A crusher frequency converter torque control method and system

Abstract

The application belongs to the technical field of crushers, and discloses a crusher frequency converter torque control method and system, which comprises the following steps: optimizing the rotor torque signal of the motor, comparing the optimized rotor torque signal with a preset torque signal, and generating a rotor torque deviation; introducing the rotor torque deviation as a feedback signal into a torque compensation mechanism, correcting the torque component of the stator current in combination with the load change characteristics, and controlling the actual output torque of the motor by the PID regulator in the inner loop of the frequency converter based on the corrected torque component of the stator current; predicting the crushing fineness of the crusher in the future time period, and optimizing the torque control compensation mechanism based on the prediction result of the crushing fineness. Through real-time monitoring of the working torque of the crushing rotor and dynamic response to the change trend, the application can automatically execute the acceleration control logic and adjust the rotating speed to maintain the stability of the crushing fineness when the rotor torque is detected to decrease due to wear.

Description

Technical Field

[0001] This invention relates to the field of crusher technology, and in particular to a torque control method and system for a crusher frequency converter. Background Technology

[0002] A crusher is an industrial device used to crush large materials into smaller particles. It is commonly used in mining, metallurgy, building materials, chemical and other fields. Its main working principle is to change the particle size of materials through mechanical energy (such as compression, shearing, impact or grinding) so that the raw materials meet the particle size requirements of downstream processes, while improving material utilization and transmission efficiency.

[0003] Automatic adjustment of crushing particle size based on torque detection results means that the system monitors the torque signal of the motor or rotor in real time. When it detects that the torque change may lead to insufficient crushing or coarser particle size, it actively adjusts the control strategy, such as increasing the motor speed, increasing the feed pressure, or increasing the crushing time, to compensate for the particle size deviation and ultimately achieve closed-loop dynamic control of the crushing particle size.

[0004] During long-term operation of a crusher, the distance between the rotating hammer and the fixed liner gradually increases due to continuous friction and wear. This weakens the compression and impact on the material during crushing, resulting in coarser particle size and poorer crushing effect. Traditional ore crushing equipment usually adopts a fixed crushing distance design. When the crushing particle size is too large, the machine must be stopped for manual adjustment, which not only affects the continuity of crushing but also prolongs the downtime for equipment adjustment, thereby reducing the overall crushing efficiency.

[0005] Therefore, how to provide a torque control method and system for a crusher frequency converter is an urgent problem to be solved. Summary of the Invention

[0006] This invention provides a torque control method and system for a crusher frequency converter, which solves the problem that traditional ore crushing equipment in the prior art usually adopts a fixed crushing spacing design, and when the crushing particle size is too large, the machine must be stopped for manual adjustment, which reduces the crushing efficiency.

[0007] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or to describe the scope of protection of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the detailed description that follows.

[0008] According to a first aspect of the present invention, a torque control method for a crusher frequency converter is provided.

[0009] In one embodiment, the torque control method for the crusher frequency converter includes:

[0010] The crusher's operating data is collected and uploaded to the cloud. The crusher's operating data includes the motor's original signal, rotor torque signal, and crushing fineness data.

[0011] The rotor torque signal of the motor is optimized, and the optimized rotor torque signal is compared with the preset torque signal to generate the rotor torque deviation.

[0012] The rotor torque deviation is introduced as a feedback signal into the torque compensation mechanism. The torque component of the stator current is corrected in combination with the load change characteristics. Based on the corrected stator current torque component, the inner loop of the frequency converter controls the actual output torque of the motor through the PID regulator.

[0013] The crushing fineness index is extracted from the crushing fineness data to predict the crushing fineness of the crusher in the future time period, and the torque control compensation mechanism is optimized based on the predicted crushing fineness.

[0014] In one embodiment, the step of introducing rotor torque deviation as a feedback signal into the torque compensation mechanism, correcting the torque component of the stator current based on load variation characteristics, and controlling the actual output torque of the motor through a PID regulator in the inner loop of the frequency converter based on the corrected stator current torque component includes:

[0015] Frequency domain analysis techniques are used to extract load variation characteristics from the original motor signal, and a mapping relationship between load and torque is constructed based on these characteristics.

[0016] The rotor torque deviation is input into the feedforward compensator and converted into a correction amount for the stator current torque component. The compensation coefficient is adjusted based on the mapping relationship between load and torque to generate the corrected stator current torque component.

[0017] The corrected stator current torque component and the constant excitation current component are combined to form the stator current reference value, and the stator current reference value is converted into a current command in the stationary coordinate system through inverse transformation technology.

[0018] The inverter's inner loop tracks the current command through a PID controller, generates pulse signals to adjust the inverter switch, and controls the motor's actual output torque to track the target torque signal. A current limiting mechanism is also introduced during the current control process.

