Multi-point object distance measuring method and device

Abstract

The invention discloses a multi-point object distance measurement method and device, and relates to the technical field of precision measurement and automatic detection, and the method comprises the following steps: S100, building a nanosecond time auditing reference, collecting the phase information of an incident pulse, the time delay of an echo signal, and the echo energy density, generating a time consistency matrix, and obtaining a time consistency matrix; object surface reflection feature fingerprints are constructed and are used for providing a unified time reference; and S200, constructing an anti-fact playback sequence, generating a shadow time sequence by using reflection feature fingerprint guidance, replacing a suspicious signal section in the sampling data, calculating a delay distribution trend, extracting an abnormal echo trajectory, and generating a position set of a pseudo echo pulse. According to the invention, by constructing a time auditing reference and introducing anti-fact playback and phase conjugate registration, abnormal echo recognition and signal separation are realized, and by combining dynamic risk control and a multi-dimensional regulation mechanism, the stability and precision of high-speed distance measurement are improved.

Description

Technical Field

[0001] This invention relates to the field of precision measurement and automated detection technology, specifically to a multi-point object distance measurement method and device. Background Technology

[0002] Multi-point object distance measurement refers to a measurement method that arranges multiple equally spaced measurement points or sensors along the object's movement path. These measurement points synchronously or sequentially collect distance and time pulse data as the object passes by, and the data is then comprehensively calculated using algorithms to obtain high-precision length or displacement results. Unlike traditional single-point measurement, this method effectively suppresses jitter, slippage errors, and environmental interference through multi-point redundant sampling and statistical processing, ensuring high stability and accuracy even under complex conditions such as material speed changes and vibrations. To further improve measurement resolution, the principle of quantum ranging is introduced. By utilizing the quantum entanglement, single-photon interference, and phase superposition characteristics of photons, sub-nanosecond time difference resolution and phase-locked calibration are achieved, forming quantum coherent correlations between measurement points. This maintains high sensitivity and low drift distance output even in noisy environments, constructing a cross-point quantum consistency model, significantly improving the ranging accuracy and environmental adaptability for continuous production lines and high-speed moving objects.

[0003] The existing technology has the following shortcomings: In existing technologies, when multi-point object distance measurement systems are applied to the online detection of high-speed moving materials, if the material surface has structural features such as micro-depressions, specular reflections, or uneven local gloss, the pulse signal emitted by the trigger sensor is prone to secondary reflection during propagation, forming a delayed echo. This delayed echo, at high sampling frequencies, can be superimposed on adjacent sampling windows, causing the system to misidentify spurious peak signals as genuine trigger points during the signal determination stage, resulting in repeated pulse counts. Because existing technologies typically rely on single trigger responses for distance determination, they lack time difference correction and spurious peak identification mechanisms for multi-source echo signals, leading to abrupt and drastic changes in measurement results within a very short time. Such anomalies can cause the control system to misjudge that the material has reached the preset length, leading to feeding stoppages, equipment malfunctions, or premature shearing mechanism actions, resulting in serious consequences such as mechanical impact, material miscutting, and damage to equipment components, severely impacting the stability of the production line and the reliability of measurement.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-point object distance measurement method and apparatus to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-point object distance measurement method, comprising the following steps: S100 establishes a nanosecond-level time audit benchmark, collects the phase information of the incident pulse, the time delay of the echo signal and the echo energy density, generates a time consistency matrix, and constructs the reflection feature fingerprint of the object surface to provide a unified time reference benchmark. S200, construct counterfactual playback sequence, use reflection feature fingerprint to guide the generation of shadow time series, replace suspicious signal segments in the sampled data, infer the delay distribution trend, extract abnormal echo trajectories, and generate a set of pseudo echo pulse positions; S300 establishes a gradient orthogonal registration process based on the location set of pseudo-echo pulses, performs phase conjugate registration on the time-delayed trajectory, realizes the temporal separation of overlapping pulse signals within the continuous sampling window, and outputs a de-echo image containing the real signal trajectory. S400 generates a dynamic time risk model based on echo-de-echo images, constructs a Hamiltonian variational risk distribution field, uses risk gradients to generate time gating commands for controlling the measurement window, freezes time intervals with high misjudgment risk, and constructs a real-time feedback control sequence for regulating the signal response path based on the time gating commands. The S500 performs a micro-periodic misalignment control process based on a real-time feedback control sequence. Through the combined operation of phase conjugate energy enhancement, time grid reversible reconstruction, and dynamic rotation of polarization direction, it reconstructs the pulse signal propagation path, suppresses secondary echo interference formed by reflected signals, and achieves stable closed-loop regulation of the distance measurement process.

