Energy-saving purification power generation intelligent control method based on electricity demand of remote countryside

Abstract

The embodiment of the present application provides an energy-saving purification power generation intelligent control method based on power demand of remote villages, and relates to the technical field of water treatment and energy management technology.The method comprises the following steps: obtaining an operation state, wherein the operation state comprises a water quality state, a hydrological state and an energy state of a power generation system; determining a current event type and a dynamic purification target according to the operation state; determining an optimal power distribution scheme of each execution unit in a purification module based on the dynamic purification target and a real-time energy budget; generating a control instruction set according to the optimal power distribution scheme, and sending the control instruction set to the execution unit.Through the present application, the problem of low energy consumption control precision of the water purification system is solved, and the effect of improving the energy consumption control precision of the water purification system is achieved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of water treatment and energy management technology, and more specifically, to an energy-saving intelligent control method and system for purified power generation based on the electricity demand of remote rural areas. Background Technology

[0002] In remote rural areas, access to clean drinking water and a stable power supply are two major bottlenecks restricting development and the improvement of quality of life.

[0003] On the one hand, the water sources of mountain streams or rivers are easily affected by agricultural non-point source pollution, livestock and poultry breeding wastewater and domestic sewage, resulting in large fluctuations in water quality, making it difficult to directly meet drinking standards; on the other hand, these areas are often far from the main power grid, with unstable power supply, and the abundant water resources in the mountains have not been fully developed due to a lack of efficient utilization methods.

[0004] Existing solutions typically construct water purification and power generation as two separate systems. Since most micro-hydropower in mountainous areas are run-of-river stations without regulation capabilities, their power generation fluctuates dramatically due to seasonality and instantaneous rainfall. Simultaneously, extreme weather events such as torrential rains often bring high-turbidity water pollution, causing a sharp increase in energy consumption for water purification in a short period. This misalignment between "energy supply troughs" and "energy consumption peaks" results in either significant energy waste during the high-water season or, when faced with severe pollution, the system is unable to initiate energy-intensive deep purification processes (such as electrolysis to adjust pH) due to instantaneous power limitations, leading to system paralysis or substandard effluent. Summary of the Invention

[0005] This invention provides an energy-saving intelligent control method and system for water purification power generation based on the electricity demand of remote rural areas, so as to at least solve the problem of low energy consumption control accuracy of water purification systems in related technologies.

[0006] According to an embodiment of the present invention, an energy-saving clean power generation intelligent control method based on the electricity demand of remote rural areas is provided, comprising: The system acquires operational status, which includes water quality status, hydrological status, and energy status of the power generation system. The water quality status includes the pH and turbidity values ​​of the water flow. The hydrological status includes the flow rate of the water flow and the liquid level in the collection tank. The energy status includes the real-time power generation and the state of charge of the energy storage module. The current event type and dynamic purification target are determined based on the described operating status; Based on the dynamic purification target and the real-time energy budget, the optimal power allocation scheme for each execution unit in the purification module is determined; wherein, the energy budget is determined based on the real-time power generation in the energy state and the dischargeable power of the energy storage module. Based on the optimal power allocation scheme, a control instruction set is generated and sent to the execution unit.

[0007] In an exemplary embodiment, determining the current event type based on the running state includes: Based on the flow rate change rate in the hydrological state, and the pH and turbidity values ​​in the water quality state, determine whether the current event type is a peak water consumption event; If the current event type is a non-peak water usage event, then the pollution level of the current water quality is determined based on the water quality status.

[0008] In an exemplary embodiment, determining the optimal power allocation scheme for each execution unit in the purification module includes: The optimization problem is obtained, which takes minimizing the purification cost as the objective function and is constrained by the real-time available energy budget. Solve the optimization problem to obtain the optimal power allocation scheme, which includes the power values ​​allocated to each execution unit.

[0009] In one exemplary embodiment, generating a set of control instructions based on the optimal power allocation scheme includes: Based on the power values ​​in the optimal power allocation scheme and in conjunction with the pre-calibrated correspondence between control commands and power consumption, the hardware control parameters corresponding to each execution unit are determined. The hardware control instruction set is generated based on the hardware control parameters.

[0010] In one exemplary embodiment, the method further includes: Acquire upstream water quality data and predict the expected total energy consumption within a future time window; Based on the expected total energy consumption and the current state of charge in the energy state, calculate the predicted energy deficit; If the predicted power shortage exists, a water storage control command is generated; When polluted water is detected reaching the collection tank, a peak release command is generated.

[0011] In one exemplary embodiment, the prediction of the expected total energy consumption within a future time window includes: Based on the upstream water flow velocity and the preset river channel geometric parameters, calculate the instantaneous flow function when the polluted water body passes through the area; Based on the upstream turbidity value and the instantaneous flow function, the expected pollution load within the future time window is obtained; Calculate the instantaneous purification power demand function based on the pollution load and the expected peak turbidity. The expected value of total energy consumption is determined based on the instantaneous purification power demand function.

[0012] According to another embodiment of the present invention, an energy-saving intelligent control system for purified power generation based on the electricity demand of remote rural areas is provided, comprising: The sensing module is used to acquire the operating status, which includes water quality status, hydrological status, and energy status of the power generation system; wherein, the water quality status includes the pH value and turbidity value of the water flow; the hydrological status includes the flow rate of the water flow and the liquid level of the collection tank; and the energy status includes the real-time power generation and the state of charge of the energy storage module. A power generation module is used to convert hydropower into electrical energy; The purification module includes multiple power-adjustable execution units configured to perform water purification operations; The intelligent control module is used to execute the following methods: The current event type and dynamic purification target are determined based on the described operating status; Based on the dynamic purification target and the real-time energy budget, the optimal power allocation scheme for each execution unit in the purification module is determined; wherein, the energy budget is determined based on the real-time power generation in the energy state and the dischargeable power of the energy storage module. Based on the optimal power allocation scheme, a control instruction set is generated and sent to the execution unit.

