CN119048285A

CN119048285A - Energy consumption hierarchical metering loss analysis method and system based on energy consumption trend recursion algorithm

Info

Publication number: CN119048285A
Application number: CN202411550674.9A
Authority: CN
Inventors: 胡小龙; 滕硕
Original assignee: Shenzhen Sibiyun Technology Co ltd
Current assignee: Shenzhen Sibiyun Technology Co ltd
Priority date: 2024-11-01
Filing date: 2024-11-01
Publication date: 2024-11-29

Abstract

The invention provides an energy consumption hierarchical metering loss analysis method and system based on an energy consumption trend recursion algorithm, wherein the energy consumption hierarchical metering loss analysis method and system comprises the steps of collecting energy consumption data of all levels in real time to obtain initial energy consumption data, carrying out trend analysis on the initial energy consumption data based on the energy consumption trend recursion algorithm to obtain energy consumption change trend data, carrying out hierarchical processing on the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels, respectively carrying out energy consumption metering on the energy consumption data of all levels to obtain energy consumption metering values of all levels, comparing the energy consumption metering values of all levels with corresponding preset standard values, if the energy consumption metering values of all levels exceed the preset standard value range, combining the energy consumption change trend data to analyze the stability and the persistence of energy consumption change of all levels, and making a corresponding early warning scheme according to the analyzed stability and persistence. In the invention, the defect that the energy consumption of each stage cannot be adaptively and accurately graded at present is overcome.

Description

Energy consumption hierarchical metering loss analysis method and system based on energy consumption trend recursion algorithm

Technical Field

The invention relates to the technical field of data processing, in particular to an energy consumption hierarchical metering loss analysis method and system based on an energy consumption trend recursion algorithm.

Background

In various fields such as industrial production, commercial operation and resident life, accurate metering and analysis of energy consumption are important for reducing energy cost and realizing sustainable development.

Traditional energy consumption metering methods are often single, and lack deep analysis and grading treatment on energy consumption data. In general, the energy consumption is simply recorded, and the change trend of the energy consumption and the specific conditions of different levels of energy consumption cannot be accurately known. Moreover, in the face of complex energy utilization systems, it is difficult to quickly locate specific links and causes of energy consumption loss, so that effective preventive and improvement measures cannot be timely taken.

In addition, the existing energy consumption management technology is difficult to accurately predict the energy consumption trend when analyzing the energy consumption, and is generally only used for grading treatment by adopting a general structure when grading measurement, and cannot reflect the current actual change condition, so that the energy consumption of each stage cannot be graded adaptively and accurately.

Disclosure of Invention

The invention mainly aims to provide an energy consumption hierarchical metering loss analysis method and system based on an energy consumption trend recursion algorithm, and aims to overcome the defect that the energy consumption of each level cannot be adaptively and accurately classified.

In order to achieve the above purpose, the invention provides an energy consumption hierarchical metering loss analysis method based on an energy consumption trend recursion algorithm, which comprises the following steps:

The energy consumption data of each level is acquired in real time to obtain initial energy consumption data, and trend analysis is carried out on the initial energy consumption data based on an energy consumption trend recursion algorithm to obtain energy consumption change trend data;

Carrying out grading treatment on the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels, wherein the energy consumption change trend data is used for assisting in determining the division standard of each level;

Energy consumption metering is respectively carried out on the energy consumption data of each level, and energy consumption metering values of each level are obtained;

comparing the energy consumption metering values of all levels with corresponding preset standard values, and if the energy consumption metering values exceed the range of the preset standard values, analyzing the stability and the persistence of energy consumption changes of all levels by combining the energy consumption change trend data;

and (5) according to the analyzed stability and persistence, a corresponding early warning scheme is provided.

Further, according to the analyzed stability and persistence, a corresponding early warning scheme is provided, including:

if the energy consumption change trend is rising and the loss exceeds the preset range, judging whether equipment aging exists or not by carrying out deep analysis on the equipment operation parameters;

If the equipment is not aged, the energy consumption behavior data are analyzed to determine whether an unreasonable energy consumption factor exists, and a loss analysis result is obtained;

And when the loss analysis result exceeds a preset threshold range, early warning prompt is carried out.

Further, the step of classifying the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels includes:

analyzing each parameter in a preset energy consumption model structure to obtain a basic grading standard;

carrying out statistical analysis on the energy consumption change trend data to determine trend influence factors;

And combining the basic grading standard with the trend influence factor, and grading the initial energy consumption data to obtain the energy consumption data corresponding to different levels.

Further, the statistical analysis is performed on the energy consumption variation trend data to determine a trend influence factor, including:

calculating the mean value and standard deviation of the energy consumption change trend data to obtain trend data statistical characteristics;

and inputting the statistical characteristics of the trend data into a pre-trained neural network model for calculation, and outputting the trend influence factors.

Further, the analyzing the stability and the persistence of the energy consumption change of each stage by combining the energy consumption change trend data includes:

calculating the variance of the energy consumption change trend data, and determining the stability of energy consumption change of each level according to the variance calculation result;

and analyzing the slope change condition of the energy consumption change trend data at a plurality of continuous time points, and determining the persistence of energy consumption change of each stage according to the slope change condition.

