Water load dynamic control method and system

Abstract

The application provides a water load dynamic regulation method and system, and belongs to the technical field of intelligent water saving and carbon emission control. The method comprises the following steps: obtaining real-time water flow data and real-time electric power data of a water pump of a target water system; combining a real-time carbon emission factor of a power grid to calculate a dynamic carbon emission weight of unit water quantity in a current period; generating a load regulation instruction based on the weight when the real-time carbon emission factor of the power grid meets a carbon emission condition; and sending the instruction to a variable frequency regulation device to reduce the operation load of the water pump. The application introduces a real-time carbon emission factor of a power grid to construct a dynamic mapping of water energy consumption and carbon equivalent, so as to actively control the frequency reduction and peak shaving of the water pump in a high carbon emission period of the power grid, and finally realize the collaborative closed-loop regulation and control of saving water resources and reducing indirect carbon emission of the power grid.

Description

Technical Field

[0001] This invention relates to the field of intelligent water conservation and carbon emission control technology, and in particular to a method and system for dynamic regulation of water load. Background Technology

[0002] Water systems not only need to meet basic daily water supply and distribution needs, but also face macro-application needs such as optimizing energy consumption throughout the entire water resource transmission and distribution lifecycle and effectively reducing overall carbon emissions.

[0003] Existing water use control schemes typically employ a static parameter management model. This scheme primarily relies on flow meters to monitor system water consumption and subsequently estimates total carbon emissions by directly multiplying the accumulated water consumption by a fixed average carbon emission conversion factor. Regarding equipment control, current technologies only rely on preset constant network water pressure indicators or fixed water consumption quotas to perform basic physical flow maintenance control on the speed of water supply pumps.

[0004] The existing solution is completely detached from the real-time operation of the external power grid during the pump control process, and cannot reflect the actual carbon intensity changes of the power grid at different times. This results in lagging and inaccurate dynamic carbon emission accounting results for water energy consumption. In addition, due to the lack of real-time carbon emission sensing and linkage capabilities, when the external power grid is in a high carbon emission state, the variable frequency pumps still passively maintain the normal operating load and cannot adaptively reduce peak loads and consumption during high carbon emission phases, resulting in persistently high indirect carbon emissions from the overall water load. Summary of the Invention

[0005] This invention provides a method and system for dynamic water load control, which addresses the shortcomings of existing technologies and achieves coordinated closed-loop control of water resource conservation and reduction of indirect carbon emissions from the power grid.

[0006] This invention provides a method for dynamic control of water load, comprising the following steps: Acquire real-time water flow data of the target water system, and real-time electrical power data of the water pump that powers the target water system; Based on the real-time water flow data, the real-time power data, and the real-time carbon emission factor of the power grid for the current time period, the dynamic carbon emission weight per unit volume of water for the current time period is obtained. When it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions, a load adjustment command for reducing the operating load of the water pump is generated based on the dynamic carbon emission weight. The load adjustment command is sent to the frequency converter connected to the water pump so that the frequency converter controls the water pump to reduce the current operating load.

[0007] According to a method for dynamic water load control provided by the present invention, the step of obtaining the dynamic carbon emission weight per unit volume of water in the current time period based on the real-time water flow data, the real-time power data, and the real-time carbon emission factor of the power grid in the current time period includes: Calculate the ratio of the real-time electrical power data to the real-time water flow data to obtain the energy consumption value per unit volume of water in the current time period; The energy consumption value per unit of water is multiplied by the real-time carbon emission factor of the power grid to obtain the dynamic carbon emission weight per unit of water in the current time period.

[0008] According to a method for dynamic water load control provided by the present invention, when it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions, a load adjustment command for reducing the operating load of the water pump is generated based on the dynamic carbon emission weight, comprising: When it is determined that the real-time carbon emission factor of the power grid is greater than the preset carbon emission determination threshold, the real-time water demand level parameter of the target water system is obtained. When it is determined that the real-time water demand level parameter is within the preset non-critical water use range, a comprehensive carbon reduction gain value is calculated based on the real-time water flow data and the real-time carbon emission factor of the power grid. Using the dynamic carbon emission weight as the feedback input, and taking the goal of maximizing the comprehensive carbon reduction gain as the control objective, the load adjustment command is calculated and generated based on the proportional-integral-derivative adjustment algorithm.

[0009] According to a method for dynamic water load control provided by the present invention, the step of calculating a comprehensive carbon reduction gain value based on the real-time water flow data and the real-time carbon emission factor of the power grid includes: The difference between the preset water flow threshold and the real-time water flow data is determined as the water saving amount; The expected energy consumption optimization of the water pump is obtained based on the current candidate load adjustment amount; Multiply the water-saving amount by the preset implicit carbon coefficient of water resource production to obtain the indirect carbon reduction amount; Multiply the expected energy consumption optimization amount by the real-time carbon emission factor of the power grid to obtain the direct carbon reduction amount; The indirect carbon reduction amount is summed with the direct carbon reduction amount to obtain the comprehensive carbon reduction gain value.

[0010] According to a method for dynamic water load control provided by the present invention, the step of acquiring real-time water flow data of a target water system and real-time electrical power data of the water pumps providing power to the target water system includes: The water flow signal of the target water system and the power signal of the water pump are synchronously sampled according to the preset synchronization sampling period through the hardware timestamp synchronization mechanism to obtain initial time series data containing water flow subsequence and power subsequence. The initial time series data is first processed by moving average filtering to filter out power grid harmonic interference components, and then the instantaneous surge anomalies caused by the start and stop of the water pump are removed from the initial time series data to obtain the real-time water flow data and the real-time power data.

[0011] According to a method for dynamic water load control provided by the present invention, after sending the load adjustment command to a frequency converter connected to the water pump, the method further includes: After the water pump executes the load adjustment command, the actual carbon reduction data calculated after the water pump reduces its operating load is obtained; The real-time water flow data, the real-time carbon emission factor of the power grid, and the actual carbon reduction data are hashed locally to generate a hash digest corresponding to the current load adjustment event. After encapsulating the hash digest using a preset lightweight encryption algorithm, the encapsulated hash digest is uploaded to a cloud database to trigger the cloud database to perform cross-validation on the encapsulated hash digests uploaded by multiple device nodes and generate an immutable carbon emission reduction certificate.

