Dead-time compensation method and device, electronic equipment, medium and program product

Abstract

Embodiments of the present disclosure disclose a dead-time compensation method and device, electronic equipment, medium and program product. A specific embodiment of the method comprises: performing current sampling processing on a target circuit to generate a set of current signal information; for each current signal information in the set of current signal information, performing the following processing steps: amplifying a voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information; converting the amplified voltage signal information to generate voltage digital signal information; inputting the current digital signal information into a pre-trained current signal feature point prediction model to obtain a set of current signal feature point prediction information; generating a set of target current signal feature point information based on the set of current signal feature point prediction information; and performing dead-time compensation on the target circuit based on the set of target current signal feature point information. The embodiment can avoid circuit short circuit.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of circuitry, and more specifically to dead-zone compensation methods, apparatus, electronic devices, media, and program products. Background Technology

[0002] In motor control applications, due to the conductance modulation effect and tail current effect of power devices, the actual turn-on / turn-off time of the switching transistors will inevitably lag behind the control waveform. To avoid DC bus voltage short circuit, a certain dead area needs to be inserted between the drive signals of the upper and lower switching transistors in the same bridge arm. Currently, the common method for dead area compensation is to use zero-crossing detection to compensate for the dead area.

[0003] However, the above methods typically present the following technical problems:

[0004] First, when using the zero-crossing detection method to compensate for the dead zone, the current ripple will cause the accuracy of the zero-crossing detection results to be low, resulting in low dead zone insertion accuracy and easy to cause circuit short circuit.

[0005] Second, zero-crossing detection methods usually require a comparator to compare three output results, but the comparator only has two outputs. An additional logic unit is needed to implement the third output, which can easily lead to a waste of resources.

[0006] Third, the accuracy of the positive and negative current time zones obtained by the zero-crossing detection method is low, resulting in low accuracy of dead zone compensation and easy to cause short circuits.

[0007] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0009] Some embodiments of this disclosure provide dead-zone compensation methods, apparatuses, electronic devices, computer-readable media, and program products to address one or more of the technical problems mentioned in the background section above.

[0010] In a first aspect, some embodiments of this disclosure provide a dead-time compensation method, which includes: performing current sampling processing on a target circuit to generate a current signal information set; for each current signal information in the current signal information set, performing the following processing steps: amplifying the voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information; converting the amplified voltage signal information to generate voltage digital signal information; converting the voltage digital signal information into current digital signal information; inputting the current digital signal information into a pre-trained current signal feature point prediction model to obtain a current signal feature point prediction information group; generating a target current signal feature point information group based on the current signal feature point prediction information group; and performing dead-time compensation on the target circuit based on the target current signal feature point information group.

[0011] Secondly, some embodiments of this disclosure provide a dead-zone compensation device, comprising: a current sampling unit configured to perform current sampling processing on a target circuit to generate a current signal information set; and a processing unit configured to perform the following processing steps for each current signal information in the current signal information set: amplifying the voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information; converting the amplified voltage signal information to generate voltage digital signal information; converting the voltage digital signal information into current digital signal information; inputting the current digital signal information into a pre-trained current signal feature point prediction model to obtain a current signal feature point prediction information group; generating a target current signal feature point information group based on the current signal feature point prediction information group; and performing dead-zone compensation on the target circuit based on the target current signal feature point information group.

