An environmental engineering energy consumption optimization control method based on big data analysis

Abstract

The application relates to the technical field of energy consumption control, and discloses an environmental engineering energy consumption optimization control method based on big data analysis, which comprises the following steps: firstly, calculating the average flow velocity by using a sliding window; combining physical distance to calculate transmission delay, and extracting time sequence aligned concentration data; subsequently, fusing the aligned concentration and the square of the flow velocity to construct dynamic pressure density; calculating the running state curvature by using the relative change rate of the dynamic pressure density; at the same time, calculating the transient impact standard deviation based on the concentration discrete variance, and generating a compensation coefficient by combining the limit power; then, mapping the original energy consumption demand by comprehensively considering the reference power, the state curvature and the compensation coefficient; finally, obtaining the transient offset energy by integrating the original energy consumption difference, and constructing a normalized exponential kernel weight; performing discrete convolution smoothing on the original energy consumption by using the kernel weight, and converting the optimal control frequency according to the fluid mechanical proportionality law.

Description

Technical Field

[0001] This invention relates to the field of energy consumption control technology, specifically to an environmental engineering energy consumption optimization and control method based on big data analysis. Background Technology

[0002] With increasingly stringent requirements for ecological and environmental protection, environmental treatment engineering has been widely applied. In environmental engineering projects such as wastewater treatment and waste gas scrubbing and purification, the energy consumption of core fluid machinery equipment, such as aeration blowers and circulating water pumps, accounts for a significant portion. Optimizing energy consumption control for these core devices is crucial for reducing the overall operating costs of environmental engineering projects. Currently, common existing technologies mainly rely on traditional preset parameter control schemes, such as static PID regulation or frequency conversion control technology based on simple thresholds. Some environmental engineering projects are also beginning to introduce conventional big data analysis or predictive models. However, existing technologies still have many shortcomings in complex real-world application scenarios. First, multi-source sensors in environmental engineering often exhibit differences in physical spatial distribution. The velocity and concentration sensors in the main inlet pipeline are physically distant from the core treatment equipment. Existing technologies often ignore the dynamic delay of pollutant transport with the fluid. This leads to misalignment of the collected parameters in the time dimension. The control system cannot achieve time-series alignment of heterogeneous data. This easily causes control commands to be issued ahead of or behind schedule. Second, flow velocity and pollutant concentration are heterogeneous data in different physical dimensions. Existing control methods often employ simple linear concatenation to directly establish energy consumption mappings. This approach ignores differences in data scale. When facing environmental engineering projects of varying scales, energy characterization is easily distorted due to data scale interference. This leads to highly inaccurate state assessments of system operating intensity. Furthermore, environmental treatment projects frequently face sudden high-load pollution shocks. Existing steady-state control logic reacts very slowly. Conventional big data models also struggle to accurately capture the sudden oscillations in pollution loads within local timeframes. When encountering a sudden surge of high-concentration pollution plumes, equipment cannot promptly and forcibly increase processing power. This easily causes momentary emissions exceeding standards in the engineering system. Finally, to cope with dynamically fluctuating processing demands, control systems often frequently adjust their output. However, directly sending drastically fluctuating raw energy consumption demands to the frequency converter generates abrupt frequency control commands. Long-term execution of such high-frequency oscillation commands by fluid machinery in environmental engineering can cause severe mechanical fatigue damage. Existing technologies lack adaptive smoothing mechanisms based on the system's own energy state. The system struggles to balance energy consumption optimization with the mechanical protection of underlying equipment hardware.

[0003] In summary, there is a need for an environmental engineering energy consumption optimization and control method based on big data analysis to address the problems existing in current technologies, such as spatiotemporal data misalignment, distortion of multidimensional feature mapping, weak resistance to sudden shocks, and mechanical fatigue caused by high-frequency commands. Summary of the Invention

[0004] This invention provides an environmental engineering energy consumption optimization and control method based on big data analysis, which helps to solve the problems mentioned in the background art.

[0005] This invention provides the following technical solution: an environmental engineering energy consumption optimization and control method based on big data analysis, comprising:

[0006] The mean velocity is calculated by averaging instantaneous flow velocity and sampled parameters using a sliding window.

[0007] Using the quotient of physical distance and uniform flow velocity as the transmission delay, the instantaneous concentration is shifted forward by this delay to extract the aligned concentration.

[0008] The dynamic pressure density is constructed by multiplying the aligned concentration by the square of the mean flow velocity, and the operating curvature is calculated from the relative rate of change of dynamic pressure density between adjacent time windows.

[0009] Calculate the discrete variance of the relative mean of the aligned concentration within the local window and take the square root to obtain the standard deviation of the transient shock.

[0010] The margin coefficient is calculated based on the limit and the reference power. The compensation coefficient is calculated by combining the impact standard deviation and the alignment concentration ratio.

[0011] The original energy consumption demand is mapped by multiplying the base power by the state curvature after adding the constant and the compensation coefficient respectively.

[0012] The transient offset energy is obtained by integrating the square of the difference between the original energy consumption and the baseline power, and then used as the exponential denominator to construct the normalized exponential kernel weight.

[0013] The optimal power is obtained by performing discrete convolution on the original energy consumption sequence with kernel weights. The cube root of the ratio of the optimal power to the reference power is then multiplied by the reference frequency to obtain the control frequency.

[0014] Optionally, the step of calculating the mean velocity using instantaneous flow velocity and sampled parameters through a sliding window averaging operation includes:

[0015] Flow velocity sensors and pollutant concentration density sensors are installed at the main inlet pipe of the environmental engineering project.

[0016] A power acquisition module and a frequency converter control interface are installed at the core processing equipment;

[0017] Obtain the set system sampling interval time and the corresponding number of sampling points, and start real-time collection of the instantaneous flow velocity at the current moment;

[0018] Obtain the direct measurement values ​​of instantaneous flow velocity corresponding to multiple sampling intervals backward from the current time;

[0019] The mean velocity within the current time window is obtained by summing the direct measurements of each instantaneous flow velocity within the current time window and then dividing the sum by the number of sampling points.

[0020] Optionally, the step of using the quotient of physical distance and uniform flow velocity as the transmission delay, and advancing the instantaneous concentration to extract the aligned concentration according to this delay, includes:

[0021] Obtain the physical distance constant between the sensors in the processing pipeline and begin real-time acquisition of instantaneous pollutant concentration density;

[0022] Divide the physical distance constant by the uniform flow velocity within the current time window to obtain the dynamic transmission delay time at the current time.

[0023] Extract the concentration density data directly measured by the sensor at the time corresponding to the dynamic transmission delay time calculated backward from the current time from the system's historical cache;

[0024] The extracted data is used as the pollutant concentration density aligned to the current processing section.

[0025] Optionally, the step of constructing the dynamic pressure density by multiplying the aligned concentration by the square of the mean flow velocity, and calculating the operating state curvature from the relative rate of change of dynamic pressure density in adjacent time windows, includes:

[0026] Get the set time window length constant;

[0027] The equivalent dynamic pressure density at the current moment is obtained by multiplying the pollutant concentration density aligned to the current treatment section by the square of the uniform flow velocity within the current time window.

