Digital energy consumption management system

Abstract

The invention belongs to the technical field of energy consumption management, and particularly relates to a digital energy consumption management system which comprises an energy consumption data acquisition module, a target length acquisition module, a data sequence storage module and an energy consumption data management module. Dividing a data sequence formed by the energy consumption data of all the users in one day according to the candidate length, obtaining a binary sequence of the subsequences according to the size relationship between the real frequency of the data in the subsequences and a frequency threshold value, obtaining all characteristics of the candidate length, and calculating the number of the subsequences with the characteristics according to the number of the subsequences with the characteristics. And calculating the advantage degree of each feature as a concern feature, taking the feature with the advantage degree greater than 1 as the concern feature, taking the candidate length with at least one concern feature as a target length, performing arithmetic coding on the data sequence, obtaining a compression result of the energy consumption data, and storing the compression result so as to facilitate digital display. According to the invention, the demand of storage space of the management system and the cost of storage hardware are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption management, and more specifically, to a digital energy consumption management system. Background Art

[0002] The construction of smart parks effectively integrates multi-level technologies, such as 5G, AI, and the Internet of Things, to achieve full-scenario smart services, truly empower park management decisions, and achieve the goal of reducing costs and increasing efficiency. At the same time, as the core of the digital economy, smart parks comprehensively improve park management and operation capabilities and promote the efficiency of digital transformation through digital infrastructure construction, artificial intelligence, and digital platforms.

[0003] In the related technology, for example, the Chinese patent application document with application publication number CN117875733A discloses an intelligent energy consumption management method and system for a smart park based on artificial intelligence, including: when the system is running, the power usage and consumption in the park are measured through the data acquisition module, and the current, voltage, power and electrical energy parameters can be monitored in real time, as well as the water usage and energy consumption in the park. The water flow rate and total water volume can be monitored in real time. At the same time, the data set is sent to the storage module for sorting and storage through the data transmission module. After the data analysis module removes invalid data and processes missing values, it is sent to the digital twin model module for calculation and analysis to obtain the power consumption coefficient Dhxs and the water volume coefficient Slxs. At the same time, the power consumption coefficient Dhxs and the water volume coefficient Slxs are compared with the standard threshold value, which promotes the operation of the hierarchical strategy scheme under the control module, optimizes the reduction of power consumption, reduces unnecessary cost expenditure, and at the same time moves closer to the social concept of energy conservation and emission reduction.

[0004] Energy consumption data is often used for long-term historical data analysis to help park managers understand energy consumption trends and optimize energy management strategies. In order to support historical data analysis, a large amount of energy consumption data needs to be compressed, stored and backed up. Although the storage and backup of energy consumption data are of great significance to the long-term management and optimization of smart parks, it also increases the storage space requirements of the management system and the cost of storage hardware. Summary of the invention

[0005] In order to solve the technical problem that the storage and backup of the above energy consumption data increase the storage space demand for the management system of the smart park and the cost of storage hardware, the present invention provides a digital energy consumption management system, which includes the following modules: an energy consumption data acquisition module, which is used to collect the energy consumption data of each user every day, and compile the energy consumption data of all users in one day into a data sequence, wherein the energy consumption data includes electricity consumption and water consumption; a target length acquisition module, which is used to calculate the true frequency of each type of data in the data sequence; and divide the data sequence into multiple lengths equal to the candidate length. subsequence; according to the relationship between the true frequency of the data in the subsequence and the frequency threshold, obtain the binary sequence of the subsequence, the frequency threshold is equal to the inverse of the number of types of all data; length equal to All binary numbers of are taken as all features of the candidate length; according to the number of subsequences with each feature, the degree of dominance of each feature as the focus feature is calculated, and the features with a dominance greater than 1 are taken as the focus features of the candidate length; the candidate length with at least one focus feature is taken as the target length ; A data sequence storage module is used to perform arithmetic coding on the data sequence according to the target length, and obtain the interval to which each data belongs in turn. When the data is the first in the subsequence of the feature of interest with the target length, When data is obtained, the interval is divided by frequency threshold and the interval to which the data belongs is obtained; in the interval to which the last data belongs, the shortest binary number is used as the compression result of the energy consumption data and stored; the energy consumption data management module is used to digitally display the electricity consumption ranking, water consumption ranking, electricity consumption trend and water consumption trend according to the compression result of the stored energy consumption data.

[0006] The present invention divides a data sequence into multiple subsequences by candidate lengths, and sets all features of the candidate lengths. A binary sequence of the subsequence is obtained by a size relationship between the real frequency of data in the subsequence and a frequency threshold. A subsequence with each feature is obtained according to the binary sequence of the subsequence. The dominance of each feature as a focus feature is calculated according to the number of subsequences with each feature. Features with a dominance greater than 1 are used as focus features of the candidate length. The candidate length with at least one focus feature is used as a target length. The obtained target length can expand the size of an interval corresponding to the last data in the data sequence, thereby improving the compression rate of the data sequence, reducing the amount of data, and further reducing the storage space requirements of the management system and the cost of storage hardware.

