A digital energy consumption management system

Abstract

The present invention belongs to the technical field of energy consumption management, and particularly relates to a digital energy consumption management system. The system includes: an energy consumption data acquisition module, a target length acquisition module, a data sequence storage module, and an energy consumption data management module; dividing the data sequence composed of the energy consumption data of all users in one day according to the candidate length, obtaining the binary sequence of the subsequence according to the size relationship between the true frequency of the data in the subsequence and the frequency threshold, obtaining all the features of the candidate length, calculating the superiority degree of each feature as a concerned feature according to the number of subsequences with each feature, taking the feature with a superiority degree greater than 1 as the concerned feature, and taking the candidate length with at least one concerned feature as the target length for arithmetic coding of the data sequence, obtaining the compression result of the energy consumption data and storing it for digital display. The present invention reduces the requirements for the storage space of the management system and the cost of storage hardware.

Description

Technical Field

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

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

[0003] In related technologies, for example, the Chinese patent application document with the publication number CN117875733A discloses an intelligent energy consumption management method and system for smart parks based on artificial intelligence, including: when the system is running, measuring the electricity consumption and consumption in the park through a data acquisition module, being able to monitor current, voltage, power and electrical energy parameters in real time, and measuring the water consumption and energy consumption in the park, being able to monitor water flow rate and total water volume in real time. At the same time, the data set is sent to the storage module for sorting and storage through a data transmission module. After the data is processed by the data analysis module to remove invalid data and handle missing values, it is sent to the digital twin model module for calculation and analysis to obtain the electricity consumption coefficient Dhxs and the water volume coefficient Slxs. At the same time, the electricity consumption coefficient Dhxs and the water volume coefficient Slxs are compared with the standard threshold values to promote the operation of the hierarchical strategy plan under the control module, optimize the reduction of electricity consumption, reduce unnecessary cost expenditures, and at the same time move 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. To support historical data analysis, a large amount of energy consumption data needs to be compressed and stored and backed up; although the storage and backup of energy consumption data are of great significance for the long-term management and optimization of smart parks, they also increase the storage space requirements of the management system and the cost of storage hardware. Summary of the Invention

[0005] To solve the technical problem that the storage and backup of the above energy consumption data increase the storage space requirements of the management system of the smart park and the cost of storage hardware, the present invention provides a digital energy consumption management system, and the system 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 form a data sequence of the energy consumption data of all users in one day, and the energy consumption data includes electricity consumption and water consumption; a target length acquisition module, which is used to count the true frequency of each type of data in the data sequence; divide the data sequence into multiple lengths equal to the candidate length a 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, where the frequency threshold is equal to the reciprocal of the number of all data types; the length is equal to all binary numbers are used as all features of the candidate length; according to the number of subsequences with each feature, calculate the degree of advantage of each feature as a concerned feature, and use the feature with a degree of advantage greater than 1 as the concerned feature of the candidate length; use the candidate length with at least one concerned feature as the target length ; a data sequence storage module, which is used to perform arithmetic coding on the data sequence according to the target length, and sequentially obtain the intervals to which each data belongs. When the data is the th data in the subsequence with the concerned feature of the target length, perform interval division through the frequency threshold and obtain the interval to which the data belongs; in the interval to which the last data belongs, use the binary number with the shortest length as the compression result of the energy consumption data and store it; an energy consumption data management module, which is used to digitally display the electricity consumption ranking, water consumption ranking, electricity consumption trend, and water consumption trend according to the stored compression result of the energy consumption data.

[0006] In the present invention, the data sequence is divided into multiple subsequences by the candidate length, and all features of the candidate length are set. According to the relationship between the true frequency of the data in the subsequence and the frequency threshold, the binary sequence of the subsequence is obtained. According to the binary sequence of the subsequence, the subsequences with each feature are obtained. According to the number of subsequences with each feature, the degree of advantage of each feature as a concerned feature is calculated, and the feature with a degree of advantage greater than 1 is used as the concerned feature of the candidate length. The candidate length with at least one concerned 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 ratio of compressing the data sequence, reducing the amount of data, and further reducing the storage space requirement of the management system and the cost of storage hardware.

[0007] Preferably, the candidate length is an integer within the range of is the preset upper limit.