[0012] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0013] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0014] Fifthly, some embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0015] The above-described embodiments of this disclosure have the following beneficial effects: Dead-zone compensation methods of some embodiments of this disclosure can avoid circuit short circuits. Specifically, the reason why circuit short circuits are easily caused is that when using a zero-crossing detection method to compensate for the dead zone, current ripple leads to low accuracy of the zero-crossing detection result, resulting in low accuracy of dead-zone insertion and easily causing circuit short circuits. Based on this, the dead-zone compensation method of some embodiments of this disclosure first performs current sampling processing on the target circuit to generate a current signal information set. Thus, three-phase currents can be obtained through current sampling. Secondly, for each current signal information in the aforementioned current signal information set, the following processing steps are performed: First, the voltage signal in the voltage signal information corresponding to the aforementioned current signal information is amplified to generate amplified voltage signal information. Thus, the current signal information can be converted into voltage signal information within the effective range of the analog-to-digital converter, so that the sampled analog signal can be converted into a digital signal. Second, the amplified voltage signal information is converted to generate voltage digital signal information. Thus, the amplified voltage signal information can be converted into voltage digital signal information for subsequent zero-crossing information detection. Third, the voltage digital signal information is converted into current digital signal information. Fourth, the aforementioned digital current signal information is input into a pre-trained current signal feature point prediction model to obtain a set of predicted current signal feature points. This allows for a more accurate set of predicted current signal feature points based on the pre-trained model. Fifth, based on this set of predicted current signal feature points, a target current signal feature point information set is generated. This provides more accurate zero-crossing information. Sixth, based on the target current signal feature point information set, dead-zone compensation is performed on the target circuit. This allows for individual dead-zone compensation for each phase of the target circuit based on the more accurate zero-crossing information. This enables more accurate dead-zone compensation and helps prevent short circuits. Attached Figure Description

[0016] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and the originals and elements are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart of some embodiments of the dead zone compensation method according to this disclosure;

[0018] Figure 2 This is a schematic diagram of the structure of some embodiments of the dead zone compensation device according to the present disclosure;

[0019] Figure 3This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0020] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0021] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0022] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0023] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0024] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0025] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0026] refer to Figure 1 The diagram illustrates a flow 100 of some embodiments of a dead-zone compensation method according to the present disclosure. This dead-zone compensation method includes the following steps:

[0027] Step 101: Perform current sampling processing on the target circuit to generate a current signal information set.

[0028] In some embodiments, the entity executing the dead-time compensation method (e.g., a computing device) may perform current sampling processing on the target circuit to generate a set of current signal information.

[0029] In practice, the aforementioned executing entity can perform current sampling processing on the target current through the following steps to generate a current signal information set:

[0030] The first step involves current sampling of the target circuit to generate first and second current signal information. The first current signal information includes a sequence of first current signal values, and the second current signal information includes a second current signal value corresponding to each of the first current signal values ​​included in the first current signal information. For example, the sequence of first current signal values ​​can be the first current signal values ​​corresponding to a preset time granularity. The preset time granularity can be, but is not limited to, one second, ten milliseconds, five milliseconds, etc. In practice, the target circuit can be sampled using a three-resistor lower bridge current sampling method in conjunction with a PWM (Pulse Width Modulation) controller. Here, the three-resistor lower bridge sampling method involves connecting a resistor in series with each of the three lower bridge arms of the PWM drive circuit, and then sampling the current in the circuit containing the lower bridge arm after the series resistor connection. All the resistors connected in series in the three-resistor lower bridge current sampling method have the same value.

[0031] The second step is to perform the following conversion steps for each first current signal value included in the aforementioned first current signal information:

[0032] The first sub-step involves determining the sum of the first current signal value and the corresponding second current signal value as the sum of the current signals.

[0033] The second sub-step involves determining the difference between the preset total current value and the sum of the aforementioned current signals as the third current signal value. The preset total current value can be 0.

[0034] The third step is to combine the determined third current signal values ​​into third current signal information.

[0035] The fourth step is to combine the first current signal information, the second current signal information, and the third current signal information into a current signal information set.

[0036] Step 102: For each current signal in the above current signal information set, perform the following processing steps:

[0037] Step 1021: Amplify the voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information.

[0038] In some embodiments, the execution entity may amplify the voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information.

[0039] In practice, firstly, for each current signal value included in the aforementioned current signal information, the executing entity can determine a voltage signal value by multiplying the current signal value by the corresponding resistance value. Secondly, the executing entity can combine the determined voltage signal values ​​into voltage signal information. Then, the executing entity can use a voltage amplifier to amplify the voltage signal in the voltage signal information to generate amplified voltage signal information. The voltage amplifier can be a device that increases the signal voltage. For example, the voltage amplifier can be, but is not limited to, a single-stage amplifier, a transformer-coupled amplifier, a resonant amplifier, etc. The range of the voltage signal in the voltage signal information can be within the effective range of the analog-to-digital converter (ADC).