[0012] The present invention also provides a dynamic water load control system, comprising the following modules: The data acquisition module is used to acquire real-time water flow data of the target water system and real-time electrical power data of the water pump that provides power to the target water system. The carbon power calculation module is used to obtain the dynamic carbon emission weight per unit volume of water in the current time period based on the real-time water flow data, the real-time power data and the real-time carbon emission factor of the power grid in the current time period. The carbon computing power calculation module is also used to generate a load adjustment command to reduce the operating load of the water pump based on the dynamic carbon emission weight when it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions. The variable frequency control module is used to send the load adjustment command to the variable frequency control device connected to the water pump, so that the variable frequency control device controls the water pump to reduce the current operating load.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the water load dynamic control method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the water load dynamic control method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the dynamic water load control method as described above.

[0016] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By acquiring real-time water flow data of the target water system and real-time electrical power data of the pumps powering the system, the basic physical quantities of water energy consumption for constructing a dynamic carbon accounting model are determined. Based on the real-time water flow data, real-time electrical power data, and the real-time carbon emission factor of the power grid for the current period, the dynamic carbon emission weight per unit volume of water for the current period is obtained. This accurately transforms the aforementioned basic physical quantities of water energy consumption into real-time carbon emission equivalent indicators to guide subsequent intervention mechanisms. When the real-time carbon emission factor of the power grid meets preset carbon emission conditions, a load adjustment command is generated based on the dynamic carbon emission weight to reduce the operating load of the water pumps. This proactively outputs targeted energy-saving control decisions during high-carbon emission phases of the power grid based on the aforementioned real-time carbon emission equivalent indicators. By sending the load adjustment command to the variable frequency control equipment connected to the water pumps, the equipment controls the water pumps to reduce their current operating load. This directly translates the aforementioned energy-saving control decisions into actions to control the water pumps to reduce their current operating load, ultimately achieving a synergistic closed-loop control mechanism that conserves water resources and reduces indirect carbon emissions from the power grid. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is the overall architecture diagram of the intelligent water-saving monitoring system provided by the present invention.

[0019] Figure 2 This is a schematic diagram of the internal hardware structure of the intelligent monitoring device provided by the present invention.

[0020] Figure 3 This is a flowchart illustrating the dynamic water load control method provided by the present invention.

[0021] Figure 4This is a schematic diagram of the water load dynamic control system provided by the present invention.

[0022] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0024] It should be noted that in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a 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 a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships according to the accompanying drawings, are only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the system or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0026] The following is combined with Figures 1 to 5 This invention describes the water load dynamic control method, system, electronic device, storage medium, and computer program product provided by the present invention.

[0027] This application provides a method for dynamic water load control. In specific engineering implementations, the executing entity for the dynamic water load control method in this application includes, but is not limited to, embedded edge computing processors, industrial-grade microcontrollers, system-on-a-chip with integrated neural network processing units, and intelligent monitoring devices with edge computing capabilities.

[0028] Reference Figure 1 , Figure 1 This is an overall architecture diagram of the intelligent water-saving monitoring system provided by this invention. Figure 1 As shown, the intelligent water-saving monitoring system is divided into three layers in terms of topology: physical layer, edge computing layer, and application and network layer.

[0029] The physical layer includes a variable frequency water pump, a piping system, a pressure sensor, and an ultrasonic flow meter. The variable frequency water pump, pressure sensor, and ultrasonic flow meter are physically mounted on the piping system. The pressure sensor acquires real-time water pressure signals within the piping system. The ultrasonic flow meter acquires real-time water flow signals flowing through the piping system. The variable frequency water pump provides the physical power for the water transport process within the piping system. The outputs of the pressure sensor and ultrasonic flow meter are connected to the edge computing layer via physical connection interfaces.

[0030] The edge computing layer includes an intelligent monitoring device. This device integrates a data acquisition module, a carbon computing module, and a frequency converter control module. The data acquisition module receives real-time water pressure signals from a pressure sensor and real-time water flow signals from an ultrasonic flow meter via a physical connection interface. The carbon computing module is connected to both the data acquisition module and the frequency converter control module. It receives sensor signals from the data acquisition module and also receives real-time carbon emission factors from the power grid issued by the application and network layers. Based on the real-time water flow signals and the real-time carbon emission factors from the power grid, the carbon computing module executes dynamic carbon accounting logic to generate frequency converter control commands. The frequency converter control module receives these commands and sends corresponding drive signals to the frequency converter pumps in the physical layer, directly adjusting the pumps' operating load.

[0031] The application and network layers include a cloud server and a real-time carbon intensity input interface for the power grid. The real-time carbon intensity input interface continuously transmits real-time carbon emission factors from the power grid to intelligent monitoring devices in the edge computing layer via an external data link. The cloud server establishes a wide area network (WAN) data communication connection with the intelligent monitoring devices. The cloud server receives water-saving and carbon reduction accounting data uploaded in real-time by the intelligent monitoring devices. The cloud server performs storage, cross-validation, and data dashboard visualization operations on the water-saving and carbon reduction accounting data.

[0032] In the following embodiments of this application, an intelligent monitoring device is uniformly selected as the execution subject. (Refer to...) Figure 2 , Figure 2This is a schematic diagram of the internal hardware structure of the intelligent monitoring device provided by the present invention. In its specific physical implementation, the intelligent monitoring device includes a power management module, a microprocessor unit, a signal conditioning circuit, a storage and encryption module, and a communication module.

[0033] The power management module includes a photovoltaic input port and an AC power input port. It is physically connected to the microprocessor unit, signal conditioning circuit, storage and encryption module, and communication module via internal power lines. The power management module continuously supplies operating power to these components.

[0034] The microprocessor unit comprises a microcontroller unit and a neural network processing unit. The microprocessor unit establishes bidirectional data communication links with the signal conditioning circuit, storage and encryption module, and communication module via an internal bidirectional data bus. As the core computing hub of the intelligent monitoring device, the microprocessor unit coordinates and schedules the operational status of each module within the device.

[0035] The signal conditioning circuit includes a signal amplification circuit and an analog-to-digital converter (ADC). The signal conditioning circuit receives the underlying analog signal transmitted from the physical layer sensors. It then amplifies the voltage amplitude of the underlying analog signal using the signal amplification circuit. Finally, the signal conditioning circuit discretizes the amplified analog signal into a digital signal recognizable by the microprocessor unit using the ADC. The signal conditioning circuit then sends the digital signal to the microprocessor unit.

[0036] The storage and encryption module includes a flash memory chip and a hardware security encryption chip. The storage and encryption module receives the data to be stored from the microprocessor unit. It then invokes the hardware security encryption chip to perform a cryptographic encapsulation operation on the data. Finally, the storage and encryption module persistently writes the encrypted ciphertext generated by the cryptographic encapsulation operation into the flash memory chip for local storage.

