Intelligent control method and system for cable processing

Abstract

The invention discloses an intelligent control method and system for cable processing, relates to the technical field of cable processing, and aims to solve the technical problems that in an existing cable processing system, due to the fact that hidden defects caused by material microcosmic differences and cutter abrasion are difficult to detect, long-term operation of products is unstable, and fault diagnosis is difficult. The method comprises the following steps: acquiring material microscopic characteristic characterization information of an insulating layer of a cable to be processed, synchronously acquiring dynamic physical signals in the processing process in the process of performing peeling processing on the cable to be processed by utilizing a peeling cutter, and calculating the material microscopic characteristic characterization information of the insulating layer of the cable to be processed according to the material microscopic characteristic characterization information and the dynamic physical signals. Identifying whether the composite physical interaction abnormity caused by the combined action of the material characteristics of the insulating layer and the peeling cutter exists in the peeling processing process or not; and if it is identified that the composite physical interaction is abnormal, closed-loop adaptive differential adjustment is performed on control parameters of peeling processing according to the characteristics of the dynamic physical signals.

Description

Technical Field

[0001] This application relates to the field of cable processing technology, and in particular to an intelligent control method and system for cable processing. Background Technology

[0002] In modern industrial production, automated cable processing systems play a crucial role in meeting the demands for high precision and efficiency. These systems typically perform high-speed, high-precision stripping and crimping of cables, ensuring products meet stringent quality standards. However, in actual production, even highly intelligent systems often encounter unforeseen challenges. These challenges often stem from subtle differences in the materials themselves and gradual changes in processing tools, leading to hidden defects in the products and affecting their long-term reliability. Summary of the Invention

[0003] This application provides an intelligent control method and system for cable processing, which aims to solve the technical problem that hidden defects caused by material micro-differences and tool wear are difficult to detect in existing cable processing systems, leading to long-term product instability and difficulty in fault diagnosis.

[0004] In a first aspect, to address the aforementioned technical problems, this invention provides an intelligent control method for cable processing. This method includes: acquiring microscopic characteristic information of the insulation layer of the cable to be processed, the microscopic characteristic information reflecting the toughness of the insulation layer material; simultaneously acquiring dynamic physical signals during the stripping process using a stripping tool, the dynamic physical signals being generated by the physical interaction between the stripping tool and the insulation layer of the cable to be processed; identifying, based on the microscopic characteristic information and the dynamic physical signals, whether there is a composite physical interaction anomaly caused by the combined action of the insulation layer's material characteristics and the stripping tool during the stripping process; if a composite physical interaction anomaly is identified, performing closed-loop adaptive differential adjustment on the control parameters of the stripping process based on the characteristics of the dynamic physical signals, the closed-loop adaptive differential adjustment being used to suppress microscopic defects on the stripped end face caused by the composite physical interaction anomaly.

[0005] Secondly, this application provides an intelligent control system for cable processing. The system includes: an acquisition unit for acquiring microscopic characteristic information of the insulation layer of the cable to be processed, the microscopic characteristic information reflecting the toughness of the insulation layer material; a collection unit for simultaneously collecting dynamic physical signals during the stripping process using a stripping tool, the dynamic physical signals being generated by the physical interaction between the stripping tool and the insulation layer of the cable to be processed; an identification unit for identifying, based on the microscopic characteristic information and the dynamic physical signals, whether there is a composite physical interaction anomaly caused by the combined action of the insulation layer material characteristics and the stripping tool during the stripping process; and an adjustment unit for, if a composite physical interaction anomaly is identified, performing closed-loop adaptive differential adjustment on the control parameters of the stripping process based on the characteristics of the dynamic physical signals, the closed-loop adaptive differential adjustment being used to suppress microscopic defects on the stripped end face caused by the composite physical interaction anomaly.

[0006] This application has at least the following beneficial effects: The intelligent control method for cable processing disclosed in this application, by acquiring the microscopic characteristic information of the insulation layer of the cable to be processed and simultaneously collecting dynamic physical signals during the stripping process, can comprehensively determine whether there is a composite physical interaction anomaly caused by the combined effect of the insulation layer material characteristics and the stripping tool during the stripping process. Once the anomaly is identified, the system will perform closed-loop adaptive differential adjustment of the control parameters for the stripping process according to the characteristics of the dynamic physical signals, thereby effectively suppressing microscopic defects on the stripped end face caused by the composite physical interaction anomaly. This method overcomes the limitations of existing technologies in detecting microscopic defects, solves the problem of "burrs" or "irregular tears" that are difficult to detect by traditional visual inspection, and avoids cables with hidden defects from entering the subsequent crimping stage, thereby improving the quality and reliability of cable processing from the source. Through this refined and adaptive control strategy, this application can significantly reduce the risk of unstable electrical connections or functional failures in products during long-term operation, effectively solve the problem of difficult product fault diagnosis and maintenance in existing technologies, and greatly improve the service life and brand reputation of products. Attached Figure Description

[0007] Figure 1 This is a flowchart illustrating an intelligent control method for cable processing provided in this application. Detailed Implementation

[0008] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0009] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0010] In modern industrial production, automated cable processing systems play a crucial role in meeting the demands for high precision and efficiency. These systems typically perform high-speed, high-precision stripping and crimping of cables, ensuring products meet stringent quality standards. However, in actual production, even highly intelligent systems often encounter unforeseen challenges. These challenges often stem from subtle differences in the materials themselves and gradual changes in processing tools, leading to hidden defects in the products and affecting their long-term reliability. Traditional cable processing methods struggle to effectively identify and suppress the resulting complex physical interactions caused by differences in the microscopic properties of cable insulation materials and the non-uniform microscopic wear of stripping tools. This results in microscopic defects on the stripped end face, affecting subsequent crimping quality and ultimately causing electrical instability or functional failure during long-term operation.

[0011] In view of the above problems, this application provides an intelligent control method for cable processing. By introducing the synergistic analysis of material micro-characteristic information and dynamic physical signals, this application can accurately identify complex physical interaction anomalies that are difficult to detect by traditional methods. Through closed-loop adaptive differential adjustment, it can effectively suppress the generation of micro-defects on the stripped end face, thereby significantly improving the quality and reliability of cable processing and avoiding later failures caused by hidden defects in traditional methods.

[0012] The following specific embodiments will provide a detailed introduction and explanation of the precision fertilization program control method and system for intelligent agricultural equipment provided in this application.

[0013] Reference Figure 1This application provides an intelligent control method for cable processing, which may include the following steps: S1. Obtain the microscopic properties of the insulation layer of the cable to be processed.

[0014] Among these features, the microscopic properties of the material are used to reflect the toughness of the insulation material of the cable to be processed. This directly affects the interaction between the cutting tool and the material during the stripping process.

[0015] Specifically, the intelligent control method for cable processing in this application first requires obtaining the microscopic characteristic information of the insulation layer of the cable to be processed. This information reflects the toughness of the insulation layer material. For example, it can be done manually, with operators judging the hardness of the insulation layer material based on experience and inputting the corresponding parameters. Alternatively, laboratory analysis methods can be used to conduct mechanical tests on the insulation layer material, such as tensile strength tests or impact toughness tests, and then the test results can be used as the microscopic characteristic information of the material.

[0016] S2. During the stripping process of the cable to be processed using the stripping tool, the dynamic physical signals of the processing process are collected simultaneously.

[0017] Among them, dynamic physical signals are generated by the physical interaction between the stripping tool and the insulation layer of the cable to be processed, and can reflect the physical interaction state between the stripping tool and the insulation layer, such as force, displacement, vibration or acoustic emission. These signals can capture subtle changes in the processing process in real time.