[0040] Step 1022: Convert the amplified voltage signal information to generate digital voltage signal information.

[0041] In some embodiments, the executing entity may perform conversion processing on the amplified voltage signal information to generate digital voltage signal information. In practice, the executing entity may use an analog-to-digital converter to perform conversion processing on the amplified voltage signal information to generate digital voltage signal information. The digital voltage signal information may include: a digital voltage signal value.

[0042] Step 1023: Convert the above voltage digital signal information into current digital signal information.

[0043] In some embodiments, the execution entity can convert the voltage digital signal information into current digital signal information. In practice, firstly, for each voltage digital signal value included in the voltage digital signal information, the execution entity can determine the current digital signal value as the ratio of the voltage digital signal value to the resistance value corresponding to the voltage digital signal information. Then, the execution entity can combine the determined current digital signal values ​​into current digital signal information.

[0044] Step 1024: Input the above-mentioned digital current signal information into the pre-trained current signal feature point prediction model to obtain the current signal feature point prediction information group.

[0045] In some embodiments, the executing entity can input the aforementioned digital current signal information into a pre-trained current signal feature point prediction model to obtain a current signal feature point prediction information group. The current signal feature point prediction information in the aforementioned current signal feature point prediction information group includes: current signal prediction point type, current signal prediction point time, and current signal prediction point confidence level. The aforementioned current signal prediction point type includes: a first current signal prediction point type, a second current signal prediction point type, a third current signal prediction point type, and a fourth current signal prediction point type. Here, the aforementioned pre-trained current signal feature point prediction model can be a neural network model that takes digital current signal information as input and outputs the current signal feature point prediction information group. For example, the pre-trained current signal feature point prediction model can be a one-dimensional residual neural network model. The first current signal prediction point corresponding to the first current signal prediction point type can characterize the lowest point of the current waveform within a certain current change cycle within a preset time period. The second current signal prediction point corresponding to the second current signal prediction point type can characterize the first zero point of the current waveform within a certain current change cycle within a preset time period. The third current signal prediction point type represents the highest point of the current waveform within a certain current change cycle over a preset time period. The fourth current signal prediction point type represents the second zero point of the current waveform within a certain current change cycle over a preset time period. The current waveform represents the current waveform corresponding to each current signal included in the current signal information.

[0046] Optionally, the pre-trained current signal feature point prediction model can be obtained through the following steps:

[0047] The first step is to obtain the training sample set.

[0048] In some embodiments, the aforementioned executing entity can obtain a training sample set from a terminal device via a wired or wireless connection. The training samples in the training sample set include: sample signal information and a sample label set. The sample labels in the sample label set may be current signal feature point information corresponding to the sample signal information. The sample labels may include: the time of the current signal feature points. For example, the training sample set may include 1000 training samples. The sample signal information may be current signal information composed of 200,000 current signal feature points randomly selected from 2 million current signal feature points. The sample label set may include: labels for the time of the current signal feature points corresponding to the 200,000 current signal feature points included in the sample signal information.

[0049] The second step is to select training samples from the above training sample set.

[0050] In some embodiments, the execution entity may select training samples from the training sample set. In practice, the execution entity may randomly select training samples from the training sample set.

[0051] The third step is to input the sample signal information included in the above training samples into the initial current signal feature point prediction model to obtain the initial current signal feature point prediction information group.

[0052] In some embodiments, the execution entity may input the sample signal information included in the training samples into the initial current signal feature point prediction model to obtain an initial current signal feature point prediction information group. The initial current signal feature point prediction information in the initial current signal feature point prediction information group includes: the confidence level of the initial current signal feature points. Here, the initial current signal feature point prediction model is a one-dimensional residual neural network model. The one-dimensional residual neural network model includes: six residual blocks and thirty-two one-dimensional convolutional layers. The initial current signal feature point prediction information in the initial current signal feature point prediction information group may further include: the type of initial current signal feature points and the time of initial current signal feature points.

