CN116974320A - Intelligent file management regulation and control method and integrated control system - Google Patents
Intelligent file management regulation and control method and integrated control system Download PDFInfo
- Publication number
- CN116974320A CN116974320A CN202311235910.3A CN202311235910A CN116974320A CN 116974320 A CN116974320 A CN 116974320A CN 202311235910 A CN202311235910 A CN 202311235910A CN 116974320 A CN116974320 A CN 116974320A
- Authority
- CN
- China
- Prior art keywords
- data
- monitoring point
- sequence
- data acquisition
- humidity
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 65
- 230000033228 biological regulation Effects 0.000 title claims abstract description 46
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 44
- 230000007774 longterm Effects 0.000 claims abstract description 38
- 241000254158 Lampyridae Species 0.000 claims abstract description 37
- 230000008878 coupling Effects 0.000 claims abstract description 25
- 238000010168 coupling process Methods 0.000 claims abstract description 25
- 238000005859 coupling reaction Methods 0.000 claims abstract description 25
- 238000007418 data mining Methods 0.000 claims abstract description 5
- 238000012544 monitoring process Methods 0.000 claims description 253
- 238000005259 measurement Methods 0.000 claims description 20
- 238000009825 accumulation Methods 0.000 claims description 19
- 230000035772 mutation Effects 0.000 claims description 11
- 230000011218 segmentation Effects 0.000 claims description 11
- 230000001174 ascending effect Effects 0.000 claims description 8
- 230000001932 seasonal effect Effects 0.000 claims description 8
- 238000000354 decomposition reaction Methods 0.000 claims description 5
- 230000001105 regulatory effect Effects 0.000 claims description 5
- 238000004590 computer program Methods 0.000 claims description 4
- 238000001514 detection method Methods 0.000 claims description 4
- 230000008569 process Effects 0.000 description 13
- 238000005457 optimization Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000007781 pre-processing Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000001419 dependent effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 239000000969 carrier Substances 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000001276 controlling effect Effects 0.000 description 1
- 238000001816 cooling Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention relates to the technical field of temperature regulation and control, and provides an archive intelligent management regulation and control method and an integrated control system, wherein the archive intelligent management regulation and control method comprises the following steps: acquiring a data sequence; acquiring weighting factors according to the distribution characteristics of elements in the long-term data sequence; acquiring a temperature and humidity coupling coefficient according to the weighting factors of the elements in the data sequence; acquiring a data time-lag radius by using a data mining algorithm; obtaining the moving step length of fireflies according to the data time-lag radius; acquiring a target regulation value of each controller in the integrated control system according to the moving step length; respectively sending the target regulation and control values to a control system of each type of controller; the control system of each type of controller generates a control instruction of each controller in each type of controller; and realizing intelligent management of temperature and humidity data in the archive according to the control instruction of each controller. According to the invention, the accuracy of the integrated control system in controlling the temperature and humidity in the archive is improved by analyzing the coupling relation between the temperature and humidity data and the time lag of influence.
Description
Technical Field
The invention relates to the technical field of temperature regulation and control, in particular to an archive intelligent management regulation and control method and an integrated control system.
Background
Scientific regulation and control of temperature, humidity and the like of a file storage place is a key link of file protection management task, the temperature of the file storage place, such as a film warehouse and a tape warehouse, is controlled to be 14-24 ℃, and the change range of daily temperature in the storage place with equipment is controlled to be 14-24 DEG CWithin that, the relative humidity is controlled to be 45% -60%, and the variation range of the daily humidity in the storage place with equipment is controlled to be +.>Within the inner part. The temperature is too high, the original moisture of the record carrier is easy to evaporate, the record carrier is dried and hardened, and the durability is obviously reduced; the humidity in the archives is too high, and the phenomena such as mildew and the like are easy to occur, so that harmful substances are generated.
The temperature and humidity control of the archive in the current stage is mainly based on various methods such as data prediction, data optimization and PID algorithm, wherein the common algorithms for data prediction comprise a Smith estimation algorithm, an index moving average EMA algorithm and the like, the Smith estimation algorithm has high requirements on parameter precision, the sensitivity to errors of a built prediction model is high, the short-term prediction precision of the EMA algorithm is high, and the long-term prediction precision is low; the PID algorithm has poor control capability in a control system with higher delay, and is difficult to realize accurate control on a regulation object with larger time lag.
Disclosure of Invention
The invention provides an archive intelligent management regulation method and an integrated control system, which are used for solving the problem of low control accuracy of temperature and humidity caused by mutual coupling relation and time stagnation among data, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for intelligently managing and controlling files, including the following steps:
acquiring a data sequence of each monitoring point in the archive at each data acquisition moment;
taking a sequence formed by the data sequences of all the data acquisition moments of each monitoring point according to the sequence of the time ascending order as a long-term data sequence of each monitoring point; acquiring a weighting factor of each element in the long-term data sequence of each monitoring point according to the distribution characteristics of the elements in the data period in the long-term data sequence of each monitoring point; acquiring a temperature and humidity coupling coefficient of each monitoring point at each data acquisition time according to the data sequence of each data acquisition time of different monitoring points and the weighting factors of all elements in the data sequence;
acquiring a temperature association weighting sequence and a humidity association weighting sequence of each monitoring point at each data acquisition moment by using a data mining algorithm; acquiring the data time lag radius of each monitoring point at each data acquisition time according to the temperature association weighting sequences and the humidity association weighting sequences of the same data acquisition time of different monitoring points;
Taking the monitoring point closest to the linear distance of each controller in the archive as a monitoring representative point of each controller; acquiring the moving step length of the firefly corresponding to each controller according to the data time lag radius of the monitoring representative point of each controller at the data acquisition time in each day; acquiring a target regulation and control value of each controller in the integrated control system according to the moving step length, and generating a control instruction of each type of control system based on the target regulation and control value; and each controller adjusts output power according to a control instruction of the class control system to which the controller belongs, so that intelligent management of temperature and humidity data in the archive is realized.
