Low abnormal rate of magnetic material diamond wire horizontal electroplating sand control system

Abstract

This invention provides a low-abnormality horizontal electroplating control system for diamond wire magnetic materials, belonging to the field of diamond wire electroplating technology. This invention uses a real-time pre-control compensation mechanism to capture and suppress early-stage anomalies, while dynamic optimization at the meso-level continuously adjusts process parameters to their optimal state. These two mechanisms work collaboratively within a macro-steady-state baseline framework, fundamentally changing the passive nature of traditional production models and enabling proactive management of production anomalies, significantly reducing the probability of defects such as agglomerates and incomplete plating. By establishing a learning loop that feeds micro-level intervention experience back to the meso-level optimization model, and by incorporating slow-time-varying factors such as plating solutions into the macro-baseline modeling, the system can continuously absorb knowledge from production practice, automatically adapting to long-term changes such as plating solution aging and raw material batch differences, reducing reliance on manual experience.

Description

Technical Field

[0001] This invention relates to the field of electroplated diamond wire technology, and more particularly to a low-abnormality magnetic diamond wire horizontal electroplating sand-coating control system. Background Technology

[0002] Diamond wire, especially diamond wire used for cutting hard and brittle materials such as magnetic materials, is a core superhard material tool. Its manufacturing process mainly involves electroplating, in which diamond particles are firmly embedded on the surface of a metal wire. Horizontal electroplating is a key step, and its control directly determines the uniformity of the diamond coating, the bonding strength, and the final cutting performance and service life of the diamond wire.

[0003] In existing diamond wire horizontal electroplating production, programmable logic controllers (PLCs) are typically used to automate the production line. Operators set fixed process parameters according to the process card, such as the main electroplating current, wire speed, and wire tension. The production equipment then executes the operation according to these set values. Quality monitoring during the production process relies heavily on manual inspections and offline sampling inspections of finished diamond wires. The quality of the batch of products is judged by measuring indicators such as abrasive density and coating thickness.

[0004] Existing technical solutions have obvious limitations. Electroplating with abrasive is a complex electrochemical and physical process with multiple coupled parameters. There are nonlinear interactions between parameters such as current density, line speed, and plating solution state. Existing control methods usually set these parameters in isolation and statically, lacking a coordinated adjustment mechanism. The production process is dynamic. For example, the concentration of abrasive in the plating solution will decrease as production is consumed, but fixed process parameters cannot adapt to this change, resulting in fluctuations in product quality. There is room for improvement. Summary of the Invention

[0005] This invention provides a low-abnormality magnetic material diamond wire horizontal electroplating sanding control system, which adopts a multi-level collaborative control system that integrates steady-state baseline modeling, dynamic process optimization and real-time micro-disturbance pre-control, and can realize closed-loop intelligent control of the sanding process.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, a low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system is provided, the system comprising: The steady-state baseline management module is used to obtain steady-state baseline parameters for the target product. The steady-state baseline parameters include the process parameter safety range and the target sand density benchmark value. The dynamic optimization module is used to generate optimized process parameters for the current production cycle based on the steady-state baseline parameters and in combination with recent historical production data. The pre-control fine-tuning module is used to acquire real-time process parameters of the production line, identify micro-disturbance states based on the real-time process parameters, and then generate real-time pre-control compensation instructions based on the micro-disturbance states. The collaborative execution module is used to generate a final execution instruction based on the optimized process parameters and the real-time pre-control compensation instruction, and send it to the electroplating production line to perform sand-applying control.

[0007] Optionally, the steady-state baseline management module is specifically used for: Obtain a set of historical production data, which includes historical process parameter combinations and their corresponding historical quality inspection results and historical anomaly records; Based on the aforementioned historical production data set, a baseline generation model is trained, taking product specification requirements as input and steady-state baseline parameters as output. The product specifications of the target product are input into the baseline generation model to obtain the steady-state baseline parameters output by the model.

[0008] Optionally, the dynamic optimization module is specifically used for: Extract process parameter sequences and quality parameter sequences from the recent historical production data to construct a training sample set; A correlation model between process parameters and quality indicators is trained using the training sample set. Using the target sand density benchmark value as the optimization objective, and under the constraint of the process parameter safety range, the optimization algorithm is invoked to perform optimization calculations on the associated model to generate the optimized process parameters.