[0053] Fourth step: For each initial current signal feature point prediction information in the above initial current signal feature point prediction information group, in response to determining that the confidence level of the initial current signal feature points included in the above initial current signal feature point prediction information is greater than the preset confidence level, the above initial current signal feature point prediction information is determined as the initial target current signal feature point information.

[0054] In some embodiments, for each initial current signal feature point prediction information in the aforementioned initial current signal feature point prediction information group, the executing entity may, in response to determining that the confidence level of the initial current signal feature points included in the aforementioned initial current signal feature point prediction information is greater than a preset confidence level, determine the aforementioned initial current signal feature point prediction information as initial target current signal feature point information. The initial target current signal feature point information in the aforementioned initial target current signal feature point information group may include: initial target current signal feature point time and initial target current signal feature point type. Here, the setting of the preset confidence level is not limited. For example, the preset difference value may be 90%.

[0055] The fifth step is to define the determined initial target current signal feature point information as an initial target current signal feature point information group.

[0056] In some embodiments, the execution entity may determine the determined initial target current signal feature point information as an initial target current signal feature point information group.

[0057] The sixth step is to determine the difference between the initial target current signal feature point information group and the sample label set included in the training samples, based on the preset loss function.

[0058] In some embodiments, based on a preset loss function, the execution entity can determine the difference between the initial target current signal feature point information group and the sample label set included in the training samples. Here, the preset loss function may be:

[0059]

[0060] Where L represents the aforementioned difference value. N represents the number of initial target current signal feature points included in the aforementioned initial target current signal feature point information group. i represents the sequence number of the initial target current signal feature point information. xi represents the initial target current signal feature point time included in the i-th initial target current signal feature point information in the initial target current signal feature point information group. This represents the time of the current signal feature points included in the i-th sample label in the sample label set. The preset loss function can also be, but is not limited to: Mean Squared Error Loss (MSE), SVM, Cross Entropy Loss, etc.

[0061] Step 6: Based on the above differences, adjust the network parameters of the initial current signal feature point prediction model.

[0062] In some embodiments, based on the aforementioned difference value, the executing entity can adjust the network parameters of the initial current signal feature point prediction model. In practice, the executing entity can adjust the network parameters of the initial current signal feature point prediction model in response to determining that the aforementioned difference value does not meet a preset condition. The preset condition can be that the aforementioned difference value is less than or equal to a preset difference value. For example, the difference between the difference value and the preset difference value can be calculated to obtain the loss difference. Based on this, methods such as backpropagation and stochastic gradient descent are used to propagate the error value from the last layer of the model forward to adjust the parameters of each layer. Of course, as needed, a network dropout method can also be used to keep the network parameters of some layers unchanged without adjustment; no limitation is made in this regard. Here, the setting of the preset difference value is not limited. For example, the preset difference value can be 0.5.