[0007] Preferably, step S100 includes: The high-frequency pulse transmission operation is performed by using a narrow pulse source with an extremely short rise time to emit continuous excitation pulses. The sampling time base calibration is completed through delay line matching technology, and the initial phase information of each echo signal is collected to construct a time reference base. For each echo signal, a delay time measurement operation is performed. The propagation path is estimated using the difference between the time calibration value and the received timestamp. The maximum energy point is selected by sampling through a sliding window to determine the effective echo delay time. An energy density acquisition mechanism is introduced to integrate and normalize the power curve within the sampling window, and then register it with the phase and delay information on the time axis to form a three-dimensional feature structure of the reflected signal. A time consistency matrix is ​​constructed, and the measurement point parameters within multiple periods are normalized and expanded into a set of time vectors. The reflection behavior feature map is extracted through feature matching and induction as a unified time reference standard.

[0008] Preferably, step S200 includes: Shadow time series are generated based on time consistency matrix and reflection feature fingerprint, and the time distribution of echo signal in the current measurement period is mapped to historical stable response structure to construct a control framework. The current echo signal is fitted using the shadow time series as a reference model, and the data segments that deviate from the shadow model in terms of time, amplitude and energy distribution are marked. The multipath interference interval is inferred based on the delay growth trend. The marked suspicious data segments are replaced with the corresponding segments in the shadow time series, and the phase continuity of the replaced waveform is checked to improve the trajectory smoothness and data reliability. Based on the time difference before and after the replacement, a delay change curve is constructed, and jump points that do not conform to the stable response trend are identified and marked as suspected points of pseudo-echo pulses. Finally, a set of pseudo-echo positions is formed for subsequent registration processing.

[0009] Preferably, during the shadow time series replacement process, the phase abruptness point and energy jump amplitude of the replaced waveform are monitored synchronously, and the time coordinate and energy level are kept in a smooth transition through continuity verification after replacement; when the phase difference exceeds the preset threshold, the replacement is immediately stopped and recorded as a high-risk pseudo-peak source, so as to ensure the accuracy of pseudo-echo identification and the stability and consistency of the delay trajectory.

[0010] Preferably, step S300 includes: Delay trajectory distribution map is constructed based on the location set of pseudo-echo pulses, the information of abrupt changes in time growth rate and delay slope is identified, and nonlinear distortion features in the delay trajectory are extracted by envelope filtering. Extract the main phase peak position of each echo signal and perform time axis sliding matching operation to align the waveform structure to the physical propagation response center, and use the phase conjugate structure to symmetrically repair the asymmetric section; Overlap rate analysis and amplitude suppression processing are performed on all registered signal sequences, and the slope trend of the residual reflection band at the tail is converged and adjusted through a time smoothing mechanism to improve the waveform separation effect. Based on the reconstructed signal sequence after registration, a real signal trajectory image is constructed, false echo trajectories are removed, and phase feature information is superimposed to generate a two-dimensional image with temporal structure and energy distribution characteristics for feedback analysis.

[0011] Preferably, when constructing a real signal trajectory image, the phase change rate is used as the color dimension, and the effective echo signal within the continuous sampling window is displayed as intensity strips across the time axis. The consistency of the response trajectory is compared and analyzed by superimposing a reference shadow sequence.

[0012] Preferably, step S400 includes: A point-by-point risk assessment of the full-time domain signal trajectory is performed based on the echo image. A risk scoring sequence is constructed based on the phase evolution rate, delay fluctuations, and energy density fluctuations, and an initial time risk curve is formed. Based on the initial risk curve, a Hamiltonian variational risk distribution field is established. The risk density vector flow field is generated by calculating the risk gradient direction and the rate of change, and the continuous risk growth interval is calibrated. Based on the high-risk intervals identified by the risk distribution field, a time gating instruction is generated. During the high-risk time period, a shielding control is applied to the sampling window and a time buffer offset is set to construct an envelope-type freeze zone. A real-time feedback control sequence is generated based on the dynamic response relationship between time-gated commands and echo images, and the sampling frequency, response intensity, and signal weight allocation are dynamically adjusted according to risk changes to achieve stable ranging control.

