Current sensor pin bending processing control system and processing equipment

Abstract

The application discloses a current sensor pin bending processing control system and processing equipment, relates to the technical field of current sensor pin bending processing, and through fusion collection of three types of disturbance parameters with different physical dimensions and response mechanisms, namely, bending acceleration ZWa, pin surface contact impedance disturbance ZBd and friction spectrum response ZMc, the sensing coverage range of the system for multi-source abnormal phenomena in the processing process is significantly expanded, the identification blind area of a traditional system relying on a single force or displacement signal is made up for, and the sensitivity to non-explicit disturbance characteristics is improved. Nonlinear interaction modeling of the three disturbance factors is carried out through the micro-disturbance coupling strength index Ψ, the system can not only identify single-variable fluctuations, but also capture the collaborative evolution trend between variables. This structure enables the system to have the ability to identify the structural imbalance and coupling intensification trend of the disturbance in advance, and has higher fault foresight and active response capability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of current sensor pin bending processing, in particular to a current sensor pin bending processing control system and processing equipment. BACKGROUND

[0002] In the packaging process of electrical detection devices, the pin member often serves as the key physical lead-out end for connecting external circuits, and its forming shape plays a decisive role in subsequent welding adaptation, electrical performance consistency and assembly reliability. Especially in the batch packaging scene of current sensors, pin bending processing not only needs to achieve size consistency control, but also must ensure the stability of dynamic response and the real-time control of the processing process.

[0003] In the existing current sensor pin bending equipment, the action control method based on fixed trajectory template is generally adopted, and the bending task is completed by statically setting the path instruction. Although this method is simple in structure, it is highly sensitive to changes in pin material, process errors, environmental disturbances and other factors, and is prone to problems such as slight bending angle deviation, end position drift and processing process instability during processing.

[0004] The root cause of the above phenomenon is that the existing system generally lacks real-time sensing capability for the "perturbation behavior" of the bending process, and fails to build a traceable disturbance model or trajectory offset response mechanism. Under high-speed processing conditions, the pin structure may experience transient torque disturbance due to material elasticity differences, local stress release, processing inertia lag and other factors, and such disturbances are not sensed or modeled in the conventional control model, resulting in the inability of subsequent trajectory control to actively correct, forming a state drift of "slip delay" or "inertial deviation" of the bending path. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a current sensor pin bending processing control system and processing equipment, which solves the problems mentioned in the background art.

[0006] To achieve the above purpose, the present application realizes the following technical scheme: a current sensor pin bending processing control system, comprising a disturbance signal acquisition and preprocessing module, a micro-perturbation energy modeling and atlas generation module, a bending path error mapping module, a real-time correction control module, a feedback trend analysis and steady-state evaluation module, and an optimization strategy switching and task scheduling module;

[0007] The disturbance signal acquisition and preprocessing module samples the real-time state data in the current sensor pin processing process through a sensor, fits it into an original data set SM, and pre-processes it to obtain a pin data set ZM;

[0008] The perturbation energy modeling and spectrum generation module analyzes the pin dataset ZM, constructs the perturbation energy spectrum, and calculates the perturbation energy offset function D.

[0009] The bending path error mapping module receives the current processing trajectory data based on the acquired pin dataset ZM, and performs spatial error mapping with the pre-stored standard trajectory to obtain the spatial error E;

[0010] The real-time correction control module calculates the correction magnitude F based on the acquired energy offset function D and spatial error E; the feedback trend analysis and steady-state assessment module processes the continuous change trend of the correction magnitude F within a fixed period T, obtains the continuous stability evaluation function G, and identifies whether it has entered the high disturbance and critical instability region.

[0011] The optimization strategy switching and task scheduling module integrates the acquired disturbance energy offset function D, correction magnitude F, and continuous stability evaluation function G to calculate the strategy response exponential function H, and then performs dynamic bending strategy switching.

[0012] Preferably, the disturbance signal acquisition and preprocessing module includes a disturbance state signal acquisition unit and a cleaning and normalization unit;

[0013] The disturbance state signal acquisition unit is responsible for collecting state data through sensors during the pin bending process, including bending acceleration ZWa, pin surface contact impedance disturbance ZBd, and triboelectric spectrum response ZMc, and fitting it into the original dataset SM.

[0014] Among them, the bending acceleration ZWa is sampled by a high-frequency laser displacement sensor and obtained by performing first derivative;

[0015] The bending acceleration ZWa is obtained using the following formula:

[0016] ;

[0017] In the formula, x(t+Δt) represents the displacement at time t+Δt, x(t) represents the displacement at time t, x(t-Δt) represents the displacement at time t-Δt, and Δt represents the time interval;

[0018] The contact impedance disturbance ZBd on the pin surface is obtained by using a composite current and voltage probe and by acquiring the instantaneous change in ohmic value.

[0019] The pin surface contact impedance disturbance ZBd is obtained using the following formula:

[0020] ;

[0021] In the formula, V(t) represents the voltage value at time t, I(t) represents the current value at time t, and Ro represents the initial contact resistance without load;

[0022] The triboacoustic spectrum response ZMc was acquired by a piezoelectric acoustic emission sensor and the frequency spectrum intensity was obtained after FFT.

[0023] The triboelectric spectral response ZMc is obtained using the following formula:

[0024] ;

[0025] In the formula, f1 and f2 represent the defined target frequency range, 1.5kHz–10kHz, used for processing contact friction, F{Sraw(t)} represents the Fourier transform of the original acoustic signal, and max represents the point with the largest spectral amplitude in the frequency range [f1,f2], which is to capture the resonance or abnormal amplification peak of a certain frequency band.

[0026] The cleaning and normalization unit performs noise reduction and normalization on the acquired raw dataset SM to obtain the pin dataset ZM;

[0027] Noise reduction is achieved by removing noise from the original dataset SM using a sliding filter method;

[0028] Normalization was performed on the original dataset SM using the Min-Max linear normalization method to obtain the pin dataset ZM;

[0029] The pin dataset ZM is obtained using the following formula:

[0030] ;

[0031] In the formula, ZMo represents the o-th data in the pin dataset ZM, SMo represents the o-th data in the original dataset SM, minSMo represents the valley value of the o-th data in the original dataset SM, and maxSMo represents the peak value of the o-th data in the original dataset SM.

[0032] Preferably, the perturbation energy modeling and map generation module includes a perturbation coupling index construction unit and a perturbation energy shift function generation unit;

[0033] The perturbation coupling index construction unit constructs a nonlinear interaction function based on the obtained pin dataset ZM to capture the joint coupling behavior between the three perturbation quantities and obtain the micro-perturbation coupling strength index Ψ.