[0063] The relevant content in the optional section serves as an inventive point of this disclosure, thereby solving the second technical problem mentioned in the background art: "Zero-crossing detection methods typically require comparators to compare three output results, but comparators only have two outputs. Adding additional logic units to implement the third output easily leads to resource waste." Factors causing resource waste often include: zero-crossing detection methods typically require comparators to compare three output results, but comparators only have two outputs. Adding additional logic units to implement the third output easily leads to resource waste. Solving these factors can avoid resource waste. To achieve this effect, firstly, a training sample set is obtained. The training samples in the training sample set include: sample signal information and a sample label set. Secondly, training samples are selected from the training sample set. Then, the sample signal information included in the training samples is input into the initial current signal feature point prediction model to obtain the initial current signal feature point prediction information group. The initial current signal feature point prediction information in the initial current signal feature point prediction information group includes: the confidence level of the initial current signal feature points. Therefore, the initial current signal feature point prediction information group output by the initial current signal feature point prediction model can be obtained, so as to optimize the initial current signal feature point prediction model in the subsequent process. Next, for each initial current signal feature point prediction information in the above initial current signal feature point prediction information group, in response to determining that the confidence level of the initial current signal feature point included in the above initial current signal feature point prediction information is greater than a preset confidence level, the above initial current signal feature point prediction information is determined as the initial target current signal feature point information. Thus, relatively accurate initial target current signal feature point information can be obtained. For example, if the confidence level of the initial current signal feature point is less than or equal to the preset confidence level, it indicates that the accuracy of the above initial current signal feature point information is low, and no further processing is performed. Then, the determined initial target current signal feature point information is defined as the initial target current signal feature point information group. Next, based on a preset loss function, the difference value between the initial target current signal feature point information in the above initial target current signal feature point information group and the sample labels corresponding to the sample label set included in the above training samples is determined. Therefore, the difference between the initial target current signal feature point information and the expected result can be obtained, which can be used to adjust the initial current signal feature point prediction model. Finally, based on the above difference value, the network parameters of the initial current signal feature point prediction model are adjusted. Thus, the network parameters of the initial current signal feature point prediction model can be continuously adjusted according to the difference values ​​of different training samples to obtain a more accurate initial current signal feature point prediction model. This allows for obtaining relatively accurate zero-crossing information without additional logic units, thereby avoiding resource waste.

[0064] Optionally, in response to the above difference value satisfying a preset condition, the above initial current signal feature point prediction model is determined as the trained current signal feature point prediction model.

[0065] In some embodiments, the execution entity may determine the initial current signal feature point prediction model as the trained current signal feature point prediction model in response to the difference value satisfying a preset condition. The preset condition may be that the difference value is less than or equal to a preset difference value.

[0066] Step 1025: Based on the above current signal feature point prediction information group, generate the target current signal feature point information group.

[0067] In some embodiments, based on the aforementioned current signal feature point prediction information group, the executing entity can generate a target current signal feature point information group.

[0068] In practice, firstly, for each current signal feature point prediction information in the aforementioned current signal feature point prediction information group, the executing entity can, in response to determining that the confidence level of the current signal prediction points included in the aforementioned current signal feature point prediction information is greater than a preset confidence level, determine the aforementioned current signal feature point prediction information as target current signal feature point information. The aforementioned target current signal feature point information includes: target current signal feature point time and target current signal feature point type. The aforementioned target current signal feature point types include: a first current signal feature point type, a second current signal feature point type, a third current signal feature point type, and a fourth current signal feature point type. Then, the executing entity can combine the determined target current signal feature point information into a target current signal feature point information group. The target current signal feature point information in the aforementioned target current signal feature point information group has an arrangement order. Here, the arrangement order of the target current signal feature point information in the aforementioned target current signal feature point information group can be based on the arrangement order of the target current signal feature point time. The first current signal feature point type corresponds to the first current signal prediction point type included in the aforementioned current signal feature point prediction information. The second current signal feature point type corresponds to the second current signal prediction point type included in the aforementioned current signal feature point prediction information. The third current signal feature point type corresponds to the third current signal prediction point type included in the aforementioned current signal feature point prediction information. The fourth current signal feature point type corresponds to the fourth current signal prediction point type included in the aforementioned current signal feature point prediction information.

[0069] Step 1026: Based on the target current signal feature point information group, perform dead zone compensation on the target circuit.

[0070] In some embodiments, based on the target current signal feature point information group, the execution entity can perform dead-zone compensation on the target circuit.

[0071] In practice, for each target current signal feature point in the aforementioned target current signal feature point information group, the aforementioned execution entity can perform dead-zone compensation for the aforementioned current through the following compensation steps:

[0072] The first step, in response to determining that the target current signal feature point type included in the above target current signal feature point information characterizes the second current signal feature point type, can be performed as follows:

[0073] The first sub-step involves determining the target current signal feature point information corresponding to the aforementioned target current signal feature point information, representing the fourth current signal feature point type, as the positive current time zone end information. This positive current time zone end information includes the positive current time zone end time.