[0037] The communication module includes a fourth-generation mobile communication antenna, a fifth-generation mobile communication antenna, and a long-range radio networking antenna. The communication module receives network transmission commands output by the microprocessor unit. The communication module activates the physical communication channels in the fourth-generation mobile communication antenna, fifth-generation mobile communication antenna, or long-range radio networking antenna, and sends the data packets corresponding to the network transmission commands to an external wide-area Internet of Things (IoT) network.

[0038] Reference Figure 3 , Figure 3 This is a schematic flowchart of the water load dynamic control method provided by the present invention, as shown below. Figure 3 As shown, the water load dynamic control method provided in this application embodiment includes steps 110 to 140: Step 110: Obtain real-time water flow data of the target water system and real-time electrical power data of the water pump that powers the target water system.

[0039] The intelligent monitoring device acquires real-time water flow data of the target water system and real-time electrical power data of the pumps providing power to the target water system at the current operating moment through a data acquisition module. In this embodiment, the target water system refers to a complete hydraulic loop including the water supply network, water terminals, and circulation loop. The real-time water flow data represents the instantaneous flow rate through a specified pipe section of the target water system at the current moment, in cubic meters per hour. The real-time electrical power data represents the instantaneous active power consumed by the pumps providing power to the target water system under the current operating conditions, in kilowatts.

[0040] In practical implementation, the intelligent monitoring device establishes a data communication connection via a communication bus with a flow sensor mounted on the target water system pipeline, reading real-time water flow data from the flow sensor. Simultaneously, the intelligent monitoring device establishes a data communication connection via the communication bus with an electrical parameter acquisition module mounted on the water pump's power supply circuit, reading real-time electrical power data from the electrical parameter acquisition module. Specific types of flow sensors include ultrasonic flow meters, electromagnetic flow meters, or turbine flow meters. Specific types of electrical parameter acquisition modules include AC power transmitters or multi-functional power meters.

[0041] After acquiring real-time water flow data and real-time electrical power data, the intelligent monitoring device performs data preprocessing operations on the real-time water flow data and real-time electrical power data to remove noise components and abnormal disturbance components in the data, thereby obtaining clean data that meets the accuracy requirements of subsequent dynamic carbon accounting.

[0042] Step 120: Based on real-time water flow data, real-time power data, and the real-time carbon emission factor of the power grid for the current period, obtain the dynamic carbon emission weight per unit volume of water for the current period.

[0043] The intelligent monitoring device periodically acquires the real-time carbon emission factor of the power grid, as released by the power grid side, for the current time period via a low-power communication network. The real-time carbon emission factor of the power grid reflects the mass of carbon dioxide emitted per kilowatt-hour of electricity produced in the power system during the current time period, expressed in grams of carbon dioxide per kilowatt-hour. The real-time carbon emission factor of the power grid fluctuates dynamically with the output ratio of different power generation types in the power system. When the output of clean energy sources such as hydropower, wind power, and photovoltaic power is relatively high, the real-time carbon emission factor of the power grid is relatively low; when the output of fossil energy sources such as coal-fired power is relatively high, the real-time carbon emission factor of the power grid is relatively high. The intelligent monitoring device acquires the real-time carbon emission factor of the power grid every 15 minutes, every 30 minutes, or every hour, with the specific acquisition cycle determined based on the update frequency of the carbon emission factor data released by the power grid dispatching agency.

[0044] After acquiring the real-time carbon emission factor of the power grid, the intelligent monitoring device correlates real-time water flow data, real-time power data, and the real-time carbon emission factor of the power grid to construct a water resource lifecycle energy consumption model. This model couples the physical flow of water with the energy consumption of the water pump over time, quantifying the energy consumed per unit volume of water during transport. Based on the output of the water resource lifecycle energy consumption model and the real-time carbon emission factor of the power grid, the intelligent monitoring device calculates the dynamic carbon emission weight per unit volume of water for the current period. The dynamic carbon emission weight represents the indirect carbon dioxide emissions generated per unit volume of water transported under the current power grid carbon intensity conditions, expressed in grams of carbon dioxide per cubic meter. .

[0045] Dynamic carbon emission weighting transforms the traditional "water consumption" indicator into a time-sensitive "dynamic carbon intensity" indicator. During periods of high grid carbon emission factor, even when transporting the same volume of water, the dynamic carbon emission weight is significantly higher than during periods of low grid carbon emission factor. The intelligent monitoring device establishes a local lookup table, storing the calculated dynamic carbon emission weight based on the current time and grid load level for quick retrieval in subsequent steps.

[0046] Step 130: When it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions, a load adjustment command for reducing the operating load of the water pump is generated based on the dynamic carbon emission weight.

[0047] The intelligent monitoring device determines the real-time carbon emission factor of the power grid for the current period. It compares this real-time carbon emission factor with pre-configured carbon emission conditions. These conditions are based on historical carbon emission data of the power grid in the target water system's area and established carbon reduction targets. When the real-time carbon emission factor of the power grid meets these conditions, it indicates that the carbon emission intensity of the power system is at a high level for the current period, meaning that every kilowatt-hour of electricity consumed by the water pumps during this period is accompanied by high indirect carbon emissions.

[0048] Assuming the real-time carbon emission factor of the power grid meets the carbon emission conditions, the intelligent monitoring device further considers the current water demand status of the target water system to make a comprehensive decision. When the water demand of the target water system is not critical, the intelligent monitoring device uses dynamic carbon emission weight as the core feedback parameter to perform load optimization calculations, determine the required reduction in pump operating frequency or power, and generate load adjustment instructions accordingly. The load adjustment instructions include specific control parameters such as the target frequency value or target power ratio.

[0049] By proactively reducing the operating load of water pumps during periods of high carbon emissions, the intelligent monitoring device implements a peak-shaving and valley-filling water use strategy. While meeting the minimum water supply pressure requirements of the target water system, some non-urgent water loads are shifted from high-carbon-emission periods to low-carbon-emission periods, thereby reducing indirect carbon emissions during high-carbon-emission periods without affecting basic water supply security.

[0050] Step 140: Send the load adjustment command to the frequency converter connected to the water pump so that the frequency converter controls the water pump to reduce the current operating load.

[0051] The intelligent monitoring device sends the load adjustment command generated in step 130 to the frequency converter electrically connected to the water pump via a communication interface. Upon receiving the load adjustment command, the frequency converter adjusts the power frequency output to the water pump according to the target frequency value or target power ratio parameter contained in the load adjustment command, thereby reducing the pump's speed and operating load. Specific types of frequency converters include frequency converters or soft starters. The communication methods between the intelligent monitoring device and the frequency converter include analog signal transmission and digital bus communication. In analog signal transmission, the intelligent monitoring device outputs a 4-20 mA current signal or a 0-10 V voltage signal as a frequency setting command. In digital bus communication, the intelligent monitoring device sends a frequency setting register write command to the frequency converter via an RS485 communication bus using the Modbus protocol.

