Energy acquisition monitoring system based on big data

Abstract

The invention discloses an energy acquisition and monitoring system based on big data, particularly relates to the field of energy monitoring, is used for solving the problem of energy consumption abnormity identification and management optimization in steel production, and is characterized in that energy parameters are acquired in a production process, and an energy signature reference model of multi-dimensional energy consumption characteristics is constructed based on historical data and equipment operation characteristics; an accurate reference is provided for dynamic comparison and anomaly detection; potential anomalies are accurately identified by utilizing comprehensive analysis of energy consumption deviation distribution and load characteristics, and key influence factors and root causes are deeply mined through a multivariable machine learning algorithm; in combination with a predictive scheduling engine and an online optimization algorithm, process parameters, equipment running states and task schedules are dynamically adjusted, abnormities are effectively eliminated, and energy consumption balance is recovered; therefore, the real-time monitoring and intelligent anomaly detection of the process energy consumption are realized, the refinement level of energy management is improved, the energy utilization efficiency and production stability of iron and steel enterprises are improved, and the potential production risk is reduced at the same time.

Description

Technical Field

[0001] The present invention relates to the field of energy monitoring, and more specifically, to an energy collection and monitoring system based on big data. Background Art

[0002] In the steel production process, each process link such as ironmaking, steelmaking and steel rolling has significant energy consumption characteristics, which form a unique energy signature. Traditional energy monitoring systems mainly rely on total energy consumption statistics or simple threshold alarms, which makes it difficult to deeply analyze and identify potential energy consumption anomalies in each process link. This monitoring method cannot detect hidden process anomalies in time, resulting in energy waste and reduced production efficiency. With the development of big data technology and Internet of Things devices, in-depth analysis based on multi-source data has become possible, which helps to build a refined energy monitoring system, realize real-time monitoring of process energy consumption and intelligent anomaly detection, thereby improving energy management level and production optimization capabilities.

[0003] The existing energy monitoring system cannot accurately identify and analyze energy consumption anomalies in various process links of steel production. It lacks in-depth mining and dynamic comparison of energy signatures, resulting in untimely anomaly detection and inability to effectively guide energy optimization and troubleshooting. This deficiency limits the intelligence and refinement of energy management, and it is difficult to meet the needs of modern steel companies for efficient and accurate energy monitoring. Therefore, there is an urgent need for an energy acquisition and monitoring system based on big data, which can achieve accurate monitoring and anomaly detection of process energy consumption through multi-source data fusion and in-depth analysis, improve energy utilization efficiency and reduce production risks.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an energy collection and monitoring system based on big data, which collects energy parameters through the production process, and then constructs an energy signature reference model of multi-dimensional energy consumption characteristics based on historical data and equipment operation characteristics, so as to provide an accurate benchmark for dynamic comparison and anomaly detection; utilizes the comprehensive analysis of energy consumption deviation distribution and load characteristics to accurately identify potential anomalies, and deeply mines key influencing factors and root causes through multivariate machine learning algorithms; combines predictive scheduling engines and online optimization algorithms to dynamically adjust process parameters, equipment operating status and task scheduling, effectively eliminate anomalies and restore energy consumption balance; thereby realizing real-time monitoring of process energy consumption and intelligent anomaly detection, improving the level of refinement of energy management, improving the energy utilization efficiency and production stability of steel enterprises, and reducing potential production risks, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An energy collection and monitoring system based on big data, comprising: a data collection module, a data processing module, a feature modeling module, an anomaly detection module, a correlation analysis module and a scheduling optimization module;

[0008] Data acquisition module: using pre-arranged sensors, corresponding energy-related parameters are uploaded to the big data processing platform in real time, and the output data set is passed to the data processing module;

[0009] Data processing module: converts the uploaded data into a unified format and removes noise signals to ensure the accuracy and consistency of the data, and indexes and stores the data by timestamp and process number. The output standardized data set is passed to the feature modeling module;

[0010] Feature modeling module: Based on historical data and equipment operation characteristics, it extracts the multi-dimensional energy consumption characteristics of each process link, builds an energy signature reference model as a benchmark for normal energy consumption status, and transmits the output energy signature reference model to the anomaly detection module;

[0011] Anomaly detection module: continuously receives real-time energy consumption data, conducts dynamic comparison with the energy signature reference model through comprehensive evaluation of the deviation distribution of multi-dimensional energy consumption status and load concentration characteristics, identifies abnormal data points that exceed the normal energy consumption range, and transmits the detected abnormal data to the correlation analysis module;

[0012] Correlation analysis module: performs in-depth correlation analysis on detected abnormal data, combines equipment status and production parameters, uses multivariate machine learning algorithms to determine the root cause of the abnormality and classify and archive it. The output abnormal classification archive and root cause analysis results are passed to the scheduling optimization module;

[0013] Scheduling Optimization Module: Uses the predictive scheduling engine and online energy consumption optimization algorithm to evaluate the execution effect of the adjustment plan. By adjusting key process temperatures, switching backup fuel supply pipelines, and issuing production line operation sequence adjustment instructions, it can immediately eliminate energy consumption anomalies and ensure production and furnace temperature balance.

[0014] In a preferred embodiment, the data acquisition module includes the following contents:

[0015] Using multiple types of sensors arranged at key process nodes, multiple energy parameters are collected in real time at fixed sampling intervals to construct an energy state vector to characterize the comprehensive energy consumption state at each sampling moment; through a dynamic partition scheduling strategy, the data collected by multiple types of sensors are classified and stored in distributed data nodes, and the timestamp is used as the primary key to generate an original data set with a unique identifier; the cleaned original data set is marked according to the timestamp and process number to generate a structured data set.

[0016] In a preferred embodiment, the data processing module includes the following contents:

[0017] Extract multi-dimensional energy parameters from the data set uploaded by the data acquisition module, and preliminarily analyze the type of source sensor and data encoding format;

[0018] Based on the rule-based dynamic mapping table, the data encoding formats uploaded by sensors from different sources are converted and an initial data set with a unified format is generated;

[0019] The noise signal in the initial data set is removed by using the wavelet transform method;

[0020] An index matrix is ​​constructed by combining the timestamp and process number, and the cleaned data is allocated to the time series storage unit of the corresponding process to ensure that the data is continuous in the time dimension and independent in the process dimension;

[0021] Generate a standardized dataset after format conversion, noise removal and indexing.

