CN115269659A - Remote monitoring system for energy consumption detection of combined air conditioning box - Google Patents

Remote monitoring system for energy consumption detection of combined air conditioning box Download PDF

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CN115269659A
CN115269659A CN202211133816.2A CN202211133816A CN115269659A CN 115269659 A CN115269659 A CN 115269659A CN 202211133816 A CN202211133816 A CN 202211133816A CN 115269659 A CN115269659 A CN 115269659A
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CN115269659B (en
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杜国栋
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Jiangsu Taiente Environmental Technology Co ltd
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Abstract

The invention relates to the technical field of data processing, in particular to a remote monitoring system for energy consumption detection of a combined air-conditioning box. The system comprises: the data acquisition module is used for acquiring energy consumption time sequence data corresponding to the combined air-conditioning box; the data transmission module is used for constructing a hierarchical description by using the numbered data sequence to obtain a hierarchical sequence of each multiple layer; acquiring a hierarchical curve of a corresponding hierarchy through a hierarchical sequence of each multiple layer, and calculating the regularity of the hierarchical curve; selecting a layer curve corresponding to the layer with the maximum regularity to calculate the minimum period of the layer curve; the product of the minimum period and the layer multiplication corresponding to the minimum period is the optimal dictionary window size; encoding and compressing time-consuming sequence data by using a dictionary window with the size of the optimal dictionary window size and transmitting the time-consuming sequence data; and the energy consumption detection module is used for receiving and transmitting the obtained energy consumption time sequence data to analyze the energy consumption condition of the combined air-conditioning box. The invention can transmit energy time sequence data in real time for energy detection.

Description

Remote monitoring system for energy consumption detection of combined air conditioning box
Technical Field
The invention relates to the technical field of data processing, in particular to a remote monitoring system for energy consumption detection of a combined air-conditioning box.
Background
In energy consumption of China, a combined air-conditioning system occupies a large energy consumption proportion, and the combined air-conditioning energy consumption detection system usually acquires energy consumption data through an energy consumption metering device installed on site, such as a flow meter or an electric energy meter, stores and transmits the acquired data, so that a user can conveniently and timely master the use condition, and an energy-saving plan can be effectively formulated.
The data volume collected by the energy consumption detection is large, so that compression processing is needed when the data are transmitted, the compression effect of the traditional compression algorithm on the data with large redundancy degree is good, but frequent power change is always accompanied to the combined air-conditioning box, and the collected energy consumption data are not the data with large redundancy degree, so that the compression degree of the energy consumption data of the combined air-conditioning box by the traditional compression algorithm is low, inconvenience is brought to the data transmission and storage, and energy-saving reaction is difficult to accurately and timely make.
Disclosure of Invention
In order to solve the technical problem, the invention provides a remote monitoring system for energy consumption detection of a combined air-conditioning box, which is characterized in that the size of a sliding window is obtained in a self-adaptive manner by analyzing the distribution condition of data in a data section, and a sliding window dictionary is utilized to perform compression transmission on the acquired energy consumption data; the adopted technical scheme is as follows:
one embodiment of the invention provides a remote monitoring system for energy consumption detection of a combined air-conditioning box, which comprises the following components: the data acquisition module is used for acquiring energy consumption time sequence data corresponding to the combined air conditioning cabinet;
the data transmission module is used for counting the number of different types of energy consumption time sequence data to establish a statistical histogram; calculating the frequency of each type of energy consumption time sequence data by using the statistical histogram, and sequencing all types of energy consumption time sequence data according to the frequency to obtain a sequencing sequence; acquiring a serial number data sequence based on the position of each type of energy consumption time sequence data in the sequencing sequence; constructing a hierarchical description by using the serial number data sequence to obtain a hierarchical sequence of each multiple layer; acquiring a hierarchical curve of a corresponding hierarchy through a hierarchical sequence of each multiple layer, and calculating the regularity of the hierarchical curve; selecting a layer curve corresponding to the layer with the maximum regularity to calculate the minimum period of the layer curve; the product of the minimum period and the layer corresponding to the minimum period is the optimal dictionary window size; encoding and compressing time-consuming sequence data by using a dictionary window with the size of the optimal dictionary window size and transmitting the time-consuming sequence data;
and the energy consumption detection module is used for receiving and transmitting the obtained energy consumption time sequence data to analyze the energy consumption condition of the combined air-conditioning box.