[0013] Preferably, a local gain adjustment mechanism is introduced into the real-time feedback control sequence to improve the energy recognition sensitivity of the sampling window when the ranging signal strength is insufficient or the trajectory boundary is blurred, so as to enhance the accuracy and stability of trajectory judgment.

[0014] Preferably, step S500 includes: The micro-periodic misalignment control operation is performed based on the real-time feedback control sequence. The emission time points of adjacent periodic pulses are randomly arranged by nanosecond-level time offset to break the time domain overlap between the secondary echo and the main echo, and the phase change is recorded as a reference for dynamic parameter adjustment. After completing the micro-periodic misalignment, a phase conjugate energy enhancement operation is performed based on the energy difference between the main signal and the secondary signal. The main echo energy is amplified by phase inversion and superposition with the reference waveform, and the secondary echo of phase mismatch is weakened. By combining the energy-enhanced signal structure with a time-grid reversible reconstruction operation, the sampling accuracy is improved in the main echo interval by adjusting the sampling density and maintaining the time structure reversibility to maintain the ranging continuity. After time rearrangement is completed, a dynamic polarization direction rotation operation is performed. The difference in reflection response is controlled by changing the pulse vibration direction, and a phase synchronization calibration mechanism is introduced to ensure the precise alignment of the main wave propagation path.

[0015] A multi-point object distance measurement device includes a time-series audit benchmark module, a pseudo-peak identification and deduction module, a time-series registration and de-echo module, a risk modeling and gating module, and a path reconstruction and control module. The timing audit benchmark module establishes a nanosecond-level timing audit benchmark, collects the phase information of the incident pulse, the time delay of the echo signal and the echo energy density, generates a timing consistency matrix, and constructs the reflection feature fingerprint of the object surface to provide a unified timing reference benchmark. The pseudo-peak identification and deduction module constructs a counterfactual playback sequence, uses reflection feature fingerprints to guide the generation of shadow time series, replaces suspicious signal segments in the sampled data, calculates the delay distribution trend, extracts abnormal echo trajectories, and generates a set of pseudo-echo pulse positions. The timing registration de-echo module establishes a gradient orthogonal registration process based on the location set of pseudo-echo pulses, performs phase conjugate registration on the time-delayed trajectory, realizes the timing separation of overlapping pulse signals within the continuous sampling window, and outputs a de-echo image containing the real signal trajectory. The risk modeling and gating module generates a dynamic time risk model based on the echo-de-echo image, constructs a Hamiltonian variational risk distribution field, uses the risk gradient to generate time gating commands for controlling the measurement window, freezes time intervals with high misjudgment risk, and constructs a real-time feedback control sequence for regulating the signal response path based on the time gating commands. The path reconstruction and control module executes a micro-periodic misalignment control process based on a real-time feedback control sequence. Through the combined operation of phase conjugate energy enhancement, time grid reversible reconstruction, and dynamic rotation of polarization direction, it reconstructs the pulse signal propagation path, suppresses secondary echo interference formed by reflected signals, and achieves stable closed-loop regulation of the distance measurement process.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention constructs a nanosecond-level time audit benchmark to achieve precise acquisition and consistent modeling of incident phase, echo delay, and energy density. It further introduces a counterfactual playback mechanism and a shadow time series substitution strategy to accurately identify and eliminate abnormal echo trajectories. Based on this, phase conjugate registration decouples and separates overlapping pulse signals within a continuous sampling window, obtaining a clean trajectory representation of the true signal. Simultaneously, by combining Hamiltonian variational risk modeling and time-gated command generation, it achieves dynamic freezing of high-risk time segments and real-time feedback control of the response path. Finally, through collaborative mechanisms such as micro-period misalignment control, energy enhancement, time grid reconstruction, and dynamic polarization rotation, it completes fine modulation of the pulse signal propagation path and adaptive suppression of secondary echoes. This method significantly improves the measurement stability and anti-interference capability of multi-point ranging systems in high-speed motion and complex surface reflection environments, ensuring the continuity, accuracy, and reliability of ranging results and control response. It is widely applicable to critical applications such as online cutting, feeding control, and dimensional monitoring, and has significant engineering value for improving the operating efficiency and measurement reliability of automated production lines. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of a multi-point object distance measurement method according to the present invention.