[0022] In a preferred embodiment, the feature modeling module includes the following contents:

[0023] From the standardized data set, the data sequence is divided by process number to form an independent time series data set for each process link, so as to conduct group analysis on the energy consumption characteristics of different processes;

[0024] For the time series data of each process link, the adaptive weighted curve decomposition method is applied to decompose it into basic consumption trend and periodic fluctuation components;

[0025] For the basic consumption trend, calculate the key inflection point characteristics, including the maximum change rate and the time position of the energy consumption inflection point, to capture the key moments of energy demand;

[0026] For periodic fluctuation components, short-time Fourier transform is used to extract the main frequency components, and the energy concentration is calculated to quantify the fluctuation intensity;

[0027] The trend features and fluctuation features are combined to form a multi-dimensional energy consumption feature vector, and the high-dimensional features are embedded into a low-dimensional space through a dimensionality reduction algorithm to form a visual feature distribution map;

[0028] The density clustering algorithm is used to perform density clustering analysis on the multidimensional energy consumption feature vectors in the feature distribution map. By setting the core point density threshold and the minimum number of samples, the dense areas of energy consumption characteristics are identified and divided into clusters with similar energy consumption patterns. The center point of the cluster with the highest density is extracted as the typical energy consumption state of each process link, and the upper and lower limits of the feature range are calculated to form an energy signature reference model with boundary conditions as a normal energy consumption benchmark for real-time comparison.

[0029] In a preferred embodiment, the anomaly detection module includes the following contents:

[0030] The typical energy consumption state center value and energy consumption characteristic boundary of each process link are extracted from the energy signature reference model as the comparison benchmark; the real-time energy consumption data set is loaded into the comparison module point by point, and the energy consumption deviation symmetry index and instantaneous load energy consumption compression rate index are calculated and processed for the real-time energy consumption state vector at each time point.

[0031] In a preferred embodiment, the energy consumption deviation symmetry index measures whether the deviation of energy consumption data in each dimension is evenly distributed by performing a symmetry analysis on the deviation of the real-time energy consumption state vector; specifically, the energy consumption deviation symmetry index decomposes the energy consumption deviation into two dimensions: absolute value and square value during the calculation process: the average absolute deviation reflects the overall level of the deviation amplitude, while the square root of the mean square deviation emphasizes the contribution of the dimension with a large deviation; the absolute value of the difference between the two is used to characterize the distribution characteristics of the deviation of each dimension, namely, the energy consumption deviation symmetry index.

[0032] In a preferred embodiment, the instantaneous load energy consumption compression rate index is intended to analyze the concentration of real-time energy consumption data in load distribution and reveal whether there is abnormal load aggregation in the energy consumption state vector. In the calculation, the real-time energy consumption data is first converted into a standard load value with relative weight significance through normalization processing to ensure that the data of each dimension are compared under a unified dimension. Subsequently, the instantaneous load energy consumption compression rate index quantifies the load concentration by calculating the ratio of the extreme difference of the load value to the total load. At the same time, a logarithmic function is introduced to enhance the sensitivity to the load extreme value ratio, thereby identifying abnormal characteristics caused by extreme load distribution.

[0033] In a preferred embodiment, the energy consumption deviation symmetry index and the instantaneous load energy consumption compression rate index are weighted and summed to form a comprehensive comparison parameter for comprehensively evaluating the degree of abnormality of real-time energy consumption data; a comprehensive comparison threshold is set, and when the comprehensive comparison parameter is greater than the comprehensive comparison threshold, it is determined that there is an energy consumption abnormality at the corresponding time point; the information of the abnormal data point is recorded and stored in the abnormal data log.

[0034] In a preferred embodiment, the association analysis module includes the following contents:

[0035] Extract deviation values, corresponding process links and time points from abnormal data logs as initial input for deep correlation analysis;

[0036] Combine the equipment status data set and the process parameter data set to perform correlation modeling on the abnormal occurrence time point and its neighboring time window, and preliminarily screen the key influencing factors;

[0037] A multivariate time series regression model is applied, with energy consumption deviation as the target variable and all variables in the equipment status data and process parameter data as input factors. The contribution of each factor to energy consumption deviation is quantified by calculating the product of the partial derivative coefficient and the actual value of each factor.

[0038] Sort the factors in descending order according to their impact contribution, select the top N factors with the largest contribution values ​​as key factors, form an abnormal feature factor set, and record their corresponding feature deviation values ​​and time periods;

[0039] Based on the abnormal feature factor set, the clustering algorithm is used to classify abnormal events, classify abnormalities with similar patterns into the same category, and mark the classification results and root cause types to form an abnormal classification archive.

[0040] In a preferred embodiment, the scheduling optimization module includes the following contents:

[0041] Extract the anomaly type, key factor set and root cause analysis results from the anomaly classification archive as input basis for scheduling and optimization;

[0042] Based on the predictive scheduling model, the time series prediction algorithm is used to make short-term predictions on the future status of key factors, and a prediction function is constructed. The prediction is completed by combining the multivariate regression model with other related factors.

[0043] Match the prediction results with the energy signature reference model, simulate future energy consumption deviations, calculate the dynamic response effects of different adjustment schemes, and select the adjustment strategy with the shortest response time and the best recovery effect to form the optimal adjustment scheme;

[0044] After determining the adjustment plan, the key process parameters are revised through the online optimization algorithm. The adjustment plan includes:

[0045] Update the key process temperature setting values ​​to ensure that the temperature parameters gradually approach the energy signature reference model;

[0046] Switch backup fuel supply lines and adjust supply priorities based on gas flow demand;

[0047] Adjust the production line operation sequence by optimizing the task allocation matrix, dynamically adjusting the task priority and scheduling sequence according to the root cause analysis results of energy consumption anomalies and real-time process load status;

[0048] The adjusted energy consumption data is dynamically compared with the energy signature reference model. If the deviation value gradually decreases and tends to the predetermined range, the adjustment effect is recorded and the abnormal state is marked as recovered.