[0008] Preferably, the obtaining of the binary sequence of the subsequence according to the relationship between the true frequency of the data in the subsequence and the frequency threshold includes: setting the element corresponding to the data with a true frequency greater than or equal to the frequency threshold as the first digit, and setting the element corresponding to the data with a true frequency less than the frequency threshold as the second digit; forming the binary sequence of the subsequence by the elements corresponding to all the data in the subsequence.

[0009] The present invention obtains the binary sequence of each subsequence based on the magnitude relationship between the true frequency of the data in the subsequence and the frequency threshold, realizes the feature recognition of each subsequence, and provides data support for calculating the advantage degree of each feature as the concerned feature in the subsequent process.

[0010] Preferably, the method for obtaining the subsequences with each feature is as follows: for any one feature, if the binary number composed of the first elements in the binary sequence of the subsequence is equal to this feature, then mark this subsequence as the subsequence with this feature; otherwise, mark this subsequence as the subsequence without this feature.

[0011] Preferably, the calculation formula for the advantage degree of the feature as the concerned feature is: if the number of subsequences with this feature is equal to 0, then the advantage degree of this feature as the concerned feature ; if the number of subsequences with this feature is greater than 0, then the advantage degree of this feature as the concerned feature ; where, is the frequency threshold, is the number of subsequences with this feature, is the true frequency of the last data of the th subsequence with this feature, is the symbol of successive multiplication.

[0012] The present invention calculates the advantage degree of each feature as the concerned feature according to the number of subsequences with each feature, and is used to screen out the concerned features that can expand the size of the interval corresponding to the last data in the data sequence.

[0013] Preferably, one method for using the candidate length with at least one concerned feature as the target length is: arbitrarily select a candidate length from the candidate lengths with at least one concerned feature as the target length .

[0014] Preferably, another method for using the candidate length with at least one concerned feature as the target length is: for the candidate lengths with at least one concerned feature , calculate the advantage degree of the candidate length , and use the candidate length with the maximum advantage degree 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 has a 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 determining the interval to which a piece of data belongs, set the frequency of each type of data to the frequency threshold. Divide the intervals based on the frequencies of all types of data to obtain the interval to which the data belongs, thereby expanding the size of the interval corresponding to the last data in the data sequence, increasing the compression ratio for compressing the data sequence, reducing the amount of data, and thus lowering the storage space requirements of the management system and the cost of storage hardware.

[0019] Preferably, the step of dividing the intervals based on the frequencies of all types of data to obtain the interval to which the data belongs includes: for the th data in the data sequence: when holds, divide based on the frequencies of all types of data, and divide into sub - intervals, being the number of all data types; when holds, divide the interval to which the th data in the data sequence belongs based on the frequencies of all types of data, and divide the interval to which the th data in the data sequence belongs into sub - intervals; take the obtained sub - intervals as the sub - intervals corresponding to each type of data respectively. From the sub - intervals corresponding to all types of data, obtain the sub - interval corresponding to the th data in the data sequence, and use it as the interval to which the th data in the data sequence belongs.

[0020] The beneficial effects of the present invention are as follows:

[0021] The present invention divides the data sequence into multiple subsequences by the candidate length, sets all the features of the candidate length, obtains the binary sequence of the subsequence based on the relationship between the true frequency of the data in the subsequence and the frequency threshold, obtains the subsequences with each feature according to the binary sequence of the subsequence, calculates the degree of superiority of each feature as the concerned feature based on the number of subsequences with each feature, takes the feature with a superiority degree greater than 1 as the concerned feature of the candidate length, and takes the candidate length with at least one concerned feature 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 increasing the compression ratio for compressing the data sequence, reducing the amount of data, and thus lowering the storage space requirements of the management system and the cost of storage hardware. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] 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 understandable. In the drawings, several embodiments of the present invention are shown by way of example and not limitation, and like or corresponding reference numerals represent like or corresponding parts, wherein:

[0023] Figure 1 is a system block diagram schematically showing a digital energy consumption management system in the present invention;

[0024] Figure 2 is a system block diagram schematically showing the target length acquisition module 200. Detailed Embodiments

[0025] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026] Next, the detailed embodiments of the present invention will be described in detail in conjunction with the accompanying drawings.