[0018] During the stripping process of cables using a stripping tool, it is necessary to simultaneously acquire dynamic physical signals during the process. These dynamic physical signals are generated by the physical interaction between the stripping tool and the insulation layer of the cable. For example, a force sensor can be installed on the stripping equipment to monitor the cutting force experienced by the stripping tool in real time during the cutting process, and the force signal can be used as the dynamic physical signal. Alternatively, a displacement sensor can be installed to monitor the downward displacement of the stripping tool in real time, and the displacement signal can be used as the dynamic physical signal.

[0019] S3. Based on the microscopic characteristics of the material and dynamic physical signals, identify whether there are abnormal composite physical interactions caused by the combined action of the material properties of the insulating layer and the peeling tool during the peeling process.

[0020] Among them, composite physical interaction anomalies refer to physical interaction phenomena that deviate from the ideal peeling state, caused by the combined effect of the microscopic properties of the insulating layer material and the wear state of the peeling tool. For example, abnormal friction, tearing, or vibration occurs between the tool and the material.

[0021] Based on the aforementioned material microstructure characterization information and dynamic physical signals, this application identifies whether there are complex physical interaction anomalies during the peeling process caused by the combined effects of the insulating layer's material properties and the peeling tool. For example, the collected dynamic physical signals can be compared with a preset signal pattern corresponding to an ideal peeling state. If certain characteristics of the dynamic physical signals (such as amplitude and frequency) significantly deviate from the ideal pattern, it can be preliminarily determined that there are complex physical interaction anomalies.

[0022] S4. If an abnormality in the composite physical interaction is identified, then the control parameters for the peeling process are adjusted using closed-loop adaptive differential adjustment based on the characteristics of the dynamic physical signal.

[0023] Among them, closed-loop adaptive differential control is used to suppress microscopic defects on the peeling end face caused by abnormal complex physical interactions. Closed-loop adaptive differential control is a precise control strategy that continuously and slightly adjusts the control parameters of the peeling process (such as blade depth and cutting speed) based on real-time feedback dynamic physical signals to suppress abnormalities in real time and ensure peeling quality.

[0024] Specifically, if the aforementioned complex physical interaction anomaly is identified, closed-loop adaptive differential adjustment is performed on the control parameters of the peeling process based on the characteristics of the dynamic physical signal. This closed-loop adaptive differential adjustment is used to suppress microscopic defects on the peeling end face caused by the complex physical interaction anomaly. For example, when an anomaly is identified, the depth of penetration or cutting speed of the peeling tool can be manually adjusted according to the real-time trend of the dynamic physical signal. Alternatively, a simple set of rules can be preset so that when a certain indicator of the dynamic physical signal exceeds a threshold, the adjustment of the control parameters is automatically triggered; for example, when the cutting force is too large, the depth of penetration of the blade is reduced.

[0025] The intelligent control method for cable processing disclosed in this application comprehensively and in real-time reflects the intrinsic state of the stripping process by collaboratively acquiring the microscopic material characteristics of the insulation layer of the cable to be processed and the dynamic physical signals during the processing. Traditional methods often rely solely on visual detection or simple mechanical feedback, making it difficult to capture the complex physical interaction anomalies caused by the combined effects of material microscopic differences and tool wear. This application, through comprehensive analysis of these two types of information, can identify these hidden anomalies earlier and more accurately. Once an anomaly is identified, the system immediately performs closed-loop adaptive differential adjustment of the control parameters for the stripping process based on the characteristics of the dynamic physical signals. This differential adjustment is a refined, real-time control strategy that continuously adjusts key parameters such as blade depth and cutting speed with high-frequency, small-amplitude corrections, thereby effectively suppressing the generation of microscopic defects on the stripped end face caused by complex physical interaction anomalies. For example, when abnormal friction or tearing signs are detected between the tool and the insulation layer, the system can immediately fine-tune the blade depth to reduce friction or change the cutting angle, thereby avoiding the formation of microscopic burrs or irregular tears. This closed-loop adaptive adjustment mechanism enables the system to respond to dynamic changes during the processing in real time, nip defects in the bud, significantly improve the quality of the peeled end face, lay a good foundation for the subsequent terminal crimping process, and ultimately effectively solve the problem of long-term electrical connection instability caused by hidden defects in traditional methods.

[0026] In some of the embodiments described above in this application, information on the microscopic properties of the insulation layer of the cable to be processed is proposed. However, in the implementation process, relying solely on traditional material testing methods or preset parameters may not accurately and in real-time reflect the actual toughness of the insulation material, especially for cables with subtle differences between batches or within the same batch. This inaccurate characterization of material properties may lead to inaccurate identification of subsequent composite physical interaction anomalies, thereby affecting the effectiveness of adjusting the stripping process control parameters.

[0027] In response, this application further proposes a method for obtaining the material microstructure characterization information of the insulation layer of a cable to be processed, specifically including: applying a micro-vibration excitation signal of a preset mode to the insulation layer and collecting the acoustic response signal generated by the insulation layer in response to the micro-vibration excitation signal; analyzing the spectral characteristics and propagation characteristics of the acoustic response signal; determining a material toughness-brittleness index for quantifying the toughness or brittleness of the insulation layer based on the spectral characteristics and the propagation characteristics, and using the material toughness-brittleness index as the material microstructure characterization information.

[0028] Specifically, applying a preset-mode micro-vibration excitation signal to the insulation layer refers to exciting the cable insulation layer with minute vibrations at a specific frequency, amplitude, and duration using a non-contact or light-contact excitation device, such as a piezoelectric ceramic actuator or an ultrasonic transducer. This preset mode can be optimized according to the characteristics of different cable materials to ensure that the excitation signal can effectively penetrate the insulation layer and induce a measurable acoustic response. Acquiring the acoustic response signal generated by the insulation layer in response to the micro-vibration excitation signal involves using a highly sensitive acoustic sensor, such as a miniature microphone or acoustic emission sensor, to capture the sound wave signal generated by the insulation layer after excitation in real time. These sound wave signals carry rich information about the internal structure and mechanical properties of the insulation layer.

[0029] Analyzing the spectral characteristics and propagation properties of acoustic response signals can be understood as performing digital signal processing on the acquired acoustic signals. This includes analyzing spectral features such as frequency components, energy distribution, and harmonic structure using Fast Fourier Transform (FFT), and evaluating propagation characteristics such as sound wave propagation speed, attenuation rate, reflection, and refraction modes within the insulating layer through time-domain analysis or correlation analysis. These characteristics are directly related to the material's physical parameters such as elastic modulus, density, and damping coefficient.

[0030] In practical applications, the material toughness-brittleness index, used to quantify the toughness or brittleness of insulation layers, is determined based on spectral characteristics and propagation properties. This involves mapping complex acoustic parameters into a single or multi-dimensional index using pre-established physical models, empirical formulas, or machine learning algorithms, based on acoustic signal analysis results. This index directly reflects the toughness or brittleness tendency of the insulation material during the peeling process. For example, the decay rate of high-frequency components, the intensity of specific resonant peaks, or changes in sound velocity can all serve as the basis for constructing the toughness-brittleness index. This index is then used as information characterizing the material's microscopic properties for subsequent anomaly identification in complex physical interactions.

[0031] This application's solution actively applies micro-vibration excitation signals to the insulation layer and collects its acoustic response, enabling the non-invasive acquisition of real-time, dynamic mechanical information of the insulation material. The spectral characteristics and propagation properties of the acoustic response signal directly reflect the material's internal structure, density, elastic modulus, and damping, among other microscopic physical properties. For example, for highly ductile materials, sound wave propagation speed may be faster, and high-frequency attenuation may be relatively small; while for brittle materials, sound wave propagation may be accompanied by more scattering and energy dissipation, leading to faster attenuation or abnormal resonance of specific spectral components. Through in-depth analysis of these acoustic characteristics, the toughness or brittleness of the insulation layer can be accurately quantified, forming a material toughness-brittleness index. This index, as a refined characterization of the material's microscopic properties, provides more accurate and reliable benchmark data for subsequent identification of abnormal composite physical interactions between the stripping tool and the insulation layer. Compared to relying solely on general material parameters, this characterization method based on real-time acoustic response can more effectively capture subtle differences in the state of the cable insulation layer before actual processing, thereby significantly improving the accuracy and sensitivity of anomaly identification.