[0009] Optionally, the pre-control fine-tuning module is specifically used for: The real-time process parameters are acquired in real time at a first acquisition frequency. The real-time process parameters include current signals, vibration signals, and process images. The real-time process parameters are input into the anomaly precursor prediction model to calculate the probability of the anomaly precursor at the current moment. When the probability of the abnormal precursor exceeds the first probability threshold used to characterize the significance of the risk, the corresponding real-time pre-control compensation instruction is generated from the pre-control rule base containing multiple intervention strategies.

[0010] Optionally, when the pre-control fine-tuning module generates the corresponding real-time pre-control compensation instruction from the pre-control rule base containing multiple intervention strategies, it is specifically used for: The probability of the abnormal precursor is input into the pre-control rule base for matching with the features extracted from the real-time process parameters. Obtain the corresponding basic compensation strategy based on the matching results; Based on the magnitude of the probability of the abnormal precursor, the compensation range defined in the basic compensation strategy is dynamically adjusted to form the final real-time pre-control compensation instruction.

[0011] Optionally, the system further includes a dynamic constraint adjustment module, which is used after the dynamic optimization module generates the optimized process parameters for the current production cycle: Monitor the statistical characteristics of the probability of the abnormal precursor within the second time window; When the statistical characteristics indicate increased disturbance in the production process, the safe range of the process parameters is narrowed to constrain the optimization range of the optimization algorithm; When the statistical characteristics indicate that the production process disturbance has decreased, the safe range of the process parameters should be maintained or expanded.

[0012] Optionally, the system further includes an evaluation feedback module, which is used after the pre-control fine-tuning module generates a real-time pre-control compensation command based on the micro-disturbance state: Record the real-time pre-control compensation command, the probability of the abnormal precursor that triggers the command, and the production result data after compensation; The effectiveness of the real-time pre-control compensation instructions is evaluated based on the production result data; The real-time pre-control compensation commands and their triggering conditions that have been evaluated as valid are converted into empirical parameters and fed back into the correlation model of process parameters and quality indicators for model parameter correction.

[0013] Optionally, the historical production data set also includes plating solution characteristic parameters collected synchronously with the process parameters; when training the baseline generation model based on the historical production data set, the steady-state baseline management module is specifically used for: The product specifications and the plating solution characteristic parameters are used together as model inputs to jointly train the baseline generation model.

[0014] Optionally, the system further includes an associated storage module, which is used after the collaborative execution module sends the command to the electroplating production line to perform sandblasting control: The steady-state baseline parameters, the optimized process parameters, the real-time pre-control compensation instructions, the final execution instructions, and the actual quality data generated after execution are stored in association with the production batch identifier according to the timestamp.

[0015] Secondly, a method for controlling the horizontal electroplating of magnetic diamond wire with low anomaly rate is provided, specifically including the following steps: Obtain steady-state baseline parameters for the target product, including the process parameter safety range and the target sand density benchmark value; Based on the steady-state baseline parameters and combined with recent historical production data, optimized process parameters for the current production cycle are generated. The system acquires real-time process parameters of the production line, identifies micro-disturbance states based on these parameters, and then generates real-time pre-control compensation commands based on the micro-disturbance states. Based on the optimized process parameters and the real-time pre-control compensation instructions, a final execution instruction is generated and sent to the electroplating production line to perform sanding control.

[0016] Thirdly, an electronic device is provided, comprising: a processor and a memory; the memory is used to store a computer program, which, when executed by the processor, causes the electronic device to perform the low-abnormality magnetic diamond wire horizontal electroplating sanding control system described in the first aspect.

[0017] In one possible design, the electronic device described in the third aspect may further include a transceiver. This transceiver may be a transceiver circuit or an interface circuit. The transceiver can be used for communication between the electronic device described in the third aspect and other electronic devices.

[0018] In the embodiments of the present invention, the electronic device described in the third aspect may be a terminal, or a chip (system) or other component or assembly disposed in the terminal, or a system containing the terminal.

