Energy consumption optimization and energy-saving regulation and control method for environmental protection equipment based on multi-source data fusion

Abstract

The invention provides an environmental protection equipment energy consumption optimization and energy-saving regulation and control method based on multi-source data fusion, and relates to the technical field of environmental protection equipment energy-saving control, and the method comprises the steps: collecting the operation data of environmental protection equipment, carrying out the multi-scale feature extraction, recognizing the energy consumption abnormity through combining the energy efficiency curve reconstruction, and carrying out the energy-saving control of the environmental protection equipment; energy consumption sensitive parameters are extracted, energy-saving space is calculated based on load fluctuation, and an optimized parameter adjusting scheme and an optimized time sequence control sequence are generated by integrating the environment load change rate and the equipment thermal inertia coefficient. The energy consumption abnormity can be accurately identified, the energy-saving space is scientifically quantified, the intelligent optimization and dynamic regulation and control of the energy consumption of the environmental protection equipment are realized, and the operation efficiency of the equipment is obviously improved.

Description

Energy consumption optimization and energy-saving control method for environmental protection equipment based on multi-source data fusion Technical Field

[0001] This invention relates to the field of energy-saving control technology for environmental protection equipment, and in particular to a method for energy consumption optimization and energy-saving regulation of environmental protection equipment based on multi-source data fusion. Background Technology

[0002] As a crucial means of environmental governance, the energy consumption of environmental protection equipment is receiving increasing attention. With the growing demand for environmental protection, the application scope of various environmental protection equipment is constantly expanding, including wastewater treatment equipment, exhaust gas treatment devices, and solid waste treatment systems. However, these devices generally suffer from high energy consumption and low efficiency during operation.

[0003] Currently, energy consumption management of environmental protection equipment mainly relies on periodic inspections and experience-based adjustments, lacking scientific data support and intelligent control methods. Traditional energy consumption management of environmental protection equipment typically employs single-parameter monitoring or simple control strategies under fixed operating conditions, which still suffers from problems such as insufficient data utilization, simplistic energy consumption anomaly identification mechanisms, and a lack of foresight in parameter control. Summary of the Invention

[0004] This invention provides a method for optimizing and controlling the energy consumption of environmental protection equipment based on multi-source data fusion, which can at least solve some of the problems existing in the prior art.

[0005] A first aspect of this invention provides a method for energy consumption optimization and energy-saving control of environmental protection equipment based on multi-source data fusion, comprising: collecting multi-dimensional operation monitoring data of environmental protection equipment and performing wavelet packet decomposition to obtain multi-scale frequency domain features; performing sliding window statistics on the multi-dimensional operation monitoring data to obtain a load fluctuation sequence and combining it with the multi-scale frequency domain features to obtain an equipment state feature matrix; acquiring historical operating condition records of the equipment and extracting energy efficiency curves; performing probability density reconstruction on the energy efficiency curves through kernel density estimation; calculating energy efficiency deviation and identifying abnormal energy consumption periods in combination with the equipment state feature matrix; extracting the combination of operating parameters corresponding to the abnormal energy consumption periods and performing principal component analysis to obtain a subset of energy consumption sensitive parameters. Based on the subset of energy-sensitive parameters and the load fluctuation sequence, the expected energy consumption trajectory is determined. The expected energy consumption trajectory is dynamically planned and segmented to calculate the energy-saving space quantification value. Based on the energy-saving space quantification value, the energy consumption optimization target is determined. Multiple sets of parameter adjustment candidate schemes are obtained by iteratively solving the problem using a sequence quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. The real-time change rate of the environmental load is obtained and the load gradient vector is calculated. Based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, the advance time corresponding to the parameter adjustment is calculated. The parameter change path is generated according to the advance time and the load gradient vector. The timing control sequence is generated based on the parameter change path and the actuator and then issued for execution.

[0006] In one optional implementation, the process of collecting multi-dimensional operation monitoring data of environmental protection equipment and performing wavelet packet decomposition to obtain multi-scale frequency domain features, and then performing sliding window statistics on the multi-dimensional operation monitoring data to obtain a load fluctuation sequence and combining the multi-scale frequency domain features to obtain an equipment state feature matrix includes: collecting multi-dimensional operation monitoring data corresponding to environmental protection equipment through pre-set sensors and reconstructing the time series according to a preset sampling period; performing wavelet packet decomposition on the reconstructed multi-dimensional operation monitoring data to extract frequency domain components of different frequency bands; performing energy normalization processing on the frequency domain components to obtain a multi-scale frequency domain feature vector; dividing the multi-dimensional operation monitoring data into sliding windows according to a preset window length; calculating the load statistics within each sliding window and constructing a load fluctuation time series; performing a difference operation on the load fluctuation time series to obtain a load change rate sequence as the load fluctuation sequence; extracting the multi-scale frequency domain feature vector and the timestamp in the load fluctuation sequence; performing time alignment based on the timestamps; concatenating the aligned multi-scale frequency domain feature vector and the load fluctuation sequence column-wise to form a two-dimensional array structure; and performing a transpose operation on the two-dimensional array structure to obtain the equipment state feature matrix.

[0007] In one optional implementation, acquiring historical operating condition records of the equipment and extracting energy efficiency curves, reconstructing the probability density of the energy efficiency curves using kernel density estimation, and calculating energy efficiency deviation and identifying abnormal energy consumption periods in conjunction with the equipment state feature matrix includes: acquiring historical operating condition records of the equipment and dividing them into segments according to load intervals; extracting energy efficiency data points corresponding to each load interval and constructing segmented energy efficiency curves; performing adaptive bandwidth kernel density estimation on the segmented energy efficiency curves to obtain a probability density distribution surface; calculating an energy efficiency quantile set based on the probability density distribution surface; constructing an energy efficiency benchmark confidence band based on the energy efficiency quantile set; extracting frequency domain feature components and load fluctuation components from the equipment state feature matrix; and performing energy efficiency deviation calculation on the equipment state feature matrix. The frequency domain feature components are weighted and aggregated to obtain a comprehensive frequency domain index. The load fluctuation components are decomposed to obtain a load trend term and a load disturbance term. The comprehensive frequency domain index, the load trend term, and the load disturbance term are mapped to the energy efficiency assessment space through tensor decomposition and the current energy efficiency estimate is calculated. The current energy efficiency estimate is compared with the energy efficiency benchmark confidence band to calculate the energy efficiency deviation. The energy efficiency deviation is accumulated over time to construct a time-weighted cumulative sequence. The time-weighted cumulative sequence is used to detect change points to identify the moment of sudden change in energy efficiency status. The time period of continuous energy efficiency deviation is extracted with the moment of sudden change in energy efficiency status as the dividing point and labeled according to the severity of the deviation to obtain the energy consumption abnormal period.

[0008] In one optional implementation, extracting the operating parameter combinations corresponding to the abnormal energy consumption periods and performing principal component analysis to obtain a subset of energy-sensitive parameters, and determining the expected energy consumption trajectory based on the subset of energy-sensitive parameters and the load fluctuation sequence includes: extracting the operating parameter combinations corresponding to the abnormal energy consumption periods and constructing a time-varying parameter tensor; performing Tucker decomposition on the time-varying parameter tensor to obtain a core tensor and a parameter factor matrix; performing modal expansion on the core tensor to calculate the higher-order coupling strength between parameters; performing L1 norm-constrained sparse reconstruction on the parameter factor matrix to obtain the dominant factor pattern; and solving for the parameter sensitivity score based on the higher-order coupling strength between parameters and the dominant factor pattern. The parameter sensitivity scores are used to sort each operating parameter in descending order to determine a subset of energy-sensitive parameters. The subset of energy-sensitive parameters and the load fluctuation sequence are concatenated according to timestamps to obtain a multivariate time-series data matrix. The load fluctuation sequence is subjected to moving average filtering to extract the trend component and Fourier transform to extract the periodic component. The multivariate time-series data matrix is ​​decomposed to obtain a latent variable matrix. The latent variable matrix is ​​subjected to variational inference iterative optimization to obtain the latent variable mean sequence and latent variable variance sequence. The energy-sensitive parameter subset, the trend component, and the periodic component are combined and weighted summation and hyperbolic tangent transform to obtain the predicted energy consumption output value. The predicted energy consumption output value is iteratively solved to obtain the expected energy consumption trajectory.

[0009] In one optional implementation, the expected energy consumption trajectory is dynamically segmented using programming to obtain a quantified value of the energy-saving space. Based on the quantified value of the energy-saving space, an energy consumption optimization target is determined. Multiple candidate parameter adjustment schemes are obtained through iterative solution using a sequential quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. This includes: calculating the curvature sequence of the expected energy consumption trajectory using second-order difference calculation and identifying energy consumption curvature abrupt change points using a preset curvature threshold; using these energy consumption curvature abrupt change points as candidate segmentation points and dividing the trajectory into multiple energy consumption operation stages using a dynamic programming algorithm; determining the energy consumption uncertainty boundary based on a pre-acquired latent variable variance sequence and obtaining the upper bound of energy consumption confidence by combining the energy consumption mean of each energy consumption operation stage; calculating the theoretical optimal energy consumption trajectory corresponding to each energy consumption operation stage using a reverse recursive approach based on the Bellman optimality principle; and combining the energy consumption confidence... The upper bound is solved to obtain the energy-saving space quantification value; based on the energy-saving space quantification value and the expected energy consumption trajectory, the energy-saving potential ratio is solved and quantile analysis is performed to obtain the energy consumption optimization target value. A quadratic optimization problem is constructed with the energy consumption optimization target value as a constraint. The first-order partial derivative of the quadratic optimization problem is calculated to obtain the gradient vector, and the second-order partial derivative is calculated to obtain the Hessian matrix. The Hessian matrix is ​​corrected for positive definiteness and inverted with the gradient vector to obtain the search direction. A linear search is performed along the search direction until the L2 norm of the difference between the parameter vectors of two iterations is less than a preset convergence threshold to obtain parameter adjustment candidate schemes; based on the parameter adjustment candidate schemes and the expected energy consumption trajectory, the predicted energy consumption value is solved; based on the predicted energy consumption value and the energy consumption optimization target value, the optimal parameter adjustment scheme is solved.