[0074] The second sub-step is to determine the time interval corresponding to the time of the target current signal feature point included in the target current signal feature point information and the time of the end of the positive current time zone included in the positive current time zone end information as the positive current time zone.

[0075] The second step, in response to determining that the target current signal feature point type included in the above target current signal feature point information characterizes the fourth current signal feature point type, can be performed as follows:

[0076] The first sub-step involves determining the target current signal feature point information corresponding to the aforementioned target current signal feature point information, representing the second current signal feature point type, as the negative current time zone end information. This negative current time zone end information includes the negative current time zone end time.

[0077] The second sub-step is to determine the time interval corresponding to the time of the target current signal feature point included in the target current signal feature point information and the time of the end of the negative current time zone included in the negative current time zone end information as the negative current time zone.

[0078] The third step is to add a preset dead time to each of the determined positive current time zones in order to perform dead time compensation on the target circuit.

[0079] The fourth step is to reduce the preset dead time for each of the determined negative current time zones in order to perform dead time compensation for the target circuit.

[0080] The relevant content in step 1026 serves as an inventive point of this disclosure, thereby solving the third technical problem mentioned in the background art: "The accuracy of the positive and negative current time zones obtained by the zero-crossing detection method is low, resulting in low dead-zone compensation accuracy and a tendency to cause short circuits." Factors causing short circuits often include: the low accuracy of the positive and negative current time zones obtained by the zero-crossing detection method leads to low dead-zone compensation accuracy, easily causing short circuits. Solving these factors can prevent short circuits. To achieve this effect, firstly, in response to determining that the target current signal feature point information includes a target current signal feature point type representing a second current signal feature point type, the following determination step can be performed: the target current signal feature point information corresponding to the next target current signal feature point type representing a fourth current signal feature point type is determined as the positive current time zone end information. The positive current time zone end information includes: the positive current time zone end time. The time interval corresponding to the time of the target current signal feature point included in the above target current signal feature point information and the end time of the positive current time zone included in the above positive current time zone end information is determined as the positive current time zone. This yields a more accurate positive current time zone for subsequent dead-zone compensation. Next, in response to determining that the target current signal feature point type included in the above target current signal feature point information represents the fourth current signal feature point type, the following generation steps can be performed: The target current signal feature point information corresponding to the next target current signal feature point type representing the second current signal feature point type is determined as the negative current time zone end information. The negative current time zone end information includes the negative current time zone end time. The time interval corresponding to the time of the target current signal feature point included in the above target current signal feature point information and the end time of the negative current time zone included in the above negative current time zone end information is determined as the negative current time zone. This yields a more accurate negative current time zone for subsequent dead-zone compensation. Then, a preset dead-zone duration is added to each determined positive current time zone to perform dead-zone compensation on the target circuit. Finally, the preset dead time is reduced for each determined negative current time zone to compensate for the dead time in the target circuit. Thus, dead time compensation can be performed on the accurate positive and negative current time zones based on the preset dead time, thereby preventing short circuits.

[0081] The above-described embodiments of this disclosure have the following beneficial effects: Dead-zone compensation methods of some embodiments of this disclosure can avoid circuit short circuits. Specifically, the reason why circuit short circuits are easily caused is that when using a zero-crossing detection method to compensate for the dead zone, current ripple leads to low accuracy of the zero-crossing detection result, resulting in low accuracy of dead-zone insertion and easily causing circuit short circuits. Based on this, the dead-zone compensation method of some embodiments of this disclosure first performs current sampling processing on the target circuit to generate a current signal information set. Thus, three-phase currents can be obtained through current sampling. Secondly, for each current signal information in the aforementioned current signal information set, the following processing steps are performed: First, the voltage signal in the voltage signal information corresponding to the aforementioned current signal information is amplified to generate amplified voltage signal information. Thus, the current signal information can be converted into voltage signal information within the effective range of the analog-to-digital converter, so that the sampled analog signal can be converted into a digital signal. Second, the amplified voltage signal information is converted to generate voltage digital signal information. Thus, the amplified voltage signal information can be converted into voltage digital signal information for subsequent zero-crossing information detection. Third, the voltage digital signal information is converted into current digital signal information. Fourth, the aforementioned digital current signal information is input into a pre-trained current signal feature point prediction model to obtain a set of predicted current signal feature points. This allows for a more accurate set of predicted current signal feature points based on the pre-trained model. Fifth, based on this set of predicted current signal feature points, a target current signal feature point information set is generated. This provides more accurate zero-crossing information. Sixth, based on the target current signal feature point information set, dead-zone compensation is performed on the target circuit. This allows for individual dead-zone compensation for each phase of the target circuit based on the more accurate zero-crossing information. This enables more accurate dead-zone compensation and helps prevent short circuits.