[0027] The present invention provides a digital energy consumption management system. As Figure 1 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 will be specifically described below.

[0028] The problems of traditional industrial parks mainly stem from the following aspects:

[0029] 1. The security governance problem is prominent, mainly because the traditional industrial park has poor comprehensive perception ability, and the security means are backward and single, making it difficult to meet the growing demand for refined security management.

[0030] 2. It has various forms and unclear common needs, mainly because the traditional industrial parks have different planning and construction, management mechanisms, construction years, and park business forms, and the needs and construction focuses of intelligent upgrading and transformation are different, making it difficult to meet the personalized needs of different groups.

[0031] 3. There is a lack of unified industry standards and operation specifications, mainly because manufacturers have launched intelligent products separately and built their own ecological chains, resulting in the formation of scattered "information islands" and making it difficult to achieve intelligent interconnection of the entire scenario.

[0032] 4. The investment in informatization construction is large but the results are meager, mainly because of the lack of systematic and forward-looking planning, which leads to isolation among systems, inability to achieve data sharing and collaborative work, and difficulty in bringing into play the overall benefits.

[0033] The construction of smart parks effectively integrates multi-level technologies such as 5G, AI, and the Internet of Things to achieve full-scenario intelligent services, truly empower park management decisions, and achieve the goal of cost reduction and efficiency improvement. At the same time, as the core of the digital economy, smart parks comprehensively enhance park management and operation capabilities through means such as digital infrastructure construction, artificial intelligence, and digital platforms, and promote the efficiency of digital transformation.

[0034] Among them, the AIoT (Artificial Intelligence of Things, integrating AI (Artificial Intelligence) technology and IoT (Internet of Things) technology) integrated management platform provides a complete and sound solution for various scenarios mainly for smart parks. It uses one system to generate linkages for multiple intelligent applications, uniformly deploys and manages them, and through data fusion, scenario linkage, algorithm innovation, etc., realizes precise analysis and scientific governance of the entire area of smart parks. It coordinates the relationships among park users, construction parties, and management parties through a market-driven and sustainable construction and operation model, enabling effective cooperation among all links in the smart park chain and seamless connection and effective operation among different service modules.

[0035] In the energy consumption management system of the AIoT integrated management platform, energy consumption data is often used for long-term historical data analysis. Combined with the digital big screen, through scientific regulation, it enables efficient operation of energy, helps 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.

[0036] The energy consumption data acquisition module 100 is used to collect the energy consumption data of each user every day and form a data sequence of the energy consumption data of all users in one day.

[0037] Specifically, for the smart park managed by the AIoT integrated management platform, individual users are divided according to the comprehensive building number, unit number, floor number, and household number, and all users are sorted in order.

[0038] Among them, for the user with household number on the nd unit of the th floor of building , its sorted serial number is , and the serial number is equal to ; 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 floor where the user is located, is the user account number, is the number of all floors in the th unit of Building is the number of all users on each floor in the th unit of Building is the number of all floors in the th unit of Building is the number of all users on each floor in the th unit of Building is the number of all users on each floor in the th unit of Building

[0039] Exemplarily, when Building has two units, and the number of all floors in each unit is equal to 28, and the number of all users on each floor is equal to 6; Building has two units, and the number of all floors in each unit is equal to 30, where the number of all users on each floor in the th unit is equal to 6, and the number of all users on each floor in the th unit is equal to 4; when Building has two units, and the number of all floors in each unit is equal to 30, and the number of all users on each floor is equal to 6; for the user with the account number in the th floor of the th unit of Building , the sorted serial number ; for the user with the account number in the th floor of the th unit of Building , the sorted serial number =(28×6 + 28×6 + 30×6 + 30×4)+(30×6)+(22×6)+4 = 952.

[0040] Furthermore, each user is equipped with an energy consumption data acquisition device to collect the daily energy consumption data of each user; the energy consumption data acquisition device includes an electric meter and a water meter, and each user is equipped with one electric meter and one water meter; the energy consumption data includes electricity consumption and water consumption, the electricity consumption is collected by the electric meter, and the unit of electricity consumption is degree, the water consumption is collected by the water meter, and the unit of water consumption is ton.