Further, after the corresponding early warning scheme is made, the method further includes:

adding the initial energy consumption data, the energy consumption change trend data and the energy consumption metering values of all levels into a data table to obtain an energy consumption data table;

Generating an identification value based on the energy consumption change trend data and the energy consumption metering values of all levels, and identifying the energy consumption data table based on the identification value;

generating a transmission key based on the initial energy consumption data and the identification value;

and encrypting the energy consumption data table based on the transmission key, and transmitting the encrypted energy consumption data table to an energy consumption management terminal.

Further, the generating a transmission key based on the initial energy consumption data and the identification value includes:

Performing character cleaning on the initial energy consumption data to obtain a cleaning data character string, wherein the character cleaning comprises cleaning specified characters in the initial energy consumption data;

acquiring a standard binary tree template pre-stored in a database, and sequentially adding characters in the cleaning data character string into each node of the standard binary tree template one by one to obtain a character binary tree;

dividing the identification value to obtain a plurality of numbers, wherein the identification value comprises a plurality of number characters;

Generating a graph according to a preset rule based on the numbers;

the graph is superimposed on the appointed position of the character binary tree, and the graph is subjected to equal-ratio stretching or scaling, so that the graph and the character binary tree meet the preset position relation;

After the graph is subjected to equal-ratio stretching or scaling, acquiring characters in the graph from the character binary tree as target characters;

And generating the transmission key by adopting a preset rule based on the target character.

The invention also provides an energy consumption hierarchical metering loss analysis system based on the energy consumption trend recursion algorithm, which comprises the following steps:

the analysis unit is used for acquiring the energy consumption data of each level in real time to obtain initial energy consumption data, and carrying out trend analysis on the initial energy consumption data based on an energy consumption trend recursion algorithm to obtain energy consumption change trend data;

the grading unit is used for grading the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels, wherein the energy consumption change trend data is used for assisting in determining the division standard of each level;

The metering unit is used for respectively metering the energy consumption of the energy consumption data of each level to obtain energy consumption metering values of each level;

The comparison unit is used for comparing the energy consumption metering values of all levels with corresponding preset standard values, and analyzing the stability and the persistence of energy consumption changes of all levels by combining the energy consumption change trend data if the energy consumption metering values exceed the range of the preset standard values;

and the early warning unit is used for making a corresponding early warning scheme according to the analyzed stability and persistence.

The invention also provides a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of any of the methods described above when the computer program is executed.

The invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method of any of the preceding claims.

The energy consumption hierarchical metering loss analysis method and system based on the energy consumption trend recursion algorithm comprise the steps of collecting all levels of energy consumption data in real time to obtain initial energy consumption data, carrying out trend analysis on the initial energy consumption data based on the energy consumption trend recursion algorithm to obtain energy consumption change trend data, carrying out hierarchical processing on the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels, wherein the energy consumption change trend data are used for assisting in determining dividing standards of all levels, carrying out energy consumption metering on all levels of energy consumption data to obtain energy consumption metering values of all levels, comparing the energy consumption metering values of all levels with corresponding preset standard values, analyzing the stability and the persistence of energy consumption change of all levels in combination with the energy consumption change trend data if the energy consumption metering values exceed the preset standard value range, and making a corresponding early warning scheme according to the analyzed stability and persistence. According to the method, the initial energy consumption data is classified by analyzing the energy consumption change trend data and combining with the preset energy consumption model structure, so that the classification is more reasonable, the current energy consumption data change is met, the subsequent classified metering is facilitated, and then the early warning is carried out.

Drawings

FIG. 1 is a schematic diagram of steps of an energy consumption hierarchical metering loss analysis method based on an energy consumption trend recursion algorithm in an embodiment of the present invention;

FIG. 2 is a block diagram of an energy consumption hierarchical metering loss analysis system based on an energy consumption trend recursion algorithm in accordance with an embodiment of the present invention;

Fig. 3 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present invention.

The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.

Referring to fig. 1, in one embodiment of the present invention, there is provided an energy consumption hierarchical metering loss analysis method based on an energy consumption trend recursion algorithm, including the steps of:

Step S1, acquiring energy consumption data of each level in real time to obtain initial energy consumption data, and carrying out trend analysis on the initial energy consumption data based on an energy consumption trend recursion algorithm to obtain energy consumption change trend data;

step S2, carrying out grading treatment on the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels, wherein the energy consumption change trend data is used for assisting in determining the division standard of each level;

step S3, energy consumption metering is carried out on the energy consumption data of each level respectively, and energy consumption metering values of each level are obtained;

S4, comparing the energy consumption metering values of all levels with corresponding preset standard values, and if the energy consumption metering values exceed the range of the preset standard values, analyzing the stability and the persistence of energy consumption change of all levels by combining the energy consumption change trend data;

and S5, according to the analyzed stability and persistence, a corresponding early warning scheme is made.

In this embodiment, as described in the above step S1, the sensors and the data acquisition system are first utilized to monitor and collect data in all directions and in real time for the energy usage conditions of different levels. For example, in an industrial production scene, energy consumption such as electric energy, water energy, heat energy and the like of various production workshops and equipment is monitored, in a commercial building, electricity consumption, air conditioner energy consumption and the like of different floors and different functional areas are collected, and in a home environment, energy consumption of various electric appliances is recorded in real time. The collected data should include detailed information of the type of energy source, the time of use, the amount of use, etc., for accurate analysis and processing to follow. By means of real-time acquisition, dynamic changes of energy use can be mastered in time, and latest data support is provided for follow-up trend analysis and management decisions.