[0010] In one optional implementation, acquiring the real-time rate of change of environmental load and calculating the load gradient vector, and adjusting the corresponding advance time based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, includes: acquiring the numerical sequence of environmental load within a continuous time window and performing sliding window difference calculation to obtain the load change; calculating the real-time rate of change of environmental load based on the load change and a pre-set sliding step size; performing time series decomposition on the real-time rate of change to obtain a trend term and a fluctuation term; calculating the first derivative of the trend term to obtain the trend gradient component; extracting the amplitude corresponding to the fluctuation term and performing normalization processing to obtain the normalized fluctuation amplitude; and concatenating the trend gradient component and the normalized fluctuation amplitude into a vector to obtain the load gradient vector and performing L2 calculation. The load gradient modulus is calculated using the norm; the heat capacity and heat transfer coefficient parameters of the environmental protection equipment are obtained and the thermal inertia coefficient of the equipment is calculated; the response delay baseline value is obtained by combining the load gradient modulus; the adjustment amplitude of each parameter in the optimal parameter adjustment scheme is extracted and weighted to obtain the total parameter adjustment amplitude; the delay time correction value is calculated based on the total parameter adjustment amplitude and the response delay baseline value; the total delay time is obtained by combining the equipment thermal inertia time constant; the fluctuation phase sequence corresponding to the fluctuation term is extracted and phase expansion is performed to obtain a continuous phase curve; the phase change rate is obtained by performing first derivative calculation; the phase compensation amount is obtained by combining the total delay time; and the advance time corresponding to parameter adjustment is determined based on the phase compensation amount and the total delay time, combined with the current time.

[0011] In one optional implementation, generating a parameter change path based on the advance time and the load gradient vector, and generating and issuing a timing control sequence based on the parameter change path and the actuator includes: taking the advance time as the path start time, taking the target value of each parameter in the optimal parameter adjustment scheme as the path end value, determining the load change trend direction based on the trend gradient component in the load gradient vector, determining the parameter change time allocation ratio based on the load change trend direction, solving for the path speed adjustment factor based on the normalized fluctuation amplitude in the load gradient vector and the time allocation ratio, normalizing the time interval from the path start time to the current time and calculating the position correction amount based on the path speed adjustment factor, and then... The numerical deviation is determined by the endpoint value and the current value of each parameter, and the instantaneous change is calculated by combining the position correction amount. The parameter change path is determined based on the current value of each parameter and the instantaneous change. The response bandwidth parameter and action dead zone parameter of the actuator are obtained. The effective control time is marked based on the parameter change path and the action dead zone parameter, and the corresponding parameter value is extracted to form a discrete control point sequence. The time interval between adjacent discrete control points is calculated, and the discrete control point sequence is time-resampled in combination with the response bandwidth parameter to obtain a sparse control point sequence. The parameter value of each control point in the sparse control point sequence is quantized and encoded to obtain a control command code. The control command code is timestamped with the corresponding effective control time to obtain a timing control sequence and is sent to the actuator for execution.

[0012] A second aspect of this invention provides an energy consumption optimization and energy-saving control system for environmental protection equipment based on multi-source data fusion, comprising: a state feature extraction module, used to collect multi-dimensional operation monitoring data of environmental protection equipment and perform wavelet packet decomposition to obtain multi-scale frequency domain features, perform sliding window statistics on the multi-dimensional operation monitoring data to obtain a load fluctuation sequence, and combine the multi-scale frequency domain features to obtain an equipment state feature matrix; an energy efficiency anomaly identification module, used to acquire historical operating condition records of the equipment and extract energy efficiency curves, perform probability density reconstruction on the energy efficiency curves through kernel density estimation, calculate energy efficiency deviation and identify abnormal energy consumption periods in combination with the equipment state feature matrix; and a parameter scheme optimization module, used to extract the combination of operating parameters corresponding to the abnormal energy consumption periods and perform principal component analysis. A subset of energy-sensitive parameters is obtained through analysis. Based on the subset of energy-sensitive parameters and the load fluctuation sequence, the expected energy consumption trajectory is determined. The expected energy consumption trajectory is dynamically planned and segmented to calculate the energy-saving space quantification value. Based on the energy-saving space quantification value, the energy consumption optimization target is determined. Multiple sets of parameter adjustment candidate schemes are obtained by iteratively solving the sequence quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. A control sequence execution module is used to obtain the real-time change rate of the environmental load and calculate the load gradient vector. Based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, the advance time corresponding to parameter adjustment is calculated. The parameter change path is generated according to the advance time and the load gradient vector. The timing control sequence is generated based on the parameter change path and the actuator and then issued for execution.

[0013] A third aspect of the present invention provides an electronic device, including: a processor and a memory for storing processor-executable instructions, wherein the processor is configured to invoke the instructions stored in the memory to perform the aforementioned method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0015] In this invention, multi-scale frequency domain features and load fluctuation sequences are extracted from multi-dimensional operation monitoring data by combining wavelet packet decomposition and sliding window statistics. This constructs a more comprehensive equipment state feature matrix, significantly improving the accuracy of energy consumption status characterization. Kernel density estimation is used to reconstruct the probability density of the energy efficiency curve, and the energy efficiency deviation is calculated in conjunction with the equipment state feature matrix, enabling accurate identification of abnormal energy consumption periods. Principal component analysis is used to screen a subset of energy-sensitive parameters, and the expected energy consumption trajectory is determined based on the load fluctuation sequence. Dynamic programming is used to calculate the quantifiable value of the energy-saving space in segments, providing accurate quantitative basis for energy consumption optimization. Based on the sequential quadratic programming algorithm, multiple sets of parameter adjustment candidate schemes are iteratively solved, maximizing energy-saving benefits while ensuring environmental protection effects, thus improving the scientificity and feasibility of the control scheme. The optimal lead time for parameter adjustment is calculated by using the load gradient vector and the equipment thermal inertia coefficient, generating a smooth parameter change path, achieving accurate response to dynamic changes in environmental load, and avoiding energy waste caused by equipment adjustment lag. Attached Figure Description

[0016] Figure 1 is a flowchart illustrating the method for optimizing and controlling energy consumption of environmental protection equipment based on multi-source data fusion according to an embodiment of the present invention; Figure 2 is a flowchart illustrating the calculation of environmental load response delay using the method for optimizing and controlling energy consumption of environmental protection equipment based on multi-source data fusion according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0019] Figure 1 is a flowchart illustrating the energy consumption optimization and energy-saving control method for environmental protection equipment based on multi-source data fusion according to an embodiment of the present invention. As shown in Figure 1, the method includes: collecting multi-dimensional operation monitoring data of environmental protection equipment and performing wavelet packet decomposition to obtain multi-scale frequency domain features; performing sliding window statistics on the multi-dimensional operation monitoring data to obtain a load fluctuation sequence and combining it with the multi-scale frequency domain features to obtain an equipment state feature matrix; acquiring historical operating condition records of the equipment and extracting energy efficiency curves; reconstructing the energy efficiency curves using kernel density estimation; calculating the energy efficiency deviation and identifying abnormal energy consumption periods in combination with the equipment state feature matrix; extracting the operating parameter combinations corresponding to the abnormal energy consumption periods and performing principal component analysis to obtain energy consumption sensitive parameters. A parameter subset is used to determine the expected energy consumption trajectory based on the energy consumption sensitive parameter subset and the load fluctuation sequence. The expected energy consumption trajectory is dynamically planned and segmented to calculate the energy-saving space quantification value. Based on the energy-saving space quantification value, the energy consumption optimization target is determined. Multiple sets of parameter adjustment candidate schemes are obtained by iteratively solving the problem using a sequence quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. The real-time change rate of the environmental load is obtained and the load gradient vector is calculated. Based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, the advance time corresponding to the parameter adjustment is calculated. The parameter change path is generated according to the advance time and the load gradient vector. The timing control sequence is generated based on the parameter change path and the actuator and issued for execution.

[0020] In one optional implementation, the process of collecting multi-dimensional operation monitoring data of environmental protection equipment and performing wavelet packet decomposition to obtain multi-scale frequency domain features, and then performing sliding window statistics on the multi-dimensional operation monitoring data to obtain a load fluctuation sequence and combining the multi-scale frequency domain features to obtain an equipment state feature matrix includes: collecting multi-dimensional operation monitoring data corresponding to environmental protection equipment through pre-set sensors and reconstructing the time series according to a preset sampling period; performing wavelet packet decomposition on the reconstructed multi-dimensional operation monitoring data to extract frequency domain components of different frequency bands; performing energy normalization processing on the frequency domain components to obtain a multi-scale frequency domain feature vector; dividing the multi-dimensional operation monitoring data into sliding windows according to a preset window length; calculating the load statistics within each sliding window and constructing a load fluctuation time series; performing a difference operation on the load fluctuation time series to obtain a load change rate sequence as the load fluctuation sequence; extracting the multi-scale frequency domain feature vector and the timestamp in the load fluctuation sequence; performing time alignment based on the timestamps; concatenating the aligned multi-scale frequency domain feature vector and the load fluctuation sequence column-wise to form a two-dimensional array structure; and performing a transpose operation on the two-dimensional array structure to obtain the equipment state feature matrix.

[0021] Operational monitoring data is collected through multiple sensors pre-installed on the environmental protection equipment. These sensors include temperature, pressure, flow, and current sensors, forming a multi-dimensional data acquisition network. The sampling period is set to 10 seconds, meaning all sensor data is collected every 10 seconds. The raw data may contain missing or anomalies, requiring time series reconstruction. Linear interpolation is used to fill in missing data points, ensuring data continuity and integrity.

[0022] The reconstructed data is subjected to wavelet packet decomposition to extract frequency domain components from different frequency bands. During wavelet packet decomposition, a three-level decomposition structure is selected, and the db4 wavelet basis function is used to decompose the original signal into eight frequency bands. For example, for the acquired temperature data, wavelet packet decomposition yields eight sub-signals in different frequency bands, each corresponding to different frequency characteristics.

[0023] The decomposed frequency domain components are subjected to energy normalization. The energy of each frequency band sub-signal is calculated by taking the square root of the sum of the squares of each point in the sub-signal. For example, the energy value of a certain frequency band sub-signal is 256.78. Then, the energy of all frequency band sub-signals is divided by the total energy to obtain the normalized energy distribution. Assuming the normalized energies of the eight frequency bands are 0.15, 0.23, 0.18, 0.12, 0.09, 0.08, 0.07, and 0.08, respectively, they constitute a multi-scale frequency domain feature vector.

[0024] Multidimensional operational monitoring data is divided into sliding windows of a preset length. The window length is set to 5 minutes, and the sliding step is 1 minute. Within each window, the load statistics of the equipment are calculated. Load statistics include average power, peak power, and power standard deviation. Taking a certain environmental water treatment equipment as an example, within a 5-minute window, the average power is 35.6 kW, the peak power is 42.3 kW, and the power standard deviation is 3.2 kW. The load statistics of continuous windows are arranged in chronological order to form a load fluctuation time series.

[0025] Differential calculations are performed on the load fluctuation time series to calculate the change in load statistics between adjacent time points. For example, if the average power of two adjacent time windows is 35.6 kW and 37.2 kW respectively, the corresponding difference value is 1.6 kW. Differential calculations reflect the rate of change of equipment load, and the differentially derived series is used as the load fluctuation series. The load fluctuation series can reflect the energy consumption characteristics of equipment at different operating stages.