[0041] Furthermore, in the order of the user numbers from small to large, the energy consumption data of all users in one day are composed into a data sequence; since the energy consumption data includes electricity consumption and water consumption, therefore, the data sequence composed of the electricity consumption of all users in one day is recorded as the electricity consumption data sequence, and the data sequence composed of the water consumption of all users in one day is recorded as the water consumption data sequence.

[0042] Exemplarily, when the number of all users is equal to 10, and the electricity consumption of these 10 users on a certain day is respectively equal to 3, 2, 4, 2, 2, 1, 2, 3, 4, 3, the energy consumption data of these 10 users in one day are composed into a data sequence, then the data sequence is {3, 2, 4, 2, 2, 1, 2, 3, 4, 3}.

[0043] 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 the smart park, it also increases the demand for the storage space of the management system and the cost of storage hardware. Therefore, it is necessary to adopt an efficient data compression algorithm to compress the energy consumption data, reduce the data volume, and thus reduce the demand for the storage space of the management system and the cost of storage hardware.

[0044] Furthermore, it should be noted that arithmetic coding is a compression algorithm that can adapt to various types of data. The size of its compression ratio depends on the size of the interval corresponding to the last data in the data sequence. The larger the interval, the larger the compression ratio. On the contrary, the smaller the interval, the smaller the compression ratio. And 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 data in the data sequence.

[0045] In summary, the present invention divides the data sequence into multiple subsequences through the candidate length, sets all the characteristics of the candidate length, obtains the binary sequence of the subsequence according to the size relationship between the true frequency of the data in the subsequence and the frequency threshold, obtains the subsequences with each characteristic according to the binary sequence of the subsequence, calculates the superiority degree of each characteristic as the concerned characteristic according to the number of subsequences with each characteristic, takes the characteristic with the superiority degree greater than 1 as the concerned characteristic of the candidate length, and takes the candidate length with at least one concerned characteristic 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 ratio of compressing the data sequence, reducing the data volume, and further reducing the demand for the storage space of the management system and the cost of storage hardware.

[0046] The data feature acquisition module 200 is used to obtain the target length.

[0047] The system block diagram of the target length acquisition module 200 is referred to Figure 2 , including sub-modules 201 to 206, specifically:

[0048] The frequency statistics sub-module 201 is used to statistically calculate the true frequency of each type of data in the data sequence.

[0049] Specifically, statistically calculate the frequency of each type of data in the data sequence. The frequency of each type of data refers to the number of times each type of data appears in the data sequence. Take the ratio of the frequency of each type of data to the total number of all data in the data sequence as the true frequency of each type of data.

[0050] 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 total number of data types is equal to 4, and through statistics, the frequencies of these 4 types of data in the data sequence 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.

[0051] The sequence division sub-module 202 is used to divide the data sequence into multiple subsequences by the candidate length.

[0052] Specifically, take any integer within the range as the candidate length, and denote it as , and divide the data sequence into multiple subsequences with a length equal to ; is the preset upper limit, and the specific value of the preset upper limit can be set according to the actual application scenario and requirements, and the value-taking method of the preset upper limit is [3, 8]. In the present invention, the preset upper limit is set to 5.

[0053] Exemplarily, for the data sequence {3, 2, 4, 2, 2, 1, 2, 3, 4, 3}: when the candidate length is, divide the data sequence into 5 subsequences with a length equal to 2 through the candidate length, which are {3, 2}, {4, 2}, {2, 1}, {2, 3}, {4, 3} respectively; when the candidate length is, divide the data sequence into 3 subsequences with a length equal to 3 through the candidate length, which are {3, 2, 4}, {2, 2, 1}, {2, 3, 4} respectively.

[0054] The feature recognition sub-module 203 is used to obtain the binary sequence of the subsequence according to the magnitude relationship between the true frequency of the data in the subsequence and the frequency threshold.

[0055] Specifically, taking the reciprocal of the number of types of all data as the frequency threshold, then the frequency threshold , is the number of types of all data.

[0056] Exemplarily, 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 .