[0034] The perturbation coupling strength index Ψ is obtained by the following formula:

[0035] ;

[0036] In the formula, d represents the derivative symbol, ZWa(t) represents the bending acceleration at time t, ZBd(t) represents the contact impedance disturbance of the pin surface at time t, ZMc(t) represents the triboelectric response at time t, and cos() represents the cosine function. All of these values ​​are derived from the disturbance signal acquisition and preprocessing module and have undergone sliding filter noise reduction and Min-Max normalization. They are of uniform dimension. The constructed micro-perturbation coupling strength index Ψ is a dimensionless coupling trend index, used for qualitative analysis of disturbance strength trends and construction of control criteria.

[0037] Specifically, before the bending acceleration ZWa, pin surface contact impedance disturbance ZBd, and triboacoustic response ZMc are incorporated into the micro-perturbation coupling strength index Ψ, they have all undergone unified normalization processing through the disturbance signal acquisition and preprocessing module. All variables are dimensionless disturbance trend factors. The constructed coupling terms are only used to qualitatively reflect the changing trend of disturbance path coupling rather than direct physical and mechanical quantities, ensuring comparability and fusion between disturbance terms from different sources.

[0038] The perturbation coupling strength index Ψ does not refer to a specific physical quantity value, but is used to comprehensively reflect the nonlinear linkage trend between acceleration fluctuations, contact resistance anomalies and acoustic spectrum changes during the processing. It belongs to the trend perturbation index of normalized modeling and is suitable for constructing perturbation evolution maps and stability assessment models.

[0039] The perturbation energy offset function generation unit performs integral modeling of the perturbation behavior based on the obtained micro-perturbation coupling strength index Ψ, obtains the perturbation energy offset function D, and compares it with the preset offset threshold TD to construct an energy spectrum.

[0040] The perturbation energy shift function D is obtained by the following formula:

[0041] ;

[0042] In the formula, This represents the steady-state disturbance accumulation coefficient. Ψ(t) represents the dynamic perturbation evolution coefficient, and Ψ(t) represents the perturbation coupling strength index at time t.

[0043] Specific steady-state disturbance cumulative coefficient and dynamic perturbation evolution coefficient The control parameters with engineering significance are not derived from fixed physical constants, but are empirically configured based on the system's sensitivity to disturbance response. Specifically, β1 is used to adjust the system's response to the micro-disturbance coupling strength index Ψ itself, and an initial setting range of 0.8 to 1.2 is usually recommended to ensure that the system can promptly identify the gradual accumulation of steady-state disturbances. β2 is used to adjust the system's dynamic response capability to the disturbance change rate dΨ(t) / dt, and an initial setting range of 0.3 to 0.8 is recommended to enhance the system's ability to identify state changes caused by critical disturbance trends.

[0044] Energy maps are obtained through matching in the following ways:

[0045] When the perturbation energy offset function D ≤ offset threshold TD, it indicates that the offset is stable and the energy spectrum is a stable straight line;

[0046] When the perturbation energy offset function D > offset threshold TD, it indicates an abnormal offset, and the energy spectrum shows an cumulative trend, which is an upward straight line.

[0047] Preferably, the bending path error mapping module includes a trajectory reconstruction and attitude synchronization unit and a spatial error calculation unit;

[0048] The trajectory reconstruction and attitude synchronization unit records the acquisition trajectory of the pin dataset ZM, and performs dynamic attitude reconstruction and standard spatiotemporal alignment on the trajectory point data during pin processing to obtain the trajectory point sequence Pact.

[0049] The trajectory point sequence Pact is obtained using the following formula:

[0050] Pact(t)={xi(t),yi(t),zi(t)|i=1,2,...,N};

[0051] In the formula, Pact(t) represents the sequence of trajectory points at time t, (xi(t), yi(t), zi(t)) represents the three-dimensional spatial position of the needle end during the bending process, i represents the sampling number, and N represents the number of trajectory points;

[0052] By introducing a local rotation registration matrix XR and a displacement vector XT, rigid registration processing is performed between the pin path and the standard trajectory. The processing formula is as follows:

[0053] .

[0054] Preferably, the spatial error calculation unit evaluates the geometric offset intensity ep of each point based on the trajectory point sequence Pact(t) at time t through Euclidean space difference, and introduces the path deformation sensitivity index function to nonlinearly amplify the high error section to obtain the spatial error E;

[0055] Based on the three-dimensional spatial position (xi(t), yi(t), zi(t)) of the pin tip during the bending process, a three-dimensional error vector is constructed. And obtain the offset intensity ep;

[0056] Three-dimensional error vector Obtain it using the following formula:

[0057] ;

[0058] In the formula, Let represent the three-dimensional error vector of the i-th trajectory point at time t, and let (oxi(t), oyi(t), ozi(t)) represent the spatial coordinates of the i-th point in the standard reference trajectory;

[0059] The offset intensity ep is obtained using the following formula:

[0060] ;

[0061] In the formula, epi(t) represents the offset intensity of the i-th trajectory point at time t;

[0062] Spatial error E is obtained using the following formula:

[0063] ;

[0064] In the formula, k represents the path deformation amplification factor, and sin represents the sine function.

[0065] Specifically, the path deformation amplification factor k can be set based on empirical values ​​of process conditions or according to the spatial distribution density of trajectory errors during processing. This factor is used to enhance the error response weight in the middle section of the path. By default, the path deformation amplification factor k can be set to 1.0, which means that the trajectory offset in the middle section is given twice the weight. For situations with concentrated stress or springback in the middle section, the path deformation amplification factor k can be set to 1.5 to 2.0 to enhance the error perception sensitivity of critical sections of the processing path deformation. The parameter settings have clear adjustment logic and engineering feasibility, and technical personnel in the field can quickly configure or fine-tune them based on actual working conditions.

[0066] Preferably, the real-time correction control module includes a dynamic correction intensity calculation unit and a correction trigger determination and fine-tuning unit;

[0067] The dynamic correction intensity calculation unit calculates the correction magnitude F based on the obtained energy offset function D and spatial error E;

[0068] The correction magnitude F is obtained using the following formula:

[0069] ;

[0070] In the formula, λ represents the control adjustment sensitivity coefficient, μ represents the evolution trend response weight, D(t) represents the energy offset function at time t, E(t) represents the spatial error at time t, and QE represents a non-zero constant.

[0071] Specifically, the control adjustment sensitivity coefficient λ is used to determine the overall sensitivity of the control system to disturbance response. The higher the value, the faster the system responds to disturbance fluctuations. The recommended initial value is 1.0. In scenarios where the control system has low rigidity or high cycle time, it can be increased to 1.2 to 1.5 to improve the trajectory correction speed. In high-rigidity systems, it can be set to 0.8 to avoid false triggering of correction behavior.

[0072] The evolution trend response weight μ is used to reflect the system's attention to the rate of change of disturbance dD(t) / dt. For processing conditions with significant disturbance acceleration and prominent evolution trend, μ is set to 0.7 to 0.9 to enhance the trend-driven intervention mechanism; for processing scenarios with stable disturbance and constant rhythm, μ can also be set to 0.3 to 0.5 to suppress misjudgment behavior of ineffective fluctuations.