[0019] Figure 2 This is a schematic diagram of a multi-point object distance measuring device according to the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The multi-point object distance measurement method shown includes the following steps: S100 establishes a nanosecond-level time audit benchmark, collects the phase information of the incident pulse, the time delay of the echo signal and the echo energy density, generates a time consistency matrix, and constructs the reflection feature fingerprint of the object surface to provide a unified time reference benchmark. To achieve high-precision time reference construction for multi-point ranging of high-speed moving objects, the following method is used to establish a nanosecond-level time audit benchmark and extract stable reflection feature fingerprints, which serve as a unified time scale for subsequent ranging processes. The specific implementation steps are as follows: By transmitting high-frequency active pulses to each object as it passes through the ranging area, the signal source uses a narrow pulse source with an extremely short rise time. A frequency-tunable control method is employed to ensure continuous excitation pulses within a controllable frequency range, guaranteeing precise synchronization between different measurement points. After transmission, the pulse signal travels through a propagation channel to the target object's surface, where it is affected by factors such as surface structure, material reflection characteristics, and changes in the incident angle, generating corresponding echo signals. The system samples the return signals on a nanosecond timescale and uses dedicated delay line matching technology to calibrate the sampler's time base, ensuring a strictly uniform sampling starting point for all return signals on the time axis. During sampling, the system focuses on acquiring the initial phase characteristics of each return signal, including phase abrupt change points, amplitude inflection points, and waveform leading-edge slope information. These phase parameters are used to establish a complete time reference frame and serve as the fundamental data source for delay identification and spurious peak removal throughout the measurement process.

[0022] After high-precision sampling, a delay time measurement is performed on each returned signal. The delay time measurement is determined based on the difference between the time calibration value of the pulse excitation source and the actual timestamp of the echo signal arriving at the receiver. This difference represents the complete time path traversed during signal propagation, including the true object distance component and any possible non-ideal reflection paths. To improve measurement accuracy, a point-by-point time window sliding screening mechanism is introduced. A sampling window of equal width is set near each sampling point, and the maximum energy response point within this window is collected as the true arrival time of the signal, thereby eliminating the ambiguity caused by the vibration of the signal leading edge. Through multiple sampling and superposition techniques, the instantaneous delay change is transformed into a stable statistical parameter, ensuring that each measurement point obtains a consistent delay response curve in different measurement cycles. The delay time values ​​obtained in the above process are automatically aligned to the same time starting point and correspond one-to-one with the phase data collected in the previous step, forming the first-level mapping of the reflection response in the time dimension.

[0023] After accurately aligning the phase information and delay time data, an energy density acquisition mechanism is further introduced to perform integral statistics on the overall power distribution of the echo signal within the receiving window. Energy density acquisition employs a fixed-bandwidth filter and envelope tracking to extract the instantaneous power change curve of the echo signal within the sampling window. This power change curve, after normalization, reflects the reflectivity of the object's surface under specific angle, distance, and material conditions. To reduce the error caused by energy drift, a dynamic baseline adaptive mechanism is introduced, allowing the baseline of the energy curve to be fine-tuned according to the material and surface condition of different batches of objects, thus ensuring the comparability of the sampling results. The final energy density curve is then precisely registered on the time axis with the phase and delay information obtained in the previous two steps, completing the construction of the three-dimensional feature structure of the reflected signal. This three-dimensional structure, with time as the horizontal axis, phase abrupt change points as the vertical axis, and energy density as the color level, forms a unique reflection response spectrum.

[0024] Based on the combined structure of the three parameters mentioned above, a time consistency matrix is ​​constructed after each complete measurement cycle. The time consistency matrix uses the measurement point number and measurement round as its basic dimensions, expanding the phase start point, delay time, and energy density of the same measurement point across different cycles along the time axis to calculate the time stability index for each measurement point. To this end, differential normalization is performed on all measurement point signals during matrix construction, fitting the measurement result of any cycle to its historical reference mean to eliminate the influence of periodic fluctuations. In the time consistency matrix, each measurement point is mapped to a three-dimensional vector structure with phase sequence, delay characteristics, and energy weights. After pattern matching and feature summarization of all measurement point vectors, a complete fingerprint of the current object surface reflection behavior is formed. This fingerprint serves as a unified time reference standard, providing a basis for subsequent steps to identify echo delay anomalies and construct shadow time series, while ensuring that different measurement points have equivalent time start points, establishing a precise time synchronization foundation for the comparison and collaborative judgment of continuous multi-point data.