[0057] Furthermore, for all the data in each subsequence, successively judge the magnitude relationship between the true frequency of each data and the frequency threshold: set the element corresponding to the data with a true frequency greater than or equal to the frequency threshold as the first digit, and set the element corresponding to the data with a true frequency less than the frequency threshold as the second digit; finally, form the binary sequence of the subsequence with all the elements corresponding to the data in the subsequence, so as to obtain the binary sequence of each subsequence;

[0058] In one embodiment, the first digit is 1 and the second digit is 0; in another embodiment, the first digit is 0 and the second digit is 1.

[0059] Exemplarily, for the data sequence {3, 2, 4, 2, 2, 1, 2, 3, 4, 3}:

[0060] (1) The frequency threshold , when the candidate length , the corresponding 5 subsequences with a length of 2 are {3, 2}, {4, 2}, {2, 1}, {2, 3}, {4, 3} respectively. When the first digit is 1 and the second digit is 0, set the element corresponding to the data with a true frequency greater than or equal to as 1, and set the element corresponding to the data with a true frequency less than as 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.

[0061] (2) When the candidate length , the corresponding 3 subsequences with a length of 3 are {3, 2, 4}, {2, 2, 1}, {2, 3, 4} respectively. When the first digit is 1 and the second digit is 0, set the element corresponding to the data with a true frequency greater than or equal to as 1, and set the element corresponding to the data with a true frequency less than Set the elements corresponding to the data to 0, then the binary sequences of the 3 subsequences with a length equal to 3 are {1, 1, 0}, {1, 1, 0}, and {1, 1, 0} respectively.

[0062] It should be noted that the present invention obtains the binary sequence of the subsequence through the magnitude relationship between the true frequency of the data in the subsequence and the frequency threshold, realizes the feature recognition of each subsequence, and provides data support for calculating the superiority degree of each feature as the concerned feature in the subsequent process.

[0063] The feature setting sub-module 204 is used to obtain all features of the candidate length.

[0064] Specifically, each binary number with a length equal to is used as a feature of the candidate length, and in this way, all features of the candidate length are obtained. is the candidate length, and the length of the subsequence is equal to .

[0065] Exemplarily, when the candidate length , all binary numbers with a length equal to are used as all features of the candidate length. There are 2 binary numbers with a length equal to , including: 0, 1. Then there are 2 all features of the candidate length , which are 0 and 1 respectively; when the candidate length , all binary numbers with a length equal to are used as all features of the candidate length. There are 4 binary numbers with a length equal to , including: 00, 01, 10, 11. Then there are 4 all features of the candidate length , which are 00, 01, 10, and 11 respectively.

[0066] The feature statistics sub-module 205 is used to calculate the superiority degree of each feature as the concerned feature according to the number of subsequences with each feature, and use the features with a superiority degree greater than 1 as the concerned features of the candidate length.

[0067] Specifically, for any one feature, it is successively determined whether the binary number composed of the first elements in the binary sequence of each subsequence is equal to this feature: if the binary number composed of the first elements in the binary sequence of the subsequence is equal to this feature, then mark this subsequence as a subsequence with this feature; if the binary number composed of the first elements in the binary sequence of the subsequence is not equal to this feature, then mark this subsequence as a subsequence without this feature.

[0068] Exemplarily, the candidate length There are 2 features in total, which are 0 and 1 respectively; for the candidate length For the first feature "0" of , among the binary sequences of 5 subsequences with a length equal to 2, in the binary sequences {0,1} and {0,1} of the subsequences {4,2} and {4,3}, if the binary number formed by the front elements is equal to this feature, then mark the subsequences {4,2} and {4,3} as subsequences with this feature. In the binary sequences {1,1}, {1,0}, and {1,1} of the subsequences {3,2}, {2,1}, and {2,3}, if the binary number formed by the front elements is not equal to this feature, then mark the subsequences {3,2}, {2,1}, and {2,3} as subsequences without this feature; for the candidate length For the second feature "1" of , among the binary sequences of 5 subsequences with a length equal to 2, in the binary sequences {1,1}, {1,0}, and {1,1} of the subsequences {3,2}, {2,1}, and {2,3}, if the binary number formed by the front elements is equal to this feature, then mark the subsequences {3,2}, {2,1}, and {2,3} as subsequences with this feature. In the binary sequences {0,1} and {0,1} of the subsequences {4,2} and {4,3}, if the binary number formed by the front elements is not equal to this feature, then mark the subsequences {4,2} and {4,3} as subsequences without this feature.