[0007] Preferably, the candidate length for Integer in the range, The preset upper limit.

[0008] Preferably, the method of obtaining the binary sequence of the subsequence based on the size relationship between the true frequency of the data in the subsequence and the frequency threshold includes: setting the elements corresponding to the data whose true frequency is greater than or equal to the frequency threshold as the first digital number, and setting the elements corresponding to the data whose true frequency is less than the frequency threshold as the second digital number; and forming the binary sequence of the subsequence from the elements corresponding to all the data in the subsequence.

[0009] The present invention obtains the binary sequence of the subsequence through the size relationship between the real frequency of the data in the subsequence and the frequency threshold, realizes the feature recognition of each subsequence, and provides data support for the subsequent calculation of the advantage degree of each feature as the focus feature.

[0010] Preferably, the method for obtaining the subsequence with each feature is: for any feature, if the first If the binary number composed of elements is equal to the feature, the subsequence is marked as a subsequence with the feature; otherwise, the subsequence is marked as a subsequence without the feature.

[0011] Preferably, the calculation formula for the degree of dominance of the feature as a focus feature is: if the number of subsequences with the feature is equal to 0, then the degree of dominance of the feature as a focus feature is ; If the number of subsequences with this feature is greater than 0, the advantage of this feature as a focus feature ; In the formula, is the frequency threshold, is the number of subsequences with this feature, For the first The true frequency of the last data of the subsequence, is the multiplication symbol.

[0012] The present invention calculates the degree of dominance of each feature as a focus feature according to the number of subsequences having each feature, so as to screen out the focus feature that can expand the size of the interval corresponding to the last data in the data sequence.

[0013] Preferably, the candidate length having at least one feature of interest is used as the target length One method is to arbitrarily select a candidate length as the target length from the candidate lengths having at least one feature of interest. .

[0014] Preferably, the candidate length having at least one feature of interest is used as the target length Another method is: for candidate lengths with at least one feature of interest , calculate the candidate length The candidate length with the largest advantage is taken as the target length.

[0015] The present invention calculates the candidate length The advantage degree is determined, and the candidate length with the largest advantage degree is taken as the target length, so that the size of the interval corresponding to the last data in the data sequence is expanded to the maximum extent, the compression rate of the data sequence is maximized and the amount of data is reduced, thereby minimizing the storage space requirements of the management system and the cost of storage hardware.

[0016] Preferably, the candidate length The calculation formula for the degree of advantage is: ; In the formula, Candidate length Features of interest, The first The frequency of the data, , Candidate length The number of all subsequences under is the candidate length, The first The true frequency of the data, , is the length of the data sequence, is the symbol for cumulative multiplication; The data is of candidate length The first When the data is The frequency of the data is set to The true frequency of the data; The data does not have a candidate length The first When the data is The frequency of each data point is set as the frequency threshold.

[0017] Preferably, the data is the first When there are data, the interval is divided by the frequency threshold and the interval to which the data belongs is obtained, including: setting the frequency of each data as the frequency threshold, dividing the interval by the frequency of all data and obtaining the interval to which the data belongs; when the data is not the first in the subsequence of the feature of interest with the target length When there are data, the interval is divided according to the real frequency of the data and the interval to which the data belongs is obtained, including: setting the frequency of each data to the real frequency of each data, dividing the interval according to the frequencies of all kinds of data and obtaining the interval to which the data belongs.

[0018] The present invention is to obtain a target length The first When the interval to which a piece of data belongs is determined, the frequency of each type of data is set as the frequency threshold, and the interval is divided according to the frequencies of all types of data to obtain the interval to which the data belongs, so as to expand the size of the interval corresponding to the last data in the data sequence, improve the compression rate of the data sequence, reduce the amount of data, and thus reduce the storage space requirements of the management system and the cost of storage hardware.

[0019] Preferably, the step of dividing the intervals by the frequencies of all types of data and obtaining the intervals to which the data belongs includes: for the first Data: When When the frequency of all data is Divide the interval into Divide into sub-intervals, is the number of types of all data; when When the frequency of all kinds of data is used to calculate the first The interval to which the data belongs is divided into intervals, and the first The interval to which the data belongs is divided into subintervals; the obtained subintervals are used as the subintervals corresponding to each type of data, and the first subinterval in the data sequence is obtained from the subintervals corresponding to all types of data. The subinterval corresponding to the data is used as the first The interval to which the data belongs.