[0073] The spatial error suppression constant QE is introduced to prevent the denominator term in the formula from approaching zero when the error is extremely small, which would cause the correction magnitude to be abnormally amplified.

[0074] Preferably, the correction trigger determination and fine-tuning unit compares the acquired correction magnitude F with the preset magnitude threshold TF to determine whether trajectory correction is triggered;

[0075] Whether trajectory correction is triggered is determined by matching in the following way:

[0076] When the correction magnitude F is less than the magnitude threshold TF, trajectory correction is not triggered.

[0077] When the correction magnitude F is greater than or equal to the magnitude threshold TF, trajectory correction is triggered.

[0078] When trajectory correction is triggered, a correction control vector CX is generated based on the correction magnitude F, and the angle θ, velocity v, and acceleration a at the next turning point are adjusted.

[0079] Specifically, after the system determines the trigger condition based on the disturbance energy offset function and spatial error, it generates a correction control vector CX to adjust the control parameters for the next bending action. This control vector contains three parameters: bending angle θ, bending speed v, and bending acceleration a. The system automatically adjusts these three parameters in conjunction with the disturbance intensity level to achieve dynamic compensation for trajectory errors or mechanical disturbances.

[0080] When the system determines that the current disturbance intensity is high, it will appropriately increase the bending angle θ, so that the actual angle of the pin after forming is slightly greater than the original set value, thereby offsetting the end error caused by material springback or path offset.

[0081] At the same time, the bending speed v will also be increased in order to quickly complete the forming when the disturbance lasts for a short period of time, so as to reduce the impact of dynamic disturbance on trajectory formation.

[0082] In addition, the system will increase the acceleration 'a' accordingly, so that the bending action has a stronger inertial response capability in the initial stage, effectively offsetting the initial path deviation caused by disturbance fluctuations.

[0083] The corrected control vector CX is obtained using the following formula:

[0084] ;

[0085] In the formula, Co represents the initial control reference vector. Under normal circumstances, there is no need for correction, and the system adopts the default combination of control values. kz represents the control gain factor, and tanh represents the hyperbolic tangent function.

[0086] Specifically, the initial control reference vector Co=[θ, v, a] is usually preloaded by the system during the task initialization phase. Its sources may include standard parameter configurations provided by the manufacturing execution system, historical average values ​​extracted based on qualified batch trajectories, or settings provided by the control program in the CNC machine tool, which has repeatability and industrial practical basis.

[0087] The initial control reference vector Co=[θ, v, a] represents the default combination of control parameters preset for each bend point under the condition of no disturbance correction requirement, namely the set of standard bend angle θ, velocity v and acceleration a.

[0088] The control gain factor kz is used to adjust the response strength of the disturbance correction command to the control vector. Its value can be set by the process engineer according to the material properties and equipment characteristics, and is generally selected in the range of 0.1 to 1.0 to ensure that the system has good flexible control capability and response adaptability.

[0089] Adjust angle θ: Offset the standard bending angle to compensate for the target angle error caused by disturbance or springback;

[0090] Adjust speed v: Reduce speed appropriately to stabilize output control and avoid rapid processing that could exacerbate disturbances;

[0091] Adjusting acceleration 'a': Reduces sudden changes in system inertia and controls the dynamic stability of the bending dynamic process.

[0092] Preferably, the feedback trend analysis and steady-state assessment module collects and statistically processes the correction magnitude F within a fixed period T, extracts its volatility and stability characteristics, and outputs a continuous stability evaluation function G.

[0093] The stability evaluation function G is obtained through the following formula:

[0094] ;

[0095] In the formula, F(t) represents the correction magnitude at time t, and pF represents the average correction magnitude within a fixed period T;

[0096] Based on the historical evolution trend of the stability evaluation function G, it is compared with the preset slight fluctuation boundary threshold Glow and critical instability boundary threshold Ghig to identify the disturbance region of the system and automatically divide the current state into operating intervals.

[0097] The division of the operating range is obtained by matching in the following way:

[0098] When the stability evaluation function G < the slight fluctuation boundary threshold Glow, it indicates the first operating interval, the steady state region;

[0099] When the slight fluctuation boundary threshold Glow ≤ stability evaluation function G < critical instability boundary threshold Ghig, it indicates the second operating range, the high disturbance response zone;

[0100] When the stability evaluation function G ≥ the critical instability boundary threshold Ghig, it represents the third operating interval, the critical instability evolution zone.

[0101] Preferably, the optimization strategy switching and task scheduling module integrates the acquired disturbance energy offset function D, correction magnitude F, and continuous stability evaluation function G to construct a composite strategy response exponential function H that reflects the system operation.

[0102] The obtained perturbation energy offset function D, correction magnitude F, and continuous stability evaluation function G are normalized to transform them together with log(D+1) into a dimensionless index. The coefficient AO is used for harmonic balance to ensure that the function H has consistency between numerical calculation and physical interpretation.

[0103] The policy response exponential function H is obtained through the following formula:

[0104] ;

[0105] In the formula, log represents the logarithmic function, and AO represents the weighting coefficient for evaluating the trend response;

[0106] Specifically, the strategy response exponential function H is a normalized trend function used to integrate processing disturbances, execution feedback strength, and control trend stability, representing the urgency of the current system operating state. All functions involved in the calculation (disturbance energy offset function D, correction magnitude F, and continuous stability evaluation function G) have been uniformly expressed using dynamic constraints. Therefore, H can serve as an evaluation criterion for determining whether the current processing task requires triggering a strategy switch.

[0107] The obtained policy response exponential function H is compared with the preset low policy response threshold Hth1 and high policy response threshold Hth2, and the policy execution mode is dynamically selected.

[0108] The strategy execution mode is selected through matching in the following ways:

[0109] When the policy response exponential function H < the low policy response threshold Hth1, it indicates the standard mode, and no policy switching is required.

[0110] When the low policy response threshold Hth1 ≤ policy response exponential function H < high policy response threshold Hth2, it indicates the compensation mode, which corrects the angle θ, velocity v and acceleration a, and reduces the processing cycle to suppress disturbance accumulation.

[0111] When the high policy response threshold Hth2 ≥ the policy response exponential function H, it indicates a delay mode, compensation is paused, and the correction trend is monitored.

[0112] A current sensor pin bending processing control device includes a bending control device body and a controller. The controller includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it realizes the function of the system.

[0113] Specifically, the disturbance parameters and judgment functions used by each control module of this invention are all constructed based on the original, collectable physical quantities, and the index dimensions are unified through normalization and exponential coupling. The correction magnitude F, continuous stability evaluation function G, and policy response exponential function H output by the system are all explicit values, and are equipped with threshold judgment rules and policy response mechanisms.