[0028] Obtain the equivalent dynamic pressure density of a historical moment corresponding to a time window length constant calculated backward from the current moment;

[0029] The difference is obtained by subtracting the equivalent dynamic pressure density at the current moment from the equivalent dynamic pressure density at the previous moment, and then dividing the difference by the equivalent dynamic pressure density at the previous moment to obtain the curvature of the current running state.

[0030] Optionally, the step of calculating and square-taking the discrete variance of the aligned concentration relative to the mean within a local window to obtain the transient shock standard deviation includes:

[0031] Obtain the constant number of samples in the local moving window and the constant time interval for sampling;

[0032] Calculate the arithmetic mean of the pollutant concentration densities within the local moving window that are aligned to the current processing section;

[0033] Calculate the difference between each historical aligned concentration data distributed at sampling intervals within the window and the arithmetic mean;

[0034] Square each of the differences and then sum them.

[0035] Divide the summation result by the constant number of samples in the local moving window, and finally take the square root of the division result to obtain the transient shock standard deviation at the current moment.

[0036] Optionally, the margin coefficient calculated based on the limit and reference power, and the compensation coefficient calculated by combining the impact standard deviation and the alignment concentration ratio, include:

[0037] Obtain the baseline rated power of the core equipment under standard operating conditions and the maximum allowable power limit of the engineering equipment hardware;

[0038] The difference is obtained by subtracting the reference rated power from the maximum limit allowable power, and the margin factor is obtained by dividing the difference by the reference rated power.

[0039] The deviation ratio is obtained by dividing the current transient shock standard deviation by the pollutant concentration density aligned to the current treatment section.

[0040] Multiply the deviation ratio by the margin coefficient, and finally add a constant 1 to the product to obtain the current transient impact compensation coefficient.

[0041] Optionally, the step of multiplying the reference power by the state curvature after adding the constant and the compensation coefficient respectively to map the original energy consumption demand includes:

[0042] Add a constant 1 to the curvature of the running state at the current moment to obtain the state curvature addition term;

[0043] The original energy consumption demand state quantity at the current moment is calculated by multiplying the core equipment's base rated power under standard operating conditions, the state curvature addition term, and the current transient impact compensation coefficient.

[0044] Optionally, the step of integrating the square of the difference between the original energy consumption and the reference power to obtain the transient offset energy, and using it as the denominator of the exponent to construct the normalized exponential kernel weight, includes:

[0045] Calculate the difference between the original energy consumption demand state quantity and the baseline rated power for each historical sequence within the local moving window recorded in the cache;

[0046] After squaring each of the differences, multiply them by the sampling interval time constant, and sum all the product results to obtain the transient offset energy integral;

[0047] The numerator is obtained by multiplying the number of time backtracking steps, the sampling interval time constant, and the square of the reference rated power.

[0048] Divide the numerator by the transient offset energy integral and take the negative value of the division result as the exponent of the natural exponential function to obtain the kernel function exponent term corresponding to the time backtracking step number;

[0049] The normalized denominator is obtained by summing the kernel function exponents corresponding to all time backsteps within the local moving window.

[0050] Divide the kernel function exponent corresponding to a single time step backward by the normalized denominator to obtain the convolution kernel distribution weights corresponding to the time step backward.

[0051] Optionally, the step of performing discrete convolution on the original energy consumption sequence with kernel weights to obtain the optimal power, and taking the cube root of its ratio to the reference power and multiplying it by the reference frequency as the control frequency, includes:

[0052] Obtain the known constant of the reference operating frequency of the core equipment;

[0053] Multiply the elements of the convolution kernel distribution weight matrix corresponding to each backstep at each time with the original energy consumption demand state of the historical sequence recorded in the corresponding cache, and sum all the product results to obtain the optimal target power command after smoothing and filtering.

[0054] Divide the optimal target power command by the reference rated power to obtain the ratio, and calculate the cube root of the ratio.

[0055] Finally, the cube root is multiplied by the known constant of the core equipment's reference operating frequency to obtain the final control frequency command for directly driving the frequency converter.

[0056] The present invention has the following beneficial effects:

[0057] 1. By collecting real-time flow velocity and pollutant concentration data from the main inlet pipeline of environmental engineering projects, and combining this with the basic power parameters of the core treatment equipment, this technical solution constructs a complete automatic frequency conversion control link, from data spatiotemporal alignment, system load perception, pollution mutation compensation, to smooth command output. In specific environmental engineering application scenarios such as large-scale wastewater treatment or industrial waste gas scrubbing and purification, there is often a significant physical distance between the monitoring sensors at the front end of the inlet pipeline and the core treatment equipment such as aerators and circulating water pumps at the back end. This directly leads to a serious misalignment between the pollutant concentration data measured at the front end and the actual time it takes for the pollutants to reach the treatment equipment. Furthermore, complex environmental treatment systems are highly susceptible to unpredictable sudden surges in high-concentration pollutants. Directly adjusting the operation of fans or pumps frequently and drastically based on these drastically fluctuating instantaneous concentration data would cause highly destructive mechanical fatigue and hardware wear. The reason for designing this comprehensive control scheme is precisely to fundamentally overcome these mutually constraining engineering physical dilemmas. This innovative solution utilizes real-time uniform flow velocity and physical distance to calculate dynamic transmission delay, precisely shifting historical concentration data forward on the timeline. This perfectly eliminates timing errors caused by physical spatial distribution, ensuring "eye-hand synchronization" of the underlying control system. More uniquely, addressing the highly destructive challenge of sudden "pollution plumes" in this specific environment, the solution does not rely on slow steady-state assessments. Instead, it keenly captures the discrete variance of concentration data within a local timeframe and, in conjunction with the equipment's own physical power limit threshold, dynamically calculates the transient impact compensation coefficient. This mechanism endows the system with the ability to instantly and safely increase processing power when encountering extreme high-load sudden changes, constructing a robust shock-resistant defense and fundamentally curbing the risk of exceeding environmental emission standards. Finally, to completely mitigate the fatal damage caused by high-intensity regulation commands to heavy machinery, the solution extracts the characteristics of the system's own energy fluctuations as an attenuation factor, constructing a dynamically adaptive smoothing filter kernel. This process extremely smoothly "softens" the original, intense energy consumption demands and strictly follows fluid mechanics laws to transform them into control frequencies. This mechanism ensures a swift and efficient response to environmental pollution control while greatly eliminating the step and abrupt changes in frequency conversion control signals. It provides excellent flexible mechanical protection for the underlying core electromechanical equipment, thereby significantly reducing the overall energy consumption of the system and extending the service life of the hardware facilities while ensuring that environmental treatment meets absolute standards.

[0058] 2. By simultaneously installing flow velocity and concentration sensors at the main inlet pipe of the environmental engineering system, and configuring corresponding power and frequency conversion acquisition and control interfaces at the core processing equipment, the continuous instantaneous flow velocity is subjected to sliding window summation and average division calculations based strictly on the set sampling interval and number of sampling points. This effectively filters out the high-frequency noise and pulsation interference that inevitably accompany direct measurement by the underlying hardware sensors in complex fluid physics environments. This processing method, which converts instantaneous physical quantities into uniform characteristics within a local time window, greatly smooths the transient disordered oscillations at the fluid dynamics level, laying an extremely stable and solid physical velocity benchmark for subsequent cross-spatial dynamic transmission delay calculations. At the same time, the real-time extraction of uniform flow velocity completely avoids the severe distortion of the underlying control benchmark caused by the direct participation of single abnormal extreme value data in subsequent calculations. This makes the entire environmental engineering underlying data acquisition system more fault-tolerant and environmentally resistant to interference, thus ensuring that the high-dimensional data stream input to the control system has extremely high numerical stability and objective reliability even in extremely harsh industrial monitoring environments.