[0019] Fourthly, a computer-readable storage medium is provided, comprising: a computer program or instructions; when the computer program or instructions are executed on a computer, causing the computer to perform the low-abnormality magnetic diamond wire horizontal electroplating sandblasting control system described in the first aspect.

[0020] In summary, the above methods and systems have the following technical effects: This invention can capture and suppress the early signs of anomalies through a real-time pre-control compensation mechanism, while dynamic optimization at the meso level continuously adjusts process parameters to the optimal state. The two work together under the macro steady-state baseline framework, fundamentally changing the passive situation of the traditional production mode, realizing proactive management of production anomalies, and significantly reducing the probability of defects such as sand agglomeration and incomplete plating. By establishing a learning loop that feeds back micro-level intervention experience to the meso-level optimization model, and by incorporating slow-time-varying factors such as plating solutions into the macro-level baseline modeling, the system can continuously absorb knowledge from production practice and automatically adapt to long-term changes such as plating solution aging and raw material batch differences, reducing reliance on human experience. This self-improvement capability enables the production process to maintain a highly efficient and stable operating state in the long term, ensuring the long-term consistency of product quality. Through the control loops at different time scales performing their respective functions, the macro-level baseline ensures overall safety, the meso-level optimization is responsible for periodic efficiency improvement, and the micro-level pre-control handles instantaneous disturbances. The structured design ensures that optimization behavior is always carried out within the dynamically adjusted safety boundary, allowing for bold exploration of better processes without sacrificing production stability, significantly improving the robustness and scientific nature of the entire control system. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating the low-abnormality magnetic material diamond wire horizontal electroplating sanding control system provided in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0023] In this embodiment of the invention, "instruction" can include direct and indirect instructions, as well as explicit and implicit instructions. The information indicated by a certain piece of information is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed. It can also instruct only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Simultaneously, common parts of various pieces of information can be identified and uniformly indicated to reduce the instruction overhead caused by individually indicating the same information.

[0024] Furthermore, the specific indication method can also be any existing indication method, such as, but not limited to, the above-mentioned indication methods and their various combinations. Specific details of various indication methods can be found in existing technologies, and will not be elaborated upon here. As described above, for example, when multiple pieces of information of the same type need to be indicated, the indication methods for different pieces of information may differ. In specific implementation, the required indication method can be selected according to specific needs. This embodiment of the invention does not limit the selected indication method; therefore, the indication methods involved in this embodiment of the invention should be understood to cover various methods that enable the party to be indicated to obtain the information to be indicated.

[0025] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information messages sent separately, and the sending period and / or timing of these sub-information messages can be the same or different. The specific sending method is not limited in this embodiment of the invention. The sending period and / or timing of these sub-information messages can be predefined, for example, according to a protocol, or configured by the sending device by sending configuration information to the receiving device.

[0026] "Predefined" or "pre-configured" can be achieved by pre-saving corresponding codes, tables, or other means that can be used to indicate relevant information in the device. This embodiment of the invention does not limit the specific implementation method. "Saving" can refer to saving in one or more memories. These memories can be separate installations or integrated into the encoder, decoder, processor, or electronic device. Alternatively, some memories can be separately installed, while others are integrated into the decoder, processor, or electronic device. The type of memory can be any form of storage medium, and this embodiment of the invention does not limit this.

[0027] In the embodiments of this invention, the “protocol” may refer to a protocol family in the field of communication, a standard protocol with a similar protocol family frame structure, or a related protocol applied to a future low-abnormality magnetic material diamond wire horizontal electroplating sanding control system. The embodiments of this invention do not specifically limit this.

[0028] In this embodiment of the invention, descriptions such as "when," "under the circumstances," "if," and "if" all refer to the device making corresponding processing under certain objective circumstances, and are not limited to a specific time. They do not require the device to make a judgment action during implementation, nor do they imply any other limitations.