[0019] In one embodiment, the current limiting mechanism includes field weakening control, which is used to actively weaken the magnetic field when the motor speed is at the base speed, so that the motor can maintain torque at high speed; the parameter settings of the current limiting mechanism include torque limiting, ramp time, field weakening parameters and protection functions.

[0020] In one embodiment, the step of extracting load variation characteristics from the original motor signal using frequency domain analysis techniques and constructing a load-torque mapping relationship based on these characteristics includes:

[0021] The original motor signal is subjected to a fast Fourier transform to obtain the spectral distribution characteristics. The non-stationary load in the spectral distribution characteristics is subjected to a short-time Fourier transform to generate a time-varying spectrum to capture transient load disturbances.

[0022] The power spectral density is used to identify the multidimensional spectral feature vectors that characterize load changes in the spectral distribution features, and the dimensionality of the multidimensional spectral feature vectors is reduced to obtain the load change features.

[0023] The load variation characteristics are sampled and aligned with the synchronously acquired rotor torque signal, and then input into the fractional-order state-space model. The fractional-order state-space model is used to generate the mapping relationship between load and torque.

[0024] In one embodiment, the step of aligning the load variation characteristics with the synchronously acquired rotor torque signal and inputting them into a fractional-order state-space model, and generating the load-torque mapping relationship through the fractional-order state-space model, includes:

[0025] The load variation characteristics are processed by variable-order differentiation to extract load memory characteristics at different time scales. The load memory characteristics are then cross-correlation analyzed with the synchronously acquired rotor torque signal.

[0026] Based on the cross-correlation analysis results, the extreme values ​​of the cross-correlation between the load memory characteristics and the rotor torque signal are searched in a multi-order hysteresis window to obtain the optimal hysteresis of the load response to torque changes.

[0027] The optimal hysteresis is used to adjust the load change characteristics in time to achieve sample alignment between the load change characteristics and the rotor torque signal, and the aligned sample pairs are introduced into the fractional-order state-space model.

[0028] The optimal cross-correlation parameter combination is determined within the global search range using the fractional-order particle swarm optimization algorithm, and the optimal cross-correlation parameter combination is used as the mapping relationship between load and torque.

[0029] In one embodiment, the step of synthesizing the corrected stator current torque component and the constant excitation current component into a stator current reference value, and converting the stator current reference value into a current command in a stationary coordinate system using an inverse transformation technique includes:

[0030] The torque current component corrected by the torque compensation mechanism and the constant excitation current component are combined in the synchronous rotating coordinate system to form the stator current reference value.

[0031] Based on the current rotor position angle, the synthesized current reference value is transformed to the stationary coordinate system through inverse transformation to obtain the current component in the stationary coordinate system;

[0032] The current component in the stationary coordinate system is used as the control target to generate current commands and drive the inverter output.

[0033] In one embodiment, the step of extracting crushing fineness indices from crushing fineness data, predicting the crushing fineness of the crusher in a future time period, and optimizing the torque control compensation mechanism based on the predicted crushing fineness includes:

[0034] Tensor decomposition technology was used to extract the series of crushing fineness indicators from crushing fineness data;

[0035] The crushing fineness index sequence is input into a long short-term memory network, and the crushing fineness trend in the future time period is predicted based on historical crushing fineness data to obtain the crushing fineness prediction result.

[0036] A reinforcement learning agent is constructed based on the prediction results of the crushing fineness, with the crushing fineness deviation as the state input, and the optimized target current command is output according to the torque compensation mechanism.

[0037] In one embodiment, the optimized target current command output based on the torque compensation mechanism includes:

[0038] The current crushing fineness deviation is compared with the preset threshold. If the current crushing fineness deviation is greater than the preset threshold, it indicates that the particle size of the crushed material has increased. The rotor torque signal of the motor is fed back synchronously and the next step is executed. If the current crushing fineness deviation is less than or equal to the preset threshold, the current parameters are maintained.

[0039] When the particle size of the crushed material increases, the rotor torque signal of the motor decreases and the rotor torque deviation is recalculated. Based on the new rotor torque deviation, the torque compensation mechanism is repeatedly executed to correct the torque component of the stator current.

[0040] In one embodiment, the step of inputting the crushing fineness index sequence into a long short-term memory network and predicting the crushing fineness trend in future time periods based on historical crushing fineness data to obtain the predicted crushing fineness result includes:

[0041] The crushing fineness index sequence is used as the original time series input, and the crushing fineness index sequence is cut into several sample sets by a sliding window mechanism at a fixed time step.

[0042] A time series prediction network consisting of several long short-term memory layers was constructed, and the network was trained by combining historical fragmentation fineness data and sample sets. The fragmentation fineness trend in future time periods was output through a fully connected layer.

[0043] The crushing fineness trend over a future time period is compared with the target fineness range, and the crushing fineness deviation is calculated based on the comparison results.