[0052] After receiving and executing load adjustment commands, the variable frequency drive (VFD) reduces the operating frequency of the water pump, consequently decreasing its output flow rate and head, and thus reducing its energy consumption. Once the grid's carbon emission factor returns to a level below normal carbon emission conditions, the intelligent monitoring device recalculates the dynamic carbon emission weight and determines whether to restore the water pump's normal operating load based on the latest dynamic carbon emission weight. When it is determined that the load reduction strategy no longer needs to be implemented, the intelligent monitoring device generates a load restoration command and sends it to the VFD, which then restores the water pump's operating frequency to its normal operating value.

[0053] In a more specific implementation, step 110, which involves acquiring real-time water flow data of the target water system and real-time electrical power data of the pumps powering the target water system, specifically includes the following steps: Step 210: Using a hardware timestamp synchronization mechanism, the water flow signal and the electric power signal of the water pump of the target water system are synchronously sampled according to a preset synchronization sampling period to obtain initial time series data containing water flow subsequence and electric power subsequence.

[0054] The intelligent monitoring device incorporates a hardware timestamp synchronization mechanism. This mechanism, based on the device's internal hardware timer, simultaneously sends sampling commands to the flow sensor and electrical parameter acquisition module at each sampling trigger moment, ensuring that the water flow signal and electrical power signal acquired at the same sampling moment are strictly aligned on the time reference. This hardware timestamp synchronization mechanism eliminates the problem of inconsistent sampling times for multi-source data caused by software scheduling delays or communication timing deviations.

[0055] The intelligent monitoring device synchronously samples the water flow signal and the electrical power signal of the water pump in the target water system according to a preset synchronous sampling period. The preset synchronous sampling period is 100 milliseconds. That is, the intelligent monitoring device simultaneously reads the water flow signal output by the flow sensor and the electrical power signal output by the electrical parameter acquisition module every 100 milliseconds.

[0056] In the specific physical connection implementation, the intelligent monitoring device establishes a data connection with the ultrasonic flow meter via an RS482 communication bus using the Modbus communication protocol. The ultrasonic flow meter is installed on the water supply pipeline of the target water system to collect the instantaneous flow rate value of the target water system at the current moment. Simultaneously, the intelligent monitoring device establishes a data connection with the high-precision electrical parameter acquisition module via an RS482 communication bus using the Modbus communication protocol. The high-precision electrical parameter acquisition module is installed in the water pump's power supply circuit and collects the water pump's operating power value under the current operating conditions. .

[0057] The intelligent monitoring device analyzes the water flow and electrical power signals acquired in each synchronous sampling period, and arranges the analysis results from multiple consecutive sampling periods in chronological order to generate initial time series data containing water flow and electrical power subsequences. Each data point in the water flow subsequence corresponds to the instantaneous flow rate value at a specific sampling time. Each data point in the power subsequence corresponds to the instantaneous operating power value of the water pump at the same sampling time. The data points with the same index in the water flow subsequence and the electrical power subsequence have a strict one-to-one correspondence in the time dimension.

[0058] Step 220: First, perform moving average filtering on the initial time series data to filter out the power grid harmonic interference components, and then remove the instantaneous surge anomalies caused by the start and stop of the water pump in the initial time series data to obtain real-time water flow data and real-time power data.

[0059] The intelligent monitoring device performs two-stage data cleaning operations on the initial time series data generated in step 210.

[0060] In the first-level data cleaning operation, the intelligent monitoring device performs moving average filtering on the water flow and power subsequences in the initial time series data. The principle of moving average filtering is as follows: the arithmetic mean of the values ​​at the current sampling point and multiple adjacent sampling points within a preset window length is taken, and this arithmetic mean is used as the filtered output value for the current sampling point. The purpose of moving average filtering is to filter out the grid harmonic interference components superimposed on the water flow and power signals. Grid harmonic interference components are high-frequency noise components introduced into the acquired signal by high-order harmonics generated by nonlinear loads in the power system through electromagnetic coupling or common-mode interference paths. Moving average filtering effectively attenuates the amplitude of high-frequency noise components by averaging multiple sampling points, retaining the low-frequency useful signal components that reflect the true changing trends of water flow and power.

[0061] In the second-level data cleaning operation, the intelligent monitoring device performs outlier removal on the time series data after moving average filtering. The intelligent monitoring device identifies transient surge anomalies in the initial time series data caused by the start and stop of water pumps. Transient surge anomalies refer to extreme data points where, at the moment of pump start-up or shutdown, the electrical power signal or water flow signal experiences a rapid spike or drop within a short period due to the inrush current of the motor or the water hammer effect in the pipeline. The intelligent monitoring device identifies transient surge anomalies by setting data change rate thresholds or amplitude upper and lower limit thresholds. When the rate of change of a sampling point exceeds the data change rate threshold, or when the absolute value of a sampling point exceeds the amplitude upper and lower limit threshold range, the intelligent monitoring device marks that sampling point as a transient surge anomaly and removes it from the time series data. The time position of the removed transient surge anomaly is filled using linear interpolation or by replacing it with adjacent valid data points.

[0062] After two stages of data cleaning, the intelligent monitoring device outputs standardized real-time water flow data and real-time electrical power data. This standardized real-time water flow data and real-time electrical power data constitute the input time-series dataset that meets the accuracy requirements for subsequent dynamic carbon accounting.

[0063] In an alternative implementation, when faced with specific engineering constraints preventing the direct acquisition of real-time pump power data via physical circuits, the intelligent monitoring device performs an equivalent calculation of energy consumption parameters based on a fluid dynamics model. The intelligent monitoring device acquires real-time fluid pressure parameters of the physical pipe network in the target water system via an analog input interface, and simultaneously acquires real-time pump speed parameters via a digital pulse interface. The intelligent monitoring device substitutes the real-time fluid pressure and speed parameters into a pre-constructed fluid pressure and speed regression equation for solution. Through equation solving, the intelligent monitoring device calculates the expected energy consumption estimate of the target water system under the current hydraulic work state. The intelligent monitoring device uses this expected energy consumption estimate as an equivalent substitute input source for the pump's real-time power data, inputting it into subsequent calculation units to maintain the continuous calculation process of dynamic carbon emission weights.