[0114] This invention provides a control system and processing equipment for bending current sensor pins, which has the following beneficial effects:

[0115] (1) During system operation, the system can acquire and clean non-contact micro-perturbation signals such as bending acceleration ZWa, pin surface contact impedance perturbation ZBd and triboelectric response ZMc through the perturbation signal acquisition and preprocessing module. The system can obtain core state information reflecting structural abnormalities and deformation trends without interrupting processing, thereby avoiding the delayed response problem caused by the inability of traditional systems to detect early perturbations.

[0116] The perturbation energy modeling and spectrum generation module establishes a perturbation spectrum based on multi-source dynamic signals, comprehensively reflecting the energy accumulation process and evolution trend. This enables the system to identify "instability precursors that have not yet manifested as offsets," allowing for proactive intervention in subtle anomalies during processing. The bending path error mapping module significantly improves the ability to locate key points of bending trajectory deformation through dynamic mapping of spatial errors and a mid-section enhancement identification strategy. This addresses the problem of overly average error judgments in existing equipment, thereby improving shaping accuracy and structural consistency.

[0117] (2) By fusing and collecting three types of disturbance parameters with different physical dimensions and response mechanisms—bending acceleration ZWa, pin surface contact impedance disturbance ZBd, and triboacoustic response ZMc—the system's perception coverage of multi-source anomalies during processing is significantly expanded. This compensates for the blind spots of traditional systems that rely on single force or displacement signals, and enhances the sensitivity to non-obvious disturbance characteristics. By using the micro-perturbation coupling strength index Ψ to perform nonlinear interactive modeling of the three disturbance factors, the system can not only identify single-variable fluctuations but also capture the co-evolution trend between variables. This structure enables the system to identify structural imbalances and coupling intensification trends of disturbances in advance, and has higher fault foresight and proactive response capabilities.

[0118] (3) By introducing a nonlinear weighting function into the error calculation, the system gives higher detection weight to the errors in deformation-sensitive areas such as the bend and reversal points in the bending path. This solves the problem of the traditional average error index being sluggish at key positions, ensuring that errors in key areas can be amplified and identified, and improving the accuracy of risk identification. The system can output a quantitative spatial error index based on the dynamic comparison results between each processing trajectory and the standard model. It is particularly suitable for dynamic analysis of differential trajectories under multiple batches, multiple models, or different elastic materials, effectively solving the problem that traditional static templates are difficult to adapt to the needs of flexible manufacturing. The spatial error E is not only used as an independent evaluation index, but can also be used as the input basis for subsequent correction amplitude functions and trend evaluation functions. The high-precision, high-dimensional error data stream constructs a precise and controllable feedback closed-loop mechanism for the entire control system, improving the system's responsiveness to small offsets.

[0119] (4) By performing time-series sampling and fluctuation analysis of the correction amplitude within a fixed period T, the system can extract the stability change trend from an overall evolutionary perspective, thereby achieving quantitative modeling of disturbance volatility. This processing mechanism enables the system to move beyond relying solely on instantaneous state judgments and gain insight into the evolutionary patterns of disturbances over longer periods, thus identifying potential risks of small fluctuations accumulating into instability in advance. By comparing the stability function G with the boundary threshold, three operating states are classified: steady state, high disturbance response, and critical instability. The system achieves dynamic classification and perception of the operating environment, providing a basis for differentiated triggering of subsequent control behaviors. This mechanism overcomes the shortcomings of traditional control systems that are "single-state and rigid in response," enhancing the system's ability to respond flexibly to different disturbance intensities. Attached Figure Description

[0120] Figure 1 This is a schematic flowchart of the current sensor pin bending processing control system of the present invention;

[0121] Figure 2 This is a frame diagram of the current sensor pin bending processing control equipment of the present invention;

[0122] Figure 3 This is a schematic diagram of the strategy response index acquisition process of the present invention;

[0123] Figure 4 This is a line graph of the perturbation coupling strength index of the present invention. Detailed Implementation

[0124] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0125] Example 1

[0126] This invention provides a current sensor pin bending processing control system; please refer to [link / reference]. Figures 1 to 4 It includes a disturbance signal acquisition and preprocessing module, a disturbance energy modeling and spectrum generation module, a bending path error mapping module, a real-time correction control module, a feedback trend analysis and steady-state evaluation module, and an optimization strategy switching and task scheduling module.

[0127] The disturbance signal acquisition and preprocessing module samples the real-time status data of the current sensor pins during the processing of the current sensor pins through the sensor, fits it into the original dataset SM, and performs preprocessing to obtain the pin dataset ZM;

[0128] The perturbation energy modeling and spectrum generation module analyzes the pin dataset ZM, constructs the perturbation energy spectrum, and calculates the perturbation energy offset function D.

[0129] The bending path error mapping module receives the current processing trajectory data based on the acquired pin dataset ZM, and performs spatial error mapping with the pre-stored standard trajectory to obtain the spatial error E;

[0130] The real-time correction control module calculates the correction magnitude F based on the acquired energy offset function D and spatial error E; the feedback trend analysis and steady-state assessment module processes the continuous change trend of the correction magnitude F within a fixed period T, obtains the continuous stability evaluation function G, and identifies whether it has entered the high disturbance and critical instability region.

[0131] The optimization strategy switching and task scheduling module integrates the acquired disturbance energy offset function D, correction magnitude F, and continuous stability evaluation function G to calculate the strategy response exponential function H, and then performs dynamic bending strategy switching.

[0132] In this embodiment, the system synchronously acquires and cleans non-contact micro-perturbation signals such as bending acceleration ZWa, pin surface contact impedance perturbation ZBd, and triboelectric response ZMc through the perturbation signal acquisition and preprocessing module. This allows the system to obtain core state information reflecting structural anomalies and deformation trends without interrupting processing, thereby avoiding the delayed response problem caused by the inability of traditional systems to detect early perturbations.

[0133] The perturbation energy modeling and spectrum generation module establishes a perturbation spectrum based on multi-source dynamic signals, comprehensively reflecting the energy accumulation process and evolution trend. This enables the system to identify "instability precursors that have not yet manifested as offsets," allowing for proactive intervention in subtle anomalies during processing. The bending path error mapping module significantly improves the ability to locate key points of bending trajectory deformation through dynamic mapping of spatial errors and a mid-section enhancement identification strategy. This addresses the problem of overly average error judgments in existing equipment, thereby improving shaping accuracy and structural consistency.

[0134] The real-time correction control module combines energy disturbances and geometric errors to construct correction commands, achieving a self-adjusting mechanism of "compensation while running." Compared with traditional rigid control schemes that require shutdown for debugging, this system supports flexible dynamic correction and has strong disturbance rejection stability. The feedback trend analysis and steady-state assessment module evaluates the volatility of the correction behavior within a fixed period, enabling it to capture critical turning points in the disturbance evolution trend in real time, preventing "latent accumulated disturbances" from turning into sudden anomalies, thereby improving the robustness and predictability of the overall system operation.