[0013] In an exemplary embodiment, determining the current event type based on the running state includes: Based on the flow rate change rate in the hydrological state, and the pH and turbidity values ​​in the water quality state, determine whether the current event type is a peak water consumption event; If the current event type is a non-peak water usage event, then the pollution level of the current water quality is determined based on the water quality status.

[0014] According to yet another embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0015] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0016] This invention, by constructing a dual collaborative mechanism of dynamic coupling of power load and feedforward control of potential energy, can match unstable energy supply with dynamically changing purification tasks, transforming the system from a passive energy consumer into an adaptive energy distribution center. Simultaneously, by predicting pollution load and actively adjusting the water level in the collection tank, energy is pre-accumulated in physical space. When a severe pollution impact arrives, the system can instantly release the dual peak power superimposed with physical potential energy and the energy storage battery, thus completely breaking down the misalignment between energy supply troughs and energy consumption peaks. Therefore, it can solve the problem of low energy consumption control accuracy in water purification systems, achieving the effect of improving the energy consumption control accuracy of water purification systems. Attached Figure Description

[0017] Figure 1 This is a structural block diagram of an energy-saving purification power generation intelligent control system based on the electricity demand of remote rural areas according to an embodiment of the present invention; Figure 2 This is a simulation diagram of the effect according to an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0019] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0020] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.

[0021] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "coupled" can refer to an electrical connection that enables signal transmission.

[0022] As used herein, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from the given value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement system).

[0023] This embodiment provides an energy-saving intelligent control method and system for water purification power generation based on the electricity demand of remote rural areas. In one specific embodiment, the system includes an intelligent control module integrating a multimodal state perception link, a real-time optimization solution kernel, and a potential energy feedforward scheduling engine, as well as a system suitable for coordinated control of water flow valves and multi-stage purification execution units. This allows the system to predict and accumulate physical potential energy under the constraint of non-stationary runoff energy input, and simultaneously perform continuous power allocation to variable frequency water pumps and electrolysis units. This architecture solves the technical problems of low energy efficiency and weak shock resistance caused by the rigidity of energy supply and the sudden misalignment of pollution load in existing runoff micro-hydropower and water purification coupling systems. It achieves the beneficial effects of improving the overall energy utilization efficiency of the system, resisting extreme pollution shocks with small-capacity electrochemical energy storage, and ensuring the continuity of water and power supply.

[0024] like Figure 1 As shown, this embodiment provides an energy-saving purification and power generation intelligent control system based on the electricity demand of remote rural areas. The system is deployed beside a stream with a natural drop in elevation in remote rural areas, forming a self-sustaining energy and water treatment micro-circulation physical node. The system includes a sensing module, a power generation module, a purification module, an energy storage module, and an intelligent control module.

[0025] The sensing module, deployed at the hydraulic boundary, continuously captures internal fluid and electrical states, as well as external environmental physical quantities. This module includes a main monitoring node rigidly fixed to the raw water inlet pipe, electrically coupled to a pH probe based on the glass electrode method and a flow-through turbidity probe. The pH probe is adapted to output a millivolt-level analog voltage mapped to hydrogen ion activity. The turbidity probe includes infrared LEDs and photodetectors distributed at a 90-degree angle, suitable for generating a 4-20mA current loop signal proportional to the scattering intensity of suspended particles in the water. In addition, the sensing module includes an ultrasonic level gauge mounted vertically on the top of the inlet collection tank via a flange. This gauge emits sound pulses and captures echoes, calculating the absolute water level elevation of the collection tank based on the sound wave transit time and temperature-compensated sound velocity parameters.

[0026] Furthermore, the sensing module also includes a remote sensor node deployed at a predetermined physical distance upstream of the system inlet. This remote sensor node is embedded with an immersion turbidity probe and an ultrasonic Doppler current meter, suitable for capturing feedforward digital characteristics of water quality before the polluted fluid front reaches the main treatment system. The analog-to-digital conversion subsystem built into the sensing module discretizes the aforementioned continuous analog signal into a quantized digital sequence and encapsulates it into a standardized serial data frame carrying a GPS clock timestamp, continuously pushing the data stream to the control layer via an RS-485 physical layer differential bus.

[0027] The power generation module is physically coupled to the hydraulic drop section to convert the kinetic energy and gravitational potential energy of the fluid flowing through the collection pool into controlled electrical energy. The module includes a mixed-flow micro-hydro turbine generator set and an inlet regulating valve. The inlet regulating valve is an electric butterfly valve containing an absolute photoelectric encoder, which is fixed to the inlet of a pressure steel pipe connected to the turbine casing at the bottom of the collection pool. Responding to opening control commands, the electric regulating butterfly valve drives an actuator to change the geometry of the flow surface, thereby adjusting the hydraulic torque acting on the turbine runner and simultaneously changing the water discharge rate of the collection pool. Additionally, the power generation module is electrically coupled to a dedicated power conversion controller, which includes a high-frequency Hall sensor to adapt to real-time sampling of the terminal voltage and line current waveforms of the generator stator three-phase windings. After internal full-bridge rectification and active power integration, the terminal voltage and line current generate a floating-point value representing the current instantaneous power generation, which is then encapsulated into a message with a specific ID and broadcast to the local controller bus (CAN).