Preferably, the frequency of each type of energy consumption timing data is:
Figure DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 724019DEST_PATH_IMAGE002
indicating the frequency of occurrence of the category j energy source time series data,
Figure 228818DEST_PATH_IMAGE003
representing the number of occurrences of the j-th energy sequence data,
Figure 150638DEST_PATH_IMAGE004
the number of times of occurrence of the ith type of energy time series data is shown, and n is the total type number of the energy time series data.
Preferably, the sorting all types of energy consumption time series data according to the frequency to obtain a sorting sequence, including: and arranging the sequences according to the sequence of frequencies from large to small to obtain a sequencing sequence.
Preferably, obtaining a hierarchical sequence of each magnification level comprises:
obtaining the data number of the hierarchical sequence of each multiple layer:
Figure 667070DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 632621DEST_PATH_IMAGE006
to represent
Figure 234504DEST_PATH_IMAGE007
The number of data of the hierarchical sequence of the hierarchy,
Figure 960014DEST_PATH_IMAGE004
representing the number of times of the ith type of data, and n representing the total type number of the data;
Figure 452657DEST_PATH_IMAGE008
represents rounding down; obtaining a hierarchical sequence of each multiple layer
Figure 464475DEST_PATH_IMAGE009
Wherein
Figure 694599DEST_PATH_IMAGE010
To represent
Figure 128860DEST_PATH_IMAGE007
The value of the corresponding data in the hierarchical sequence of multiple layers.
Preferably, calculating the regularity of the hierarchical curve comprises:
in a length of
Figure 495251DEST_PATH_IMAGE006
Is/are as follows
Figure 68184DEST_PATH_IMAGE007
Hierarchical sequence segment setting of multiple layers
Figure 379079DEST_PATH_IMAGE011
An initiation point (wherein
Figure 180813DEST_PATH_IMAGE012
) The position of the initiation point is:
Figure 526344DEST_PATH_IMAGE013
wherein K represents the distance between the initial points, K represents the number of the initial points,
Figure 273107DEST_PATH_IMAGE006
to represent
Figure 71299DEST_PATH_IMAGE007
The number of data in the hierarchical sequence of the multiple layers is such that the corresponding initial point positions are respectively located
Figure 942303DEST_PATH_IMAGE014
. Two same sizes are arranged
Figure DEST_PATH_IMAGE015
The window of (2) is slid and sized
Figure 798133DEST_PATH_IMAGE016
The initial size is 2, the step length is set to be 1, and the difference between two windows in each iteration process is calculated and counted, so that the data structure similarity is calculated
Figure 463600DEST_PATH_IMAGE017
. The calculation formula of the data structure similarity is as follows:
Figure 749088DEST_PATH_IMAGE018
wherein the content of the first and second substances,
Figure 141892DEST_PATH_IMAGE019
indicates the size of the radix point correspondences is
Figure 196436DEST_PATH_IMAGE016
A window of (1), wherein
Figure 298384DEST_PATH_IMAGE020
；
Figure DEST_PATH_IMAGE021
Indicating that even dot correspondences have a size of
Figure 726960DEST_PATH_IMAGE016
The window of (1);
Figure 408608DEST_PATH_IMAGE022
means representing data within the corresponding window;
Figure 583238DEST_PATH_IMAGE023
means representing data within the corresponding window;
Figure 860283DEST_PATH_IMAGE024
representing the variance of the data within the corresponding window;
Figure 120364DEST_PATH_IMAGE025
representing the variance of the data within the corresponding window;
Figure 871282DEST_PATH_IMAGE026
represent
Figure 25052DEST_PATH_IMAGE019
Window and
Figure 327857DEST_PATH_IMAGE021
covariance of data within the window;
Figure 685020DEST_PATH_IMAGE027
and
Figure 364263DEST_PATH_IMAGE028
is to calculate the constant of the time that,
Figure 372539DEST_PATH_IMAGE029
，
Figure 846246DEST_PATH_IMAGE030
；
Figure 690705DEST_PATH_IMAGE031
is the maximum range of data in the time series segment;
iterating the window K, and calculating to obtain the similarity of the data structures of the corresponding windows
Figure 642481DEST_PATH_IMAGE032
The maximum value of (2) is obtained by solving the mean value of the similarity of a plurality of groups of window data structures
Figure 770842DEST_PATH_IMAGE007
The regularity of the hierarchical sequence curve of the multiple layers is as follows:
Figure 415450DEST_PATH_IMAGE033
wherein B represents
Figure 12785DEST_PATH_IMAGE007
The regularity of the hierarchical sequence curve of the multiple layers, n represents the number of point pairs of the initial point,
Figure 895815DEST_PATH_IMAGE017
is shown as
Figure 488470DEST_PATH_IMAGE034
Data structure similarity for an initial point pair.