The energy consumption trend recursive algorithm is a powerful algorithm specially used for analyzing time series data. The method can accurately predict the change trend of the future energy consumption through continuous iteration and analysis of the historical energy consumption data. The algorithm takes into account the effects of various factors on energy consumption, such as seasonal variations, changes in equipment operating conditions, fluctuations in production activities, and the like. For example, in terms of seasonal variation, the summer air conditioning power consumption will generally increase substantially, and the algorithm predicts the current summer energy consumption trend according to the change rule of the historical summer energy consumption data. In the aspect of the running state of the equipment, if a certain equipment fails or ages, the energy consumption can be abnormally changed, and the algorithm can capture the change trend in time and perform early warning. The trend analysis of the initial energy consumption data can be used for providing important reference basis for subsequent grading processing and decision.

As described in the above step S2, the preset energy consumption model structure is designed in advance according to different application scenarios and requirements. For example, in industrial production, it may be classified by different production workshops, production lines, equipment types, etc., in commercial buildings, it may be classified by floors, functional areas, tenants, etc., and in urban energy management, it may be classified by different areas, industries, etc. The energy consumption change trend data plays a key role in assisting in determining the division standards of each level in the grading process. If the trend of energy consumption of a certain area or equipment is rapidly increased, the situation that the energy consumption situation of the area or equipment is possibly changed greatly is indicated, and the area or equipment needs to be divided into higher energy consumption levels for more strict monitoring and management. For example, if the energy consumption of one production plant continues to rise over a period of time with a significantly higher trend than other plants, the energy consumption level of that plant may be increased so that the manager is more concerned with the energy usage of that plant and takes corresponding energy saving measures. By means of hierarchical processing, the complex energy consumption data can be classified and managed, so that energy management is more refined and efficient.

As described in the above step S3, for the energy consumption data of different levels, the accurate energy consumption measurement is performed by adopting the corresponding measuring device and method. For example, a high-precision electric energy meter may be used for measuring electric energy, a water meter or a flowmeter may be used for measuring water energy, a calorimeter may be used for measuring heat energy, or the like. During the metering process, accuracy and reliability of the metering device are ensured, and calibration and maintenance are performed regularly. Meanwhile, the metering data can be calibrated and corrected by combining a data analysis technology, so that the metering accuracy is improved. For example, if a great difference is found between the readings of a certain metering device and the readings of other related devices, possible reasons can be found out through data analysis, and corresponding adjustment is performed. After the energy consumption data of each level are measured, the obtained energy consumption measurement values of each level can intuitively reflect the specific condition of the energy consumption of each level, and quantized data support is provided for subsequent comparative analysis and management decision.

As described in the above step S4, the preset standard value is set according to historical data, industry standard or energy saving objective, and is used for judging whether the energy consumption is within a reasonable range. For example, in industrial production, the preset standard value can be set according to the average energy consumption level of the enterprises of the same type or the related national standard, and in commercial buildings, the reasonable energy consumption standard can be set according to the type, area, using functions and other factors of the buildings. And comparing the energy consumption metering values of all levels with corresponding preset standard values, and timely finding out the abnormal energy consumption condition. If the energy consumption metering value of a certain level exceeds the preset standard value range, the level is indicated to have the problems of energy waste, equipment failure or energy disuse. At this time, the stability and the persistence of the energy consumption change can be further known by analyzing the energy consumption change trend data. If the energy consumption change trend is in an unstable state, such as large fluctuation, frequent change and the like, the problems of unstable equipment operation, irregular energy consumption behavior and the like are possibly caused, and if the energy consumption change trend is in a continuously ascending or descending state, the long-term influence factors such as equipment aging, production process change and the like are possibly caused. For example, if the energy consumption measurement value of a production plant exceeds the preset standard value range for several months continuously and the energy consumption change trend is in a continuously rising state, specific reasons for causing the rising of the energy consumption can be found out by further analyzing factors such as equipment operation data, production plans and the like, and corresponding measures are taken for improvement.

As described in step S5, a corresponding early warning scheme is formulated according to the analysis results of the stability and the persistence of the energy consumption changes of each stage. The early warning scheme can be customized according to different conditions so as to ensure that relevant personnel can be timely and effectively reminded to take measures. For example, if the energy consumption change of a certain level is unstable and exceeds the preset standard value range, the manager can be immediately notified by means of short messages, mails, audible and visual alarms and the like so that the manager can take measures in time to adjust the energy consumption change, and if the energy consumption change of a certain level shows a continuous rising trend but does not exceed the preset standard value range, the manager can be reminded of paying attention to the energy use condition of the level by means of regular reports, early warning prompts and the like so as to take preventive measures in advance. The early warning scheme can also combine historical data and experience to provide corresponding solutions and suggestions. For example, if the energy consumption of a certain device is continuously rising, it may be recommended to overhaul, replace or optimize the operating parameters, etc. of the device, and if the energy consumption of a certain area is irregular, it may be recommended to enhance energy management, develop energy saving training, etc. In the embodiment, the initial energy consumption data is classified by analyzing the energy consumption change trend data and combining a preset energy consumption model structure, so that the classification is more reasonable, the current energy consumption data change is met, the follow-up classified metering is facilitated, and then the early warning is carried out. Through the timely and effective early warning scheme, the energy waste and the further deterioration of equipment faults can be avoided, and the efficiency and the effect of energy management are improved.