[0025] S200, construct counterfactual playback sequence, use reflection feature fingerprint to guide the generation of shadow time series, replace suspicious signal segments in the sampled data, infer the delay distribution trend, extract abnormal echo trajectories, and generate a set of pseudo echo pulse positions; To effectively identify and eliminate delayed pseudo-echo signals that may occur during high-speed ranging, after establishing a unified time reference standard, counterfactual playback processing is further carried out to construct a shadow time series. Based on this, abnormal trajectories are extracted to identify and locate potential pseudo-echo signal regions. The specific implementation steps are as follows: Based on the acquired and calibrated time consistency matrix and reflection feature fingerprint, a shadow time series is generated for each echo sequence generated by an object passing through the ranging area. The shadow time series is constructed by retrieving the average response behavior of corresponding measurement points from historical stable sample data, specifically including the phase start point, delay time node, and corresponding energy density distribution curve of the measurement point within the reference period. These data have been normalized and interpolated during construction to ensure consistency in time coordinates and energy levels. Based on this, a point recombination operation on the time axis maps the temporal distribution of the echo signal in the current measurement period to the stable response structure in historical samples, enabling a unified description of the waveform trajectory of the same measurement point in different periods. This shadow time series does not directly replace the current measurement value but participates in subsequent difference analysis and pseudo-peak evaluation in a structural comparison manner, providing a comparative framework for establishing anomaly identification baselines in the next step.

[0026] Using the shadow time series as a reference model, a point-by-point fitting analysis is performed on the current echo signal. During the fitting process, interval matching is performed on the phase start point, echo delay value, and energy density of each measurement point, and the microscopic offsets in time, amplitude, and energy distribution between the measured values ​​and the shadow model are calculated. If these offsets fluctuate within the tolerable error range, it indicates that the current response is a natural disturbance; however, if there is a significant deviation, such as the echo time suddenly leading or lagging behind the shadow time series standard point by more than a set window, or the energy density showing a sudden increase or decrease and lasting for several sampling points, it can be considered that there is a suspicious signal segment. At this time, these segments are marked with time periods, and their start and end times, peak positions, and the trend of changes in the waveforms before and after them are recorded. Furthermore, the potential multipath propagation interference is inferred through the delay growth trend line. During the inference process, the historical stability index of the measurement point in the preceding time consistency matrix is ​​comprehensively considered. If it shows high stability in the past period, the current anomaly can be given higher weight and included in the sequence to be processed as a key suspected object.

[0027] After the suspicious signal segments are identified, a counterfactual replacement process is performed. Counterfactual replacement does not directly delete the abnormal data; instead, it replaces the abnormal segments in the current sampled data with the corresponding waveform from the shadow time series to obtain a reference trajectory corrected by a stable model. This replacement process strictly controls the continuity of time coordinates and energy levels to ensure a smooth transition between the inserted data and the preceding and following real data. To enhance the credibility of the replacement results, phase continuity is checked on the replaced waveform, i.e., the slope difference and energy jump amplitude at the phase abrupt change point of the waveform before and after the interpolation segment are checked to see if they are within the natural transition range. If the replaced waveform still exhibits significant jump characteristics, the replacement operation is rolled back and marked as a high-risk spurious peak source for further evaluation. This processing method effectively eliminates nonlinear echo patterns caused by short-term reflection interference, mirror deflection, or abrupt changes in material structure, thereby improving the temporal consistency and energy distribution balance of the overall ranging sequence.

[0028] Based on the data after shadow replacement, a delay variation curve is constructed according to the time difference trajectory before and after replacement. This curve, with time as the horizontal axis, records the trend of response delay values ​​at the same measurement point over multiple cycles, identifying jump points that do not conform to the stable response trend. Jump points refer to delay behaviors exhibiting abnormal rises, falls, or repeated rebounds in time, often representing spurious echoes. A dynamic boundary window is constructed around the jump points to further analyze their echo trajectory morphology, energy density curve, and phase structure symmetry. If multiple indicators jointly suggest that the jump region has non-natural formation characteristics, its center position is marked as a suspected point of the spurious echo pulse. Multiple suspected points are spatially aggregated to form a set of spurious echo locations that can be used for subsequent time registration and signal stripping operations. This set not only includes time location information but also the credibility index, energy offset vector, and deviation magnitude from the shadow model for each point, providing quantitative support for phase alignment and error filtering in subsequent steps.