[0026] Extract multi-scale frequency domain feature vectors and timestamps from the load fluctuation sequence. Since the two types of data are collected at different frequencies, time alignment is required. Time alignment uses the nearest neighbor interpolation method to map data from different time scales onto a unified time axis. For example, if the timestamp of the frequency domain feature is 10:30:15 AM on January 15, 2024, while the timestamp of the most recent load fluctuation data is 10:30:00 AM on January 15, 2024, then the frequency domain feature will be mapped to that time point.

[0027] The aligned multi-scale frequency domain feature vectors are concatenated column-wise with the load fluctuation sequence to form a two-dimensional array structure. The consistency of timestamps is maintained during concatenation to ensure that each row of data corresponds to the same time point. For example, at time point 10:30:00 on January 15, 2024, the frequency domain feature vector is [0.15, 0.23, 0.18, 0.12, 0.09, 0.08, 0.07, 0.08], and the load fluctuation value is 1.6 kW. The concatenated row vector would then be [0.15, 0.23, 0.18, 0.12, 0.09, 0.08, 0.07, 0.08, 1.6].

[0028] Transposing the two-dimensional array structure yields the equipment state feature matrix. The transpose operation causes the rows of the matrix to correspond to different feature dimensions, and the columns to correspond to different time points. This structure facilitates subsequent model analysis of the correlations between features. Taking a certain waste gas treatment equipment as an example, the transposed feature matrix contains 9 rows (8 frequency domain features plus 1 load fluctuation feature) and 288 columns (corresponding to 24 hours in a day, with 12 sampling points per hour).

[0029] In this embodiment, by performing wavelet packet decomposition on multi-dimensional operation monitoring data and extracting multi-band energy features, a fine characterization of equipment operation signals at different time and frequency scales is achieved. This more fully reflects the potential change patterns of equipment under steady-state and non-steady-state conditions, improving the ability to identify early anomalies, hidden faults, and complex operation modes. By constructing a load fluctuation sequence and introducing a load change rate description, the equipment load change process is quantitatively expressed, which is more conducive to revealing the impact of load disturbances on equipment status during operation and enhancing the perception accuracy of operational risks and performance degradation trends. Through feature alignment and fusion based on timestamps, multi-scale frequency domain features and load fluctuation features are modeled in a unified manner, avoiding the misalignment problem of multi-source features in the time dimension. This allows the correlation between different types of features to be effectively preserved and utilized, significantly improving the stability and discrimination performance of subsequent state assessment, anomaly detection, and operation optimization algorithms, and providing a more reliable data foundation for the refined operation monitoring and intelligent operation and maintenance of environmental protection equipment.

[0030] In one optional implementation, acquiring historical operating condition records of the equipment and extracting energy efficiency curves, reconstructing the probability density of the energy efficiency curves using kernel density estimation, and calculating energy efficiency deviation and identifying abnormal energy consumption periods in conjunction with the equipment state feature matrix includes: acquiring historical operating condition records of the equipment and dividing them into segments according to load intervals; extracting energy efficiency data points corresponding to each load interval and constructing segmented energy efficiency curves; performing adaptive bandwidth kernel density estimation on the segmented energy efficiency curves to obtain a probability density distribution surface; calculating an energy efficiency quantile set based on the probability density distribution surface; constructing an energy efficiency benchmark confidence band based on the energy efficiency quantile set; extracting frequency domain feature components and load fluctuation components from the equipment state feature matrix; and performing energy efficiency deviation calculation on the equipment state feature matrix. The frequency domain feature components are weighted and aggregated to obtain a comprehensive frequency domain index. The load fluctuation components are decomposed to obtain a load trend term and a load disturbance term. The comprehensive frequency domain index, the load trend term, and the load disturbance term are mapped to the energy efficiency assessment space through tensor decomposition and the current energy efficiency estimate is calculated. The current energy efficiency estimate is compared with the energy efficiency benchmark confidence band to calculate the energy efficiency deviation. The energy efficiency deviation is accumulated over time to construct a time-weighted cumulative sequence. The time-weighted cumulative sequence is used to detect change points to identify the moment of sudden change in energy efficiency status. The time period of continuous energy efficiency deviation is extracted with the moment of sudden change in energy efficiency status as the dividing point and labeled according to the severity of the deviation to obtain the energy consumption abnormal period.

[0031] Obtain historical operating records of environmental protection equipment, collecting at least one year of operational data, including key indicators such as operating load and energy consumption. Taking a wastewater treatment aeration system as an example, historical data records information such as hourly influent flow rate, dissolved oxygen concentration, and power consumption. The historical data is segmented according to load ranges, using an equal-width segmentation method to divide the load range into 10 intervals. For example, the influent flow rate range from 0 cubic meters per hour to 1000 cubic meters per hour is divided into 10 equal-width intervals, each with a width of 100 cubic meters per hour.

[0032] Energy efficiency data points were extracted for each load range. The energy efficiency data was expressed using the unit energy consumption index, i.e., the energy consumed per unit load. For wastewater treatment aeration systems, kilowatt-hours per cubic meter (kWh / m³) was used as the energy efficiency index. In the first load range (0-100 m³ / hour), the extracted energy efficiency data points were 0.28, 0.31, 0.25, and 0.29 kWh / m³, etc.; in the second load range (100-200 m³ / hour), the data points were 0.22, 0.20, 0.23, and 0.21 kWh / m³, etc. Statistical analysis was performed on the energy efficiency data points for each load range, and piecewise energy efficiency curves were plotted.

[0033] Adaptive bandwidth kernel density estimation is performed on the piecewise energy efficiency curves to obtain the probability density distribution surface. The kernel density estimation uses a Gaussian kernel function, and the bandwidth parameter is adaptively adjusted according to the data distribution characteristics of each interval. For example, a larger bandwidth of 2.5 is used in sparse data regions, and a smaller bandwidth of 0.8 is used in dense data regions. After kernel density estimation, a two-dimensional probability density distribution surface of the energy efficiency index is formed for different load intervals.

[0034] The energy efficiency quantile set is calculated based on the probability density distribution surface. Five quantile points—25%, 50%, 75%, 90%, and 95%—are selected to represent different energy efficiency levels. Taking the third load range of 200-300 m³ / h as an example, the energy efficiency values ​​corresponding to the five quantile points are 0.19, 0.18, 0.17, 0.16, and 0.155 kWh / m³, respectively. Connecting the quantiles of each load range forms a quantile curve, which constitutes the energy efficiency baseline confidence band. The 50% quantile curve represents the expected energy efficiency level, the 25% and 75% quantile curves form the core confidence interval, and the 90% and 95% quantile curves define the energy efficiency anomaly boundaries.

[0035] Frequency domain feature components and load fluctuation components are extracted from the equipment state feature matrix. The frequency domain feature components contain the energy distribution of eight frequency bands, while the load fluctuation component reflects the dynamic changes in equipment load. The frequency domain feature components are then weighted and aggregated to obtain a comprehensive frequency domain index. The weighting coefficients are determined based on the importance of each frequency band to energy efficiency; low-frequency and high-frequency bands are assigned higher weights of 0.25 and 0.20, respectively, while the mid-frequency band is assigned a weight of 0.10. Taking a certain waste gas treatment equipment as an example, the energy distribution of the eight frequency bands is 0.15, 0.22, 0.18, 0.12, 0.10, 0.08, 0.07, and 0.08, and the comprehensive frequency domain index after weighted aggregation is 0.135.

[0036] The load fluctuation component is decomposed into a trend term and a load disturbance term using a locally weighted regression method. The trend term reflects the long-term trend of load change, while the disturbance term reflects short-term fluctuations. For example, if the original load fluctuation data for a certain period is 35.6, 37.2, 38.5, 36.8, and 37.9 kW, the decomposed trend term is 35.8, 36.7, 37.4, 37.8, and 38.1 kW, and the disturbance term is -0.2, 0.5, 1.1, -1.0, and -0.2 kW.

[0037] The integrated frequency domain index, load trend term, and load disturbance term are mapped to the energy efficiency assessment space through tensor decomposition. Tensor decomposition employs a high-order singular value decomposition method to retain the main components and remove noise interference. After mapping, the current energy efficiency estimate is calculated, for example, 0.21 kWh / m³. The current energy efficiency estimate is then compared with the energy efficiency benchmark confidence band to determine which quantile interval the estimate falls into, and the percentage deviation from the expected energy efficiency level is calculated. If the current energy efficiency estimate of 0.21 is higher than the 50th quantile of the corresponding load interval (0.18), the deviation is 16.7%, indicating that the current energy efficiency is lower than the expected level.

[0038] Energy efficiency deviations are accumulated over time to construct a time-weighted cumulative sequence. The time weighting employs an exponential decay function, giving higher weight to recent deviations. For example, if the energy efficiency deviations at five consecutive time points are 15%, 18%, 20%, 22%, and 25%, the corresponding weighting coefficients are 0.4, 0.3, 0.15, 0.1, and 0.05, resulting in a calculated weighted cumulative value of 18.85%. After constructing the complete time-weighted cumulative sequence, change point detection is performed to identify abrupt changes in energy efficiency status. Change point detection uses a cumulative sum method with a detection threshold of 5%. When the cumulative sum exceeds the threshold, it is determined to be a point of abrupt change in energy efficiency status.

[0039] Using the moment of abrupt change in energy efficiency status as the dividing point, the time period of continuous energy efficiency deviation is extracted. Continuous deviation is defined as the energy efficiency estimate deviating from the core interval of the baseline confidence band for three or more consecutive time points. Abnormal energy consumption periods are labeled according to the severity of the deviation; deviations exceeding the 90th percentile are defined as Level 1 anomalies, and deviations exceeding the 95th percentile are defined as Level 2 anomalies. In an application at a wastewater treatment plant, a period of continuous energy efficiency deviation was identified from 14:00 on January 18, 2024 to 6:00 on January 19, 2024, with the period from 20:00 to 23:00 on January 18 being a Level 2 anomaly requiring priority handling.