[0135] Example 2

[0136] This embodiment is an explanation based on Embodiment 1. Please refer to it.Figure 1 and Figure 4 Specifically: the disturbance signal acquisition and preprocessing module includes a disturbance state signal acquisition unit and a cleaning and normalization unit;

[0137] The disturbance state signal acquisition unit is responsible for collecting state data through sensors during the pin bending process, including bending acceleration ZWa, pin surface contact impedance disturbance ZBd, and triboelectric spectrum response ZMc, and fitting it into the original dataset SM.

[0138] Among them, the bending acceleration ZWa is sampled by a high-frequency laser displacement sensor and obtained by performing first derivative;

[0139] The contact impedance disturbance ZBd on the pin surface is obtained by using a composite current and voltage probe and by acquiring the instantaneous change in ohmic value.

[0140] The triboacoustic spectrum response ZMc was acquired by a piezoelectric acoustic emission sensor and the frequency spectrum intensity was obtained after FFT.

[0141] The cleaning and normalization unit performs noise reduction and normalization on the acquired raw dataset SM to obtain the pin dataset ZM;

[0142] Noise reduction is achieved by removing noise from the original dataset SM using a sliding filter method;

[0143] Normalization was performed on the original dataset SM using the Min-Max linear normalization method to obtain the pin dataset ZM;

[0144] The pin dataset ZM is obtained using the following formula:

[0145] ;

[0146] In the formula, ZMo represents the o-th data in the pin dataset ZM, SMo represents the o-th data in the original dataset SM, minSMo represents the valley value of the o-th data in the original dataset SM, and maxSMo represents the peak value of the o-th data in the original dataset SM.

[0147] The perturbation energy modeling and map generation module includes a perturbation coupling index construction unit and a perturbation energy shift function generation unit;

[0148] The perturbation coupling index construction unit constructs a nonlinear interaction function based on the obtained pin dataset ZM to capture the joint coupling behavior between the three perturbation quantities and obtain the micro-perturbation coupling strength index Ψ.

[0149] The perturbation coupling strength index Ψ is obtained by the following formula:

[0150] ;

[0151] In the formula, d represents the derivative symbol, ZWa(t) represents the bending acceleration at time t, ZBd(t) represents the contact impedance disturbance of the pin surface at time t, ZMc(t) represents the triboelectric response at time t, and cos() represents the cosine function.

[0152] Specific examples:

[0153] Table 1: Calculation table of perturbation coupling strength index;

[0154] Time step Bending acceleration ZWa Pin surface contact impedance perturbation ZBd Friction spectrogram response ZMc Perturbation coupling strength index Ψ t1 0.45 0.60 0.70 0.459 t2 0.48 0.58 0.72 0.436 t3 0.50 0.55 0.75 0.403 t4 0.52 0.53 0.74 0.391 t5 0.49 0.57 0.71 0.433

[0155] The perturbation energy offset function generation unit performs integral modeling of the perturbation behavior based on the obtained micro-perturbation coupling strength index Ψ, obtains the perturbation energy offset function D, and compares it with the preset offset threshold TD to construct an energy spectrum.

[0156] The perturbation energy shift function D is obtained by the following formula:

[0157] ;

[0158] In the formula, This represents the steady-state disturbance accumulation coefficient. Ψ(t) represents the dynamic perturbation evolution coefficient, and Ψ(t) represents the perturbation coupling strength index at time t.

[0159] Energy maps are obtained through matching in the following ways:

[0160] When the perturbation energy offset function D ≤ offset threshold TD, it indicates that the offset is stable and the energy spectrum is a stable straight line;

[0161] When the perturbation energy offset function D > offset threshold TD, it indicates an abnormal offset, and the energy spectrum shows an cumulative trend, which is an upward straight line.

[0162] In this embodiment, by fusing and acquiring three types of disturbance parameters with different physical dimensions and response mechanisms—bending acceleration ZWa, pin surface contact impedance disturbance ZBd, and triboacoustic response ZMc—the system's perception coverage of multi-source anomalies during processing is significantly expanded. This compensates for the blind spots of traditional systems that rely on single force or displacement signals, and enhances the sensitivity to inconspicuous disturbance characteristics. By using the micro-perturbation coupling strength index Ψ to perform nonlinear interactive modeling of the three disturbance factors, the system can not only identify single-variable fluctuations but also capture the co-evolution trend between variables. This structure enables the system to identify structural imbalances and coupling intensification trends of disturbances in advance, resulting in higher fault foresight and proactive response capabilities.

[0163] After acquiring the original signal, sliding filter noise reduction and Min-Max normalization are introduced to effectively suppress the impact of signal spike noise, sampling error, and unit differences on model stability. This ensures the comparability and computational reliability of the data used in perturbation modeling, improving the system's engineering practicality. The system expresses the cumulative trend of disturbances during processing in the form of a graph, not only quantifying the "stability" or "instability" of the processing state but also enabling the control system to autonomously identify offset trends based on the graph morphology. Compared to traditional control logic that relies on threshold judgment, energy graphs have the advantages of more intuitive, continuous, and evolutionary recognition.

[0164] This embodiment can continuously acquire and update disturbance maps without pausing processing or relying on manual intervention, ensuring that the system can stably identify the migration evolution trend even under high-speed processing conditions. This achieves true "on-the-fly identification," significantly reducing the risk of false positives and false negatives. The disturbance energy migration function, as the first-stage quantitative discrimination index in the system, directly serves the subsequent spatial trajectory correction and strategy switching judgment modules, providing solid support for the entire control system to form a closed-loop process with physical rationality, data logic continuity, and control predictability.

[0165] Example 3

[0166] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: the bending path error mapping module includes a trajectory reconstruction and attitude synchronization unit and a spatial error calculation unit;

[0167] The trajectory reconstruction and attitude synchronization unit records the acquisition trajectory of the pin dataset ZM, and performs dynamic attitude reconstruction and standard spatiotemporal alignment on the trajectory point data during pin processing to obtain the trajectory point sequence Pact.

[0168] The trajectory point sequence Pact is obtained using the following formula:

[0169] Pact(t)={xi(t),yi(t),zi(t)|i=1,2,...,N};

[0170] In the formula, Pact(t) represents the sequence of trajectory points at time t, (xi(t), yi(t), zi(t)) represents the three-dimensional spatial position of the needle end during the bending process, i represents the sampling number, and N represents the number of trajectory points;

[0171] By introducing a local rotation registration matrix XR and a displacement vector XT, rigid registration processing is performed between the pin path and the standard trajectory. The processing formula is as follows:

[0172] .

[0173] The spatial error calculation unit evaluates the geometric offset intensity ep of each point based on the trajectory point sequence Pact(t) at time t through Euclidean space difference, and introduces the path deformation sensitivity index function to nonlinearly amplify the high error section to obtain the spatial error E.