Preferably, selecting the hierarchical curve corresponding to the multiple layer with the greatest regularity to calculate the minimum period of the hierarchical curve, including:
calculating a data relation function:
Figure 444925DEST_PATH_IMAGE035
wherein, Y represents a data relation function,
Figure 388610DEST_PATH_IMAGE036
indicating a sequence of numbered data
Figure DEST_PATH_IMAGE037
The number value of the point is set to,
Figure 603560DEST_PATH_IMAGE038
representing a numbered data sequence
Figure 457246DEST_PATH_IMAGE039
The number value of the point;
Figure 302711DEST_PATH_IMAGE006
indicating the length of the numbered data sequence;
Figure 733693DEST_PATH_IMAGE040
representing a data point interval;
Figure 793746DEST_PATH_IMAGE041
a mean value of the number values representing the number data sequence;
and (3) obtaining a corresponding minimum period through the extreme point of the data relation function Y:
Figure 767518DEST_PATH_IMAGE042
wherein the content of the first and second substances,
Figure 659250DEST_PATH_IMAGE043
expressing a time data degree of relationship function
Figure 357954DEST_PATH_IMAGE044
An individual maximum point;
Figure 399859DEST_PATH_IMAGE045
representing the number of maximum points of the data relation function;
Figure 821613DEST_PATH_IMAGE046
is a rounding function.
The embodiment of the invention at least has the following beneficial effects: according to the method, the hierarchy description of the energy time sequence data is obtained through analysis of the collected energy time sequence data, the smoothness degree of a hierarchy curve of each time-series layer is further analyzed, the optimal dictionary window size is obtained, the time-consuming data is encoded, compressed and transmitted by utilizing the dictionary window with the size being the optimal dictionary window size, the compression speed and the compression ratio of an LZ77 compression algorithm are considered, and the effect similar to real-time transmission is achieved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a system block diagram of a remote monitoring system for energy consumption detection of a combined air conditioning cabinet according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description, the structure, the features and the effects of the remote monitoring system for energy consumption detection of a combined air conditioning box according to the present invention are provided with reference to the accompanying drawings and the preferred embodiments. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the remote monitoring system for energy consumption detection of the combined air conditioning cabinet provided by the invention in detail with reference to the accompanying drawings.
Example (b):
the main application scenarios of the invention are as follows: the energy consumption of the combined air conditioning box is detected, and the acquired energy consumption data is compressed and transmitted.
Referring to fig. 1, a system block diagram of a remote monitoring system for energy consumption detection of a combined air conditioning cabinet according to an embodiment of the present invention is shown, where the method includes the following modules:
and the data acquisition module is used for acquiring energy consumption time sequence data corresponding to the combined air conditioner box.
The meter is installed in the combined air conditioner box, energy consumption time sequence data corresponding to the combined air conditioner box are collected, the collected energy consumption time sequence data are processed, namely, sampling frequency and transmission frequency are set, the energy consumption time sequence data in a fixed time period are compressed and transmitted, and the data to be transmitted are the energy consumption time sequence data in the fixed time period.
The data transmission module is used for counting the number of different types of energy consumption time sequence data to establish a statistical histogram; calculating the frequency of each type of energy consumption time sequence data by using the statistical histogram, and sequencing all types of energy consumption time sequence data according to the frequency to obtain a sequencing sequence; acquiring a serial number data sequence based on the position of each type of energy consumption time sequence data in the sequencing sequence;
the compression rate of the LZ77 compression algorithm generally depends on the size of a sliding window and the data entropy, which is determined by the data itself, so that the compression rate of the data is desired to be increased, the only variable is the size of the sliding window, the sliding window is a dynamic dictionary of the LZ77 encoding, the sliding window dictionary is large, that is, a longer character string can be accommodated, but the corresponding search difficulty is large, that is, the compression speed is increased as the sliding window is larger; the sliding serial port is smaller, the searching speed is higher, but the length of the character string which can be accommodated is limited by the size of the window, and the compression of the long character string is not facilitated. Therefore, in order to take account of the compression speed and the compression rate of the algorithm, the smoothness of each layer of curve is analyzed by acquiring the hierarchical description of the data to acquire the optimal sliding window dictionary.