[0059] 3. By dividing the acquired pipeline physical distance constant by the mean flow velocity within the current time window, the dynamic transmission delay time of the fluid is accurately calculated in real time. This dynamic delay time is then used to perform precise time backward calculation and displacement extraction in the system's historical cache data sequence. The extracted historical concentration data is then used as the aligned concentration of the current processing section, fundamentally overcoming the severe misalignment of the data time axis caused by the dispersed physical spatial locations of multi-source sensors in environmental engineering. This forced displacement mapping process from spatial features to temporal features accurately restores the physical dynamic hysteresis process of the real fluid medium transmission in a closed pipeline, ensuring that the concentration state faced by the core processing equipment at the current moment is the same batch of pollutants that were actually measured by the sensor at the inlet end. This rigorous spatiotemporal feature alignment process completely eliminates the phenomenon of system response being ahead or behind due to blind spots in physical spatial span. This allows all subsequent state assessments and energy consumption calculations to be closely based on the same frequency characteristic data at the same physical spatiotemporal interface, greatly improving the underlying rigor of multi-dimensional heterogeneous sensor data fusion and the objectivity and authenticity of the system's operating logic.

[0060] 4. By multiplying the spatiotemporally aligned pollutant concentration density with the square of the mean flow velocity, an equivalent dynamic pressure density representing the true energy scale of the environmental fluid at the current moment is constructed. Furthermore, the ratio of the dynamic difference between equivalent dynamic pressure densities in adjacent time windows to the historical dynamic pressure density is extracted as the operating state curvature. This cleverly integrates and reduces the heterogeneous characteristics of flow velocity and concentration in different physical dimensions into a unified fluid dynamic energy measurement category. This deep multiplicative fusion process realistically reflects the comprehensive destructive energy state of the fluid medium carrying the pollutant load. The further calculation of the relative change rate completely eliminates the data scale interference caused by the absolute range limitation of sensors or the difference in project hardware scale in different environmental treatment projects. Using this dimensionless relative operating state curvature to directly characterize the dynamic geometric growth or decay trend of the actual treatment load of the system, it can not only extremely sensitively capture the abnormal operating deviations that accumulate slowly within the fluid environment, but also effectively shield the long-term drift of the absolute numerical baseline under normal operating conditions. This gives the core optimization algorithm a strong cross-scenario operating condition generalization and adaptability capability and objective accuracy in judging the operating intensity state.

[0061] 5. By calculating the local arithmetic mean of multiple aligned concentrations continuously collected within a local moving window, and calculating the sum of squares of the differences between each historical aligned concentration sequence and the local mean, and then using division scaling and square root processing to accurately obtain the transient impact standard deviation, this method can quantify and capture the extreme abrupt changes and discrete oscillation characteristics of pollutant concentration density within a specific time range using extremely rigorous and highly sensitive mathematical statistical methods. This deep extraction method, which focuses on local high-frequency variance characteristics, completely makes up for the fundamental technical shortcomings of conventional steady-state mean load assessment algorithms, which are severely slow to respond to sudden, high-risk, high-concentration pollution plumes. As the core mathematical geometric metric most sensitive to the degree of environmental deviation from the mean, the transient impact standard deviation directly locks into the extreme abnormal change behavior of the fluid concentration at the system inlet, providing an absolutely objective and quantitatively accurate underlying benchmark for judging the impact intensity for the subsequent construction of a targeted emergency protective underlying power compensation mechanism. This greatly improves the dynamic perception breadth and rapid response sensitivity of heavy environmental control systems when facing unpredictable severe impact conditions.

[0062] 6. By pre-extracting the baseline rated power of the core equipment and the maximum allowable power indicated on the hardware nameplate, the inherent margin coefficient characterizing the upper limit of the equipment's physical potential is accurately calculated. The aforementioned transient impact standard deviation and the real-time deviation ratio of the aligned concentration density are then multiplied and reconstructed using this margin coefficient. This ultimately generates a transient impact compensation coefficient that dynamically adapts to the rapidly changing operating conditions, enabling the system to intelligently and absolutely safely force an increase in the operating power of the underlying equipment when facing a sudden impact of high-risk pollution loads. This calculation logic, based entirely on the multiplication coefficient mapping of the hardware's physical limits, fully and unreservedly releases the core fluid machinery's potential in response to... For idle redundant processing capabilities under emergency high-pressure conditions, the natural physical constraint effect of introducing a margin upper limit parameter strictly prevents serious engineering accidents such as overheating or even burning of core motors due to excessive compensation by the control algorithm. The characteristic ratio representing transient fluctuations of fluids is deeply bound to the expansion and fault tolerance potential allowed by the underlying hardware, ensuring that the generated final compensation coefficient can resist the impact of various sudden extreme environmental deterioration with the most aggressive and compliant response under the premise of fully guaranteeing the absolute operational safety of physical electromechanical equipment. Thus, at the algorithm level, the risk of instantaneous environmental physical emissions exceeding the standard caused by the overly conservative response of conventional control systems is resolutely eliminated.

[0063] 7. By adding a constant of 1 to the state curvature, which characterizes the overall trend of slow, cumulative environmental changes, to a state curvature additive term, and then performing a nonlinear multiplication operation with the core equipment's baseline rated power and the transient impact compensation coefficient specifically designed to cope with sudden extreme and severe operating conditions, the original energy consumption demand state quantities that the underlying system must possess under the current complex and intertwined environmental conditions are accurately and completely mapped. This successfully constructs a dual-track driven energy consumption comprehensive extrapolation model within the core main control algorithm, which balances steady-state accurate tracking and strong transient suppression. This deep multiplicative joint mapping mechanism ensures that the energy consumption fluctuations of the underlying equipment under normal stable conditions are completely controlled by pure flow. The curvature change law of the body's energy state firmly ensures the ultimate energy-saving effect of the daily stable operation cycle. However, once the sensor network detects and encounters a local sudden severe pollution impact, the multiplicative geometric amplification effect of the compensation coefficient will instantly take over the dominant position of energy consumption, and extremely rapidly push up the overall original energy consumption demand expectation defense line. This transformation process, which deeply integrates the multi-dimensional and cross-frequency band environmental composite load characteristics and directly reduces the dimension to project into the theoretical power index of the underlying hardware single operation, makes the final generated theoretical energy consumption demand data series contain both flexible adaptive adjustment to the macro long-term operation trend and rigid defense and resistance mechanism against micro-level severe sudden disturbances.

