference data of different segments in each reference data of the belonging time period;
acquiring control parameters according to the temperature predicted value and the temperature data at the current moment, setting a basic domain and membership function of the control parameters, fuzzifying the control parameters, setting a fuzzy rule of PID parameter self-adaption and defuzzifying the control parameters to obtain an output value of a PID controller, and combining air conditioner operation data to realize accurate control of an air conditioning system;
and setting a fuzzy rule according to the temperature predicted value and the temperature data at the current moment, so as to realize the accurate control of the air conditioning system.
Optionally, the method for acquiring the current day data and the plurality of reference data of each period in the current day data includes the following specific steps:
acquiring a plurality of day reference data of the day data according to the day data and the distribution of the history data of each day in each period; dividing the current day data and the plurality of day reference data through preset time periods to obtain the current day data of each time period in the current day data, and taking the reference data of the same time period in the reference data as the reference data of each time period in the current day data to obtain the plurality of reference data of each time period in the current day data.
Optionally, the method for obtaining the segmentation time in the current day temperature data and the segmentation time in the reference temperature data of each period includes the following specific steps:
acquiring the current day temperature data or reference temperature data of any period as target temperature data, acquiring a target temperature curve of the target temperature data, and recording a trend term obtained by decomposing the time sequence of the target temperature curve as a target temperature trend line;
and taking the ratio of the difference value of the trend item data at adjacent moments in the target temperature trend line to the time interval at the adjacent moments as the temperature change degree at the next moment in the adjacent moments, acquiring the temperature change degree at each moment in the target temperature trend line, carrying out linear normalization on the absolute values of all the temperature change degrees, marking the obtained result as the temperature change rate at each moment, and taking the moment with the temperature change rate larger than a first preset threshold value as the segmentation moment of the target temperature data.
Optionally, the method for obtaining the comprehensive similarity degree between the data of the current day of each segment and each reference data of the period to which the data belongs includes the following specific steps:
acquisition of the first
No. in time period>
The temperature profile of the day of the segment, corresponding to +. >
Obtaining +.>
The temperature trend line of the current day of each segment is obtained to obtain the +.>
A reference temperature trend line for each segment in the plurality of reference temperature data for each time period;
acquisition of the first
No. in time period>
A temperature trend line of the same segment, and +.>
No. H of the time period>
The minimum value of the DTW distances between the reference temperature trend lines of each segment in the reference temperature data is taken as the +.>
No. in time period>
Day temperature data and +.>
A first similar distance of the reference temperature data, expressed as
, wherein />
Indicate->
No. in time period>
A segmented current day temperature trend line, +.>
Represents the +.f corresponding to the minimum of the DTW distance>
No. H of the time period>
Segmented reference temperature trend lines in the individual reference temperature data; will be
Marked as +.>
No. in time period>
Day temperature data and +.>
A first degree of similarity of the reference temperature data, wherein +.>
An exponential function that is based on a natural constant;
obtaining a first similarity degree of the current day temperature data of each segment and each reference data of the belonging time period, obtaining a second similarity degree of the current day flow data of each segment and each reference data of the belonging time period, and taking an square root of a square sum of the first similarity degree and the second similarity degree as a comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period.
Optionally, the method for obtaining the long-term distribution parameter of each segment in the current day data according to the comprehensive similarity includes the following specific steps:
wherein ,
indicate->
First->
Local reachable density under the distance neighborhood, +.>
Represent the first
First->
Number of reference data points in the distance neighborhood, +.>
Indicate->
First->
First->
Reference data points->
Indicate->
The segmented data points and->
The overall degree of similarity of the individual reference data points,
indicate->
The reference data point is at->
First->
Local reachable density under the distance neighborhood, +.>
Indicate->
First->
Local outlier factors under the distance neighborhood;
will be the first
First->
Local outlier factor in the distance neighborhood as +.>
Long-term distribution parameters of the segments.
Optionally, the method for acquiring the short-term distribution parameter of each segment in the current day data according to the long-term distribution parameter includes the following specific steps:
wherein ,
representing the +.>
Short-term distribution parameters of individual segments,/->
Representing the +.>
The number of other segments before the segment, +.>
Representing the +. >
Other segments and->
A time interval of a segment, said time interval being obtained by a difference between a first moment of a subsequent segment and a last moment of a preceding segment,
representing the +.>
Long-term distribution parameters of the other segments, +.>
Representing a symbolic function +_>
An exponential function based on a natural constant is represented.
Optionally, the correction characteristic parameter of each segment in the current day data is obtained by correcting the long-term distribution parameter according to the short-term distribution parameter, which comprises the following specific steps:
wherein ,
representing the +.>
Correction characteristic parameters of individual segments, +.>
Representing the +.>
Short-term distribution parameters of individual segments,/->
Representing the +.>
Long-term distribution parameters of the segments.