Preferably, the method for obtaining the weighting factor of each element in the long-term data sequence of each monitoring point according to the distribution characteristics of the elements in the data period in the long-term data sequence of each monitoring point comprises the following steps:
the method comprises the steps of utilizing a sequence decomposition STL algorithm based on local weighted regression to obtain a seasonal item in a long-term data sequence of each monitoring point, utilizing a mutation point detection algorithm to obtain mutation points in the seasonal item, and taking a time interval between any two mutation points as one data period in the long-term data sequence of each monitoring point;
Taking the average value of the distribution variance of each element in each data period in the long-term data sequence of each monitoring point on all data periods in the long-term data sequence of each monitoring point as the long-term variance of each element of each monitoring point;
taking the inverse of the square of the long-term variance of each element of each monitoring point as a first product factor, taking the accumulation of the first product factor on all elements of each monitoring point as a denominator, taking a preset parameter as a numerator, and taking the ratio of the numerator to the denominator as a second product factor;
the weighting factor of each element consists of a first product factor and a second product factor, wherein the weighting factors are in direct proportion to the first product factor and the second product factor.
Preferably, the method for obtaining the temperature and humidity coupling coefficient of each monitoring point at each data acquisition time according to the data sequence of each data acquisition time of different monitoring points and the weighting factors of all elements in the data sequence comprises the following steps:
acquiring a data period of each data acquisition time of each monitoring point, acquiring a preset number of adjacent times of each data acquisition time by using a neighbor algorithm in the data period, and taking a set formed by the preset number of adjacent times according to a time sequence as a neighbor time set of each data acquisition time of each monitoring point;
Acquiring a weighted element sequence, a temperature related sequence and a humidity related sequence of each monitoring point at each data acquisition moment according to the weighted factors of each element;
acquiring element association indexes of each monitoring point at each data acquisition time according to a weighted element sequence, a temperature related sequence and a humidity related sequence of each monitoring point and the rest of each monitoring point at each data acquisition time, and taking accumulation of the element association indexes on a neighbor time set of each data acquisition time as element association stability of each monitoring point and the rest of each monitoring point at each data acquisition time;
acquiring a data period of each data acquisition time, and respectively acquiring the mode and the average value of element association stability of each monitoring point and all other monitoring points at all times in the data period; and taking the absolute value of the difference between the mode and the mean value of the element association stability and the reciprocal of the sum of the preset parameters as a first accumulation factor, and taking the mean value of the first accumulation factor between each monitoring point and all the rest monitoring points as the temperature and humidity coupling coefficient of each monitoring point at each data acquisition time.
Preferably, the method for obtaining the weighted element sequence, the temperature related sequence and the humidity related sequence at each data acquisition time of each monitoring point according to the weighted factor of each element comprises the following steps:
Acquiring a weighting factor of each element in a data sequence of each monitoring point, taking the product of the weighting factor of each element and the numerical value of each element as a weighted data value of each element, and taking a sequence formed by the weighted data values of all elements at each data acquisition time of each monitoring point according to the descending order of the weighting factors as a weighted element sequence at each data acquisition time of each monitoring point;
the sequence obtained after deleting the weighted data value corresponding to the temperature element from the weighted element sequence is used as a temperature related sequence of each monitoring point at each data acquisition moment; and deleting the weighted data value corresponding to the humidity element from the weighted element sequence to obtain a sequence serving as a humidity related sequence of each monitoring point at each data acquisition time.
Preferably, the method for obtaining the element association index of each monitoring point at each data acquisition time according to the weighted element sequence, the temperature related sequence and the humidity related sequence of each monitoring point and each other monitoring point at each data acquisition time comprises the following steps:
taking the similarity measurement between each monitoring point and the weighted element sequences of the rest monitoring points at each data acquisition moment as a first measurement factor;
Taking the similarity measurement between each monitoring point and the temperature related sequences of the rest monitoring points at each data acquisition moment as a second measurement factor;
taking the similarity measurement between each monitoring point and the humidity related sequences of the rest monitoring points at each data acquisition time as a third measurement factor;
taking the absolute value of the difference between the first measurement factor and the second measurement factor as a first temperature difference value, taking the absolute value of the difference between the first measurement factor and the third measurement factor as a first humidity difference value, and taking the sum of the first temperature difference value and the first humidity difference value as an element association index of each monitoring point and each other monitoring point at each data acquisition moment.
Preferably, the method for acquiring the data time lag radius of each monitoring point at each data acquisition time according to the temperature association weighting sequence and the humidity association weighting sequence of the same data acquisition time of different monitoring points comprises the following steps:
measuring distances between each monitoring point and temperature association weighted sequences and humidity association weighted sequences of the rest of each monitoring point at each data acquisition time are respectively obtained, and the measuring distances between the temperature association weighted sequences and the humidity association weighted sequences are respectively used as single temperature association distances and single humidity association distances between each monitoring point and the rest of each monitoring point at each data acquisition time;
Acquiring the temperature influence distance and the humidity influence distance of each monitoring point at each data acquisition time according to the single temperature correlation distance and the single humidity correlation distance;
and obtaining the maximum value in the temperature influence distance and the humidity influence distance of each data acquisition time of each monitoring point, and taking the product of the maximum value and the temperature and humidity coupling coefficient of each data acquisition time of each monitoring point as the data time lag radius of each data acquisition time of each monitoring point.