[0028] The purification module, located downstream of the water treatment network, receives raw water and performs a tandem physical-chemical treatment. This module comprises multiple independently addressable execution units with continuously adjustable input power, specifically a variable frequency pump and an electrolysis unit. The variable frequency pump is driven by a space vector pulse width modulation (SPWM) AC inverter, which responds to an externally given frequency signal to adjust the fundamental frequency output to the stator windings, thereby continuously changing the mechanical speed of the centrifugal pump impeller. As the mechanical speed changes, the head and volumetric flow rate of the fluid output by the centrifugal pump shift, thus altering the fluid permeation velocity in the downstream multi-media quartz sand filter tank, simultaneously causing a nonlinear change in the input electrical power on the pump stator side. The electrolysis unit includes a titanium-based ruthenium-iridium plated inert anode plate and a stainless steel cathode plate spanning the fluid pipeline. This electrolysis unit is powered by a closed-loop programmable adjustable DC switching power supply. For example, in response to the terminal voltage, a water reduction reaction occurs at the cathode solid-liquid interface, precipitating hydrogen gas and increasing the local hydroxide ion concentration, while an oxygen evolution reaction occurs at the anode; and in response to the change in the duty cycle parameter of the adjustable DC switching power supply, the effective electrolysis current flowing through the electrolyzer changes, thereby adjusting the Faraday generation rate of hydroxide ions to neutralize the acid load of the raw water, and simultaneously causing a linear shift in the active power consumption of the electrolysis unit.

[0029] The energy storage module is mounted on the DC bus and acts as an electrochemical energy buffer. This module includes a lithium iron phosphate battery cluster and a master-slave battery management system (BMS). The slave control board of the BMS measures the terminal voltage and surface temperature of each individual cell in real time via a distributed sampling harness, while the master control board measures the bus charging and discharging current via a shunt. The microcontroller of the BMS is embedded in a state-of-charge (SOC) estimation coprocessor, which periodically iteratively calculates and outputs the current SOC percentage of the battery cluster. Combined with a pre-stored temperature-voltage safety envelope, it dynamically calculates the maximum allowable continuous discharge power boundary value of the battery cluster at the current sampling time.

[0030] The intelligent control module includes an industrial-grade computing node integrating an ARM architecture microprocessor and a hardware floating-point arithmetic unit. This module bridges the RS-485 bus and controller area network via opto-isolated physical ports, collecting all underlying telemetry data from the sensing module, power generation module, and energy storage module using a polling mechanism. The intelligent control module's read-only memory contains a multi-tasking preemptive real-time operating system. Its runtime memory resident event recognition threads, multi-objective optimization solution threads, and communication scheduling threads. These threads execute control algorithm graphs and map the calculated floating-point target values ​​into pulse-width modulation duty cycle signals and register write instructions, which are then routed to the inverter and adjustable DC power supply of the purification module via a digital-to-analog converter and serial port.

[0031] Reference Figure 2 This embodiment provides an energy-saving intelligent control method for purified power generation based on the electricity demand of remote rural areas. The method includes the following steps: S100: Multimodal State Awareness and Event Type Recognition This step maps the continuously fluctuating hydraulic and electrical field quantities in the physical domain into discrete, processor-manageable digital state vectors using a defined communication protocol. Specifically: S110: The bus polling thread running in the kernel of the intelligent control module sends a read request message to the address of the sensing module via the transceiver based on a preset timer interrupt cycle. In response to the read request, the sensing module encapsulates and sends back the current pH value, current turbidity value, and current flow rate value latched in its memory-mapped area. Subsequently, the preprocessing subroutine of the intelligent control module performs cyclic redundancy check on the received data frame, and after the check passes, deserializes and extracts the current flow rate floating-point variable from the payload area. .

[0032] S120: Based on extraction The preprocessing subroutine pushes it into a fixed-length sliding window queue of traffic history residing in memory. This queue is configured to store discrete traffic values ​​containing the past sixty sampling periods. This is based on the current system clock tick. The preprocessing subroutine calls the cumulative division instruction to calculate the arithmetic mean of all discrete elements within the historical flow sliding window. Based on this arithmetic mean, the processor's arithmetic logic unit performs a difference operation. To generate the rate of change of flow used to characterize the transient flow fluctuation gradient. ,in This is the physical time span constant for the sliding window mapping.

[0033] S130: The event discrimination engine within the intelligent control module captures the generated traffic change rate. Simultaneously read the current pH value from the shared memory area. and current turbidity value In response to this data, the event discrimination engine performs multi-dimensional conditional logic arbitration: The engine retrieves the traffic surge threshold by addressing the configuration sector of non-volatile memory. Cleaning pH lower limit Upper limit of pH level for cleaning and the upper limit of clean turbidity The processor evaluates the Boolean expression: Condition one, ; Condition two, and .

[0034] When the result of the concurrent logical AND operation of conditions one and two is true, the event discrimination engine triggers a state machine transition and sets the event type flag in the system global register to the code "peak water usage event".

[0035] S140: When the result of the logical AND operation in S130 is false, the program counter of the intelligent control module jumps to the water quality classification subroutine entry point, which loads the current pH value. and current turbidity value The two-dimensional feature coordinates are then projected onto a predefined water quality state classification hyperplane matrix. This classification matrix is ​​configured to consist of multiple mutually exclusive parameter boundary polygons. These polygons are configured with strict rules for determining the assignment of closed and open intervals. Specifically, the classification matrix is ​​hard-coded into three defined polygonal regions within the two-dimensional coordinate system: Polygon 1 (L1-clean) is defined as a coordinate set. ; Polygon 2 (L2 - heavily polluted) is defined as a coordinate set ; Polygon 3 (L3 - lightly contaminated) is defined as the complement of all coordinate points other than Polygon 1 and Polygon 2.