[0049] The technical effects and advantages of the energy collection and monitoring system based on big data of the present invention are as follows:

[0050] The present invention combines multiple types of sensors to collect energy-related parameters in real time, adopts a unified format conversion, noise signal removal, and a preprocessing method of timestamp and process number indexing to ensure the accuracy and consistency of the data, and then builds an energy signature reference model of multi-dimensional energy consumption characteristics based on historical data and equipment operation characteristics, providing an accurate benchmark for real-time energy consumption monitoring and dynamic comparison; through a comprehensive evaluation of energy consumption state deviation distribution and load concentration characteristics, accurately identify abnormal data points, and use multivariate machine learning algorithms to combine equipment status and process parameters to carry out deep correlation analysis, effectively locate the root cause of abnormalities and key influencing factors; at the same time, through a predictive scheduling engine and an online energy consumption optimization algorithm, a refined adjustment plan is generated, including process parameter revision, equipment switching, and task scheduling adjustment, so as to achieve instant elimination of energy consumption anomalies, and finally dynamically update the energy signature reference model, further improving the adaptability and monitoring accuracy of the model. Furthermore, through real-time collection, dynamic comparison, deep analysis and closed-loop optimization of energy consumption data, the intelligent, refined and efficient energy management is realized, which significantly improves the energy utilization efficiency and production stability of steel production, and reduces energy waste and potential equipment operation risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a structural schematic diagram of an energy collection and monitoring system based on big data of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Embodiment 1: Figure 1 The present invention provides an energy collection and monitoring system based on big data, comprising: a data collection module, a data processing module, a feature modeling module, an anomaly detection module, a correlation analysis module and a scheduling optimization module;

[0054] Data acquisition module: using pre-arranged sensors, corresponding energy-related parameters are collected in real time and uploaded to the big data processing platform. The output data set is passed to the data processing module;

[0055] Data processing module: converts the uploaded data into a unified format and removes noise signals to ensure the accuracy and consistency of the data, and indexes and stores the data by timestamp and process number. The output standardized data set is passed to the feature modeling module;

[0056] Feature modeling module: Based on historical data and equipment operation characteristics, it extracts the multi-dimensional energy consumption characteristics of each process link, builds an energy signature reference model as a benchmark for normal energy consumption status, and transmits the output energy signature reference model to the anomaly detection module;

[0057] Anomaly detection module: continuously receives real-time energy consumption data, conducts dynamic comparison with the energy signature reference model through comprehensive evaluation of the deviation distribution of multi-dimensional energy consumption status and load concentration characteristics, identifies abnormal data points that exceed the normal energy consumption range, and transmits the detected abnormal data to the correlation analysis module;

[0058] Correlation analysis module: performs in-depth correlation analysis on detected abnormal data, combines equipment status and production parameters, uses multivariate machine learning algorithms to determine the root cause of the abnormality and classify and archive it. The output abnormal classification archive and root cause analysis results are passed to the scheduling optimization module;

[0059] Scheduling Optimization Module: Uses the predictive scheduling engine and online energy consumption optimization algorithm to evaluate the execution effect of the adjustment plan. By adjusting key process temperatures, switching backup fuel supply pipelines, and issuing production line operation sequence adjustment instructions, it can immediately eliminate energy consumption anomalies and ensure production and furnace temperature balance.

[0060] In the steel production process, the energy consumption of each process link often shows a complex alternation between transient and steady state. Real-time collection of high-dimensional, multi-category energy parameters is the basis for building an accurate energy monitoring system. Traditional collection methods lack effective screening and association of multi-dimensional features, making it difficult to meet the high requirements of modern energy management for data accuracy, real-time performance and system compatibility. By rationally deploying multiple types of sensors and adopting efficient data collection and transmission mechanisms, high-quality input support can be provided for subsequent data processing and abnormal analysis.

[0061] The data acquisition module includes the following:

[0062] By using multiple types of sensors (including power monitoring modules, gas flow meters, temperature sensors and pressure sensors) arranged at key process nodes such as ironmaking, steelmaking and steel rolling, multiple energy parameters such as power consumption P(t), gas flow F(t), temperature T(t) and pressure p(t) are collected in real time at fixed sampling intervals to construct the energy state vector X(t) = [P(t), F(t), T(t), p(t)] T , to characterize the comprehensive energy consumption state at each sampling moment.

[0063] Through the dynamic partition scheduling strategy, the data collected by multiple types of sensors are classified and stored in distributed data nodes, and the timestamp t is used as the primary key to generate the original data set with a unique identifier. Ensure that the data collection process has temporal continuity and spatial distribution integrity;

[0064] During the data upload process, edge computing devices are used to perform preliminary cleaning of the original data set, and physically unacceptable abnormal points are removed based on a multivariate nonlinear filtering algorithm. For example, when detecting gas flow fluctuations, the fluctuation identification formula based on empirical mode decomposition is applied: ΔF = F(t)-F trend (t), if |ΔF|>∈, F(t) is eliminated; where F trend (t) represents the trend value of the gas flow corresponding to time t. It is the long-term trend of flow change extracted by data decomposition or trend analysis algorithm (such as empirical mode decomposition EMD or sliding average method), and is used to represent the flow benchmark under normal conditions.

[0065] ΔF is the flow deviation, which is calculated as the difference between the original flow value and the trend flow value and is used to detect the abnormal fluctuation amplitude of the flow.

[0066] ∈ represents the abnormal detection threshold of gas flow fluctuation, and its value needs to be set according to the analysis results of historical data to ensure that abnormal points can be accurately identified.

[0067] The cleaned raw data set is labeled by timestamp and process number to generate a structured data set Provide standardized input for subsequent format unification and multi-dimensional feature extraction.

[0068] D clean Represents the cleaned data set after format conversion and noise removal.

[0069] X ′ (t i ) represents the time t i The energy state vector after cleaning corresponding to the moment.

[0070] t i Represents time point i, which is a valid time point included in the cleaning data set.

[0071] It is a time index set, which contains all timestamps that are considered valid. The original data corresponding to these time points have been cleaned to exclude unreliable data.