[0064] 8. By performing discrete integral summation based on the system sampling interval constant on the square of the dynamic difference between the original energy consumption demand state quantity and the baseline rated power within the local moving buffer window, the transient offset energy integral is obtained. This energy integral value is then creatively used as the denominator parameter of the natural exponential function, thereby constructing an extremely smooth and self-normalized exponential convolution kernel distribution weight sequence. This successfully forms an adaptive smoothing mechanism within the deep architecture of the system that dynamically adjusts the filtering intensity entirely based on the degree of endogenous energy oscillation. When the calculated transient offset energy increases sharply, it indicates that the current system is experiencing severe disorder in the original demand instructions. During periods of high risk and disruption due to fluctuations and frequent step jumps, the deviation energy in the denominator of the exponential term forces the overall exponential decay to become more gradual, thereby automatically increasing the proportion of historical data in the current weight matrix allocation and greatly enhancing the low-pass filtering physical damping of harmful oscillations. This design, which completely abandons external static preset filters, gives the system a high degree of self-sensing and numerical self-convergence protection characteristics. It can spontaneously weave a time-dimensional buffer protection network based entirely on the real-time turbulence intensity of its own operation control commands, and ingeniously uses the mathematical limit law to completely cut off the transmission path of high-frequency harmful oscillation commands to the mechanical execution end.

[0065] 9. By using the dynamically generated adaptive exponential kernel weight matrix from the aforementioned steps, discrete convolution operations are performed on the original energy consumption demand sequence within the local buffer window to thoroughly filter out potential malicious steps in the signal waveform and obtain an extremely smooth optimal target power command. Simultaneously, strictly adhering to the objective physical laws of large-scale fluid processing machinery, a rigorous cube root operation is performed on the ratio of the optimal power command to the reference power, and this is multiplied and mapped to the core equipment's reference operating frequency to generate the final frequency conversion control command. This perfectly bridges the gap between abstract data deduction and the execution of underlying physical actions; the high-intensity discrete convolution smoothing process achieves millisecond-level smoothness. At the second level, it completely eliminates spike signal disturbances that could cause severe mechanical fatigue and structural fracture in heavy-duty fans or water pumps, ensuring the absolute smoothness of the control signals sent. Finally, by using extremely rigorous cube root mathematical operations for frequency conversion, it completely eliminates the crude and distorted subjective linear mapping behavior in conventional engineering control logic, ensuring that every hertz of the optimized operating frequency can perfectly match the cubic proportional relationship between the physical power and speed of the actual centrifugal machinery. Thus, while achieving flexible protection in the environmental governance process, it truly delivers the most realistic long-term physical energy saving and life extension effect of the underlying heavy-duty hardware. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of the basic process of the present invention.

[0067] Figure 2 This is a flowchart of the spatiotemporal alignment and transmission delay processing of the present invention.

[0068] Figure 3 This is a flowchart of the dual-track feature fusion and energy consumption mapping process of the present invention.

[0069] Figure 4 This is a flowchart of the adaptive smoothing filtering and instruction output of the present invention. Detailed Implementation

[0070] 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.

[0071] Example 1, refer to Figure 1 An environmental engineering energy consumption optimization and control method based on big data analysis includes:

[0072] The mean velocity is calculated by averaging instantaneous flow velocity and sampled parameters using a sliding window.

[0073] Using the quotient of physical distance and uniform flow velocity as the transmission delay, the instantaneous concentration is shifted forward by this delay to extract the aligned concentration.

[0074] The dynamic pressure density is constructed by multiplying the aligned concentration by the square of the mean flow velocity, and the operating curvature is calculated from the relative rate of change of dynamic pressure density between adjacent time windows.

[0075] Calculate the discrete variance of the relative mean of the aligned concentration within the local window and take the square root to obtain the standard deviation of the transient shock.

[0076] The margin coefficient is calculated based on the limit and the reference power. The compensation coefficient is calculated by combining the impact standard deviation and the alignment concentration ratio.

[0077] The original energy consumption demand is mapped by multiplying the base power by the state curvature after adding the constant and the compensation coefficient respectively.

[0078] The transient offset energy is obtained by integrating the square of the difference between the original energy consumption and the baseline power, and then used as the exponential denominator to construct the normalized exponential kernel weight.

[0079] The optimal power is obtained by performing discrete convolution on the original energy consumption sequence with kernel weights. The cube root of the ratio of the optimal power to the reference power is then multiplied by the reference frequency to obtain the control frequency.

[0080] The calculation of mean velocity using instantaneous flow velocity and sampled parameters through a sliding window averaging operation includes:

[0081] Flow velocity sensors and pollutant concentration density sensors are installed at the main inlet pipe of the environmental engineering project.

[0082] A power acquisition module and a frequency converter control interface are installed at the core processing equipment;

[0083] Obtain the set system sampling interval time and the corresponding number of sampling points, and start real-time collection of the instantaneous flow velocity at the current moment;

[0084] Obtain the direct measurement values ​​of instantaneous flow velocity corresponding to multiple sampling intervals backward from the current time;

[0085] The mean velocity within the current time window is obtained by summing the direct measurements of each instantaneous flow velocity within the current time window and then dividing the sum by the number of sampling points.

[0086] The method of using the quotient of physical distance and uniform flow velocity as the transmission delay, and extracting aligned concentrations by shifting the instantaneous concentration forward according to this delay, includes:

[0087] Obtain the physical distance constant between the sensors in the processing pipeline and begin real-time acquisition of instantaneous pollutant concentration density;

[0088] Divide the physical distance constant by the uniform flow velocity within the current time window to obtain the dynamic transmission delay time at the current time.

[0089] Extract the concentration density data directly measured by the sensor at the time corresponding to the dynamic transmission delay time calculated backward from the current time from the system's historical cache;

[0090] The extracted data is used as the pollutant concentration density aligned to the current processing section.

[0091] The process of constructing the dynamic pressure density by multiplying the aligned concentration by the square of the mean flow velocity, and calculating the operating state curvature from the relative rate of change of dynamic pressure density in adjacent time windows, includes:

[0092] Get the set time window length constant;

[0093] The equivalent dynamic pressure density at the current moment is obtained by multiplying the pollutant concentration density aligned to the current treatment section by the square of the uniform flow velocity within the current time window.

[0094] Obtain the equivalent dynamic pressure density of a historical moment corresponding to a time window length constant calculated backward from the current moment;

[0095] The difference is obtained by subtracting the equivalent dynamic pressure density at the current moment from the equivalent dynamic pressure density at the previous moment, and then dividing the difference by the equivalent dynamic pressure density at the previous moment to obtain the curvature of the current running state.

[0096] The calculation of the discrete variance and square root of the relative mean of the aligned concentration within the local window to obtain the standard deviation of the transient impact includes:

[0097] Obtain the constant number of samples in the local moving window and the constant time interval for sampling;

[0098] Calculate the arithmetic mean of the pollutant concentration densities within the local moving window that are aligned to the current processing section;

[0099] Calculate the difference between each historical aligned concentration data distributed at sampling intervals within the window and the arithmetic mean;

[0100] Square each of the differences and then sum them.

[0101] Divide the summation result by the constant number of samples in the local moving window, and finally take the square root of the division result to obtain the transient shock standard deviation at the current moment.

[0102] The margin coefficient, calculated based on the limit and reference power, is used to derive the compensation coefficient from the ratio of the impact standard deviation to the alignment concentration, including:

[0103] Obtain the baseline rated power of the core equipment under standard operating conditions and the maximum allowable power limit of the engineering equipment hardware;

[0104] The difference is obtained by subtracting the reference rated power from the maximum limit allowable power, and the margin factor is obtained by dividing the difference by the reference rated power.