[0040] In this embodiment, by segmenting historical operating conditions according to load intervals and constructing an energy efficiency probability distribution and energy efficiency benchmark confidence band based on adaptive bandwidth kernel density estimation, the energy efficiency benchmark no longer relies on fixed thresholds or single empirical curves. This effectively reduces the interference of operating condition fluctuations and sample discreteness on energy efficiency evaluation results, and improves the objectivity and adaptability of energy efficiency comparison. By jointly modeling frequency domain operating characteristics, load trend terms, and load disturbance terms and mapping them to a unified energy efficiency evaluation space, energy efficiency estimation can simultaneously characterize the comprehensive impact of changes in internal equipment operating status and external load changes. This avoids the misjudgment problem caused by energy efficiency evaluation based solely on energy consumption data or single operating parameters, and significantly improves the robustness and interpretability of energy efficiency estimation results to complex operating conditions and dynamic disturbances. By performing time-weighted accumulation of energy efficiency deviation and introducing a change point detection mechanism, the transformation process of energy efficiency anomalies from "instantaneous fluctuations" to "continuous deterioration" is effectively distinguished, enabling more accurate identification of energy consumption anomaly periods that are truly meaningful for operation and maintenance, and reducing false alarms caused by occasional disturbances.

[0041] In one optional implementation, extracting the operating parameter combinations corresponding to the abnormal energy consumption periods and performing principal component analysis to obtain a subset of energy-sensitive parameters, and determining the expected energy consumption trajectory based on the subset of energy-sensitive parameters and the load fluctuation sequence includes: extracting the operating parameter combinations corresponding to the abnormal energy consumption periods and constructing a time-varying parameter tensor; performing Tucker decomposition on the time-varying parameter tensor to obtain a core tensor and a parameter factor matrix; performing modal expansion on the core tensor to calculate the higher-order coupling strength between parameters; performing L1 norm-constrained sparse reconstruction on the parameter factor matrix to obtain the dominant factor pattern; and solving for the parameter sensitivity score based on the higher-order coupling strength between parameters and the dominant factor pattern. The parameter sensitivity scores are used to sort each operating parameter in descending order to determine a subset of energy-sensitive parameters. The subset of energy-sensitive parameters and the load fluctuation sequence are concatenated according to timestamps to obtain a multivariate time-series data matrix. The load fluctuation sequence is subjected to moving average filtering to extract the trend component and Fourier transform to extract the periodic component. The multivariate time-series data matrix is ​​decomposed to obtain a latent variable matrix. The latent variable matrix is ​​subjected to variational inference iterative optimization to obtain the latent variable mean sequence and latent variable variance sequence. The energy-sensitive parameter subset, the trend component, and the periodic component are combined and weighted summation and hyperbolic tangent transform to obtain the predicted energy consumption output value. The predicted energy consumption output value is iteratively solved to obtain the expected energy consumption trajectory.

[0042] The system extracts combinations of operating parameters corresponding to periods of abnormal energy consumption, including multi-dimensional parameters such as influent flow rate, dissolved oxygen concentration, aeration rate, water temperature, and sludge concentration. Taking the aeration system of a wastewater treatment plant as an example, 16 operating parameters were recorded during the identified periods of abnormal energy consumption, with each parameter containing data from 72 time points in the time series. These multi-dimensional parameters are organized into a three-dimensional data structure in chronological order, forming a time-varying parameter tensor. The three dimensions of this tensor represent the number of time points, parameter types, and parameter recording batches, with a size of 72×16×3.

[0043] Tucker decomposition was performed on the time-varying parameter tensor to obtain the core tensor and parameter factor matrices. Tucker decomposition is a multilinear algebraic tool capable of extracting key features from tensor data. During the decomposition, the core tensor was set to 6×5×2 dimensions, and the parameter factor matrices were set to 72×6, 16×5, and 3×2 dimensions, respectively. Iterative calculations were performed using alternating least squares, stopping when the relative error was less than 0.001. After 15 iterations, the core tensor and three parameter factor matrices were obtained. The core tensor contains information about the interaction relationships between parameters, while the parameter factor matrices describe the main variation patterns of each dimension.

[0044] Modal expansion is performed on the core tensor to calculate the higher-order coupling strengths between parameters. Modal expansion is the operation of expanding a three-dimensional tensor into a matrix along different dimensions. Expansion is performed on the second mode, resulting in a 5×12 matrix. Singular value decomposition is performed on this matrix to extract the singular vectors corresponding to the principal singular values, constructing a parameter coupling matrix. Larger matrix element values ​​indicate stronger coupling relationships between corresponding parameters. For example, the coupling strength between dissolved oxygen concentration and aeration rate is 0.85, indicating a high correlation between these two parameters; while the coupling strength between water temperature and sludge concentration is only 0.12, indicating a weaker correlation.

[0045] The parameter factor matrix is ​​subjected to L1 norm-constrained sparse reconstruction to obtain the dominant factor patterns. The L1 norm constraint makes the elements in the factor matrix sparser, highlighting salient features. In the sparse reconstruction, a regularization coefficient of 0.15 is set, and the proximal gradient descent method is used for optimization. After 30 iterations, a sparse parameter factor matrix is ​​obtained, where non-zero elements represent dominant factor patterns. For example, in the second parameter factor matrix, the weights for aeration rate, dissolved oxygen, and recirculation ratio are 0.38, 0.31, and 0.25, respectively, while the weights of other parameters are close to zero, indicating that these three parameters are dominant factors.

[0046] The parameter sensitivity score is calculated based on the high-order coupling strength between parameters and the dominant factor model. The score calculation comprehensively considers the parameter's own weight and its coupling relationship with other parameters. For each parameter, its weighted sum in the dominant factor model is first calculated, and then multiplied by the average coupling strength between the current parameter and other parameters to obtain the comprehensive sensitivity score. For example, the sensitivity scores for aeration rate, dissolved oxygen, return ratio, water temperature, and sludge concentration are 0.42, 0.37, 0.29, 0.18, and 0.15, respectively. The operating parameters are sorted in descending order according to the aforementioned scores, and the top 5 parameters are taken as the subset of energy consumption sensitive parameters.

[0047] A subset of energy-sensitive parameters and the load fluctuation sequence are concatenated according to timestamps to obtain a multivariate time-series data matrix. This matrix contains 6 columns: the first 5 columns are energy-sensitive parameters, and the last column is the load fluctuation sequence. Each row of the matrix corresponds to a data record at the same time point. A moving average filter is applied to the load fluctuation sequence with a window length of 5 to obtain a smoothed trend component. A Fourier transform is performed on the extracted trend component, and the sampling frequency is set to 24 times / day to calculate the power spectral density. The main periodic components are identified based on the peak values ​​of the power spectrum. In this embodiment, two significant periods, 24 hours and 12 hours, are identified, with amplitudes of 3.2 kW and 1.8 kW, respectively.

[0048] The latent variable matrix is ​​obtained by decomposing the multivariate time-series data matrix. A variational autoencoder structure is adopted, with both the encoder and decoder being two-layer neural networks, and the dimension of the latent variables is set to 3. The encoder maps the input data to the latent variable space, and the decoder reconstructs the original data from the latent variables. During training, the batch size is set to 32, the learning rate to 0.005, and the number of training epochs to 200. Variational inference iterative optimization is performed on the latent variable matrix, using the mean field approximation method, with a maximum number of iterations set to 50 and a convergence threshold of 0.0001. During the iteration process, the parameters of the latent variable distribution are continuously updated to obtain the latent variable mean sequence and the latent variable variance sequence.

[0049] The predicted energy consumption output is obtained by weighted summation and hyperbolic tangent transformation of a subset of energy-sensitive parameters, trend components, and periodic components. The weight coefficients of each component are determined using gradient descent, with the weights for the sensitive parameter subset set at 0.5, the trend component at 0.3, and the periodic component at 0.2. After weighted summation, a nonlinear transformation using the hyperbolic tangent function is applied to map the output value to a reasonable range. The predicted energy consumption output is iteratively solved using a forward rolling prediction method with a prediction step size of 24 hours. In each prediction step, the prediction result from the previous moment is used as one of the input parameters for the next moment, progressively constructing a complete expected energy consumption trajectory.

[0050] In this embodiment, a time-varying parameter tensor is constructed from multi-parameter operating data during periods of abnormal energy consumption. Tucker decomposition is introduced to model the high-dimensional, multi-source, and strongly coupled relationships between operating parameters, which can explicitly characterize the high-order correlation structure between parameters. This significantly improves the comprehensiveness and accuracy of the analysis of the causes of energy consumption anomalies. By extracting the dominant factor pattern through sparse constraints, the interference of non-critical parameters on the analysis results is avoided, making the screening of energy-sensitive parameters more focused and stable. By jointly constructing a parameter sensitivity score with the high-order coupling strength and the dominant factor pattern, a quantitative ranking of the importance of operating parameters is achieved, which improves the objectivity and interpretability of determining the subset of energy-sensitive parameters and provides a clear target for subsequent optimization and regulation. By integrating energy-sensitive parameters, multi-scale load trends and periodic characteristics, and introducing variational inference to probabilistically model latent variables, energy consumption prediction can not only characterize long-term trends and periodic fluctuations, but also explicitly quantify the uncertainty of prediction.

[0051] In one optional implementation, the expected energy consumption trajectory is dynamically segmented using programming to obtain a quantified value of the energy-saving space. Based on the quantified value of the energy-saving space, an energy consumption optimization target is determined. Multiple candidate parameter adjustment schemes are obtained through iterative solution using a sequential quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. This includes: calculating the curvature sequence of the expected energy consumption trajectory using second-order difference calculation and identifying energy consumption curvature abrupt change points using a preset curvature threshold; using these energy consumption curvature abrupt change points as candidate segmentation points and dividing the trajectory into multiple energy consumption operation stages using a dynamic programming algorithm; determining the energy consumption uncertainty boundary based on a pre-acquired latent variable variance sequence and obtaining the upper bound of energy consumption confidence by combining the energy consumption mean of each energy consumption operation stage; calculating the theoretical optimal energy consumption trajectory corresponding to each energy consumption operation stage using a reverse recursive approach based on the Bellman optimality principle; and combining the energy consumption confidence... The upper bound is solved to obtain the energy-saving space quantification value; based on the energy-saving space quantification value and the expected energy consumption trajectory, the energy-saving potential ratio is solved and quantile analysis is performed to obtain the energy consumption optimization target value. A quadratic optimization problem is constructed with the energy consumption optimization target value as a constraint. The first-order partial derivative of the quadratic optimization problem is calculated to obtain the gradient vector, and the second-order partial derivative is calculated to obtain the Hessian matrix. The Hessian matrix is ​​corrected for positive definiteness and inverted with the gradient vector to obtain the search direction. A linear search is performed along the search direction until the L2 norm of the difference between the parameter vectors of two iterations is less than a preset convergence threshold to obtain parameter adjustment candidate schemes; based on the parameter adjustment candidate schemes and the expected energy consumption trajectory, the predicted energy consumption value is solved; based on the predicted energy consumption value and the energy consumption optimization target value, the optimal parameter adjustment scheme is solved.