[0029] In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. Additionally, to facilitate a clear description of the technical solutions of the embodiments of the present invention, the terms "first" and "second" are used in the embodiments of the present invention to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in the embodiments of this invention, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this invention should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0030] The network architecture and business scenarios described in the embodiments of this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0031] Figure 1 This is a flowchart illustrating the method provided in an embodiment of the present invention. The low-abnormality magnetic diamond wire horizontal electroplating sandblasting control system includes: The steady-state baseline management module is used to obtain steady-state baseline parameters for the target product. The steady-state baseline parameters include the process parameter safety range and the target sand density benchmark value. The dynamic optimization module is used to generate optimized process parameters for the current production cycle based on the steady-state baseline parameters and in combination with recent historical production data. The pre-control fine-tuning module is used to acquire real-time process parameters of the production line, identify micro-disturbance states based on the real-time process parameters, and then generate real-time pre-control compensation instructions based on the micro-disturbance states. The collaborative execution module is used to generate a final execution instruction based on the optimized process parameters and the real-time pre-control compensation instruction, and send it to the electroplating production line to perform sand-applying control. The steady-state baseline management module is specifically used for: Obtain a set of historical production data, which includes historical process parameter combinations and their corresponding historical quality inspection results and historical anomaly records; Based on the aforementioned historical production data set, a baseline generation model is trained, taking product specification requirements as input and steady-state baseline parameters as output. The product specifications of the target product are input into the baseline generation model to obtain the steady-state baseline parameters output by the model.

[0032] In this embodiment, the steps of acquiring and constructing the historical production data set include: extracting time-stamped combinations of historical process parameters, quality inspection results, and anomaly records from the manufacturing execution system and the underlying controller; aligning them by timestamp and matching them with the production batch number as the unique identifier; and performing normalization processing on continuous process parameters.

[0033] The baseline generation model employs a multi-layer feedforward neural network. Its training process involves vectorizing the product specification requirements of batches from historical production data that have not experienced anomalies and meet quality standards, and using this vectorized requirement as input. Its stable process parameter safety range and the average density of the top sand are used as labels. The steady-state baseline parameters are output by iterative training using the backpropagation algorithm and a loss function that minimizes the square of the Euclidean distance. ; Where L(θ) represents the loss function with the neural network weight parameter θ as the independent variable, Σ is the summation over the training sample set, Yi represents the true label of the i-th sample, i.e., the stable process parameter safety range and the mean vector of sand density, f represents the mapping function of the feedforward neural network, and Xi represents the input vector of the i-th sample, i.e., the product specification requirements. ||2 represents the square of the Euclidean distance.

[0034] The dynamic optimization module is specifically used for: Extract process parameter sequences and quality parameter sequences from the recent historical production data to construct a training sample set; A correlation model between process parameters and quality indicators is trained using the training sample set. Using the target sand density benchmark value as the optimization objective, and under the constraint of the process parameter safety range, the optimization algorithm is invoked to perform optimization calculations on the associated model to generate the optimized process parameters.

[0035] In this embodiment, when constructing the training sample set, a sliding time window is set to extract the recent process parameter sequence and quality parameter sequence within this window. The correlation model between the process parameters and quality indicators is a regression neural network. Furthermore, the specific process of calling the optimization algorithm is as follows: using the particle swarm optimization algorithm, a target function is constructed under the constraint of the safe range of the process parameters. The target function is minimized through iterative search, and the optimized process parameters are output. ; in, Let represent the objective function value of the optimization calculation, w represent the preset weight coefficient used to adjust the optimization focus, P represent the process parameter vector to be optimized, and f(P) represent the predicted quality output of the correlation model for the input process parameter vector P, such as the predicted sand density. This indicates the set target sand density benchmark value.

[0036] The pre-control fine-tuning module is specifically used for: The real-time process parameters are acquired in real time at a first acquisition frequency. The real-time process parameters include current signals, vibration signals, and process images. The real-time process parameters are input into the anomaly precursor prediction model to calculate the probability of the anomaly precursor at the current moment. When the probability of the abnormal precursor exceeds the first probability threshold used to characterize the significance of the risk, the corresponding real-time pre-control compensation instruction is generated from the pre-control rule base containing multiple intervention strategies.

[0037] In this embodiment, the first acquisition frequency is set to be much higher than the frequency of conventional process monitoring, for example, 200Hz to 1000Hz. The anomaly precursor prediction model uses a long short-term memory network. By inputting a fixed-length time series data window into the network, it analyzes the characteristic deviation changes in the time dimension and outputs a value between 0 and 1 as the probability of the anomaly precursor. When this probability is greater than a set first probability threshold, a significant micro-perturbation state is identified.