[0044] According to a second aspect of the present invention, a torque control system for a crusher frequency converter is provided.

[0045] In one embodiment, the crusher frequency converter torque control system includes:

[0046] The data acquisition module is used to collect the operating data of the crusher and upload it to the cloud. The operating data of the crusher includes the original signal of the motor, the rotor torque signal and the crushing fineness data.

[0047] The torque deviation calculation module is used to optimize the rotor torque signal of the motor, compare the optimized rotor torque signal with the preset torque signal, and generate the rotor torque deviation.

[0048] The torque correction module is used to introduce the rotor torque deviation as a feedback signal into the torque compensation mechanism, and correct the torque component of the stator current in combination with the load change characteristics. Based on the corrected stator current torque component, the inverter inner loop controls the actual output torque of the motor through the PID regulator.

[0049] The crushing fineness assessment module is used to extract crushing fineness indicators from crushing fineness data, predict the crushing fineness of the crusher in the future time period, and optimize the torque control compensation mechanism based on the predicted crushing fineness.

[0050] According to a third aspect of the present invention, a computer device is provided.

[0051] In one embodiment, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0052] According to a fourth aspect of the present invention, a computer-readable storage medium is provided.

[0053] In one embodiment, a computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the steps of the above method.

[0054] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0055] 1. This invention monitors the working torque of the crushing rotor in real time and dynamically responds to its changing trend. When the rotor torque is detected to decrease due to wear, it can automatically execute acceleration control logic to adjust the speed to maintain stable crushing fineness without stopping the machine to manually adjust the crushing gap. This effectively compensates for the problem of coarser particle size caused by the increase in gap and allows the next adjustment strategy to be optimized based on the wear pattern, thereby enabling the breaker hammer to release a larger allowable amount of wear and further extending the adjustment interval of the crusher.

[0056] 2. This invention combines fineness prediction with rotor torque feedback control to achieve dynamic and precise adjustment of motor torque. It can optimize torque compensation strategy in advance based on future crushing fineness trends, and correct stator current torque components in real time in response to changes in load characteristics. This ensures consistent output particle size and reduces human intervention and equipment adjustment lag. The stepless control of rotational speed achieves closed-loop adaptive control of the crushing process, which not only improves the intelligence and operating efficiency of the equipment, but also greatly reduces production interruption time caused by frequent shutdowns for adjustment. Ultimately, it ensures the consistency and stability of the output particle size of the ore processed by the crusher.

[0057] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0059] Figure 1 This is a flowchart illustrating a torque control method for a crusher frequency converter according to an exemplary embodiment;

[0060] Figure 2 This is a schematic block diagram of a torque control system for a frequency converter in a crusher, according to an exemplary embodiment.

[0061] Figure 3 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment;

[0062] Figure 4 This is a schematic diagram of the structure of a crusher in a torque control method for a crusher frequency converter according to an exemplary embodiment. Detailed Implementation

[0063] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some embodiments may include or substitute parts and features of other embodiments. The scope of the embodiments herein encompasses the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0064] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings. They are used solely for the convenience of describing the document and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements; they can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0065] In this document, unless otherwise stated, the term "multiple" means two or more.

[0066] In this article, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0067] In this article, the term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0068] It should be understood that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order constraint on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the diagram may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0069] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0070] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0071] Figure 1 An embodiment of the torque control method for a crusher frequency converter according to the present invention is shown.

[0072] In this optional embodiment, the torque control method for the crusher frequency converter includes:

[0073] Step S101: Collect the operating data of the crusher and upload it to the cloud. The operating data of the crusher includes the original signal of the motor, the rotor torque signal and the crushing fineness data.

[0074] Step S102: Optimize the rotor torque signal of the motor, compare the optimized rotor torque signal with the preset torque signal, and generate rotor torque deviation;

[0075] Step S103: The rotor torque deviation is introduced as a feedback signal into the torque compensation mechanism. The torque component of the stator current is corrected in combination with the load change characteristics. Based on the corrected stator current torque component, the inner loop of the frequency converter controls the actual output torque of the motor through the PID regulator.

[0076] Step S104: Extract the crushing fineness index from the crushing fineness data, predict the crushing fineness of the crusher in the future time period, and optimize the torque control compensation mechanism based on the predicted crushing fineness.