[0032] Through the above technical solution, this application enables non-contact, real-time, and high-precision characterization of the microscopic properties of cable insulation materials. By actively exciting and analyzing acoustic responses, richer and more accurate material mechanical information can be obtained than with traditional methods, and this information can be quantified into an intuitive material toughness-brittleness index. This refined characterization of material properties allows subsequent identification of anomalies in complex physical interactions to be based on a more solid and targeted data foundation, effectively avoiding misjudgments or omissions caused by inaccurate material property information. Consequently, the control parameters for the stripping process can be adjusted more precisely and promptly, significantly suppressing microscopic defects on the stripped end face caused by the combined effects of material properties and cutting tools, thereby improving the quality and reliability of cable processing.

[0033] In some preferred embodiments, a specific example is given below. Assume the cable to be processed is a high-voltage cable in an automotive wiring harness, and its insulation material is cross-linked polyethylene (XLPE). To obtain information on the microscopic properties of this insulation material, firstly, a micro-vibration excitation signal with a preset frequency (e.g., 50 kHz) and pulse width (e.g., 10 microseconds) is applied to the insulation layer using an ultrasonic transducer mounted above the cable but not in contact with it. Simultaneously, on the other side of the insulation layer, a high-sensitivity broadband microphone array is used to acquire the acoustic response signal generated by the insulation layer in response to the excitation signal.

[0034] The acquired acoustic response signal is then input into a signal processing unit. This unit performs a Fourier transform on the signal to analyze its spectral characteristics, such as observing the intensity and bandwidth of the dominant frequency peak and the presence of subharmonic or superharmonic components. Simultaneously, by measuring the time difference between the excitation signal and the arrival of the acoustic response signal, and combining this with the cable's geometry, the propagation speed of the sound wave in the insulation layer is calculated, and its attenuation characteristics are analyzed.

[0035] Based on these spectral characteristics and propagation properties, for example, if high-frequency components attenuate rapidly and have a low sound velocity, it may indicate that the material is brittle; if high-frequency components attenuate slowly and have a high sound velocity, it may indicate that the material is tough. A pre-trained neural network model takes the spectral energy distribution, the amplitude ratio of a specific frequency, and the sound velocity as input, and outputs a material toughness-brittleness index between 0 and 1, where 0 represents extremely brittle and 1 represents extremely tough. For example, for this XLPE insulation layer, if the calculated material toughness-brittleness index is 0.3, it indicates that the insulation layer of this batch of cables is relatively brittle. This material toughness-brittleness index of 0.3 is then used as information characterizing the material's microstructure and input into a subsequent composite physical interaction anomaly identification module. This module works in conjunction with dynamic physical signals to determine whether there are anomalies during the stripping process, thereby guiding the closed-loop adaptive differential adjustment of the stripping tool.

[0036] In some embodiments of this application, the aforementioned dynamic physical signals include force and displacement coordinated signals, vibration signals, and acoustic emission signals. During the stripping process of the cable to be processed using a stripping tool, dynamic physical signals are simultaneously acquired during the processing. Specifically, this includes: real-time monitoring of the cutting force and corresponding downward displacement of the stripping tool in the cutting direction to obtain a force and displacement coordinated signal; real-time monitoring of the mechanical vibration generated by the stripping tool during the cutting process to obtain a vibration signal; real-time monitoring of the elastic waves released when the stripping tool interacts with the insulation layer to obtain an acoustic emission signal; and using the force and displacement coordinated signal, the vibration signal, and the acoustic emission signal as the dynamic physical signals.

[0037] Among them, the force-displacement co-sensor signal refers to the real-time correspondence between the cutting force and the downward displacement of the peeling tool when cutting the insulation layer. Synchronous measurement using high-precision force and displacement sensors allows for the acquisition of the macroscopic mechanical response of the physical interaction between the peeling tool and the insulation layer. The vibration signal can be understood as the mechanical vibration caused by factors such as material inhomogeneity, tool wear, or mismatched processing parameters during the peeling process. These vibrations can be monitored in real time using accelerometers or laser vibrometers to reflect the dynamic stability during processing. In practical applications, the acoustic emission signal specifically refers to the transient elastic waves released when the microstructure of the material changes (such as crack initiation, propagation, and fiber breakage) during the interaction between the peeling tool and the insulation layer. These elastic waves can be captured by acoustic emission sensors to sensitively reflect the microscopic damage to the insulation material during the peeling process. By comprehensively acquiring these three signals, the physical interaction state during the peeling process can be fully characterized from different dimensions.

[0038] This application's solution, by simultaneously acquiring force and displacement signals, vibration signals, and acoustic emission signals, enables a multi-dimensional and comprehensive characterization of the physical interaction between the stripping tool and the insulation layer of the cable to be processed. The force and displacement signals provide macroscopic mechanical response information, revealing the overall stress and deformation characteristics of the insulation layer under the action of the stripping tool; the vibration signals reflect the dynamic stability of the processing and potential mechanical impact or friction anomalies; while the acoustic emission signals can capture early signs of microscopic damage within the insulation layer, exhibiting extremely high sensitivity for identifying minute defects such as brittle fracture or ductile tearing within the material. Therefore, through the synergistic analysis of these multi-source dynamic physical signals, the complex physical interaction anomalies caused by the combined effects of the insulation material properties and the stripping tool can be identified more accurately and comprehensively, avoiding misjudgments or omissions that may occur with a single signal source.

[0039] Through the above technical solution, this application can obtain richer and more comprehensive dynamic physical signals of the processing, thereby significantly improving the accuracy and reliability of identifying potential complex physical interaction anomalies during the peeling process. This multi-signal fusion acquisition method enables the system to perform refined monitoring of the peeling process from multiple levels, including macroscopic mechanical response, mesoscopic dynamic stability, and microscopic material damage. This provides a more solid data foundation for subsequent anomaly identification and closed-loop adaptive adjustment, ultimately helping to more effectively suppress the generation of microscopic defects on the peeling end face.

[0040] In some embodiments described above in this application, a scheme is proposed for identifying whether there are complex physical interaction anomalies during the peeling process based on material microstructure characterization information and dynamic physical signals. Specifically, the above-mentioned identification of whether there are complex physical interaction anomalies caused by the combined action of the insulating layer's material properties and the peeling tool during the peeling process based on material microstructure characterization information and dynamic physical signals may further include the following steps: extracting the morphological characteristics of the force and displacement coordinated signal, the spectral energy distribution characteristics of the vibration signal, and the frequency domain characteristics of the acoustic emission signal; comparing the morphological characteristics, spectral energy distribution characteristics, and frequency domain characteristics with preset ideal interaction modes corresponding to different material microstructure characterization information; when at least two of the morphological characteristics, spectral energy distribution characteristics, and frequency domain characteristics simultaneously deviate from the corresponding ideal interaction mode, it is determined that there is a complex physical interaction anomaly.

[0041] Specifically, the morphological characteristics of the force-displacement co-signal refer to the geometric or mathematical properties of the curve shape, slope, peak value, inflection point, and area of ​​the relationship between cutting force and downward displacement during the peeling process. These characteristics can intuitively reflect the macroscopic mechanical response between the peeling tool and the insulating material, such as material deformation, fracture behavior, and cutting resistance. For example, when the insulating material is brittle, the force-displacement co-signal may exhibit a sudden drop or a sharp peak; when the material is tougher, it may exhibit a smoother curve and a longer plastic deformation region.