[0105] The deviation ratio is obtained by dividing the current transient shock standard deviation by the pollutant concentration density aligned to the current treatment section.

[0106] Multiply the deviation ratio by the margin coefficient, and finally add a constant 1 to the product to obtain the current transient impact compensation coefficient.

[0107] The process of multiplying the base power by the state curvature (after adding constants) and the compensation coefficient to map the original energy consumption demand includes:

[0108] Add a constant 1 to the curvature of the running state at the current moment to obtain the state curvature addition term;

[0109] The original energy consumption demand state quantity at the current moment is calculated by multiplying the core equipment's base rated power under standard operating conditions, the state curvature addition term, and the current transient impact compensation coefficient.

[0110] The transient offset energy is obtained by integrating the square of the difference between the original energy consumption and the reference power, and then used as the denominator of the exponent to construct the normalized exponent kernel weight, including:

[0111] Calculate the difference between the original energy consumption demand state quantity and the baseline rated power for each historical sequence within the local moving window recorded in the cache;

[0112] After squaring each of the differences, multiply them by the sampling interval time constant, and sum all the product results to obtain the transient offset energy integral;

[0113] The numerator is obtained by multiplying the number of time backtracking steps, the sampling interval time constant, and the square of the reference rated power.

[0114] Divide the numerator by the transient offset energy integral and take the negative value of the division result as the exponent of the natural exponential function to obtain the kernel function exponent term corresponding to the time backtracking step number;

[0115] The normalized denominator is obtained by summing the kernel function exponents corresponding to all time backsteps within the local moving window.

[0116] Divide the kernel function exponent corresponding to a single time step backward by the normalized denominator to obtain the convolution kernel distribution weights corresponding to the time step backward.

[0117] The process of performing discrete convolution on the original energy consumption sequence using kernel weights to obtain the optimal power, and then multiplying the cube root of its ratio to the baseline power by the baseline frequency to obtain the control frequency, includes:

[0118] Obtain the known constant of the reference operating frequency of the core equipment;

[0119] Multiply the elements of the convolution kernel distribution weight matrix corresponding to each backstep at each time with the original energy consumption demand state of the historical sequence recorded in the corresponding cache, and sum all the product results to obtain the optimal target power command after smoothing and filtering.

[0120] Divide the optimal target power command by the reference rated power to obtain the ratio, and calculate the cube root of the ratio.

[0121] Finally, the cube root is multiplied by the known constant of the core equipment's reference operating frequency to obtain the final control frequency command for directly driving the frequency converter.

[0122] Example 2 provides a more specific method for optimizing and controlling energy consumption in environmental engineering based on big data analysis. The core steps are detailed below:

[0123] The calculation of mean velocity using instantaneous flow velocity and sampled parameters through a sliding window averaging operation includes:

[0124] The real-time flow velocity data collected by the sensor contains high-frequency noise, and direct use of it will cause severe oscillations in subsequent time delay calculations. The purpose of this step is to perform digital homogenization processing on the flow velocity using a time window, providing a stable physical transmission velocity benchmark for subsequent spatiotemporal feature alignment.

[0125] Initially, flow rate sensors and pollutant concentration density sensors need to be installed at the main inlet pipe of the environmental engineering, and power acquisition modules and frequency converter control interfaces need to be installed at the core treatment equipment (such as aeration blowers or circulating water pumps).

[0126] Get the set system sampling interval time and the corresponding number of sampling points and began real-time data collection. Instantaneous flow rate at any moment ;

[0127] To eliminate high-frequency pulsations from the flow velocity sensor, the mean flow velocity at the current moment is calculated using historical data from a sliding window, as shown in the following formula:

[0128] In the formula: for The uniform flow velocity within the time window at a given moment; The number of sampling points to be obtained; To calculate backwards from the current time The instantaneous flow velocity is directly measured at each sampling interval; The system sampling interval time is used to obtain the data.

[0129] By simultaneously installing flow velocity and concentration sensors at the main inlet pipe of environmental engineering, and configuring corresponding power and frequency conversion acquisition and control interfaces at the core processing equipment, the continuous instantaneous flow velocity is subjected to sliding window summation and average division operations based strictly on the set sampling interval and number of sampling points. This effectively filters out the high-frequency noise and pulsation interference that inevitably accompany direct measurement by the underlying hardware sensors in complex fluid physics environments. This processing method, which converts instantaneous physical quantities into uniform characteristics within a local time window, greatly smooths the transient disordered oscillations at the fluid dynamics level, laying an extremely stable and solid physical velocity benchmark for subsequent dynamic transmission delay calculations across spatial dimensions. At the same time, the real-time extraction of uniform flow velocity completely avoids the severe distortion of the underlying control benchmark caused by the direct participation of single abnormal extreme value data in subsequent calculations. This makes the entire environmental engineering underlying data acquisition system more fault-tolerant and environmentally resistant to interference, thus ensuring that the high-dimensional data stream input to the control system has extremely high numerical stability and objective reliability even in extremely harsh industrial monitoring environments.

[0130] Further reference Figure 2 The step of using the quotient of physical distance and uniform flow velocity as the transmission delay, and extracting aligned concentrations by shifting the instantaneous concentration forward according to this delay, includes:

[0131] In environmental engineering, it takes physical time for pollutants measured by concentration sensors to reach the core treatment equipment area. This step aims to address the uncommon problem of "data timing asynchrony due to physical distance." By calculating the dynamic delay time, past concentration data is mapped onto the current fluid cross-section, achieving alignment of spatial features with temporal features.

[0132] Obtain the physical distance constant between the sensors in the processing pipeline. It also began collecting instantaneous pollutant concentration and density data in real time. ;

[0133] To determine the time span of pollutant transport from the measurement point to the treatment point, the dynamic transport delay time is first calculated based on the physical distance of the pipeline and the uniform flow velocity. The formula is as follows:

[0134] In the formula: for The dynamic transmission delay time at any given moment; To handle the physical distance constant between pipe sensors; for The uniform flow velocity within the time window at a given moment;

[0135] After obtaining the dynamic transmission delay time, the historical concentration data is shifted forward by this delay time to construct a concentration state sequence aligned with spatiotemporal features, as shown in the following formula:

[0136] In the formula: To align with the contaminant concentration density of the current processing section; For the system's historical cache, the data from the time before the current time. The concentration density data is directly measured by the second-hour sensor.

[0137] By dividing the acquired pipeline physical distance constant by the mean flow velocity within the current time window, the dynamic fluid transmission delay time is accurately calculated in real time. This dynamic delay time is then used to perform precise time backward calculation and displacement extraction in the system's historical cache data sequence. The extracted historical concentration data is then used as the aligned concentration for the current processing section, fundamentally overcoming the severe misalignment of the data time axis caused by the dispersed physical locations of multiple sensors in environmental engineering. This forced displacement mapping process from spatial features to temporal features accurately restores the physical dynamic hysteresis process of the actual fluid medium transmission within a closed pipeline, ensuring that the concentration state faced by the core processing equipment at the current moment is precisely the batch of pollutants actually measured by the sensor at the inlet. This rigorous spatiotemporal feature alignment completely eliminates the phenomenon of system response being ahead or behind due to blind spots in physical space, allowing all subsequent state assessments and energy consumption calculations to be closely centered on the same frequency characteristic data at the same physical spatiotemporal interface. This greatly enhances the underlying rigor of multi-dimensional heterogeneous sensor data fusion and the objectivity and authenticity of the system's operational logic.