[0029] S300 establishes a gradient orthogonal registration process based on the location set of pseudo-echo pulses, performs phase conjugate registration on the time-delayed trajectory, realizes the temporal separation of overlapping pulse signals within the continuous sampling window, and outputs a de-echo image containing the real signal trajectory. To further separate and eliminate spurious echo signals, after extracting the set of spurious echo pulse positions, fine registration of the delay trajectory is performed. Interference signals are stripped using a phase conjugation mechanism, and a time trajectory image of the true signal is constructed. The specific implementation steps are as follows: Based on the obtained set of pseudo-echo pulse locations, an initial distribution map of the delay trajectory is constructed. Each time point in this location set corresponds to a signal interval identified as a potential pseudo-echo, and includes the associated signal intensity, phase slope, and preceding and following delay characteristic curves. In this step, this information is first mapped onto a continuous time axis, forming a multi-dimensional delay distribution structure with time markers and intensity values. Subsequently, gradient identification is performed on each marked interval to identify its instantaneous growth rate, slope abrupt changes, and amplitude asymmetry exhibited on the time axis. To avoid interference from high-frequency jitter, a three-segment envelope filtering method is introduced, that is, the average delay level and its changing trend are calculated in the front, center, and rear regions, respectively. Through this processing, the hidden nonlinear distortion trend in the delay trajectory can be analyzed as a whole, establishing a preliminary gradient reference basis for subsequent registration.

[0030] After completing the initial delay distribution analysis, the phase structure matching and construction began. For each marked time period, the main phase peak position and rising edge structure of the original echo signal waveform were extracted and compared point-by-point with the ideal model corresponding to the pseudo-echo position set. Specifically, local sub-waveform segments were extracted from the original waveform, and by fine-grained sliding these segments along the time axis, the position point that most closely approximates the phase envelope shape with the standard model was found. This position point was defined as the phase alignment target point, representing the true response center of the current waveform in the physical propagation sense. After determining the alignment point, the echo waveform was adjusted forward or backward based on this point to maximize the symmetry of the entire waveform structure. The phase conjugation operation here is not a reverse mapping of the signal, but rather uses the mirror characteristics of the waveform to symmetrically repair its forward delay and backward echo, eliminating the nonlinear stretching or compression effects caused by propagation path disturbances.

[0031] Based on the aforementioned operations, synchronous alignment is performed on all pseudo-echo segments, forming multiple reconstructed time-segment signal sets. In these sets, previously overlapping waveforms are separated due to differences in their phase initiation points and temporal characteristics, resulting in the absence of multiple amplitude peaks simultaneously in adjacent sampling windows. To ensure the integrity of waveform separation, the overlap rate of adjacent windows is calculated for each aligned signal segment, analyzing whether phase drift continues across two consecutive sampling periods. If waveform repetition occurs across periods, attenuation is applied to the lower amplitude waveform, causing it to gradually fade out of the main trajectory region during subsequent waveform construction. Simultaneously, a local time smoothing mechanism is introduced to converge the slope of any potentially residual tail reflection bands in all non-conjugate waveforms, preventing secondary interference at the sampling window boundaries. This process makes the echo response distribution on the time axis tend towards a single peak, significantly improving signal identification within the sampling window.

[0032] A two-dimensional image of the real signal trajectory is constructed using all reconstructed signal sequences after registration. This image is plotted with time on the horizontal axis and sampling intensity on the vertical axis, with phase change rate added as a color dimension, forming a comprehensive representation of temporal structure, intensity level, and phase characteristics. During image construction, all response trajectories identified as false echo start points are removed and replaced with smooth curves formed by phase conjugate alignment. The entire image presents the effective response within each sampling window in a point-by-point scanning manner, and the continuous response trajectory is presented as intensity strips across the time axis, forming a complete ranging reflection mainline. By overlaying a reference shadow sequence on the image, the consistency of response and trajectory deviation across different time periods can be further observed, ensuring that the extracted trajectory has a true physical propagation basis. Simultaneously, this image also serves as the basic data carrier for subsequent time risk assessment and interference evaluation, constituting the core of the visualization feedback after real signal separation in the entire ranging link.