[0174] Based on the three-dimensional spatial position (xi(t), yi(t), zi(t)) of the pin tip during the bending process, a three-dimensional error vector is constructed. And obtain the offset intensity ep;

[0175] Three-dimensional error vector Obtain it using the following formula:

[0176] ;

[0177] In the formula, Let represent the three-dimensional error vector of the i-th trajectory point at time t, and let (oxi(t), oyi(t), ozi(t)) represent the spatial coordinates of the i-th point in the standard reference trajectory;

[0178] The offset intensity ep is obtained using the following formula:

[0179] ;

[0180] In the formula, epi(t) represents the offset intensity of the i-th trajectory point at time t;

[0181] Spatial error E is obtained using the following formula:

[0182] ;

[0183] In the formula, k represents the path deformation amplification factor, and sin represents the sine function.

[0184] In this embodiment, the real-time spatial motion trajectory of the pin end is recorded and dynamically restored by the trajectory reconstruction and attitude synchronization unit. The system can completely restore the actual motion path during the processing, avoid data breakpoints or distortions caused by sensor delay or spatial drift, and enhance the stability of trajectory recognition under dynamic changing conditions.

[0185] This embodiment introduces a rotation matrix and a displacement vector to rigidly register the trajectory point sequence, ensuring that the machining trajectory and the standard trajectory have the same coordinate system in three-dimensional space. This avoids false deviations caused by coordinate drift in traditional error judgment, significantly improving the reliability and traceability of error measurement. By constructing a three-dimensional error vector point by point and calculating its spatial offset intensity, the system can not only obtain the overall trajectory error but also accurately identify the abnormal offset of a specific trajectory point during machining, realizing a technological evolution from "overall error judgment" to "local dynamic recognition".

[0186] This embodiment introduces a nonlinear weighting function into the error calculation, giving higher detection weight to errors in deformation-sensitive areas such as bends and reversal points in the bending path. This solves the problem of traditional average error indices being sluggish at critical locations, ensuring that errors in key areas can be amplified and identified, thus improving the accuracy of risk identification. The system can output a quantitative spatial error index based on the dynamic comparison results of each processing trajectory with the standard model. It is particularly suitable for dynamic analysis of differential trajectories under multiple batches, multiple models, or different elastic materials, effectively solving the problem that traditional static templates are difficult to adapt to the needs of flexible manufacturing. The spatial error E is not only used as an independent evaluation index, but also as the input basis for subsequent correction amplitude functions and trend evaluation functions. The high-precision, high-dimensional error data stream constructs a precise and controllable feedback closed-loop mechanism for the entire control system, improving the system's responsiveness to small offsets.

[0187] Example 4

[0188] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 and Figure 3 Specifically: the real-time correction control module includes a dynamic correction intensity calculation unit and a correction trigger determination and fine-tuning unit;

[0189] The dynamic correction intensity calculation unit calculates the correction magnitude F based on the obtained energy offset function D and spatial error E;

[0190] The correction magnitude F is obtained using the following formula:

[0191] ;

[0192] In the formula, λ represents the control adjustment sensitivity coefficient, μ represents the evolution trend response weight, D(t) represents the energy offset function at time t, E(t) represents the spatial error at time t, and QE represents a non-zero constant.

[0193] The correction trigger determination and fine-tuning unit compares the acquired correction magnitude F with the preset magnitude threshold TF to determine whether trajectory correction is triggered.

[0194] Whether trajectory correction is triggered is determined by matching in the following way:

[0195] When the correction magnitude F is less than the magnitude threshold TF, trajectory correction is not triggered.

[0196] When the correction magnitude F is greater than or equal to the magnitude threshold TF, trajectory correction is triggered.

[0197] When trajectory correction is triggered, a correction control vector CX is generated based on the correction magnitude F, and the angle θ, velocity v, and acceleration a at the next turning point are adjusted.

[0198] The corrected control vector CX is obtained using the following formula:

[0199] ;

[0200] In the formula, Co represents the initial control reference vector, kz represents the control gain factor, and tanh represents the hyperbolic tangent function.

[0201] This embodiment couples the energy disturbance behavior during processing with the spatial error trajectory behavior, avoiding the problem of minor errors being falsely triggered or superimposed instability being ignored, fundamentally improving the accuracy and logical scientific nature of correction trigger determination. By introducing control sensitivity and response weights and other adjustment parameters, the system achieves continuous quantitative evaluation of the correction magnitude, enabling the control strategy to have "gradual adjustment" capabilities. This breaks through the previous "either / or" binary correction logic, exhibiting higher robustness and flexible control levels in complex processing scenarios.

[0202] This embodiment establishes a precise discrimination mechanism between dynamic correction intensity and amplitude threshold, filtering out false feedback caused by minor disturbances at the source. This allows the system to truly focus on the actual deviation scenarios requiring intervention, effectively improving the execution efficiency and processing rhythm of the control system. When the system triggers correction, it can automatically generate a new control vector based on the correction intensity, comprehensively adjusting the angle, speed, and acceleration at the next turning point, forming a data-driven "precise control chain." This overcomes the previous control limitations of only being able to adjust a single angle or based solely on static setpoints.

[0203] This embodiment introduces a nonlinear function to adjust the gain amplitude during the control command construction process, enabling the system to maintain smooth and stable control output even under high disturbance intensity. This avoids a new round of errors or mechanical fatigue caused by over-correction, thereby improving equipment lifespan and long-term stability. All correction behaviors are based on online calculation and prediction generation. The entire process does not require interruption of the processing or pausing of mechanical actions. It can automatically adjust control parameters before each bending point, greatly improving the system's operating efficiency and intelligence level. This is a key manifestation of the transition from "post-response compensation" to "predictive pre-adjustment".

[0204] Example 5

[0205] This embodiment is an explanation based on Embodiment 4. Please refer to it. Figure 3 Specifically: the feedback trend analysis and steady-state assessment module collects and statistically processes the correction magnitude F within a fixed period T, extracts its volatility and stability characteristics, and outputs a continuous stability evaluation function G.

[0206] The stability evaluation function G is obtained through the following formula:

[0207] ;

[0208] In the formula, F(t) represents the correction magnitude at time t, and pF represents the average correction magnitude within a fixed period T;

[0209] Based on the historical evolution trend of the stability evaluation function G, it is compared with the preset slight fluctuation boundary threshold Glow and critical instability boundary threshold Ghig to identify the disturbance region of the system and automatically divide the current state into operating intervals.

[0210] The division of the operating range is obtained by matching in the following way:

[0211] When the stability evaluation function G < the slight fluctuation boundary threshold Glow, it indicates the first operating interval, the steady state region;

[0212] When the slight fluctuation boundary threshold Glow ≤ stability evaluation function G < critical instability boundary threshold Ghig, it indicates the second operating range, the high disturbance response zone;

[0213] When the stability evaluation function G ≥ the critical instability boundary threshold Ghig, it represents the third operating interval, the critical instability evolution zone.