In a second aspect, another embodiment of the present invention provides an intelligent air conditioner control system for building automation control, the system comprising:
the data acquisition module acquires the current day temperature data and current day people flow data in the current day data, and acquires the historical temperature data and the historical people flow data in the historical data of a plurality of days;
and a control parameter correction module: performing time interval division on the historical data and the current day data to obtain current day data and a plurality of reference data of each time interval in the current day data;
Acquiring the current day temperature data in the current day data of each period and the reference temperature data in the reference data, acquiring the segmentation time in the current day temperature data of each period and the segmentation time in the reference temperature data according to the current day temperature data or the data change of adjacent time in the reference temperature data, segmenting the current day data according to the segmentation time in the current day temperature data of each period to obtain segmented current day data, and segmenting the reference data according to the segmentation time in each reference temperature data of each period to obtain segmented reference data;
acquiring the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period according to the similarity of the current day data of each segment and the reference data of different segments in each reference data of the belonging time period;
acquiring long-term distribution parameters of each segment in the current day data according to the comprehensive similarity degree, acquiring short-term distribution parameters of each segment in the current day data according to the long-term distribution parameters, correcting long-term distribution parameters according to the short-term distribution parameters, acquiring correction characteristic parameters of each segment in the current day data, acquiring a temperature predicted value according to the correction characteristic parameters, and acquiring control parameters according to the temperature predicted value and the temperature data at the current moment;
The control parameter fuzzification module is used for setting a basic domain and membership function of the control parameters and fuzzifying the control parameters;
the fuzzy rule setting module is used for setting fuzzy rules with PID parameter self-adaption;
the PID parameter defuzzification module defuzzifies the control parameters of the fuzzification through a fuzzy rule and a membership function to obtain an output value of the PID controller;
and the PID control module is used for combining the air conditioner operation data according to the output value of the PID controller to realize the accurate control of the air conditioning system.
The beneficial effects of the invention are as follows: according to the invention, the characteristic of the current moment in the input data of the traditional fuzzy PID is replaced by the characteristic of the current section by quantifying the temperature data change characteristic and the corresponding people flow change characteristic of the section to which the current moment belongs, and the correction characteristic parameter is obtained by combining a data prediction algorithm according to the short-term distribution parameter of the data in the section to which the current moment belongs and the long-term distribution parameter obtained in the process of comparing with the historical data, so that the temperature predicted value more accurate to the current moment is obtained.
In the process of acquiring correction characteristic parameters, a self-adaptive LOF local anomaly detection algorithm is adopted, and under the condition of considering the long-term data distribution characteristics in the current segmented data and the short-term data distribution characteristics of the current day data, the acquired long-term distribution parameters are corrected, so that the calculated correction characteristic parameters are more accurate; the abnormal problem of the local abnormal factor caused by the hysteresis characteristic of temperature change and the accumulation process of the flow of people is avoided when the local abnormal factor of the current segment is calculated; the temperature value of the current segment obtained by prediction is more accurate, and when PID parameters are obtained in the fuzzy PID control process, the problem that the temperature hysteresis and the disturbance of factors such as the flow of people are used for obtaining the wrong PID parameters is solved, so that more accurate control can be realized.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a block diagram of an intelligent air conditioner control system for building automation control according to one embodiment of the present invention;
fig. 2 is a flowchart of an intelligent air conditioner control method for building automatic control according to another embodiment of the present invention;
FIG. 3 shows the present invention
A fuzzy rule diagram of parameter setting;
FIG. 4 shows the present invention
A fuzzy rule diagram of parameter setting; />
FIG. 5 shows the present invention
Schematic diagram of fuzzy rule of parameter setting.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a block diagram of an intelligent air conditioner control system for building automatic control according to an embodiment of the present invention is shown, where the system includes:
the existing fuzzy PID control combines a fuzzy theory and a PID control algorithm, and the PID parameters are set by using a fuzzy rule; the indoor temperature change has time hysteresis characteristics, and is interfered by factors such as people flow, so that the air conditioning system is poor in effect in the control process; if the PID parameters are obtained by using a traditional fuzzy PID control algorithm so as to obtain the control quantity of the air conditioner parameters, the control quantity value of the error at the current moment can be obtained due to the hysteresis of temperature change and corresponding people flow change; therefore, the change characteristics of temperature data and corresponding people flow change characteristics in the time period to which the current moment belongs are required to be quantized, so that the characteristics of the current moment are replaced by the characteristics of the current time period in the traditional fuzzy PID control parameters, and further, the accurate control quantity of the current time period is obtained, and the accurate control of an air conditioning system is realized.
The data acquisition module S101 is used for acquiring air conditioner operation data, and acquiring current day and historical temperature data and people flow data.
Control parameter correction module S102:
(1) Performing time interval division on the historical data and the current day data to obtain current day data and a plurality of reference data of each time interval in the current day data;
(2) Segmenting the current day data and the reference data according to the change between the data, and acquiring the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period;
(3) And acquiring long-term distribution parameters of each segment in the current day data according to the comprehensive similarity degree, acquiring short-term distribution parameters of each segment in the current day data according to the long-term distribution parameters, correcting long-term distribution parameters according to the short-term distribution parameters, acquiring correction characteristic parameters of each segment in the current day data, acquiring a temperature predicted value according to the correction characteristic parameters, and correcting control parameters of the fuzzy PID according to the temperature predicted value.
And the control parameter fuzzification module S103 is used for setting a basic domain and membership function of the control parameters and fuzzifying the control parameters.
The fuzzy rule setting module S104 sets the fuzzy rule of the PID parameter adaptation.