LZ77 compression algorithm run flow: determining the size of a sliding window dictionary, performing phrase matching between data in a cache region and the sliding window dictionary, if matched characters cannot be found, coding unmatched symbols into mark symbols, and if matched characters cannot be found, coding the longest matched symbols into phrase marks, wherein the phrases comprise offset in the sliding window dictionary, the number of matched symbols and the first symbol of the cache region after matching is finished.
Statistical data type, numbering: the types of data in the acquired energy consumption time sequence data are various, and the size difference is different, namely, when the acquired energy consumption data are directly processed, the processing difficulty is higher, and the calculation is not facilitated. For the convenience of calculation, the types of the energy consumption time sequence data are counted, new numbers are given to the types of the energy consumption time sequence data to replace the data, and a statistical histogram is established according to the number of each type of the energy consumption time sequence data, wherein the statistical histogram is used for calculating the energy consumption time sequence dataThe vertical axis S in the middle statistical histogram represents the number, i.e., the number of times a certain type of data appears, and the horizontal axis a represents the energy consumption value. For example, the energy consumption time series data is: [10, 30, 20, 40, 50, 60, 30, 20, 40]Then, then
Figure 212143DEST_PATH_IMAGE047
,
Figure 352138DEST_PATH_IMAGE048
,
Figure 444072DEST_PATH_IMAGE049
,
Figure 861278DEST_PATH_IMAGE050
,
Figure 625971DEST_PATH_IMAGE051
,
Figure 909054DEST_PATH_IMAGE052
Calculating the frequency of occurrence of each type of data, namely:
Figure 292762DEST_PATH_IMAGE001
in the formula
Figure 813742DEST_PATH_IMAGE002
Indicating the frequency of occurrence of category j energy timing data,
Figure 483758DEST_PATH_IMAGE003
representing the number of occurrences of category j energy timing data,
Figure 473711DEST_PATH_IMAGE004
representing the number of times of occurrence of the ith type energy time sequence data, and n represents the total type number of the data; sorting n types of data from large to small according to the frequency, if the frequencies are the same, sorting according to the sequence of the data, and obtaining a sorting sequence through sorting, which is shown as an example: if the energy consumption time sequence data is as follows:[10、30、10、30、20、40、50、60、30、20、40]and the sequence obtained by sequencing the occurrence frequency according to the sequencing rule is as follows:
Figure 520164DEST_PATH_IMAGE053
assigning the sequencing sequence to the data of the corresponding type, and converting the original energy consumption time sequence data sequence into a number data sequence, such as: the energy consumption time sequence data is as follows: [10, 30, 20, 40, 50, 60, 30, 20, 40]. The corresponding numbering data sequence is as follows: [2,1,2,1,3,4,5,6,1,3,4]Compared with the original energy consumption time sequence data sequence, the converted serial number sequence has greatly reduced calculation amount, and is convenient for the calculation of the subsequent curve smoothing degree.
Constructing a hierarchical description: for LZ77 compression encoding, the compression is performed on redundant data, i.e. the greater the data similarity between the corresponding sliding window dictionary region and the forward buffer region, the greater the corresponding compression rate. Because the data values in the energy consumption time sequence are directly different, namely the data values are difficult to calculate when similarity calculation is carried out, the energy consumption values of the same energy consumption time sequence sub-sequences are equal to each other through analysis, namely, hierarchical description is built, and the appropriateness of the corresponding hierarchy is obtained through obtaining the corresponding hierarchical sequence.