[0036] When the collected coordinates fall precisely on the shared boundary line of adjacent polygons, the system executes a conservative attribution logic that tilts towards higher pollution levels, that is, it adopts a semi-closed and semi-open interval determination formula that is 'greater than or equal to' the higher level threshold and 'less than' the lower level threshold.

[0037] The above-mentioned upper limit of clean turbidity The value is based on the pre-stored Class III water quality standard of the "Surface Water Environmental Quality Standard", and is superimposed with a 15% safety redundancy calculated from the historical high-water period drift variance, thus hard-coded as follows. .

[0038] Subsequently, by performing a coordinate inclusion test, the unique intersection polygon that the current pH and turbidity values ​​fall into is determined. Based on this, the water quality classification subroutine outputs a discrete pollution level enumeration identifier. Then, in response to the output of this enumeration identifier, the system addresses a pre-stored target mapping hash dictionary, uses the pollution level identifier as a key, and retrieves the corresponding set of target pH values. and target turbidity value This is used to construct and lock the dynamic purification target vector for the current control cycle. .

[0039] For example, suppose there is a traffic surge threshold. for At the clock stamp The current traffic parsed by the bus for (combine ), moving average flow Calculated as (combine Processor computational throughput change rate Because the comparator determines... Boolean conditions If the value is false, the system suppresses the setting of the peak water usage flag. During the same period, the analog-to-digital conversion link measured... for , for The water quality classification subroutine then assigns coordinates ( Substitute the matrix into the inclusion test and match to the matrix. and The region enclosed by polygon two. Based on this matching result, the dictionary search program returns the preset strict control benchmark corresponding to key value level three, that is, to construct a dynamic purification target vector. And so on.

[0040] It should be noted that the clean water quality conditions used in S130 to assist in judging "peak water usage events" are intended to quickly exclude situations where both flow surges and water quality deterioration are caused simultaneously by rainstorms and flood peaks. The threshold values ​​(e.g.) are specific to these conditions. The boundary is relatively loose. However, the water quality classification matrix used for fine-grained control in S140 has a threshold for the "L1 - Clean" level (e.g., ...). S140 is a more stringent definition used to distinguish whether a purification operation needs to be initiated. The two serve different control logic branches and have different threshold definitions. The judgment condition of S140 can be regarded as a further refinement of S130.

[0041] Assuming the water quality coordinates collected after rain in the mountainous area are: In this scenario, using traditional control logic is highly prone to misjudgment. For example, a controller based solely on pH value might misjudge... A controller that incorrectly judges water quality as good when it is within its "clean" range (e.g., 6.5-8.5) may fail to initiate necessary turbidity treatment processes, resulting in substandard turbid water output. Conversely, a simple logic controller that lumps all unclean states into "contamination" may indiscriminately activate the entire heavily contaminated treatment process, including a high-power electrolysis unit, causing unnecessary energy waste, since pH adjustment is only needed in this case and is not the primary concern.

[0042] Here, by dividing the area into polygonal regions using S140, the point can be accurately located. Classification: This point clearly does not belong to polygon one (because...) It does not belong to polygon two (because neither pH nor NTU has reached the heavy pollution threshold), therefore, it is uniquely and correctly identified as polygon three (L3 light pollution). Based on this precise location, the subsequent target setting unit S230 will generate an asymmetric purification target vector with the primary objective of significantly reducing turbidity and only requiring the maintenance of pH stability. Finally, the real-time optimization solver S240, under energy budget constraints, will allocate the vast majority of power to the execution units related to turbidity treatment, while allocating only a very low maintenance power to the electrolysis unit, and so on.

[0043] S200: Energy Budget Assessment and Real-Time Optimization Solution This step is used to converge a set of electrical power allocation vectors that minimize the water quality deviation cost function under the rigid constraints of the physical energy power boundary; specifically: S210: The energy statistics daemon within the intelligent control module listens to the controller local area network bus and parses the payload of specific identifiers, extracting the real-time power generation floating-point numbers written by the power generation module controller. and the dischargeable power mapped by the battery management system message. and system power consumption Based on this extracted value, the processor executes an addition instruction. The result is written to a buffer register and used as the upper limit scalar of the absolute energy budget available for the system during the current control cycle. .

[0044] S220: Response to Budget Cap Scalar The system activates a real-time optimization solution engine residing in memory, which includes a multivariate optimization problem space with nonlinear constraints.

[0045] The system first allocates memory to construct a purification cost function for quantifying the Euclidean distance between the water quality state vector and the target vector. The cost function is configured as follows:

[0046] In the formula, and It is a dimensionless weight multiplier configured in non-volatile memory, used to define the gradient preference in multi-objective optimization; The expected effluent turbidity standard. To determine the expected pH level of the effluent, the objective here is to minimize the purification cost, i.e. The goal is to minimize the value.

[0047] S230: In order to map control domain variables to the predicted state domain, the optimization solver calls the physical dynamics representation model in memory.

[0048] For the turbidity physical blocking effect, the engine load attenuation model is as follows:

[0049] It is important to note here that variables In this model, the term "turbidity treatment power" is used in a generalized sense. It represents not only the motor power consumption of the variable frequency pump itself but also the equivalent power of other execution units related to turbidity treatment (such as the flocculant dosing pump). In the optimized control logic of this system, when it is necessary to improve the turbidity removal rate, the intelligent control module will not only adjust the pump speed to optimize the contact time and flow state of the filter media (e.g., appropriately reducing the flow rate within a certain range to increase sedimentation and filtration efficiency) but will also simultaneously increase the flocculant dosage. Both of these operations will increase the total power consumption of the system. Therefore, the variable... It is a macroscopic representation of the total power cost that the system pays to achieve the turbidity reduction target; the exponential decay model is an empirical model based on fitting a large amount of experimental data. It reflects the macroscopic law that under the coordinated control of the system, the greater the input of the comprehensive "turbidity treatment power", the faster the rate of effluent turbidity reduction, rather than simply describing the physical relationship between filtration flow rate and efficiency.