[0069] Exemplarily, there are 4 features in total for the candidate length , which are: 00, 01, 10, 11; since there are 3 subsequences with a length equal to 3, and in the binary sequences {1,1,0}, {1,1,0}, and {1,1,0} of the 3 subsequences {3,2,4}, {2,2,1}, and {2,3,4}, the binary numbers formed by the front elements are all equal to the 4th feature "11" of the candidate length , therefore, mark the subsequences {3,2,4}, {2,2,1}, and {2,3,4} as subsequences with the feature "11", and there are no subsequences with the features "00", "01", and "10".

[0070] Furthermore, for any feature, calculate the degree of dominance of this feature as a feature of interest according to the number of subsequences with this feature: if the number of subsequences with this feature is equal to 0, then the degree of dominance of this feature as a feature of interest ; if the number of subsequences with this feature is greater than 0, then the calculation formula for the degree of dominance of this feature as a feature of interest is:

[0071] ;

[0072] In the formula, is the degree of advantage of this feature as the feature of interest, is the frequency threshold, is the number of subsequences with this feature, is the true frequency of the last data of the subsequence with this feature, and

[0073] is the product symbol.

[0074] Accordingly, calculate the degree of advantage of each feature as the feature of interest, and then use all features with a degree of advantage greater than 1 as all features of interest for the candidate length; it should be specifically noted that if the degree of advantage of all features as the feature of interest is less than or equal to 1, the candidate length does not have a feature of interest. ;

[0075] (1) For the first feature "0" of the candidate length , 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 the data "2", and the true frequency of the data "2" is 0.4. The last data of the second subsequence {4, 3} with this feature is the data "3", and the true frequency of the data "3" is 0.3. Then, the degree of advantage of the first feature "0" of the candidate length as the feature of interest is

[0076] .

[0077] .

[0077] (3) For the candidate length For the first 3 features "00", "01", and "10", there is no subsequence with features "00", "01", and "10", and the number of subsequences with the first 3 features "00", "01", and "10" is equal to 0. Then, the degree of advantage of the first 3 features "00", "01", and "10" as the features of interest .

[0078] (4)For the 4th feature "11" of the candidate length , the subsequences {3, 2, 4}, {2, 2, 1}, and {2, 3, 4} are marked as the subsequences with this feature. Therefore, there are a total of 3 subsequences with this feature. 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 degree of advantage of the 4th feature "11" of the candidate length as the feature of interest .

[0079] (5)In summary, since the degree of advantage of the 2nd feature "1" of the candidate length as the feature of interest , and the degree of advantage of the 4th feature "11" of the candidate length as the feature of interest , therefore, the feature "1" is used as the feature of interest for the candidate length , and the feature "11" is used as the feature of interest for the candidate length .

[0080] It should be noted that the present invention calculates the degree of advantage of each feature as the feature of interest according to the number of subsequences with each feature, and is used to screen out the features of interest that can expand the size of the interval corresponding to the last data in the data sequence.

[0081] The target length acquisition sub-module 206 is used to use the candidate length with at least one feature of interest as the target length.

[0082] In one embodiment, any one of the candidate lengths with at least one feature of interest is selected as the target length .

[0083] Exemplarily, the feature of interest of the candidate length is the feature "1", and the feature of interest of the candidate length is the feature "11". Therefore, the candidate length and candidate lengths are all candidate lengths with at least one feature of interest, and any one of the candidate lengths is randomly selected as the target length .

[0084] In another embodiment, for a candidate length with at least one feature of interest , the degree of advantage of the candidate length is calculated, and the candidate length with the greatest degree of advantage is used as the target length .

[0085] Among them, the calculation formula for the degree of advantage of the candidate length is:

[0086] ;

[0087] In the formula, is the feature of interest of the candidate length , is the frequency of the th data in the data sequence, , is the number of all subsequences under the candidate length , is the candidate length, is the true frequency of the th data in the data sequence, , is the length of the data sequence, and the number of all data in the data sequence is equal to , is the product symbol.