[0077] In this optional embodiment, the step of introducing the rotor torque deviation as a feedback signal into the torque compensation mechanism, correcting the torque component of the stator current in combination with load change characteristics, and controlling the actual output torque of the motor through a PID regulator in the inner loop of the frequency converter based on the corrected stator current torque component includes:

[0078] Frequency domain analysis is used to extract load variation characteristics from the original motor signal, and a load-torque mapping relationship is constructed based on these characteristics. The rotor torque deviation is input to the feedforward compensator and converted into a correction amount for the stator current torque component. The compensation coefficient is adjusted based on the load-torque mapping relationship to generate the corrected stator current torque component. The corrected stator current torque component is combined with the constant excitation current component to form a stator current reference value. The stator current reference value is then converted into a current command in a stationary coordinate system using inverse transformation technology. The inverter inner loop tracks the current command through a PID controller, generates pulse signals to adjust the inverter switch, and controls the actual output torque of the motor to track the target torque signal. A current limiting mechanism is introduced during the current control process.

[0079] It should be noted that by extracting load characteristics in the frequency domain and establishing a load-torque mapping, feedforward compensation and dynamic correction of rotor torque deviation are achieved, improving the accuracy of stator current commands. At the same time, through inverse transformation and PID control closed loop, the motor output torque accurately tracks the target torque. Combined with the current limiting mechanism, the stability, anti-disturbance capability and operational safety of the system under complex load changes are effectively enhanced, achieving high-precision and high-responsive electric drive control of the crushing process.

[0080] In this optional embodiment, the current limiting mechanism includes field weakening control, which is used to actively weaken the magnetic field when the motor speed is at the base speed, so that the motor can maintain torque at high speed; the parameter settings of the current limiting mechanism include torque limiting, ramp time, field weakening parameters and protection functions.

[0081] In this optional embodiment, the step of extracting load variation features from the original motor signal using frequency domain analysis techniques and constructing a load-torque mapping relationship based on these features includes:

[0082] The original motor signal is subjected to a Fast Fourier Transform (FFT) to obtain the spectral distribution characteristics. The non-stationary load in the spectral distribution characteristics is then subjected to a Short-Time Fourier Transform (SFT) to generate a time-varying spectrum to capture transient load disturbances. The power spectral density is used to identify the multidimensional spectral feature vectors representing load changes in the spectral distribution characteristics, and the multidimensional spectral feature vectors are then dimensionality-reduced to obtain the load change characteristics. The load change characteristics are then sample-aligned with the synchronously acquired rotor torque signal and input into a fractional-order state-space model. The fractional-order state-space model is used to generate the mapping relationship between load and torque.

[0083] It should be noted that the fractional state-space model is a modeling method that extends the traditional integer state-space modeling method to the fractional calculus framework. It can more realistically describe complex dynamic systems with "memory" and "multi-scale response". When dealing with industrial signals containing hysteresis, inertia or nonlocal response characteristics, such as motor loads and crushing processes, it has greater physical rationality and mathematical expression.

[0084] In this optional embodiment, the step of aligning the load variation characteristics with the synchronously acquired rotor torque signal and inputting them into a fractional-order state-space model, and generating the load-torque mapping relationship through the fractional-order state-space model, includes:

[0085] The load variation characteristics are processed by variable-order differentiation to extract load memory characteristics at different time scales. The load memory characteristics are then cross-correlation analyzed with the synchronously acquired rotor torque signal. Based on the cross-correlation analysis results, the extreme values ​​of the cross-correlation between the load memory characteristics and the rotor torque signal are searched in a multi-order hysteresis window to obtain the optimal hysteresis of the load response to torque change. The optimal hysteresis is used to adjust the load variation characteristics in time to achieve sample alignment between the load variation characteristics and the rotor torque signal, and the aligned sample pairs are introduced into the fractional-order state-space model. The optimal cross-correlation parameter combination is determined in the global search range using the fractional-order particle swarm optimization algorithm, and the optimal cross-correlation parameter combination is used as the mapping relationship between load and torque.

[0086] It should be noted that the fractional-order particle swarm optimization (PSO) algorithm is an extension of the classic PSO algorithm. It introduces a fractional-order velocity and position update mechanism during the search process, giving the algorithm better global optimization and dynamic trade-off capabilities. It is suitable for high-dimensional, complex, and multi-peak parameter optimization tasks, and is used to accurately find the optimal combination of cross-correlation parameters describing the load-torque mapping relationship within a large search space. Specifically, it includes:

[0087] Initialize the position, velocity, and fitness values ​​of the particle swarm, and assign a fractional-order history memory factor to each particle to introduce long-memory characteristics; calculate the fitness of each particle, and record the current individual optimal value and the global optimal value; update the particle velocity according to the idea of ​​fractional-order calculus, that is, introduce the weight of multiple historical iterations and the memory decay function on the basis of the traditional velocity update formula, so that the velocity depends not only on the current state but also on the historical trajectory; adjust the particle position using the updated velocity, while keeping it within a preset boundary range; repeat the iterative process of fitness calculation, local and global optimal update, velocity and position adjustment until the stopping condition is met (such as the maximum number of iterations or error threshold); finally, output the position of the globally optimal particle as the optimal solution to the problem, thereby achieving global optimization of parameters such as fractional-order state-space models.