In an embodiment, the step of providing a corresponding early warning scheme according to the analyzed stability and persistence includes:

In this embodiment, first, the change condition of the energy consumption measurement values of each stage is continuously monitored, and when the energy consumption change trend is found to be in an ascending state, this means that the energy consumption is in a situation of continuously increasing in a period of time. Meanwhile, the actual energy consumption is compared with the preset standard energy consumption, and the loss value is calculated. If the loss exceeds the preset range, the problem of energy utilization efficiency is possibly caused, and the reason is required to be further analyzed. At this time, various operation parameters of the apparatus, such as the operation time, the operation temperature, the pressure, the rotation speed, etc., of the apparatus are collected. The above parameters may reflect the operating state and performance of the device. And analyzing the change trend of the operation parameters of the equipment. For example, if the operating temperature of the device continues to rise, it may mean that the heat dissipation system of the device is problematic, or that there is wear on the internal components of the device, resulting in increased heat generation. Comparing the operation parameters of the equipment in different time periods, and judging whether abnormal changes exist. If the operating parameters of the device change significantly from the historical data, it may be implied that the device has aged or failed. Through the deep analysis of the operation parameters of the equipment, whether the equipment has aging phenomenon or not can be primarily judged. If the operating parameters of the device exhibit aging-related characteristics, such as reduced performance, increased failure rate, etc., it may be determined that device aging is one possible cause of increased energy consumption and loss outside of the preset range.

When it is determined that the equipment has no aging phenomenon by deep analysis of the equipment operation parameters, it is necessary to turn to analysis of the energy consumption behavior data to find other reasons which may cause the increase of energy consumption and the loss to exceed the preset range. In particular, performance data may be collected, including time of use, frequency of use, pattern of use, etc. of the device. For example, some devices remain on for non-operating periods, or the devices are subject to unreasonable behavior during use, such as excessive use, frequent start-up and shut-down.

Then, the relation between the energy consumption behavior data and the energy consumption variation is analyzed. For example, if the energy consumption of an area suddenly increases within a certain period of time, it may be a result of irrational energy usage by analyzing the energy usage behavior data to find that a large number of devices are operating simultaneously within the period of time.

Further, it is determined whether there are disjustification energy factors. And judging whether factors such as energy waste, unreasonable equipment use modes and the like exist or not through analysis of the energy consumption behavior data. For example, if it is found that some devices are still operating at high power under low load conditions, or the device is not powered down in time after use, these are all undesirable energy factors.

Finally, a loss analysis result is obtained. And (3) comprehensively analyzing the equipment operation parameters and the energy consumption behavior data, and determining specific reasons for causing the energy consumption to rise and the energy consumption to exceed a preset range to obtain a loss analysis result. And presetting an early warning threshold range of a loss analysis result. This threshold range may be adjusted according to the actual situation, e.g., determined according to industry standards, energy conservation goals of the enterprise, or historical data.

When the loss analysis result exceeds a preset threshold range, the problem of energy consumption is serious, and measures are needed to be taken in time for intervention. The early warning prompt can be carried out in various modes, such as sending a short message, informing related management personnel by mail, displaying early warning information in an energy management system, or triggering audible and visual alarm and the like. The warning cues should contain critical information such as the level of energy consumption anomalies, specific problem descriptions (e.g., equipment aging, irrational energy, etc.), and suggested actions to take. Therefore, related personnel can quickly know the severity and specific conditions of the problem, and take corresponding measures in time to process.

In an embodiment, the step of classifying the initial energy consumption data according to a preset energy consumption model structure and the energy consumption variation trend data to obtain energy consumption data corresponding to different levels includes:

In this embodiment, the preset energy consumption model structure is designed according to a specific application scenario and requirement, and includes a plurality of parameters, where the parameters may reflect different aspects of energy consumption. For example, in an industrial production scenario, the energy consumption model structure may include parameters such as equipment type, production process, operating time, etc., and in a commercial building, parameters such as floor, functional area, equipment power, etc. Different combinations of the above parameters may be used to distinguish between different energy consumption levels.

And carrying out deep analysis on each parameter in the energy consumption model structure, and determining the influence degree of each parameter on energy consumption. For example, the energy consumption difference of different device types can be analyzed through historical data statistics, or the influence rule of different working time on the energy consumption can be analyzed. And (5) formulating a basic grading standard according to the influence degree of the parameters. The basic grading criterion may be a range of values or a classification rule for initially dividing the initial energy consumption data into different levels. For example, energy consumption may be divided into three levels, high, medium, and low, depending on the device power level.

The energy consumption change trend data is obtained by carrying out trend analysis on the initial energy consumption data, and reflects the change condition of energy consumption along with time. For example, the energy consumption variation trend data may include information of a rate of increase in energy consumption, a fluctuation range, and the like.

And carrying out statistical analysis on the energy consumption change trend data, and extracting key features. For example, statistics such as the mean, standard deviation, median, etc. of the energy consumption variation trend data may be calculated to understand the central trend and the degree of dispersion of the energy consumption variation.