And the PID parameter defuzzification module S105 defuzzifies the control parameters of the fuzzification through a fuzzy rule and a membership function to obtain the output value of the PID controller.
And the PID control module S106 combines the air conditioner operation data according to the output value of the PID controller to realize the accurate control of the air conditioning system.
Referring to fig. 2, a flowchart of an intelligent air conditioner control method for building automatic control according to another embodiment of the present invention is shown, and the method includes the following steps:
step S201, collecting air conditioner operation data, and collecting temperature data and people flow data of the current day and history.
The aim of the embodiment is to realize accurate control of an air conditioning system, so that air conditioning operation data are required to be collected firstly, and the air conditioning operation data are collected by arranging a temperature sensor, a humidity sensor, a pressure sensor, an ultrasonic flowmeter and a power sensor in the air conditioning system; the model of the sensor and the detecting instrument is not limited in this embodiment, and the sampling time interval in this embodiment is 1 minute to sample, so that the air conditioner operation data is obtained.
Furthermore, the purpose of controlling the air conditioner is to adjust the indoor temperature, and meanwhile, the indoor temperature is greatly influenced by the change of the traffic flow of people in the building, so that temperature data and traffic flow data need to be acquired, wherein the temperature data are acquired by installing a temperature sensor in the building, and the traffic flow data are acquired in real time by installing a traffic flow counter at an entrance or a gate of the building; sampling is carried out in 1 minute at the sampling time interval, so that the temperature data and the people flow data of the same day are obtained; meanwhile, the historical temperature data and the people flow data are acquired when the reference is needed according to the historical data, and the temperature data and the people flow data in the last three months are acquired as the historical data in the embodiment; the day data includes the temperature data and the traffic data of the day, and the history data includes the history temperature data and the traffic data.
So far, the air conditioner operation data, the temperature data of the day and the history and the people flow data are collected.
Step S202, time interval division is carried out on the historical data and the current day data, and the current day data and a plurality of reference data of each time interval in the current day data are obtained.
It should be noted that, the temperature data and the people flow data in the building have more regular distribution characteristics in the time dimension, for example, the people flow at the same time of the rest day is larger than the people flow at the same time of the working day, the people flow in the morning is smaller than the people flow in the noon and evening in a certain day, and the temperature is affected by the seasonal variation; therefore, it is necessary to divide the time period of the history data and the day data, including the time period of each day and the day data in the history, and acquire the reference data in the history data according to the day of the week to which the day data belongs.
Specifically, firstly, dividing each day of data in the historical data to obtain historical data of a plurality of days, and dividing each day of data and each time period of the data of the same day, wherein the embodiment uses one hour as a preset time period to divide the data of a plurality of time periods in the data of the same day, and recording the data of the same day of each time period; and simultaneously acquiring historical data of a plurality of time periods in the historical data.
Further, the day of the week to which the day data belongs is obtained, and a plurality of historical data of the day of the week in the historical data are used as reference data of the day data; according to the historical data of each time period in the corresponding reference data of the current day data and the corresponding relation of the time periods, obtaining a plurality of reference data of each time period in the current day data; it should be noted that, the data of the same day includes temperature data and people flow data of corresponding time period, and the reference data also includes temperature data and people flow data of corresponding time period; for example, when the day data is Saturday data, the historical data of all Saturday in the historical data is used as reference data of the day data, and the time interval division is carried out on the data of each hour of Saturday on the day and the data of all Saturday in the historical data, and the historical data of the same time interval is used as reference data of the day data of each time interval.
The current day data and a plurality of reference data of each period in the current day data are acquired.
Step S203, segmenting the current day data and the reference data according to the change between the data, and obtaining the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period.
It should be noted that, there are data points with larger variation in the temperature data or the people flow data of each period, and the data of each period is divided into a plurality of segments by the data points; according to the similarity between different segments in the current day data of each period and different segments in the plurality of reference data of the same period, the temperature data and the people flow data, and the comprehensive similarity degree between the current day data of each period and different reference data is obtained; and further provides a reference for the acquisition of the subsequent correction characteristic parameters through the comprehensive similarity degree.
Specifically, the temperature data in the current day data of each period is recorded as the current day temperature data of each period, the people flow data in the current day data is recorded as the current day people flow data of each period, the temperature data in the reference data is recorded as the reference temperature data of each period, and the people flow data in the reference data is recorded as the reference people flow data of each period; taking the temperature data of the day of any period as an example, taking the temperature data of the day as time in an abscissa and the temperature data in an ordinate as temperature data to obtain a temperature curve of the day of the period, performing STL time sequence decomposition on the temperature curve of the day, and recording a trend term obtained by decomposition as a temperature trend line of the day of the period; it should be noted that, the current temperature trend line characterizes the normal temperature change in the period, and avoids the interference of the abnormal value possibly existing on the temperature change.