[0088] This invention can accurately identify the dynamic response relationship between load change characteristics and rotor torque at multiple time scales, and realize adaptive alignment and modeling of load and torque signals, thereby constructing a more stable, robust and adaptable torque control system, effectively improving the intelligent response and energy efficiency control capabilities of crushing equipment under complex working conditions.

[0089] In this optional embodiment, the step of synthesizing the corrected stator current torque component and the constant excitation current component into a stator current reference value, and converting the stator current reference value into a current command in a stationary coordinate system using an inverse transformation technique includes:

[0090] The torque current component corrected by the torque compensation mechanism and the constant excitation current component are combined to form a stator current reference value in a synchronous rotating coordinate system. Based on the current rotor position angle, the combined current reference value is transformed to a stationary coordinate system through an inverse transformation to obtain the current component in the stationary coordinate system. The current component in the stationary coordinate system is used as the control target to generate current commands and drive the inverter output.

[0091] In this optional embodiment, the step of extracting crushing fineness indices from crushing fineness data, predicting the crushing fineness of the crusher in a future time period, and optimizing the torque control compensation mechanism based on the predicted crushing fineness includes:

[0092] Tensor decomposition is used to extract the fineness index sequence from the fineness data. The fineness index sequence is then input into a long short-term memory network to predict the fineness trend in the future time period based on historical fineness data, thus obtaining the fineness prediction result. Based on the fineness prediction result, a reinforcement learning agent is constructed, which takes the fineness deviation as the state input and outputs the optimized target current command according to the torque compensation mechanism.

[0093] In this optional embodiment, the step of outputting the optimized target current command based on the torque compensation mechanism includes:

[0094] The current crushing fineness deviation is compared with a preset threshold. If the current crushing fineness deviation is greater than the preset threshold, it indicates that the particle size of the crushed material has increased. The rotor torque signal of the motor is fed back synchronously and the next step is executed. If the current crushing fineness deviation is less than or equal to the preset threshold, the current parameters are maintained. When the particle size of the crushed material increases, the rotor torque signal of the motor decreases and the rotor torque deviation is recalculated. Based on the new rotor torque deviation, the torque compensation mechanism is repeatedly executed to correct the torque component of the stator current.

[0095] In this optional embodiment, the step of inputting the crushing fineness index sequence into a long short-term memory network and predicting the crushing fineness trend within future time periods based on historical crushing fineness data to obtain the predicted crushing fineness result includes:

[0096] The fineness index sequence is used as the original time series input, and the fineness index sequence is cut into several sample sets by a sliding window mechanism at a fixed time step. A time series prediction network composed of several long short-term memory layers is constructed, and the time series prediction network is trained by combining historical fineness data and sample sets. The fineness trend in the future time period is output through a fully connected layer. The fineness trend in the future time period is compared with the target fineness range, and the fineness deviation is calculated based on the comparison results.

[0097] It should be noted that this invention generates training samples through a sliding window and constructs an LSTM network to predict the crushing fineness in multiple steps. The system can identify future fineness change trends in advance and dynamically evaluate the crushing quality based on the deviation between the prediction results and the target range, providing data support for subsequent control decisions, thereby improving the stability and intelligence level of the crushing process.

[0098] Figure 2 An embodiment of the torque control system for a crusher frequency converter according to the present invention is shown.

[0099] In this optional embodiment, the crusher frequency converter torque control system includes:

[0100] The data acquisition module 201 is used to collect the operating data of the crusher and upload it to the cloud. The operating data of the crusher includes the original signal of the motor, the rotor torque signal and the crushing fineness data.

[0101] The torque deviation calculation module 202 is used to optimize the rotor torque signal of the motor, compare the optimized rotor torque signal with the preset torque signal, and generate the rotor torque deviation.

[0102] The torque correction module 203 is used to introduce the rotor torque deviation as a feedback signal into the torque compensation mechanism, correct the torque component of the stator current in combination with the load change characteristics, and control the actual output torque of the motor through the PID regulator in the inner loop of the frequency converter based on the corrected stator current torque component.

[0103] The crushing fineness assessment module 204 is used to extract crushing fineness indicators from crushing fineness data, predict the crushing fineness of the crusher in the future time period, and optimize the torque control compensation mechanism based on the predicted crushing fineness.

[0104] The torque control method for a crusher frequency converter provided by the present invention will be further explained below with reference to specific implementation methods.

