Method, apparatus, device and medium for visualizing energy efficiency of digital transmission system

Abstract

The embodiment of the invention discloses a method, equipment, a device and a medium for visualizing the energy efficiency of a digital transmission system. The method comprises the following steps: acquiring a model of the digital transmission system; acquiring real-time control parameters of variable frequency driving; the real-time control parameters are input into the model; receiving a predicted output power of at least one component of the digital driveline from the model; and displaying the predicted energy efficiency of the at least one component in a visual interface based on the predicted output power. The developed visual interface makes data and analysis transparent, thereby saving the time and workload for operators to check anomalies and providing guidance for future planning and decision making.

Description

Technical Field

[0001] This invention relates to the technical field of digital drive systems, and more particularly to a method, apparatus, device, and medium for visualizing the energy efficiency of digital drive systems. Background Technology

[0002] In recent years, global energy consumption has been increasing. Total global energy consumption rose significantly to 580 terajoules in 2022 and is expected to continue increasing in the following year. However, conventional energy sources are finite, and we are still experiencing a global energy crisis and oil and gas shortages. Efforts are underway to address these issues, which can be divided into two objectives. One objective is to develop other sustainable alternatives, such as wind and nuclear power. The other objective is to optimize energy use methods, i.e., increase efficiency.

[0003] Industry in many countries remains energy-intensive. As estimated, the industrial sector accounts for 55% of the world's total energy consumption. Raising awareness of the environmental issues associated with electricity production sets a ceiling on energy consumption.

[0004] Digital drive systems integrate key components and power facilities, converting electrical energy into mechanical energy, with motors consuming approximately 70% of industrial electricity. Therefore, it is clear that there is a need to improve the energy efficiency of drive systems. Summary of the Invention

[0005] Embodiments of the present invention provide a method, apparatus, device, and medium for visualizing the energy efficiency of a digital drive system.

[0006] In a first aspect, a method for visualizing the energy efficiency of a digital drive system is provided. The components of the digital drive system include a variable frequency drive, a motor, and an application device, and the method includes: Obtain the model of the digital drive system; Obtain real-time control parameters for the variable frequency drive; Input real-time control parameters into the model; Receive the predicted output power of at least one component of the digital drive system from the model; and The predicted energy efficiency of at least one component is displayed in a visual interface based on the predicted output power.

[0007] In a second aspect, an apparatus for visualizing the energy efficiency of a digital drive system is provided. The components of the digital drive system include a variable frequency drive, a motor, and an application device, and the apparatus includes: The first acquisition module is configured to acquire a model of the digital drive system; The second acquisition module is configured to acquire real-time control parameters of the frequency converter drive; The input module is configured to input real-time control parameters into the model; and A receiving module configured to receive from the model the predicted output power of at least one component of the digital drive system; and A display module configured to display the predicted energy efficiency of at least one component in a visual interface based on the predicted output power.

[0008] In a third aspect, an electronic device is provided. The electronic device includes a processor and a memory, wherein an application program executable by the processor is stored in the memory for causing the processor to perform a method for visualizing the energy efficiency of a digital drive system as described in any of the preceding claims.

[0009] In a fourth aspect, a computer-readable medium is provided that includes computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement a method for visualizing the energy efficiency of a digital drive system as described in any of the preceding claims.

[0010] In the fifth aspect, a computer program product comprising a computer program, which, when executed by a processor, is used to perform a method for visualizing the energy efficiency of a digital drive system as described in any of the preceding items.

[0011] Based on the above technical solutions, the developed visualization interface makes data and analysis transparent, thereby saving operators time and workload in checking for anomalies and providing guidance for future planning and decision-making. Attached Figure Description

[0012] To make the technical solutions of the embodiments of this disclosure clearer, only the accompanying drawings used to describe the embodiments will be included below. Obviously, the drawings described below are only some examples of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0013] Figure 1 This is a flowchart of a method for visualizing the energy efficiency of a digital drive system according to an embodiment of the present invention.

[0014] Figure 2 This is a first schematic diagram of a digital drive system model according to an embodiment of the present invention.

[0015] Figure 3 This is a second schematic diagram of a digital drive system model according to an embodiment of the present invention.

[0016] Figure 4 This is a schematic diagram illustrating the visualization and optimization process of a digital drive system according to an embodiment of the present invention.

[0017] Figure 5 This is a schematic diagram of the diagnostic workflow for energy consumption and efficiency according to an embodiment of the present invention.

[0018] Figure 6 This is a schematic diagram illustrating the power loss of a digital drive system according to an embodiment of the present invention.

[0019] Figure 7 This is a structural diagram of a device for visualizing the energy efficiency of a digital drive system according to an embodiment of the present invention.

[0020] Figure 8 This is a structural diagram of an electronic device according to an embodiment of the present invention.

[0021] List of reference numerals in the attached diagram: Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the following examples are provided to further explain this invention in detail.

[0023] For the sake of brevity and intuitiveness, the present invention will be described below through several representative embodiments. Numerous details in the embodiments are provided solely to aid in understanding the invention. However, it is obvious that the technical solutions of the present invention can be implemented without being limited to these details. To avoid unnecessarily obscuring the invention, some embodiments are not described in detail, but only outlines are given. In the following text, "comprising" means "comprising but not limited to," and "according to..." means "at least according to..., but not limited to...". When the number of elements is not specifically indicated below, it means that the elements may be one or more, or can be understood as at least one.

[0024] Following the investigation, at least the following technical problems have been identified in the existing technology: Technical Problem 1: Existing technologies mainly focus on the overall equipment and make recommendations based on the value of the product produced or the cost in the process. Few studies consider the mechanical and electrical characteristics of the transmission system.

[0025] Technical Issue 2: Existing technologies for industrial efficiency cannot establish robust evaluation models that take into account the differences in end-user instruments. For example, the U.S. Department of Energy's Industrial Technologies Program provides pumping system evaluation tools that can only identify the current operating status of the pumping system.

[0026] Technical Issue 3: Despite progress in finding optimal parameters for drivetrain systems, the lack of visualization tools results in data opacity for users and fails to help them make decisions to improve drivetrain efficiency.

[0027] Therefore, tools for the analysis, diagnosis, optimization, and visualization of a complete transmission system are needed.

[0028] Existing technologies regarding the energy efficiency of drive systems typically consider the internal structure of the motor, with little regard for the dynamic characteristics of external loads. Others place drive systems within limited application scenarios, with vehicles and trains being a preferred area of ​​research. Research focuses on specific end-user instruments and their key energy consumption patterns.

[0029] Traditional Method 1: Energy efficiency of the motor in the transmission system

[0030] The electric motor acts as a converter in a drive system, transmitting electrical input to torque output. Traditional methods for evaluating motor energy efficiency include the nameplate method, differential method, current method, statistical method, equivalent circuit method, and loss analysis method. However, these methods are inaccurate due to their reliance on technical manuals and empirical parameters. They also disrupt operation. P. Pillay et al. created a method using a genetic algorithm (GA) to identify some unavailable parameters online. Gomaa F. Abdelnaby et al. proposed a controller for meaningless speed control of permanent magnet synchronous motors (PMSMs) using a PI controller and fuzzy control. Chong Liu developed software for fault diagnosis and efficiency monitoring of motor systems, measuring power factor, current, and torque. These methods are suitable for measuring motor energy efficiency individually and do not consider load type.

[0031] Traditional Method 2: Energy Efficiency of Transmission Systems in Vehicles and Trains

[0032] A key theme in drivetrain efficiency is its performance within vehicles. Jorge O. Estima et al. studied traction in two drivetrain topologies for electric and hybrid vehicles, including pulse-width modulation (PWM) inverters connected to a PMSM, where energy efficiency was specifically considered, different modes were used, and a variable voltage control strategy was applied. Similarly, Sheldon S. Williamson et al. presented efficiency plots of inverters and traction motors in hybrid electric vehicles (HEVs) and fuel cell vehicles (FCVs). Agostinho Rocha et al. proposed a simulated annealing optimization algorithm to minimize train traction energy. These methods have limitations in analyzing drivetrains within vehicle or train contexts.

[0033] Traditional Method 3: Energy Efficiency of Specific User Instruments

[0034] The system efficiency of drive systems with user-defined instruments has not received much attention. Xun Wang developed a portable online fan efficiency testing system based on an equivalent circuit model. Yan Qiu Du et al. optimized the circulating water system of a thermal power plant by adding variable frequency control and calculating the optimal frequency. These methods are applicable to specific end users.

[0035] However, specifically, the above methods have drawbacks. Limitation 1: No universal motor efficiency analysis algorithm has been established. Limitation 2: Systems integrating different variable frequency speed control methods are required, including v / f control, vector control, and slip control. Limitation 3: The problem of multi-drive systems with dynamic loads in industrial applications needs to be addressed. Limitation 4: Few studies consider the diversity of end-users and are only applicable to specific instruments, making it difficult to scale up.

[0036] Embodiments of the present invention provide a method for evaluating and optimizing the energy consumption of drive systems with different applications (e.g., fans, water pumps, compressors). Accurate analysis and diagnostics based on key energy consumption instruments from OEMs and end-users help identify energy loss saving potential and intelligently optimize the overall drive system's energy efficiency. Transparency and optimization of individual and multiple drive systems are achieved based on the application using smart terminals (e.g., IoT 2050, edge devices, IPC nanometers, etc.). Control signals are then transmitted to the drives and motors to help optimize drive system energy efficiency and performance.

[0037] Figure 1 This is a flowchart illustrating a method for visualizing the energy efficiency of a digital drive system according to an embodiment of the present invention. The components of the digital drive system include a variable frequency drive, a motor, and an application device. Figure 1 As shown, the method includes: Step 101: Obtain the model of the digital drive system.

[0038] In one embodiment, obtaining a model of the digital drive system includes: determining the mechanism model of the variable frequency drive, the mechanism model of the motor, and the mechanism model of the application device through mechanism modeling; and combining the mechanism model of the variable frequency drive, the mechanism model of the motor, and the mechanism model of the application device to generate a mechanism model of the digital drive system.

[0039] Step 102: Obtain the real-time control parameters of the variable frequency drive.

[0040] The control parameters include at least one of the following: r65: slip frequency / f_Slip; r66: output frequency / f_outp; r1337: actual slip compensation / Slip comp act val; r1770: motor model speed adaptive proportional component / MotModn_adapt Kp; r0063_1: actual slip compensation / Slip comp act val; r0063_2: actual slip compensation / Slipcomp act val; r21: actual speed value (smoothed); r24: output frequency (smoothed); r25: CO: output voltage (smoothed); r27: actual absolute current value (smoothed); r29: actual excitation current value (smoothed); r30: actual torque current value (smoothed). (smoothed); r31: Actual torque value (smoothed); r32: Actual effective power value (smoothed); r62: Frequency setting value after filtering; r87: Actual power factor value; r39_0: Total energy consumption; r39_1: Received energy; r39_2: Energy feedback; r41: Energy saved; r83: Flux setting value; r84: Actual flux value; r1598: Total flux setting value; r1348: Actual U / f control energy saving factor; r26: DC link voltage (smoothed); r1315: Actual U / f control energy saving factor; r1337: Actual U / f control energy saving factor; r1310: Actual U / f control energy saving factor; r1311: Actual U / f control energy saving factor; r1312: Actual U / f control energy saving factor.

[0041] The above exemplary description provides typical examples of control parameters, and those skilled in the art will recognize that this specification is exemplary and not intended to limit the scope of protection of the methods for implementing the present invention.

[0042] In one embodiment, obtaining a model of a digital drive system includes: determining training samples, the training samples including historical values ​​of control parameters of a variable frequency drive, wherein the label set of the training samples includes historical output power of the variable frequency drive corresponding to historical values, historical output power of the motor corresponding to historical values, and historical output power of the application device corresponding to historical values; inputting the training samples into a neural network model to receive a predicted output power set from the neural network model, the predicted output power set including the predicted output power of the variable frequency drive, the predicted output power of the motor, and the predicted output power of the application device; determining a loss function value based on the difference between the predicted output power set and the label set; configuring the model parameters of the neural network model so that the loss function value is lower than a preset threshold; and determining the configured neural network model as an artificial intelligence model of the digital drive system.

[0043] Figure 2 This is a first schematic diagram of a digital drive system model according to an embodiment of the present invention.

[0044] exist Figure 2 In this process, training sample 11 is input into neural network model 20. Training sample 11 contains the values ​​of historical control parameters of the variable frequency drive and a label set. The label set contains: (1) the output power of the variable frequency drive corresponding to the values ​​of historical control parameters; (2) the output power of the motor corresponding to the values ​​of historical control parameters; and (3) the output power of the application device corresponding to the values ​​of historical control parameters. Neural network model 20 outputs a predicted output power set, which contains the predicted output power of the variable frequency drive, the predicted output power of the motor, and the predicted output power of the application device. The loss function value is determined based on the difference between the predicted output power set and the label set. The model parameters of neural network model 20 are configured so that the loss function value is lower than a preset threshold, and the configured neural network model 20 is determined as an artificial intelligence model of the digital drive system.

[0045] In one embodiment, obtaining a model of a digital drive system includes: determining a first training sample, the first training sample including first historical values ​​of control parameters of a variable frequency drive, wherein the label of the first training sample includes historical output power of the variable frequency drive corresponding to the first historical value; inputting the first training sample into a first neural network model to receive predicted output power of the variable frequency drive from the first neural network model; determining a first loss function value based on a first difference between the predicted output power of the variable frequency drive and the label of the first training sample; configuring model parameters of the first neural network model such that the first loss function value is lower than a preset first threshold; and determining the configured first neural network model as an artificial intelligence model of the variable frequency drive.

[0046] In one embodiment, obtaining a model of a digital drive system includes: determining a second training sample, the second training sample including second historical values ​​of control parameters of a variable frequency drive, wherein the label of the second training sample includes the historical output power of the motor corresponding to the second historical value; inputting the second training sample into a second neural network model to receive the predicted output power of the motor from the second neural network model; determining a second loss function value based on a second difference between the predicted output power of the motor and the label of the second training sample; configuring the model parameters of the second neural network model so that the second loss function value is lower than a preset second threshold; and determining the configured second neural network model as an artificial intelligence model of the motor.

[0047] In one embodiment, obtaining a model of a digital drive system includes: determining a third training sample, the third training sample including a third historical value of the control parameters of the variable frequency drive, wherein the label of the third training sample includes the historical output power of the application device corresponding to the third historical value; inputting the third training sample into a third neural network model to receive the predicted output power of the application device from the third neural network model; determining a third loss function value based on a third difference between the predicted output power of the application device and the label of the third training sample; configuring the model parameters of the third neural network model so that the third loss function value is lower than a preset third threshold; and determining the configured third neural network model as the artificial intelligence model of the application device.

[0048] Figure 3 This is a second schematic diagram of a digital drive system model according to an embodiment of the present invention.

[0049] exist Figure 3 In the digital drive chain, the artificial intelligence model 30 includes a first neural network model 37 for frequency conversion drive, a second neural network model 38 for motor, and a third neural network model 39 for application device.

[0050] The first training sample 31 is input into the first neural network model 37. The first training sample 31 contains the values ​​and labels of the historical control parameters of the frequency converter drive. The labels include the historical output power of the frequency converter drive corresponding to the historical control parameter values. The predicted output power 32 of the frequency converter drive is received from the first neural network model 37. A first loss function value is determined based on a first difference between the predicted output power 32 and the label of the first training sample 31. The model parameters of the first neural network model 37 are configured so that the first loss function value is lower than a preset first threshold, and the configured first neural network model 37 is determined as the artificial intelligence model of the frequency converter drive.

[0051] The second training sample 33 is input into the second neural network model 38. The second training sample 33 contains the values ​​and labels of historical control parameters of the variable frequency drive. The labels include the historical output power of the motor corresponding to the historical control parameter values. The predicted output power 34 of the motor is received from the second neural network model 38, and a second loss function value is determined based on the second difference between the predicted output power 34 and the labels of the second training sample 33; the model parameters of the second neural network model 38 are configured so that the second loss function value is lower than a preset second threshold; the configured second neural network model 38 is determined as the artificial intelligence model of the motor.

[0052] The third training sample 35 is input into the third neural network model 39. The third training sample 35 contains the values ​​and labels of the historical control parameters of the frequency converter drive. The labels include the historical output power of the application device corresponding to the values ​​of the historical control parameters. The predicted output power 36 of the application device is received from the third neural network model 39, and a third loss function value is determined based on the third difference between the predicted output power 36 and the labels of the third training sample; the model parameters of the third neural network model 39 are configured so that the third loss function value is lower than a preset third threshold; the configured third neural network model 39 is determined as the artificial intelligence model of the application device.

[0053] The AI ​​model 37 of the variable frequency drive, the AI ​​model 38 of the motor, and the AI ​​model 39 of the application device are combined to generate the AI ​​model 30 of the digital drive system.

[0054] Step 103: Input the real-time control parameters into the model; Step 104: Receive the predicted output power of at least one component of the digital drive system from the model.

[0055] Step 105: Display the predicted energy efficiency of at least one component in the visual interface based on the predicted output power.

[0056] For example, the predicted energy efficiency can be obtained by dividing the predicted output power by the total power consumption of at least one component. The total power consumption of at least one component can be measured using a power meter.

[0057] In one embodiment, the method includes: issuing an alarm message when the energy efficiency is below a predetermined threshold, wherein the threshold includes at least one of the following: an empirical value; the average energy efficiency of at least one component over a predetermined time period.

[0058] In one embodiment, the method includes: determining the real-time actual energy efficiency of at least one component; determining the energy efficiency potential of at least one component based on the difference between the predicted energy efficiency and the real-time actual energy efficiency; and adjusting the real-time control parameters of the variable frequency drive based on the energy efficiency potential.

[0059] In one embodiment, the application device includes a pump; the method includes: calibrating the pump's fluid characteristic curve based on real-time control parameters of a variable frequency drive, and updating the pump's mechanism model based on the calibrated fluid characteristic curve.

[0060] Figure 4 This is a schematic diagram illustrating the visualization and optimization process of a digital drive system according to an embodiment of the present invention.

[0061] exist Figure 4 In this digital drivetrain, there are an IOT2050 device 54, a PLC 53, a frequency converter drive 50, a motor 51, and an application device 52. The application device 52 can be a single device or multiple devices. When the application device is a single device, a single digital drivetrain 80 is formed. When the application device is multiple devices, multiple digital drivetrains 81 are formed. The application device 52 may include pumps, fans, etc.

[0062] The main functions can be described in the following two parts: Energy Efficiency Transparency 60: In the transparency phase, accurate diagnostics 63 are performed to help inform power demand and detect abnormal energy. First, energy consumption and efficiency-related data are acquired, and the parameters are visualized in the visualization process 61. Next, the efficiency of the entire drivetrain is analyzed in the energy efficiency analysis 62, and finally, a diagnosis 63 is provided.

[0063] Energy Efficiency Optimization 70: To assess and optimize energy consumption in various applications involving pumps, fans, and compressors, the toolbox can calculate the potential for efficiency improvements and obtain optimal PDS parameters 73 (e.g., default value setting algorithms or model identification, PID adaptive tuning for vector control). In multi-pump scenarios, performance curves are used to achieve optimal control 74.

[0064] Therefore, the goal of minimum energy consumption is achieved through the implementation of the optimization method. Inverter parameter optimization brings about the ideal response of the aggregate. Torque and speed oscillations are eliminated through continuous data identification and fuzzy control, ensuring stable operation. Dynamic loads can be addressed by continuously identifying and optimizing parameters. Online data is used for continuous adaptation to determine the parameters of V / f control. Fluid characteristic curves can be accurately represented through continuous adaptation using online data. Furthermore, several function-based applications are analyzed in detail, revealing technical features: Efficiency analysis of the transmission system: Technical Feature 1: Embodiments of the present invention make the operating efficiency of each part of the transmission system transparent under different load differences and provide quantitative tracking of energy loss.

[0065] Under different load conditions, the operating efficiency of each component in the transmission system lacks quantitative tracking of energy loss, resulting in opacity. To improve energy transparency and provide optimization direction for overall system efficiency, the efficiency of variable frequency drives, motors, and pumps under different load conditions can be automatically identified. Furthermore, efficiency models for variable frequency drives, motors, and pumps, along with power consumption models, can be used to calculate and display the energy loss of each component in real time. Figure 6 As shown. Figure 6 This is a schematic diagram illustrating the power loss of a digital drive system according to an embodiment of the present invention.

[0066] exist Figure 6 In the visualization interface, the total power is shown to be 80, and the power after passing through the inverter drive 41 is 77. Therefore, the power loss of inverter 41 is 3. Similarly, the power loss of motor 42 is 6, the power loss of pump 43 is 10, the power loss of throttling value 44 is 0, and the power loss of other components is 27.

[0067] Energy consumption and energy efficiency diagnosis: Technical Feature 2: Detects energy efficiency potential and automatically suggests corresponding implementation methods.

[0068] Detecting the energy efficiency potential in conventional digital drive systems relies on operator checks. By analyzing current and projected data and using historical data to predict energy consumption and efficiency, energy efficiency potential can be automatically detected, and practical, actionable recommendations can be provided to help users reduce energy loss and save energy, such as… Figure 5 As shown in the image.

[0069] Figure 5 This is a schematic diagram of the energy consumption and efficiency diagnostic workflow according to an embodiment of the present invention. Figure 5 In the process, historical data 92 (e.g., energy consumption and efficiency) is input into the prediction algorithm 90 to output predicted data, such as predicted energy consumption and predicted efficiency. The predicted data and current data 93 are input into the diagnostic module 91 to output diagnostic results 94. The prediction algorithm 90 is calibrated in the model calibration process 95 by comparing the difference between the actual data and the predicted data.

[0070] Embodiments of this invention employ a speed control mode, which improves energy efficiency and implements flow rates for specific processes. The entire equipment characteristic is transformed by the speed controller to achieve the desired flow rate. Traditional fluid characteristic curve control methods face challenges in accurately translating pump performance curves into inverter setpoints, and the five full-scale parameters are often insufficient to reflect the fluid. To accurately reflect load changes, the fluid characteristic curve can be corrected by incorporating actual speed, torque, power unit overload I²t, current, and voltage signals, and the distribution of the five parameter sets can be adaptively adjusted based on process information or data analysis of the load change range.

[0071] In summary, the embodiments of the present invention have at least the following advantages: Advantage 1: This invention enables online data analysis and optimization by combining historical data, and uses adaptive algorithms to handle dynamic loads and multi-drive systems. It is practical and can help users meet government requirements, ensure future operation, save operating costs, and improve competitiveness.

[0072] Advantage 2: The developed visual interface makes data and analysis transparent, thereby saving operators time and workload in checking for anomalies and providing guidance for future planning and decision-making.

[0073] Advantage 3: Users can optimize energy efficiency with a single click without needing prior knowledge of the entire drivetrain's domain skills. The method automatically identifies gaps and potential.

[0074] Advantage 4: The embodiments of this invention mainly involve several data analysis and intelligent optimization methods based on transmission systems; therefore, they do not involve violations of privacy rights, environmental pollution, etc. The methods can be further extended to third-party transmission systems in the future.

[0075] Figure 7 This is a structural diagram of an apparatus for visualizing the energy efficiency of a digital drive system according to an embodiment of the present invention. The components of the digital drive system include a variable frequency drive, a motor, and an application device. The apparatus 700 includes: a first acquisition module 701 configured to acquire a model of the digital drive system; a second acquisition module 702 configured to acquire real-time control parameters of the variable frequency drive; an input module 703 configured to input the real-time control parameters into the model; a receiving module 704 configured to receive predicted output power of at least one component of the digital drive system from the model; and a display module 705 configured to display the predicted energy efficiency of at least one component in a visual interface based on the predicted output power.

[0076] Embodiments of the present invention also propose an electronic device having a processor-memory architecture. Figure 8 This is a structural diagram of an electronic device according to an embodiment of the present invention. Figure 8As shown, the electronic device 800 includes a processor 801, a memory 802, and a computer program stored on the memory 802 that can run on the processor 801. When the computer program is executed by the processor 801, a method for visualizing the energy efficiency of a digital drive system as described in any of the above descriptions is implemented. The memory 802 can be implemented as various storage media, such as electrically erasable programmable read-only memory (EEPROM), flash memory, programmable programmable read-only memory (PROM), etc. The processor 801 can be implemented as including one or more central processing units (CPUs) or one or more field-programmable gate arrays (FPGAs), wherein the FPGA integrates one or more CPU cores. Specifically, the central processing unit or core can be implemented as a CPU, MCU, DSP, etc.

[0077] It should be noted that not all steps and modules in the above process and structure diagrams are necessary; some steps or modules may be omitted as needed. The execution sequence of each step is not fixed and can be adjusted as required. The division of each module is merely for the convenience of describing the functional division used. In actual implementations, a module can be divided into multiple modules, and the functions of multiple modules can also be implemented by the same module. These modules can be in the same device or different devices.

[0078] The hardware modules in each implementation can be implemented mechanically or electronically. For example, a hardware module may include specially designed permanent circuitry or logic devices (e.g., a dedicated processor, such as an FPGA or ASIC) to perform a specific operation. A hardware module may also include programmable logic devices or circuitry (e.g., a general-purpose processor or other programmable processor) temporarily configured by software to perform a specific operation. The specific use of a mechanical method, or the dedicated permanent circuitry or temporarily configured circuitry (e.g., software configuration) used to implement the hardware module, can be determined based on cost and time considerations.

[0079] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of protection of the present invention.

[0080] In this patent application, nouns and pronouns relating to people are not limited to specific genders.

Claims

1. A method for visualizing the energy efficiency of a digital drive system, wherein components of the digital drive system include a variable frequency drive, a motor, and an application device, the method comprising: Obtain the model of the digital drive system described in (101); Obtain the real-time control parameters of the frequency converter drive described in (102); The real-time control parameters are input (103) into the model; Receive from the model (104) the predicted output power of at least one component of the digital drive system; and The predicted energy efficiency of at least one component is displayed (105) in a visual interface based on the predicted output power.

2. The method according to claim 1, wherein obtaining the model of the digital drive system (101) comprises: The mechanism model of the variable frequency drive, the mechanism model of the motor, and the mechanism model of the application device are determined by mechanism modeling. The mechanism model of the variable frequency drive, the mechanism model of the motor, and the mechanism model of the application device are combined to generate the mechanism model of the digital drive system.

3. The method according to claim 1, wherein obtaining the model of the digital drive system (101) comprises: Determine training samples, the training samples including historical values ​​of the control parameters of the variable frequency drive, wherein the label set of the training samples includes the historical output power of the variable frequency drive corresponding to the historical value, the historical output power of the motor corresponding to the historical value, and the historical output power of the application device corresponding to the historical value; The training samples are input into a neural network model to receive a predicted output power set from the neural network model, the predicted output power set including the predicted output power of the variable frequency drive, the predicted output power of the motor, and the predicted output power of the application device; The loss function value is determined based on the difference between the predicted output power set and the label set; Configure the model parameters of the neural network model so that the loss function value is lower than a preset threshold; The configured neural network model is determined as the artificial intelligence model of the digital drive system.

4. The method according to claim 1, wherein obtaining the model of the digital drive system (101) comprises: A first training sample is determined, the first training sample including a first historical value of the control parameter of the frequency converter, wherein the label of the first training sample includes the historical output power of the frequency converter corresponding to the first historical value; The first training sample is input into the first neural network model to receive the predicted output power of the frequency converter drive from the first neural network model; A first loss function value is determined based on a first difference between the predicted output power of the frequency converter and the label of the first training sample; Configure the model parameters of the first neural network model so that the value of the first loss function is lower than a preset first threshold; The configured first neural network model is determined as the artificial intelligence model of the frequency converter drive.

5. The method according to claim 4, wherein obtaining the model of the digital drive system (101) comprises: A second training sample is determined, the second training sample including a second historical value of the control parameter of the variable frequency drive, wherein the label of the second training sample includes the historical output power of the motor corresponding to the second historical value; The second training sample is input into the second neural network model to receive the predicted output power of the motor from the second neural network model; The second loss function value is determined based on the second difference between the predicted output power of the motor and the label of the second training sample; Configure the model parameters of the second neural network model so that the value of the second loss function is lower than a preset second threshold; The configured second neural network model is determined as the artificial intelligence model of the motor.

6. The method according to claim 5, wherein obtaining the model of the digital drive system (101) comprises: A third training sample is determined, the third training sample including a third historical value of the control parameters of the frequency converter drive, wherein the label of the third training sample includes the historical output power of the application device corresponding to the third historical value; The third training sample is input into the third neural network model to receive the predicted output power of the application device from the third neural network model; The third loss function value is determined based on the third difference between the predicted output power of the application device and the label of the third training sample; Configure the model parameters of the third neural network model so that the value of the third loss function is lower than a preset third threshold; The configured third neural network model is determined as the artificial intelligence model of the application device.

7. The method of claim 6, further comprising: The artificial intelligence model of the variable frequency drive, the artificial intelligence model of the motor, and the artificial intelligence model of the application device are combined to generate the artificial intelligence model of the digital drive system.

8. The method according to any one of claims 1 to 7, comprising: An alarm message is issued when the energy efficiency is below a predetermined threshold, wherein the threshold includes at least one of the following: Experience points; The average energy efficiency of at least one component within a predetermined time period.

9. The method according to any one of claims 1 to 7, comprising: Determine the real-time actual energy efficiency of the at least one component; The energy efficiency potential of the at least one component is determined based on the difference between the predicted energy efficiency and the actual real-time energy efficiency. The real-time control parameters of the variable frequency drive are adjusted based on the energy efficiency potential.

10. The method of claim 2, wherein the application device comprises a pump; the method comprises: The fluid characteristic curve of the pump is calibrated based on the real-time control parameters of the variable frequency drive. The mechanism model of the pump is updated based on the calibrated fluid characteristic curves.

11. An apparatus for visualizing the energy efficiency of a digital drive system, the components of which include a variable frequency drive, a motor, and an application device, the apparatus comprising: The first acquisition module (701) is configured to acquire a model of the digital drive system; The second acquisition module (702) is configured to acquire the real-time control parameters of the frequency converter drive; An input module (703) is configured to input the real-time control parameters into the model; and A receiving module (704) is configured to receive from the model the predicted output power of at least one component of the digital drive system; and Display module (705) configured to display the predicted energy efficiency of the at least one component in a visual interface based on the predicted output power.

12. An electronic device comprising a processor (801) and a memory (802), wherein an application program executable by the processor (801) is stored in the memory (802) for causing the processor (801) to execute a method for visualizing the energy efficiency of a digital drive system according to any one of claims 1 to 10.

13. A computer-readable medium comprising computer-readable instructions stored thereon, wherein the computer-readable instructions are configured to perform the function of visualizing the energy efficiency of a digital drive system according to any one of claims 1 to 10.

14. A computer program product comprising a computer program, which, when executed by a processor, performs a method for visualizing the energy efficiency of a digital drive system according to any one of claims 1 to 10.

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