Accumulating the generated and converted serial number data sequence, constructing a multi-layer accumulated sequence through the accumulated operation, wherein the initial layer is the most initial serial number data sequence and corresponds to the energy consumption time sequence data sequence, adding the first and second data of the initial layer to form the first data of a double-layer, and adding the third and fourth data of the initial layer to form the second data of the double-layer in the same way, thereby obtaining the hierarchical sequence with the corresponding layers, and if the number of the data of the hierarchical sequence of the double-layer is:
Figure 161230DEST_PATH_IMAGE005
in the formula
Figure 143092DEST_PATH_IMAGE006
To represent
Figure 744975DEST_PATH_IMAGE007
The number of data of the hierarchical sequence of the hierarchy,
Figure 457103DEST_PATH_IMAGE004
indicates the number of times of the ith type of data, n indicates the total type number of the data,
Figure 828042DEST_PATH_IMAGE007
representing a multiple of layers, if two of the layers are present
Figure 980806DEST_PATH_IMAGE054
；
Figure 804405DEST_PATH_IMAGE008
Indicating rounding down, since the number of data in the initial layer may not necessarily just be
Figure 317295DEST_PATH_IMAGE007
Therefore, the whole number is obtained by adopting a mode of rounding down
Figure 683685DEST_PATH_IMAGE007
The number of data in the hierarchical sequence of the multiple layers cannot be formed in the initial layer
Figure 866405DEST_PATH_IMAGE007
The data for the multiples are not considered. Thereby obtaining
Figure 567514DEST_PATH_IMAGE007
Hierarchical sequence of doubling layers. The shape is as follows:
Figure 493881DEST_PATH_IMAGE009
wherein
Figure 449199DEST_PATH_IMAGE010
To represent
Figure 333979DEST_PATH_IMAGE007
Corresponding data in hierarchical sequence of multiple layersThe value of (c).
By passing
Figure 522383DEST_PATH_IMAGE007
Obtaining the layer curve of the corresponding layer by the layer sequence of the multiple layers, and calculating
Figure 127808DEST_PATH_IMAGE007
The regular degree of the hierarchical sequence curve of the multiple layer is more regular and more approximately periodic, which indicates that the corresponding multiple layer is more in accordance with the periodic requirement, namely the redundancy degree reaches the maximum when the sliding window dictionary with the window size corresponding to the corresponding layer is used for compression. The calculation method of the regularity is as follows: in a length of
Figure 62266DEST_PATH_IMAGE006
Is/are as follows
Figure 239651DEST_PATH_IMAGE007
Hierarchical sequence segment setting of multiple layers
Figure 525139DEST_PATH_IMAGE011
An initiation point (wherein
Figure 934254DEST_PATH_IMAGE012
) The position of the initial point is:
Figure 113432DEST_PATH_IMAGE055
where K represents the separation distance of the initial points, K represents the number of initial points,
Figure 74435DEST_PATH_IMAGE006
to represent
Figure 457006DEST_PATH_IMAGE007
The number of data in the hierarchical sequence of the multiple layers is such that the corresponding initial point positions are respectively located
Figure 528867DEST_PATH_IMAGE014
. Two same sizes are arranged
Figure 93709DEST_PATH_IMAGE015
Is slid and sized
Figure 960034DEST_PATH_IMAGE016
The initial size is 2, the step length is set to be 1, and the difference between two windows in each iteration process is calculated and counted, so that the similarity of the data structure is calculated
Figure 95480DEST_PATH_IMAGE017
. The calculation formula of the data structure similarity is as follows:
Figure 705453DEST_PATH_IMAGE056
in the formula (I), the compound is shown in the specification,
Figure 859223DEST_PATH_IMAGE019
indicates the size of the radix point correspondences is
Figure 896449DEST_PATH_IMAGE016
A window of (1), wherein
Figure 630443DEST_PATH_IMAGE020
；
Figure 309686DEST_PATH_IMAGE021
Indicating that even dot correspondences have a size of
Figure 317962DEST_PATH_IMAGE016
The window of (1);
Figure 932614DEST_PATH_IMAGE022
means representing data within the corresponding window;
Figure 656636DEST_PATH_IMAGE023
means representing data within the corresponding window;
Figure 995695DEST_PATH_IMAGE024
representing the variance of the data within the corresponding window;
Figure 999423DEST_PATH_IMAGE025
representing the variance of the data within the corresponding window;
Figure 519397DEST_PATH_IMAGE026
to represent
Figure 975786DEST_PATH_IMAGE019
Window and
Figure 121466DEST_PATH_IMAGE021
covariance of data within the window;
Figure 855066DEST_PATH_IMAGE027
and
Figure 670576DEST_PATH_IMAGE028
is to calculate the constant of the time at which,
Figure 738895DEST_PATH_IMAGE029
，
Figure 298052DEST_PATH_IMAGE030
；
Figure 886159DEST_PATH_IMAGE031
is the maximum range of data in the time series segment.