[0020] The beneficial effects of the present invention are: The present invention divides a data sequence into multiple subsequences by candidate lengths, and sets all features of the candidate lengths. A binary sequence of the subsequence is obtained by a size relationship between the real frequency of data in the subsequence and a frequency threshold. A subsequence with each feature is obtained according to the binary sequence of the subsequence. The dominance of each feature as a focus feature is calculated according to the number of subsequences with each feature. Features with a dominance greater than 1 are used as focus features of the candidate length. The candidate length with at least one focus feature is used as a target length. The obtained target length can expand the size of an interval corresponding to the last data in the data sequence, thereby improving the compression rate of the data sequence, reducing the amount of data, and further reducing the storage space requirements of the management system and the cost of storage hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein: Figure 1 is a system block diagram schematically showing a digital energy consumption management system in the present invention; Figure 2 is a system block diagram schematically showing the target length acquisition module 200 . DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] The present invention provides a digital energy consumption management system. Figure 1 As shown, a digital energy consumption management system includes an energy consumption data acquisition module 100, a target length acquisition module 200, a data sequence storage module 300 and an energy consumption data management module 400, which are described in detail below.

[0025] The problems of traditional parks mainly come from the following aspects: 1. Security management issues are prominent, mainly because traditional parks have poor overall perception capabilities, backward and single security methods, and cannot meet the growing demand for refined security management.

[0026] 2. The forms are diverse and the common needs are unclear. This is mainly because the planning and construction, management mechanisms, construction eras, and business formats of traditional parks are different. The needs and construction priorities for smart upgrades and renovations are different, making it difficult to meet the personalized needs of different groups.

[0027] 3. The lack of unified industry standards and operating specifications is mainly because manufacturers are fighting each other to launch smart products and build their own ecological chains, resulting in the formation of scattered "information islands", making it difficult to achieve smart interconnection in all scenarios.

[0028] 4. The investment in information construction is large, but the results are minimal, mainly because of the lack of systematic and forward-looking planning, which leads to the isolation of various systems, the inability to achieve data sharing and collaborative work, and it is difficult to achieve overall benefits.

[0029] The construction of smart parks effectively integrates multi-level technologies, such as 5G, AI, and the Internet of Things, to achieve full-scenario smart services, truly empower park management decisions, and achieve the goal of reducing costs and increasing efficiency. At the same time, as the core of the digital economy, smart parks comprehensively improve park management and operation capabilities and promote the efficiency of digital transformation through digital infrastructure construction, artificial intelligence, and digital platforms.

[0030] Among them, the AIoT (Artificial Intelligence Internet of Things, integrating AI (artificial intelligence) technology and IoT (Internet of Things) technology) comprehensive management platform provides a complete and sound solution for various scenarios, mainly smart parks. It uses one system to link multiple smart applications and unify deployment and management. Through data fusion, scenario linkage, algorithm innovation and other methods, it can achieve accurate analysis and scientific governance of the entire smart park. Through a market-driven, sustainable development construction and operation model, it coordinates the relationship between park users, builders, and managers, so that all links in the smart park chain can cooperate effectively, and different service modules can be connected and operate effectively.

[0031] In the energy consumption management system of the AIoT comprehensive management platform, energy consumption data is often used for long-term historical data analysis. Combined with the digital large screen, it allows energy to operate efficiently through scientific regulation, helping park managers understand energy consumption trends and optimize energy management strategies. In order to support historical data analysis, a large amount of energy consumption data needs to be compressed, stored and backed up.

[0032] The energy consumption data collection module 100 is used to collect the energy consumption data of each user every day, and form a data sequence with the energy consumption data of all users in one day.

[0033] Specifically, for the smart park managed by the AIoT integrated management platform, individual users are divided according to the building number, unit number, floor number, and household number, and all users are sorted in order.

[0034] Among them, for the The first Unit The account number of each floor is The sorted sequence number of users is , and the serial number equal ; is the building number where the user is located. is the unit number of the unit where the user is located, is the floor number of the user, is the user's account number, For the The first The number of all floors contained in the unit, For the The first The number of all users on each floor in the unit, For the The first The number of all floors contained in the unit, For the The first The number of all users on each floor in the unit, For the The first The number of all users on each floor in the unit.

[0035] For example, when There are two units in the building, and the number of all floors contained in each unit is equal to 28, and the number of all users contained in each floor is equal to 6; There are two units in the building, and the number of floors in each unit is equal to 30. The number of users on each floor of the unit is equal to 6. The number of all users contained in each floor of the unit is equal to 4; There are two units in the building, and the number of all floors in each unit is equal to 30, and the number of all users in each floor is equal to 6; for the The first Unit The account number of each floor is The sorted sequence number of users For the The first Unit The account number of each floor is The sorted sequence number of users =(28×6+28×6+30×6+30×4)+(30×6)+(22×6)+4=952.