[0138] Further reference Figure 3 The step of constructing the dynamic pressure density by multiplying the aligned concentration by the square of the mean flow velocity, and calculating the operating state curvature from the relative rate of change of dynamic pressure density in adjacent time windows, includes:

[0139] Directly mapping high-dimensional, multi-source data (flow rate and concentration) to energy consumption is highly susceptible to distortion due to differences in data scale. This step aims to address this less common issue by merging aligned concentration and uniform flow rate into a "fluid dynamic pressure density" and calculating its relative rate of change, which serves as the sole indicator reflecting the actual operating load intensity of the system.

[0140] Get the set time window length for calculating the mean. ;

[0141] To reduce the dimensionality of concentration and flow velocity to a unified energy physics framework, the equivalent dynamic pressure density of the ambient fluid at the current moment is calculated using the following formula:

[0142] In the formula: for The equivalent dynamic pressure density at a given time; To align with the contaminant concentration density of the current processing section; for The uniform flow velocity within the time window at a given moment;

[0143] To eliminate scale interference caused by absolute values ​​and assess the dynamic fluctuation trend of the load, the operating state curvature is calculated using the current equivalent dynamic pressure density and the equivalent dynamic pressure density of the previous time window. The formula is as follows:

[0144] In the formula: for The curvature of the running state at any given moment; for The equivalent dynamic pressure density at a given time; The distance from the current time Equivalent dynamic pressure density at previous historical moments; This is a constant representing the length of the time window.

[0145] By multiplying the spatiotemporally aligned pollutant concentration density with the square of the mean flow velocity, an equivalent dynamic pressure density representing the true energy scale of the environmental fluid at the current moment is constructed. Furthermore, the ratio of the dynamic difference between equivalent dynamic pressure densities in adjacent time windows to the historical dynamic pressure density is extracted as the operating state curvature. This cleverly integrates and reduces the heterogeneous characteristics of flow velocity and concentration across different physical dimensions into a unified fluid dynamic energy measurement framework. This deep multiplicative fusion process realistically reflects the comprehensive destructive energy state of the fluid medium carrying the pollutant load. The further calculation of the relative rate of change completely eliminates data scale interference caused by sensor absolute range limitations or differences in project hardware scale in different environmental treatment projects. Utilizing this dimensionless relative operating state curvature to directly characterize the dynamic geometric growth or decay trend of the system's actual treatment load not only allows for extremely sensitive detection of slowly accumulating abnormal operating deviations within the fluid environment but also effectively shields the long-term drift of the absolute numerical baseline under normal system conditions. This endows the core optimization algorithm with strong cross-scenario adaptability and objective accuracy in determining operating intensity.

[0146] The calculation of the discrete variance and square root of the relative mean of the aligned concentration within the local window to obtain the standard deviation of the transient impact includes:

[0147] Simply relying on state curvature is slow to respond to sudden, high-concentration pollution bursts and is prone to failure. This step aims to address this uncommon problem by quantifying the dispersion (standard deviation) of aligned concentrations within a local time window to accurately capture the abrupt oscillation characteristics of pollutants, serving as a compensation benchmark.

[0148] Obtain the number of local moving window samples based on the buffer capacity setting of the PLC controller. ;

[0149] To accurately characterize the severity of sudden changes in pollution load within a local time period, the standard deviation of the transient impact is calculated based on the aligned concentration within the local moving window, using the following formula:

[0150] In the formula: for The standard deviation of transient impact at time t; The number of samples in the local moving window is a constant; Historical aligned concentration data; For this local moving window The arithmetic mean of the aligned concentrations; This is the sampling interval time constant.

[0151] By calculating the local arithmetic mean of multiple aligned concentrations continuously collected within a local moving window, and then calculating the sum of squares of the differences between each historical aligned concentration sequence and this local mean, and finally using division scaling and square root processing to accurately obtain the transient impact standard deviation, this method can quantify and capture the extreme abrupt changes and discrete oscillation characteristics of pollutant concentration density within a specific time range using extremely rigorous and highly sensitive mathematical statistical methods. This deep extraction method, which focuses on local high-frequency variance characteristics, completely makes up for the fundamental technical shortcomings of conventional steady-state mean load assessment algorithms, which are severely slow to respond to sudden, high-risk, high-concentration pollution plumes. As the core mathematical geometric metric most sensitive to the degree of environmental deviation from the mean, the transient impact standard deviation directly locks into the extreme abnormal change behavior of fluid concentration at the system inlet, providing an absolutely objective and quantitatively accurate underlying benchmark for judging the impact intensity for the subsequent construction of a targeted emergency protective underlying power compensation mechanism. This greatly improves the dynamic perception breadth and rapid response sensitivity of heavy environmental control systems when facing unpredictable severe impact conditions.

[0152] The margin coefficient, calculated based on the limit and reference power, is used to derive the compensation coefficient from the ratio of the impact standard deviation to the alignment concentration, including:

[0153] The impact standard deviation is converted into a dimensionless compensation multiplier, which directly acts on the system's energy baseline, thereby forcibly increasing the treatment power when facing high-risk pollution impacts to ensure compliance with emission standards.

[0154] Obtain the reference rated power of the core equipment under standard operating conditions. and the maximum permissible power of engineering equipment hardware ;

[0155] To generate compensation coefficients that dynamically adapt to transient changes, a ratio mapping is established using the transient shock standard deviation and the current aligned concentration, as shown in the following formula:

[0156] In the formula: This is the transient impact compensation coefficient; for The standard deviation of transient impact at time t; To align with the contaminant concentration density of the current processing section; This refers to the maximum permissible power limit of the engineering equipment hardware. This refers to the reference rated power of the core equipment under standard operating conditions.

[0157] By pre-extracting the baseline rated power of core equipment and the maximum permissible power indicated on the hardware nameplate, an inherent margin coefficient characterizing the upper limit of the equipment's physical potential is accurately calculated. This margin coefficient is then multiplied and reconstructed using the previously obtained transient impact standard deviation and the real-time deviation ratio of the aligned concentration density, ultimately generating a transient impact compensation coefficient that dynamically adapts to rapidly changing operating conditions. This allows the system to intelligently and absolutely safely force an increase in the operating power of underlying equipment when facing sudden impacts from high-risk pollution loads. This calculation logic, based entirely on multiplication coefficient mapping of hardware physical limits, fully and unreservedly releases the core fluid machinery's capacity to cope with... The idle redundancy processing capability under emergency high pressure conditions, through the natural physical clamping effect of introducing the margin upper limit parameter, strictly prevents serious engineering accidents such as overheating or even burning of the core motor due to excessive compensation by the control algorithm; by deeply binding the characteristic ratio representing transient fluctuations of fluid with the expansion and fault tolerance potential allowed by the underlying hardware, it ensures that the generated final compensation coefficient can resist the impact of various sudden extreme environmental deterioration with the most aggressive and compliant response under the premise of fully guaranteeing the absolute operational safety of physical electromechanical equipment, thereby resolutely eliminating the risk of instantaneous environmental physical emissions exceeding the standard caused by the overly conservative response of conventional control systems at the algorithm level.