[0105] like Figure 4 As shown, in Figure 4In the diagram: 1 represents the discharge port; 2 represents the discharge hopper; 3 represents the crusher liner; 4 represents the crusher liner that needs to be replaced when it wears down to a certain extent; 5 represents the rotor crusher hammer. As the usage time increases, the distance between the hammer and the liner will increase, resulting in decreased crushing efficiency and coarser particles. The main shaft rotation torque will also decrease. If the rotation speed is increased at this point, the crushing efficiency will be maintained at the level before adjustment, thus extending the adjustment time without stopping the machine to adjust the crusher hammer. The next adjustment can simply involve pulling out a relatively larger number of hammers. As the amount of ore processed increases, the rotor crusher hammer will wear down. Therefore, the crusher hammers on the rotor can be adjusted outwards to compensate for the wear and ensure crushing quality.

[0106] Traditional crushers use asynchronous motors for drive. During operation, the motor and equipment status cannot be monitored in real time. If foreign objects enter the equipment or bearings wear, the equipment cannot provide timely feedback and will continue to operate, causing damage. In contrast, the motor in this invention is a permanent magnet synchronous motor, controlled by a vector frequency converter. The motor's operating status is monitored by the frequency converter in real time. If foreign objects enter or the operating status is abnormal, the equipment will automatically detect and shut down to protect itself.

[0107] The control system uses a PLC (Programmable Logic Controller) to control the entire system. The touch screen can display the operating status and parameters of the main motor and various auxiliary machines, such as real-time current, voltage, starting torque, speed, equipment running time, motor temperature, etc. Various system parameters, such as auxiliary machine running time, main motor speed, torque, etc., can also be set through the touch screen.

[0108] The control system performs hierarchical control operations. The employee interface allows users to start and stop the basic operating functions of the main and auxiliary equipment. The engineer interface includes employee access permissions and also adds parameter settings for the main and auxiliary equipment based on production requirements. The supervisor interface includes full permissions for equipment operation and the setting and modification of all parameters.

[0109] Traditional crusher motors use three-phase asynchronous motors, while permanent magnet synchronous motors (PMSMs) offer the following functions:

[0110] Permanent magnet synchronous motor, rotor: embedded with permanent magnets such as neodymium iron boron, does not require external excitation, and relies on the permanent magnets to generate a fixed magnetic field.

[0111] Stator: Similar to an asynchronous motor, it generates a rotating magnetic field when an alternating current is applied.

[0112] Working principle:

[0113] The stator magnetic field and the rotor permanent magnet magnetic field rotate synchronously and are strictly synchronized, with the rotational speed equal to the magnetic field rotational speed.

[0114] A controller (such as a frequency converter) is needed to precisely control the position of the magnetic field.

[0115] Permanent magnet synchronous motors offer higher efficiency (90-95% or higher), a high power factor (close to 1), eliminating the need for reactive power compensation, high starting torque, and high low-speed torque. They also provide precise speed control, high precision, and high efficiency.

[0116] The equipment offers both speed and torque modes. Torque mode is more advantageous for material processing than speed mode. When the motor is controlled in torque mode by the frequency converter, the core objective is to ensure the motor's output torque precisely follows the set value, rather than controlling the speed. The motor's output torque is directly proportional to the torque component of the stator current. The frequency converter directly controls the torque by adjusting the motor's current.

[0117] In torque mode, the inverter does not actively adjust the motor speed; the speed is determined by the load. For example, if the load increases, the motor will automatically decelerate to maintain the set torque. Direct torque control directly adjusts the inverter's switching state by monitoring flux linkage and torque in real time, quickly responding to torque changes. The inverter's inner loop controls the current through a PID controller, ensuring that the actual current quickly tracks the set value.

[0118] The current limiting function prevents overcurrent damage to the motor, including field weakening control (high-speed operation). When the motor speed approaches the base speed, it actively weakens the magnetic field, allowing the motor to maintain torque at higher speeds. Key parameter settings include:

[0119] 1. Torque Limiter: Sets the maximum output torque to prevent mechanical overload.

[0120] 2. Ramp Time: Adjust the slope of torque change to avoid impact.

[0121] 3. Weakening field parameters: Optimize torque output capability in the high-speed range.

[0122] 4. Protection functions: Overcurrent, overheat, and stall protection thresholds need to be set according to the actual load.

[0123] Through torque mode, the frequency converter can achieve highly dynamic and precise torque control, and is widely used in industrial automation scenarios requiring force control. Equipment operating parameters and alarm information are uploaded to the cloud server in real time, allowing users to view the equipment's operating status anytime via computer or mobile phone.

[0124] Cloud applications bring significant advantages to IoT systems, including:

[0125] 1. Centralized management and remote monitoring.

[0126] Unified Management and Control: The cloud platform can centrally manage a massive number of IoT boxes and their connected terminal devices, enabling remote operation of configuration, upgrades, and monitoring.

[0127] Real-time monitoring: View device status, data streams, and alarm information in real time through a cloud-based dashboard, and respond quickly to anomalies.

[0128] 2. Efficient data processing and analysis.