[0036] Furthermore, each user is installed with an energy consumption data collection device, and each user's daily energy consumption data is collected through the energy consumption data collection device; the energy consumption data collection device includes an electricity meter and a water meter, and each user is installed with an electricity meter and a water meter; the energy consumption data includes electricity consumption and water consumption, the electricity consumption is collected through the electricity meter, and the unit of electricity consumption is degree, the water consumption is collected through the water meter, and the unit of water consumption is ton.

[0037] Furthermore, the energy consumption data of all users for one day are organized into a data sequence in ascending order of the user's serial number; since the energy consumption data includes electricity consumption and water consumption, the data sequence composed of the electricity consumption of all users for one day is recorded as the electricity consumption data sequence, and the data sequence composed of the water consumption of all users for one day is recorded as the water consumption data sequence.

[0038] Exemplarily, when the number of all users is equal to 10, and the electricity consumption of these 10 users on a certain day is equal to 3, 2, 4, 2, 2, 1, 2, 3, 4, 3 respectively, the energy consumption data of these 10 users for one day are organized into a data sequence, and the data sequence is {3, 2, 4, 2, 2, 1, 2, 3, 4, 3}.

[0039] It should be noted that although the storage and backup of energy consumption data are of great significance to the long-term management and optimization of smart parks, it also increases the storage space requirements of the management system and the cost of storage hardware. Therefore, it is necessary to use an efficient data compression algorithm to compress the energy consumption data, reduce the amount of data, and thereby reduce the storage space requirements of the management system and the cost of storage hardware.

[0040] It should be further explained that arithmetic coding is a compression algorithm that can adapt to various types of data. The compression rate depends on the size of the interval corresponding to the last data in the data sequence. The larger the interval, the greater the compression rate. Conversely, the smaller the interval, the smaller the compression rate. The size of the interval corresponding to the last data in the data sequence is equal to the product of the true frequencies of all the data in the data sequence.

[0041] In summary, the present invention divides a data sequence into multiple subsequences through candidate lengths, and sets all features of the candidate lengths. Through the relationship between the real frequency of the data in the subsequence and the frequency threshold, a binary sequence of the subsequence is obtained. According to the binary sequence of the subsequence, a subsequence with each feature is obtained. According to the number of subsequences with each feature, the dominance of each feature as a focus feature is calculated, and features with a dominance greater than 1 are used as focus features of the candidate length. The candidate length with at least one focus feature is used as the target length. The obtained target length can expand the size of the interval corresponding to the last data in the data sequence, thereby improving the compression rate of the data sequence, reducing the amount of data, and thereby reducing the storage space requirements of the management system and the cost of storage hardware.

[0042] The data feature acquisition module 200 is used to obtain the target length.

[0043] The system block diagram of the target length acquisition module 200 is shown in FIG. Figure 2 , including submodules 201 to 206, specifically: The frequency statistics submodule 201 is used to count the real frequency of each type of data in the statistical data sequence.

[0044] Specifically, the frequency of each data in the statistical data sequence refers to the number of times each data appears in the data sequence, and the ratio of the frequency of each data to the total number of all data in the data sequence is taken as the true frequency of each data.

[0045] Exemplarily, for the data sequence {3,2,4,2,2,1,2,3,4,3}, the total number of all data is equal to 10, and there are 4 types of data, namely 1, 2, 3, and 4. Therefore, the number of types of all data is equal to 4, and the frequencies of these 4 types of data in the data sequence obtained through statistics are 1, 4, 3, and 2, respectively. Then the frequencies of these 4 types of data are 0.1, 0.4, 0.3, and 0.2, respectively.

[0046] The sequence division submodule 202 is used to divide the data sequence into multiple subsequences according to the candidate lengths.

[0047] Specifically, Any integer in the range is taken as a candidate length and recorded as , and divide the data sequence into multiple blocks of length equal to subsequence; The preset upper limit is The specific value can be set according to the actual application scenario and requirements, and the upper limit is preset. The value of is [3,8], and the present invention will preset the upper limit Set to 5.

[0048] For example, for the data sequence {3,2,4,2,2,1,2,3,4,3}: when the candidate length When the candidate length is , the data sequence is divided into 5 subsequences of length 2, namely {3,2}, {4,2}, {2,1}, {2,3}, {4,3}; when the candidate length is When , the data sequence is divided into three subsequences of length 3 by the candidate length, namely {3,2,4}, {2,2,1}, and {2,3,4}.

[0049] The feature recognition submodule 203 is used to obtain a binary sequence of the subsequence according to the relationship between the real frequency of the data in the subsequence and the frequency threshold.

[0050] Specifically, the reciprocal of the number of types of all data is used as the frequency threshold. , is the number of types of all data.

[0051] For example, for the data sequence {3, 2, 4, 2, 2, 1, 2, 3, 4, 3}, the number of types of all data is equal to 4, then the frequency threshold .