[0064] In a more specific implementation, step 120, which involves obtaining the dynamic carbon emission weight per unit volume of water for the current time period based on real-time water flow data, real-time power data, and the real-time carbon emission factor of the power grid for the current time period, specifically includes the following steps: Step 310: Calculate the ratio of real-time electrical power data to real-time water flow data to obtain the energy consumption per unit of water volume for the current time period.

[0065] The intelligent monitoring device reads the real-time electrical power data and real-time water flow data obtained in step 110. The intelligent monitoring device uses the real-time electrical power data as the dividend and the real-time water flow data as the divisor to perform a division operation to obtain the energy consumption value per unit of water volume for the current time period.

[0066] Let the real-time electrical power data of the water pump at the current moment be... The unit is kilowatt; the real-time water flow data of the target water system at the current moment is The unit is cubic meters per hour. The formula for calculating the energy consumption per unit of water in the current time period is: Energy consumption per unit of water = .

[0067] The physical meaning of the energy consumption per unit volume of water in the current time period is: the electrical energy consumed by the water pump to deliver one cubic meter of water under the current operating conditions. The unit of energy consumption per unit volume of water is kilowatt-hours per cubic meter. The energy consumption per unit volume of water in the current time period fluctuates in real time with changes in the operating efficiency of the water pump, the pipeline resistance, and the water supply head, reflecting the hydraulic-electric coupling efficiency of the target water system at the current moment.

[0068] In the energy consumption model of the entire life cycle of water resources, the continuous-time integral form of the energy consumption per unit of water is expressed as: ; In the above formula, This represents the cumulative total energy consumption for water resource transportation over a given period. The intelligent monitoring device calculates the instantaneous energy consumption per unit volume of water in each sampling period and accumulates the instantaneous values ​​from multiple sampling periods to achieve continuous tracking of energy consumption throughout the entire lifecycle of water resources.

[0069] Step 320: Multiply the energy consumption value per unit of water by the real-time carbon emission factor of the power grid to obtain the dynamic carbon emission weight per unit of water for the current period.

[0070] Intelligent monitoring devices acquire real-time carbon emission factors of the power grid for the current period. The unit is grams of carbon dioxide per kilowatt-hour. The intelligent monitoring device will compare the energy consumption per unit of water for the current period calculated in step 310 with the real-time carbon emission factor of the power grid. Perform a multiplication operation to obtain the dynamic carbon emission weight per unit of water volume for the current time period.

[0071] The formula for calculating the dynamic carbon emission weight per unit of water volume in the current period is: ; In the above formula, This represents the dynamic carbon emission weight per unit volume of water for the current period, expressed in grams of carbon dioxide per cubic meter. Dynamic carbon emission weight It is simultaneously affected by two dynamic factors: the first dynamic factor is the real-time carbon emission factor of the power grid. This reflects the real-time fluctuation of carbon emission intensity on the power supply side as the power generation structure changes; the second dynamic factor is the energy consumption per unit of water. This reflects the real-time fluctuation of the energy efficiency of water transport on the demand side as operating conditions change. The physical meaning of multiplying the two dynamic factors is: under the current grid carbon emission level, the number of grams of carbon dioxide indirectly emitted for every cubic meter of water transported.

[0072] The intelligent monitoring device will calculate the dynamic carbon emission weight. Write the current location in the local lookup table, and simultaneously add the dynamic carbon emission weight. The load is passed to the load optimization calculation module in step 130 as the core quantitative basis for subsequent determination of whether the pump operating load needs to be reduced.

[0073] In an alternative implementation adapted to low-end microcontroller hardware, when the edge computing processor determines that the upper limit of its internal microprocessor unit's computing power resources cannot meet the floating-point operation requirements of the high-frequency dynamic evolution algorithm, it executes a computational degradation strategy in the control logic. The edge computing processor replaces the continuous dynamic carbon intensity evolution algorithm, which involves multiple multiplication, division, and integration operations, with discrete-dimensional time-sharing table lookup logic. The edge computing processor pre-programs a time-sharing carbon intensity mapping matrix table into its local persistent memory. This table contains multiple discrete time intervals divided according to a 24-hour day, and a static carbon emission weight estimate pre-bound to each interval. The edge computing processor extracts the real-time timestamp parameter of the current system hardware clock. Using this timestamp parameter as an index key, it performs memory address lookup in the time-sharing carbon intensity mapping matrix table, directly reading the corresponding static carbon emission weight estimate value within that memory address. This retrieved static carbon emission weight estimate value is then used as the basis for determining whether to generate a load adjustment instruction.

[0074] In a more specific implementation, step 130, which involves generating a load adjustment command for reducing the operating load of the water pump based on the dynamic carbon emission weight when it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions, specifically includes the following steps: Step 410: When it is determined that the real-time carbon emission factor of the power grid is greater than the preset carbon emission judgment threshold, obtain the real-time water demand level parameter of the current target water system.

[0075] The intelligent monitoring device will acquire the real-time carbon emission factor of the power grid during the current period. The value is compared with the carbon emission judgment threshold (Threshold) pre-stored in the local storage space of the smart monitoring device. The carbon emission judgment threshold (Threshold) is a fixed value or a segmented value divided by time period, pre-set based on the historical carbon emission factor distribution statistics of the power grid in the target water system area, regional carbon reduction targets, and the operating characteristics of the water system. When the real-time carbon emission factor of the power grid... When the value exceeds the carbon emission threshold, the intelligent monitoring device determines that the current period is a high-carbon emission period.

[0076] After determining that the current period falls within a high-carbon emission period, the intelligent monitoring device acquires the real-time water demand level parameter for the target water system. This real-time water demand level parameter reflects the urgency of water use at each terminal within the target water system at the current moment. The intelligent monitoring device determines the specific value of the real-time water demand level parameter by reading feedback data from pressure sensors in the target water system, scheduling information from water usage timetables, or the operating status flags of water terminals. The value range of the real-time water demand level parameter is divided into multiple discrete levels, decreasing progressively from the highest urgency critical water level to the lowest urgency non-critical water level.

[0077] Step 420: When the real-time water demand level parameter is determined to be within the preset non-critical water use range, calculate the comprehensive carbon reduction gain value based on the real-time water flow data and the real-time carbon emission factor of the power grid.

[0078] The intelligent monitoring device compares the real-time water demand level parameters obtained in step 410 with the pre-configured non-critical water demand range. The non-critical water demand range refers to the range of water demand levels in the target water system that allows for a moderate reduction in water supply without affecting production safety and basic living conditions. When the real-time water demand level parameters fall within the non-critical water demand range, the intelligent monitoring device confirms that the conditions for performing load reduction operations are met at the current moment.