[0129] Edge-cloud collaboration: IoT boxes perform initial data filtering at the edge, while the cloud performs in-depth analysis, such as big data and AI modeling, reducing latency and bandwidth consumption.

[0130] Intelligent decision-making: The cloud supports advanced analytics such as machine learning and trend prediction to optimize business logic, such as predictive maintenance and energy consumption optimization.

[0131] 3. Flexible expansion and cost optimization.

[0132] Resource allocation on demand: The elastic computing and storage resources of cloud services can be dynamically expanded with the scale of the device, avoiding excessive investment in local hardware.

[0133] Reduce edge workload: Offload complex computing tasks to the cloud to reduce reliance on the hardware performance of IoT boxes.

[0134] 4. Data storage and long-term value mining.

[0135] Massive storage: The cloud provides low-cost, highly reliable data storage, such as time-series databases, which support long-term historical data backtracking.

[0136] Data fusion: Aggregating data across devices and regions to uncover holistic insights, such as supply chain optimization and user behavior analysis.

[0137] 5. High availability and security.

[0138] Redundant backup: Cloud-based multi-copy storage and disaster recovery mechanisms ensure no data loss and uninterrupted service.

[0139] End-to-end security: The cloud platform provides security capabilities such as device authentication, data encryption (TLS / SSL), and access control (IAM) to fill the gaps in edge security.

[0140] 6. Business agility and accelerated innovation.

[0141] Rapid iteration: Cloud applications can dynamically update functions without the need for frequent firmware upgrades to IoT boxes.

[0142] Exploring new models: Supporting innovative business models such as data monetization (e.g., open APIs) and on-demand services (SaaS).

[0143] 7. Cross-regional collaboration.

[0144] Global coverage: Through multi-node deployment of cloud services, unified management of devices across countries and regions can be achieved, meeting compliance requirements such as GDPR.

[0145] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 3 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0146] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0147] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0148] In addition, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0149] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0150] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A torque control method for a crusher frequency converter, characterized in that, The method includes: The crusher's operating data is collected and uploaded to the cloud. The crusher's operating data includes the motor's original signal, rotor torque signal, and crushing fineness data. The rotor torque signal of the motor is optimized, and the optimized rotor torque signal is compared with the preset torque signal to generate the rotor torque deviation. The rotor torque deviation is introduced as a feedback signal into the torque compensation mechanism. The torque component of the stator current is corrected in combination with the load change characteristics. Based on the corrected stator current torque component, the inner loop of the frequency converter controls the actual output torque of the motor through the PID regulator. The crushing fineness index is extracted from the crushing fineness data to predict the crushing fineness of the crusher in the future time period, and the torque control compensation mechanism is optimized based on the predicted crushing fineness.

2. The torque control method for a crusher frequency converter according to claim 1, characterized in that, The rotor torque deviation is introduced as a feedback signal into the torque compensation mechanism. Combined with load variation characteristics, the torque component of the stator current is corrected. Based on the corrected stator current torque component, the inverter inner loop controls the actual output torque of the motor through a PID regulator, including: Frequency domain analysis techniques are used to extract load variation characteristics from the original motor signal, and a mapping relationship between load and torque is constructed based on these characteristics. The rotor torque deviation is input into the feedforward compensator and converted into a correction amount for the stator current torque component. The compensation coefficient is adjusted based on the mapping relationship between load and torque to generate the corrected stator current torque component. The corrected stator current torque component and the constant excitation current component are combined to form the stator current reference value, and the stator current reference value is converted into a current command in the stationary coordinate system through inverse transformation technology. The inverter's inner loop tracks the current command through a PID controller, generates pulse signals to adjust the inverter switch, and controls the motor's actual output torque to track the target torque signal. A current limiting mechanism is also introduced during the current control process.

3. The torque control method for a crusher frequency converter according to claim 2, characterized in that, The current limiting mechanism includes field weakening control, which is used to actively weaken the magnetic field when the motor speed is at the base speed, so that the motor can maintain torque at high speed. The parameters of the current limiting mechanism include torque limiting, ramp time, field weakening parameters, and protection functions.

4. The torque control method for a crusher frequency converter according to claim 2, characterized in that, The process of extracting load variation characteristics from the original motor signal using frequency domain analysis techniques and constructing a load-torque mapping relationship based on these characteristics includes: The original motor signal is subjected to a fast Fourier transform to obtain the spectral distribution characteristics. The non-stationary load in the spectral distribution characteristics is subjected to a short-time Fourier transform to generate a time-varying spectrum to capture transient load disturbances. The power spectral density is used to identify the multidimensional spectral feature vectors that characterize load changes in the spectral distribution features, and the dimensionality of the multidimensional spectral feature vectors is reduced to obtain the load change features. The load variation characteristics are sampled and aligned with the synchronously acquired rotor torque signal, and then input into the fractional-order state-space model. The fractional-order state-space model is used to generate the mapping relationship between load and torque.