Iterating the window K, and calculating to obtain the similarity of the data structures of the corresponding windows
Figure 872570DEST_PATH_IMAGE032
The maximum value of (2) is obtained by solving the mean value of the similarity of a plurality of groups of window data structures
Figure 693764DEST_PATH_IMAGE007
The regularity of the hierarchical sequence curve of the multiple layers is as follows:
Figure 666399DEST_PATH_IMAGE057
in the formula, B represents
Figure 233647DEST_PATH_IMAGE007
The regularity of the hierarchical sequence curve of the multiple layers, n represents the number of point pairs of the initial point,
Figure 518522DEST_PATH_IMAGE017
is shown as
Figure 702379DEST_PATH_IMAGE034
Data structure similarity for an initial point pair.
Obtaining an optimal dictionary window size: by passing
Figure 213126DEST_PATH_IMAGE007
The method comprises the following steps of obtaining an optimal multiple layer according to the regularity of a hierarchical sequence curve of the multiple layer, selecting the multiple layer with the maximum regularity B, obtaining a minimum period in a corresponding hierarchical curve, wherein the data volume corresponding to the minimum period is the optimal window size, and the minimum period calculation method comprises the following steps:
Figure 900459DEST_PATH_IMAGE035
wherein Y represents the degree of relationship of the data,
Figure 353306DEST_PATH_IMAGE036
indicating a sequence of numbered data
Figure 634246DEST_PATH_IMAGE037
The number value of the point is set to,
Figure 338897DEST_PATH_IMAGE038
indicating a sequence of numbered data
Figure 270950DEST_PATH_IMAGE039
The number value of the point;
Figure 770064DEST_PATH_IMAGE006
indicating the length of the numbered data sequence;
Figure 538300DEST_PATH_IMAGE040
representing an interval of data points, the data correlation function being spaced only from the data points
Figure 905696DEST_PATH_IMAGE011
(ii) related;
Figure 567622DEST_PATH_IMAGE041
the number value means of the number data sequence. The maximum point of the data relationship is always at a multiple of the period, i.e. the maximum point
Figure 378583DEST_PATH_IMAGE040
、
Figure 614873DEST_PATH_IMAGE058
、
Figure 395748DEST_PATH_IMAGE059
…. The corresponding period is thus determined by determining the extreme point of the data relationship Y
Figure 787546DEST_PATH_IMAGE060
I.e. period
Figure 894042DEST_PATH_IMAGE061
The computational expression of (a) is:
Figure 620559DEST_PATH_IMAGE042
in the formula (I), the compound is shown in the specification,
Figure 205124DEST_PATH_IMAGE043
to express a function of time relation
Figure 451428DEST_PATH_IMAGE044
An individual maximum point;
Figure 853460DEST_PATH_IMAGE045
representing the number of maximum points of the time relation function;
Figure 677059DEST_PATH_IMAGE046
is a rounding function. Thus, the minimum cycle number of the hierarchical sequence curve corresponding to the multiple layers is obtained, and the corresponding optimal dictionary window size is as follows:
Figure 206261DEST_PATH_IMAGE062
where F represents the optimal dictionary window size,
Figure 431706DEST_PATH_IMAGE007
representing the magnification level, E represents the minimum number of cycles, thereby obtaining the optimal LZ77 dictionary window size of
Figure 676742DEST_PATH_IMAGE063
。
And the energy consumption detection module is used for receiving and transmitting the obtained energy consumption time sequence data to analyze the energy consumption condition of the combined air-conditioning box.