Preferably, the method for respectively obtaining the temperature influence distance and the humidity influence distance of each monitoring point at each data acquisition time according to the single temperature correlation distance and the single humidity correlation distance comprises the following steps:
the single temperature association distance between each monitoring point and all other monitoring points at each data acquisition time is formed into a sequence according to the ascending order of data to be used as a temperature distance sequence, the segmentation threshold value of all elements in the temperature distance sequence obtained by using a threshold segmentation algorithm is used as a second accumulation factor, and the average value of the second accumulation factor in the data period of each data acquisition time is used as the temperature influence distance of each monitoring point at each data acquisition time;
And forming a sequence of single humidity association distances between each monitoring point and all other monitoring points at each data acquisition time as a humidity distance sequence according to the ascending order of data, taking a segmentation threshold value of all elements in the humidity distance sequence obtained by using a threshold segmentation algorithm as a third accumulation factor, and taking the average value of the third accumulation factor in a data period of each data acquisition time as the humidity influence distance of each monitoring point at each data acquisition time.
Preferably, the method for obtaining the movement step length of the firefly corresponding to each controller according to the data time lag radius of the data acquisition time in each day of the monitoring representative point of each controller comprises the following steps:
taking the average value of the data time-lag radii of all the data acquisition moments in each day of the monitoring representative points of each controller as a numerator, taking the maximum value in the average value of the data time-lag radii of all the data acquisition moments in each day of all the monitoring points in the archive as a denominator, and taking the sum of the ratio of the numerator to the denominator and preset parameters as a step length regulation and control coefficient of the firefly corresponding to each controller;
taking the difference value of the maximum iteration times and the current iteration times of each firefly as a base number, taking the ratio of the calculated result of the step length regulating coefficient of each firefly as an index to the maximum iteration times as a first step length factor, and taking the sum of the product of the first step length factor and the initial value of the moving step length and the final value of the moving step length as the moving step length of the firefly corresponding to each controller.
Preferably, the method for obtaining the target regulation and control value of each controller in the integrated control system according to the movement step length and generating the control instruction of each type of control system based on the target regulation and control value includes:
respectively obtaining the moving step length of the firefly corresponding to each controller, and taking an optimal solution obtained by utilizing a firefly algorithm based on the moving step length as a target regulation value of each controller;
uploading the target regulation values of all controllers to a system database, and generating control instructions of each type of control system by the integrated control system according to the received target regulation values of all controllers, wherein each type of control system controls the output power of each controller in each type of control system according to the received control instructions.
In a second aspect, an embodiment of the present invention further provides an integrated control system for intelligent archive management and regulation, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the methods described above when executing the computer program.
The beneficial effects of the invention are as follows: according to the method, the temperature and humidity coupling coefficient is constructed according to the coupling degree between the temperature and humidity data at different moments of each monitoring point after the weighting treatment, and the temperature and humidity coupling coefficient considers the correlation relationship of different elements in the data sequence at each moment in the data period of the season term; and secondly, constructing a data time lag radius by the influence degree of each element at each moment in a strong correlation task of temperature and humidity, wherein the data time lag radius considers the correlation degree among different events when an integrated control system regulates and controls the archive and the time lag of the influence of temperature and humidity data at each moment.
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 flowchart of a method for intelligent file management and control according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an implementation of a method for intelligent file management and control according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a control flow of an integrated control system for intelligent file management and control according to an embodiment of the present invention.
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 flowchart of a method for intelligent file management and control according to an embodiment of the invention is shown, the method includes the following steps:
and S001, acquiring a data sequence of each data acquisition moment of each monitoring point in the archive.
According to the invention, the archive storehouse with equipment is used as a storage place of archives, and the temperature and humidity in the archive storehouse are intelligently regulated and controlled. M monitoring points are arranged in the archive, a DHT11 type temperature and humidity sensor is installed at each monitoring point, temperature and humidity data in the archive at each moment are collected in real time, and the collected data are transmitted to a system database. And secondly, acquiring the working state and output power of each controller in the archive in the past m days from a system database of a temperature and humidity integrated control system of the archive, setting state parameters according to the working state, and setting the state parameters of the controllers in the working state and the static state to be 1 and 0 respectively. The controller comprises equipment such as an air conditioner, a blower, a dehumidifier and the like, in the invention, the size of M, m is respectively taken as an empirical value 50 and 365, and an implementer can set a proper M, m and a sensor model according to the actual situation of a file storage place.
In order to prevent the phenomenon of data loss in the data transmission process, the acquired temperature data, humidity data and output power are processed by using a k-nearest neighbor filling method, and then the temperature, humidity data and output power after the filling processing are subjected to the maximum normalization processing to complete the preprocessing of the data, wherein the maximum normalization and k-nearest neighbor filling are known techniques, the specific process is not repeated, and the obtained temperature, humidity data and output power by the preprocessing and the state of the controller are utilizedThe state parameters form a data sequence of each monitoring point, and the data sequence at the time t of the a-th monitoring point is recorded asThe data sequence comprises a preprocessing result of temperature and humidity data of each monitoring point at each data acquisition time, a preprocessing result of output power of each controller and a state parameter of each controller.
So far, the data sequence of each monitoring point at each moment is used for calculating the subsequent regulation and control indexes.
Step S002, obtaining the weighting factors of each element in the long-term data sequence of each monitoring point based on the distribution characteristics of the elements in the data period in the long-term data sequence of each monitoring point.
The temperature and humidity in the archive are not only influenced by factors in the archive room, but also are more easily influenced by external factors, for example, the temperature difference between the indoor temperature and the outdoor temperature is large in a heavy rain day, the humidity at the indoor wall of the archive room is increased, and archive carriers in an archive cabinet close to the wall are easy to generate damp and mildew; the temperature difference between the morning and evening in spring and autumn is large, and the degree of the temperature and the humidity required to be regulated and controlled in the archives at different moments is different.