[0079] The intelligent monitoring device executes a carbon reduction accounting algorithm based on real-time water flow data and the real-time carbon emission factor of the power grid to calculate a comprehensive carbon reduction gain value. This comprehensive carbon reduction gain value quantifies the total carbon emission reduction that can be achieved by implementing load reduction operations in the current period. It comprehensively considers the contributions of both the indirect carbon emission reduction from water resource production processes due to water conservation and the direct carbon emission reduction from reducing water pump energy consumption. Based on the magnitude of the comprehensive carbon reduction gain value, the intelligent monitoring device determines the current water-saving and carbon reduction efficiency and uses it as an optimization target parameter for generating subsequent load adjustment commands.

[0080] Step 430: Using dynamic carbon emission weights as feedback inputs, and taking the goal of maximizing the comprehensive carbon reduction gain as the control objective, load regulation commands are generated based on the proportional-integral-derivative adjustment algorithm.

[0081] The intelligent monitoring device will use the dynamic carbon emission weights calculated in step 120. This serves as the feedback input for the proportional-integral-derivative (PID) control algorithm. The PID is a classic closed-loop control algorithm consisting of three control elements: a proportional element, an integral element, and a derivative element. The proportional element is based on the dynamic carbon emission weight. The deviation from the expected carbon emission weight target value generates an immediate response. The integral element affects the dynamic carbon emission weight. Historical deviations are accumulated to eliminate steady-state errors. The differential step applies dynamic carbon emission weights. The changing trend is predicted to suppress overshoot.

[0082] The intelligent monitoring device sets the control objective of the proportional-integral-derivative (PID) control algorithm to maximize the overall carbon reduction gain. This maximization objective maximizes the overall carbon reduction gain while meeting the minimum water supply pressure constraint of the target water system. The PID algorithm iteratively calculates the output regulation based on the deviation between the feedback input and the control objective. The intelligent monitoring device then converts this regulation into a load regulation command. This load regulation command includes the target frequency value that the variable frequency drive (VFD) equipment needs to set.

[0083] The proportional-integral-derivative (PI-DE) control algorithm runs continuously within each control cycle. This is achieved when the dynamic carbon emission weight... Due to the real-time carbon emission factor of the power grid When the load decreases, the proportional-integral-derivative (PI-DE) control algorithm automatically adjusts the output load control command to gradually restore the pump's operating frequency. The entire adjustment process is executed locally on the intelligent monitoring device, without waiting for remote commands from the cloud server.

[0084] In a more specific implementation, step 420, which involves calculating the comprehensive carbon reduction gain value based on real-time water flow data and the real-time carbon emission factor of the power grid, specifically includes the following steps: Step 510: The difference between the preset water flow threshold and the real-time water flow data is determined as the water saving amount.

[0085] The intelligent monitoring device reads a preset water flow threshold from its local storage. This preset water flow threshold is a baseline water flow value pre-set based on the target water system's design water supply specifications, historical water consumption statistics, and water consumption quota standards. The preset water flow threshold represents the standard water consumption of the target water system under current operating conditions. The intelligent monitoring device subtracts the real-time water flow data obtained in step 110 from the preset water flow threshold to obtain the difference. The intelligent monitoring device determines the difference obtained from the subtraction operation as the water saving amount. .

[0086] Water saving The physical meaning is: within the current sampling period, the extent to which the actual water consumption of the target water system is lower than the standard water consumption. When the real-time water flow data is lower than the preset water flow threshold, the water saving amount... A positive value indicates that the target water system has achieved water saving in the current cycle. When the real-time water flow data is equal to or higher than the preset water flow threshold, the water saving amount is... A value of zero or negative indicates that no effective water conservation has been achieved in the current cycle.

[0087] Step 520: Calculate the expected energy consumption optimization of the water pump based on the current candidate load adjustment.

[0088] The intelligent monitoring device predicts the expected energy consumption optimization of the water pump after performing load reduction operations based on the candidate load adjustment output of the current iteration of the proportional-integral-derivative control algorithm. The candidate load adjustment represents the magnitude by which the proportional-integral-derivative (PI-DE) control algorithm intends to reduce the pump's operating frequency in the current iteration step. Based on the pump's power-frequency characteristic curve, the intelligent monitoring device maps the candidate load adjustment to the power difference of the pump before and after frequency adjustment, and determines this power difference as the expected energy consumption optimization amount. Expected energy consumption optimization The physical meaning of is: under the current candidate load adjustment, the amount of electrical energy consumed by the water pump per unit time is reduced, and the unit is kilowatt-hour.

[0089] The power-frequency characteristic curve of the water pump is obtained in advance through actual operation tests of the water pump at different frequencies, and stored in the local storage space of the intelligent monitoring device in the form of a lookup table or a fitted polynomial. The intelligent monitoring device calculates the expected energy consumption optimization amount each time. At that time, the calculation is performed by referring to a table or substituting into a polynomial based on the current operating frequency of the water pump and the candidate load adjustment amount.

[0090] Step 530: Multiply the water saving amount by the preset implicit carbon coefficient of water resource production to obtain the indirect carbon reduction amount.

[0091] The intelligent monitoring device reads the preset implicit carbon coefficient of water resource production from the local storage space. Implicit carbon coefficient in water resource production This reflects the indirect carbon dioxide emissions generated per unit volume of water during the water production and treatment processes, such as water intake, purification, and disinfection. The unit is grams of carbon dioxide per cubic meter. (Water resource production implicit carbon coefficient) The values ​​are predetermined based on the tap water production process, water source type, and energy consumption statistics of the target water system's location.

[0092] The intelligent monitoring device will calculate the water saving amount in step 510. Implicit carbon coefficient in water resource production Performing multiplication yields the indirect carbon reduction. The expression for calculating indirect carbon reduction is: Indirect carbon reduction = .

[0093] The physical meaning of indirect carbon reduction is: due to the saving of... The volume of water, and the corresponding reduction in carbon dioxide emissions during water resource production and treatment. Indirect carbon reduction reflects the contribution of water-saving behaviors to reducing emissions from hidden energy consumption in upstream water production processes.

[0094] Step 540: Multiply the expected energy consumption optimization amount by the real-time carbon emission factor of the power grid to obtain the direct carbon reduction amount.

[0095] The intelligent monitoring device will optimize the expected energy consumption calculated in step 520. Real-time carbon emission factors of the power grid during the current period Performing multiplication yields the direct carbon reduction. The expression for calculating direct carbon reduction is: Direct carbon reduction = .