And determining a trend influence factor according to the statistical analysis result. The trend influence factor is a numerical value or an index and is used for measuring the influence degree of the energy consumption change trend on the grading treatment. For example, if the trend of energy consumption changes exhibits a rapid increase, the trend influencing factor may be set to a larger value to increase the sensitivity of the classification.

And combining the basic grading standard with the trend influencing factors to obtain a comprehensive grading standard. The manner of combining may be weighted summation, multiplication, or other suitable mathematical method. For example, the device power parameters and trend impact factors in the base ranking criteria may be weighted and summed to obtain a new ranking indicator for more accurate ranking of the initial energy consumption data.

And finally, grading the initial energy consumption data by utilizing the comprehensive grading standard. Specifically, each initial energy consumption data point is compared to a ranking criteria to determine the level to which it belongs. And after the grading treatment, obtaining the energy consumption data corresponding to different grades. The data can be used for subsequent energy consumption metering, analysis, early warning and other works. For example, the energy consumption data of different levels can be respectively subjected to statistical analysis to know the characteristics and problems of the energy consumption of different levels so as to take targeted energy-saving measures.

In an embodiment, the performing statistical analysis on the energy consumption variation trend data to determine a trend impact factor includes:

In this embodiment, the energy consumption change trend data is obtained by trend analysis of initial energy consumption data, which reflects the change of energy consumption with time. For example, the energy consumption variation trend data may include information of a rate of increase in energy consumption, a fluctuation range, and the like. These data are time series data, and the energy consumption change at different time points is recorded.

The mean is the arithmetic mean of a set of data that reflects the central tendency of the data. For energy consumption trend data, the mean is calculated by summing the values of all the data points and dividing by the total number of data points. The standard deviation is a measure of the degree of dispersion of a set of data, which reflects the degree of deviation of the data from the mean. For energy consumption change trend data, the standard deviation is calculated by calculating the difference between each data point and the mean value, then adding the squares of the differences, dividing the sum by the total number of data points, and finally taking the square root.

The above neural network model is a machine learning tool that can build complex relationships between inputs and outputs by learning a large amount of data. In this embodiment, the neural network model is used to calculate the trend impact factor from the trend data statistics. The trend influence factor is a numerical value reflecting the degree of influence of the energy consumption change trend on the energy consumption classification process. Through calculation of the neural network model, trend influence factors can be determined more accurately, and therefore accuracy of energy consumption classification is improved.

The neural network model described above requires training prior to use. The training process generally includes the step of collecting a plurality of marked energy consumption trend data samples including trend data statistics and corresponding trend impact factors. And inputting the sample data into a neural network model, and enabling the output of the model to be as close to the labeling value in the sample as possible by adjusting the parameters of the model. This process is repeated until the performance of the model meets the expected requirements. The trained neural network model can accurately calculate the trend impact factors according to the new trend data statistical characteristics.

In an embodiment, the analyzing the stability and the persistence of the energy consumption change of each stage in combination with the energy consumption change trend data includes:

In this embodiment, the variance is a measure of the degree of data dispersion that reflects the average of the squares of the degrees of deviation of the data from the mean. For energy consumption trend data, the variance is calculated by first calculating the difference between each data point and the mean, then summing the squares of these differences, and dividing by the total number of data points. The smaller the variance is, the smaller the deviation degree between the energy consumption change trend data and the mean value is, namely the more stable the energy consumption change is. Conversely, the larger the variance, the less stable the energy consumption variation. For example, if the variance is close to zero, it indicates that the energy consumption change trend data is very close to the mean value, the energy consumption change of each stage is stable, and if the variance is large, it indicates that the energy consumption change trend data is large in fluctuation, and the energy consumption change of each stage is unstable.

For the energy consumption variation trend data of a plurality of consecutive time points, the trend of the energy consumption variation can be analyzed by calculating the slope between the adjacent two time points. The calculation formula of the slope is that the slope= (energy consumption value of the latter time point-energy consumption value of the former time point)/(the latter time point-the former time point).

By analyzing the slope change condition at a plurality of continuous time points, whether the trend of the energy consumption change is continuous or not can be known. If the slope change is small and tends to be stable, the energy consumption change is continuous, and if the slope change is large, the energy consumption change is not continuous. For example, if the slopes of successive time points all fluctuate within a small range, this indicates that the energy consumption variation of each stage is sustained, and if the slopes have a large variation between different time points, this indicates that the energy consumption variation of each stage is not sustained.

In an embodiment, after the corresponding early warning scheme is made, the method further includes:

In this embodiment, a data table structure is constructed for storing and managing the energy consumption related data. The data table may include a plurality of fields, which respectively correspond to different types of data such as initial energy consumption data, energy consumption change trend data, energy consumption measurement values of each level, and the like. And filling the sorted initial energy consumption data, energy consumption change trend data and energy consumption metering values of all levels into corresponding fields of a data table one by one. Ensuring the accuracy and integrity of the data for subsequent analysis and processing. For example, the initial energy consumption value at a certain time point, the corresponding energy consumption change trend analysis result and the energy consumption metering value at the level are respectively filled in the corresponding positions of the data table.

And further, combining the energy consumption change trend data with the energy consumption measurement values of all levels, and generating a unique identification value through a preset algorithm. This identification value may reflect the characteristics and status of the current energy consumption data. For example, certain key features of the energy consumption variation trend data (such as trend direction, variation amplitude, etc.) and statistical information of the energy consumption measurement values of each level (such as sum, average, etc.) may be combined, and the identification value may be generated through a hash algorithm or other encryption algorithm.