5. The torque control method for a crusher frequency converter according to claim 4, characterized in that, The step of aligning the load variation characteristics with the synchronously acquired rotor torque signal and inputting them into a fractional-order state-space model, and generating the load-torque mapping relationship through the fractional-order state-space model includes: The load variation characteristics are processed by variable-order differentiation to extract load memory characteristics at different time scales. The load memory characteristics are then cross-correlation analyzed with the synchronously acquired rotor torque signal. Based on the cross-correlation analysis results, the extreme values ​​of the cross-correlation between the load memory characteristics and the rotor torque signal are searched in a multi-order hysteresis window to obtain the optimal hysteresis of the load response to torque changes. The optimal hysteresis is used to adjust the load change characteristics in time to achieve sample alignment between the load change characteristics and the rotor torque signal, and the aligned sample pairs are introduced into the fractional-order state-space model. The optimal cross-correlation parameter combination is determined within the global search range using the fractional-order particle swarm optimization algorithm, and the optimal cross-correlation parameter combination is used as the mapping relationship between load and torque.

6. The torque control method for a crusher frequency converter according to claim 5, characterized in that, The step of synthesizing the corrected stator current torque component and the constant excitation current component into a stator current reference value, and converting the stator current reference value into a current command in a stationary coordinate system using inverse transformation technology includes: The torque current component corrected by the torque compensation mechanism and the constant excitation current component are combined in the synchronous rotating coordinate system to form the stator current reference value. Based on the current rotor position angle, the synthesized current reference value is transformed to the stationary coordinate system through inverse transformation to obtain the current component in the stationary coordinate system; The current component in the stationary coordinate system is used as the control target to generate current commands and drive the inverter output.

7. The torque control method for a crusher frequency converter according to claim 1, characterized in that, The process of extracting crushing fineness indices from crushing fineness data, predicting the crushing fineness of the crusher in the future time period, and optimizing the torque control compensation mechanism based on the predicted crushing fineness includes: Tensor decomposition technology was used to extract the series of crushing fineness indicators from crushing fineness data; The crushing fineness index sequence is input into a long short-term memory network, and the crushing fineness trend in the future time period is predicted based on historical crushing fineness data to obtain the crushing fineness prediction result. A reinforcement learning agent is constructed based on the prediction results of the crushing fineness, with the crushing fineness deviation as the state input, and the optimized target current command is output according to the torque compensation mechanism.

8. The torque control method for a crusher frequency converter according to claim 7, characterized in that, The optimized target current command output based on the torque compensation mechanism includes: The current crushing fineness deviation is compared with the preset threshold. If the current crushing fineness deviation is greater than the preset threshold, it indicates that the particle size of the crushed material has increased. The rotor torque signal of the motor is fed back synchronously and the next step is executed. If the current crushing fineness deviation is less than or equal to the preset threshold, the current parameters are maintained. When the particle size of the crushed material increases, the rotor torque signal of the motor decreases and the rotor torque deviation is recalculated. Based on the new rotor torque deviation, the torque compensation mechanism is repeatedly executed to correct the torque component of the stator current.

9. A torque control method for a crusher frequency converter according to claim 8, characterized in that, The step of inputting the crushing fineness index sequence into a long short-term memory network and predicting the crushing fineness trend in future time periods based on historical crushing fineness data to obtain the crushing fineness prediction result includes: The crushing fineness index sequence is used as the original time series input, and the crushing fineness index sequence is cut into several sample sets by a sliding window mechanism at a fixed time step. A time series prediction network consisting of several long short-term memory layers was constructed, and the network was trained by combining historical fragmentation fineness data and sample sets. The fragmentation fineness trend in future time periods was output through a fully connected layer. The crushing fineness trend over a future time period is compared with the target fineness range, and the crushing fineness deviation is calculated based on the comparison results.

10. A torque control system for a crusher frequency converter, characterized in that, The system includes: The data acquisition module is used to collect the operating data of the crusher and upload it to the cloud. The operating data of the crusher includes the original signal of the motor, the rotor torque signal and the crushing fineness data. The torque deviation calculation module is used to optimize the rotor torque signal of the motor, compare the optimized rotor torque signal with the preset torque signal, and generate the rotor torque deviation. The torque correction module is used to introduce the rotor torque deviation as a feedback signal into the torque compensation mechanism, and correct the torque component of the stator current in combination with the load change characteristics. Based on the corrected stator current torque component, the inverter inner loop controls the actual output torque of the motor through the PID regulator. The crushing fineness assessment module is used to extract crushing fineness indicators from crushing fineness data, predict the crushing fineness of the crusher in the future time period, and optimize the torque control compensation mechanism based on the predicted crushing fineness.

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