[0052] The curvature sequence is obtained by performing second-order difference calculations on the expected energy consumption trajectory. The second-order difference operation reflects the curvature of the energy consumption trajectory by calculating the rate of change of energy consumption at adjacent time points. Specifically, for three consecutive points in the time series, the perpendicular distance between the second point and the line connecting the two preceding and following points is calculated as the curvature value for that point. Taking a certain waste gas treatment equipment as an example, its expected energy consumption trajectory data for 24 hours are 85, 87, 90, 92, 95, 98, 100, 102, 105, 107, 110, 112, 114, 112, 108, 105, 102, 98, 95, 93, 90, 88, 86, and 84 kWh. The second-order difference calculation of this sequence yields the curvature sequence 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, -2, -1, 0, -1, 0, 0, 0, 0, 0, 0 kilowatt-hours.

[0053] Energy consumption curvature abrupt change points are identified by combining a preset curvature threshold. For example, in this embodiment, the curvature threshold is set to ±1.5 kWh. When the value in the curvature sequence exceeds the threshold, the corresponding time point is marked as a curvature abrupt change point. In the aforementioned example, the curvature values ​​at the 13th and 14th time points are -2, exceeding the threshold, and are therefore identified as curvature abrupt change points. Using the identified curvature abrupt change points as candidate segmentation points, multiple energy consumption operation stages are obtained through a dynamic programming algorithm. The dynamic programming algorithm aims to minimize the energy consumption variance within each stage, while considering a penalty term for the number of stages to avoid over-segmentation. In practical applications, the maximum number of stages is set to 5, and the penalty coefficient is 0.2. After dynamic programming calculation, the above energy consumption trajectory is divided into 3 operation stages: hours 1-12 are the first stage, hours 13-17 are the second stage, and hours 18-24 are the third stage.

[0054] The energy consumption uncertainty boundary is determined based on the pre-acquired latent variable variance sequence. The latent variable variance sequence reflects the degree of uncertainty in the prediction result; the larger the variance value, the higher the uncertainty of the prediction. In this embodiment, the values ​​of the latent variable variance sequence over 24 hours are 0.12, 0.13, 0.15, 0.14, 0.13, 0.12, 0.11, 0.10, 0.09, 0.10, 0.11, 0.12, 0.14, 0.17, 0.18, 0.16, 0.14, 0.12, 0.11, 0.10, 0.09, 0.10, 0.11, and 0.12. The uncertainty boundary is calculated with a 95% confidence level, and the corresponding expansion coefficient is 1.96. The standard error is obtained by multiplying the latent variable variance sequence by the square root of the expansion coefficient, and then added to the expected energy consumption trajectory to obtain the upper boundary of the energy consumption uncertainty.

[0055] The upper confidence bounds for energy consumption are obtained by combining the average energy consumption for each energy consumption operation phase. The average energy consumption data for each operation phase are calculated: 98.3, 108.2, and 90.6 kWh for the first, second, and third phases, respectively. The upper confidence bounds for energy consumption are determined by combining the average energy consumption for each phase with the maximum value of the uncertainty boundary within that phase. Specifically, the upper confidence bounds for energy consumption for each phase are 98.3 + 0.15 × 1.96 = 98.9, 108.2 + 0.18 × 1.96 = 108.9, and 90.6 + 0.12 × 1.96 = 90.8 kWh, respectively.

[0056] The theoretical optimal energy consumption trajectory for each energy consumption operation stage is calculated using the Bellman optimality principle in reverse recursion. The Bellman principle states that any sub-strategy of the optimal strategy is also optimal. Applying this principle, the optimal energy consumption value for each time point is calculated in reverse, starting from the last time point. During the calculation, equipment operating constraints are considered, such as a maximum load change rate not exceeding 10%. For the three operation stages in this embodiment, the theoretical optimal energy consumption trajectories are 83, 84, 86, 87, 89, 90, 92, 93, 95, 96, 98, 99, 105, 102, 99, 97, 95, 92, 90, 88, 86, 84, 82, and 80 kWh, respectively.

[0057] The energy-saving potential is quantified by combining the upper bound of the energy consumption confidence. The difference between the actual expected energy consumption trajectory and the theoretical optimal energy consumption trajectory is accumulated to obtain the total energy-saving potential. Considering the constraint of the upper bound of the energy consumption confidence, it is ensured that the optimized energy consumption will not exceed the reliable operating range. In this embodiment, the total energy-saving potential is 85 kWh, which, when distributed over 24 hours, translates to an average energy saving of 3.54 kWh per hour.

[0058] The energy-saving potential ratio is obtained by quantifying the energy-saving space and calculating the expected energy consumption trajectory. Dividing the energy-saving space by the total expected energy consumption yields the energy-saving potential ratio. In this embodiment, the total expected energy consumption is 2338 kWh, and the energy-saving potential ratio is 85 / 2338 = 3.64%. A quantile analysis is performed on the energy-saving potential ratio, calculating the 25%, 50%, and 75% quantiles, which are 2.5%, 3.5%, and 4.8%, respectively. Based on the quantile analysis results, the energy consumption optimization target value is determined to be 96.5% of the expected energy consumption, i.e., the total target energy consumption is 2338 × 96.5% = 2256.2 kWh.

[0059] A quadratic optimization problem is constructed with energy consumption optimization objective value as the constraint. The optimization variables are parameters in a subset of energy consumption-sensitive parameters, and the optimization objective is to minimize the sum of squares of the deviations from the theoretically optimal energy consumption trajectory. The constraints include: total energy consumption does not exceed the optimization objective value; the values ​​of each parameter are within the allowable range; and the parameter changes at adjacent time points do not exceed the maximum rate of change. In this embodiment, dissolved oxygen concentration and reflux ratio are used as optimization variables, with values ​​ranging from 1.5 to 4.0 mg / L and 50% to 120%, respectively, and maximum rates of change of 0.5 mg / L / hour and 10% / hour, respectively.

[0060] The gradient vector is obtained by calculating the first-order partial derivatives of the quadratic optimization problem. The gradient vector represents the rate of change of the objective function with respect to each optimization variable, indicating the direction of the fastest decrease in function value. At the initial point of dissolved oxygen = 3.0 mg / L and reflux ratio = 100%, the calculated gradient vectors are 0.25 and 0.18, indicating that the objective function is more sensitive to dissolved oxygen than to reflux ratio. The Hessian matrix is ​​obtained by calculating the second-order partial derivatives. The Hessian matrix describes the local curvature of the objective function. At the same initial point, the calculated Hessian matrix is ​​a 2×2 matrix with diagonal elements of 0.15 and 0.08 and off-diagonal elements of 0.03.

[0061] The Hessian matrix is ​​corrected for positive definiteness and inverted using the gradient vector to obtain the search direction. Since the optimization problem may be non-convex, the Hessian matrix may not be positive definite, requiring correction to ensure the stability of the optimization algorithm. The correction method involves checking the smallest eigenvalue of the Hessian matrix; if it is less than a set threshold of 0.01, an appropriate positive number is added to the diagonal elements. The corrected Hessian matrix is ​​then used in matrix operations with the gradient vector to obtain search directions -1.56 and -1.94. A linear search is performed along these search directions, using the Armijo criterion to determine the step size, with an initial step size of 1 and a shrinkage factor of 0.5. Iterative calculations are repeated until the L2 norm of the difference between the parameter vectors of two iterations is less than the preset convergence threshold of 0.001. After 12 iterations, the following parameter adjustment candidate schemes are obtained: dissolved oxygen = 2.5 mg / L, reflux ratio = 85%.

[0062] In this embodiment, by performing curvature analysis on the expected energy consumption trajectory and introducing dynamic programming to divide the energy consumption operation stages, the energy consumption change process is transformed from a continuous sequence into a stage structure with clear boundaries and characteristics. This allows for adaptive identification of critical moments when energy consumption behavior undergoes substantial changes, thereby more accurately characterizing the energy consumption features of different operation stages. By constructing a theoretically optimal energy consumption trajectory based on the Bellman optimality principle and comparing it with the actual achievable upper bound of energy consumption confidence, the actual energy-saving space of the equipment in each operation stage can be quantified, significantly improving the interpretability and decision-making value of energy-saving potential assessment. By introducing quantile analysis of the proportion of energy-saving potential to determine the energy consumption optimization target, the optimization target can reflect the overall operation distribution characteristics rather than extreme samples, enhancing the robustness of target setting. By constructing a constrained quadratic optimization problem and adopting an iterative solution strategy based on gradients and Hessian matrices, the numerical stability and convergence of the parameter adjustment process and the feasibility of the solution are guaranteed. This allows for more efficient approximation of the optimal solution while meeting energy consumption constraints, reducing ineffective adjustments and repeated trial and error.

[0063] In one optional implementation, acquiring the real-time rate of change of environmental load and calculating the load gradient vector, and adjusting the corresponding advance time based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, includes: acquiring the numerical sequence of environmental load within a continuous time window and performing sliding window difference calculation to obtain the load change; calculating the real-time rate of change of environmental load based on the load change and a pre-set sliding step size; performing time series decomposition on the real-time rate of change to obtain a trend term and a fluctuation term; calculating the first derivative of the trend term to obtain the trend gradient component; extracting the amplitude corresponding to the fluctuation term and performing normalization processing to obtain the normalized fluctuation amplitude; and concatenating the trend gradient component and the normalized fluctuation amplitude into a vector to obtain the load gradient vector and performing L2 calculation. The load gradient modulus is calculated using the norm; the heat capacity and heat transfer coefficient parameters of the environmental protection equipment are obtained and the thermal inertia coefficient of the equipment is calculated; the response delay baseline value is obtained by combining the load gradient modulus; the adjustment amplitude of each parameter in the optimal parameter adjustment scheme is extracted and weighted to obtain the total parameter adjustment amplitude; the delay time correction value is calculated based on the total parameter adjustment amplitude and the response delay baseline value; the total delay time is obtained by combining the equipment thermal inertia time constant; the fluctuation phase sequence corresponding to the fluctuation term is extracted and phase expansion is performed to obtain a continuous phase curve; the phase change rate is obtained by performing first derivative calculation; the phase compensation amount is obtained by combining the total delay time; and the advance time corresponding to parameter adjustment is determined based on the phase compensation amount and the total delay time, combined with the current time.

[0064] The environmental load numerical sequence within a continuous time window is collected, and the load change is obtained by performing sliding window differential calculation. The environmental load numerical sequence reflects the changes of key indicators such as pollutant concentration and flow rate over time. Taking an industrial wastewater treatment plant as an example, wastewater flow load data for 24 consecutive hours is collected, with a sampling interval of 1 hour. The resulting load sequence contains 24 data points, with values ​​ranging from 125 to 180 cubic meters per hour, showing a diurnal variation characteristic of first increasing and then decreasing. A sliding window differential calculation is performed, with the window length set to 3 hours. The difference between the last value and the first value within the window is calculated to obtain the load change sequence. This sequence is mostly positive in the morning and mostly negative in the afternoon and evening, reflecting the diurnal pattern of industrial wastewater discharge.