Using optimal sliding dictionary windows
Figure 253217DEST_PATH_IMAGE063
The energy consumption time sequence data are coded and compressed, the coding efficiency is guaranteed while the coding compression rate is guaranteed, the effect similar to real-time transmission is achieved, the energy consumption condition of the combined air conditioning box is analyzed through the energy consumption data obtained through receiving and transmitting by the remote monitoring system, and corresponding measures are taken, such as early warning, control of corresponding switches and the like.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And that specific embodiments have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (6)

1. A remote monitoring system for energy consumption detection of a combined air conditioning cabinet is characterized by comprising:
the data acquisition module is used for acquiring energy consumption time sequence data corresponding to the combined air-conditioning box;
the data transmission module is used for counting the number of different types of energy consumption time sequence data to establish a statistical histogram; calculating the frequency of each type of energy consumption time sequence data by using the statistical histogram, and sequencing all types of energy consumption time sequence data according to the frequency to obtain a sequencing sequence; acquiring a serial number data sequence based on the position of each type of energy consumption time sequence data in the sequencing sequence; constructing a hierarchical description by using the serial number data sequence to obtain a hierarchical sequence of each multiple layer; acquiring a hierarchical curve of a corresponding hierarchy through a hierarchical sequence of each multiple layer, and calculating the regularity of the hierarchical curve; selecting a layer curve corresponding to the layer with the maximum regularity to calculate the minimum period of the layer curve; the product of the minimum period and the layer multiplication corresponding to the minimum period is the optimal dictionary window size; carrying out coding compression and transmission on the time-consuming sequence data by utilizing the dictionary window with the size being the optimal size of the dictionary window;
and the energy consumption detection module is used for receiving and transmitting the obtained energy consumption time sequence data to analyze the energy consumption condition of the combined air-conditioning box.
2. The system according to claim 1, wherein the frequency of each type of energy consumption time series data is:
Figure 511827DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE003
indicating the frequency of occurrence of category j energy timing data,
Figure 114846DEST_PATH_IMAGE004
representing the number of occurrences of category j energy timing data,
Figure DEST_PATH_IMAGE005
the number of times of occurrence of the ith type of energy time series data is shown, and n is the total type number of the energy time series data.
3. The system according to claim 1, wherein the sorting of the energy consumption time series data of all types according to the frequency to obtain a sorting sequence comprises: and arranging according to the sequence of the frequencies from large to small to obtain a sequencing sequence.
4. The system according to claim 1, wherein the step of obtaining the hierarchical sequence of each hierarchy level comprises:
obtaining the data number of the hierarchical sequence of each multiple layer:
Figure DEST_PATH_IMAGE007
wherein the content of the first and second substances,
Figure 270234DEST_PATH_IMAGE008
to represent
Figure DEST_PATH_IMAGE009
The number of data of the hierarchical sequence of the hierarchy,
Figure 57931DEST_PATH_IMAGE005
representing the number of times of the ith type of data, and n representing the total type number of the data;
Figure 849169DEST_PATH_IMAGE010
represents rounding down; obtaining a hierarchical sequence of each multiple layer
Figure DEST_PATH_IMAGE011
Wherein
Figure 521459DEST_PATH_IMAGE012
Represent
Figure 84683DEST_PATH_IMAGE009
The value of the corresponding data in the hierarchical sequence of multiple layers.
5. The system according to claim 1, wherein the calculating the regularity of the hierarchical curve comprises:
in a length of
Figure 777701DEST_PATH_IMAGE008
Is/are as follows
Figure 197181DEST_PATH_IMAGE009
Hierarchical sequence segment setting of multiple layers
Figure DEST_PATH_IMAGE013
An initiation point (wherein
Figure 876424DEST_PATH_IMAGE014
) The position of the initiation point is:
Figure 274913DEST_PATH_IMAGE016
wherein K represents the distance between the initial points, K represents the number of the initial points,
Figure 686303DEST_PATH_IMAGE008
represent
Figure 738221DEST_PATH_IMAGE009
The number of data in the hierarchical sequence of the multiple layers is such that the corresponding initial point positions are respectively located
Figure DEST_PATH_IMAGE017
(ii) a Two same sizes are arranged