[0042] The spectral energy distribution characteristics of vibration signals refer to the energy distribution of vibration signals generated by stripping tools or cables within different frequency ranges during the stripping process. This includes the dominant frequency, harmonic components, energy concentration areas, and broadband noise. By analyzing these characteristics, the dynamic interaction at the microscopic level between the stripping tool and the insulation layer can be revealed, such as tool chatter, material resonance response, or impact caused by irregular cutting. For example, abnormal spectral energy distribution may indicate tool wear, internal material defects, or an unstable cutting process.

[0043] The frequency domain characteristics of acoustic emission signals refer to the characteristics of transient elastic wave signals generated when a peeling tool interacts with an insulating layer in the frequency domain, such as dominant frequency, bandwidth, amplitude, and energy. Acoustic emission signals are usually closely related to phenomena such as microscopic damage, crack propagation, friction, or tearing of materials. By analyzing their frequency domain characteristics, real-time information on changes in the microstructure inside the insulating layer can be captured, such as the initiation and propagation of microcracks, fiber breakage, or interface debonding.

[0044] Ideal interaction patterns refer to the baseline patterns, under normal, defect-free stripping conditions, that define the morphological characteristics of force and displacement signals, the spectral energy distribution characteristics of vibration signals, and the frequency domain characteristics of acoustic emission signals that should be exhibited by the physical interaction between the stripping tool and the insulation layer in response to specific material microstructure characterization information (such as the material's toughness-brittleness index). These ideal interaction patterns can be established through prior experimental calibration, simulation, or learning based on a large amount of historical data. In practical applications, corresponding ideal interaction pattern databases can be established for different types of cable insulation materials.

[0045] This application compares the extracted real-time morphological features, spectral energy distribution features, and frequency domain features with a preset ideal interaction mode, aiming to quantify the deviation between the current processing state and the ideal state. This comparison can be achieved through various mathematical methods, such as calculating the Euclidean distance between feature vectors, correlation coefficients, or using machine learning models for classification.

[0046] When at least two of the morphological characteristics, spectral energy distribution characteristics, and frequency domain characteristics simultaneously deviate from the corresponding ideal interaction mode, a complex physical interaction anomaly is determined to exist. This multi-dimensional, multi-signal collaborative judgment mechanism can effectively avoid misjudgments caused by single signal fluctuations or noise, improving the accuracy and robustness of anomaly identification. For example, if only the force and displacement signals are abnormal, it may be due to non-material characteristic problems such as unstable cable clamping; however, if it is accompanied by abnormal vibration signals and acoustic emission signals, it is more likely to indicate a deep-seated complex physical interaction problem caused by the combined effect of the insulation material characteristics and the stripping tool.

[0047] This application's solution, by comprehensively analyzing the characteristics of multi-source dynamic physical signals and comparing them with preset ideal interaction modes, can more comprehensively and accurately capture the complex physical interaction state between the peeling tool and the insulation layer during the peeling process. Traditional peeling anomaly detection methods often rely on threshold judgments of single physical quantities (such as force or displacement), making it difficult to effectively distinguish complex anomalies caused by the combined effects of multiple factors such as material properties, tool condition, or processing parameters. This application achieves multi-scale characterization of macroscopic mechanical response, microscopic dynamic behavior, and material damage mechanisms by extracting the morphological characteristics of force and displacement co-signals, the spectral energy distribution characteristics of vibration signals, and the frequency domain characteristics of acoustic emission signals. It is precisely because of this synergistic analysis of multi-dimensional features that the system can identify complex physical interaction anomalies that are difficult to reveal with a single signal, thus providing a more reliable basis for subsequent closed-loop adaptive adjustment.

[0048] Through the above technical solution, this application can accurately identify complex physical interaction anomalies during the stripping process. Compared with methods that rely solely on a single physical signal for judgment, this application significantly improves the accuracy and robustness of anomaly detection by fusing multimodal features of force and displacement signals, vibration signals, and acoustic emission signals, and employing a judgment criterion that at least two features simultaneously deviate from the ideal mode. This effectively reduces the false alarm rate and false negative rate. This refined anomaly identification capability enables the system to detect potential microscopic defects on the stripped end face earlier and more accurately, providing a solid foundation for subsequent closed-loop adaptive differential adjustment. This effectively suppresses the generation of microscopic defects on the stripped end face caused by complex physical interaction anomalies, thereby improving the quality and reliability of cable processing.

[0049] The aforementioned intelligent control method for cable processing proposes a scheme to suppress microscopic defects on the stripping end face by implementing closed-loop adaptive differential adjustment of the control parameters after identifying complex physical interaction anomalies. However, in actual stripping processes, the occurrence of complex physical interaction anomalies is often instantaneous, dynamic, and complex. If only general closed-loop adaptive adjustment is performed, it may be difficult to achieve a rapid and accurate response to the anomalies, resulting in poor adjustment effects or the generation of new processing problems. For example, when the interaction anomalies between the insulation material properties and the stripping tool change rapidly, traditional adjustment mechanisms may fail to capture and make fine adjustments in time, thereby affecting the microscopic quality of the stripped end face.

[0050] In response, this application further proposes that if the above-mentioned composite physical interaction anomaly is identified, then based on the characteristics of the above-mentioned dynamic physical signal, a closed-loop adaptive differential adjustment is performed on the control parameters of the peeling process. Specifically, this includes: if the above-mentioned composite physical interaction anomaly is identified, calculating the differential adjustment amount of the blade pressing depth and cutting speed based on the real-time change trend of the above-mentioned dynamic physical signal; and continuously making small-amplitude corrections to the control parameters according to the differential adjustment amount at a preset high-frequency control cycle.

[0051] Specifically, the real-time change trend of the aforementioned dynamic physical signals refers to the continuous monitoring and analysis of dynamic physical signals such as force and displacement coordination signals, vibration signals, and acoustic emission signals, to obtain information such as the rate of change and acceleration of these signals in the time dimension. For example, the instantaneous derivative of the cutting force or vibration amplitude can be calculated to reflect the speed and direction of the anomaly's evolution. The blade penetration depth refers to the depth to which the stripping tool penetrates the insulation layer in the direction perpendicular to the cable axis, directly affecting the integrity of the stripping and the degree of damage to the insulation layer. The cutting speed refers to the speed at which the stripping tool peels along the cable axis, affecting the stripping efficiency and the heat and stress generated during the stripping process. In practical applications, the differential adjustment amount is calculated based on the real-time change trend of the dynamic physical signals using a preset differential control algorithm (such as the differential term in a PID controller), indicating the magnitude and direction of the adjustment to the blade penetration depth and cutting speed. This adjustment amount reflects the predictive correction requirements for the current abnormal state and its changing trend. Furthermore, the preset high-frequency control cycle refers to an extremely short time interval, such as milliseconds or microseconds, within which the control parameters are corrected once. Continuous small-scale corrections mean that each adjustment is very small to avoid system oscillations or over-adjustment, ensuring the smoothness and accuracy of the peeling process.

[0052] This application's solution introduces a differential adjustment mechanism based on the real-time changing trends of dynamic physical signals, enabling more precise and proactive control of complex physical interaction anomalies occurring during the peeling process. Once a complex physical interaction anomaly is identified, the system no longer adjusts solely based on the current static value of the anomaly but further analyzes the changing trends of the anomaly signal. For example, if the cutting force is rapidly increasing, the differential adjustment will pre-calculate a larger negative adjustment to preemptively suppress further force increases. This trend-based adjustment allows the control system to predict the direction and speed of the anomaly's development, thus intervening before the anomaly fully worsens. Simultaneously, continuous small-amplitude corrections to the control parameters using a high-frequency control cycle ensure the real-time nature and stability of the adjustment. Each minute adjustment is quickly fed back to the movement of the peeling tool, avoiding the lag or overshoot that may occur in traditional control, thereby more effectively suppressing microscopic defects on the peeling end face caused by complex physical interaction anomalies.