[0065] The real-time rate of change of environmental load is calculated based on the load change and a pre-set sliding step size. The sliding step size is set to 1 hour, and the real-time rate of change represents the magnitude of load change per unit time. The calculation method is to divide the load change by the window length to obtain the real-time rate of change sequence. The peak of this sequence occurs around 9:00 AM, approximately 4.67 cubic meters per hour; the trough occurs around 5:00 PM, approximately -4.67 cubic meters per hour, reflecting the rate of change of wastewater flow load.

[0066] Time series decomposition of the real-time rate of change yields a trend term and a fluctuation term. A seasonal trend decomposition method is used to separate the real-time rate of change sequence into a trend component and a periodic fluctuation component. During the decomposition process, the decomposition period is set to 24 hours, and the number of iterations is 5. The resulting trend term sequence exhibits smooth change characteristics, with a value range between -4.10 and 4.33 cubic meters per hour. The fluctuation term sequence reflects short-term random fluctuations, with an amplitude not exceeding ±1.51 cubic meters per hour and an average absolute amplitude of 0.72 cubic meters per hour.

[0067] The trend gradient component is obtained by calculating the first derivative of the trend term. The first derivative reflects the direction and speed of trend change. An approximate calculation is performed using the central difference method; for each point in the sequence, the corresponding derivative value is obtained by dividing the difference between two adjacent points by the time interval. The calculated trend gradient component sequence remains within ±0.67 cubic meters per hour for most time points, but its value approaches zero near load inflection points.

[0068] The amplitude corresponding to the fluctuation term is extracted and normalized to obtain the normalized fluctuation amplitude. The fluctuation amplitude refers to the absolute value of the fluctuation term sequence, reflecting the degree of deviation from the trend. The absolute value of each element in the fluctuation term sequence is taken to obtain the fluctuation amplitude sequence. The fluctuation amplitude sequence is then normalized by dividing each element by the maximum value of the sequence, 1.51, to obtain the normalized fluctuation amplitude sequence, whose value range is 0 to 1 and is dimensionless.

[0069] The trend gradient components and normalized fluctuation amplitudes are concatenated as vectors to obtain the load gradient vector, and the L2 norm is calculated to obtain the load gradient magnitude. For each time point in the sequence, the corresponding trend gradient value and normalized fluctuation amplitude are combined into a two-dimensional vector. The L2 norm of the two-dimensional vector, i.e., the square root of the sum of the squares of the vector components, is calculated to obtain the load gradient magnitude. In this embodiment, the average value of the load gradient magnitude sequence is approximately 0.8, and the maximum value is approximately 1.2, which occurs at the moment when the fluctuation amplitude is the largest and the trend change is significant.

[0070] The heat capacity and heat transfer coefficient parameters of environmental protection equipment are obtained, and the thermal inertia coefficient of the equipment is calculated. The heat capacity parameter reflects the equipment's ability to store thermal energy, while the heat transfer coefficient parameter reflects the rate of heat transfer. Taking a wastewater biological treatment reactor as an example, the corresponding heat capacity is 2.8 × 10^6 joules / degree Celsius, and the heat transfer coefficient is 3.5 × 10^6 joules / degree Celsius. 4 Watts per degree Celsius. The thermal inertia coefficient of the equipment is calculated by dividing the heat capacity by the heat transfer coefficient, resulting in a thermal inertia coefficient of 80 seconds.

[0071] The response delay baseline value is obtained by combining the load gradient magnitude. The response delay baseline value is inversely proportional to the load gradient magnitude, indicating that the more drastic the load change, the earlier the equipment needs to start adjusting to cope with the change. The baseline calculation formula is to divide the preset maximum delay time of 240 seconds by the load gradient magnitude, with upper and lower limits set between 60 and 240 seconds. During periods of drastic load changes, the response delay baseline value can be as low as 100 seconds; while during periods of slow load changes, the response delay baseline value can reach 240 seconds.

[0072] The adjustment ranges of each parameter in the optimal parameter adjustment scheme are extracted and weighted summed to obtain the total parameter adjustment range. The optimal parameter adjustment scheme includes multiple operating parameters, such as aeration rate, reflux ratio, and reagent dosage. The adjustment range of each parameter is the percentage change relative to its current value. Based on the degree of influence of each parameter on the system response, different weights are assigned, and the weighted total adjustment range is calculated. The absolute value of the total parameter adjustment range is typically between 5% and 20%, reflecting the intensity of the adjustment.

[0073] The delay time correction value is calculated based on the total parameter adjustment amplitude and the response delay baseline value, and then combined with the equipment thermal inertia coefficient to obtain the total delay time. The delay time correction value is calculated by multiplying the absolute value of the total parameter adjustment amplitude by a preset coefficient of 50 seconds / percentage. The total delay time is the sum of the equipment thermal inertia coefficient and the delay time correction value, typically in the range of 100 to 200 seconds, reflecting the time required for the system to complete its response from the issuance of the parameter adjustment command.

[0074] The wave phase sequence corresponding to the wave term is extracted and phase expansion is performed to obtain a continuous phase curve. The phase change rate is then calculated using the first derivative. The wave phase sequence is extracted from the wave term using a Hilbert transform, representing the periodic variation of the wave. The phase value ranges from -π to π. To avoid phase jumps, phase expansion is performed to obtain a continuously monotonically increasing phase curve. The first derivative of the continuous phase curve is calculated to obtain a phase change rate sequence, with an average value of approximately 0.25 radians per hour.

[0075] The phase compensation is obtained by combining the total delay time. The phase compensation is calculated by multiplying the total delay time by the phase change rate, reflecting the amount of phase change during the delay period. For a total delay time of 100 seconds, the phase compensation is approximately 0.007 radians. Phase compensation ensures that, considering the delay time, parameter adjustments accurately correspond to the phase of load changes.

[0076] Based on the phase compensation amount and the total delay time, the advance time corresponding to parameter adjustment is determined in conjunction with the current time. The method for calculating the advance time of parameter adjustment is to subtract the total delay time from the current time, ensuring that the system has completed response preparation before the load change occurs. Taking the current time as 14:30:00 and the total delay time as 100 seconds as an example, the advance time of parameter adjustment is 14:28:20.

[0077] In this embodiment, by decomposing the rate of change of environmental load into trends and fluctuations, and further introducing trend gradients and normalized fluctuation amplitudes to construct load gradient modulus, the intensity and direction of environmental load changes are uniformly quantified and expressed. This allows for more sensitive capture of the process of load transitioning from gradual to drastic changes, providing more reliable prior information for determining the timing of parameter adjustments, reducing energy consumption fluctuations and control failure risks caused by delayed responses. By combining load change characteristics with equipment thermal inertia characteristics, and introducing response delay benchmarks and delay time correction mechanisms, the timing of parameter adjustments no longer relies on fixed lead times or empirical time constants. This significantly improves the matching degree between parameter adjustments and the actual response process of the equipment, reducing the probability of over-adjustment or under-adjustment. By continuously processing the load fluctuation phase and calculating the phase compensation amount in conjunction with the total delay time, fine alignment of parameter adjustment times in periodic load disturbance scenarios is achieved. This effectively avoids misalignment between parameter adjustments and load peaks and troughs, thereby reducing energy consumption oscillations introduced by adjustments and improving the stability and energy-saving effect of system operation.

[0078] Figure 2 is a flowchart of the environmental load response delay calculation method based on multi-source data fusion for environmental protection equipment energy consumption optimization and energy-saving control according to an embodiment of the present invention.

[0079] In one optional implementation, generating a parameter change path based on the advance time and the load gradient vector, and generating and issuing a timing control sequence based on the parameter change path and the actuator includes: taking the advance time as the path start time, taking the target value of each parameter in the optimal parameter adjustment scheme as the path end value, determining the load change trend direction based on the trend gradient component in the load gradient vector, determining the parameter change time allocation ratio based on the load change trend direction, solving for the path speed adjustment factor based on the normalized fluctuation amplitude in the load gradient vector and the time allocation ratio, normalizing the time interval from the path start time to the current time and calculating the position correction amount based on the path speed adjustment factor, and then... The numerical deviation is determined by the endpoint value and the current value of each parameter, and the instantaneous change is calculated by combining the position correction amount. The parameter change path is determined based on the current value of each parameter and the instantaneous change. The response bandwidth parameter and action dead zone parameter of the actuator are obtained. The effective control time is marked based on the parameter change path and the action dead zone parameter, and the corresponding parameter value is extracted to form a discrete control point sequence. The time interval between adjacent discrete control points is calculated, and the discrete control point sequence is time-resampled in combination with the response bandwidth parameter to obtain a sparse control point sequence. The parameter value of each control point in the sparse control point sequence is quantized and encoded to obtain a control command code. The control command code is timestamped with the corresponding effective control time to obtain a timing control sequence and is sent to the actuator for execution.

[0080] The path start time is defined as the advance execution time of the parameter adjustment, and the target values ​​of each parameter in the optimal parameter adjustment scheme are defined as the path end time. The path start time is the advance execution time of the parameter adjustment calculated using the aforementioned method, for example, 14:28:20. The optimal parameter adjustment scheme includes target adjustment values ​​for multiple operating parameters, such as a target aeration volume of 1200 cubic meters per hour, a target return ratio of 150%, and a target chemical dosage of 25 liters per hour for a wastewater treatment device. These target values ​​are based on the energy-optimal operating conditions obtained through multi-source data analysis.

[0081] The direction of load change trend is determined based on the trend gradient component in the load gradient vector. This trend gradient component reflects the acceleration characteristics of environmental load changes. When the trend gradient component is positive, it indicates that the rate of load change is increasing, and the load change trend direction is defined as upward. When the trend gradient component is negative, it indicates that the rate of load change is decreasing, and the load change trend direction is defined as downward. When the trend gradient component is close to zero, it indicates that the rate of load change is basically constant, and the load change trend direction is defined as stable. Taking the wastewater treatment scenario at 9:00 AM as an example, the trend gradient component at this time is 0.58, which is determined to be an upward trend.

[0082] The time allocation ratio for parameter changes is determined based on the direction of the load change trend. This time allocation ratio determines the time distribution between the acceleration and deceleration phases during parameter adjustment. During an upward trend, a faster-than-faster adjustment strategy is preferred, with a time allocation ratio of 0.4:0.6, meaning the acceleration phase accounts for 40% of the total adjustment time and the deceleration phase accounts for 60%. During a downward trend, a slower-than-faster adjustment strategy is preferred, with a time allocation ratio of 0.6:0.4. During a stable trend, a uniform adjustment strategy is adopted, with a time allocation ratio of 0.5:0.5. Based on the upward trend in the previous example, the time allocation ratio is determined to be 0.4:0.6.