[0050] For the acid-base electrochemical neutralization effect, the engine loads an ion evolution model based on Faraday's law: First, the logarithmic inverse function is called to map the sensor feedback value to hydrogen ion concentration. Subsequently, the concentration gradient was predicted based on the relationship between current and mass conversion equivalence.

[0051] Finally, the negative logarithmic function is called to remap the predicted concentration back to the acid-base scale:

[0052] In this set of simultaneous models, and These represent the input power variables of the variable frequency pump and the input power variables of the electrolysis unit, respectively, as the optimization solver iterates in the search space. The physical control cycle duration defined for the timer interrupt; coefficient (dimensions are) )and (dimensions are) ) is the equipment energy conversion efficiency characteristic constant that is fixed by fitting the historical operating condition data matrix using the least squares method.

[0053] The values ​​of the aforementioned characteristic constants are determined as follows: During the initial system installation phase, the built-in step response test program automatically traverses the effective power ranges of the water pump and electrolyzer, and uses the least squares method to perform nonlinear surface fitting on the effluent water quality time series, which is then hard-coded and stored in read-only memory. Specifically, the step response test program is configured to use the Latin hypercube sampling method to sample no fewer than 500 uniformly distributed discrete test points within the water pump power range [100W, 1000W] and the electrolyzer power range [50W, 2000W]. At each test point, the system maintains constant power operation for at least 15 minutes to establish hydraulic and chemical steady state, followed by simultaneous acquisition of input electrical power and effluent water quality time series data. Then, a total of no fewer than 1000 sets of steady-state data samples are obtained, and the processor uses the least squares method to perform nonlinear surface fitting on the effluent water quality time series to calculate the characteristic constants. and The fitting process is configured with strict error control logic, iteratively optimizing until the root mean square error (RMSE) is less than a preset 5% tolerance threshold. If the fitting error exceeds this threshold, the system triggers an abnormal interruption and requests recalibration to ensure the representation accuracy of the underlying physical model. The constant values ​​after fitting convergence are finally hard-coded and stored in read-only memory. Among them, constants... It is a comprehensive empirical coefficient that integrates the electrolysis Faraday constant, the Faraday efficiency of the electrolyzer, and the effective volume of the treated water, and maintains constant properties in application scenarios where the volume of the collection tank is constant.

[0054] S240: Optimize the solver engine configuration to execute the optimization algorithm to minimize the cost function. The objective is gradient descent, and the feasible search region is strictly constrained to the inequality boundary. and nonnegative boundaries Inside.

[0055] The optimization algorithm includes a gradient descent solver for the driving term, given the cost function. The solution space contains nonlinear exponential decay terms and absolute value terms, exhibiting multimodal nonconvexity. To overcome the risk of getting trapped in local minima and to approximate the global optimum, the solver is configured with a multi-point concurrent initialization routine. This routine, based on a uniform grid partitioning strategy, forces the generation of at least five spatially orthogonal initial search anchor points within the feasible solution space. For each anchor point, the solver instantiates an independent gradient descent convergence thread. After all threads have executed, the main control program extracts the multiple local minima obtained through convergence and executes a scalar comparison instruction to select the corresponding... The coordinate node with the smallest value is selected as the final globally optimal power allocation scheme. For a single convergence thread, the solver includes an initial search step size scalar. Its value is set based on the finest response granularity of the actuator (inverter resolution). To implement computational error control and suppress iterative oscillations at the bottom of the multimodal objective function, the processor calls a backtracking search routine based on the Amiho criterion after each partial derivative calculation and weight update, dynamically decaying the search step size scalar according to the local curvature of the cost function. The convergence condition of this engine includes a dual logical AND gate criterion: One is the absolute value of the scalar difference in the cost function between two adjacent iterations. Less than the preset tolerance of approximating the device noise floor (dimensionless); Secondly, the Euclidean norm gradient descent of the power allocation vector is less than... .

[0056] If any condition is triggered or the number of iterations exceeds the preset hardware watchdog limit of 500, the engine terminates the search. In abnormal discrete operating conditions that trigger the hardware watchdog limit, the engine activates the exception handling backup mechanism, forcibly reverting to the valid power allocation vector of the previous control cycle to eliminate divergent mathematical singularities and ensure the continuity and robustness of the control signals of the underlying actuators.

[0057] Therefore, the engine calculates partial derivatives and updates the step size in the two-dimensional solution space using interior-point methods or sequential quadratic programming iterative procedures, until two adjacent iterations are completed. The difference falls within the preset convergence tolerance band that approaches zero.

[0058] Finally, the engine extracts the finally converged node coordinate variable values ​​from memory and serializes them into a setpoint for an optimal power allocation scheme. The command is submitted to the instruction generation queue. In the event of an abnormal discrete condition that triggers the hardware watchdog limit, the engine activates the exception handling backup mechanism and forcibly rolls back to the valid power allocation vector of the previous control cycle to ensure the continuity and robustness of the control signals of the underlying actuator.