[0072] It indicates that the cleaned data vector is a subset of the original dataset and contains only data points that have passed the validity filter.

[0073] The cleaned and classified energy consumption data is uploaded to the big data processing platform through industrial protocols (such as MQTT or OPC UA), and the energy state vector at each moment is indexed within the platform to ensure efficient transmission of data from real-time collection to final storage, forming a time-continuous and logically rigorous energy consumption data flow to meet the high-quality input requirements of subsequent modeling and comparison.

[0074] Through the above methods, the data acquisition module not only completes the real-time collection and cleaning of high-dimensional energy parameters, but also uses structured data streams and dynamic partition storage strategies to solve the problems of poor time continuity and insufficient data accuracy in traditional collection methods, laying a solid foundation for subsequent format conversion, multidimensional feature extraction and energy signature modeling. This method has the technical characteristics of high precision and high efficiency, and reflects the technical depth of the solution with multivariate filtering and distributed storage design.

[0075] In the process of energy collection and monitoring, the real-time data uploaded by the sensor may have problems such as inconsistent formats, noise interference, and timestamp misalignment due to the complex sources. These defects will directly affect the accuracy and consistency of subsequent feature extraction and energy signature modeling. In order to ensure that the data can be processed efficiently and analyzed accurately, it is necessary to design targeted data format conversion and noise removal technology logic to ensure that the data structure and content are highly reliable and available at the physical and logical levels.

[0076] The data processing module includes the following:

[0077] Datasets uploaded from the data collection module Extract multi-dimensional energy parameters, including power consumption, gas flow, temperature and pressure, and preliminarily analyze the type of source sensor and data encoding format;

[0078] Design a rule-based dynamic mapping table to convert the data encoding formats uploaded by sensors from different sources, such as unifying different units of power data into kilowatt-hours, unifying different flow meter units into cubic meters per hour for flow data, and generating an initial data set with a unified format

[0079] The wavelet transform method is used to remove the noise signal in the initial data set. The specific method is as follows:

[0080] For each time point, the energy state vector X fmt (t) Perform wavelet decomposition to obtain low-frequency components L and high-frequency components H, and only retain components that meet the threshold condition |H|<λ H Components to filter high-frequency noise and reconstruct noise-free energy consumption data

[0081] Combined with timestamp ti Construct an index matrix with process number k Allocate the cleaned data to the time series storage unit of the corresponding process to ensure that the data is continuous in the time dimension and independent in the process dimension;

[0082] Generate a standardized dataset after format conversion, noise removal and indexing The standardized dataset has high consistency and accuracy, providing input for multi-dimensional feature extraction and energy signature modeling in the feature modeling module.

[0083] Through the above processing, the data processing module completes the comprehensive processing from raw data to standardized data, and solves the format inconsistency and noise interference problems in multi-source data integration by using technical means such as dynamic format mapping, wavelet noise reduction and index construction, providing high-quality and standardized data input for subsequent energy signature modeling. It logically and rigorously connects the collection content of the data acquisition module and lays a precise foundation for the modeling work of the feature modeling module, reflecting the processing logic and data quality assurance capabilities.

[0084] In energy monitoring and management, extracting multi-dimensional energy consumption characteristics is the core step to achieve accurate modeling and anomaly detection. Based on historical data and equipment operation characteristics, in-depth analysis of the energy consumption characteristics of each process link can not only reflect the equipment operation rules, but also provide a clear benchmark for anomaly judgment. However, energy consumption characteristics involve complex relationships in multiple dimensions and levels, and traditional univariate analysis methods cannot effectively capture such high-dimensional correlation characteristics. Therefore, mining potential rules from historical data and constructing an energy signature reference model that can be used for real-time monitoring lays the foundation for dynamic comparison and anomaly detection.

[0085] The feature modeling module includes the following:

[0086] Standardized datasets generated from the data processing module In the process, the data sequence is divided according to the process number k to form an independent time series data set D for each process link. k ={X std (t i ,k)|k∈K}, so as to conduct group analysis on the energy consumption characteristics of different processes;

[0087] The adaptive weighted curve decomposition method is applied to the time series data of each process link to decompose it into the basic consumption trend and the periodic fluctuation component W k (t), the formula is as follows: X std (t i ,k)=T k (t i )+W k (ti )+ε k (t i ), where T k (t i ) reflects the long-term energy consumption trend, W k (t i ) represents the periodic fluctuation characteristics, ε k (t i ) is random interference.

[0088] For basic consumption trend T k (t), calculate key inflection point characteristics, including maximum rate of change and the time position t of the energy consumption inflection point peak , to capture critical moments of energy demand;

[0089] For the periodic fluctuation component W k (t), short-time Fourier transform is used to extract the main frequency component ν k , and calculate the energy concentration Quantify the intensity of volatility;

[0090] The trend characteristics and fluctuation characteristics are combined to form a multi-dimensional energy consumption feature vector F k =

[0091] [Δ max ,t peak ,ν k ,E k ], and embed the high-dimensional features into low-dimensional space through dimensionality reduction algorithms (such as t-SNE) to form a visual feature distribution map;

[0092] The density clustering algorithm is used to perform density clustering analysis on the multidimensional energy consumption feature vectors in the feature distribution map. By setting the core point density threshold ε and the minimum sample number MinPts, the dense area of ​​energy consumption characteristics is identified and divided into clusters with similar energy consumption patterns; the center point of the cluster with the highest density is extracted. As the typical energy consumption state of each process link, the upper and lower limits of the calculated characteristic range are formed to form an energy signature reference model with boundary conditions. Serves as the normal energy consumption benchmark for subsequent real-time comparison.

[0093] The feature modeling module uses a density clustering algorithm to accurately divide the multidimensional distribution of energy consumption characteristics, successfully extracts the typical energy consumption status of each process link, and constructs an energy signature reference model containing central values ​​and boundary conditions. This model can clearly reflect the normal energy consumption range of each process link, which not only provides a clear judgment standard for subsequent real-time comparison, but also significantly improves the accuracy and reliability of energy consumption anomaly detection, avoiding resource waste and inefficiency caused by feature overlap or misjudgment in traditional methods. Through this process, the establishment of the energy signature model closely matches the need for in-depth exploration of multidimensional energy consumption laws in background technology, and helps the intelligent and refined upgrade of energy management in the steel industry.