[0083] The path speed adjustment factor is obtained by solving the normalized fluctuation amplitude and time allocation ratio in the load gradient vector. The normalized fluctuation amplitude reflects the intensity of short-term fluctuations. When the fluctuation amplitude is large, parameter adjustment should be smoother to avoid following short-term fluctuations. The path speed adjustment factor is calculated by subtracting the product of the normalized fluctuation amplitude and a preset weight from 1, where the preset weight is usually set to 0.5. For the case where the normalized fluctuation amplitude is 0.54, the calculated path speed adjustment factor is 0.73. The value range of the path speed adjustment factor is from 0.5 to 1, with a larger value indicating a faster path change rate.

[0084] The position correction is calculated by normalizing the time interval from the path's start time to the current time and combining it with the path speed adjustment factor. The time interval normalization method is to divide the elapsed time by the total adjustment time. Assuming the path's start time is 14:28:20, the current time is 14:29:10, and the total adjustment time is 120 seconds, then the normalized time is 50 seconds / 120 seconds = 0.417. The position correction is calculated using a power function mapping. For an upward trend, the position correction equals the normalized time raised to the power of the power factor, where the exponent is 1 divided by the product of the path speed adjustment factor and the preceding term of the time allocation ratio. In this embodiment, the exponent is 1 / (0.73 × 0.4) = 3.42, and the position correction is (0.417). 3.42 =0.051.

[0085] The numerical deviation is determined based on the path endpoint value and the current values ​​of each parameter, and the instantaneous change is calculated by combining this with the position correction. The numerical deviation is the difference between the path endpoint value and the current parameter value. Taking aeration airflow as an example, if the endpoint value is 1200 cubic meters per hour and the current value is 1000 cubic meters per hour, then the numerical deviation is 200 cubic meters per hour. The instantaneous change is calculated by multiplying the numerical deviation by the position correction, resulting in 10.2 cubic meters per hour, indicating that the aeration airflow should increase by 10.2 cubic meters per hour at the current moment.

[0086] The parameter change path is determined based on the current values ​​and instantaneous changes of each parameter. The parameter change path refers to the continuous trajectory of parameter values ​​from the start time to the end time. A complete parameter change path can be constructed by calculating the instantaneous changes at each sampling time and accumulating them to the current value. The aeration airflow change path is recorded with a time precision of 0.5 seconds, gradually increasing from 1000 cubic meters per hour to 1200 cubic meters per hour over time, forming a smooth S-shaped curve. The change paths of the reflux ratio and reagent dosage are calculated using the same method.

[0087] Obtain the response bandwidth and dead zone parameters of the actuator. The response bandwidth parameter reflects the frequency characteristics of the actuator's response to commands, usually measured in Hertz (Hz); the dead zone parameter reflects the minimum identifiable change range of the actuator. Taking an aeration blower in a wastewater treatment plant as an example, its response bandwidth is 0.25 Hz, meaning it can perform an effective adjustment at least once every 4 seconds; the dead zone is 2% of the airflow, meaning the actuator will not respond if the airflow change is less than 2% of the current value.

[0088] Effective control moments are marked based on parameter change paths and action dead zones, and corresponding parameter values ​​are extracted to form a discrete control point sequence. The method for marking effective control moments is to detect whether the difference in parameter values ​​between two adjacent points on the parameter change path exceeds the action dead zone. Taking aeration airflow as an example, when the airflow increases from 1000 cubic meters per hour to 1020 cubic meters per hour, the change is 2%, which just reaches the action dead zone threshold. This is marked as an effective control moment, and the parameter value of 1020 cubic meters per hour is recorded as a discrete control point. Continuing to detect subsequent moments, when the airflow reaches 1040.4 cubic meters per hour, the change relative to the previous control point again reaches 2%, and this is marked as the next effective control moment. This marking process is repeated to obtain the discrete control point sequence for aeration airflow.

[0089] The time interval between adjacent discrete control points is calculated, and the discrete control point sequence is resampled using the response bandwidth parameter to obtain a sparse control point sequence. The time interval between adjacent discrete control points is calculated; for example, the time interval from an airflow of 1020 cubic meters per hour to 1040.4 cubic meters per hour is 2.3 seconds. If the time interval is less than the minimum allowable interval of 4 seconds by the response bandwidth, time resampling is required. The resampling method involves uniformly selecting control points at the minimum interval of 4 seconds, discarding intermediate control points. After resampling, the resulting sparse control point sequence meets the response capability of the actuator.

[0090] The parameter values ​​of each control point in the sparse control point sequence are quantized and encoded to obtain control command codes. Quantization encoding converts the actual parameter values ​​into command codes that the actuator can recognize. The aeration air volume is represented by a 16-bit binary number, with a range of 0 to 2000 cubic meters per hour and a resolution of approximately 0.03 cubic meters per hour. 1040.4 cubic meters per hour is quantized into the binary code 10000010011101, which is represented as 8477 in decimal. Similarly, the reflux ratio and reagent dosage are quantized and encoded.

[0091] The control command code is timestamped with its corresponding valid control time to obtain a time-series control sequence, which is then sent to the actuator for execution. The timestamp precision is milliseconds. For example, the control command code 8477 for an aeration volume of 1040.4 cubic meters per hour is timestamped with 14:28:24.000 to form a single control command. All parameter control commands are organized chronologically to form a complete time-series control sequence. Finally, this sequence is sent to the actuator's control unit, which executes the corresponding control commands sequentially according to the timestamps.

[0092] In this embodiment, by using the lead time as the starting point of the path and the optimal parameter target value as the ending point of the path, and combining the trend component of the load gradient vector and the normalized fluctuation amplitude for time allocation and speed adjustment, dynamic path planning for the parameter change process is realized. This can adaptively match the trend and fluctuation characteristics of environmental load changes, making parameter changes smoother and conforming to the load change law, reducing energy loss and equipment stress caused by adjustment lag or overshoot. By combining the response bandwidth and action dead zone parameters of the actuator to mark and resample the discrete control points, efficient mapping of parameter adjustment paths to executable control sequences is realized, effectively avoiding the problems of actuator response delay, dead zone effect and high frequency oscillation, making the issued control commands more accurate and feasible in actual execution, and improving the reliability and stability of control. By quantizing and encoding the parameter values ​​of sparse control points and binding them with timestamps to form a time-series control sequence, the parameter adjustment process is digitized, structured and traceable, ensuring a close correspondence between control commands and operating status, and facilitating rapid response under complex working conditions, realizing refined real-time energy consumption optimization.

[0093] A second aspect of this invention provides an energy consumption optimization and energy-saving control system for environmental protection equipment based on multi-source data fusion, comprising: a state feature extraction module, used to collect multi-dimensional operation monitoring data of environmental protection equipment and perform wavelet packet decomposition to obtain multi-scale frequency domain features, perform sliding window statistics on the multi-dimensional operation monitoring data to obtain a load fluctuation sequence, and combine the multi-scale frequency domain features to obtain an equipment state feature matrix; an energy efficiency anomaly identification module, used to acquire historical operating condition records of the equipment and extract energy efficiency curves, perform probability density reconstruction on the energy efficiency curves through kernel density estimation, calculate energy efficiency deviation and identify abnormal energy consumption periods in combination with the equipment state feature matrix; and a parameter scheme optimization module, used to extract the combination of operating parameters corresponding to the abnormal energy consumption periods and perform principal component analysis. A subset of energy-sensitive parameters is obtained through analysis. Based on the subset of energy-sensitive parameters and the load fluctuation sequence, the expected energy consumption trajectory is determined. The expected energy consumption trajectory is dynamically planned and segmented to calculate the energy-saving space quantification value. Based on the energy-saving space quantification value, the energy consumption optimization target is determined. Multiple sets of parameter adjustment candidate schemes are obtained by iteratively solving the sequence quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. A control sequence execution module is used to obtain the real-time change rate of the environmental load and calculate the load gradient vector. Based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, the advance time corresponding to parameter adjustment is calculated. The parameter change path is generated according to the advance time and the load gradient vector. The timing control sequence is generated based on the parameter change path and the actuator and then issued for execution.

[0094] A third aspect of the present invention provides an electronic device, including: a processor and a memory for storing processor-executable instructions, wherein the processor is configured to invoke the instructions stored in the memory to perform the aforementioned method.

[0095] A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the aforementioned method.

[0096] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy consumption optimization and energy-saving control of environmental protection equipment based on multi-source data fusion, characterized in that, include: Multi-dimensional operation monitoring data of environmental protection equipment is collected and wavelet packet decomposition is performed to obtain multi-scale frequency domain features. Sliding window statistics are performed on the multi-dimensional operation monitoring data to obtain load fluctuation sequence, and the equipment state feature matrix is ​​obtained by combining the multi-scale frequency domain features. Historical operating condition records of the equipment are acquired and energy efficiency curves are extracted. The energy efficiency curves are reconstructed using kernel density estimation and probability density. The energy efficiency deviation is calculated and abnormal energy consumption periods are identified by combining the equipment state feature matrix. The operating parameter combinations corresponding to the abnormal energy consumption periods are extracted and principal component analysis is performed to obtain a subset of energy-sensitive parameters. The expected energy consumption trajectory is determined based on the subset of energy-sensitive parameters and the load fluctuation sequence. The expected energy consumption trajectory is dynamically segmented and the quantified value of the energy-saving space is calculated. The energy consumption optimization target is determined based on the quantified value of the energy-saving space. Multiple sets of parameter adjustment candidate schemes are obtained by iteratively solving the problem using a sequence quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. The system acquires the real-time rate of change of environmental load and calculates the load gradient vector. Based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, it calculates the advance time corresponding to parameter adjustment. Based on the advance time and the load gradient vector, it generates a parameter change path. Based on the parameter change path and the actuator, it generates a timing control sequence and issues it for execution.

2. The method according to claim 1, characterized in that, The process of collecting multi-dimensional operation monitoring data of environmental protection equipment and performing wavelet packet decomposition to obtain multi-scale frequency domain features, and then performing sliding window statistics on the multi-dimensional operation monitoring data to obtain a load fluctuation sequence and combining the multi-scale frequency domain features to obtain an equipment state feature matrix includes: collecting multi-dimensional operation monitoring data corresponding to environmental protection equipment through pre-set sensors and reconstructing the time series according to a preset sampling period; performing wavelet packet decomposition on the reconstructed multi-dimensional operation monitoring data to extract frequency domain components of different frequency bands; performing energy normalization processing on the frequency domain components to obtain multi-scale frequency domain feature vectors; dividing the multi-dimensional operation monitoring data into sliding windows according to a preset window length; calculating the load statistics within each sliding window and constructing a load fluctuation time series; performing difference operations on the load fluctuation time series to obtain a load change rate sequence as the load fluctuation sequence; extracting the multi-scale frequency domain feature vector and the timestamp in the load fluctuation sequence; performing time alignment based on the timestamps; concatenating the aligned multi-scale frequency domain feature vector and the load fluctuation sequence column-wise to form a two-dimensional array structure; and performing a transpose operation on the two-dimensional array structure to obtain the equipment state feature matrix.