Further, the ratio of the difference value of the trend item data at the adjacent time in the current day temperature trend line to the time interval at the adjacent time is used as the temperature change degree at the next time in the adjacent time, and the temperature change degree at each time in the current day temperature trend line in the period is obtained; it should be noted that, for the temperature change degree at the first moment in the period, a quadratic linear interpolation method is adopted to fill in trend item data for obtaining; the absolute values of all the temperature change degrees are subjected to linear normalization, the obtained result is recorded as the temperature change rate of each moment, a first preset threshold value is given for judging the temperature change, the first preset threshold value of the embodiment is calculated by adopting 0.58, the moment when the temperature change rate is larger than the first preset threshold value is extracted and recorded as the segmentation moment of the temperature curve of the current day of the period, the temperature curve of the current day of the period is segmented through the segmentation moment, and a plurality of segmented temperature curves of the current day and temperature data of the current day of the period are obtained; segmenting the current day temperature data and the reference temperature data of all the time periods according to the method, and acquiring the current day temperature data of each segment in each time period and the reference temperature data of each segment in each reference temperature data; it should be noted that, the segment time is only applicable to the current day temperature curve of the corresponding period, and the segment time of the reference temperature data of the corresponding period needs to be recalculated.
Further, because the people flow data are time sequence data as well, and meanwhile, the people flow influences the temperature change, the current day people flow data of each period are segmented according to the segmentation time of the current day temperature data of the corresponding period, and the current day people flow data of each segment in each period is obtained; and segmenting each piece of reference people flow data in each time period according to the segmentation time of the reference temperature data in the corresponding reference data in the corresponding time period, and obtaining the reference people flow data of each segment in each piece of reference people flow data.
It should be further noted that, taking the temperature data as an example, since there is a difference between the segmentation result of the current day temperature data and the reference temperature data of each period, that is, a difference between the time ranges of the segments, the embodiment obtains the first similarity degree between the current day temperature data and the reference temperature data of the corresponding period of each segment by analyzing the temperature trend line through the DTW distance.
Specifically, obtain the first
No. in time period>
The temperature profile of the day of the segment, corresponding to +.>
Obtaining +.>
The temperature trend line of the current day of each segment is obtained to obtain the +. >
A reference temperature trend line for each segment in the plurality of reference temperature data for each time period; get->
No. in time period>
A temperature trend line of the same segment, and +.>
No. H of the time period>
The minimum value of the DTW distances between the reference temperature trend lines of each segment in the reference temperature data is taken as the +.>
No. in time period>
Day temperature data and +.>
The first similar distance of the reference temperature data is denoted +.>
, wherein />
Indicate->
No. in time period>
A segmented current day temperature trend line, +.>
Represents the +.f corresponding to the minimum of the DTW distance>
No. H of the time period>
Segmented reference temperature trend lines in the individual reference temperature data; will->
Marked as +.>
No. in time period>
Day temperature data and +.>
The first degree of similarity of the reference temperature data, expressed as +.>
, wherein />
An exponential function that is based on a natural constant; it should be noted that the present embodiment adopts +.>
The inverse proportion relation and normalization processing are presented, and an implementer can select other inverse proportion and normalization functions according to actual conditions; the first degree of similarity of the temperature data of the day of each segment and each reference data of the belonging period is obtained according to the method. / >
Further, since the current-day traffic data and the reference traffic data of each period have been segmented, the current-day traffic data of each segment is obtained according to the calculation method of the first similarity, and the similarity with each reference traffic data of the period to which the current-day traffic data belongs is recorded as the second similarity; in the second similarity calculation process, the DTW distance between the current-day people flow data of each segment and the reference people flow data of each segment in each reference people flow data still needs to be obtained, and the minimum value of the DTW distance is obtained to obtain the second similarity; in the first place
No. H of the time period>
For example, the data of the same day and +.>
No. H of the time period>
Comprehensive similarity of individual reference data->
The specific calculation method of (a) is as follows:
wherein ,
indicate->
No. in time period>
Day temperature data and +.>
First degree of similarity of the reference temperature data, < >>
Indicate->
No. in time period>
Day people flow data and +.>
A second degree of similarity of the individual reference people flow data; and acquiring the comprehensive similarity degree of the data of the current day of each segment and each reference data of the belonging time period according to the method.
The comprehensive similarity degree of the data of the current day of each segment and the reference data of the period of the current day is obtained and is used for distance measurement in a follow-up LOF algorithm, and further the long-term distribution parameters and the short-term distribution parameters are obtained.
Step S204, obtaining long-term distribution parameters of each segment in the current day data according to the comprehensive similarity degree, obtaining short-term distribution parameters of each segment in the current day data according to the long-term distribution parameters, correcting the long-term distribution parameters according to the short-term distribution parameters, obtaining correction characteristic parameters of each segment in the current day data, and obtaining a temperature predicted value according to the correction characteristic parameters.
After the comprehensive similarity degree between each segment of the current day data and the reference data is obtained, the long-term distribution parameter of each segment is quantified through the LOF local anomaly detection algorithm, so that the characteristics of the long-term distribution parameter can be obtained by referring to the historical data.