The method comprises the steps of taking a sequence formed by data sequences of all data acquisition moments in m days of each monitoring point according to the ascending order of time as a long-term data sequence of each monitoring point in an archive, inputting the corresponding long-term data sequence of any one monitoring point as an algorithm, and carrying out sequence decomposition on the long-term data sequence of each monitoring point by using an STL sequence decomposition algorithm to obtain trend items, season items and residual items of each long-term data sequence, wherein the STL sequence decomposition algorithm is a known technology, and specific processes are not repeated. And secondly, carrying out mutation point detection on a seasonal item corresponding to each monitoring point by utilizing a BG sequence segmentation algorithm, taking a time interval between any two mutation points as one data period in a long-term data sequence of each monitoring point, for example, carrying out mutation point detection on a seasonal item of a long-term data sequence of an a-th monitoring point, taking a time interval between the 1 st mutation point and the 2 nd mutation point as a first data period of the a-th monitoring point, wherein the BG sequence segmentation algorithm is a known technology, and the specific process is not repeated.
In general, the temperature and humidity at each monitoring point have little change in a short time, that is, the fluctuation of the values of elements in the data sequence at each monitoring point in the same data period is small, but certain interrelation exists among the elements in the data sequence, and in order to influence the fluctuation of the data, the elements in the data sequence are weighted.
Based on the analysis, a weighting factor W is constructed, and is used for carrying out weighting processing on the data of each factor in the data sequence of each monitoring point, and calculating the weighting factor of the ith element in the data sequence of the a-th monitoring point：
In the method, in the process of the invention,is the long-term variance of the ith element at monitoring point a,/->Is the distribution variance of the ith element in the b-th data period in the long-term data sequence of monitoring point a,/for the data sequence of monitoring point a>Is the number of periods corresponding to the long-term data sequence of monitoring point a, < >>Is the number of elements within the data sequence at each time instant of the monitoring point a.
Wherein the larger the distribution variance of elements in different data periods in the seasonal term of the long-term data sequence of the a-th monitoring point，The larger the value of (2), the first product factor +.>The smaller the value of (2); while the larger the data fluctuation in the seasonal term of the long-term data sequence of the a-th monitoring point, the larger the long-term variance of all elements, the second product factor +.>The larger the value of (2), the weighting factor +.>The smaller the value of (2).
So far, the weighting factors of each factor in the data sequence of each monitoring point are obtained and used for carrying out subsequent weighting processing on the data.
And step S003, acquiring the temperature and humidity coupling coefficient of each monitoring point at each data acquisition moment according to the data sequence and the weighting factors of all elements in the data sequence.
Further, a data period of each monitoring point at each data acquisition time is obtained, k adjacent times of each data acquisition time are obtained by using a k adjacent algorithm in the data period, a set formed by the k adjacent times according to time sequence is used as an adjacent time set of each monitoring point at each data acquisition time, the size of k takes a checked value of 10, and an adjacent time set of an a-th monitoring point t data acquisition time is recorded as. Secondly, obtaining a weighting factor of each element in the data sequence of each monitoring point, taking the product of the weighting factor and the element value as the weighted data value of the element, taking a sequence formed by the weighted data values of all the elements at each moment of each monitoring point according to the descending order of the weighting factors as the weighted element sequence at each moment of each monitoring point, and marking the weighted element sequence at the t moment of the a-th monitoring point as->The invention aims to realize the regulation and control of the temperature and the humidity in the archive by utilizing the integrated control system, so that the weighted data values corresponding to the temperature and the humidity are deleted from the weighted element sequences respectively, and +.>The sequence obtained by deleting the weighted data values corresponding to the temperature and the humidity is marked as a temperature related sequence +. >Humidity-related sequence->。
Based on the analysis, a temperature and humidity coupling coefficient U is constructed, used for representing the coupling degree between temperature and humidity data at different moments of each monitoring point, and the temperature and humidity coupling coefficient at the moment t of the a-th monitoring point is calculated：
In the method, in the process of the invention,is the first temperature difference value of the a monitoring point and the c monitoring point at the moment f; />、/>The weighted element sequences of the (f) th moment in the neighbor moment set of the (a) th monitoring point and the (c) th monitoring point t moment are respectively +.>、Respectively weighted element sequences->、/>Corresponding temperature-dependent sequences,/->、/>Respectively the sequences->And->、/>And->Pearson correlation coefficient therebetween;
element association index at time f of a-th and c-th monitoring points, +.>Is the first humidity difference value at the time f of the a-th monitoring point and the c-th monitoring point, and the calculation principle and +.>The same shall not be repeated here again>The larger the value of the temperature and humidity data is, the larger the relevance between the temperature and humidity data corresponding elements in the data sequences of the a-th monitoring point and the c-th monitoring point is.
The element association stability of the t moment of the a-th monitoring point and the c-th monitoring point is k, and k is the moment number in the t moment neighbor moment set. />The larger the value of the temperature and humidity data of the a-th monitoring point and the c-th monitoring point is, the more stable the relevance of the temperature and humidity data of the a-th monitoring point and the c-th monitoring point is at the time t.
Is the temperature and humidity coupling coefficient at the time t of the a monitoring point, M is the number of monitoring points in the archive,、/>the method is characterized in that the mode, the average value and the +.f of element association stability of the a-th monitoring point and the c-th monitoring point at all times in a data period of the t time are respectively>Is a parameter regulating factor and has the function of preventing denominator from being 0 #>The size of (2) is 0.001.
The temperature and humidity coupling coefficient reflects the degree of mutual coupling between temperature and humidity data at different moments of each monitoring point. The greater the relevance between the corresponding elements of the temperature and humidity data in the data sequences of the a-th and c-th monitoring points at the moment f, the greater the relevance between the weighted element sequences and the temperature correlation sequenceThe smaller the correlation between the series of humidity correlations, the first metricAnd a second scale factor->The greater the difference between the first temperature difference +.>The larger the value of (2), the higher the first metric factor +.>And a third metric factor->The greater the difference between the first humidity difference +.>The greater the value of +.>The greater the value of (2);
the more stable the relevance of temperature and humidity data of the a-th and c-th monitoring points at the time t is, the more stable the value condition of the element relevance index in the neighbor time set at the time t is,the greater the value of (2); the more stable the distribution of element association stability of the a-th monitoring point and the c-th monitoring point at all times in the data period of the t moment is, the mode is- >Mean->The closer the size of ++>The smaller the value of (2), the first accumulation factor +.>The greater the value of (2); i.e. < ->The larger the value of (c) is, the stronger the coupling between temperature and humidity in the data sequence at the time t of the a monitoring point is. The temperature and humidity coupling coefficient considers the correlation relationship of different elements in the data sequence at each moment in the data period of the season term, and has the advantages of eliminating the influence of the correlation caused by the fluctuation of the monitoring data on the normal coupling relationship between the temperature and humidity data, and being beneficial to obtaining the prediction result with higher accuracy later.