[0053] Through the above technical solution, this application can significantly improve the response speed and adjustment accuracy to complex physical interaction anomalies during the cable stripping process. By introducing the analysis of real-time changes in dynamic physical signals, the system possesses the ability to proactively predict the development of anomalies, enabling earlier and more precise intervention. The high-frequency, small-amplitude continuous correction mechanism ensures the stability and continuity of the control process, effectively avoiding secondary damage or new defects caused by untimely or excessive adjustment. Therefore, the suppression effect of microscopic defects on the stripped end face is greatly optimized, and the quality and consistency of cable stripping are significantly improved, thus providing a higher-quality processing foundation for subsequent terminal crimping and other processes.

[0054] In some preferred embodiments, a specific example is given below. Suppose that during the stripping of a specific type of cable, the system, through comprehensive analysis of material microstructure characterization information and dynamic physical signals, identifies an anomaly in the composite physical interaction caused by the combined action of the insulation material's toughness and the stripping tool. Specifically, during the stripping process, the force and displacement signals show a rapid increase in cutting force within a short period, while the energy of a specific frequency band in the vibration signal also increases synchronously, indicating abnormal friction or adhesion between the tool and the insulation layer. At this point, the control system immediately initiates closed-loop adaptive differential adjustment. First, the system monitors the rate of change of the cutting force in real time (i.e., performs differential processing on the cutting force signal) and combines this with the energy change trend of the vibration signal. Based on these real-time trends, a differential adjustment amount is calculated for the blade depth and cutting speed. For example, if the cutting force increases too quickly, the system calculates a negative differential adjustment amount for the depth of cut and a positive differential adjustment amount for the cutting speed, aiming to mitigate the anomaly by fine-tuning the blade position and speed before the cutting force reaches its peak. Subsequently, based on the calculated differential adjustment, the control system continuously makes small-amplitude corrections to the blade depth of cut and cutting speed at a preset high-frequency control cycle (e.g., every 10 milliseconds). For example, every 10 milliseconds, the blade depth of cut is slightly increased, and the cutting speed is slightly increased. This high-frequency, small-amplitude correction continues until newly acquired dynamic physical signals show that the abnormal composite physical interaction has been effectively suppressed, such as the rate of change of cutting force stabilizing and vibration energy returning to normal levels. In this way, the system can dynamically and accurately respond to instantaneous anomalies during the processing, ensuring the microscopic quality of the peeled end face.

[0055] In some embodiments described above in this application, closed-loop adaptive differential adjustment of the control parameters for peeling is proposed to suppress microscopic defects on the peeling end face caused by abnormal complex physical interactions. However, in practical applications, the lack of real-time evaluation and feedback on the adjustment effect may lead to redundancy or over-adjustment in the adjustment process, or even introduce new processing instability factors, thereby affecting the overall efficiency and quality of the peeling process.

[0056] In response, this application further proposes an optimization scheme, which specifically includes: after each small correction, re-acquiring the dynamic physical signal, and judging whether the composite physical interaction anomaly has been suppressed based on the newly acquired dynamic physical signal; if it is judged to be suppressed, then the adjustment is stopped.

[0057] Specifically, after each minor correction, the dynamic physical signal is re-acquired. This means that after applying the differential adjustment to the blade depth and cutting speed, the dynamic physical signal generated by the physical interaction between the stripping tool and the insulation layer of the cable to be processed is immediately acquired again. Determining whether the composite physical interaction anomaly has been suppressed can be understood as analyzing the characteristics of the newly acquired dynamic physical signal, such as the morphological characteristics of the force and displacement coordinated signal, the spectral energy distribution characteristics of the vibration signal, and the frequency domain characteristics of the acoustic emission signal, and comparing them with a preset ideal interaction mode or anomaly threshold to determine whether the degree of the composite physical interaction anomaly has been reduced to an acceptable range or completely eliminated. If suppression is determined, the adjustment stops, meaning that once it is confirmed that the composite physical interaction anomaly has been effectively alleviated or eliminated, the current closed-loop adaptive differential adjustment process is immediately terminated to avoid unnecessary continuous adjustments.

[0058] The solution proposed in this application effectively solves the aforementioned problems by introducing a real-time feedback and termination mechanism for the adjustment effect. Specifically, after each small-scale correction of the control parameters, the system immediately re-acquires the dynamic physical signals during the machining process. These newly acquired dynamic physical signals can reflect the current physical interaction state between the peeling tool and the insulation layer in real time. By analyzing these dynamic physical signals, it can be determined whether the previously performed differential adjustment has effectively suppressed the complex physical interaction anomaly. For example, if the characteristics of the dynamic physical signals (such as force, vibration, and acoustic emission) have returned to the normal range or are close to the ideal interaction mode, it indicates that the anomaly has been suppressed. It is precisely because of this real-time effect evaluation that the system can stop the adjustment in time after the anomaly is effectively suppressed, thereby avoiding the negative impacts that over-adjustment may bring, such as reduced machining efficiency or the introduction of new machining defects.

[0059] Through the above technical solution, this application can achieve refined and adaptive control of the wire stripping process. This solution not only ensures that abnormalities in complex physical interactions can be effectively suppressed, but also avoids unnecessary continuous adjustments through a real-time feedback mechanism, significantly improving the efficiency and accuracy of adjustment. Therefore, it can effectively prevent prolonged processing time, accelerated tool wear, or the generation of new stripping end-face defects caused by over-adjustment, thereby ensuring the quality stability and production efficiency of cable stripping.

[0060] In some preferred embodiments, assuming that an anomaly in the composite physical interaction is identified during the peeling process, such as an abnormal peak in the force-displacement signal or a significant increase in the energy of a specific frequency band of the vibration signal, the system then calculates and performs differential adjustments to the blade depth and cutting speed based on the real-time trend of the dynamic physical signal. After completing one fine-tuning, the system immediately re-acquires the dynamic physical signal. If the peak in the newly acquired force-displacement signal disappears and the energy of the vibration signal returns to normal levels, the system determines that the composite physical interaction anomaly has been effectively suppressed and immediately stops subsequent differential adjustments. Conversely, if the anomaly persists, the system will continue to perform the next small-amplitude correction until the anomaly is suppressed or the preset maximum number of adjustments is reached.

[0061] In some embodiments described above, closed-loop adaptive differential adjustment of the control parameters for the stripping process can effectively suppress microscopic defects on the stripped end face caused by abnormal composite physical interactions. However, even after optimization, the quality of the stripping process may still potentially affect the subsequent terminal crimping process, resulting in suboptimal microscopic contact quality between the conductor and the terminal, thereby impacting the overall electrical performance and reliability of the cable. To address this, this application further proposes an intelligent control method for cable processing, which combines the adjustment results of the stripping process with the subsequent crimping process to achieve compensatory adjustment of the crimping control parameters, thereby further optimizing the microscopic contact quality between the conductor and the terminal.

[0062] In some embodiments, the above method further includes: generating corresponding crimping warning information based on the result of the closed-loop adaptive differential adjustment; and, during the subsequent crimping of the cable terminals, performing compensatory adjustments on the crimping control parameters based on the crimping warning information and real-time monitoring data of the crimping process to optimize the micro-contact quality between the conductor and the terminal.