[0033] S400 generates a dynamic time risk model based on echo-de-echo images, constructs a Hamiltonian variational risk distribution field, uses risk gradients to generate time gating commands for controlling the measurement window, freezes time intervals with high misjudgment risk, and constructs a real-time feedback control sequence for regulating the signal response path based on the time gating commands. To enhance the dynamic discrimination capability during ranging signal processing, after constructing the real signal trajectory, a time-dimensional risk assessment and control mechanism is further introduced. This involves building a risk distribution field and time-gated response logic to actively freeze high-risk interference zones and dynamically adjust the response path. The specific implementation steps are as follows: After obtaining the echo image, a point-by-point risk assessment analysis is performed on the signal trajectory across the entire time domain. The reflected waveform at each time point is composed of the actual trajectory generated after the phase conjugate registration process in the previous stage. This trajectory includes the phase evolution rate of the echo signal, the delay fluctuation curve, and the local energy density fluctuation. After mapping these parameters to a unified time coordinate system, a local risk scoring unit is established around each time point. A composite scoring standard is constructed by combining the degree of waveform abrupt change, the historical trajectory offset amplitude, and the signal energy concentration, thereby assigning a risk score value based on physical propagation behavior to each sampling moment. The higher the risk score value, the more obvious the potential signal interference, spurious peak overlap, or trajectory drift at that time point, indicating a higher potential for misjudgment. Subsequently, all score values ​​are arranged into a continuous data sequence on the time axis, forming an initial time risk curve reflecting the signal stability throughout the entire ranging period.

[0034] Based on the initial risk curve, a Hamiltonian variable field construction mechanism is introduced to project the discrete values ​​of the risk curve onto a continuous risk potential energy space. This risk potential energy space is constructed based on the principles of Hamiltonian dynamical systems, and its rate of change on the time axis is no longer determined solely by a single risk value, but simultaneously considers the gradient direction and rate of change of the risk value within the time neighborhood. To this end, symmetrical small intervals are set on both sides of each time point to calculate the directionality and acceleration characteristics of risk value changes, thereby constructing a risk density vector flow field. This vector flow field is formally represented as a multi-peaked fluctuating distribution, with each high-risk peak interval identified as a potentially high-risk misjudgment area. By tracking the ascending and descending segments with maximum gradients within this distribution, the starting and ending points of each continuous risk growth interval can be precisely delineated, further identifying the critical time segments where freezing operations should be prioritized.

[0035] After identifying high-risk time segments, the time-gating command for the measurement window is constructed. This time-gating command is a set of time-based control trigger signals used to dynamically control the signal response channel during the ranging process. Specifically, within each high-risk segment, the time-gating command applies a signal shielding command to the corresponding sampling window, preventing it from triggering valid recording of reflected data during the measurement process, thus avoiding the miscalculation of false peaks or non-true trajectories into the ranging results. When constructing the gating command, the signal fluctuation behavior at each time point in the echo image must be considered to ensure that the freezing operation does not affect the continuity structure of adjacent true waveforms. To achieve this, time buffer offsets are introduced on the rising and falling edges of the gating command, activating it before the main signal peak appears and delaying its closure after the signal ends, thus forming an envelope-like freezing band. The entire gating process uses the echo image and the risk field as references, ensuring thorough shielding of high-risk periods while preserving the integrity and continuity of the main ranging signal to the greatest extent possible.

[0036] After constructing the time-gating command, to improve the adaptability and feedback capability of the control strategy, a real-time feedback control sequence for regulating the signal response path is further generated based on the dynamic response relationship between the gated interval and the echo image. This control sequence uses the internal clock beat of the ranging device as the driving reference, monitors the waveform evolution in each sampling period during the measurement process in real time, and dynamically adjusts the response intensity, sampling frequency, and signal weight allocation method of the ranging sampling according to the time period frozen by the aforementioned gate command. Specifically, when a certain segment is identified as a high-risk area and has been blocked by the gate command, the control sequence will reduce the processing priority of the ranging signal in that area and redirect processing resources to the adjacent medium- and low-risk segments to improve the overall ranging stability. Simultaneously, a local gain adjustment mechanism is introduced into the control sequence, allowing for a temporary increase in the energy recognition sensitivity of the corresponding sampling window to obtain a clearer trajectory judgment when the ranging signal strength is insufficient or the trajectory boundary is blurred. The sequence is refreshed in real time during each sampling period. The strategy is adjusted based on the response trajectory and risk feedback of the previous period to form a complete closed-loop control chain, thereby realizing the active control of the ranging signal in the time dimension and the adaptive suppression of high-risk behaviors.