And identifying the energy consumption data table by using the generated identification value. The identification can be used as a unique label of the data table, so that the identification and management in the subsequent processing and transmission processes are facilitated. For example, the identification value may be recorded in a particular field of the energy consumption data table, or as part of the file name of the data table.

Further, some key parts of the initial energy consumption data, such as energy consumption peak value, average value and the like in a specific time period, and the identification value generated before are selected as basic data for generating the transmission key.

And processing the selected initial energy consumption data and the identification value by adopting a specific encryption algorithm to generate a key for data transmission encryption. This key should be of sufficient complexity and security to ensure confidentiality of the energy consumption data during transmission. For example, a symmetric encryption algorithm may be used to mix, transform, etc. the initial energy consumption data and the identification value to generate an encryption key.

Finally, the energy consumption data table is encrypted by using the generated transmission key. The encryption can adopt methods such as symmetric encryption or asymmetric encryption, and the like, so that the data is prevented from being illegally stolen or tampered in the transmission process. For example, using the AES symmetric encryption algorithm, each data field in the energy consumption data table is encrypted so that only the receiver that has the correct key can decrypt and read the data.

And transmitting the encrypted energy consumption data table to an energy consumption management terminal through a safe communication channel. The communication channel may be a dedicated network connection, an encrypted file transfer protocol, etc. to ensure the security and reliability of data transmission. For example, the encrypted energy consumption data table may be transmitted from the data collection device to a remote energy consumption management server using HTTPS protocol.

After receiving the encrypted energy consumption data table, the energy consumption management terminal decrypts the encrypted energy consumption data table by using the corresponding transmission key, and recovers the original energy consumption data. These data may then be further analyzed, processed and managed.

In an embodiment, the generating a transmission key based on the initial energy consumption data and the identification value includes:

Generating a graph according to a preset rule based on the numbers;

In this embodiment, the initial energy consumption data is obtained by collecting all levels of energy consumption data in real time, and includes various types of character information including Chinese characters, numbers, letters, symbols, and the like. However, in generating the transmission key, the presence of some designated characters may interfere with the generation of the key or reduce the security of the key.

A specified character that needs to be cleared is determined. The specified characters can be special symbols, spaces, line-feed symbols and the like, and are determined according to actual conditions. For example, a comma, a semicolon, a bracket, or the like may be set as a designated character to clear. The initial energy consumption data is checked character by character, and when a specified character is found, it is deleted from the data. After washing, a washing data character string containing only valid characters is obtained.

The standard binary tree template pre-stored in the database is a tree data structure with a specific structure and is used for organizing and storing character information. Which can provide an orderly way to process and manage the cleaned data strings.

Each character is added in turn to a node of the standard binary tree template starting with the first character of the cleaning data string. The characters may be assigned one by one to different nodes of the binary tree in a particular order, such as from left to right, top to bottom, etc. For example, if the cleaning data string is "ABCDE," first "a" is added to the root node of the binary tree, then "B" is added to the left or right child node of the root node, and so on until all characters are added to the binary tree, forming a binary tree of characters.

The identification value is generated based on the energy consumption change trend data and the energy consumption measurement values of all levels, and comprises a plurality of digital characters. And extracting the digital characters in the identification values, and dividing. The partitioning may be performed according to a specific rule, such as by a fixed number of bits, separators, etc. For example, if the identification value is "123456789", the division may be performed every two digits to obtain a plurality of digits, such as [12, 34, 56, 78, 9].

The digits can be used as input parameters to generate a specific pattern according to a preset rule. The rules may be based on mathematical formulas, algorithms, or specific graph generation algorithms. It should be noted that the graph is typically formed by a closed curve, such as an ellipse, a circle, a polygon, etc.

For example, the number characters in the identification value may be divided into 4 groups of numbers, and a preset rule is adopted, 3 coordinate points are obtained in the coordinate system based on the 4 groups of numbers, for example, the 4 groups of numbers are 13,24, 38, and 16, then three coordinate points (13, 24), (24, 38), and 38,16) may be generated, and a triangle is generated based on the 3 coordinate points, which is not described in detail herein.

And then, superposing the graph on the appointed position of the character binary tree, and carrying out equal-ratio stretching or scaling on the graph to ensure that the graph and the character binary tree meet the preset position relation. First, a designated position of the binary tree is determined, and the generated graphic is superimposed on this position. The specified location may be a node of the binary tree, a branch, or around the entire binary tree.

And performing equal-ratio stretching or scaling operation on the graph to enable the graph and the character binary tree to meet a preset position relation. This positional relationship may be that the graph completely covers a certain node, is scaled appropriately to the binary tree size, etc. For example, if the pattern is too large, an equal scaling down can be performed to match the size of the binary tree of characters, and if the pattern is too small, an equal scaling up can be performed to better integrate with the binary tree.

After the graph is scaled or scaled and combined with the binary tree of characters, characters within the graph are determined. The character is considered as a target character to be used for generating a transmission key. Each node of the binary tree is checked to determine if it is within the scope of the graph. If so, the character is extracted as the target character. For example, if the graph overlays certain nodes of the binary tree, the characters on those nodes are determined to be target characters.