[0214] The optimization strategy switching and task scheduling module integrates the acquired disturbance energy offset function D, correction magnitude F, and continuous stability evaluation function G to construct a composite strategy response exponential function H that reflects the system operation.

[0215] The policy response exponential function H is obtained through the following formula:

[0216] ;

[0217] In the formula, log represents the logarithmic function, and AO represents the weighting coefficient for evaluating the trend response;

[0218] The obtained policy response exponential function H is compared with the preset low policy response threshold Hth1 and high policy response threshold Hth2, and the policy execution mode is dynamically selected.

[0219] The strategy execution mode is selected through matching in the following ways:

[0220] When the policy response exponential function H < the low policy response threshold Hth1, it indicates the standard mode, and no policy switching is required.

[0221] When the low policy response threshold Hth1 ≤ policy response exponential function H < high policy response threshold Hth2, it indicates the compensation mode, which corrects the angle θ, velocity v and acceleration a.

[0222] When the high policy response threshold Hth2 ≥ the policy response exponential function H, it indicates a delay mode, compensation is paused, and the correction trend is monitored.

[0223] In this embodiment, by performing time-series sampling and fluctuation analysis of the correction amplitude within a fixed period T, the system can extract the stability change trend from an overall evolutionary perspective, achieving quantitative modeling of disturbance volatility. This processing mechanism allows the system to move beyond relying solely on instantaneous state judgments and instead perceive the evolutionary patterns of disturbances over longer periods, thereby identifying potential risks of small fluctuations accumulating into instability in advance. By comparing the stability function G with boundary thresholds, three operating states are classified: steady state, high disturbance response, and critical instability. The system achieves dynamic classification and perception of the operating environment, providing a basis for differentiated triggering of subsequent control behaviors. This mechanism overcomes the shortcomings of traditional control systems that are "single-state and rigid in response," enhancing the system's ability to respond flexibly to disturbances of varying intensities.

[0224] The system integrates three core elements—disturbance energy (D), correction intensity (F), and stability evaluation function (G)—in its control logic to construct a composite response index (H) that reflects the overall state of the operating environment. This effectively enhances the control decision-making layer's ability to integrate and judge multi-source data, upgrading the strategy switching logic from "single-factor driven" to "multi-factor fusion and synergistic driven." The system dynamically matches different operating modes, including standard mode, compensation mode, and delay mode, based on the level of the response index (H). It can not only provide control responses of varying depths according to the degree of disturbance but also automatically switch to observation and buffering states when identifying instability evolution trends, preventing the system from entering abnormal operating conditions such as over-correction or frequent oscillations, thus enhancing the robustness of the overall control system.

[0225] This embodiment integrates the stability feedback function with the correction strategy execution mechanism, achieving a closed-loop control system that links the entire process of "disturbance monitoring—trend analysis—strategy generation—execution control." This mechanism endows the system with the ability to self-regulate, self-diagnose, and self-correct in the face of changing environments, representing a typical upgrade from "single-loop control" to "multi-level intelligent closed-loop control." By using the stable trend function in conjunction with the logarithmic response exponential model, the system can identify and quantify sudden disturbances and critical precursors in the short term, providing sufficient reaction time for delayed strategy switching. This enhances the system's resistance to sudden disturbances and its adaptive recovery capability, making it a key technical means to ensure the stable operation of high-precision machining processes.

[0226] Example 6

[0227] For the current sensor pin bending processing control system and processing equipment, please refer to [reference needed]. Figure 2Specifically, it includes the bending control equipment body and the controller. The controller includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it realizes the functions of the system.

[0228] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A current sensor pin bending processing control system, characterized in that: It includes a disturbance signal acquisition and preprocessing module, a perturbation energy modeling and spectrum generation module, a bending path error mapping module, a real-time correction control module, a feedback trend analysis and steady-state assessment module, and an optimization strategy switching and task scheduling module; The disturbance signal acquisition and preprocessing module samples the real-time status data of the current sensor pins during the processing of the current sensor pins through the sensor, fits it into the original dataset SM, and performs preprocessing to obtain the pin dataset ZM; The perturbation energy modeling and spectrum generation module analyzes the pin dataset ZM, constructs the perturbation energy spectrum, and calculates the perturbation energy offset function D. The bending path error mapping module receives the current processing trajectory data based on the acquired pin dataset ZM, and performs spatial error mapping with the pre-stored standard trajectory to obtain the spatial error E; The real-time correction control module calculates the correction magnitude F based on the acquired energy offset function D and spatial error E; The feedback trend analysis and steady-state assessment module processes the continuous change trend of the correction amplitude F within a fixed period T, obtains the continuous stability evaluation function G, and identifies whether it has entered the high disturbance and critical instability region. The optimization strategy switching and task scheduling module integrates the acquired disturbance energy offset function D, correction magnitude F, and continuous stability evaluation function G to calculate the strategy response exponential function H, and then performs dynamic bending strategy switching.

2. The current sensor pin bending processing control system according to claim 1, characterized in that: The disturbance signal acquisition and preprocessing module includes a disturbance state signal acquisition unit and a cleaning and normalization unit; The disturbance state signal acquisition unit is responsible for collecting state data through sensors during the pin bending process, including bending acceleration ZWa, pin surface contact impedance disturbance ZBd, and triboelectric spectrum response ZMc, and fitting it into the original dataset SM. Among them, the bending acceleration ZWa is sampled by a high-frequency laser displacement sensor and obtained by performing first derivative; The contact impedance disturbance ZBd on the pin surface is obtained by using a composite current and voltage probe and by acquiring the instantaneous change in ohmic value. The triboacoustic spectrum response ZMc was acquired by a piezoelectric acoustic emission sensor and the frequency spectrum intensity was obtained after FFT. The cleaning and normalization unit performs noise reduction and normalization on the acquired raw dataset SM to obtain the pin dataset ZM; Noise reduction is achieved by removing noise from the original dataset SM using a sliding filter method; Normalization was performed on the original dataset SM using the Min-Max linear normalization method to obtain the pin dataset ZM; The pin dataset ZM is obtained using the following formula: ； In the formula, ZMo represents the o-th data in the pin dataset ZM, SMo represents the o-th data in the original dataset SM, minSMo represents the valley value of the o-th data in the original dataset SM, and maxSMo represents the peak value of the o-th data in the original dataset SM.