Specifically, taking an abscissa as temperature data and an ordinate as human flow data, and establishing a distribution coordinate system; taking any one of the segments in the day data as an example, taking the average value of the temperature data of the segment on the same day as an abscissa and the average value of the flow data of people on the same day as an ordinate to obtain segment data points in a distribution coordinate system; acquiring a time period to which the segment belongs, taking the average value of the reference temperature data of each reference data of the corresponding time period as an abscissa, and taking the average value of the reference people flow data of each reference data as an ordinate to obtain a plurality of reference data points positioned in a distribution coordinate system; obtaining a distribution coordinate system corresponding to each segment according to the method, wherein each distribution coordinate system comprises a segment data point and a plurality of reference data points; the present embodiment employs the first segment of data points 
Local anomaly detection of LOF from data distribution features in the neighborhood, wherein the present embodiment employs +.>
Calculating; by +.>
The specific calculation method for obtaining the long-term distribution parameters of the segments is as follows:
wherein ,
indicate->
First->
Local reachable density under the distance neighborhood, +.>
Represent the first
First->
Number of reference data points in the distance neighborhood, +.>
Indicate->
First->
First->
Reference data points->
Indicate->
The segmented data points and->
The overall degree of similarity of the individual reference data points,
indicate->
The reference data point is at->
First->
Local reachable density under the distance neighborhood, +.>
Indicate->
First->
Local outlier factors under the distance neighborhood; it should be noted that, the formula for obtaining the local outlier factor is a known technology, only the traditional reachable distance metric is replaced by the comprehensive similarity degree for calculation, and other existing embodiments are not repeated; will be->
First->
Local outlier factor in the distance neighborhood as +.>
The long-term distribution parameters of the segments are quantified through the comprehensive similarity degree of the segments and the reference data, so that the influence of the historical data of the same period on the current day data is better referenced, and the characteristic performance in the current day data and the historical data is better reflected; according to the above formula The method obtains the long-term distribution parameters of each segment in the current day data.
It should be further noted that, the temperature change and the people flow change in each segment in the day data are continuously accumulated, so that the temperature and the people flow performance of each segment in the day data are comprehensively influenced by other segments before each segment in the day data, and the influence is recorded as a short-term distribution parameter; the larger the long-term distribution parameters of other segments, namely the larger the local outlier factor, the larger the data change of the same day in the corresponding segments, and the larger the influence on the short-term distribution parameters of each segment; the smaller the time difference between other segments and each segment, the larger the influence of the change brought by the long-term distribution parameters on each segment, the larger the influence of the short-term distribution parameters; therefore, the short-term distribution parameters of each segment are required to be obtained by combining the long-term distribution parameters of other segments in the data of the same day, and the correction characteristic parameters of each segment are obtained by correcting the long-term distribution parameters according to the short-term distribution parameters.
Specifically, the date data is
For example, the short-term distribution parameter of the segment is obtained>
The specific calculation method of (a) is as follows:
wherein ,
representing the +. >
The number of other segments before the segment, +.>
Representing the +.>
Other segments and->
A time interval of a segment, said time interval being obtained by the difference between the first moment of the following segment and the last moment of the preceding segment,/->
Representing the +.>
Long-term distribution parameters of the other segments, +.>
Representing a sign function, in particular +.>
，/>
An exponential function that is based on a natural constant; the smaller the time interval is, the greater the influence on the short-term distribution parameters is; the longer-term distribution parameters of other segments are greater than 1 and the greater, indicating that the segment has a greater difference from the corresponding reference data, for the +.>
The greater the short-term distribution parameter impact of the individual segments; the other segments have a long-term distribution parameter of less than 1, which itself changes less, for the +.>
The short-term distribution parameters of the individual segments have little influence.
Further, correcting the long-term distribution parameters according to the short-term distribution parameters to obtain correction characteristic parameters of each segment in the current day data, thereby obtaining the third segment in the current day data
For example, the correction characteristic parameter of the segment is obtained>
The calculation method of (1) is as follows:
wherein ,
representing the +.>
Short-term distribution parameters of individual segments,/- >
Representing the +.>
Long-term distribution parameters of individual segments; and acquiring correction characteristic parameters of each segment in the current day data according to the method.
It should be further noted that, after the correction characteristic parameter of each segment in the current day data is obtained, the current day temperature data is subjected to weighted fitting to obtain a temperature change curve, so as to obtain a temperature predicted value.
Specifically, obtaining correction characteristic parameters of each segment in the current day data, obtaining an average value of the current day temperature data of each segment, taking the average value as a comprehensive temperature value of each segment, carrying out softmax normalization on the correction characteristic parameters of all segments, taking the obtained result as a comprehensive temperature value weight of a corresponding segment, carrying out weighted curve fitting on the comprehensive temperature value of each segment in the current day data by a least square method, obtaining a temperature change curve, and obtaining a temperature predicted value of the last segment by the temperature change curve; the weighted curve fitting is in the prior art, and this embodiment is not described in detail.
So far, the temperature predicted value of the last segment in the current day data is obtained, wherein the current moment belongs to the last segment, the correction of the control parameters of the fuzzy PID is realized according to the temperature predicted value and the temperature data of the current moment, and the accurate control of the air conditioner system is completed.
Step S205, control parameters are obtained according to the temperature predicted value and the temperature data at the current moment, basic domains and membership functions of the control parameters are set, the control parameters are fuzzified, the PID parameter self-adaptive fuzzy rules are set, the control parameters are defuzzified, the output value of a PID controller is obtained, and the air conditioner operation data are combined, so that the accurate control of an air conditioning system is realized.