[0094] The anomaly detection module includes the following:

[0095] Extract the typical energy consumption state center value of each process link from the energy signature reference model generated by the feature modeling module and energy consumption characteristic boundary As a comparison benchmark.

[0096] The real-time energy consumption data set processed by the data processing module is loaded point by point into the comparison module. The real-time energy consumption state vector X at each time point is real (t i )=[x 1 (t i ),x 2 (t i ),...,x n (t i )] T , perform the following calculation and processing of the energy consumption deviation symmetry index and the instantaneous load energy consumption compression rate index.

[0097] Calculate the energy consumption deviation symmetry index EDSI:

[0098] The energy consumption deviation symmetry index is used to measure whether the deviation of real-time energy consumption data in various dimensions is symmetrical, reflecting the balance of the energy consumption state vector in multiple dimensions.

[0099] The energy consumption deviation symmetry index measures whether the deviation of energy consumption data in each dimension is evenly distributed by performing a symmetry analysis on the deviation of the real-time energy consumption state vector. Specifically, the energy consumption deviation symmetry index decomposes the energy consumption deviation into two dimensions: absolute value and square value during the calculation process: the average absolute deviation reflects the overall level of the deviation amplitude, while the square root of the mean square deviation emphasizes the contribution of the dimension with a larger deviation. The absolute value of the difference between the two is used to characterize the distribution characteristics of the deviation in each dimension. If the difference is large, it means that the deviation is mainly concentrated in certain dimensions, indicating that the asymmetry of the energy consumption state between the dimensions is increasing, which may indicate a potential abnormal state. The energy consumption deviation symmetry index provides a theoretical basis for identifying abnormal deviation characteristics by quantifying the symmetry of multi-dimensional energy consumption data.

[0100] The calculation process is as follows: Where Δx j (t i ) represents the jth dimension at time point t i The energy consumption deviation.

[0101] Energy consumption deviation symmetry index EDSI: in, Represents the average absolute value of the deviation of each dimension, Represents the root mean square of the deviation in each dimension. The larger the absolute value of the energy consumption deviation symmetry index, the more significant the asymmetry of the energy consumption deviation in each dimension, which may indicate an abnormality.

[0102] Calculate the instantaneous load energy consumption compression index ILECRI:

[0103] The instantaneous load energy consumption compression rate index is used to evaluate the concentration of real-time energy consumption data in load distribution and reflect whether there is abnormal load concentration in the energy consumption state vector.

[0104] The instantaneous load energy consumption compression rate index aims to analyze the concentration of real-time energy consumption data in load distribution and reveal whether there is abnormal load aggregation in the energy consumption state vector. In the calculation, the real-time energy consumption data is first converted into a standard load value with relative weight significance through normalization processing to ensure that the data of each dimension are compared under a unified dimension. Subsequently, the instantaneous load energy consumption compression rate index quantifies the load concentration by calculating the ratio of the extreme difference of the load value to the total load, and introduces a logarithmic function to enhance the sensitivity to the load extreme value ratio, so as to more accurately identify the abnormal characteristics caused by extreme load distribution. By quantifying the compression and concentration of the load distribution, this index provides a strong basis for detecting abnormal load behavior in the energy consumption state.

[0105] The calculation process is as follows: Among them, w jis the energy consumption weight of the jth dimension, is the normalized energy consumption value.

[0106] Instantaneous load energy consumption compression index: in, and are the maximum and minimum values ​​of the normalized energy consumption values, respectively. The first term measures the relative difference in energy consumption values, and the second term enhances the sensitivity to extreme load concentration through a logarithmic function. The higher the instantaneous load energy consumption compression rate index value, the more unbalanced the load distribution is, which may indicate abnormal energy consumption.

[0107] The energy consumption deviation symmetry index and the instantaneous load energy consumption compression rate index are weighted and summed to form a comprehensive comparison parameter CPI, which is used to comprehensively evaluate the abnormality of real-time energy consumption data.

[0108] Set the comprehensive comparison threshold θ CPI , when CPI(t i )>θ CPI (comprehensive comparison parameter is greater than the comprehensive comparison threshold), the corresponding time point t is determined i There is energy consumption anomaly. Record the information of abnormal data points, including the deviation value ΔX(t i ), corresponding to process step k and time point t i , and save it in the abnormal data log

[0109] The anomaly detection module realizes multi-dimensional and complex comparison of real-time energy consumption data by introducing the energy consumption deviation symmetry index and the instantaneous load energy consumption compression rate index. This process not only improves the accuracy and sensitivity of anomaly detection by quantifying the deviation symmetry and load concentration, but also effectively integrates the abnormal characteristics of different dimensions through comprehensive comparison parameters, ensuring the comprehensiveness and reliability of anomaly judgment. Through accurate anomaly recognition and pattern marking, the anomaly detection module provides high-quality data input for subsequent deep correlation analysis and intelligent anomaly processing, significantly improving the intelligence and responsiveness of the energy monitoring system.

[0110] Accurately locating energy consumption anomalies is the core link in optimizing energy management and production efficiency. The identification of abnormal data is only the first step. In the future, it is necessary to combine the operating status of production equipment and process parameters to deeply explore the potential causes of the anomalies. Since energy consumption anomalies may be caused by the combined action of multi-dimensional factors, the multivariate correlation analysis technology can be used to accurately locate the root cause of the anomaly through the linkage modeling of historical data and real-time parameters, and provide a reliable basis for subsequent energy consumption optimization.

[0111] The association analysis module includes the following:

[0112] Extract the deviation value, corresponding process link and time point from the abnormal data log generated by the anomaly detection module as the initial input for deep correlation analysis;

[0113] Combined with the equipment status dataset S k (t) = {s 1 (t),s 2 (t),...,s m (t)} and process parameter data set P k (t) = {p 1 (t),p 2 (t),...,p l (t)}, the abnormality occurs at time t i and its neighborhood time window [t i -Δt,t i +Δt] to conduct association modeling and preliminarily screen possible key influencing factors.