The target character is determined by the interaction of the graphic with a binary tree of characters, which may be used as a basis for generating the transmission key. And processing the target character by adopting a preset rule to generate a transmission key. This rule may be an encryption algorithm, a hash function, or other specific key generation method. For example, the target character may be combined, transformed, encrypted, etc., to generate a transmission key of sufficient complexity and security for encrypted transmission of the energy consumption data table.

In an embodiment, the generating the identification value based on the energy consumption variation trend data and the energy consumption metering value of each stage includes:

Extracting the energy consumption change trend data and the digital characters in the energy consumption measurement values of each level to obtain a corresponding first character string and a corresponding second character string;

sequentially adding the digital characters in the first character string into a first matrix and sequentially adding the digital characters in the second character string into a second matrix according to a preset rule, wherein the rows and columns of the first matrix and the second matrix are the same;

Calculating the product of the first matrix and the second matrix to obtain a product matrix;

And screening the digital characters of the product matrix, screening a plurality of target digital characters, and combining to obtain the identification value.

In this embodiment, the energy consumption variation trend data and the energy consumption measurement values of each stage are obtained through the previous steps, and include digital characters and other non-digital characters. For example, the energy consumption variation trend data may be a descriptive text containing numerals and letters, and the energy consumption measurement values of each stage may be numerical values composed of numerals and units. All the digital characters are extracted from the energy consumption change trend data, and are combined into a character string, namely a first character string. Likewise, the digital characters are extracted from the energy consumption metering values of each stage to form a second character string. For example, if the energy consumption variation trend data is "trend is increased by 10%", the number "10" is extracted to constitute the first character string. If the energy consumption measurement value of each stage is 50 kilowatt-hours, the number 50 is extracted to form a second character string.

Further, the first matrix and the second matrix are sized, and the number of rows and columns may be set according to actual requirements, but the rows and columns of the two matrices must be identical. For example, a matrix of 3x3 may be set.

For the numeric characters in the first string, they are added to the elements of the first matrix in order from left to right and from top to bottom. If the number of numeric characters in the first string is less than the number of elements of the matrix, then the addition or specific padding process may be repeated. Also, for the numeric characters in the second string, they are added to the second matrix according to the same rule.

The matrix multiplication is a mathematical operation for calculating the product of two matrices. In this step, the first matrix and the second matrix are subjected to matrix multiplication. The calculation rule of matrix multiplication is that, for two matrices a and B, each element of their product C is equal to the sum of the products of the ith row of a and the jth column of B.

A new matrix, called a product matrix, is obtained by matrix multiplication. The size of this product matrix is the same as the size of the first matrix and the second matrix. Each element in the product matrix is checked to screen out the digital characters conforming to the specific rule. The screening rules may be selecting only numbers at a particular location, selecting only numbers greater than a certain threshold, etc. For example, the bit number of each element in the product matrix may be selected as the target number character.

And combining the plurality of the screened target digital characters according to a certain sequence to obtain a character string, wherein the character string is the identification value. For example, if the screened target numeric characters are [1, 2, 3, 4], they may be combined into "1234" as the identification value.

Referring to fig. 2, in an embodiment of the present invention, there is further provided an energy consumption hierarchical metering loss analysis system based on an energy consumption trend recursion algorithm, including:

In this embodiment, for specific implementation of each unit in the above system embodiment, please refer to the description in the above method embodiment, and no further description is given here.

Referring to fig. 3, in an embodiment of the present invention, there is further provided a computer device, which may be a server, and an internal structure thereof may be as shown in fig. 3. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected by a system bus. Wherein the computer is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program, when being executed by a processor, carries out the above-mentioned method.

It will be appreciated by those skilled in the art that the architecture shown in fig. 3 is merely a block diagram of a portion of the architecture in connection with the present inventive arrangements and is not intended to limit the computer devices to which the present inventive arrangements are applicable.

An embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the above method. It is understood that the computer readable storage medium in this embodiment may be a volatile readable storage medium or a nonvolatile readable storage medium.

In summary, the method and the system for analyzing the energy consumption hierarchical metering loss based on the energy consumption trend recursion algorithm provided by the embodiment of the invention comprise the steps of collecting all levels of energy consumption data in real time to obtain initial energy consumption data, carrying out trend analysis on the initial energy consumption data based on the energy consumption trend recursion algorithm to obtain energy consumption change trend data, carrying out hierarchical processing on the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels, wherein the energy consumption change trend data is used for assisting in determining dividing standards of all levels, respectively carrying out energy consumption metering on all levels of energy consumption data to obtain energy consumption metering values of all levels, comparing the energy consumption metering values of all levels with corresponding preset standard values, if the energy consumption metering values exceed the preset standard value range, combining the energy consumption change trend data to analyze the stability and the persistence of energy consumption change of all levels, and making a corresponding early warning scheme according to the analyzed stability and persistence. According to the method, the initial energy consumption data is classified by analyzing the energy consumption change trend data and combining with the preset energy consumption model structure, so that the classification is more reasonable, the current energy consumption data change is met, the subsequent classified metering is facilitated, and then the early warning is carried out.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium provided by the present invention and used in embodiments may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, apparatus, article, or method that comprises the element.

The foregoing description is only of the preferred embodiments of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes using the descriptions and drawings of the present invention or direct or indirect application in other related technical fields are included in the scope of the present invention.