[0038] Optionally, when the pre-control fine-tuning module generates the corresponding real-time pre-control compensation instruction from the pre-control rule base containing multiple intervention strategies, it is specifically used for: The probability of the abnormal precursor is input into the pre-control rule base for matching with the features extracted from the real-time process parameters. Obtain the corresponding basic compensation strategy based on the matching results; Based on the magnitude of the probability of the abnormal precursor, the compensation range defined in the basic compensation strategy is dynamically adjusted to form the final real-time pre-control compensation instruction.

[0039] In this embodiment, the pre-control rule base is a database that stores various "conditional action" mapping relationships; the matching operation is: combining the extracted process parameter frequency domain features, such as the energy spectrum peak value of the high-frequency harmonic component of the current, or image features, such as the local contrast of the image, with the probability of abnormal precursors, and matching the pre-control rule base to determine the abnormality type and the corresponding basic compensation strategy.

[0040] When dynamically adjusting the compensation range, an adjustment function is used to calculate and generate a final real-time pre-control compensation instruction that includes the execution object, adjustment direction, compensation range, and duration. ; in, This indicates the final compensation magnitude in the generated real-time pre-control compensation command. This indicates the baseline compensation level defined in the matched basic compensation strategy. This represents the probability of the anomaly precursor obtained from the current calculation. This represents a monotonically increasing function with the probability of abnormal precursors as the independent variable (such as a linear function or a sigmoid function, used to achieve a greater adjustment force as the probability increases).

[0041] The system also includes a dynamic constraint adjustment module, which is used after the dynamic optimization module generates optimized process parameters for the current production cycle: Monitor the statistical characteristics of the probability of the abnormal precursor within the second time window; When the statistical characteristics indicate increased disturbance in the production process, the safe range of the process parameters is narrowed to constrain the optimization range of the optimization algorithm; When the statistical characteristics indicate that the production process disturbance has decreased, the safe range of the process parameters should be maintained or expanded.

[0042] In this embodiment, the monitored statistical features include the mean, variance, peak value, and frequency of abnormal precursor probabilities within the second time window. When the statistical features, such as the probability mean or the frequency of exceeding the threshold, exceed the set disturbance enhancement threshold, the upper and lower limits of the process parameter safety range are shrunk proportionally or non-linearly. Conversely, if the statistical features remain below the set disturbance reduction threshold, the upper and lower limits of the process parameter safety range are maintained or appropriately expanded to achieve dynamic boundary constraints.

[0043] The system further includes an evaluation feedback module, which is used after the pre-control fine-tuning module generates a real-time pre-control compensation command based on the micro-disturbance state: Record the real-time pre-control compensation command, the probability of the abnormal precursor that triggers the command, and the production result data after compensation; The effectiveness of the real-time pre-control compensation instructions is evaluated based on the production result data; The real-time pre-control compensation commands and their triggering conditions that have been evaluated as valid are converted into empirical parameters and fed back into the correlation model of process parameters and quality indicators for model parameter correction.

[0044] In this embodiment, when evaluating the effectiveness of real-time pre-control compensation commands, production result data within a preset time window after the intervention command is issued is extracted. Indicators such as the rate of decline in the probability of abnormal precursors and the rate of reduction in the variance of process parameters are calculated and compared with a preset benchmark. For interventions deemed effective, their triggering features and compensation strategies are parameterized into "state-action" pairs as empirical parameters. These pairs are then used as new samples or constraint weights and injected into the correlation model for parameter correction and retraining.

[0045] The historical production data set also includes plating solution characteristic parameters collected synchronously with the process parameters; when training the baseline generation model based on the historical production data set, the steady-state baseline management module is specifically used for: The product specifications and the plating solution characteristic parameters are used together as model inputs to jointly train the baseline generation model.

[0046] In this embodiment, the plating solution characteristic parameters include the concentration of main metal ions, the concentration of diamond grit, the pH value, and the content of organic additives. The joint training process involves: vectorizing the product specification requirements... Vector of plating solution characteristic parameters Feature concatenation is performed to form a new input vector with higher dimensions: ; The new input vector $X'$ is then fed into the baseline generation model to output steady-state baseline parameters that adapt to changes in the current electrochemical environment.