[0052] Furthermore, for all the data in each subsequence, the relationship between the real frequency of each data and the frequency threshold is determined in turn: the element corresponding to the data whose real frequency is greater than or equal to the frequency threshold is set as the first digital number, and the element corresponding to the data whose real frequency is less than the frequency threshold is set as the second digital number; finally, the elements corresponding to all the data in the subsequence are combined into a binary sequence of the subsequence, so as to obtain a binary sequence of each subsequence; In one embodiment, the first digital number is 1 and the second digital number is 0; in another embodiment, the first digital number is 0 and the second digital number is 1.

[0053] For example, for the data sequence {3,2,4,2,2,1,2,3,4,3}: (1) Frequency threshold , when the candidate length , the corresponding 5 subsequences of length 2 are {3,2}, {4,2}, {2,1}, {2,3}, {4,3}. When the first digit is 1 and the second digit is 0, the true frequency is greater than or equal to The elements corresponding to the data are set to 1, and the true frequency is less than The elements corresponding to the data are set to 0, then the binary sequences of the 5 subsequences with a length of 2 are {1,1}, {0,1}, {1,0}, {1,1}, {0,1} respectively.

[0054] (2) When the candidate length , the corresponding three subsequences of length 3 are {3,2,4}, {2,2,1}, {2,3,4}. When the first digit is 1 and the second digit is 0, the true frequency is greater than or equal to The elements corresponding to the data are set to 1, and the true frequency is less than The elements corresponding to the data are set to 0, then the binary sequences of the three subsequences with a length of 3 are {1,1,0}, {1,1,0}, and {1,1,0} respectively.

[0055] It should be noted that the present invention obtains the binary sequence of the subsequence through the relationship between the real frequency of the data in the subsequence and the frequency threshold, realizes the feature recognition of each subsequence, and provides data support for the subsequent calculation of the advantage degree of each feature as the focus feature.

[0056] The feature setting submodule 204 is used to obtain all features of the candidate length.

[0057] Specifically, the length is equal to Each binary number of is used as a feature of the candidate length, so as to obtain all the features of the candidate length. is the candidate length, and the length of the subsequence is equal to .

[0058] For example, when the candidate length When the length is equal to All binary numbers of as all features of candidate length, length equal to There are 2 binary numbers in total, including: 0 and 1, then the candidate length There are 2 features in total, 0 and 1 respectively; when the candidate length When the length is equal to All binary numbers of as all features of candidate length, length equal to There are 4 binary numbers in total, including: 00, 01, 10, 11, then the candidate length There are 4 features in total, namely 00, 01, 10, and 11.

[0059] The feature statistics submodule 205 is used to calculate the dominance of each feature as a focus feature according to the number of subsequences having each feature, and to use features with a dominance greater than 1 as focus features of candidate lengths.

[0060] Specifically, for any feature, the first binary sequence of each subsequence is determined in turn. Is the binary number composed of elements equal to the feature: If the first The binary number composed of elements is equal to the feature, then the subsequence is marked as a subsequence with the feature. If the binary number composed of elements is not equal to the feature, the subsequence is marked as a subsequence without the feature.

[0061] For example, the candidate length There are 2 features in total, 0 and 1 respectively; for the candidate length The first feature "0" of the binary sequence of 5 subsequences with a length of 2 is the first feature "0" of the binary sequence {0,1}, {0,1} of the subsequences {4,2} and {4,3}. If the binary number composed of elements is equal to the feature, then the subsequences {4,2} and {4,3} are marked as subsequences with the feature, and the first one in the binary sequence {1,1}, {1,0}, {1,1} of the subsequences {3,2}, {2,1}, {2,3} is If the binary number composed of elements is not equal to the feature, the subsequences {3,2}, {2,1}, {2,3} are marked as subsequences without the feature; for the candidate length The second feature "1" of the binary sequence of 5 subsequences with a length of 2, the first If the binary number composed of elements is equal to the feature, then the subsequences {3,2}, {2,1}, {2,3} are marked as subsequences with the feature, and the first one in the binary sequence {0,1}, {0,1} of the subsequences {4,2}, {4,3} is If the binary number composed of elements is not equal to the feature, the subsequences {4,2} and {4,3} are marked as subsequences that do not have this feature.

[0062] For example, the candidate length There are 4 features in total, namely: 00, 01, 10, 11; since there are 3 subsequences of length 3, and the first The binary number composed of elements is equal to the candidate length Therefore, the subsequences {3,2,4}, {2,2,1} and {2,3,4} are marked as subsequences with the feature “11”, and there are no subsequences with the features “00”, “01” and “10”.

[0063] Furthermore, for any feature, the dominance of the feature as a focus feature is calculated based on the number of subsequences with the feature: if the number of subsequences with the feature is equal to 0, then the dominance of the feature as a focus feature is ; If the number of subsequences with this feature is greater than 0, the calculation formula for the advantage of this feature as a focus feature is: ; In the formula, is the advantage of this feature as a feature of interest, is the frequency threshold, is the number of subsequences with this feature, For the first The true frequency of the last data of the subsequence, is the multiplication symbol.