[0158] The process of multiplying the base power by the state curvature (after adding constants) and the compensation coefficient to map the original energy consumption demand includes:

[0159] By combining the steady-state load curvature and the transient impact compensation coefficient, the reference power is directly mapped to the theoretical operating power required at present.

[0160] To derive the theoretical operating energy consumption that the system must currently possess, a nonlinear multiplication mapping calculation is performed on the baseline rated power, operating curvature, and transient impact compensation coefficient. The formula is as follows:

[0161] In the formula: for The state quantity of the original energy consumption demand at any given moment; This refers to the baseline rated power of the core equipment under standard operating conditions. for The curvature of the running state at any given moment; This is the transient impact compensation coefficient.

[0162] By adding a constant 1 to the state curvature, which characterizes the overall trend of slow, cumulative environmental changes, to transform it into a state curvature additive term, and then performing a nonlinear multiplication operation with the core equipment's baseline rated power and the transient impact compensation coefficient specifically designed to cope with sudden extreme and severe operating conditions, the original energy consumption demand state quantities that the underlying system must possess under the current complex and intertwined environmental conditions are accurately and completely mapped. This successfully constructs a dual-track driven energy consumption comprehensive extrapolation model within the core main control algorithm, which balances steady-state accurate tracking and strong transient suppression. This deep multiplicative joint mapping mechanism ensures that the energy consumption fluctuations of the underlying equipment under normal stable conditions are completely controlled by pure fluid dynamics. The curvature change law of mechanical energy state firmly guarantees the ultimate compression and energy saving effect of daily stable operation cycle. However, once the sensor network detects and encounters a local sudden severe pollution impact, the multiplicative geometric amplification effect of the compensation coefficient will instantly take over the dominant position of energy consumption, and extremely rapidly push up the overall original energy consumption demand expectation defense line. This transformation process of deeply mathematically integrating multi-dimensional and cross-frequency band environmental composite load characteristics and directly reducing the dimension to project into the theoretical power index of the underlying hardware single operation makes the final generated theoretical energy consumption demand data series contain both flexible adaptive adjustment to macro long-term operation trends and rigid defense and resistance mechanism against micro-level severe sudden disturbances.

[0163] Further reference Figure 4 The process of integrating the square of the difference between the original energy consumption and the reference power to obtain the transient offset energy, and using it as the denominator of the exponent to construct the normalized exponential kernel weight, includes:

[0164] Directly executing raw energy consumption commands can lead to frequent step changes in control signals due to data fluctuations, resulting in severe fatigue damage to electromechanical actuators. This solution does not rely on empirically preset filters. Instead, it extracts the system's local demand power fluctuations to construct transient energy consumption integrals and uses these integrals to calculate convolution kernel weights with adaptive time decay characteristics, thereby achieving endogenous smoothing suppression of high-frequency fluctuations.

[0165] First, the transient offset energy integral of the theoretical operating power deviating from the reference power within the moving window is calculated to characterize the degree of energy fluctuation within the system. The formula is as follows:

[0166] In the formula: This is the integral of the transient offset energy; The original energy consumption demand state quantity is the historical sequence recorded in the cache; This refers to the baseline rated power of the core equipment under standard operating conditions. The sampling interval time constant; The number of samples in the local moving window is a constant;

[0167] Next, using the transient offset energy integral as the bandwidth adjustment factor of the kernel function, a self-normalized exponential smoothing kernel function distribution weight is constructed, the formula of which is as follows:

[0168] In the formula: Number of steps backward in time The corresponding convolution kernel distribution weights; and All are summation control index constants; This refers to the baseline rated power of the core equipment under standard operating conditions. This is the integral of the transient offset energy; This is the sampling interval time constant.

[0169] By performing discrete integral summation based on the system sampling interval constant on the square of the dynamic difference between the original energy demand state quantity and the baseline rated power within the local moving buffer window, the transient offset energy integral is obtained. This energy integral value is then creatively used as the denominator parameter of the natural exponential function, thereby constructing an extremely smooth and self-normalized exponential convolution kernel distribution weight sequence. This successfully forms an adaptive smoothing mechanism within the deep system architecture that dynamically adjusts the filtering intensity entirely based on the degree of endogenous energy oscillation. When the calculated transient offset energy increases sharply, it indicates that the current system is experiencing a period of severe disorder in the original demand command. During periods of high-risk disruption caused by frequent step jumps, the deviation energy in the denominator of the exponential term forces the overall exponential decay to become more gradual, thereby automatically increasing the proportion of historical data in the current weight matrix allocation and greatly enhancing the low-pass filtering physical damping of harmful oscillations. This design, which completely abandons external static preset filters, gives the system a high degree of self-sensing and numerical self-convergence protection characteristics. It can spontaneously weave a time-dimensional buffer protection network based entirely on the real-time turbulence intensity of its own operation control commands, and ingeniously uses the mathematical limit law to completely cut off the transmission path of high-frequency harmful oscillation commands to the mechanical execution end.

[0170] The process of performing discrete convolution on the original energy consumption sequence using kernel weights to obtain the optimal power, and then multiplying the cube root of its ratio to the baseline power by the baseline frequency to obtain the control frequency, includes:

[0171] The adaptive kernel function is discretely convolved with the local original energy consumption sequence to obtain a smooth, step-free optimal power command, which is then explicitly converted into the final frequency control command issued to the frequency converter.

[0172] Obtain the reference operating frequency corresponding to the core equipment ;

[0173] To obtain the optimal power command to eliminate step mechanical damage, an adaptive kernel function is used to perform convolution filtering on the original energy demand sequence, as shown in the following formula:

[0174] In the formula: The optimal target power command after smoothing filtering; Number of steps backward in time The corresponding convolution kernel distribution weight matrix elements; The original energy consumption demand state quantity is the historical sequence recorded in the cache;

[0175] Finally, based on the objective physical laws governing the relationship between environmental engineering fluid dynamics load and motor speed, the smoothed optimal power is explicitly converted into the frequency converter control frequency to complete the issuance of system control commands. The formula is as follows:

[0176] In the formula: The final calculated control frequency command generated for the direct drive inverter; The reference operating frequency of the core equipment is a known constant; The optimal target power command after smoothing filtering; This refers to the reference rated power of the core equipment under standard operating conditions.

[0177] generate Then, it is sent directly to the frequency converter as an analog or bus digital command to perform the operation.

[0178] By using the dynamically generated adaptive exponential kernel weight matrix from the aforementioned steps, discrete convolution operations are performed on the original energy consumption demand sequence within the local buffer window to thoroughly filter out potential malicious steps in the signal waveform and obtain an extremely smooth optimal target power command. Simultaneously, strictly adhering to the objective physical laws of large-scale fluid handling machinery, a rigorous cube root operation is performed on the ratio of the optimal power command to the reference power, and this is multiplied and mapped to the core equipment's reference operating frequency to generate the final frequency conversion control command. This perfectly bridges the gap between abstract data deduction and the execution of underlying physical actions; the high-intensity discrete convolution smoothing process achieves millisecond-level smoothing. At the operational level, it completely eliminates spike signal disturbances that could cause severe mechanical fatigue and structural fracture in heavy-duty fans or water pumps, ensuring the absolute smoothness of the control signals sent. Finally, it uses extremely rigorous cube root mathematical operations for frequency conversion, completely eliminating the crude and distorted subjective linear mapping behavior in conventional engineering control logic. This ensures that every hertz of the optimized operating frequency perfectly matches the cubic proportional relationship between the physical power and speed of the actual centrifugal machinery. Thus, while achieving flexible protection in the environmental governance process, it truly delivers the most realistic long-term physical energy saving and life extension effects for the underlying heavy-duty hardware.