[0082] Further reference Figure 2 As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a dead-zone compensation device, which are similar to... Figure 1 Corresponding to the method embodiments shown, this dead-zone compensation device can be specifically applied to various electronic devices.

[0083] like Figure 2As shown, the dead-time compensation device 200 in some embodiments includes a current sampling unit 201 and a processing unit 202. The current sampling unit 201 is configured to perform current sampling processing on the target circuit to generate a current signal information set. The processing unit 202 is configured to perform the following processing steps for each current signal information in the current signal information set: amplify the voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information; convert the amplified voltage signal information to generate voltage digital signal information; convert the voltage digital signal information into current digital signal information; input the current digital signal information into a pre-trained current signal feature point prediction model to obtain a current signal feature point prediction information group; generate a target current signal feature point information group based on the current signal feature point prediction information group; and perform dead-time compensation on the target circuit based on the target current signal feature point information group.

[0084] It is understandable that the units described in the dead zone compensation device 200 are related to the reference. Figure 1 The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the dead-zone compensation device 200 and the units contained therein, and will not be repeated here.

[0085] The following is for reference. Figure 3 This document illustrates a structural schematic of an electronic device (e.g., a computing device) 300 suitable for implementing some embodiments of the present disclosure. The electronic devices in some embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0086] like Figure 3 As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0087] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0088] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 309, or installed from a storage device 308, or installed from a ROM 302. When the computer program is executed by the processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0089] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0090] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0091] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: perform current sampling processing on the target circuit to generate a current signal information set; for each current signal information in the current signal information set, perform the following processing steps: amplify the voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information; convert the amplified voltage signal information to generate voltage digital signal information; convert the voltage digital signal information into current digital signal information; input the current digital signal information into a pre-trained current signal feature point prediction model to obtain a current signal feature point prediction information group; generate a target current signal feature point information group based on the current signal feature point prediction information group; and perform dead-zone compensation on the target circuit based on the target current signal feature point information group.

[0092] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The units described in some embodiments of this disclosure can be implemented in software or in hardware. The described units can also be housed in a processor; for example, a processor may be described as including a current sampling unit and a processing unit. The names of these units do not necessarily limit the unit itself; for example, a current sampling unit may also be described as "performing current sampling processing on a target circuit to generate a current signal information set."

[0095] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0096] Some embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements any of the dead-zone compensation methods described above.

[0097] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A dead-time compensation method, comprising: performing current sampling processing on a target circuit to generate a current signal information set; for each current signal information in the current signal information set, performing the following processing steps: amplifying a voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information; performing conversion processing on the amplified voltage signal information to generate voltage digital signal information; converting the voltage digital signal information into current digital signal information; inputting the current digital signal information into a pre-trained current signal feature point prediction model to obtain a current signal feature point prediction information group, the current signal feature point prediction information in the current signal feature point prediction information group including: a current signal prediction point type, the current signal prediction point type including: a first current signal prediction point type, a second current signal prediction point type, a third current signal prediction point type, and a fourth current signal prediction point type, the first current signal prediction point type corresponding to a first current signal prediction point representing a minimum point in a current change cycle within a preset time period in a current waveform graph, the second current signal prediction point type corresponding to a second current signal prediction point representing a first 0 point in a current change cycle within a preset time period in a current waveform graph, the third current signal prediction point type corresponding to a third current signal prediction point representing a maximum point in a current change cycle within a preset time period in a current waveform graph, and the fourth current signal prediction point type corresponding to a fourth current signal prediction point representing a second 0 point in a current change cycle within a preset time period in a current waveform graph; generating a target current signal feature point information group based on the current signal feature point prediction information group; performing dead-time compensation on the target circuit based on the target current signal feature point information group.