[0064] In this way, the advantage of each feature as a focus feature is calculated, and then all features with an advantage greater than 1 are taken as all focus features of the candidate length; it should be noted that if the advantage of all features as focus features is less than or equal to 1, the candidate length does not have a focus feature.

[0065] For example, for the data sequence {3,2,4,2,2,1,2,3,4,3}, the frequencies of the four data types 1, 2, 3, and 4 are 0.1, 0.4, 0.3, and 0.2 respectively, and the frequency threshold ; (1) For candidate length The first feature "0" of , the subsequences {4,2} and {4,3} are marked as subsequences with this feature. Therefore, there are 2 subsequences with this feature, and the last data of the first subsequence {4,2} with this feature is data "2", and the true frequency of data "2" is 0.4. The last data of the second subsequence {4,3} with this feature is data "3", and the true frequency of data "3" is 0.3. Then the candidate length The first feature "0" is the advantage of the feature of interest .

[0066] (2) For candidate length The second feature "1" of , the subsequences {3,2}, {2,1} and {2,3} are marked as subsequences with this feature. Therefore, there are 3 subsequences with this feature, and the last data of the first subsequence {3,2} with this feature is data "2", and the true frequency of data "2" is 0.4. The last data of the second subsequence {2,1} with this feature is data "1", and the true frequency of data "1" is 0.1. The last data of the third subsequence {2,3} with this feature is data "3", and the true frequency of data "3" is 0.3. Then the candidate length The second feature "1" is the advantage of the feature of interest .

[0067] (3) For candidate length The first three features of "00", "01" and "10" are "00", "01" and "10", there is no subsequence with the features "00", "01" and "10", and the number of subsequences with the first three features "00", "01" and "10" is equal to 0, then the first three features "00", "01" and "10" are the advantages of being the focus features. .

[0068] (4) For candidate length The fourth feature "11" of the sequence {3,2,4}, {2,2,1} and {2,3,4} are marked as subsequences with this feature. Therefore, there are 3 subsequences with this feature, and the last data of the first subsequence {3,2,4} with this feature is data "4", and the true frequency of data "4" is 0.2. The last data of the second subsequence {2,2,1} with this feature is data "1", and the true frequency of data "1" is 0.1. The last data of the third subsequence {2,3,4} with this feature is data "4", and the true frequency of data "4" is 0.2. Then the candidate length The dominance of the fourth feature "11" as the focus feature .

[0069] (5) In summary, due to the candidate length The second feature "1" is the advantage of the feature of interest , and the candidate length The dominance of the fourth feature "11" as the focus feature Therefore, feature "1" is selected as the candidate length The feature of interest is "11", and the feature "11" is used as the candidate length Features of interest.

[0070] It should be noted that the present invention calculates the degree of dominance of each feature as a focus feature according to the number of subsequences having each feature, so as to screen out the focus feature that can expand the size of the interval corresponding to the last data in the data sequence.

[0071] The target length acquisition submodule 206 is used to take the candidate length having at least one focus feature as the target length.

[0072] In one embodiment, a candidate length is arbitrarily selected from the candidate lengths having at least one feature of interest as a target length. .

[0073] For example, the candidate length The feature of interest is feature "1", and the candidate length The feature of interest is feature "11", so the candidate length and candidate length All of them are candidate lengths with at least one feature of interest, and any candidate length is selected as the target length .

[0074] In another embodiment, for a candidate length having at least one feature of interest, , calculate the candidate length The advantage degree of the candidate length with the largest advantage degree is taken as the target length .

[0075] Among them, the candidate length The calculation formula for the degree of advantage is: ; In the formula, Candidate length Features of interest, The first The frequency of the data, , Candidate length The number of all subsequences under is the candidate length, The first The true frequency of the data, , is the length of the data sequence, and the number of all data in the data sequence is equal to , is the multiplication symbol.

[0076] Among them, when The data is of candidate length The first When the data and When The frequency of the data is set to The actual frequency of the data ,Right now ; When the The data does not have a candidate length The first When the data When The frequency of the data is set as the frequency threshold ,Right now .

[0077] It should be noted that the present invention calculates the candidate length The advantage degree is determined, and the candidate length with the largest advantage degree is taken as the target length, so that the size of the interval corresponding to the last data in the data sequence is expanded to the maximum extent, the compression rate of the data sequence is maximized and the amount of data is reduced, thereby minimizing the storage space requirements of the management system and the cost of storage hardware.