3. The method according to claim 1, characterized in that, The process of acquiring historical operating condition records of equipment and extracting energy efficiency curves, reconstructing the energy efficiency curves using kernel density estimation, and calculating energy efficiency deviation and identifying abnormal energy consumption periods in conjunction with the equipment state feature matrix includes: acquiring historical operating condition records of equipment and dividing them into segments according to load intervals; extracting energy efficiency data points corresponding to each load interval and constructing segmented energy efficiency curves; performing adaptive bandwidth kernel density estimation on the segmented energy efficiency curves to obtain a probability density distribution surface; calculating an energy efficiency quantile set based on the probability density distribution surface; constructing an energy efficiency benchmark confidence band based on the energy efficiency quantile set; extracting frequency domain feature components and load fluctuation components from the equipment state feature matrix; and reconstructing the frequency domain feature components... The load fluctuation component is subjected to frequency band energy weighted aggregation to obtain a comprehensive frequency domain index. The load fluctuation component is decomposed into a load trend term and a load disturbance term. The comprehensive frequency domain index, the load trend term, and the load disturbance term are mapped to the energy efficiency assessment space by tensor decomposition and the current energy efficiency estimate is calculated. The current energy efficiency estimate is compared with the energy efficiency benchmark confidence band to calculate the energy efficiency deviation. The energy efficiency deviation is accumulated over time to construct a time-weighted cumulative sequence. The time-weighted cumulative sequence is used to detect change points to identify the moment of sudden change in energy efficiency status. The time period of continuous energy efficiency deviation is extracted with the moment of sudden change in energy efficiency status as the dividing point and labeled according to the severity of deviation to obtain the energy consumption abnormal period.

4. The method according to claim 1, characterized in that, Extracting the operating parameter combinations corresponding to the abnormal energy consumption periods and performing principal component analysis to obtain a subset of energy-sensitive parameters, and determining the expected energy consumption trajectory based on the subset of energy-sensitive parameters and the load fluctuation sequence includes: extracting the operating parameter combinations corresponding to the abnormal energy consumption periods and constructing a time-varying parameter tensor; performing Tucker decomposition on the time-varying parameter tensor to obtain a core tensor and a parameter factor matrix; performing modal expansion on the core tensor to calculate the higher-order coupling strength between parameters; performing L1 norm-constrained sparse reconstruction on the parameter factor matrix to obtain the dominant factor pattern; solving for the parameter sensitivity score based on the higher-order coupling strength between parameters and the dominant factor pattern; and determining the expected energy consumption trajectory based on the parameter sensitivity. The scoring values ​​are sorted in descending order for each operating parameter to determine a subset of energy-sensitive parameters. The subset of energy-sensitive parameters and the load fluctuation sequence are concatenated according to timestamps to obtain a multivariate time-series data matrix. The load fluctuation sequence is subjected to moving average filtering to extract the trend component and Fourier transform to extract the periodic component. The multivariate time-series data matrix is ​​decomposed to obtain a latent variable matrix. The latent variable matrix is ​​subjected to variational inference iterative optimization to obtain the latent variable mean sequence and latent variable variance sequence. The energy-sensitive parameter subset, the trend component, and the periodic component are combined and weighted summation and hyperbolic tangent transform to obtain the predicted energy consumption output value. The predicted energy consumption output value is iteratively solved to obtain the expected energy consumption trajectory.

5. The method according to claim 1, characterized in that, The expected energy consumption trajectory is segmented using dynamic programming, and the quantified value of the energy-saving space is calculated. Based on the quantified value of the energy-saving space, the energy consumption optimization target is determined. Multiple candidate parameter adjustment schemes are obtained through iterative solution using a sequential quadratic programming algorithm, and the optimal parameter adjustment scheme is selected. This includes: calculating the curvature sequence of the expected energy consumption trajectory using second-order difference calculation and identifying energy consumption curvature abrupt change points using a preset curvature threshold; using these energy consumption curvature abrupt change points as candidate segmentation points and dividing the trajectory into multiple energy consumption operation stages using a dynamic programming algorithm; determining the energy consumption uncertainty boundary based on a pre-acquired latent variable variance sequence and obtaining the upper bound of energy consumption confidence by combining the mean energy consumption of each energy consumption operation stage; calculating the theoretical optimal energy consumption trajectory corresponding to each energy consumption operation stage using the Bellman optimality principle in reverse recursion; and obtaining the optimal energy consumption trajectory by combining the upper bound of energy consumption confidence. The energy-saving space is quantified; based on the energy-saving space quantification value and the expected energy consumption trajectory, the energy-saving potential ratio is obtained and quantile analysis is performed to obtain the energy consumption optimization target value. A quadratic optimization problem is constructed with the energy consumption optimization target value as a constraint. The first-order partial derivative of the quadratic optimization problem is calculated to obtain the gradient vector, and the second-order partial derivative is calculated to obtain the Hessian matrix. The Hessian matrix is ​​corrected for positive definiteness and inverted with the gradient vector to obtain the search direction. A linear search is performed along the search direction until the L2 norm of the difference between the parameter vectors of two iterations is less than a preset convergence threshold to obtain parameter adjustment candidate schemes. Based on the parameter adjustment candidate schemes and the expected energy consumption trajectory, the predicted energy consumption value is obtained. Based on the predicted energy consumption value and the energy consumption optimization target value, the optimal parameter adjustment scheme is obtained.

6. The method according to claim 1, characterized in that, The process involves acquiring the real-time rate of change of environmental load and calculating the load gradient vector. Based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, the adjustment timing is calculated. This includes: acquiring the numerical sequence of environmental load within a continuous time window and performing sliding window difference calculation to obtain the load change; calculating the real-time rate of change of environmental load based on the load change and a pre-set sliding step size; performing time series decomposition on the real-time rate of change to obtain a trend term and a fluctuation term; calculating the first derivative of the trend term to obtain the trend gradient component; extracting the amplitude corresponding to the fluctuation term and normalizing it to obtain the normalized fluctuation amplitude; and concatenating the trend gradient component and the normalized fluctuation amplitude into a vector to obtain the load gradient vector and performing L2 norm calculation. Load gradient modulus; obtain the heat capacity parameters and heat transfer coefficient parameters of the environmental protection equipment and calculate the thermal inertia coefficient of the equipment. Combine the load gradient modulus to obtain the response delay benchmark value. Extract the adjustment amplitude of each parameter in the optimal parameter adjustment scheme and sum them by weight to obtain the total parameter adjustment amplitude. Calculate the delay time correction value based on the total parameter adjustment amplitude and the response delay benchmark value. Combine the equipment thermal inertia time constant to obtain the total delay time. Extract the fluctuation phase sequence corresponding to the fluctuation term and perform phase expansion to obtain a continuous phase curve. Calculate the phase change rate by performing first derivative calculation. Combine the total delay time to obtain the phase compensation amount. Based on the phase compensation amount and the total delay time, determine the advance time corresponding to the parameter adjustment based on the current time.

7. The method according to claim 1, characterized in that, The process of generating a parameter change path based on the advance time and the load gradient vector, and generating and executing a timing control sequence based on the parameter change path and the actuator, includes: using the advance time as the path start time, using the target values ​​of each parameter in the optimal parameter adjustment scheme as the path end value, determining the load change trend direction based on the trend gradient component in the load gradient vector, determining the parameter change time allocation ratio based on the load change trend direction, solving for the path speed adjustment factor based on the normalized fluctuation amplitude in the load gradient vector and the time allocation ratio, normalizing the time interval from the path start time to the current time and calculating the position correction amount based on the path speed adjustment factor, and then calculating the position correction amount based on the path end value and the target values ​​of each parameter in the optimal parameter adjustment scheme as the path end value. The current value of the parameter determines the numerical deviation, and the instantaneous change is calculated by combining the position correction amount. The parameter change path is determined based on the current value of each parameter and the instantaneous change. The response bandwidth parameter and action dead zone parameter of the actuator are obtained. The effective control time is marked based on the parameter change path and the action dead zone parameter, and the corresponding parameter values ​​are extracted to form a discrete control point sequence. The time interval between adjacent discrete control points is calculated, and the discrete control point sequence is time-resampled in combination with the response bandwidth parameter to obtain a sparse control point sequence. The parameter values ​​of each control point in the sparse control point sequence are quantized and encoded to obtain a control command code. The control command code is timestamped with the corresponding effective control time to obtain a timing control sequence, which is then sent to the actuator for execution.

8. An environmental protection equipment energy consumption optimization and energy-saving control system based on multi-source data fusion, used to implement the method of any one of claims 1-7, characterized in that, include: The state feature extraction module is used to collect multi-dimensional operation monitoring data of environmental protection equipment and perform wavelet packet decomposition to obtain multi-scale frequency domain features. The multi-dimensional operation monitoring data is then subjected to sliding window statistics to obtain a load fluctuation sequence, and combined with the multi-scale frequency domain features to obtain an equipment state feature matrix. The energy efficiency anomaly identification module is used to acquire historical operating condition records of the equipment and extract energy efficiency curves. The energy efficiency curves are then reconstructed using kernel density estimation to obtain probability density. The energy efficiency deviation is calculated in combination with the equipment state feature matrix, and abnormal energy consumption periods are identified. The parameter scheme optimization module is used to extract the combination of operating parameters corresponding to the abnormal energy consumption period and perform principal component analysis to obtain a subset of energy consumption sensitive parameters. Based on the subset of energy consumption sensitive parameters and the load fluctuation sequence, the expected energy consumption trajectory is determined. The expected energy consumption trajectory is dynamically planned and segmented to calculate the energy-saving space quantification value. Based on the energy-saving space quantification value, the energy consumption optimization target is determined. Multiple sets of parameter adjustment candidate schemes are obtained by combining the sequence quadratic programming algorithm to iteratively solve the problem and the optimal parameter adjustment scheme is selected. The control sequence execution module is used to obtain the real-time change rate of environmental load and calculate the load gradient vector. Based on the load gradient vector and the thermal inertia coefficient of the corresponding environmental protection equipment, it calculates the advance time corresponding to parameter adjustment, generates a parameter change path according to the advance time and the load gradient vector, generates a timing control sequence based on the parameter change path and the actuator, and issues it for execution.

9. An electronic device, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method described in any one of claims 1 to 7.

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