Figure 221155DEST_PATH_IMAGE018
Is slid and sized
Figure DEST_PATH_IMAGE019
The initial size is 2, the step length is set to be 1, and the difference between two windows in each iteration process is calculated and counted, so that the similarity of the data structure is calculated
Figure 756041DEST_PATH_IMAGE020
(ii) a The calculation formula of the data structure similarity is as follows:
Figure 197387DEST_PATH_IMAGE022
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE023
indicates the size of the radix point correspondences is
Figure 184935DEST_PATH_IMAGE019
A window of (1), wherein
Figure 2718DEST_PATH_IMAGE024
；
Figure DEST_PATH_IMAGE025
Indicating that even dot correspondences have a size of
Figure 860953DEST_PATH_IMAGE019
The window of (1);
Figure 741708DEST_PATH_IMAGE026
means representing data within the corresponding window;
Figure DEST_PATH_IMAGE027
means representing data within the corresponding window;
Figure 216552DEST_PATH_IMAGE028
representing the variance of the data within the corresponding window;
Figure DEST_PATH_IMAGE029
representing the variance of the data within the corresponding window;
Figure 306868DEST_PATH_IMAGE030
to represent
Figure 691713DEST_PATH_IMAGE023
Window and
Figure 474861DEST_PATH_IMAGE025
covariance of data within the window;
Figure DEST_PATH_IMAGE031
and
Figure 437001DEST_PATH_IMAGE032
is to calculate the constant of the time that,
Figure DEST_PATH_IMAGE033
，
Figure 799849DEST_PATH_IMAGE034
；
Figure DEST_PATH_IMAGE035
is the maximum range of data in the time series segment;
iterating the window K, and calculating to obtain the similarity of the data structures of the corresponding windows
Figure 429413DEST_PATH_IMAGE036
The maximum value of (2) is obtained by solving the mean value of the similarity of a plurality of groups of window data structures
Figure 117884DEST_PATH_IMAGE009
The regularity of the hierarchical sequence curve of the multiple layers is as follows:
Figure 239424DEST_PATH_IMAGE038
wherein B represents
Figure 78067DEST_PATH_IMAGE009
The regularity of the hierarchical sequence curve of the multiple layers, n represents the number of point pairs of the initial point,
Figure 559208DEST_PATH_IMAGE020
is shown as
Figure DEST_PATH_IMAGE039
Data structure similarity to the initial point pair.
6. The system according to claim 1, wherein the selecting the hierarchical curve corresponding to the layer with the highest regularity to calculate the minimum period comprises:
calculating a data relation function:
Figure DEST_PATH_IMAGE041
wherein Y represents a data relation function,
Figure 887421DEST_PATH_IMAGE042
indicating a sequence of numbered data
Figure DEST_PATH_IMAGE043
The number value of the point is set to,
Figure 867229DEST_PATH_IMAGE044
indicating a sequence of numbered data
Figure DEST_PATH_IMAGE045
The number value of the point;
Figure 965022DEST_PATH_IMAGE008
indicating the length of the numbered data sequence;
Figure 444545DEST_PATH_IMAGE046
representing a data point interval;
Figure DEST_PATH_IMAGE047
a mean value of the number values representing the number data sequence;
and (3) obtaining a corresponding minimum period through the extreme point of the data relation function Y:
Figure DEST_PATH_IMAGE049
wherein the content of the first and second substances,
Figure 209239DEST_PATH_IMAGE050
expressing a time data degree of relationship function
Figure DEST_PATH_IMAGE051
A maximum value point;
Figure 226742DEST_PATH_IMAGE052
representing the number of maximum points of the data relation function;
Figure DEST_PATH_IMAGE053
is a rounding function.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115632661A (en) * 2022-12-22 2023-01-20 互丰科技(北京)有限公司 Efficient compression transmission method for network security information

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106933208A (en) * 2015-10-09 2017-07-07 费希尔-罗斯蒙特系统公司 Distributed industrial performance monitoring and analysis
CN108880556A (en) * 2018-05-30 2018-11-23 中国人民解放军战略支援部队信息工程大学 Destructive data compressing method, error-resilience method and encoder and decoder based on LZ77
CN114900195A (en) * 2022-07-11 2022-08-12 山东嘉通专用汽车制造有限公司 Safety state monitoring system for powder tanker

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106933208A (en) * 2015-10-09 2017-07-07 费希尔-罗斯蒙特系统公司 Distributed industrial performance monitoring and analysis
CN108880556A (en) * 2018-05-30 2018-11-23 中国人民解放军战略支援部队信息工程大学 Destructive data compressing method, error-resilience method and encoder and decoder based on LZ77
CN114900195A (en) * 2022-07-11 2022-08-12 山东嘉通专用汽车制造有限公司 Safety state monitoring system for powder tanker

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
程敏 等: "基于时间序列分段的气象数据压缩算法", 《计算机系统应用》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115632661A (en) * 2022-12-22 2023-01-20 互丰科技(北京)有限公司 Efficient compression transmission method for network security information
CN115632661B (en) * 2022-12-22 2023-03-07 互丰科技(北京)有限公司 Efficient compression transmission method for network security information

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