[0114] The equipment status data set refers to a multidimensional time series data set collected during the operation of each process link to characterize the equipment operating status. This data set covers the operating status parameters of key equipment, including but not limited to equipment vibration signals, bearing temperature, speed, operating current, lubricating oil pressure, hydraulic system pressure, etc. For example, in the steel rolling process, the equipment status data may include the real-time speed of the roller, the temperature rise curve of the bearing, and the fluctuation range of the rolling pressure; in the steelmaking process, the operating current of the electric furnace, the water flow of the cooling system, and the real-time temperature of the main transformer may be recorded in the equipment status data set. These data are collected in real time through field sensors, indexed by timestamps, and form time series data to describe the dynamic operating status of the equipment in a specific time period.

[0115] The process parameter data set refers to the set of control and environmental parameters directly related to the production process during the operation of each process link. This data set reflects the real-time changes in the operating settings and environmental conditions of the process, and usually includes production load, temperature set point, gas flow, oxygen ratio, steel specifications, raw material consumption, etc. For example, in the ironmaking process, process parameters may include temperature distribution in the blast furnace, coke charge and blast pressure; in the steel rolling process, parameters may include rolling temperature setting, steel plate thickness target value and cooling water spray volume. These data together with the equipment status data form the basis for a complete energy consumption analysis, which is used to establish the relationship between energy consumption anomalies and process operations and environmental conditions.

[0116] The multivariate time series regression model is applied, the energy consumption deviation is taken as the target variable, and all variables in the equipment status data and process parameter data are taken as input factors. By calculating the product of the partial derivative coefficient of each factor and the actual value, its contribution to the energy consumption deviation is quantified. The formula is as follows: in, Representation Factor Cumulative contribution to energy consumption deviation, is the partial derivative coefficient in the regression model, is the factor value within the time window;

[0117] Sort the factors in descending order according to their impact contribution, and select the top N factors with the largest contribution values ​​as key factors to form an abnormal characteristic factor set. And record the corresponding characteristic deviation value and time period;

[0118] Based on the abnormal feature factor set, clustering algorithms (such as Gaussian mixture model) are used to classify abnormal events, classify abnormalities with similar patterns into the same category, and mark the classification results and root cause types to form abnormal classification archives in Infer the result from the root cause;

[0119] Combined with anomaly classification archiving, the energy signature reference model is updated, the characteristic distribution of abnormal events is used as boundary condition correction, and the sensitivity of the reference model to similar events in the future is optimized to provide an enhanced comparison benchmark for subsequent processing.

[0120] The correlation analysis module accurately locates the key influencing factors of energy consumption anomalies through multi-dimensional correlation modeling and regression analysis of abnormal data, equipment status and process parameters, and classifies and archives abnormal patterns through clustering classification technology. This process not only provides a scientific basis for the clear determination of the root cause of the anomaly, but also dynamically optimizes the energy signature reference model, laying a data foundation for the intelligent scheduling and energy consumption optimization of the scheduling optimization module. Through systematic multivariate analysis, the correlation analysis module significantly improves the accuracy and decision-making efficiency of anomaly handling, and further enhances the level of intelligent energy management.

[0121] Timely elimination of energy consumption anomalies and process adjustments are key to improving energy efficiency and production stability. After detecting anomalies and analyzing their root causes, it is necessary to formulate refined adjustment strategies based on different anomaly types through intelligent scheduling and optimization algorithms. Through predictive scheduling and online optimization, dynamic adjustment of key process parameters and equipment status can be achieved, which helps to minimize the impact of anomalies on production and energy consumption, while ensuring the stable operation of the overall energy system.

[0122] The scheduling optimization module includes the following:

[0123] Extract the anomaly type, key factor set and root cause analysis results from the anomaly classification archive output by the association analysis module as the input basis for scheduling and optimization;

[0124] Based on the predictive scheduling model, the time series prediction algorithm is used to make short-term predictions on the future status of key factors and construct a prediction function: in, Factor The predicted value at the future time t+Δt, and They are the rate of change and acceleration at the current moment, respectively, and the prediction is completed through a multivariate regression model combined with other relevant factors;

[0125] The prediction results are matched with the energy signature reference model, possible energy consumption deviations in the future are simulated, the dynamic response effects of different adjustment schemes are calculated, and the adjustment strategy with the shortest response time and the best recovery effect is selected to form the optimal adjustment scheme τ opt ={ParamAdjust,DeviceSwitch,ScheduleChange};

[0126] ParamAdjust means to correct the key process parameters with abnormal energy consumption to achieve energy consumption recovery and production optimization. Specifically, it includes adjusting the process temperature setting value, gas flow ratio, oxygen concentration setting, pressure control parameters, etc. For example, in blast furnace ironmaking, when the oxygen concentration is too high and causes energy consumption deviation, the normal state can be restored by lowering the oxygen concentration setting value.

[0127] DeviceSwitch refers to switching the operating status of backup devices or different devices based on the results of abnormal root cause analysis to optimize resource utilization or reduce the impact of abnormalities. Specifically, it includes switching backup fuel supply pipelines, enabling backup pump stations, switching backup motors or redundant equipment, etc. For example, when the main gas pipeline is short of gas, the backup pipeline can be enabled to maintain system operation.

[0128] ScheduleChange means adjusting the order of production line tasks or load distribution to balance the load of each node and avoid abnormal energy consumption caused by the concentrated execution of high-energy-consuming tasks. Specifically, it includes re-planning the execution time of high-energy-consuming tasks, postponing the execution of low-priority tasks, and distributing high-energy-consuming tasks to different process nodes. For example, in the steel rolling process, the steel plate rolling task with a lower temperature can be prioritized, and the high-temperature rolling task can be postponed to the non-peak period.