So far, the temperature and humidity coupling coefficient of each monitoring point in the archive at each data acquisition time is obtained and used for calculating the self-adaptive moving step length in the follow-up firefly optimization algorithm.
And S004, acquiring the data time lag radius of each monitoring point at each data acquisition time based on the temperature association weighting sequence and the humidity association weighting sequence of the same data acquisition time of different monitoring points.
When the integrated control system sends a regulation command to each monitoring point in the archive, the time period when the temperature and humidity data at each monitoring point reach an ideal value is recorded as the regulation time period of each monitoring point, and the mutual influence relationship between the temperature and humidity data at each moment in the regulation time period is in a state of real-time change. For example, the regulation and control instruction is cooling, and the air conditioner in the archives begins to carry cold wind, and the time that needs is different when cold wind transmission arrives in the archives different position monitoring points, also can have certain loss in transmission process, and the influence relation between temperature and the humidity data also can change gradually.
In order to reduce the loss in the temperature and humidity regulation process and the influence of the space distance on the temperature and humidity regulation precision as much as possible, the invention utilizes each element in the obtained data sequence to establish a transaction database, and then counts the accumulated count of each transaction in the history data of m days to obtain a candidate 1-item set, wherein the 1-item set is used as the input of an Apriori algorithm, the threshold value of the minimum support degree is set to be 50%, the threshold value of the minimum confidence degree is set to be 80%, and the temperature and the humidity of the monitoring point are respectively calculatedThe degree is used as a target rule, a frequent item set with a strong association rule with temperature and humidity is obtained by using an Apriori data mining algorithm based on a transaction database, and the Apriori data mining algorithm is a known technology, and the specific process is not repeated. And taking the transaction in the frequent item set of the strong association rule with the highest confidence as the strong association task of the temperature and the humidity when the temperature and the humidity of the monitoring point are taken as the association rule respectively. Taking a sequence formed by weighted data values of strong correlation transaction corresponding elements of temperature and humidity at each moment of each monitoring point as a temperature correlation weighted sequence and a humidity correlation weighted sequence at each moment of each monitoring point, and taking the temperature correlation weighted sequence at the t moment of the a-th monitoring point Humidity-associated weighting sequence->。
For any monitoring point, certain time lag exists in the influence of the temperature and humidity data at the moment t on the temperature and humidity data at the subsequent moment, namely, along with the gradual increase of the time interval between the subsequent moment and the moment t, the influence of the temperature and humidity data at the moment t on the temperature and humidity data at the subsequent moment gradually decreases, namely, the temperature and humidity data at each moment has an influence radius, and the historical temperature and humidity data at each moment cannot influence the temperature and humidity data beyond the influence radius.
Based on the analysis, a data time-lag radius R is constructed, used for representing the influence range of the temperature and humidity data of each monitoring point at each moment on the temperature and humidity data at the subsequent moment, and the data time-lag radius at the time t of the a-th monitoring point is calculated：
In the method, in the process of the invention,is the single temperature related distance of the a-th and c-th monitoring points at the time f,/>、/>The temperature-related weighting sequences at the time f of the a-th monitoring point and the c-th monitoring point are respectively +.>Is the sequence->、The DTW distance between the two is a known technology, and the specific process is not described again.
Is the temperature influence distance at time t of the a-th monitoring point,/->Is the number of monitoring points in the archive, k is the number of times in the set of neighbor times at time t, +. >Is a temperature distance sequence formed by single temperature correlation distances between the a-th monitoring point and the rest M-1 monitoring points at the f moment according to the ascending order of data, and is +.>Temperature distance sequence obtained by using Ojin threshold algorithm>The segmentation threshold value and the Ojin threshold value algorithm are known techniques, and the specific process is not repeated.
Is the humidity influence distance at time t of the a-th monitoring point,/->The principle of calculation of (2) and->The same shall not be repeated here again>Is a function of taking the maximum value.
The data time lag radius reflects the influence degree of the temperature and humidity data of each monitoring point on the temperature and humidity in the archive. The larger the difference between the temperature data at the a-th and c-th monitoring points f and the temperature strong correlation data is, the lower the degree of influence of the data at the two monitoring points f is,the greater the value of (2); the greater the influence of the temperature data at the a-th monitoring point t on the temperature data at the subsequent moment, the +.>The more similar the temperature-dependent weighting sequence is to each instant in the set of neighboring instants, the temperature distance sequence +.>The smaller the difference between adjacent data, the second accumulation factor +.>The greater the value of (2); similarly, the greater the influence of the humidity data at the time t of the a-th monitoring point on the humidity data at the subsequent time, the more +. >The more similar the humidity-associated weighting sequence is to each time instant in the set of neighboring time instants, the humidity distance sequence +.>The smaller the difference between adjacent data, the third accumulation factor +.>The greater the value of (2); i.e. < ->The larger the value of the temperature and humidity data at the time t of the a monitoring point is, the larger the influence of the temperature and humidity data at the time t of the a monitoring point on the temperature and humidity in the archives at the subsequent time is. The data time lag radius considers the association degree among different transactions and the time lag of the influence of temperature and humidity data at each moment when the integrated control system regulates and controls the archive, and has the advantages of solving the problems of optimization oscillation phenomenon caused by fixed-length moving step length and too slow convergence in the traditional firefly optimization FA algorithm and improving the precision of temperature and humidity predicted values in the archive.