[0088] Among them, when the th data is the th data in the subsequence with the feature of interest of the candidate length , that is, and , the frequency of the th data is set to the true frequency of the th data , that is, ; when the th data is not the th data in the subsequence with the feature of interest of the candidate length , that is, , the frequency of the th data is set to the frequency threshold , that is, .

[0089] It should be noted that the present invention calculates the candidate length The degree of advantage, and use the candidate length with the greatest degree of advantage as the target length, so as to maximize the expansion degree of the size of the interval corresponding to the last data in the data sequence, maximize the compression rate of compressing the data sequence and reduce the data volume, and thus minimize the storage space requirements of the management system and the cost of storage hardware.

[0090] Exemplarily, the candidate length has the attention feature of feature "1", and the candidate length has the attention feature of feature "11". Therefore, the candidate length and the candidate length are both candidate lengths with at least one attention feature. Calculate the candidate lengths of the candidate length and the candidate length respectively. Then, the degree of advantage of the candidate length = 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 degree of advantage of the candidate length 0.2×0.4×0.25×0.4×0.4×0.25×0.4×0.3×0.25×0.3 = 7.2×10 -6 ; take the candidate length with the greatest degree of advantage, that is, the candidate length as the target length. Then, the target length .

[0091] The data sequence storage module 300 is used to perform arithmetic coding on the data sequence according to the target length, obtain the compression result of the energy consumption data and store it.

[0092] Specifically, perform arithmetic coding on the data sequence according to the target length, and obtain the intervals to which each data belongs in turn: when the data is the th data in the subsequence with the attention feature of the target length, divide the interval through the frequency threshold and obtain the interval to which the data belongs. When the data is not the th data in the subsequence with the attention feature of the target length, divide the interval through the true frequency of the data and obtain the interval to which the data belongs; in the interval to which the last data belongs, use the binary number with the shortest length as the compression result of the energy consumption data; store the compression result of the energy consumption data in a local or cloud storage device, and perform regular backups at the same time to ensure the security and integrity of the data.

[0093] 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.

[0094] Further, the method 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.

[0095] 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.

[0096] 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 is, the size of the interval to which the last obtained data belongs 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 is, the size of the interval to which the last obtained data belongs 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 by arithmetic coding belongs is smaller than the size of the interval to which the last data obtained by 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 ratio of compressing the data sequence, reduces the amount of data, and further reduces the storage space requirement of the management system and the cost of storage hardware.

[0097] 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.

[0098] Specifically, digitally displaying the energy consumption data includes: displaying the electricity consumption ranking, displaying the water consumption ranking, displaying the electricity consumption trend, and displaying the water consumption trend.

[0099] Among them, by calculating the total electricity consumption / water consumption of each user within the statistical period and sorting all users according to the total electricity consumption / water consumption, the electricity consumption ranking display / water consumption ranking display is realized; by calculating the total electricity consumption / water consumption of all users every day within the time period corresponding to the statistical period as the total electricity consumption / water consumption of each day, and displaying a line chart of the total electricity consumption / water consumption within the time period corresponding to the statistical period, the electricity consumption trend display / water consumption trend display is realized.

[0100] Among them, the user can select and set the statistical period, for example: the last 7 days, the last 30 days.

[0101] In the description of this specification, the meanings of "a plurality of" and "several" are at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0102] Although this specification has shown and described several 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. Many variations, modifications, and alternative means will occur to those skilled in the art without departing from the spirit and scope of the present invention. It should be understood that alternative embodiments to those described herein may be employed in practicing the present invention.

Claims

1. A digital energy consumption management system, characterized in that, Including: An energy consumption data acquisition module, which is used to collect the daily energy consumption data of each user, and form a data sequence with the energy consumption data of all users in one day. The energy consumption data includes electricity consumption and water consumption; A target length acquisition module for counting the true frequency of each type of data in a data sequence; dividing the data sequence into multiple subsequences with lengths equal to the candidate lengths ; obtaining a binary sequence of the subsequences according to the magnitude relationship between the true frequency of the data in the subsequences and the frequency threshold, where the frequency threshold is equal to the reciprocal of the number of all types of data; taking all binary numbers with lengths equal to as all features of the candidate lengths; calculating the degree of advantage of each feature as a concerned feature according to the number of subsequences with each feature, and taking the features with a degree of advantage greater than 1 as the concerned features of the candidate lengths; taking the candidate lengths with at least one concerned feature as the target lengths ; A data sequence storage module, which is used to perform arithmetic coding on a data sequence according to a target length, and sequentially obtain the intervals to which the respective data belong. When the data is the th data in a subsequence of a concerned feature having the target length, interval division is performed through a frequency threshold, and the interval to which the data belongs is obtained; In the interval where the last data belongs, the binary number with the shortest length is used as the compression result of the energy consumption data and stored; An energy consumption data management module, which 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.