[0129] After determining the adjustment plan, the key process parameters are revised through the online optimization algorithm. The adjustment plan includes:

[0130] Update the key process temperature setting value T new , ensuring that the temperature parameters gradually approach the reference model: Among them, T currentis the current temperature setting value, E is the energy consumption deviation objective function, and η is the optimization step size;

[0131] Switch the backup fuel supply line, adjust the supply priority according to the gas flow demand, and use the gas supply balance equation: Q in =Q primary +Q secondary ; Ensure ΔQ = Q demand -Q in →0; where Q in Represents the total input of gas or energy actually received by the system, usually the sum of multiple energy sources, to meet process needs.

[0132] Q primary Refers to the flow of the main energy supply source, such as the input flow of the main gas pipeline or the main power line, which is the main energy supply part of the system operation.

[0133] Q secondary Refers to the flow of backup or auxiliary energy supply sources, such as the input flow of backup gas pipelines or backup power lines, which are usually enabled to supplement the energy supply when the main energy source is insufficient.

[0134] Q demand It indicates the energy or gas flow actually required in the process of production, which is the demand calculated based on the current production tasks and equipment operating status.

[0135] ΔQ represents the difference between the energy supply and demand of the system, and is used to measure whether the current energy supply and demand are balanced. When ΔQ is close to zero, it means that the supply just meets the demand; if ΔQ is positive or negative, it means that the energy supply is excessive or insufficient.

[0136] Adjust the production line operation sequence by optimizing the task allocation matrix Where [m ov ] represents the task arrangement of the oth process node in time period v, and dynamically adjusts the task priority and scheduling order according to the root cause analysis results of energy consumption anomalies and the real-time process load status. First, the current load level of each process node is calculated by combining the equipment status data and process parameter data. where c ov is the energy consumption coefficient of the corresponding task. Then, according to the key factors of the load level and abnormal area, the task priority is reordered, the tasks with lower energy consumption and less impact on the overall load are scheduled first, and the execution of high energy consumption tasks is postponed. By introducing the load balancing objective function Φ=max(L o )-min(L o ), where Φ represents the load balancing index, max(L o ) indicates the maximum load, min(L o) represents the minimum load, and the task allocation matrix is ​​iteratively adjusted using a genetic algorithm or linear programming optimization method until the load balancing index Φ converges to the set threshold. Finally, the production schedule is updated according to the optimized task allocation plan, and the adjusted task sequence instructions are issued to each process node to achieve dynamic load balancing of the production line and reduce the sudden impact of high-energy consumption tasks.

[0137] The feedback control mechanism is used to monitor the adjustment effect in real time, and the adjusted energy consumption data is dynamically compared with the energy signature reference model. If the deviation value gradually decreases and tends to the predetermined range, the adjustment effect is recorded and the abnormal state is marked as recovered.

[0138] Finally, the energy signature reference model is updated to incorporate changes in key parameters during the adjustment process into the model boundary range, thereby improving the model's adaptability to similar abnormal events in the future.

[0139] The scheduling optimization module implements dynamic adjustment and precise control of energy consumption anomalies through the combination of predictive scheduling models and online optimization algorithms, covering multi-dimensional operations such as revision of key process parameters, switching of standby equipment, and task load optimization. Logically, it closely connects the anomaly classification and root cause analysis results of the correlation analysis module, verifies the adjustment effect and optimizes the energy signature reference model through a feedback mechanism, and provides intelligent and closed-loop technical support for the long-term optimization of energy management. Through this process, not only the immediate elimination of energy consumption anomalies is achieved, but also the production stability and efficient operation of the energy system are ensured.

[0140] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0141] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0142] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0143] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An energy collection and monitoring system based on big data, characterized in that: include: Data acquisition module, data processing module, feature modeling module, anomaly detection module, association analysis module and scheduling optimization module; Data acquisition module: using pre-arranged sensors, corresponding energy-related parameters are uploaded to the big data processing platform in real time, and the output data set is passed to the data processing module; Data processing module: converts the uploaded data into a unified format and removes noise signals to ensure the accuracy and consistency of the data, and indexes and stores the data by timestamp and process number. The output standardized data set is passed to the feature modeling module; Feature modeling module: Based on historical data and equipment operation characteristics, it extracts the multi-dimensional energy consumption characteristics of each process link, builds an energy signature reference model as a benchmark for normal energy consumption status, and transmits the output energy signature reference model to the anomaly detection module; Anomaly detection module: continuously receives real-time energy consumption data, conducts dynamic comparison with the energy signature reference model through comprehensive evaluation of the deviation distribution of multi-dimensional energy consumption status and load concentration characteristics, identifies abnormal data points that exceed the normal energy consumption range, and transmits the detected abnormal data to the correlation analysis module; Correlation analysis module: performs in-depth correlation analysis on detected abnormal data, combines equipment status and production parameters, uses multivariate machine learning algorithms to determine the root cause of the abnormality and classify and archive it. The output abnormal classification archive and root cause analysis results are passed to the scheduling optimization module; Scheduling Optimization Module: Uses the predictive scheduling engine and online energy consumption optimization algorithm to evaluate the execution effect of the adjustment plan. By adjusting key process temperatures, switching backup fuel supply pipelines, and issuing production line operation sequence adjustment instructions, it can immediately eliminate energy consumption anomalies and ensure production and furnace temperature balance.

2. The energy collection and monitoring system based on big data according to claim 1 is characterized in that: The data acquisition module includes the following: Using multiple types of sensors placed at key process nodes, multiple energy parameters are collected in real time at fixed sampling intervals to construct an energy state vector to characterize the comprehensive energy consumption state at each sampling moment; Through the dynamic partition scheduling strategy, the data collected by multiple types of sensors are classified and stored in distributed data nodes, and the timestamp is used as the primary key to generate an original data set with a unique identifier; the cleaned original data set is marked according to the timestamp and process number to generate a structured data set.

3. The energy collection and monitoring system based on big data according to claim 2 is characterized in that: The data processing module includes the following: Extract multi-dimensional energy parameters from the data set uploaded by the data acquisition module, and preliminarily analyze the type of source sensor and data encoding format; Based on the rule-based dynamic mapping table, the data encoding formats uploaded by sensors from different sources are converted and an initial data set with a unified format is generated; The noise signal in the initial data set is removed by using the wavelet transform method; An index matrix is ​​constructed by combining the timestamp and process number, and the cleaned data is allocated to the time series storage unit of the corresponding process to ensure that the data is continuous in the time dimension and independent in the process dimension; Generate a standardized dataset after format conversion, noise removal and indexing.