[0059] For example, assuming the bus data indicates that the current system is under highly acidic and turbid conditions, the captured value... , Furthermore, the dynamic purification target set in the pre-processing procedure. for The energy statistics task calculates the locked current energy budget. The optimization solution engine loads feature constants from flash memory. , Configure the weight parameters as follows: , Control cycle parameters ; Optimize engine startup constraint plane Gradient search within the range, with an initial search step size scalar set to .

[0060] This assumes that the search pointers are combined. Processor deduction Simultaneous conversion deduction And remapping At this point, the gradient evaluation subroutine reveals that the residual of the pH term decreases extremely slowly. To minimize the purification cost function, the algorithm shifts the optimization trajectory towards increasing step size in subsequent iterations. The components are tilted in direction to accelerate the reduction of the penalty weight of the acidity / alkalinity term. After a predetermined number of iterations and convergence, the scalar difference between adjacent cost functions falls into... Within the tolerance band, the algorithm fixes an optimal power allocation vector that satisfies the physical upper limit and achieves mathematical equilibrium. This serves as the control framework for this cycle.

[0061] S300: Hardware Control Command Reverse Mapping and Issuance This step performs digital-to-analog conversion logic to map the high-dimensional power numerical decision space into physical control protocol words recognizable by the device's underlying registers; specifically: S310: The scheduler wakes up the instruction generation task running within the intelligent control module and obtains the serialized optimal power allocation vector by popping it from the stack. This task uses vector pointers to access a hardware characteristic database tablespace residing in system memory. This tablespace stores discrete data pairs generated through offline factory calibration, specifically including frequency and power lookup tables mapping the inverter drive frequency to the pump stator active power, and voltage and power lookup tables mapping the DC switching power supply terminal voltage to the electrolytic cell active power. The offline calibration process is performed in a strictly controlled fluid dynamics laboratory environment, with constraints strictly limited to: ambient temperature... Standard atmospheric pressure Furthermore, the test fluid has a stable conductivity at [value missing]. The deionized water solution was prepared; under the above calibration conditions, electrical parameters were synchronously collected using a power analyzer with an accuracy class better than 0.2; the discrete data point intervals in the above lookup table were configured to be 0.5Hz and 0.1V, respectively, to ensure that the truncation error introduced by the subsequent linear interpolation algorithm was strictly suppressed to within 1% of the total system error budget.

[0062] S320: The instruction generation task will extract the power setpoint. As the search key, a binary search is performed using a frequency and power lookup table. If no exactly matching key-value pair is found, the address pointer locks the envelope. The left and right nearest discrete data points of the value are used, and the corresponding target physical frequency value is calculated by calling a first-order linear interpolation function. In parallel, the processor will The same interpolation calculations are performed on the voltage and power lookup tables as search key values ​​to extract the physical target control voltage of the electrolysis unit. .

[0063] In particular, based on When the boundary condition equals zero, the generator resets the corresponding enable register flag to the halt state.

[0064] S330: Instruction generation task extracts the calculated... and The floating-point value is converted into a hexadecimal data segment conforming to the industrial serial protocol specification. The processor then further concatenates the device slave address, function code, data segment, and calculated cyclic redundancy check word bit-by-bit, encapsulating them into a complete hardware control instruction set waveform frame. Subsequently, the main control program calls the underlying direct memory access channel of the universal asynchronous transceiver to push the waveform frame to the physical serial bus, transmitting it to the driver of the variable frequency water pump and the programmable adjustable DC switching power supply of the electrolysis unit, respectively. The underlying hardware control board parses the received protocol frame and converts it into a gate drive signal for an insulated gate bipolar transistor, thereby changing the hardware terminal outputs and forcing the purification system's state space to shift to the optimal operating coordinate point anchored by the optimization algorithm.

[0065] For example, assume that the optimal pumping power for popping from the instruction generation task stack is The lookup table scanning mechanism hits the coordinates of two adjacent calibration points in the memory area. and The processor then executes the linear interpolation subroutine: Calculate gradient and map .

[0066] The system immediately encapsulates the hexadecimal control word representing 38.5Hz into a write register frame and pushes it onto the bus. After the inverter's microprocessor parses the frame, it adjusts the modulation ratio of the pulse width modulation waveform, so the water pump rotor accelerates smoothly and anchors to the corresponding mechanical speed.

[0067] S400: Cross-domain cooperative scheduling based on the potential energy feedforward of the water collection pool This step feeds forward the potential high-energy-consuming chemical purification demand onto the timeline as a potential energy accumulation command for a physical reservoir. Specifically: S410: A pollution load prediction service deployed in the kernel, which subscribes in real time to upstream turbidity values ​​pushed by upstream remote sensor nodes at a sampling frequency of 0.5Hz via a wireless gateway. and upstream water flow velocity The system configuration parameter area stores a fixed physical river channel distance constant from the remote node to the main collection tank weir. and the equivalent cross-sectional area of ​​that section of the river. .

[0068] Pollution load prediction service based on Determine the estimated transit time of the contamination wave front. Simultaneously, the system calculates the instantaneous flow rate of the polluted water body as it passes through:

[0069] When upstream turbidity value is detected First time derivative When the gradient abruptly exceeds a preset threshold (e.g., 1.0 NTU / s), the feedforward energy integration task is activated. This task first involves analyzing the instantaneous flow rate... Duration of pollution Internal calculation of the total volume of polluted water expected to reach At the same time, the system will The pollution concentration profile of the sequence is used as a pollution intensity function; based on the above parameters, the system calculates the pollution load as the total amount of pollutants to be removed within the pollution time window.

[0070] To convert the aforementioned pollution load into executable energy dispatch commands, the system loads a pre-stored unit volume purification energy consumption mapping table to account for different initial turbidity gradients. Mapped to the instantaneous power required to achieve purification standards To calculate.