[0047] The system also includes an associated storage module, which is used after the collaborative execution module sends the command to the electroplating production line to perform sandblasting control: The steady-state baseline parameters, the optimized process parameters, the real-time pre-control compensation instructions, the final execution instructions, and the actual quality data generated after execution are stored in association with the production batch identifier according to the timestamp.

[0048] In this embodiment, the data association storage process is as follows: a database master record is established with a unique production batch identifier as the primary key, and steady-state baseline parameters and actual quality data are stored in the corresponding fields of the master record; time-varying data, including optimized process parameters, real-time pre-control compensation instruction events and final execution instructions, are stored in the time series data table or event log associated with the master record according to the high-precision timestamp as the sorting basis, so as to realize the binding and traceability of multi-source heterogeneous data in the same batch.

[0049] The above combination Figure 1 The system provided in the embodiments of the present invention is described in detail. The following details a low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control method for implementing the method provided in the embodiments of the present invention, specifically including the following steps: Obtain steady-state baseline parameters for the target product, including the process parameter safety range and the target sand density benchmark value; Based on the steady-state baseline parameters and combined with recent historical production data, optimized process parameters for the current production cycle are generated. The system acquires real-time process parameters of the production line, identifies micro-disturbance states based on these parameters, and then generates real-time pre-control compensation commands based on the micro-disturbance states. Based on the optimized process parameters and the real-time pre-control compensation instructions, a final execution instruction is generated and sent to the electroplating production line to perform sanding control.

[0050] The electronic device provided in this embodiment of the invention, exemplarily, can be a network device, or a chip (system) or other component or assembly that can be disposed in a network device. The electronic device may include a processor. Optionally, the electronic device may also include a memory and / or a transceiver. The processor is coupled to the memory and transceiver, for example, by means of a communication bus connection.

[0051] The following is a detailed introduction to the various components of the electronic device: In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0052] Alternatively, the processor can perform various functions of the electronic device, such as the methods described above, by running or executing software programs stored in memory and by calling data stored in memory.

[0053] In a specific implementation, as one example, the processor may include one or more CPUs, such as CPU0 and CPU1.

[0054] In a specific implementation, as one example, the electronic device may also include multiple processors. Each of these processors may be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0055] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0056] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device; the embodiments of the present invention do not specifically limit this.

[0057] A transceiver is used for communication with other electronic devices. For example, if the electronic device is a terminal, the transceiver can be used to communicate with a network device or with another terminal device. Similarly, if the electronic device is a network device, the transceiver can be used to communicate with a terminal or with another network device.

[0058] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0059] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the interface circuit of the electronic device. This embodiment of the invention does not specifically limit this.

[0060] It is understood that the structure of the electronic device in this embodiment does not constitute a limitation on the electronic device. The actual electronic device may include more or fewer components, or combine certain components, or have different component arrangements.

[0061] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the method described in the above method embodiments, and will not be repeated here.

[0062] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0063] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0064] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0065] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0066] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0067] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0068] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0069] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0070] In the embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0071] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0073] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0074] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system, characterized in that, The system includes: The steady-state baseline management module is used to obtain steady-state baseline parameters for the target product. The steady-state baseline parameters include the process parameter safety range and the target sand density benchmark value. The dynamic optimization module is used to generate optimized process parameters for the current production cycle based on the steady-state baseline parameters and in combination with recent historical production data. The pre-control fine-tuning module is used to acquire real-time process parameters of the production line, identify micro-disturbance states based on the real-time process parameters, and then generate real-time pre-control compensation instructions based on the micro-disturbance states. The collaborative execution module is used to generate a final execution instruction based on the optimized process parameters and the real-time pre-control compensation instruction, and send it to the electroplating production line to perform sand-applying control.

2. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 1, characterized in that, The steady-state baseline management module is specifically used for: Obtain a set of historical production data, which includes historical process parameter combinations and their corresponding historical quality inspection results and historical anomaly records; Based on the aforementioned historical production data set, a baseline generation model is trained, taking product specification requirements as input and steady-state baseline parameters as output. The product specifications of the target product are input into the baseline generation model to obtain the steady-state baseline parameters output by the model.

3. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 2, characterized in that, The dynamic optimization module is specifically used for: Extract process parameter sequences and quality parameter sequences from the recent historical production data to construct a training sample set; A correlation model between process parameters and quality indicators is trained using the training sample set. Using the target sand density benchmark value as the optimization objective, and under the constraint of the process parameter safety range, the optimization algorithm is invoked to perform optimization calculations on the associated model to generate the optimized process parameters.

4. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 1, characterized in that, The pre-control fine-tuning module is specifically used for: The real-time process parameters are acquired in real time at a first acquisition frequency. The real-time process parameters include current signals, vibration signals, and process images. The real-time process parameters are input into the anomaly precursor prediction model to calculate the probability of the anomaly precursor at the current moment. When the probability of the abnormal precursor exceeds the first probability threshold used to characterize the significance of the risk, the corresponding real-time pre-control compensation instruction is generated from the pre-control rule base containing multiple intervention strategies.

5. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 4, characterized in that, When the pre-control fine-tuning module generates the corresponding real-time pre-control compensation instruction from the pre-control rule base containing multiple intervention strategies, it is specifically used for: The probability of the abnormal precursor is input into the pre-control rule base for matching with the features extracted from the real-time process parameters. Obtain the corresponding basic compensation strategy based on the matching results; Based on the magnitude of the probability of the abnormal precursor, the compensation range defined in the basic compensation strategy is dynamically adjusted to form the final real-time pre-control compensation instruction.

6. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 4, characterized in that, The system also includes a dynamic constraint adjustment module, which is used after the dynamic optimization module generates optimized process parameters for the current production cycle: Monitor the statistical characteristics of the probability of the abnormal precursor within the second time window; When the statistical characteristics indicate increased disturbance in the production process, the safe range of the process parameters is narrowed to constrain the optimization range of the optimization algorithm; When the statistical characteristics indicate that the production process disturbance has decreased, the safe range of the process parameters should be maintained or expanded.

7. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 4, characterized in that, The system further includes an evaluation feedback module, which is used after the pre-control fine-tuning module generates a real-time pre-control compensation command based on the micro-disturbance state: Record the real-time pre-control compensation command, the probability of the abnormal precursor that triggers the command, and the production result data after compensation; The effectiveness of the real-time pre-control compensation instructions is evaluated based on the production result data; The real-time pre-control compensation commands and their triggering conditions that have been evaluated as valid are converted into empirical parameters and fed back into the correlation model of process parameters and quality indicators for model parameter correction.

8. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 2, characterized in that, The historical production data set also includes plating solution characteristic parameters collected synchronously with the process parameters; when training the baseline generation model based on the historical production data set, the steady-state baseline management module is specifically used for: The product specifications and the plating solution characteristic parameters are used together as model inputs to jointly train the baseline generation model.

9. The low-abnormality magnetic material diamond wire horizontal electroplating sandblasting control system according to claim 1, characterized in that, The system also includes an associated storage module, which is used after the collaborative execution module sends the command to the electroplating production line to perform sandblasting control: The steady-state baseline parameters, the optimized process parameters, the real-time pre-control compensation instructions, the final execution instructions, and the actual quality data generated after execution are stored in association with the production batch identifier according to the timestamp.

10. A method for controlling the horizontal electroplating and sandblasting of low-abnormality magnetic diamond wire, applied to the low-abnormality magnetic diamond wire horizontal electroplating and sandblasting control system described in any one of claims 1-9, characterized in that, Specifically, the following steps are included: Obtain steady-state baseline parameters for the target product, including the process parameter safety range and the target sand density benchmark value; Based on the steady-state baseline parameters and combined with recent historical production data, optimized process parameters for the current production cycle are generated. The system acquires real-time process parameters of the production line, identifies micro-disturbance states based on these parameters, and then generates real-time pre-control compensation commands based on the micro-disturbance states. Based on the optimized process parameters and the real-time pre-control compensation instructions, a final execution instruction is generated and sent to the electroplating production line to perform sanding control.

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