[0096] The physical meaning of direct carbon reduction is: the reduction in carbon dioxide emissions corresponding to the reduced electrical energy consumption of the water pump due to the reduced operating load of the water pump, under the current carbon emission intensity of the power grid. Direct carbon reduction reflects the direct emission reduction contribution of water pump energy efficiency optimization to the electricity consumption process. The value of direct carbon reduction is affected by the real-time carbon emission factor of the power grid. The impact is significant: during periods of high carbon emissions, the same expected energy consumption optimization... The direct carbon reduction it brings is far greater than that during periods of low carbon emissions.

[0097] Step 550: Sum the indirect carbon reduction amount with the direct carbon reduction amount to obtain the comprehensive carbon reduction gain value.

[0098] The intelligent monitoring device adds the indirect carbon reduction calculated in step 530 to the direct carbon reduction calculated in step 540 to obtain the comprehensive carbon reduction gain value G. The expression for calculating the comprehensive carbon reduction gain value G is: ; In the above formula, the summation symbol represents the cumulative summation over multiple sampling periods or multiple water-using devices. The comprehensive carbon reduction gain value G integrates the indirect carbon reduction contribution from water conservation and the direct carbon reduction contribution from energy conservation into a carbon emission equivalent index system of the same dimension, providing a single, comprehensive optimization objective function for the proportional-integral-derivative adjustment algorithm.

[0099] The intelligent monitoring device determines the current water-saving and carbon-reduction efficiency in real time based on the magnitude of the comprehensive carbon reduction gain value G. A higher comprehensive carbon reduction gain value G indicates that the current load reduction strategy has achieved a high overall benefit in terms of carbon emission reduction. Simultaneously, the intelligent monitoring device generates time-segmented carbon footprint reports based on the time-series data of the comprehensive carbon reduction gain value G and stores these reports in local storage.

[0100] In a more specific embodiment, after sending the load adjustment command to the frequency converter connected to the water pump in step 140, the intelligent monitoring device further performs the following steps: Step 610: After the water pump executes the load adjustment command, obtain the actual carbon reduction data calculated after the water pump reduces its operating load.

[0101] After the variable frequency drive (VFD) controls the water pump to reduce its current operating load according to the load adjustment command, the intelligent monitoring device acquires the actual operating parameters of the water pump after the load reduction. Based on the difference in actual power data and actual water flow data before and after the load reduction, as well as the real-time carbon emission factor of the power grid for the current period, the intelligent monitoring device calculates the actual reduction in carbon emissions caused by the execution of the load adjustment command, and defines this reduction as the actual carbon reduction data. The difference between the actual carbon reduction data and the comprehensive carbon reduction gain value in step 320 is that the comprehensive carbon reduction gain value is an expected value predicted based on the candidate load adjustment amount before the load adjustment command is executed, while the actual carbon reduction data is a measured value calculated based on the actual changes in the water pump's operating state after the load adjustment command is executed. The introduction of actual carbon reduction data ensures that subsequent data storage is based on the actual physical execution results rather than predicted estimates.

[0102] Step 620: Perform hash operations on the real-time water flow data, the real-time carbon emission factor of the power grid, and the actual carbon reduction data locally to generate a hash digest corresponding to the current load adjustment event.

[0103] The intelligent monitoring device locally concatenates and packages three types of core data related to the current load regulation event. These three types of core data include: real-time water flow data obtained in step 110, real-time carbon emission factors of the power grid obtained in step 120, and actual carbon reduction data calculated in step 610. The intelligent monitoring device performs a hash operation on the concatenated and packaged data. A hash operation is a one-way cryptographic function that maps input data of arbitrary length to a fixed-length output digest. Specific hash operation algorithms include SHA-256, SHA-3, or the SM3 national cryptographic algorithm. After performing the hash operation, the intelligent monitoring device generates a fixed-length hash digest. Each hash digest corresponds one-to-one with the current load regulation event. The hash digest is collision-resistant and irreversible: any minor alteration to the original data will result in a significant difference between the recalculated hash digest and the original hash digest, and the original data cannot be derived from the hash digest.

[0104] The hash operation is performed locally on the smart monitoring device, eliminating the need to transmit the original real-time water flow data, real-time carbon emission factors of the power grid, and actual carbon reduction data in plaintext to an external network, thus ensuring the local security of the original data during the generation and storage process.

[0105] Step 630: After encapsulating the hash digest using a preset lightweight encryption algorithm, upload the encapsulated hash digest to the cloud database to trigger the cloud database to perform cross-validation on the encapsulated hash digests uploaded by multiple device nodes and generate an immutable carbon emission reduction certificate.

[0106] The intelligent monitoring device encapsulates the hash digest generated in step 620 using a pre-configured lightweight encryption algorithm. A lightweight encryption algorithm is a cryptographic encryption algorithm suitable for operation under the limited computing power and storage resources of the intelligent monitoring device. Specific types of lightweight encryption algorithms include AES-128 symmetric encryption algorithm, PRESENT lightweight block cipher algorithm, or lightweight asymmetric encryption algorithm based on elliptic curves. The intelligent monitoring device uses the lightweight encryption algorithm to perform an encrypted encapsulation operation on the hash digest, generating an encapsulated hash digest. Even if the encapsulated hash digest is intercepted during transmission, the interceptor cannot obtain the original content of the hash digest without possessing the decryption key.

[0107] The intelligent monitoring device uploads the encapsulated hash digest to a cloud database via a wide-area Internet of Things (WAN). Specific communication methods for WAN include 4G cellular network-based communication, 5G cellular network-based communication, and NB-IoT narrowband Internet of Things (NB-IoT). In scenarios where there is no public cellular network coverage in the area where the intelligent monitoring device is located, the device uses a LoRa Mesh network to first aggregate the encapsulated hash digest to a single gateway node, which then forwards it to the cloud database via a wired network.

[0108] After receiving the encapsulated hash digest, the cloud database decrypts it to extract the original content. Simultaneously, the cloud database receives encapsulated hash digests uploaded by multiple device nodes deployed in different geographical locations. The cloud database performs cross-validation on the encapsulated hash digests uploaded by multiple device nodes. The cross-validation process includes: comparing the consistency of real-time grid carbon emission factor data from different device nodes within the same time period; verifying the reasonableness between the actual carbon reduction data uploaded by each device node and the historical operating data of the corresponding device node; and checking the timestamp continuity of the hash digests uploaded by each device node. Data records that pass cross-validation are marked as verified by the cloud database and generate tamper-proof carbon reduction certificates. These carbon reduction certificates record information such as the actual water savings, grid carbon emission intensity, and actual carbon reduction of the corresponding device node within the corresponding time period, constituting a water-saving and carbon reduction contribution certificate that can be used for subsequent carbon emission audits, carbon trading, or carbon asset management.

[0109] In an alternative implementation addressing network coverage blind spots, when an edge computing processor determines that its current physical deployment area lacks public cellular network signal coverage, it activates its built-in long-range low-power radio ad hoc communication chip. Multiple edge computing processors distributed across different geographically uncovered locations collectively construct a multi-hop mesh communication topology network via the long-range low-power radio ad hoc communication chip. These edge computing processors relay raw data collected locally from underlying sensors through the multi-hop mesh communication topology network, ultimately aggregating it at a single aggregation gateway device equipped with external network communication capabilities. Upon receiving the aggregated raw data from multiple sources, the single aggregation gateway device performs centralized calculations of integrated carbon reduction gains and unified distribution of load adjustment commands, replacing the individual edge computing processors.

[0110] Reference Figure 4 , Figure 4 This is a schematic diagram of the water load dynamic control system provided by the present invention. The system includes: The data acquisition module is used to acquire real-time water flow data of the target water system and real-time electrical power data of the water pump that powers the target water system. The carbon power calculation module is used to obtain the dynamic carbon emission weight per unit volume of water in the current period based on real-time water flow data, real-time power data and the real-time carbon emission factor of the power grid in the current period. The carbon computing power calculation module is also used to generate load adjustment instructions to reduce the operating load of water pumps based on dynamic carbon emission weights when it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions. The variable frequency control module is used to send load adjustment commands to the variable frequency control equipment connected to the water pump, so that the variable frequency control equipment can control the water pump to reduce the current operating load.

[0111] It should be noted that the water load dynamic control system provided by the present invention can execute the water load dynamic control method of any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0112] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 5 As shown, the electronic device may include a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call logical instructions in the memory 530 to execute the water load dynamic control method provided in the above embodiments.

[0113] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. 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.

[0114] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by the computer, the computer is able to execute the water load dynamic control method provided in the above embodiments.

[0115] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the water load dynamic control method provided in the above embodiments.

[0116] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, 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 can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for dynamic control of water load, characterized in that, include: Acquire real-time water flow data of the target water system, and real-time electrical power data of the water pump that powers the target water system; Based on the real-time water flow data, the real-time power data, and the real-time carbon emission factor of the power grid for the current time period, the dynamic carbon emission weight per unit volume of water for the current time period is obtained. When it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions, a load adjustment command for reducing the operating load of the water pump is generated based on the dynamic carbon emission weight. The load adjustment command is sent to the frequency converter connected to the water pump so that the frequency converter controls the water pump to reduce the current operating load.

2. The method for dynamic control of water load according to claim 1, characterized in that, The step of obtaining the dynamic carbon emission weight per unit volume of water for the current time period based on the real-time water flow data, the real-time power data, and the real-time carbon emission factor of the power grid for the current time period includes: Calculate the ratio of the real-time electrical power data to the real-time water flow data to obtain the energy consumption value per unit of water in the current time period; The energy consumption value per unit of water is multiplied by the real-time carbon emission factor of the power grid to obtain the dynamic carbon emission weight per unit of water in the current time period.

3. The method for dynamic control of water load according to claim 1, characterized in that, When it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions, the process of generating a load adjustment command to reduce the operating load of the water pump based on the dynamic carbon emission weight includes: When it is determined that the real-time carbon emission factor of the power grid is greater than the preset carbon emission determination threshold, the real-time water demand level parameter of the target water system is obtained. When it is determined that the real-time water demand level parameter is within the preset non-critical water use range, a comprehensive carbon reduction gain value is calculated based on the real-time water flow data and the real-time carbon emission factor of the power grid. Using the dynamic carbon emission weight as the feedback input, and taking the goal of maximizing the comprehensive carbon reduction gain as the control objective, the load adjustment command is calculated and generated based on the proportional-integral-derivative adjustment algorithm.

4. The method for dynamic water load control according to claim 3, characterized in that, The step of calculating the comprehensive carbon reduction gain value based on the real-time water flow data and the real-time carbon emission factor of the power grid includes: The difference between the preset water flow threshold and the real-time water flow data is determined as the water saving amount; The expected energy consumption optimization of the water pump is obtained based on the current candidate load adjustment amount; Multiply the water-saving amount by the preset implicit carbon coefficient of water resource production to obtain the indirect carbon reduction amount; Multiply the expected energy consumption optimization amount by the real-time carbon emission factor of the power grid to obtain the direct carbon reduction amount; The indirect carbon reduction amount is summed with the direct carbon reduction amount to obtain the comprehensive carbon reduction gain value.

5. The method for dynamic control of water load according to claim 1, characterized in that, The acquisition of real-time water flow data of the target water system and real-time electrical power data of the water pumps that power the target water system includes: The water flow signal of the target water system and the electric power signal of the water pump are synchronously sampled according to a preset synchronization sampling period through a hardware timestamp synchronization mechanism to obtain initial time series data containing water flow subsequence and electric power subsequence. The initial time series data is first processed by moving average filtering to filter out power grid harmonic interference components, and then the instantaneous surge anomalies caused by the start and stop of the water pump are removed from the initial time series data to obtain the real-time water flow data and the real-time power data.

6. The method for dynamic control of water load according to claim 1, characterized in that, After sending the load adjustment command to the frequency converter connected to the water pump, the method further includes: After the water pump executes the load adjustment command, the actual carbon reduction data calculated after the water pump reduces its operating load is obtained; The real-time water flow data, the real-time carbon emission factor of the power grid, and the actual carbon reduction data are hashed locally to generate a hash digest corresponding to the current load adjustment event. After encapsulating the hash digest using a preset lightweight encryption algorithm, the encapsulated hash digest is uploaded to a cloud database to trigger the cloud database to perform cross-validation on the encapsulated hash digests uploaded by multiple device nodes and generate an immutable carbon emission reduction certificate.

7. A dynamic water load control system, characterized in that, include: The data acquisition module is used to acquire real-time water flow data of the target water system and real-time electrical power data of the water pump that provides power to the target water system. The carbon power calculation module is used to obtain the dynamic carbon emission weight per unit volume of water in the current time period based on the real-time water flow data, the real-time power data and the real-time carbon emission factor of the power grid in the current time period. The carbon computing power calculation module is also used to generate a load adjustment command to reduce the operating load of the water pump based on the dynamic carbon emission weight when it is determined that the real-time carbon emission factor of the power grid meets the preset carbon emission conditions. The variable frequency control module is used to send the load adjustment command to the variable frequency control device connected to the water pump, so that the variable frequency control device controls the water pump to reduce the current operating load.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the dynamic water load control method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic water load control method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the dynamic water load control method as described in any one of claims 1 to 6.

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