So far, the data time lag radius of each monitoring point at each data acquisition time is obtained and is used for obtaining the moving step length of each firefly in the firefly algorithm in a follow-up self-adaptive mode.
Step S005, self-adaptively obtaining the moving step length of fireflies based on the data time lag semi-diameter of the monitoring representative points, obtaining target regulation and control values by utilizing a firefly algorithm, and generating control instructions by the integrated control system according to the target regulation and control values of all controllers in the archive, so as to control temperature and humidity data in the archive.
According to the steps, the data time lag radius of all the moments is obtained, and the movement step length of the firefly is obtained based on the data time lag radius of each moment, wherein each firefly in the firefly optimization algorithm is used as one controller in the integrated control system, each movable position of the firefly represents the output value of the controller, the monitoring point closest to the straight line of each controller in the archive is used as the monitoring representative point of each controller, and the monitoring point of the corresponding controller of the xth firefly is used as the monitoring representative point of the corresponding controller of the xth fireflyThe representative point is marked as a monitoring point j, and when the optimal output value of the controller is obtained by utilizing a firefly optimization algorithm on the h day of the integrated control system, the moving step length of the xth firefly at the p-th iteration is calculated：
In the method, in the process of the invention,is the step length regulation coefficient of the xth firefly,>is the average value of the data time lag radius of all the time points in the h day of the monitoring point j, and is>Is the maximum value in the data time-lag radius average value of all monitoring points on the h day;
p is the maximum number of iterations, the magnitude of P takes an empirical value of 300,、/>the initial value, the end value and the +.>、/>The empirical values of 0.07 and 0.01 are respectively adopted.
According to the steps, the moving step length of each firefly is obtained in each iteration process when the firefly FA algorithm predicts the temperature and humidity data in a self-adaptive manner, then the target regulation and control value of each controller is obtained by using the firefly FA algorithm, the firefly FA algorithm is a known technology, and the specific process is not repeated. In the invention, the implementation flow of the intelligent file management and control method based on the firefly FA algorithm is shown in FIG. 2.
According to the above steps, the target regulation and control value of each controller obtained by the integrated control system in the h day by using the firefly algorithm is obtained respectively, and further, the optimal values of all the controller output values in the prediction time archive are uploaded to the system database based on the data communication protocol of TCP/IP, and the integrated control flow is shown in figure 3. The system database generates control instructions of each type of control system in the archive according to the received controller target regulation and control values, each type of control system regulates and controls the output power of each controller in each type of control system in real time according to the received control instructions, the controllers comprise equipment such as an air conditioner, a blower and a dehumidifier, and the like, the control system of each controller realizes classified regulation and control management on all controllers, and intelligent management regulation and control on the temperature and humidity in the archive is completed.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. An intelligent archive management and control method is characterized by comprising the following steps:
acquiring a data sequence of each monitoring point in the archive at each data acquisition moment;
taking a sequence formed by the data sequences of all the data acquisition moments of each monitoring point according to the sequence of the time ascending order as a long-term data sequence of each monitoring point; acquiring a weighting factor of each element in the long-term data sequence of each monitoring point according to the distribution characteristics of the elements in the data period in the long-term data sequence of each monitoring point; acquiring a temperature and humidity coupling coefficient of each monitoring point at each data acquisition time according to the data sequence of each data acquisition time of different monitoring points and the weighting factors of all elements in the data sequence;
acquiring a temperature association weighting sequence and a humidity association weighting sequence of each monitoring point at each data acquisition moment by using a data mining algorithm; acquiring the data time lag radius of each monitoring point at each data acquisition time according to the temperature association weighting sequences and the humidity association weighting sequences of the same data acquisition time of different monitoring points;
taking the monitoring point closest to the linear distance of each controller in the archive as a monitoring representative point of each controller; acquiring the moving step length of the firefly corresponding to each controller according to the data time lag radius of the monitoring representative point of each controller at the data acquisition time in each day; acquiring a target regulation and control value of each controller in the integrated control system according to the moving step length, and generating a control instruction of each type of control system based on the target regulation and control value; and each controller adjusts output power according to a control instruction of the class control system to which the controller belongs, so that intelligent management of temperature and humidity data in the archive is realized.
2. The archive intelligent management and control method according to claim 1, wherein the method for obtaining the weighting factor of each element in the long-term data sequence of each monitoring point according to the distribution characteristics of the elements in the data period in the long-term data sequence of each monitoring point is as follows:
the method comprises the steps of utilizing a sequence decomposition STL algorithm based on local weighted regression to obtain a seasonal item in a long-term data sequence of each monitoring point, utilizing a mutation point detection algorithm to obtain mutation points in the seasonal item, and taking a time interval between any two mutation points as one data period in the long-term data sequence of each monitoring point;
taking the average value of the distribution variance of each element in each data period in the long-term data sequence of each monitoring point on all data periods in the long-term data sequence of each monitoring point as the long-term variance of each element of each monitoring point;
taking the inverse of the square of the long-term variance of each element of each monitoring point as a first product factor, taking the accumulation of the first product factor on all elements of each monitoring point as a denominator, taking a preset parameter as a numerator, and taking the ratio of the numerator to the denominator as a second product factor;
The weighting factor of each element consists of a first product factor and a second product factor, wherein the weighting factors are in direct proportion to the first product factor and the second product factor.