[0071] It should be noted that for each moment within the window... The system solves the equations in reverse:

[0072] This allows us to calculate the real-time power consumption curve required to maintain constant effluent turbidity; similarly... This serves as a proportional multiplier for the conversion of chemical load into electrical power, reflecting the instantaneous electrolysis power demand. This yields the instantaneous purification power requirement curve needed to maintain the target water quality. The instantaneous purification power demand curve It depends on the real-time change profile of turbidity when the flood peak passes.

[0073] Finally, the system obtains the expected value of total energy consumption by performing time integration on the power curve:

[0074] This value is set in system memory as the expected total energy consumption parameter. (Unit: kWh).

[0075] Generally, in practical engineering applications, to reduce the real-time computational load on the intelligent control module, the above definite integral operation is equivalently mapped to a cumulative summation process of pollution load within the discrete control domain. Specifically, when the pollution concentration distribution and flow velocity fluctuations within the prediction window are within a preset steady-state tolerance range, the above integration process simplifies to a linear calculation mode:

[0076] In the formula, This represents the total volume of polluted water within that time window. The average unit purification energy consumption is determined by an inverse dynamics model based on the expected pollution peak.

[0077] For example, suppose the remote sensor measures River cross-sectional area The instantaneous flow rate is calculated as follows: If the pollution duration is The expected total volume of polluted water to reach ;like for The system retrieves the concentration of water per unit volume that needs to be purified based on the model. Required average specific energy consumption for The simplified calculation of the total energy consumption expectation value calculated by the system is as follows: If the remaining battery power is only... ,but The shortfall will immediately trigger the action of the inlet regulating valve in the subsequent S430, which will raise the liquid level by sacrificing the current power generation, accumulating enough gravitational potential energy to fill the 1.0 kWh shortfall, and so on.

[0078] S420: The potential energy scheduling engine internally mounted on the intelligent control module... Initiate a resource audit; the engine queries the battery management system through the application layer interface to read the remaining floating-point number of charge representing the current available depth of chemical energy storage. Then, the arithmetic logic unit of the potential energy scheduling engine performs a differential comparison: exist In such cases, the system determines that a risk of physical power depletion is imminent, and establishes the difference between the two as the predicted power gap parameter. .

[0079] S430: Predicting power shortage parameters When the potential energy is not zero, the potential energy dispatching engine triggers a state transition at the highest level of the system, entering a strong potential energy accumulation mode. In this mode, the engine seizes local control of the power generation module, calculates a set of restricted valve opening duty cycle values, encapsulates them into a water storage suppression control frame (i.e., water storage control command), and pushes it to the actuator of the inlet regulating valve via the fieldbus. Subsequently, the inlet regulating valve, equipped with a servo motor, responds to this control frame, driving the valve plate to rotate to reduce the flow cross-sectional area. The sharp reduction in the inlet cross-sectional area causes a sudden drop in the effective flow through the turbine stator, resulting in the active suppression of the stator induced electromotive force and instantaneous active power generation. In parallel, since the water volume ratio of the open river inflow collection pool is significantly greater than the discharge volume ratio after valve suppression, the water mass within the collection pool boundary begins to accumulate integrally, causing the absolute liquid level elevation of the collection pool to rise monotonically. Thus, the rise in liquid level elevation is directly reflected in the physical quantity of effective static head relative to the turbine tailrace. The linear increase in the flow force forces the retention of the river current energy, which would otherwise be used for immediate power generation, and statically stores it within the physical boundaries of the collection tank as gravitational potential energy. This controlled storage process is continuously monitored by a closed-loop level control system until the real-time elevation feedback from the ultrasonic level gauge reaches the upper limit of the full reservoir warning level, which is within the preset safety margin.

[0080] S440: When the main monitoring node continuously polls the water quality of the inlet channel, and the turbidity sensor of the main node feeds back the current turbidity scalar value... When a high cross-correlation occurs with historical data from remote sensors, the system determines that the pollution wavefront has physically crossed the system's intake. This high cross-correlation is achieved by calculating the turbidity time series of the main monitoring node and the data obtained from the historical data. The turbidity time series of the remote nodes after time shift is quantified by the Pearson correlation coefficient. When this correlation coefficient exceeds a preset matching threshold (e.g., 0.85) within a continuous sampling period, the system triggers a state switch. Simultaneously, the potential energy scheduling engine immediately removes the suppression on the valve, triggering the peak release mode. At this time, the engine encapsulates a valve full-opening forced frame (i.e., a peak release command) and sends it to the inlet regulating valve. The butterfly valve actuator moves to the full-bore state with maximum rotational angular velocity. In the physical field at this moment, the maximum head obtained during the previous water storage process is superimposed. With respect to the maximum allowable flow rate of a fully open valve At this point, the turbine rotor bears the maximum hydraulic torque, and the stator winding of the micro-hydro-generator unit generates peak power within a short time window. ,in, For the efficiency of the water turbine, For the density of water, To mitigate the impact of gravity and ensure safe peak power injection, the dedicated power conversion controller of the power generation module incorporates soft-start and current ramp-up control logic. When a sharp increase in turbine speed is detected, the controller does not immediately feed all power into the bus. Instead, it gradually increases the feed current to the DC bus at a preset safe ramp rate that does not exceed the maximum input current variation rate (di / dt) of the downstream purification module actuators (such as frequency converters and adjustable power supplies). Simultaneously, the intelligent control module merges this transient peak power along the DC bus with the maximum discharge current scheduled by the energy storage module, forming an energy budget pool, which is then fully injected into the optimization solution engine. Subsequently, the solution engine generates saturation operation commands, driving the electrolysis array and variable frequency pumps within the purification module to approach full-load limit conditions, thereby mitigating the high-concentration pollution load carried by the flood peak.