2. The method of claim 1, wherein, The current sampling processing on the target circuit to generate the current signal information set comprises: performing current sampling processing on the target circuit to generate first current signal information and second current signal information, wherein the first current signal information includes a first current signal value sequence, and the second current signal information includes a second current signal value corresponding to each first current signal value included in the first current signal information; for each first current signal value included in the first current signal information, performing the following conversion steps: determining a sum of the first current signal value and the second current signal value corresponding to the first current signal value as a current signal sum value; determining a difference between a preset current total value and the current signal sum value as a third current signal value; combining the determined third current signal values into third current signal information; combining the first current signal information, the second current signal information, and the third current signal information into a current signal information set.

3. The method of claim 1, wherein, The current signal feature point prediction information in the current signal feature point prediction information set comprises a current signal prediction point type, a current signal prediction point time and a current signal prediction point confidence, and the current signal prediction point type comprises a first current signal prediction point type, a second current signal prediction point type, a third current signal prediction point type and a fourth current signal prediction point type.

4. The method of claim 3, wherein, The target current signal feature point information set is generated based on the current signal feature point prediction information set, comprising: For each current signal feature point prediction information in the current signal feature point prediction information set, in response to determining that the current signal prediction point confidence included in the current signal feature point prediction information is greater than a preset confidence, the current signal feature point prediction information is determined as target current signal feature point information, wherein the target current signal feature point information comprises a target current signal feature point time and a target current signal feature point type, and the target current signal feature point type comprises a first current signal feature point type, a second current signal feature point type, a third current signal feature point type and a fourth current signal feature point type; The determined target current signal feature point information is combined into a target current signal feature point information set, wherein the target current signal feature point information in the target current signal feature point information set has an arrangement order.

5. A dead zone compensation device, comprising: a current sampling unit configured to perform current sampling processing on a target circuit to generate a current signal information set; The processing unit is configured to, for each current signal information in the set of current signal information, perform the following processing steps: amplifying a voltage signal in the voltage signal information corresponding to the current signal information to generate amplified voltage signal information; converting the amplified voltage signal information to generate voltage digital signal information; converting the voltage digital signal information into current digital signal information; inputting the current digital signal information into a pre-trained current signal feature point prediction model to obtain a set of current signal feature point prediction information, the current signal feature point prediction information in the set of current signal feature point prediction information including: a current signal prediction point type, the current signal prediction point type including: a first current signal prediction point type, a second current signal prediction point type, a third current signal prediction point type, and a fourth current signal prediction point type, the first current signal prediction point type corresponding to a first current signal prediction point representing a minimum point in a current change cycle within a preset time period in a current waveform graph, the second current signal prediction point type corresponding to a second current signal prediction point representing a first 0 point in a current change cycle within a preset time period in a current waveform graph, the third current signal prediction point type corresponding to a third current signal prediction point representing a maximum point in a current change cycle within a preset time period in a current waveform graph, and the fourth current signal prediction point type corresponding to a fourth current signal prediction point representing a second 0 point in a current change cycle within a preset time period in a current waveform graph; generating a set of target current signal feature point information based on the set of current signal feature point prediction information; and performing dead-time compensation on the target circuit based on the set of target current signal feature point information. 6.An electronic device, comprising: one or more processors; a storage having stored thereon one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1-4.

7. A computer readable medium having stored thereon a computer program, wherein, The program is executed by the processor to implement the method according to any one of claims 1-4. 8.A computer program product, comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-4.

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