[0063] Specifically, based on the results of the aforementioned closed-loop adaptive differential adjustment, the final state of the stripping process can be evaluated, and corresponding crimping warning information can be generated accordingly. This crimping warning information may include the residual risk level of microscopic defects on the stripped end face, suggested adjustment direction or magnitude of crimping parameters, etc., aiming to provide forward-looking guidance for subsequent crimping processes. For example, if there are slight but persistent abnormalities in the composite physical interaction during the stripping process, even after adjustment, minor end face unevenness may still exist. In this case, the generated crimping warning information can indicate the need for slight compensation of the crimping process. Furthermore, during the subsequent terminal crimping of the cable, the crimping warning information and real-time monitoring data of the crimping process are comprehensively utilized. The real-time monitoring data of the crimping process may include parameters such as crimping force, crimping displacement, and crimping time. Based on this information, compensatory adjustments are performed on crimping control parameters, such as the closing speed of the crimping die, crimping stroke, and holding time. These compensatory adjustments aim to compensate for the impact of minor defects that may exist during the stripping process on the crimping quality, thereby optimizing the microscopic contact quality between the conductor and the terminal and ensuring the reliability of the connection.

[0064] This application's solution transforms the final adjustment result of the stripping process into crimping early warning information, using it as one of the inputs for the subsequent crimping process, thus achieving information linkage and collaborative optimization between the two key processes of stripping and crimping. Specifically, while the closed-loop adaptive differential adjustment during the stripping process suppresses microscopic defects on the stripped end face, its adjustment result itself contains comprehensive information about factors such as cable material characteristics, tool wear, and processing environment. Transforming this information into crimping early warning information allows the crimping process to anticipate potential risks left over from the stripping process. During crimping, by combining real-time monitoring data, compensatory adjustments are made to the crimping control parameters, allowing for targeted adjustments to crimping conditions, such as increasing crimping pressure to ensure a tighter contact or adjusting the crimping stroke to accommodate minor end face unevenness. It is precisely this proactive early warning and adaptive compensation mechanism that further improves the microscopic contact quality between the conductor and the terminal, effectively preventing overall quality degradation caused by the transmission of stripping defects to downstream processes.

[0065] Through the above technical solution, this application achieves deep integration and coordinated control of the stripping and crimping processes, significantly improving the overall quality and reliability of cable processing. Compared to solutions that only optimize the stripping stage, this application can effectively compensate for the impact of minor residual defects that may exist during the stripping process on the subsequent crimping quality, thereby ensuring a more stable and reliable microscopic contact between the conductor and the terminal. This proactive early warning and compensation mechanism not only reduces the crimping failure rate caused by stripping problems but also extends the service life of cable assemblies and improves the electrical performance and safety of the products.

[0066] In some preferred embodiments, it is assumed that during the stripping process, although the system performs closed-loop adaptive differential adjustment, the local non-uniformity of the cable insulation material causes a persistent abnormality in the composite physical interaction between the stripping tool and the insulation layer, ultimately reflecting a slight microscopic roughness on the stripped end face. In this case, the system generates a crimping warning message based on this adjustment result, for example, indicating "stripping end face roughness level 2 (medium)," and suggesting an increase of 0.05 mm in the crimping stroke. The crimping system receives this warning message when subsequently crimping the cable terminals. Simultaneously, real-time monitoring data during the crimping process (such as the crimping force curve) shows that, under standard crimping parameters, the peak crimping force is slightly lower than the ideal range. Combining the warning message and real-time monitoring data, the crimping control system automatically compensates for the stroke of the crimping die, for example, by increasing the stroke by 0.05 mm from the standard stroke, and may fine-tune the holding time. In this way, even if there are slight defects on the stripped end face, the intelligent compensation during the crimping process can ensure that the conductor and the terminal form the best micro-contact, thereby avoiding potential electrical connection defects.

[0067] In some of the embodiments described above in this application, a scheme is proposed to perform compensatory adjustment on the crimping control parameters based on crimping early warning information and real-time monitoring data of the crimping process. However, in its implementation, traditional compensatory adjustment may mainly rely on macroscopic mechanical parameters or preset empirical models, making it difficult to perceive and correct potential defects in the microscopic contact state between the conductor and the terminal in real time and with precision, which may lead to unstable crimping quality or hidden electrical connection defects.

[0068] In response, this application further proposes that, in the subsequent terminal crimping process of the cable, compensatory adjustments are made to the crimping control parameters based on crimping warning information and real-time monitoring data of the crimping process. Specifically, this includes: adjusting the closing speed and crimping stroke of the crimping die according to the risk level indicated by the crimping warning information; applying a high-frequency AC excitation signal to the conductor and terminal during the subsequent terminal crimping process of the cable, and monitoring the transient impedance response between the conductor and terminal; and fine-tuning the final closing position or holding time of the crimping die based on the deviation between the transient impedance response and the preset ideal impedance range.

[0069] Specifically, the risk level indicated by the crimping warning information can be a quantitative assessment of the severity of microscopic defects on the stripped end face, for example, it can be divided into three levels: low, medium, and high. When the risk level is high, the closing speed of the crimping die can be appropriately reduced to decrease the impact during the crimping process and increase the crimping stroke to ensure more sufficient plastic deformation and contact area. Conversely, when the risk level is low, a faster closing speed and standard stroke can be used to improve production efficiency. The closing speed of the crimping die refers to the speed at which the crimping die applies pressure to the cable terminal during the crimping process; its adjustment aims to control the dynamic impact and material deformation rate during the crimping process. The crimping stroke refers to the total distance the crimping die moves from the initial position to the final closed position; its adjustment aims to control the final tightness of the crimp and the amount of plastic deformation of the material.

[0070] The purpose of applying a high-frequency AC excitation signal to the conductor and terminal is to detect the electrical characteristics of the conductor-terminal interface in real time through non-contact or micro-contact methods. The high-frequency AC excitation signal can be understood as a sine wave or pulse signal with a frequency in the range of kHz to MHz, and its amplitude and frequency can be preset according to the type of cable to be processed and the terminal material. Transient impedance response refers to the change over time in the combined electrical characteristics of resistance, inductance, and capacitance exhibited between the conductor and terminal when the high-frequency AC excitation signal is applied. By monitoring the transient impedance response, the presence of microscopic defects such as voids, oxide layers, or poor contact at the crimp interface can be assessed in real time.

[0071] In practical applications, the preset ideal impedance range is established based on a large amount of qualified crimping sample data and represents the impedance characteristics of a good electrical connection. When the transient impedance response deviates from this ideal range, it indicates that there may be a problem with the crimping quality. For example, excessively high impedance may indicate poor contact or oxide layer, while excessively low impedance may indicate over-crimping leading to material damage. To address this, the final closing position of the crimping die or the holding time can be fine-tuned. The final closing position refers to the minimum gap reached by the crimping die after completing the crimping action; fine-tuning this position allows for precise control of the crimp tightness. The holding time refers to the duration for which the crimping die maintains pressure after reaching the final closing position; fine-tuning the holding time ensures sufficient material creep and stress relaxation, thereby stabilizing the micro-contact. These fine-tuning amounts are usually small to avoid overcorrection and are achieved through closed-loop control.

[0072] This application's solution effectively addresses the limitations of traditional compensatory adjustments in ensuring microscopic contact quality by introducing a multi-layered, refined crimping control strategy. First, based on peeling warning information, the closing speed and crimping stroke of the crimping die are adjusted. This allows the crimping process to be pre-adaptively adjusted according to the initial characteristics of the cable insulation material and the risk of peeling defects, thereby optimizing the initial crimping conditions at a macroscopic level and avoiding stress concentration or material damage caused by peeling defects. Furthermore, by applying a high-frequency AC excitation signal to the conductor and terminal during the crimping process and monitoring the transient impedance response, this solution can acquire real-time, non-destructive information about the microscopic electrical contact state within the crimping interface. This real-time impedance monitoring mechanism acts like a "X-ray vision" for the crimping process, sensitively capturing defects such as tiny gaps, oxide layers, or uneven contact that are difficult to detect with traditional mechanical monitoring. Therefore, based on the deviation between the transient impedance response and the ideal impedance range, the final closing position of the crimping die or the holding time is fine-tuned, forming a precise closed-loop feedback circuit. This fine-tuning mechanism can instantly and accurately correct microscopic defects detected in real time during the crimping process, ensuring that the microscopic contact quality between the conductor and the terminal is always optimal. For example, when high impedance is detected, it may be necessary to fine-tune by increasing the final closing position or extending the holding time to promote a tighter metal contact; when low impedance is detected, it may be necessary to fine-tune by decreasing the final closing position or shortening the holding time to avoid over-crimping.