[0081] For example, such as Figure 2 As shown, based on the actual application data extracted from the operation log of a physical prototype deployed in a mountainous area, it is evident that under continuous 30-day rainy season conditions, the daily average peak value of raw water turbidity reached [data missing]. And the pH level has been pushed to its lowest point. Meanwhile, the actual feature constants extracted by the system's intelligent control module are:

[0082]

[0083] Within a typical single flood peak passage window (upstream flow velocity) ,distance Calculate the transit delay The system accurately calculates the expected total energy consumption. And based on battery status Calculate the predicted power shortage Based on this, the system will suppress the opening of the inlet regulating valve to 35%. This causes the water level in the collection tank to rise from its original level. Accumulation to When the pollution approaches, the valves open fully, and the hydro-generator, driven by the increased static head, briefly outputs [power]. (Exceeding the nominal value) (Rated value). The optimized solver, under this released generous budget, achieves... The final convergence step size after momentum decay, outputting the saturated assignment vector. Actual flow data measured by sensors at the outlet confirmed that the treated water quality remained stable at a turbidity level of less than [value missing] throughout the entire flood peak. And the pH distribution is as follows to Within the safe range, no bus power outage or restart caused by energy storage depletion occurred throughout the entire process, and so on.

[0084] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0085] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0086] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0087] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0088] Embodiments of the present invention also provide an electronic device including a memory and a processor, the memory storing a computer program and the processor being configured to run the computer program to perform the steps in any of the above method embodiments.

[0089] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0090] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0092] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0093] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0094] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy-saving, clean-generation intelligent control method for electricity demand in remote rural areas, characterized in that, include: The system acquires operational status, which includes water quality status, hydrological status, and energy status of the power generation system. The water quality status includes the pH and turbidity values ​​of the water flow. The hydrological status includes the flow rate of the water flow and the liquid level in the collection tank. The energy status includes the real-time power generation and the state of charge of the energy storage module. The current event type and dynamic purification target are determined based on the described operating status; Based on the dynamic purification target and the real-time energy budget, the optimal power allocation scheme for each execution unit in the purification module is determined; wherein, the energy budget is determined based on the real-time power generation in the energy state and the dischargeable power of the energy storage module. Based on the optimal power allocation scheme, a control instruction set is generated and sent to the execution unit.

2. The method according to claim 1, characterized in that, Determining the current event type based on the operating status includes: Based on the flow rate change rate in the hydrological state, and the pH and turbidity values ​​in the water quality state, determine whether the current event type is a peak water consumption event; If the current event type is a non-peak water usage event, then the pollution level of the current water quality is determined based on the water quality status.

3. The method according to claim 1, characterized in that, The method for determining the optimal power allocation scheme for each execution unit in the purification module includes: The optimization problem is obtained, which takes minimizing the purification cost as the objective function and is constrained by the real-time available energy budget. Solve the optimization problem to obtain the optimal power allocation scheme, which includes the power values ​​allocated to each execution unit.

4. The method according to claim 1, characterized in that, The step of generating a control command set based on the optimal power allocation scheme includes: Based on the power values ​​in the optimal power allocation scheme and in conjunction with the pre-calibrated correspondence between control commands and power consumption, the hardware control parameters corresponding to each execution unit are determined. The hardware control instruction set is generated based on the hardware control parameters.

5. The method according to claim 1, characterized in that, The method further includes: Acquire upstream water quality data and predict the expected total energy consumption within a future time window; Based on the expected total energy consumption and the current state of charge in the energy state, calculate the predicted energy deficit; If the predicted power shortage exists, a water storage control command is generated; When polluted water is detected reaching the collection tank, a peak release command is generated.

6. The method according to claim 5, characterized in that, The expected total energy consumption within the predicted future time window includes: Based on the upstream water flow velocity and the preset river channel geometric parameters, calculate the instantaneous flow function when the polluted water body passes through the area; Based on the upstream turbidity value and the instantaneous flow function, the expected pollution load within the future time window is obtained; Calculate the instantaneous purification power demand function based on the pollution load and the expected peak turbidity. The expected value of total energy consumption is determined based on the instantaneous purification power demand function.

7. An energy-saving intelligent control system for purified power generation based on the electricity needs of remote rural areas, characterized in that, include: The sensing module is used to acquire the operating status, which includes water quality status, hydrological status, and energy status of the power generation system; wherein, the water quality status includes the pH value and turbidity value of the water flow; the hydrological status includes the flow rate of the water flow and the liquid level of the collection tank; and the energy status includes the real-time power generation and the state of charge of the energy storage module. A power generation module is used to convert hydropower into electrical energy; The purification module includes multiple power-adjustable execution units configured to perform water purification operations; The intelligent control module is used to execute the following methods: The current event type and dynamic purification target are determined based on the described operating status; Based on the dynamic purification target and the real-time energy budget, the optimal power allocation scheme for each execution unit in the purification module is determined; wherein, the energy budget is determined based on the real-time power generation in the energy state and the dischargeable power of the energy storage module. Based on the optimal power allocation scheme, a control instruction set is generated and sent to the execution unit.

8. The system according to claim 7, characterized in that, Determining the current event type based on the operating status includes: Based on the flow rate change rate in the hydrological state, and the pH and turbidity values ​​in the water quality state, determine whether the current event type is a peak water consumption event; If the current event type is a non-peak water usage event, then the pollution level of the current water quality is determined based on the water quality status.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method described in any one of claims 1 to 6 when executed.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method as described in any one of claims 1 to 6.

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