3. The archive intelligent management and control method according to claim 1, wherein the method for obtaining the temperature and humidity coupling coefficient of each monitoring point at each data acquisition time according to the data sequence of each data acquisition time of different monitoring points and the weighting factors of all elements in the data sequence comprises the following steps:
acquiring a data period of each data acquisition time of each monitoring point, acquiring a preset number of adjacent times of each data acquisition time by using a neighbor algorithm in the data period, and taking a set formed by the preset number of adjacent times according to a time sequence as a neighbor time set of each data acquisition time of each monitoring point;
acquiring a weighted element sequence, a temperature related sequence and a humidity related sequence of each monitoring point at each data acquisition moment according to the weighted factors of each element;
acquiring element association indexes of each monitoring point at each data acquisition time according to a weighted element sequence, a temperature related sequence and a humidity related sequence of each monitoring point and the rest of each monitoring point at each data acquisition time, and taking accumulation of the element association indexes on a neighbor time set of each data acquisition time as element association stability of each monitoring point and the rest of each monitoring point at each data acquisition time;
Acquiring a data period of each data acquisition time, and respectively acquiring the mode and the average value of element association stability of each monitoring point and all other monitoring points at all times in the data period; and taking the absolute value of the difference between the mode and the mean value of the element association stability and the reciprocal of the sum of the preset parameters as a first accumulation factor, and taking the mean value of the first accumulation factor between each monitoring point and all the rest monitoring points as the temperature and humidity coupling coefficient of each monitoring point at each data acquisition time.
4. The archive intelligent management and control method according to claim 3, wherein the method for obtaining the weighted element sequence, the temperature related sequence and the humidity related sequence of each monitoring point at each data acquisition time according to the weighted factor of each element is as follows:
acquiring a weighting factor of each element in a data sequence of each monitoring point, taking the product of the weighting factor of each element and the numerical value of each element as a weighted data value of each element, and taking a sequence formed by the weighted data values of all elements at each data acquisition time of each monitoring point according to the descending order of the weighting factors as a weighted element sequence at each data acquisition time of each monitoring point;
The sequence obtained after deleting the weighted data value corresponding to the temperature element from the weighted element sequence is used as a temperature related sequence of each monitoring point at each data acquisition moment; and deleting the weighted data value corresponding to the humidity element from the weighted element sequence to obtain a sequence serving as a humidity related sequence of each monitoring point at each data acquisition time.
5. The archive intelligent management and control method according to claim 3, wherein the method for obtaining the element association index of each monitoring point at each data acquisition time according to the weighted element sequence, the temperature-related sequence and the humidity-related sequence of each monitoring point and each other monitoring point at each data acquisition time is as follows:
taking the similarity measurement between each monitoring point and the weighted element sequences of the rest monitoring points at each data acquisition moment as a first measurement factor;
taking the similarity measurement between each monitoring point and the temperature related sequences of the rest monitoring points at each data acquisition moment as a second measurement factor;
taking the similarity measurement between each monitoring point and the humidity related sequences of the rest monitoring points at each data acquisition time as a third measurement factor;
Taking the absolute value of the difference between the first measurement factor and the second measurement factor as a first temperature difference value, taking the absolute value of the difference between the first measurement factor and the third measurement factor as a first humidity difference value, and taking the sum of the first temperature difference value and the first humidity difference value as an element association index of each monitoring point and each other monitoring point at each data acquisition moment.
6. The archive intelligent management and control method according to claim 1, wherein the method for acquiring the data time lag radius of each monitoring point at each data acquisition time according to the temperature association weighting sequence and the humidity association weighting sequence at the same data acquisition time of different monitoring points is as follows:
measuring distances between each monitoring point and temperature association weighted sequences and humidity association weighted sequences of the rest of each monitoring point at each data acquisition time are respectively obtained, and the measuring distances between the temperature association weighted sequences and the humidity association weighted sequences are respectively used as single temperature association distances and single humidity association distances between each monitoring point and the rest of each monitoring point at each data acquisition time;
acquiring the temperature influence distance and the humidity influence distance of each monitoring point at each data acquisition time according to the single temperature correlation distance and the single humidity correlation distance;
And obtaining the maximum value in the temperature influence distance and the humidity influence distance of each data acquisition time of each monitoring point, and taking the product of the maximum value and the temperature and humidity coupling coefficient of each data acquisition time of each monitoring point as the data time lag radius of each data acquisition time of each monitoring point.
7. The archive intelligent management and control method according to claim 6, wherein the method for respectively obtaining the temperature influence distance and the humidity influence distance of each monitoring point at each data acquisition time according to the single temperature correlation distance and the single humidity correlation distance is as follows:
the single temperature association distance between each monitoring point and all other monitoring points at each data acquisition time is formed into a sequence according to the ascending order of data to be used as a temperature distance sequence, the segmentation threshold value of all elements in the temperature distance sequence obtained by using a threshold segmentation algorithm is used as a second accumulation factor, and the average value of the second accumulation factor in the data period of each data acquisition time is used as the temperature influence distance of each monitoring point at each data acquisition time;
and forming a sequence of single humidity association distances between each monitoring point and all other monitoring points at each data acquisition time as a humidity distance sequence according to the ascending order of data, taking a segmentation threshold value of all elements in the humidity distance sequence obtained by using a threshold segmentation algorithm as a third accumulation factor, and taking the average value of the third accumulation factor in a data period of each data acquisition time as the humidity influence distance of each monitoring point at each data acquisition time.
8. The archive intelligent management and control method according to claim 1, wherein the method for obtaining the movement step length of the firefly corresponding to each controller according to the data time lag radius of the data acquisition time in each day of the monitoring representative point of each controller is as follows:
taking the average value of the data time-lag radii of all the data acquisition moments in each day of the monitoring representative points of each controller as a numerator, taking the maximum value in the average value of the data time-lag radii of all the data acquisition moments in each day of all the monitoring points in the archive as a denominator, and taking the sum of the ratio of the numerator to the denominator and preset parameters as a step length regulation and control coefficient of the firefly corresponding to each controller;
taking the difference value of the maximum iteration times and the current iteration times of each firefly as a base number, taking the ratio of the calculated result of the step length regulating coefficient of each firefly as an index to the maximum iteration times as a first step length factor, and taking the sum of the product of the first step length factor and the initial value of the moving step length and the final value of the moving step length as the moving step length of the firefly corresponding to each controller.