[0078] For example, the candidate length The feature of interest is feature "1", and the candidate length The feature of interest is feature "11", so the candidate length and candidate length are all candidate lengths with at least one feature of interest, and the candidate lengths are calculated separately and candidate length The candidate length is The degree of advantage =0.2×0.25×0.2×0.4×0.4×0.25×0.4×0.25×0.2×0.3=2.4×10 -6 ; then the candidate length The degree of advantage 0.2×0.4×0.25×0.4×0.4×0.25×0.4×0.3×0.25×0.3=7.2×10 -6 ; The candidate length with the greatest advantage is the candidate length As the target length, the target length .

[0079] The data sequence storage module 300 is used to perform arithmetic coding on the data sequence according to the target length, obtain the compressed result of the energy consumption data and store it.

[0080] Specifically, the data sequence is arithmetically encoded according to the target length, and the intervals to which each data belongs are obtained in turn: when the data is the first When there are data, the interval is divided by the frequency threshold and the interval to which the data belongs is obtained. When the data is not the first in the subsequence of the feature of interest with the target length, When a piece of data is obtained, the interval is divided according to the real frequency of the data and the interval to which the data belongs is obtained; in the interval to which the last data belongs, the shortest binary number is used as the compression result of the energy consumption data; the compressed result of the energy consumption data is stored in a local or cloud storage device, and backed up regularly to ensure the security and integrity of the data.

[0081] Among them, the said dividing the interval by the frequency threshold and obtaining the interval to which the data belongs includes: setting the frequency of each data as the frequency threshold, dividing the interval by the frequencies of all types of data and obtaining the interval to which the data belongs; the said dividing the interval by the real frequency of the data and obtaining the interval to which the data belongs includes: setting the frequency of each data as the real frequency of each data, dividing the interval by the frequencies of all types of data and obtaining the interval to which the data belongs.

[0082] Further, the method of dividing the intervals by the frequencies of all kinds of data and obtaining the intervals to which the data belongs includes: for the first Data: When When the frequency of all data is Divide the interval into Divide into sub-intervals, is the number of types of all data; when When the frequency of all kinds of data is used to calculate the first The interval to which the data belongs is divided into intervals, and the first The interval to which the data belongs is divided into subintervals; the obtained subintervals are used as the subintervals corresponding to each type of data, and the first subinterval in the data sequence is obtained from the subintervals corresponding to all types of data. The subinterval corresponding to the data is used as the first The interval to which the data belongs.

[0083] It should be noted that the present invention obtains a target length The first When the interval to which a piece of data belongs is determined, the frequency of each type of data is set as the frequency threshold, and the interval is divided according to the frequencies of all types of data to obtain the interval to which the data belongs, so as to expand the size of the interval corresponding to the last data in the data sequence, improve the compression rate of the data sequence, reduce the amount of data, and thus reduce the storage space requirements of the management system and the cost of storage hardware.

[0084] For example, for the data sequence {3,2,4,2,2,1,2,3,4,3}, the frequencies of the four data types 1, 2, 3, and 4 are 0.1, 0.4, 0.3, and 0.2 respectively, and the frequency threshold ; When arithmetic coding is used to encode the data sequence, the size of the interval to which the last data is obtained is equal to 0.2×0.4×0.2×0.4×0.4×0.1×0.4×0.3×0.2×0.3=1.8432×10 -6 ; When the method of the present invention is used to encode a data sequence, and the target length When the last data is obtained, the size of the interval is equal to 0.2×0.25×0.2×0.4×0.4×0.25×0.4×0.25×0.2×0.3=2.4×10 -6 ; When the method of the present invention is used to encode a data sequence, and the target length When the last data is obtained, the size of the interval is equal to 0.2×0.4×0.25×0.4×0.4×0.25×0.4×0.3×0.25×0.3=7.2×10 -6; Therefore, the size of the interval to which the last data obtained using arithmetic coding belongs is smaller than the size of the interval to which the last data obtained using the method of the present invention belongs. Therefore, the present invention expands the size of the interval corresponding to the last data in the data sequence, improves the compression rate of the data sequence, reduces the amount of data, and thereby reduces the storage space requirements of the management system and the cost of storage hardware.

[0085] The energy consumption data management module 400 is used to digitally display the energy consumption data according to the compression result of the stored energy consumption data.

[0086] Specifically, the digital display of energy consumption data includes: electricity consumption ranking display, water consumption ranking display, electricity consumption trend display, and water consumption trend display.

[0087] Among them, the total electricity consumption / water consumption of each user within the statistical time is calculated, and all users are sorted according to the total electricity consumption / water consumption to realize electricity consumption ranking display / water consumption ranking display; the total electricity consumption / water consumption of all users per day is calculated within the time period corresponding to the statistical time as the total electricity consumption / water consumption per day, and the total electricity consumption / water consumption within the time period corresponding to the statistical time is displayed in a line graph to realize electricity consumption trend display / water consumption trend display.

[0088] Among them, users can select and set the statistical time period, for example: the past 7 days, the past 30 days.