4. The energy collection and monitoring system based on big data according to claim 3 is characterized in that: The feature modeling module includes the following: From the standardized data set, the data sequence is divided by process number to form an independent time series data set for each process link, so as to conduct group analysis on the energy consumption characteristics of different processes; For the time series data of each process link, the adaptive weighted curve decomposition method is applied to decompose it into basic consumption trend and periodic fluctuation components; For the basic consumption trend, calculate the key inflection point characteristics, including the maximum change rate and the time position of the energy consumption inflection point, to capture the key moments of energy demand; For periodic fluctuation components, short-time Fourier transform is used to extract the main frequency components, and the energy concentration is calculated to quantify the fluctuation intensity; The trend features and fluctuation features are combined to form a multi-dimensional energy consumption feature vector, and the high-dimensional features are embedded into a low-dimensional space through a dimensionality reduction algorithm to form a visual feature distribution map; The density clustering algorithm is used to perform density clustering analysis on the multidimensional energy consumption feature vectors in the feature distribution map. By setting the core point density threshold and the minimum number of samples, the dense areas of energy consumption characteristics are identified and divided into clusters with similar energy consumption patterns. The center point of the cluster with the highest density is extracted as the typical energy consumption state of each process link, and the upper and lower limits of the feature range are calculated to form an energy signature reference model with boundary conditions as a normal energy consumption benchmark for real-time comparison.

5. The energy collection and monitoring system based on big data according to claim 4 is characterized in that: The anomaly detection module includes the following: The typical energy consumption state center value and energy consumption characteristic boundary of each process link are extracted from the energy signature reference model as the comparison benchmark; the real-time energy consumption data set is loaded into the comparison module point by point, and the energy consumption deviation symmetry index and instantaneous load energy consumption compression rate index are calculated and processed for the real-time energy consumption state vector at each time point.

6. The energy collection and monitoring system based on big data according to claim 4 is characterized in that: The energy consumption deviation symmetry index measures whether the deviation of energy consumption data in each dimension is evenly distributed by performing a symmetry analysis on the deviation of the real-time energy consumption state vector. Specifically, the energy consumption deviation symmetry index decomposes the energy consumption deviation into two dimensions: absolute value and square value. The average absolute deviation reflects the overall level of the deviation amplitude, while the square root of the mean square deviation emphasizes the contribution of the dimension with a large deviation. The absolute value of the difference between the two is used to characterize the distribution characteristics of the deviation of each dimension, namely, the energy consumption deviation symmetry index.

7. The energy collection and monitoring system based on big data according to claim 4 is characterized by: The instantaneous load energy consumption compression rate index aims to analyze the concentration of real-time energy consumption data on load distribution and reveal whether there is abnormal load aggregation in the energy consumption state vector; In the calculation, the real-time energy consumption data is first converted into a standard load value with relative weight significance through normalization processing to ensure that the data of each dimension can be compared under the same dimension; Subsequently, the instantaneous load energy consumption compression rate index quantifies the load concentration by calculating the ratio of the extreme value of the load to the total load. At the same time, a logarithmic function is introduced to enhance the sensitivity to the ratio of the load extreme values, thereby identifying the abnormal characteristics caused by extreme load distribution.

8. The energy collection and monitoring system based on big data according to claim 4 is characterized by: The energy consumption deviation symmetry index and the instantaneous load energy consumption compression rate index are weighted and summed to form a comprehensive comparison parameter, which is used to comprehensively evaluate the degree of abnormality of real-time energy consumption data; a comprehensive comparison threshold is set, and when the comprehensive comparison parameter is greater than the comprehensive comparison threshold, it is determined that there is an energy consumption abnormality at the corresponding time point; the information of the abnormal data point is recorded and stored in the abnormal data log.

9. The energy collection and monitoring system based on big data according to claim 4 is characterized in that: The association analysis module includes the following: Extract deviation values, corresponding process links and time points from abnormal data logs as initial input for deep correlation analysis; Combine the equipment status data set and the process parameter data set to perform correlation modeling on the abnormal occurrence time point and its neighboring time window, and preliminarily screen the key influencing factors; A multivariate time series regression model is applied, with energy consumption deviation as the target variable and all variables in the equipment status data and process parameter data as input factors. The contribution of each factor to energy consumption deviation is quantified by calculating the product of the partial derivative coefficient and the actual value of each factor. Sort the factors in descending order according to their impact contribution, select the top N factors with the largest contribution values ​​as key factors, form an abnormal feature factor set, and record their corresponding feature deviation values ​​and time periods; Based on the abnormal feature factor set, the clustering algorithm is used to classify abnormal events, classify abnormalities with similar patterns into the same category, and mark the classification results and root cause types to form an abnormal classification archive.

10. The energy collection and monitoring system based on big data according to claim 9 is characterized in that: The scheduling optimization module includes the following: Extract the anomaly type, key factor set and root cause analysis results from the anomaly classification archive as input basis for scheduling and optimization; Based on the predictive scheduling model, the time series prediction algorithm is used to make short-term predictions on the future status of key factors, and a prediction function is constructed. The prediction is completed by combining the multivariate regression model with other related factors. Match the prediction results with the energy signature reference model, simulate future energy consumption deviations, calculate the dynamic response effects of different adjustment schemes, and select the adjustment strategy with the shortest response time and the best recovery effect to form the optimal adjustment scheme; After determining the adjustment plan, the key process parameters are revised through the online optimization algorithm. The adjustment plan includes: Update the key process temperature setting values ​​to ensure that the temperature parameters gradually approach the energy signature reference model; Switch backup fuel supply lines and adjust supply priorities based on gas flow demand; Adjust the production line operation sequence by optimizing the task allocation matrix, dynamically adjusting the task priority and scheduling sequence according to the root cause analysis results of energy consumption anomalies and real-time process load status; The adjusted energy consumption data is dynamically compared with the energy signature reference model. If the deviation value gradually decreases and tends to the predetermined range, the adjustment effect is recorded and the abnormal state is marked as recovered.

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