In step S004, the temperature predicted value has been obtained by correcting the characteristic parameter, and according to the variation difference between the temperature predicted value and the temperature data at the current time, the accurate control is realized by combining fuzzy reasoning in fuzzy PID control.
Specifically, the current day temperature data acquired in step S001 includes the current time temperature data, and the difference obtained by subtracting the current time temperature data from the temperature predicted value is used as the temperature variation
The ratio of the temperature change amount to the sampling time interval is taken as the temperature change rate +.>
Taking the temperature change amount and the temperature change rate as input data of the fuzzy PID controller; setting temperature variation->
Is->
Temperature change rate->
Is->
The fuzzy theory of the corresponding determined temperature change quantity and temperature change rate is obtained after the fuzzy is performed, wherein the fuzzy subset is set as +. >
Respectively correspond to->
: negative big,>
: "negative middle", "18>
"Small negative", "Lei>
: "zero",
: "just small", "Dai Ji>
: "in the middle", "in the beginning>
: "positive large" is defined in the fuzzy arguments as
The method comprises the steps of carrying out a first treatment on the surface of the The membership function set in this embodiment is a gaussian function, and the actual implementation process may be set according to the specific implementation situation of the implementer.
Further, in the fuzzy PID control, three parameters in the PID controller, namely, are mainly controlled by fuzzy reasoning
、/>
And->
Make corrections in which->
、/>
And->
The domains of (2) are->
、/>
And
the method comprises the steps of carrying out a first treatment on the surface of the The embodiment sets a fuzzy control rule according to actual conditions: in the initial control phase, a larger +.>
The value increases the response speed, setting smaller +.>
The value prevents saturation of the integral, set larger +.>
The value increases the differential action to avoid overshoot; in the middle of control, a smaller +.>
The value ensures a small overshoot and ensures a response speed, setting a moderate +.>
Value and smaller ∈>
The value (or remains unchanged) ensures stability; in the later control phase, a larger +.>
Value sum->
The value decreases the static difference, setting smaller +.>
A value reducing braking action; please refer to fig. 3, 4 and 5, which show the embodiment of the present invention for +.>
、/>
And->
Fuzzy rule for three parameter settings, wherein FIG. 3 is the parameter +. >
Is +.>
Is +.5>
Is a fuzzy rule of (a).
Further, obtaining membership values of the output values to each fuzzy subset according to fuzzy rules, and performing defuzzification according to a weighted average method to obtain three parameters of the PID controller
、/>
And->
The method comprises the steps of carrying out a first treatment on the surface of the Then the parameter after parameter setting is +.>
，/>
，/>
Obtaining the output value of the PID controller, and combining the air conditioner operation data according to the output value to realize the control ofThe precise control of the air conditioning system, wherein the specific control is the prior art, and the embodiment is not repeated.
So far, the correction characteristic parameters of each period in the current day data are obtained according to the historical data, so that the temperature predicted value of the current period is obtained, and the accurate control of the air conditioning system is realized through fuzzy PID control and fuzzy rules according to the temperature predicted value and the temperature data of the current moment.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (3)
1. The intelligent air conditioner control method for building automatic control is characterized by comprising the following steps:
Acquiring current day temperature data and current day people flow data in current day data, and acquiring historical temperature data and historical people flow data in a plurality of days of historical data;
performing time interval division on the historical data and the current day data to obtain current day data and a plurality of reference data of each time interval in the current day data;
acquiring the current day temperature data in the current day data of each period and the reference temperature data in the reference data, acquiring the segmentation time in the current day temperature data of each period and the segmentation time in the reference temperature data according to the current day temperature data or the data change of adjacent time in the reference temperature data, segmenting the current day data according to the segmentation time in the current day temperature data of each period to obtain segmented current day data, and segmenting the reference data according to the segmentation time in each reference temperature data of each period to obtain segmented reference data;
acquiring the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period according to the similarity of the current day data of each segment and the reference data of different segments in each reference data of the belonging time period;
acquiring long-term distribution parameters of each segment in the current day data according to the comprehensive similarity degree, acquiring short-term distribution parameters of each segment in the current day data according to the long-term distribution parameters, correcting the long-term distribution parameters according to the short-term distribution parameters, acquiring correction characteristic parameters of each segment in the current day data, and acquiring a temperature predicted value according to the correction characteristic parameters;
Acquiring control parameters according to the temperature predicted value and the temperature data at the current moment, setting a basic domain and membership function of the control parameters, fuzzifying the control parameters, setting a fuzzy rule of PID parameter self-adaption and defuzzifying the control parameters to obtain an output value of a PID controller, and combining air conditioner operation data to realize accurate control of an air conditioning system;
the specific method for acquiring the segmentation time in the current day temperature data and the segmentation time in the reference temperature data of each period comprises the following steps:
acquiring the current day temperature data or reference temperature data of any period as target temperature data, acquiring a target temperature curve of the target temperature data, and recording a trend term obtained by decomposing the time sequence of the target temperature curve as a target temperature trend line;
taking the ratio of the difference value of the trend item data at adjacent moments in the target temperature trend line to the time interval at the adjacent moments as the temperature change degree at the next moment in the adjacent moments, acquiring the temperature change degree at each moment in the target temperature trend line, carrying out linear normalization on the absolute values of all the temperature change degrees, marking the obtained result as the temperature change rate at each moment, and taking the moment with the temperature change rate larger than a first preset threshold value as the segmentation moment of the target temperature data;
The method for obtaining the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period comprises the following specific steps:
acquisition of the first
No. in time period>
The temperature profile of the day of the segment, corresponding to +.>
Obtaining +.>
Obtaining a reference temperature trend line of each segment in a plurality of reference temperature data of a first time period;
acquisition of the first
No. in time period>
A temperature trend line of the same segment, and +.>
No. H of the time period>
The minimum value of the DTW distances between the reference temperature trend lines of each segment in the reference temperature data is taken as the +.>
No. in time period>
Day temperature data and +.>
A first similar distance of the reference temperature data, expressed as
, wherein />
Represent the first/>
No. in time period>
The current day temperature trend lines of the individual segments,
represents the +.f corresponding to the minimum of the DTW distance>
No. H of the time period>
A segmented reference temperature trend line in the reference temperature data; will->
Marked as +.>
No. in time period>
Day temperature data and +.>
A first degree of similarity of the reference temperature data, wherein +.>
An exponential function that is based on a natural constant;
Acquiring a first similarity degree of the current day temperature data of each segment and each reference data of the belonging time period, acquiring a second similarity degree of the current day flow data of each segment and each reference data of the belonging time period, and taking an square root of a square sum of the first similarity degree and the second similarity degree as a comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period;
the method for acquiring the long-term distribution parameters of each segment in the current day data according to the comprehensive similarity degree comprises the following specific steps:
wherein ,/>
Indicate->
First->
Local reachable density under the distance neighborhood, +.>
Indicate->
First->
Number of reference data points in the distance neighborhood, +.>
Indicate->
First->
First->
Reference data points->
Indicate->
The segmented data points and->
Comprehensive degree of similarity of the individual reference data points, +.>
Indicate->
Reference data point at->
First->
Local reachable density under the distance neighborhood, +.>
Indicate->
First->
Local outlier factors under the distance neighborhood;
will be the first
First->
Local outlier factor in the distance neighborhood as +. >
Long-term distribution parameters of individual segments; the method for acquiring the short-term distribution parameters of each segment in the current day data according to the long-term distribution parameters comprises the following specific steps:
wherein ,
representing the +.>
Short-term distribution parameters of individual segments,/->
Representing the +.>
The number of other segments before the segment, +.>
Representing the +.>
Other segments and->
A time interval of a segment, said time interval being obtained by a difference between a first moment of a subsequent segment and a last moment of a preceding segment,
representing the +.>
Long-term distribution parameters of the other segments, +.>
The sign function is represented by a sign function,
an exponential function that is based on a natural constant;
the method for correcting the long-term distribution parameters according to the short-term distribution parameters to obtain the correction characteristic parameters of each segment in the current day data comprises the following specific steps:
wherein ,/>
Representing the +.>
Correction characteristic parameters of individual segments, +.>
Representing the +.>
Short-term distribution parameters of individual segments,/->
Representing the +.>
Long-term distribution parameters of individual segments; />
The method for acquiring the temperature predicted value comprises the following steps: obtaining correction characteristic parameters of each segment in the current day data, obtaining the average value of the current day temperature data of each segment, taking the average value as the comprehensive temperature value of each segment, carrying out softmax normalization on the correction characteristic parameters of all segments, taking the obtained result as the comprehensive temperature value weight of the corresponding segment, carrying out weighted curve fitting on the comprehensive temperature value of each segment in the current day data by a least square method, obtaining a temperature change curve, and obtaining the temperature predicted value of the last segment by the temperature change curve.
2. The intelligent air conditioner control method for building automatic control according to claim 1, wherein the acquiring the day data and the plurality of reference data of each period in the day data comprises the following specific steps:
acquiring a plurality of day reference data of the day data according to the day data and the distribution of the history data of each day in each period; dividing the current day data and the plurality of day reference data through preset time periods to obtain the current day data of each time period in the current day data, and taking the reference data of the same time period in the reference data as the reference data of each time period in the current day data to obtain the plurality of reference data of each time period in the current day data.