[0073] Through the above technical solution, this application can significantly improve the microscopic contact quality and electrical connection reliability of cable terminal crimping. Compared with solutions that rely solely on stripping warning information for macroscopic compensation, this solution introduces a real-time, non-invasive microscopic detection and fine adjustment mechanism during the crimping process. This makes crimping control no longer a simple preset adjustment, but rather dynamically optimized based on the actual microscopic contact state. This not only effectively suppresses crimping quality problems that may be caused by microscopic defects on the stripped end face, but more importantly, it can detect and correct hidden microscopic defects generated during the crimping process in real time, thereby significantly reducing the risk of electrical connection failure caused by poor contact, oxidation, or stress concentration. Ultimately, this solution ensures a stable, low-impedance, and highly reliable microscopic contact between the conductor and the terminal, providing a solid guarantee for the long-term stable operation of the cable.

[0074] As a specific implementation, suppose that after stripping a cable, the system identifies a minor microscopic defect on the stripped end face and generates a medium-risk crimping warning. During subsequent terminal crimping, based on this medium-risk level, the crimping control system first appropriately reduces the closing speed of the crimping die by 10% and increases the crimping stroke by 0.5 mm to pre-optimize the initial crimping conditions. As the crimping die begins to close and contact the conductor and terminal, a high-frequency AC excitation signal is applied between the conductor and terminal, and its transient impedance response is monitored in real time. For example, when the crimping reaches 80% of its stroke, the system detects that the transient impedance response is slightly higher than the upper limit of the preset ideal impedance range, which may indicate a small contact gap or oxide layer between the conductor and terminal. Based on this deviation, the control system immediately fine-tunes the final closing position of the crimping die, for example, increasing the closing depth by 0.02 mm and extending the holding time by 0.1 seconds. After completing the fine-tuned crimping operation, the system monitors the transient impedance response again to confirm that it has returned to the ideal impedance range, indicating that the microscopic contact quality between the conductor and the terminal has been effectively optimized. Through this real-time feedback and fine adjustment, even in the presence of initial defects or minor deviations during the crimping process, the final crimping quality can be ensured to meet high standards, effectively avoiding the generation of hidden electrical connection defects.

[0075] In some embodiments described above in this application, closed-loop adaptive differential adjustment is proposed to suppress microscopic defects on the stripped end face, and compensatory adjustment is proposed to optimize the microscopic contact quality between the conductor and the terminal. However, even with these fine adjustments, some hidden defects caused by material properties or complex interactions during the processing may still exist. These defects may not be completely eliminated or detected in routine inspections, thus posing a potential risk to the long-term reliability of the cable.

[0076] In response, this application further proposes a method for making a final judgment on the quality of cable processing, specifically including: if the abnormality of the composite physical interaction persists after the closed-loop adaptive differential adjustment, or the microscopic contact quality fails to meet the preset standard after the compensatory adjustment, then the cable to be processed is determined to have a hidden electrical connection defect; and the cable determined to have the hidden electrical connection defect is marked or sorted.

[0077] Specifically, "the abnormal composite physical interaction persists after the closed-loop adaptive differential adjustment" means that during the stripping process, despite the implementation of closed-loop adaptive differential adjustment of the blade depth and cutting speed, continuous monitoring of dynamic physical signals reveals that the composite physical interaction mode between the stripping tool and the insulation layer of the cable being processed has not returned to the ideal state. For example, the morphological characteristics of the force and displacement co-signal, the spectral energy distribution characteristics of the vibration signal, or the frequency domain characteristics of the acoustic emission signal continuously deviate from the preset ideal interaction mode, indicating that microscopic defects on the stripping end face have not been effectively suppressed.

[0078] The phrase "the micro-contact quality after the compensatory adjustment did not meet the preset standard" means that even though compensatory adjustments were made to the crimping control parameters based on crimping warning information and real-time monitoring data during the terminal crimping process, the final micro-contact quality between the conductor and the terminal still failed to meet the preset quality standard. This preset standard can be quantified based on indicators such as resistance, contact area, and pull-out force. For example, monitoring the transient impedance response may reveal that its deviation from the ideal impedance range consistently exceeds the allowable threshold.

[0079] In practical applications, "hidden electrical connection defects" refer to those defects that are not easily detected by conventional testing methods, but may cause a decline in electrical performance, connection failure, or even safety hazards under long-term use or specific operating conditions, such as microcracks, local stress concentration, and abnormal contact resistance.

[0080] Furthermore, "performing marking or sorting operations on cables that are determined to have the aforementioned hidden electrical connection defects" means that once such defects are identified, the system will automatically mark the cable, for example by physical marking through inkjet printing or labeling, or separate it from the qualified product flow for separate processing, such as scrapping, rework, or further analysis.

[0081] This application's solution addresses the issue of hidden defects that may still exist even after adaptive and compensatory adjustments by introducing a continuous evaluation and judgment mechanism for the final effect of the stripping and crimping processes. Specifically, when abnormal composite physical interactions during the stripping process cannot be effectively suppressed through closed-loop adaptive differential adjustment, or when the microscopic contact quality after crimping fails to meet preset standards, the system can promptly identify these potential quality risks. This dual verification mechanism extends cable quality control from process control to result verification, thereby enabling the discovery and judgment of those difficult-to-detect hidden electrical connection defects. By marking or sorting defective cables, unqualified products can be effectively prevented from entering subsequent stages or final applications, improving the overall quality and reliability of cable products from the source.

[0082] Through the above technical solution, this application enables the final quality assessment of cables that have undergone stripping and crimping, providing an effective means of identification and handling, especially for hidden electrical connection defects that may still exist after adaptive and compensatory adjustments. This significantly improves the quality control level of the cable processing, avoids potential quality risks, and ensures high product reliability. Furthermore, by marking or sorting defective cables, it effectively prevents substandard products from entering the market, reducing recall and repair costs and brand reputation losses caused by product quality issues, thereby providing users with safer and more reliable cable products.

[0083] In some preferred embodiments, a specific example is given below. Suppose that during the stripping process of a batch of cables, due to batch variations in the insulation material, its toughness or brittleness fluctuates slightly in localized areas. Although the stripping system has performed closed-loop adaptive differential adjustment based on material microstructure characterization information and dynamic physical signals to suppress microscopic defects on the stripped end face, continuous monitoring reveals that the vibration signal spectrum energy distribution characteristics between the stripping blade and the insulation layer consistently deviate from the ideal pattern and fail to fully recover to normal. This indicates that there may still be minute damage on the stripped end face that is difficult to detect with the naked eye.

[0084] Subsequently, the cable enters the terminal crimping stage. Based on crimping warning information generated during the stripping stage and real-time monitoring data of the crimping process, the system performs compensatory adjustments to the crimping control parameters. However, after crimping is completed, detection of the transient impedance response between the conductor and the terminal reveals that while the deviation from the preset ideal impedance range has decreased, it still does not fully meet the preset micro-contact quality standard.

[0085] In this situation, the solution proposed in this application will determine that the cable to be processed has a hidden electrical connection defect. For example, the system will trigger an alarm and spray a red mark on the cable, or automatically sort it into the non-conforming product area by a robotic arm to prevent it from entering the subsequent assembly process. In this way, even potential defects that still exist after multi-level adaptive adjustment can be detected and dealt with in a timely manner, thereby ensuring the quality of the final product.