[0089] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0090] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A digital energy consumption management system, characterized in that: include: An energy consumption data collection module is used to collect the energy consumption data of each user every day and form a data sequence with the energy consumption data of all users in one day, wherein the energy consumption data includes electricity consumption and water consumption; The target length acquisition module is used to calculate the true frequency of each data in the statistical data sequence; divide the data sequence into multiple lengths equal to the candidate length subsequence; according to the relationship between the true frequency of the data in the subsequence and the frequency threshold, obtain the binary sequence of the subsequence, the frequency threshold is equal to the inverse of the number of types of all data; length equal to All binary numbers of are taken as all features of the candidate length; according to the number of subsequences with each feature, the degree of dominance of each feature as the focus feature is calculated, and the features with a dominance greater than 1 are taken as the focus features of the candidate length; the candidate length with at least one focus feature is taken as the target length ; The data sequence storage module is used to perform arithmetic coding on the data sequence according to the target length, and obtain the interval to which each data belongs in turn. When the data is the first in the subsequence of the feature of interest with the target length, When there is data, the interval is divided by the frequency threshold and the interval to which the data belongs is obtained; In the interval to which the last data belongs, the shortest binary number is used as the compression result of the energy consumption data and is stored; The energy consumption data management module is used to digitally display the electricity consumption ranking, water consumption ranking, electricity consumption trend and water consumption trend according to the compression results of the stored energy consumption data.

2. A digital energy consumption management system according to claim 1, characterized in that: The candidate length for Integer in the range, The preset upper limit.

3. A digital energy consumption management system according to claim 1, characterized in that: The step of obtaining a binary sequence of the subsequence according to the relationship between the real frequency of the data in the subsequence and the frequency threshold comprises: The elements corresponding to the data whose real frequency is greater than or equal to the frequency threshold are set as the first digital number, and the elements corresponding to the data whose real frequency is less than the frequency threshold are set as the second digital number; the elements corresponding to all the data in the subsequence form a binary sequence of the subsequence.

4. A digital energy consumption management system according to claim 1, characterized in that: The method for obtaining the subsequence with each feature is as follows: For any feature, if the first If the binary number composed of elements is equal to the feature, the subsequence is marked as a subsequence with the feature; otherwise, the subsequence is marked as a subsequence without the feature.

5. A digital energy consumption management system according to claim 1, characterized in that: The calculation formula for the advantage of the feature as a focus feature is: If the number of subsequences with this feature is equal to 0, the dominance of this feature as a focus feature is ; If the number of subsequences with this feature is greater than 0, the dominance of this feature as a focus feature is ; In the formula, is the frequency threshold, is the number of subsequences with this feature, For the first The true frequency of the last data of the subsequence, is the multiplication symbol.

6. A digital energy consumption management system according to claim 1, characterized in that: The candidate length having at least one feature of interest is used as the target length One method is: From the candidate lengths with at least one feature of interest, arbitrarily select a candidate length as the target length .

7. A digital energy consumption management system according to claim 1, characterized in that: The candidate length having at least one feature of interest is used as the target length Another method is: For candidate lengths with at least one feature of interest , calculate the candidate length The candidate length with the largest advantage is taken as the target length.

8. A digital energy consumption management system according to claim 1, characterized in that: The candidate length The calculation formula for the degree of advantage is: ； In the formula, Candidate length Features of interest, The first The frequency of the data, , Candidate length The number of all subsequences under is the candidate length, The first The true frequency of the data, , is the length of the data sequence, is the symbol for cumulative multiplication; The data is of candidate length The first When the data is The frequency of the data is set to The true frequency of the data; The data does not have a candidate length The first When the data is The frequency of each data point is set as the frequency threshold.

9. A digital energy consumption management system according to claim 1, characterized in that: When the data is the first When there are data, the interval is divided by the frequency threshold and the interval to which the data belongs is obtained, including: setting the frequency of each type of data as the frequency threshold, dividing the interval by the frequencies of all types of data and obtaining the interval to which the data belongs; When the data is not the first When there are data, the interval is divided according to the real frequency of the data and the interval to which the data belongs is obtained, including: setting the frequency of each data to the real frequency of each data, dividing the interval according to the frequencies of all kinds of data and obtaining the interval to which the data belongs.

10. A digital energy consumption management system according to claim 9, characterized in that: The step of dividing the intervals by the frequencies of all types of data and obtaining the intervals to which the data belongs includes: For the first Data: When When the frequency of all data is Divide the interval into Divide into sub-intervals, is the number of types of all data; when When the frequency of all kinds of data is used to calculate the first The interval to which the data belongs is divided into intervals, and the first The interval to which the data belongs is divided into sub-intervals; Will obtain subintervals are used as the subintervals corresponding to each type of data, and the first subinterval in the data sequence is obtained from the subintervals corresponding to all types of data. The subinterval corresponding to the data is used as the first The interval to which the data belongs.

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