Claims

1. A method for analyzing energy consumption loss by level measurement based on energy consumption trend recursive algorithm, characterized in that it comprises the following steps:

Collect energy consumption data at all levels in real time to obtain initial energy consumption data; perform trend analysis on the initial energy consumption data based on an energy consumption trend recursive algorithm to obtain energy consumption change trend data;

The initial energy consumption data is graded according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels; wherein the energy consumption change trend data is used to assist in determining the classification criteria for each level;

Energy consumption data at each level is measured separately to obtain energy consumption measurement values at each level;

Compare the energy consumption measurement values at each level with the corresponding preset standard values. If they exceed the preset standard value range, analyze the stability and sustainability of the energy consumption changes at each level in combination with the energy consumption change trend data;

Make corresponding early warning plans based on the analyzed stability and sustainability.

2. The energy consumption classification and measurement loss analysis method based on the energy consumption trend recursive algorithm according to claim 1 is characterized in that the corresponding early warning scheme is made according to the analyzed stability and sustainability, including:

If the energy consumption trend is increasing and the loss exceeds the preset range, an in-depth analysis of the equipment operating parameters is performed to determine whether there is equipment aging;

If the equipment is not aged, the energy usage behavior data is analyzed to determine whether there are unreasonable energy usage factors and obtain loss analysis results;

When the loss analysis result exceeds a preset threshold range, an early warning prompt is issued.

3. The energy consumption graded metering loss analysis method based on the energy consumption trend recursive algorithm according to claim 1 is characterized in that the initial energy consumption data is graded according to the preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels, including:

Analyze various parameters in the preset energy consumption model structure to obtain basic classification standards;

Performing statistical analysis on the energy consumption change trend data to determine trend influencing factors;

The basic classification standard is combined with the trend influencing factor, and the initial energy consumption data is classified to obtain energy consumption data corresponding to different levels.

4. The energy consumption classification metering loss analysis method based on energy consumption trend recursive algorithm according to claim 3 is characterized in that the statistical analysis of the energy consumption change trend data to determine the trend influencing factor includes:

Calculate the mean and standard deviation of the energy consumption change trend data to obtain statistical characteristics of the trend data;

The statistical features of the trend data are input into a pre-trained neural network model for calculation, and the trend influencing factors are output.

5. The energy consumption level measurement loss analysis method based on energy consumption trend recursive algorithm according to claim 1 is characterized in that the analysis of the stability and continuity of energy consumption changes at each level in combination with the energy consumption change trend data includes:

Calculate the variance of energy consumption trend data, and determine the stability of energy consumption changes at all levels based on the variance calculation results;

The slope changes of the energy consumption change trend data at multiple consecutive time points are analyzed, and the continuity of the energy consumption changes at each level is determined based on the slope changes.

6. The energy consumption classification and measurement loss analysis method based on energy consumption trend recursive algorithm according to claim 1 is characterized in that after making the corresponding early warning plan, it also includes:

Add the initial energy consumption data, energy consumption change trend data, and energy consumption measurement values at all levels to a data table to obtain an energy consumption data table;

Generate an identification value based on the energy consumption change trend data and the energy consumption measurement values at each level, and identify the energy consumption data table based on the identification value;

After the energy consumption data table is encrypted based on the transmission key, it is transmitted to the energy consumption management terminal.

7. The energy consumption hierarchical metering loss analysis method based on energy consumption trend recursive algorithm according to claim 6 is characterized in that the generation of a transmission key based on the initial energy consumption data and the identification value comprises:

Performing character cleaning on the initial energy consumption data to obtain a cleaned data string; wherein the character cleaning includes removing specified characters from the initial energy consumption data;

Obtain a standard binary tree template pre-stored in a database, and add the characters in the cleaned data string to each node of the standard binary tree template in sequence to obtain a character binary tree; segment the identification value to obtain a plurality of numbers; wherein the identification value includes a plurality of numeric characters;

Based on the multiple numbers, a graphic is generated according to a preset rule; the graphic is superimposed on a specified position of the character binary tree, and the graphic is stretched or scaled proportionally so that the graphic satisfies a preset positional relationship with the character binary tree;

After the graphic is stretched or scaled proportionally, the character located in the graphic is obtained in the character binary tree as the target character; based on the target character, the transmission key is generated using a preset rule.

8. An energy consumption classification metering loss analysis system based on energy consumption trend recursive algorithm, characterized by comprising:

An analysis unit is used to collect energy consumption data at all levels in real time to obtain initial energy consumption data; perform trend analysis on the initial energy consumption data based on an energy consumption trend recursive algorithm to obtain energy consumption change trend data;

A grading unit, used to grade the initial energy consumption data according to a preset energy consumption model structure and the energy consumption change trend data to obtain energy consumption data corresponding to different levels; wherein the energy consumption change trend data is used to assist in determining the classification criteria for each level;

The metering unit is used to measure the energy consumption data of each level and obtain the energy consumption measurement value of each level;

A comparison unit is used to compare the energy consumption measurement values at each level with the corresponding preset standard values. If the preset standard value range is exceeded, the stability and sustainability of the energy consumption changes at each level are analyzed in combination with the energy consumption change trend data;

The early warning unit is used to make corresponding early warning plans based on the analyzed stability and continuity.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements the steps of the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.