2. The digital energy consumption management system according to claim 1, characterized in that, The candidate length is an integer within the range of which is a preset upper limit.

3. A digital energy consumption management system according to claim 1, characterized in that, Obtaining the binary sequence of the subsequence according to 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 with a true frequency greater than or equal to the frequency threshold as the first digital, and setting the elements corresponding to the data with a true frequency less than the frequency threshold as the second digital; forming the binary sequence of the subsequence with the elements corresponding to all the data in the subsequence; where the first digital is 0 and the second digital is 1, or the first digital is 1 and the second digital is 0.

4. A digital energy consumption management system according to claim 1, characterized in that, The method for obtaining the subsequence with each feature is: For any kind of feature, if the binary number formed by the first elements in the binary sequence of the subsequence is equal to the feature, then mark the subsequence as a subsequence with this feature; otherwise, mark the subsequence as a subsequence without this feature.

5. A digital energy consumption management system according to claim 1, characterized in that, The calculation formula for the degree of advantage of the feature as the feature of concern is: If the number of subsequences with this feature is equal to 0, the degree of dominance of this feature as a feature of interest ; If the number of subsequences with this feature is greater than 0, the degree of dominance of this feature as a feature of interest ; where is the frequency threshold,[[]] is the number of subsequences with this feature,[[]] is the true frequency of the last data of the -th subsequence with this feature,[[]] is the product symbol.[[]] 6. A digital energy consumption management system according to claim 1, wherein Regarding the candidate length having at least one focus feature as the target length A method is as follows: Arbitrarily select one candidate length from the candidate lengths with at least one concerned feature as the target length .

7. A digital energy consumption management system according to claim 1, characterized in that, Taking the candidate length having at least one concerned feature as the target length Another method is as follows: For a candidate length having at least one feature of interest , calculate the degree of advantage of the candidate length , and use the candidate length with the greatest degree of advantage 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 as follows: ； In the formula, is the candidate length of the degree of advantage, is the frequency of the th data in the data sequence, , is the candidate length of all subsequences under, is the candidate length, is the th data in the data sequence of the true frequency, , is the length of the data sequence, is the product symbol; among them, when the th data is a subsequence with a candidate length of the attention feature th data, the frequency of the th data is set to the true frequency of the th data; when the th data is not a subsequence with a candidate length of the attention feature th data, the frequency of the th data is set to the frequency threshold.

9. A digital energy consumption management system according to claim 1, characterized in that When the data is the th data in the subsequence of the attention feature with the target length, interval division is performed through 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, performing interval division through the frequencies of all types of data, and obtaining the interval to which the data belongs; When the data is not the th data in the subsequence of the concerned feature with the target length, interval division is performed according to the true frequency of the data to obtain the interval to which the data belongs, including: setting the frequency of each type of data as the true frequency of each type of data, and performing interval division through the frequencies of all types of data to obtain the interval to which the data belongs.

10. A digital energy consumption management system according to claim 9, characterized in that, The interval division by the frequencies of all kinds of data and obtaining the interval to which the data belongs includes: For the th data in the data sequence: When , perform interval division on according to the frequencies of all types of data, divide into subintervals, being the number of types of all data; when , perform interval division on the interval to which the th data in the data sequence belongs according to the frequencies of all types of data, and divide the interval to which the th data in the data sequence belongs into subintervals; The obtained sub - intervals are respectively used as the sub - intervals corresponding to each type of data. From the sub - intervals corresponding to all types of data, obtain the sub - interval corresponding to the th data in the data sequence, and use it as the interval to which the th data in the data sequence belongs.

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