9. The archive intelligent management and control method according to claim 1, wherein the method for obtaining the target regulation value of each controller in the integrated control system according to the movement step length and generating the control command of each type of control system based on the target regulation value comprises the following steps:
Respectively obtaining the moving step length of the firefly corresponding to each controller, and taking an optimal solution obtained by utilizing a firefly algorithm based on the moving step length as a target regulation value of each controller;
uploading the target regulation values of all controllers to a system database, and generating control instructions of each type of control system by the integrated control system according to the received target regulation values of all controllers, wherein each type of control system controls the output power of each controller in each type of control system according to the received control instructions.
10. An archive intelligent management regulation integrated control system comprising a memory, a processor and a computer program stored in the memory and running on the processor, characterized in that the processor implements the steps of the method according to any one of claims 1-9 when executing the computer program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311235910.3A CN116974320B (en) | 2023-09-25 | 2023-09-25 | Intelligent file management regulation and control method and integrated control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311235910.3A CN116974320B (en) | 2023-09-25 | 2023-09-25 | Intelligent file management regulation and control method and integrated control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116974320A true CN116974320A (en) | 2023-10-31 |
| CN116974320B CN116974320B (en) | 2023-12-08 |
Family
ID=88479978
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311235910.3A Active CN116974320B (en) | 2023-09-25 | 2023-09-25 | Intelligent file management regulation and control method and integrated control system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116974320B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117806154A (en) * | 2024-02-27 | 2024-04-02 | 山东丰斯工程技术有限公司 | PID controller parameter optimization method and system for grate cooler |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103809630A (en) * | 2012-11-09 | 2014-05-21 | 福建新大陆电脑股份有限公司 | System for performing real-time monitoring and overtemperature outage warning management on temperatures of cold chain devices |
| US20220280450A1 (en) * | 2021-03-05 | 2022-09-08 | Philera New Zealand Ltd. | Prevention and treatment of coronavirus and related respiratory infections |
| CN115033042A (en) * | 2022-08-10 | 2022-09-09 | 启东亦大通自动化设备有限公司 | Food hydroextractor intelligence control system based on multimode thing networking |
| CN116185097A (en) * | 2023-02-09 | 2023-05-30 | 湖州新晶觅仪器设备有限公司 | Intelligent temperature regulation and control method for constant temperature tank |
-
2023
- 2023-09-25 CN CN202311235910.3A patent/CN116974320B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103809630A (en) * | 2012-11-09 | 2014-05-21 | 福建新大陆电脑股份有限公司 | System for performing real-time monitoring and overtemperature outage warning management on temperatures of cold chain devices |
| US20220280450A1 (en) * | 2021-03-05 | 2022-09-08 | Philera New Zealand Ltd. | Prevention and treatment of coronavirus and related respiratory infections |
| CN115033042A (en) * | 2022-08-10 | 2022-09-09 | 启东亦大通自动化设备有限公司 | Food hydroextractor intelligence control system based on multimode thing networking |
| CN116185097A (en) * | 2023-02-09 | 2023-05-30 | 湖州新晶觅仪器设备有限公司 | Intelligent temperature regulation and control method for constant temperature tank |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117806154A (en) * | 2024-02-27 | 2024-04-02 | 山东丰斯工程技术有限公司 | PID controller parameter optimization method and system for grate cooler |
| CN117806154B (en) * | 2024-02-27 | 2024-05-03 | 山东丰斯工程技术有限公司 | PID controller parameter optimization method and system for grate cooler |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116974320B (en) | 2023-12-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107120721B (en) | A kind of central heating dynamic gas candidate compensation method | |
| CN111563610B (en) | Building electric load comprehensive prediction method and system based on LSTM neural network | |
| CN116974320B (en) | Intelligent file management regulation and control method and integrated control system | |
| WO2016047118A1 (en) | Model evaluation device, model evaluation method, and program recording medium | |
| US20200041160A1 (en) | Air conditioning system and method for controlling same | |
| He et al. | CLeaR: An adaptive continual learning framework for regression tasks | |
| CN115016276B (en) | Intelligent water content adjustment and environment parameter Internet of things big data system | |
| CN110781595B (en) | Method, device, terminal and medium for predicting energy use efficiency (PUE) | |
| CN113819636B (en) | Control method and device of radiation air conditioning system and storage medium | |
| CN114397043B (en) | Multi-point temperature intelligent detection system | |
| CN107730045B (en) | Baseline load thermal inertia correction method based on discrete inertia dynamics system | |
| CN111898856B (en) | Analysis method of physical-data fusion building based on extreme learning machine | |
| CN114239991A (en) | Building heat supply load prediction method, device and equipment based on data driving | |
| CN114036813A (en) | Greenhouse temperature and humidity method controlled by particle swarm BP neural network PID | |
| CN116526473A (en) | Particle swarm optimization LSTM-based electrothermal load prediction method | |
| CN114117852B (en) | Regional heat load rolling prediction method based on finite difference working domain division | |
| CN113591957B (en) | Wind power output short-term rolling prediction and correction method based on LSTM and Markov chain | |
| CN115128978A (en) | Internet of things environment big data detection and intelligent monitoring system | |
| Khalil et al. | Federated learning for energy-efficient thermal comfort control service in smart buildings | |
| Yu et al. | Short-term cooling and heating loads forecasting of building district energy system based on data-driven models | |
| CN116560428A (en) | Poultry house temperature prediction control system and control method based on LMBP neural network | |
| CN114415503B (en) | Temperature big data internet of things detection and intelligent control system | |
| CN115343784A (en) | Local air temperature prediction method based on seq2seq-attention model | |
| Sabat et al. | A comparative analysis of univariate deep learning-based time-series models for temperature forecasting of the bhubaneshwar | |
| CN113625557A (en) | HVAC system model prediction control method of online optimization model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |