Generator working mode determination method and device, medium and equipment

Abstract

The invention provides a method and device for determining the working mode of a generator, a medium and equipment, and relates to the technical field of generator control, and the method comprises the steps: collecting the current data and voltage data of an accessed target load in a first target time window, so as to obtain a target feature vector T; inputting the T into a first type identification model to obtain a first classification result; if the first classification result represents that the probability that the target load belongs to any load type is smaller than a preset probability threshold value, obtaining a frequency domain voltage data matrix PC'and a frequency domain current data matrix PD '; and obtaining a second classification result according to the PC ', the PD' and the second type identification model.According to the method, the finally determined working mode is more accurate, so that the working mode of the generator can be adjusted in time, and the electric energy can be fully utilized.

Description

Background Art

[0002] In today's power application field, generators, as important power supply equipment, are widely used in industrial production, daily life and various emergency power supply scenarios. When dealing with the matching relationship between generators and loads, the existing technology mainly adopts the method of setting different powers according to different loads. This method simply takes power as the only consideration factor, and assumes that as long as the load power is the same, the load performance of the generator is the same. However, the actual situation is far more complicated than this. Different loads, even if they have the same power, have different corresponding load types, such as inductive loads, capacitive loads, and resistive loads. There are significant differences in their current characteristics, voltage characteristics, etc. during operation, which leads to the corresponding load performance is also very different. This makes it possible for some generators to drive normally when facing loads of the same power, while others cannot. This situation not only causes a waste of power resources, but also greatly limits the effective use range of generators, and cannot fully and efficiently utilize electric energy. A new technical solution is urgently needed to solve such problems. Summary of the invention

[0003] In response to the above technical problems, the present application provides a method, device, medium and equipment for determining the working mode of a generator, which at least partially solve the problems existing in the prior art.

[0004] In a first aspect of the present application, a method for determining an operating mode of a generator is provided, the method comprising: Collect the current data and voltage data of the connected target load in the first target time window to obtain the target feature vector T=(A, B, C, D), where A is the phase difference; B is the power factor; C is the voltage time domain data list; D is the current time domain data list; the end time of the first target time window is the current time; Input T into a first type identification model to obtain a first classification result; wherein the first classification result is used to characterize the probability that the target load belongs to each load type; wherein each load type has a corresponding generator operating mode; If the probability that the first classification result indicates that the target load belongs to any load type is less than the preset probability threshold, C' and D' are converted into the corresponding frequency domain voltage data matrix PC' and frequency domain current data matrix PD' through short-time Fourier transform; wherein C' and D' are respectively the voltage time domain data list and the current time domain data list of the connected target load in the second target time window; the start time of the second target time window is the same as the start time of the first target time window; the time length of the second target time window is greater than the length of the first target time window; A second classification result is obtained according to PC', PD' and the second type identification model; wherein the second classification result is used to adjust the generator operating mode according to the probability that the target load belongs to each load type.

[0005] In a second aspect of the present application, a device for determining an operating mode of a generator is provided, the device comprising: A collection unit is used to collect current data and voltage data of the connected target load within the first target time window to obtain a target feature vector T=(A, B, C, D), wherein A is the phase difference; B is the power factor; C is the voltage time domain data list; D is the current time domain data list; and the end time of the first target time window is the current time; A first classification unit, used for inputting T into a first type recognition model to obtain a first classification result; wherein the first classification result is used to characterize the probability that the target load belongs to each load type; wherein each load type has a corresponding generator operating mode; A conversion unit, for converting C' and D' into corresponding frequency domain voltage data matrix PC' and frequency domain current data matrix PD' by short-time Fourier transform if the probability represented by the first classification result that the target load belongs to any load type is less than a preset probability threshold; wherein C' and D' are respectively a voltage time domain data list and a current time domain data list of the connected target load within a second target time window; the start time of the second target time window is the same as the start time of the first target time window; and the time length of the second target time window is greater than the length of the first target time window; The second classification unit is used to obtain a second classification result according to PC', PD' and the second type identification model; wherein the second classification result is used to adjust the generator working mode according to the probability that the target load belongs to each load type.

[0006] In a third aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement the aforementioned method for determining the working mode of the generator.

[0007] In a fourth aspect of the present application, an electronic device is provided, comprising a processor and the above-mentioned non-transitory computer-readable storage medium.

[0008] This application has at least the following beneficial effects: The method for determining the working mode of the generator provided in the present application is different in phase difference, power factor, voltage change characteristics and current change characteristics when different types of AC loads are just connected to the generator, and these factors affect the load performance of the AC load, and then affect the working logic of the adapted generator. Only using a single feature to judge the load type may cause inaccurate results due to data errors or other factors. Therefore, in the present application, the current data and voltage data of the connected target load within the first target time window are collected to obtain the target feature vector T, T includes multi-dimensional data, according to T and the first category recognition model, if the load type to which the target load belongs cannot be determined, it means that the current amount of data may be small, and then the second target time window of the present application is extended for a period of time on the basis of the first time window, and then when the load type is judged again according to the frequency domain data, the original collected data volume is larger, the data dimension is richer, and the time-frequency data after short-time Fourier transform can show how the frequency components of the signal evolve over time on the two-dimensional plane of time and frequency. For example, during the motor starting process, the frequency of the current signal will gradually change from low frequency to rated frequency. This frequency change process can be clearly observed through time-frequency data, while it is difficult to directly distinguish the dynamic change of this frequency in time domain data. In addition, time-frequency data can more intuitively display the harmonic (frequency components of integer multiples of the fundamental frequency) and interharmonic (frequency components of non-integer multiples of the fundamental frequency) components in the voltage and current signals and their changes over time. By observing the time-frequency graph, the occurrence time, duration and amplitude changes of different harmonics and interharmonics can be clearly seen. In the power system, harmonics and interharmonics will have adverse effects on electrical equipment. Accurate analysis of them is crucial for evaluating power quality and equipment operation status, and time domain data is difficult to directly provide these detailed harmonic information. Therefore, in this embodiment, after conversion to time-frequency data, it is easier to analyze the transformation law of the voltage and current signals of the target load current and voltage in a short period of time when the engine is started than the original time domain data, and the judgment of the load type of the target load can be more accurate and intuitive. This makes the final working mode more accurate, so as to adjust the working mode of the generator in time and make full use of the electrical energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A flow chart of a method for determining a working mode of a generator provided in an embodiment of the present application; Figure 2 This is a structural block diagram of a device for determining an operating mode of a generator provided in an embodiment of the present application. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0012] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any corresponding variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0013] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present application, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0014] Please refer to Figure 1 As shown, an embodiment of the present application provides a method for determining a working mode of a generator, the method comprising: S100, collecting current data and voltage data of the connected target load within the first target time window to obtain a target feature vector T=(A, B, C, D), where A is the phase difference; B is the power factor; C is the voltage time domain data list; D is the current time domain data list; the end time of the first target time window is the current time.

[0015] Specifically, the generator in this application is a digital generator, which is a relatively advanced small generator. It adopts digital circuit control technology and inverter technology. The working principle of the digital generator is to first drive the generator rotor to rotate through the engine to generate AC power, which is similar to the traditional generator. However, the generated AC power is not directly output, but first converted into DC power through a rectifier, and then the DC power is inverted into high-quality AC power with stable frequency and voltage by an inverter, and finally output to the load for use. In this process, the digital circuit control technology accurately adjusts and controls the operation of the inverter to ensure the stability and high quality of the output power. It also has an intelligent control function, which can monitor the operating status of the generator in real time, such as voltage, frequency, speed, fuel quantity, etc., and automatically alarm or shut down protection when an abnormality occurs, thereby improving the reliability and safety of the generator. The digital circuit control technology can automatically adjust the engine speed according to the actual load size, reduce the speed when lightly loaded, reduce fuel consumption, and improve fuel economy.

[0016] The digital generator is equipped with a variable frequency inverter, which can control the working mode of the digital generator to supply different types of AC loads. AC loads refer to various devices or components that consume electrical energy in an AC circuit or have an impact on the AC power supply. According to their working principles and characteristics, they can be divided into resistive loads, inductive loads, capacitive loads, and composite loads.

[0017] The working principle of resistive load is to generate heat when current passes through the resistor. The current and voltage are in phase and follow Ohm's law, that is, the current is proportional to the voltage and inversely proportional to the resistance. Common equipment includes electric water heaters, electric ovens, incandescent lamps, etc. The heating wire in the electric water heater, the heating tube in the electric oven, and the filament of the incandescent lamp are all typical resistance elements, which convert electrical energy into heat energy to achieve the purpose of heating or emitting light.

[0018] The working principle of inductive load is: it works based on the principle of electromagnetic induction. When current passes through the inductor, a magnetic field is generated. The change of the magnetic field will hinder the change of the current, causing the current to lag behind the voltage by a certain phase angle. Common equipment includes motors, transformers, electromagnets, etc. Taking the motor as an example, the stator winding of the motor is inductive in nature. During operation, a magnetic field needs to be established to drive the rotor to rotate, which will consume a certain amount of reactive power and show the characteristics of an inductive load. The transformer also uses the principle of electromagnetic induction to change voltage and transmit electrical energy, and its winding also has inductive characteristics.

[0019] The working principle of capacitive load is: it works by utilizing the characteristics of capacitors storing charge and electric field energy. When the capacitor is connected to an AC circuit, the change in voltage will cause the capacitor to continuously charge and discharge, thereby forming a current, which leads the voltage by a certain phase angle. Common devices include filter capacitors, compensation capacitors, and capacitive motors in some electronic circuits. In the power circuit of electronic equipment, filter capacitors are used to smooth DC voltage and filter out AC components. Capacitor motors use capacitors to separate phases, so that the stator winding of the motor generates a rotating magnetic field, thereby driving the rotor to rotate.

[0020] Composite load refers to a load with multiple load characteristics. The relationship between current and voltage is relatively complex. It may have both a resistive energy consumption part and an inductive or capacitive energy storage part. Common equipment includes televisions and computers. These devices have both resistive components for heat generation, such as the current limiting resistor in the power supply, and a large number of capacitors and inductors for filtering and signal processing. The overall load characteristics are a combination of multiple characteristics.

[0021] In terms of the phase of current and voltage, the phase of current and voltage is the same for resistive loads. That is, when the voltage reaches its peak, the current also reaches its peak; when the voltage is zero, the current is also zero. The current and voltage signals measured by the sensor, if the phase difference between the two is 0°, can basically be judged as a resistive load. The current of an inductive load lags behind the voltage. In an AC circuit, the inductance will hinder the change of current, so that the change of current always lags behind the change of voltage. If the sensor detects that the current phase lags behind the voltage phase, such as 30°, 60° or even 90° (theoretically 90° for pure inductance), it can be judged as an inductive load. The current of a capacitive load leads the voltage. Because the current changes before the voltage changes during the charging and discharging process of the capacitor. When the sensor detects that the current phase leads the voltage phase, such as 30°, 60° or close to 90° (theoretically 90° for pure capacitance), it can be judged as a capacitive load.

[0022] In terms of the waveform characteristics of current and voltage, the current and voltage waveforms of resistive loads are usually standard sine waves, which are consistent with the AC waveform output by the inverter, and the waveform is smooth without obvious distortion or harmonic components. Due to the presence of inductance, the current waveform of inductive loads may be delayed and deformed during startup and operation. For example, when the motor starts, the current increases instantly, and the waveform may appear spiked or not smooth. During operation, due to factors such as changes in the magnetic field inside the motor, the current waveform may contain certain harmonic components, making the waveform no longer a standard sine wave. The current waveform of capacitive loads will also change during charging and discharging. When the capacitor is charged, the current rises rapidly and then gradually decreases; when discharging, the current direction is opposite. Therefore, the current waveform of capacitive loads may be asymmetric or have obvious mutations, which is significantly different from the waveforms of resistive and inductive loads.

[0023] Furthermore, the power factor is an important parameter to measure the ability of the load in the power system to effectively convert electrical energy into useful work. In this application, the power factor is calculated by the following steps: S110, calculating the voltage effective value U.

[0024] Here, C = (C1, C2, ..., C i , …, C n ); i = 1, 2, ..., n; n is the number of sampling time points in the first target time window; the time interval between any two samplings is the same; C i is the voltage value collected at the i-th sampling time point in the first target time window; D=(D1, D2, …, D i , …, D n );D i The current value collected at the i-th sampling time point in the first target time window. If the voltage is a standard sine wave, assuming that the maximum value of the voltage collected in the first time window is U m According to the relationship between the effective value and the maximum value of the sinusoidal AC voltage, the formula can be used ; If it is a non-standard sine wave, U meets the following characteristics:

[0025] S120, calculating the effective value I of the current.

[0026] Here, for sinusoidal alternating current, if the maximum value of the current collected is I m , then the effective value of the current is I = I m / √2; if it is a non-standard sine wave, then .

[0027] S130, based on U and I, obtain the apparent power S; where S=UI; the unit is volt-ampere (VA).

[0028] S140, calculating active power P.

[0029] here, .

[0030] S150, calculating the power factor cosψ; wherein cosψ=P / S; the power factor is a dimensionless value, and its value range is between 0 and 1.

[0031] The power factor of a resistive load is usually close to 1, because a resistive load only consumes active power and almost no reactive power. The power factor of an inductive load is generally less than 1, and is usually between 0.5-0.8, depending on the size of the inductance and the characteristics of the load. This is because an inductive load needs to consume a certain amount of reactive power to establish a magnetic field. The power factor of a capacitive load is also less than 1, but unlike an inductive load, the reactive power of a capacitive load is output outward, and its power factor angle is leading, while that of an inductive load is lagging. Generally, the power factor of a capacitive load is between 0.6-0.9.

[0032] In summary, since different types of AC loads have different phase differences, power factors, voltage change characteristics, and current change characteristics, using only a single feature to judge the load type may lead to inaccurate results due to data errors or other factors. Therefore, in this application, the current data and voltage data of the connected target load in the first target time window are collected to obtain the target feature vector T=(A, B, C, D). Here, C and D are both discrete sequence data, which can better reflect the changes in voltage and current of the connected target load in the first target time window. In addition, in this application, n=L×E; where L is the time length of the first time window; E is the sampling rate. Further, L=2 seconds; E≥1MHz. That is, the current and voltage data obtained in this application are obtained within a short period of time after the target load is connected, and the corresponding target feature vector in the first target time window is obtained. This is because the current or voltage of the AC load may have a large change in amplitude within a short period of time after the AC load is connected to the power supply, and the obtained data is more accurate for judging the change of the load type. In addition, in order to achieve precise control of the digital generator frequency converter, it is necessary to determine the AC load type in a short time in order to switch to the working mode of the generator corresponding to the current AC load type, so that the target load can work efficiently and avoid power loss to improve power utilization efficiency.

[0033] S200, input T into a first type identification model to obtain a first classification result; wherein the first classification result is used to characterize the probability that the target load belongs to each load type; wherein each load type has a corresponding generator operating mode.

[0034] Specifically, the first type recognition model is pre-trained and is used to classify the input feature vector to obtain the probability that the input feature vector belongs to each load type. It should be noted that the first type recognition model can be any model known to those skilled in the art that can achieve multi-classification purposes.

[0035] S300, if the first classification result indicates that the probability that the target load belongs to any load type is less than the preset probability threshold, C' and D' are converted into corresponding frequency domain voltage data matrix PC' and frequency domain current data matrix PD' through short-time Fourier transform; wherein C' and D' are respectively the voltage time domain data list and the current time domain data list of the connected target load within the second target time window; the start time of the second target time window is the same as the start time of the first target time window; the time length of the second target time window is greater than the length of the first target time window.

[0036] Specifically, the working process of the first type recognition model is as follows: if the probability that the input feature vector belongs to any load type is greater than the preset probability threshold, the load type is used as the output result. On the contrary, if the probability that the input feature vector belongs to each load type is less than the preset probability threshold, it means that the input feature vector may not belong to any load type. In this application, it may be due to the short sampling time that the first type recognition model is not enough to accurately judge the load type based on the current data. Therefore, in this embodiment, when the probability that the target load belongs to any load type represented by the first classification result is less than the preset probability threshold, that is, the first type recognition model is not enough to accurately judge the load type based on the current data, C' and D' are converted into the corresponding frequency domain voltage data matrix PC' and frequency domain current data matrix PD' through short-time Fourier transform. The short-time Fourier transform divides the data into multiple short time periods and performs Fourier transform on each short time period, which can simultaneously display the time and frequency information of the signal to form time-frequency data. In this way, it is possible to clearly observe the changes of different frequency components in the voltage data and current data over time in the second target time window.

[0037] In an exemplary embodiment of the present application, PC' meets the following characteristics:

[0038] x=1, 2, ..., y; y is the number of frequency points; p=1, 2, ..., q; q is the number of points on the time axis; PC' 1，pis the amplitude and phase information of the voltage signal at the first frequency point corresponding to the pth time point on the time axis; PC' x，p is the amplitude and phase information of the voltage signal at the xth frequency point corresponding to the pth time point on the time axis.

[0039] Further, as above, PD' meets the following characteristics:

[0040] PD' 1，p is the amplitude and phase information of the current signal at the first frequency point corresponding to the pth time point on the time axis; PD' x，p is the amplitude and phase information of the current signal at the xth frequency point corresponding to the pth time point on the time axis.

[0041] It should be noted that the second target time window is extended for a period of time based on the first time window. This is because the first type recognition model is not sufficient to accurately determine the load type based on the current data. Therefore, in this embodiment, when the load type is determined again based on the frequency domain data, the amount of data originally collected is larger, the data dimension is richer, and the time-frequency data after short-time Fourier transform can show how the frequency components of the signal evolve over time on the two-dimensional plane of time and frequency. For example, during the motor startup process, the frequency of the current signal will gradually change from low frequency to rated frequency. This frequency change process can be clearly observed through the time-frequency data, while it is more difficult to directly distinguish the dynamic change of this frequency in the time domain data. In addition, the time-frequency data can more intuitively display the harmonic (frequency components of integer multiples of the fundamental frequency) and interharmonic (frequency components of non-integer multiples of the fundamental frequency) components in the voltage and current signals and their changes over time. By observing the time-frequency diagram, the occurrence time, duration and amplitude changes of different harmonics and interharmonics can be clearly seen. In the power system, harmonics and interharmonics will have adverse effects on electrical equipment. Accurate analysis of them is crucial for evaluating power quality and equipment operating status, but time domain data is difficult to directly provide these detailed harmonic information. Therefore, in this embodiment, after conversion to time-frequency data, it is easier to analyze the transformation law of the voltage and current signals of the target load current and voltage in the short time of engine startup than the original time domain data, and the load type of the target load can be judged more accurately and more intuitively.

[0042] S400, obtaining a second classification result according to PC', PD' and a second type identification model; wherein the second classification result is used to adjust the generator operating mode according to the probability that the target load belongs to each load type.

[0043] Specifically, step S400 includes: S410, according to PC' and PD', obtain the corresponding voltage frequency domain graph APC' and current frequency domain graph APD'; S420, performing image feature extraction on APC' and APD' respectively according to the CNN model to obtain a voltage frequency domain feature vector APCT' and a current frequency domain feature vector APDT'; S430, performing feature fusion on APCT' and APDT' to obtain a fused frequency domain feature vector R; S440, inputting R into a second type recognition model to obtain a second classification result.

[0044] In this embodiment, all images are adjusted to the size required by the CNN model, and then the pixel values ​​of the images are normalized and scaled to a fixed range, which helps the convergence of the model. In order to increase the diversity of the data set and the generalization ability of the model, some data enhancement operations can be performed, such as random cropping, flipping, rotation, etc. The feature vector of the voltage image and the feature vector of the current image are spliced ​​in the feature dimension to form a longer feature vector. Finally, the second classification result is obtained.

[0045] Here, the second classification result is used to output the load type with the highest probability corresponding to the target load and greater than the preset probability threshold as the load type to which the target load belongs, according to the probability that the target load belongs to each load type. In this embodiment, the variable frequency inverter is controlled to adjust the working mode of the generator to the working mode corresponding to the load type to which the target load belongs. This allows the target load to work efficiently, avoid power loss, and improve power utilization efficiency. Here, the working mode of the generator is the control logic of the generator. This application only needs to adjust the control logic of the generator to fully and efficiently utilize the power without adjusting the circuit.

[0046] In an exemplary embodiment of the present application, the method further includes: If the first classification result indicates that the probability that the target load belongs to any load type is equal to or greater than a preset probability threshold, then the load type is used as the target load type corresponding to the target load.

[0047] In an exemplary embodiment of the present application, after performing image feature extraction on APC' and APD' according to the CNN model to obtain a voltage frequency domain feature vector APCT' and a current frequency domain feature vector APDT', the method further includes: S450, performing feature fusion on C', D', APCT' and APDT' to obtain a fused frequency domain feature vector R'.

[0048] S460: Input R' to a second type recognition model to obtain a second classification result.

[0049] In this embodiment, the original voltage time domain data and current time domain data, as well as the voltage frequency domain data and current frequency domain data obtained after short-time Fourier transform are concatenated together to obtain a fused frequency domain feature vector R'. Here, the original time domain data is added so that the final model can consider both time domain features and frequency domain features, so that the classification result obtained is more accurate.

[0050] Please refer to Figure 2 As shown, an embodiment of the present application provides a device 100 for determining a working mode of a generator, the device comprising: The acquisition unit 110 is used to collect the current data and voltage data of the connected target load within the first target time window to obtain a target characteristic vector T=(A, B, C, D), where A is the phase difference; B is the power factor; C is the voltage time domain data list; D is the current time domain data list; and the end time of the first target time window is the current time.

[0051] The first classification unit 120 is used to input T into a first type identification model to obtain a first classification result; wherein the first classification result is used to characterize the probability that the target load belongs to each load type; wherein each load type has a corresponding generator operating mode.

[0052] The conversion unit 130 is used to convert C' and D' into corresponding frequency domain voltage data matrix PC' and frequency domain current data matrix PD' through short-time Fourier transform if the probability represented by the first classification result that the target load belongs to any load type is less than a preset probability threshold; wherein C' and D' are respectively a voltage time domain data list and a current time domain data list of the connected target load within the second target time window; the start time of the second target time window is the same as the start time of the first target time window; and the time length of the second target time window is greater than the length of the first target time window.

[0053] The second classification unit 140 is used to obtain a second classification result according to PC', PD' and the second type identification model; wherein the second classification result is used to adjust the generator working mode according to the probability that the target load belongs to each load type.

[0054] In an exemplary embodiment of the present application, an electronic device capable of implementing the above method is also provided.

[0055] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.

[0056] The electronic device according to this embodiment of the present application is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0057] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).

[0058] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps described in the above “Exemplary Method” section of this specification according to various exemplary embodiments of the present application.

[0059] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).

[0060] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0061] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.

[0062] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may communicate with one or more devices that enable a user to interact with the electronic device, and / or may communicate with any device (e.g., routers, modems, etc.) that enables the electronic device to communicate with one or more other computing devices. This communication may be performed through an input / output (I / O) interface. In addition, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device through a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0063] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software, or by combining software with necessary hardware. Therefore, the technical solution according to the implementation method of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation method of the present application.

[0064] In an exemplary embodiment of the present application, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present application can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Exemplary Method" section of the present specification.

[0065] The program product may adopt any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, a system, device or component of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0066] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0067] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.

[0068] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0069] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.

[0070] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0071] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for determining a working mode of a generator, characterized in that: The method comprises: Collect the current data and voltage data of the connected target load in the first target time window to obtain the target feature vector T=(A, B, C, D), where A is the phase difference; B is the power factor; C is the voltage time domain data list; D is the current time domain data list; the end time of the first target time window is the current time; Input T into a first type identification model to obtain a first classification result; wherein the first classification result is used to characterize the probability that the target load belongs to each load type; wherein each load type has a corresponding generator operating mode; If the probability that the first classification result indicates that the target load belongs to any load type is less than the preset probability threshold, C' and D' are converted into the corresponding frequency domain voltage data matrix PC' and frequency domain current data matrix PD' through short-time Fourier transform; wherein C' and D' are respectively the voltage time domain data list and the current time domain data list of the connected target load in the second target time window; the start time of the second target time window is the same as the start time of the first target time window; the time length of the second target time window is greater than the length of the first target time window; A second classification result is obtained according to PC', PD' and the second type identification model; wherein the second classification result is used to determine the generator operating mode according to the probability that the target load belongs to each load type.

2. The method for determining the working mode of a generator according to claim 1, characterized in that: C=(C1,C2,…,C i , …, C n ); i = 1, 2, ..., n; n is the number of sampling time points in the first target time window; the time interval between any two samplings is the same; C i is the voltage value collected at the i-th sampling time point in the first target time window; D=(D1, D2, …, D i , …, D n );D i The current value is collected at the i-th sampling time point in the first target time window.

3. The method for determining the working mode of a generator according to claim 2, characterized in that: n=L×E; where L is the time length of the first time window; E is the sampling rate.

4. The method for determining the working mode of a generator according to claim 3, characterized in that: The time length L of the first time window is 2 seconds; the sampling rate E≥1MHz.

5. The method for determining the working mode of a generator according to claim 1, characterized in that: According to PC', PD' and the second type recognition model, the second classification result is obtained, including: According to PC' and PD', the corresponding voltage frequency domain diagram APC' and current frequency domain diagram APD' are obtained; According to the CNN model, image features of APC' and APD' are extracted to obtain voltage frequency domain feature vector APCT' and current frequency domain feature vector APDT'; Perform feature fusion on APCT' and APDT' to obtain the fused frequency domain feature vector R; R is input into the second type recognition model to obtain a second classification result.

6. The method for determining the working mode of a generator according to claim 1, characterized in that: The method further comprises: If the first classification result indicates that the probability that the target load belongs to any load type is equal to or greater than a preset probability threshold, then the load type is used as the target load type corresponding to the target load.

7. The method for determining the working mode of a generator according to claim 5, characterized in that: After performing image feature extraction on APC' and APD' according to the CNN model to obtain a voltage frequency domain feature vector APCT' and a current frequency domain feature vector APDT', the method further includes: Perform feature fusion on C', D', APCT' and APDT' to obtain the fused frequency domain feature vector R'; R' is input into the second type recognition model to obtain a second classification result.

8. A device for determining a working mode of a generator, characterized in that: The device comprises: A collection unit is used to collect current data and voltage data of the connected target load within the first target time window to obtain a target feature vector T=(A, B, C, D), wherein A is the phase difference; B is the power factor; C is the voltage time domain data list; D is the current time domain data list; and the end time of the first target time window is the current time; A first classification unit, used for inputting T into a first type recognition model to obtain a first classification result; wherein the first classification result is used to characterize the probability that the target load belongs to each load type; wherein each load type has a corresponding generator operating mode; A conversion unit, for converting C' and D' into corresponding frequency domain voltage data matrix PC' and frequency domain current data matrix PD' by short-time Fourier transform if the probability represented by the first classification result that the target load belongs to any load type is less than a preset probability threshold; wherein C' and D' are respectively a voltage time domain data list and a current time domain data list of the connected target load within a second target time window; the start time of the second target time window is the same as the start time of the first target time window; and the time length of the second target time window is greater than the length of the first target time window; The second classification unit is used to obtain a second classification result according to PC', PD' and the second type identification model; wherein the second classification result is used to adjust the generator working mode according to the probability that the target load belongs to each load type.

9. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the method for determining the working mode of the generator as described in any one of claims 1-7.

10. An electronic device, characterized in that: The invention comprises a processor and the non-transitory computer-readable storage medium as claimed in claim 9.

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