[0179] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0180] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for optimizing and controlling energy consumption in environmental engineering based on big data analysis, characterized in that, include: The mean velocity is calculated by averaging instantaneous flow velocity and sampled parameters using a sliding window. Using the quotient of physical distance and uniform flow velocity as the transmission delay, the instantaneous concentration is shifted forward by this delay to extract the aligned concentration. The dynamic pressure density is constructed by multiplying the aligned concentration by the square of the mean flow velocity, and the operating curvature is calculated from the relative rate of change of dynamic pressure density between adjacent time windows. Calculate the discrete variance of the relative mean of the aligned concentration within the local window and take the square root to obtain the standard deviation of the transient shock. The margin coefficient is calculated based on the limit and the reference power. The compensation coefficient is calculated by combining the impact standard deviation and the alignment concentration ratio. The original energy consumption demand is mapped by multiplying the base power by the state curvature after adding the constant and the compensation coefficient respectively. The transient offset energy is obtained by integrating the square of the difference between the original energy consumption and the baseline power, and then used as the exponential denominator to construct the normalized exponential kernel weight. The optimal power is obtained by performing discrete convolution on the original energy consumption sequence with kernel weights. The cube root of the ratio of the optimal power to the reference power is then multiplied by the reference frequency to obtain the control frequency.

2. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 1, characterized in that, The calculation of mean velocity using instantaneous flow velocity and sampled parameters through a sliding window averaging operation includes: Flow velocity sensors and pollutant concentration density sensors are installed at the main inlet pipe of the environmental engineering project. A power acquisition module and a frequency converter control interface are installed at the core processing equipment; Obtain the set system sampling interval time and the corresponding number of sampling points, and start real-time collection of the instantaneous flow velocity at the current moment; Obtain the direct measurement values ​​of instantaneous flow velocity corresponding to multiple sampling intervals backward from the current time; The mean velocity within the current time window is obtained by summing the direct measurements of each instantaneous flow velocity within the current time window and then dividing the sum by the number of sampling points.

3. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 2, characterized in that, The method of using the quotient of physical distance and uniform flow velocity as the transmission delay, and extracting aligned concentrations by shifting the instantaneous concentration forward according to this delay, includes: Obtain the physical distance constant between the sensors in the processing pipeline and begin real-time acquisition of instantaneous pollutant concentration density; Divide the physical distance constant by the uniform flow velocity within the current time window to obtain the dynamic transmission delay time at the current time. Extract the concentration density data directly measured by the sensor at the time corresponding to the dynamic transmission delay time calculated backward from the current time from the system's historical cache; The extracted data is used as the pollutant concentration density aligned to the current processing section.

4. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 3, characterized in that, The process of constructing the dynamic pressure density by multiplying the aligned concentration by the square of the mean flow velocity, and calculating the operating state curvature from the relative rate of change of dynamic pressure density in adjacent time windows, includes: Get the set time window length constant; The equivalent dynamic pressure density at the current moment is obtained by multiplying the pollutant concentration density aligned to the current treatment section by the square of the uniform flow velocity within the current time window. Obtain the equivalent dynamic pressure density of a historical moment corresponding to a time window length constant calculated backward from the current moment; The difference is obtained by subtracting the equivalent dynamic pressure density at the current moment from the equivalent dynamic pressure density at the previous moment, and then dividing the difference by the equivalent dynamic pressure density at the previous moment to obtain the curvature of the current running state.

5. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 4, characterized in that, The calculation of the discrete variance and square root of the relative mean of the aligned concentration within the local window to obtain the standard deviation of the transient impact includes: Obtain the constant number of samples in the local moving window and the constant time interval for sampling; Calculate the arithmetic mean of the pollutant concentration densities within the local moving window that are aligned to the current processing section; Calculate the difference between each historical aligned concentration data distributed at sampling intervals within the window and the arithmetic mean; Square each of the differences and then sum them. Divide the summation result by the constant number of samples in the local moving window, and finally take the square root of the division result to obtain the transient shock standard deviation at the current moment.

6. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 5, characterized in that, The margin coefficient, calculated based on the limit and reference power, is used to derive the compensation coefficient from the ratio of the impact standard deviation to the alignment concentration, including: Obtain the baseline rated power of the core equipment under standard operating conditions and the maximum allowable power limit of the engineering equipment hardware; The difference is obtained by subtracting the reference rated power from the maximum limit allowable power, and the margin factor is obtained by dividing the difference by the reference rated power. The deviation ratio is obtained by dividing the current transient shock standard deviation by the pollutant concentration density aligned to the current treatment section. Multiply the deviation ratio by the margin coefficient, and finally add a constant 1 to the product to obtain the current transient impact compensation coefficient.

7. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 6, characterized in that, The process of multiplying the base power by the state curvature (after adding constants) and the compensation coefficient to map the original energy consumption demand includes: Add a constant 1 to the curvature of the running state at the current moment to obtain the state curvature addition term; The original energy consumption demand state quantity at the current moment is calculated by multiplying the core equipment's base rated power under standard operating conditions, the state curvature addition term, and the current transient impact compensation coefficient.

8. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 7, characterized in that, The transient offset energy is obtained by integrating the square of the difference between the original energy consumption and the reference power, and then used as the denominator of the exponent to construct the normalized exponent kernel weight, including: Calculate the difference between the original energy consumption demand state quantity and the baseline rated power for each historical sequence within the local moving window recorded in the cache; After squaring each of the differences, multiply them by the sampling interval time constant, and sum all the product results to obtain the transient offset energy integral; The numerator is obtained by multiplying the number of time backtracking steps, the sampling interval time constant, and the square of the reference rated power. Divide the numerator by the transient offset energy integral and take the negative value of the division result as the exponent of the natural exponential function to obtain the kernel function exponent term corresponding to the time backtracking step number; The normalized denominator is obtained by summing the kernel function exponents corresponding to all time backsteps within the local moving window. Divide the kernel function exponent corresponding to a single time step backward by the normalized denominator to obtain the convolution kernel distribution weights corresponding to the time step backward.

9. The environmental engineering energy consumption optimization and control method based on big data analysis according to claim 8, characterized in that, The process of performing discrete convolution on the original energy consumption sequence using kernel weights to obtain the optimal power, and then multiplying the cube root of its ratio to the baseline power by the baseline frequency to obtain the control frequency, includes: Obtain the known constant of the reference operating frequency of the core equipment; Multiply the elements of the convolution kernel distribution weight matrix corresponding to each backstep at each time with the original energy consumption demand state of the historical sequence recorded in the corresponding cache, and sum all the product results to obtain the optimal target power command after smoothing and filtering. Divide the optimal target power command by the reference rated power to obtain the ratio, and calculate the cube root of the ratio. Finally, the cube root is multiplied by the known constant of the core equipment's reference operating frequency to obtain the final control frequency command for directly driving the frequency converter.

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