3. The current sensor pin bending processing control system according to claim 2, characterized in that: The perturbation energy modeling and map generation module includes a perturbation coupling index construction unit and a perturbation energy shift function generation unit; The perturbation coupling index construction unit constructs a nonlinear interaction function based on the obtained pin dataset ZM to capture the joint coupling behavior between the three perturbation quantities and obtain the micro-perturbation coupling strength index Ψ. The perturbation coupling strength index Ψ is obtained by the following formula: ； In the formula, d represents the derivative symbol, ZWa(t) represents the bending acceleration at time t, ZBd(t) represents the contact impedance disturbance of the pin surface at time t, ZMc(t) represents the triboelectric response at time t, and cos() represents the cosine function. The perturbation energy offset function generation unit performs integral modeling of the perturbation behavior based on the obtained micro-perturbation coupling strength index Ψ, obtains the perturbation energy offset function D, and compares it with the preset offset threshold TD to construct an energy spectrum. The perturbation energy shift function D is obtained by the following formula: ； In the formula, This represents the steady-state disturbance accumulation coefficient. Ψ(t) represents the dynamic perturbation evolution coefficient, and Ψ(t) represents the perturbation coupling strength index at time t. Energy maps are obtained through matching in the following ways: When the perturbation energy offset function D ≤ offset threshold TD, it indicates that the offset is stable and the energy spectrum is a stable straight line; When the perturbation energy offset function D > offset threshold TD, it indicates an abnormal offset, and the energy spectrum shows an cumulative trend, which is an upward straight line.

4. The current sensor pin bending processing control system according to claim 1, characterized in that: The bending path error mapping module includes a trajectory reconstruction and attitude synchronization unit and a spatial error calculation unit; The trajectory reconstruction and attitude synchronization unit records the acquisition trajectory of the pin dataset ZM, and performs dynamic attitude reconstruction and standard spatiotemporal alignment on the trajectory point data during pin processing to obtain the trajectory point sequence Pact. The trajectory point sequence Pact is obtained using the following formula: Pact(t)={xi(t),yi(t),zi(t)|i=1,2,...,N}; In the formula, Pact(t) represents the sequence of trajectory points at time t, (xi(t), yi(t), zi(t)) represents the three-dimensional spatial position of the needle end during the bending process, i represents the sampling number, and N represents the number of trajectory points; By introducing a local rotation registration matrix XR and a displacement vector XT, rigid registration processing is performed between the pin path and the standard trajectory. The processing formula is as follows: 。 5. The current sensor pin bending processing control system according to claim 4, characterized in that: The spatial error calculation unit evaluates the geometric offset intensity ep of each point based on the trajectory point sequence Pact(t) at time t through Euclidean space difference, and introduces the path deformation sensitivity index function to nonlinearly amplify the high error section to obtain the spatial error E. Based on the three-dimensional spatial position (xi(t), yi(t), zi(t)) of the pin tip during the bending process, a three-dimensional error vector is constructed. And obtain the offset intensity ep; Three-dimensional error vector Obtain it using the following formula: ； In the formula, Let represent the three-dimensional error vector of the i-th trajectory point at time t, and let (oxi(t), oyi(t), ozi(t)) represent the spatial coordinates of the i-th point in the standard reference trajectory; The offset intensity ep is obtained using the following formula: ； In the formula, epi(t) represents the offset intensity of the i-th trajectory point at time t; Spatial error E is obtained using the following formula: ； In the formula, k represents the path deformation amplification factor, sin represents the sine function, and π represents pi, with a value of 3.

14.

6. The current sensor pin bending processing control system according to claim 5, characterized in that: The real-time correction control module includes a dynamic correction intensity calculation unit and a correction trigger determination and fine-tuning unit; The dynamic correction intensity calculation unit calculates the correction magnitude F based on the obtained energy offset function D and spatial error E; The correction magnitude F is obtained using the following formula: ； In the formula, λ represents the control adjustment sensitivity coefficient, μ represents the evolution trend response weight, D(t) represents the energy offset function at time t, E(t) represents the spatial error at time t, and QE represents a non-zero constant.

7. The current sensor pin bending processing control system according to claim 6, characterized in that: The correction trigger determination and fine-tuning unit compares the acquired correction magnitude F with the preset magnitude threshold TF to determine whether trajectory correction is triggered. Whether trajectory correction is triggered is determined by matching in the following way: When the correction magnitude F is less than the magnitude threshold TF, trajectory correction is not triggered. When the correction magnitude F is greater than or equal to the magnitude threshold TF, trajectory correction is triggered. When trajectory correction is triggered, a correction control vector CX is generated based on the correction magnitude F, and the angle θ, velocity v, and acceleration a at the next turning point are adjusted. The corrected control vector CX is obtained using the following formula: ； In the formula, Co represents the initial control reference vector, kz represents the control gain factor, and tanh represents the hyperbolic tangent function.

8. The current sensor pin bending processing control system according to claim 7, characterized in that: The feedback trend analysis and steady-state assessment module collects and statistically processes the correction magnitude F over a fixed period T, extracts its volatility and stability characteristics, and outputs a continuous stability evaluation function G. The stability evaluation function G is obtained through the following formula: ； In the formula, F(t) represents the correction magnitude at time t, and pF represents the average correction magnitude within a fixed period T; Based on the historical evolution trend of the stability evaluation function G, it is compared with the preset slight fluctuation boundary threshold Glow and critical instability boundary threshold Ghig to identify the disturbance region of the system and automatically divide the current state into operating intervals. The division of the operating range is obtained by matching in the following way: When the stability evaluation function G < the slight fluctuation boundary threshold Glow, it indicates the first operating interval, the steady state region; When the slight fluctuation boundary threshold Glow ≤ stability evaluation function G < critical instability boundary threshold Ghig, it indicates the second operating range, the high disturbance response zone; When the stability evaluation function G ≥ the critical instability boundary threshold Ghig, it represents the third operating interval, the critical instability evolution zone.

9. The current sensor pin bending processing control system according to claim 8, characterized in that: The optimization strategy switching and task scheduling module integrates the acquired disturbance energy offset function D, correction magnitude F, and stability evaluation function G to construct a composite strategy response exponential function H that reflects the system operation. The policy response exponential function H is obtained through the following formula: ； In the formula, log represents the logarithmic function, and AO represents the weighting coefficient for evaluating the trend response; The obtained policy response exponential function H is compared with the preset low policy response threshold Hth1 and high policy response threshold Hth2, and the policy execution mode is dynamically selected. The strategy execution mode is selected through matching in the following ways: When the policy response exponential function H < the low policy response threshold Hth1, it indicates the standard mode, and no policy switching is required. When the low policy response threshold Hth1 ≤ policy response exponential function H < high policy response threshold Hth2, it indicates the compensation mode, which corrects the angle θ, velocity v and acceleration a. When the high policy response threshold Hth2 ≥ the policy response exponential function H, it indicates a delay mode, compensation is paused, and the correction trend is monitored.

10. A current sensor pin bending processing control device, characterized in that: The device includes a bending control equipment body and a controller. The controller includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the functions of the current sensor pin bending processing control system as described in any one of claims 1 to 9.

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