3. An intelligent air conditioning control system for building automation control, the system comprising:
the data acquisition module acquires the current day temperature data and current day people flow data in the current day data, and acquires the historical temperature data and the historical people flow data in the historical data of a plurality of days;
and a control parameter correction module: performing time interval division on the historical data and the current day data to obtain current day data and a plurality of reference data of each time interval in the current day data;
Acquiring the current day temperature data in the current day data of each period and the reference temperature data in the reference data, acquiring the segmentation time in the current day temperature data of each period and the segmentation time in the reference temperature data according to the current day temperature data or the data change of adjacent time in the reference temperature data, segmenting the current day data according to the segmentation time in the current day temperature data of each period to obtain segmented current day data, and segmenting the reference data according to the segmentation time in each reference temperature data of each period to obtain segmented reference data;
acquiring the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period according to the similarity of the current day data of each segment and the reference data of different segments in each reference data of the belonging time period;
acquiring long-term distribution parameters of each segment in the current day data according to the comprehensive similarity degree, acquiring short-term distribution parameters of each segment in the current day data according to the long-term distribution parameters, correcting long-term distribution parameters according to the short-term distribution parameters, acquiring correction characteristic parameters of each segment in the current day data, acquiring a temperature predicted value according to the correction characteristic parameters, and acquiring control parameters according to the temperature predicted value and the temperature data at the current moment;
The control parameter fuzzification module is used for setting a basic domain and membership function of the control parameters and fuzzifying the control parameters;
the fuzzy rule setting module is used for setting fuzzy rules with PID parameter self-adaption;
the PID parameter defuzzification module defuzzifies the control parameters of the fuzzification through a fuzzy rule and a membership function to obtain an output value of the PID controller;
the PID control module combines the air conditioner operation data according to the output value of the PID controller to realize the accurate control of the air conditioner system;
the specific method for acquiring the segmentation time in the current day temperature data and the segmentation time in the reference temperature data of each period comprises the following steps:
acquiring the current day temperature data or reference temperature data of any period as target temperature data, acquiring a target temperature curve of the target temperature data, and recording a trend term obtained by decomposing the time sequence of the target temperature curve as a target temperature trend line;
taking the ratio of the difference value of the trend item data at adjacent moments in the target temperature trend line to the time interval at the adjacent moments as the temperature change degree at the next moment in the adjacent moments, acquiring the temperature change degree at each moment in the target temperature trend line, carrying out linear normalization on the absolute values of all the temperature change degrees, marking the obtained result as the temperature change rate at each moment, and taking the moment with the temperature change rate larger than a first preset threshold value as the segmentation moment of the target temperature data;
The method for obtaining the comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period comprises the following specific steps:
acquisition of the first
No. in time period>
The temperature profile of the day of the segment, corresponding to +.>
Obtaining +.>
The temperature trend line of the current day of each segment is obtained to obtain the +.>
A reference temperature trend line for each segment in the plurality of reference temperature data for each time period;
acquisition of the first
No. in time period>
A temperature trend line of the same segment, and +.>
No. H of the time period>
The minimum value of the DTW distances between the reference temperature trend lines of each segment in the reference temperature data is taken as the +.>
No. in time period>
The day of each segmentTemperature data and->
A first similar distance of the reference temperature data, expressed as
wherein />
Indicate->
No. in time period>
The current day temperature trend lines of the individual segments,
represents the +.f corresponding to the minimum of the DTW distance>
No. H of the time period>
Segmented reference temperature trend lines in the individual reference temperature data; will->
Marked as +.>
No. in time period>
Day temperature data and +.>
A first degree of similarity of the reference temperature data, wherein +.>
An exponential function that is based on a natural constant;
Acquiring a first similarity degree of the current day temperature data of each segment and each reference data of the belonging time period, acquiring a second similarity degree of the current day flow data of each segment and each reference data of the belonging time period, and taking an square root of a square sum of the first similarity degree and the second similarity degree as a comprehensive similarity degree of the current day data of each segment and each reference data of the belonging time period;
the method for acquiring the long-term distribution parameters of each segment in the current day data according to the comprehensive similarity degree comprises the following specific steps:
wherein ,/>
Indicate->
First->
Local reachable density under the distance neighborhood, +.>
Indicate->
First->
Number of reference data points in the distance neighborhood, +.>
Indicate->
First->
First->
Reference data points->
Indicate->
The segmented data points and->
Comprehensive degree of similarity of the individual reference data points, +.>
Indicate->
The reference data point is at->
First->
The local reachable density under the distance neighborhood,
indicate->
First->
Local outlier factors in distance neighborhood；
Will be the first
First->
Local outlier factor in the distance neighborhood as +. >
Long-term distribution parameters of individual segments;
the method for acquiring the short-term distribution parameters of each segment in the current day data according to the long-term distribution parameters comprises the following specific steps:
wherein ,/>
Representing the +.>
Short-term distribution parameters of individual segments,/->
Representing the +.>
The number of other segments before the segment, +.>
Representing the +.>
Other segments and->
A time interval of a segment passing a first time of a subsequent segment and a previous time of the subsequent segmentThe difference in the last instants of the segments is obtained,
representing the +.>
Long-term distribution parameters of the other segments, +.>
The sign function is represented by a sign function,
an exponential function that is based on a natural constant;
the method for correcting the long-term distribution parameters according to the short-term distribution parameters to obtain the correction characteristic parameters of each segment in the current day data comprises the following specific steps:
wherein ,/>
Representing the +.>
Correction characteristic parameters of individual segments, +.>
Representing the +.>
Short-term distribution parameters of individual segments,/->
Representing the +.>
Long-term distribution parameters of individual segments;
the method for acquiring the temperature predicted value comprises the following steps: obtaining correction characteristic parameters of each segment in the current day data, obtaining the average value of the current day temperature data of each segment, taking the average value as the comprehensive temperature value of each segment, carrying out softmax normalization on the correction characteristic parameters of all segments, taking the obtained result as the comprehensive temperature value weight of the corresponding segment, carrying out weighted curve fitting on the comprehensive temperature value of each segment in the current day data by a least square method, obtaining a temperature change curve, and obtaining the temperature predicted value of the last segment by the temperature change curve.
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