[0086] In some embodiments, this application proposes an intelligent control system for cable processing, comprising: an acquisition unit for acquiring microscopic characteristic information of the insulation layer of a cable to be processed, the microscopic characteristic information reflecting the toughness of the insulation layer material; a collection unit for simultaneously collecting dynamic physical signals during the stripping process of the cable to be processed using a stripping tool, the dynamic physical signals being generated by the physical interaction between the stripping tool and the insulation layer of the cable to be processed; an identification unit for identifying, based on the microscopic characteristic information and the dynamic physical signals, whether there is a composite physical interaction anomaly caused by the combined action of the insulation layer material characteristics and the stripping tool during the stripping process; and an adjustment unit for, if the composite physical interaction anomaly is identified, performing closed-loop adaptive differential adjustment on the control parameters of the stripping process based on the characteristics of the dynamic physical signals, the closed-loop adaptive differential adjustment being used to suppress microscopic defects on the stripped end face caused by the composite physical interaction anomaly.

[0087] The intelligent control system for cable processing disclosed in this application acquires microscopic material characteristic information of the cable insulation layer through an acquisition unit, and simultaneously collects dynamic physical signals during the processing through a data acquisition unit, achieving comprehensive perception of the stripping process. Based on this information, the identification unit collaboratively analyzes the data to accurately identify complex physical interaction anomalies that are difficult for traditional systems to detect. Once an anomaly is identified, the adjustment unit performs closed-loop adaptive differential adjustment of the control parameters for the stripping process according to the characteristics of the dynamic physical signals, thereby effectively suppressing the generation of microscopic defects on the stripped end face. Through the collaborative work of each unit, this system fundamentally solves the problem of long-term electrical connection instability caused by hidden defects in traditional cable processing, significantly improving the quality and reliability of cable processing.

[0088] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A cable processing intelligent control method, characterized in that, include: Obtain the microscopic property characterization information of the insulation layer of the cable to be processed, wherein the microscopic property characterization information is used to reflect the toughness of the insulation layer material of the cable to be processed; During the process of stripping the cable to be processed using a stripping tool, dynamic physical signals during the processing are collected simultaneously. These dynamic physical signals are generated by the physical interaction between the stripping tool and the insulation layer of the cable to be processed. Based on the microscopic characteristics of the material and the dynamic physical signal, identify whether there is an abnormal composite physical interaction caused by the combined effect of the material properties of the insulating layer and the peeling tool during the peeling process; If the abnormality of the composite physical interaction is identified, then a closed-loop adaptive differential adjustment is performed on the control parameters of the peeling process according to the characteristics of the dynamic physical signal. The closed-loop adaptive differential adjustment is used to suppress microscopic defects on the peeling end face caused by the abnormality of the composite physical interaction.

2. The method of claim 1, wherein, The process of obtaining the microscopic characterization information of the material properties of the insulation layer of the cable to be processed includes: A micro-vibration excitation signal of a preset mode is applied to the insulating layer, and the acoustic response signal generated by the insulating layer in response to the micro-vibration excitation signal is collected. Analyze the spectral characteristics and propagation properties of the acoustic response signal; Based on the spectral characteristics and the propagation properties, a material toughness-brittleness index is determined to quantify the toughness or brittleness of the insulation layer, and the material toughness-brittleness index is used as information characterizing the microscopic properties of the material.

3. The method of claim 1, wherein, The dynamic physical signals include force and displacement signals, vibration signals, and acoustic emission signals; the simultaneous acquisition of dynamic physical signals during the stripping process of the cable to be processed using a stripping tool includes: During the process of stripping the cable to be processed using a stripping tool, the cutting force of the stripping tool in the cutting direction and the corresponding downward displacement are monitored in real time to obtain a force and displacement coordinated signal. The mechanical vibration generated by the peeling tool during the cutting process is monitored in real time to obtain vibration signals; The acoustic emission signal is obtained by real-time monitoring of the elastic waves released when the peeling tool interacts with the insulating layer; The force and displacement combined signal, the vibration signal, and the acoustic emission signal are used as the dynamic physical signal.

4. The method of claim 3, wherein, The step of identifying whether there is an abnormal composite physical interaction caused by the combined effect of the material properties of the insulating layer and the peeling tool during the peeling process, based on the material microstructure characterization information and the dynamic physical signal, includes: Extract the morphological features of the force-displacement coordinated signal, the spectral energy distribution features of the vibration signal, and the frequency domain features of the acoustic emission signal; The morphological features, the spectral energy distribution features, and the frequency domain features are compared with preset ideal interaction modes corresponding to the characterization information of the microscopic properties of different materials. When at least two of the morphological features, the spectral energy distribution features, and the frequency domain features deviate from the corresponding ideal interaction mode, it is determined that there is an anomaly in the composite physical interaction.

5. The method of claim 1, wherein, If the abnormality of the composite physical interaction is identified, then based on the characteristics of the dynamic physical signal, closed-loop adaptive differential adjustment is performed on the control parameters of the peeling process, including: If the abnormality of the composite physical interaction is identified, the differential adjustment amount of the blade pressing depth and cutting speed is calculated based on the real-time change trend of the dynamic physical signal. Based on the differential adjustment amount, the control parameters are continuously and slightly modified at a preset high-frequency control cycle.

6. The method of claim 5, wherein, After each minor correction, the dynamic physical signal is reacquired, and the composite physical interaction anomaly is determined based on the newly acquired dynamic physical signal. If the anomaly is determined to be suppressed, the adjustment is stopped.

7. The method of claim 1, wherein, The method further includes: Based on the result of the closed-loop adaptive differential adjustment, a corresponding crimping warning message is generated; During the subsequent crimping of the cable terminals, the crimping control parameters are adjusted compensatorily based on the crimping warning information and real-time monitoring data of the crimping process to optimize the micro-contact quality between the conductor and the terminal.

8. The method of claim 7, wherein, During the subsequent terminal crimping process of the cable, based on the crimping warning information and real-time monitoring data of the crimping process, compensatory adjustments are made to the crimping control parameters, including: Adjust the closing speed and pressing stroke of the pressing die according to the risk level indicated by the pressing warning information; During the subsequent terminal crimping process of the cable, a high-frequency AC excitation signal is applied to the conductor and the terminal, and the transient impedance response between the conductor and the terminal is monitored; Based on the deviation between the transient impedance response and the preset ideal impedance range, the final closing position or holding time of the pressing die is finely adjusted.

9. The method of claim 7, wherein, If the abnormal composite physical interaction persists after the closed-loop adaptive differential adjustment, or if the microscopic contact quality fails to meet the preset standard after the compensatory adjustment, then the cable to be processed is determined to have a hidden electrical connection defect. Cables found to have the aforementioned hidden electrical connection defects are marked or sorted.

10. A cable processing intelligent control system, characterized in that, include: The acquisition unit is used to acquire the microscopic characteristic information of the insulation layer of the cable to be processed, wherein the microscopic characteristic information is used to reflect the toughness of the insulation layer material of the cable to be processed. The acquisition unit is used to simultaneously acquire dynamic physical signals during the stripping process of the cable to be processed using a stripping tool. The dynamic physical signals are generated by the physical interaction between the stripping tool and the insulation layer of the cable to be processed. The identification unit is used to identify, based on the material microstructure characterization information and the dynamic physical signal, whether there is an abnormal composite physical interaction caused by the combined action of the material properties of the insulating layer and the peeling tool during the peeling process; The adjustment unit is used to perform closed-loop adaptive differential adjustment on the control parameters of the peeling process according to the characteristics of the dynamic physical signal if the abnormality of the composite physical interaction is identified. The closed-loop adaptive differential adjustment is used to suppress microscopic defects on the peeling end face caused by the abnormality of the composite physical interaction.

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