[0037] The S500 performs a micro-periodic misalignment control process based on a real-time feedback control sequence. Through the combined operation of phase conjugate energy enhancement, time grid reversible reconstruction, and dynamic rotation of polarization direction, it reconstructs the pulse signal propagation path, suppresses secondary echo interference formed by reflected signals, and achieves stable closed-loop regulation of the distance measurement process. To completely suppress secondary echoes caused by reflection interference during ranging, the signal propagation path is further refined and reconstructed based on time gating and feedback control. This is achieved by introducing a triple-linked approach of phase enhancement, time structure rearrangement, and direction rotation, thus realizing closed-loop dynamic stability adjustment of ranging accuracy. The specific implementation steps are as follows: Based on the acquired real-time feedback control sequence, the micro-periodic misalignment control process is initiated. Micro-periodic misalignment control is based on the principle of micro-timeline perturbations between continuous sampling windows. The time reference for each measurement cycle is controllably shifted at the nanosecond level to break the time-domain coincidence condition between the secondary echo and the main echo. By introducing a high-precision micro-delay generation mechanism into the clock drive source, the pulse emission time points between adjacent cycles are arranged in a randomized misalignment pattern, thereby changing the combination of reflection incident angles of the signal on the target object. This timing misalignment causes the repetitive reflection paths caused by specific structures on the object's surface to lose periodic consistency, thus gradually diminishing the energy superposition effect of these interference paths during continuous measurement. Simultaneously, guided by the feedback control sequence, the phase change caused by each misalignment is recorded in real time and archived as a reference template for dynamic parameter adjustment in subsequent control processes. This allows the misalignment operation to effectively separate the interference trajectory of the secondary echo while maintaining the integrity of the main ranging signal.

[0038] After completing the micro-periodic misalignment, a phase conjugate energy enhancement operation is implemented based on the energy distribution difference between the main and secondary signals in the current sampling period. Phase conjugate energy enhancement involves introducing phase inversion construction technology into the signal receiving link to perform phase inversion on the received main echo signal and superimpose it with a reference waveform constructed from the real trajectory data of the previous period. This process amplifies the main peak energy of the real echo in the spatial propagation path, forming a strong-interference, weak-auxiliary signal energy level structure. This significantly enhances the response of the main echo in the detector, while the secondary echo, due to its phase mismatch, cannot form effective interference and is ultimately weakened in the energy superposition. To maintain the stability of the echo along the propagation path, the time reference for all superposition operations is synchronized at the nanosecond level to avoid phase error accumulation causing drift in the ranging results. Furthermore, the energy response changes recorded in the feedback control sequence are used as dynamic gain control inputs, enabling the enhancement operation to adapt in real time.

[0039] Combining the enhanced signal response structure described above, the time sampling structure is further reversibly reconstructed. The time grid reversible reconstruction is based on a sampling density adjustment mechanism on the time axis, converting the fixed-interval sampling structure into a nonlinear adjustable density structure. Sampling density is increased in areas with concentrated echo response, while sampling distribution is reduced in areas with low interference probability, thereby improving the resolution of the main echo signal. In implementation, the actual echo trajectory within the current period is first analyzed, marking its rising edge, peak region, and falling edge. These regions are then mapped onto the time grid to form dense sampling areas. For time periods without valid signals, the sampling interval is expanded to save processing resources. This reconstructed structure is fully reversible, allowing restoration to the original time axis during signal comparison and superposition, and reconstructing the complete waveform through a built-in interpolation structure. Furthermore, the dynamic parameters of the time grid are generated in real-time by a feedback control sequence, ensuring that each reconstruction operation is highly coupled with the current measurement state, avoiding structural deviations from affecting distance calculations.

[0040] Based on the time structure rearrangement, a dynamic rotation operation of the polarization direction is performed to completely break the directional coupling characteristics of the fixed reflection path. This dynamic rotation of the polarization direction filters and weakens the response capabilities of different reflection source surfaces by changing the angle between the vibration direction of the pulse signal and the spatial propagation vector. By controlling the polarization rotation mechanism of the signal transmitter, different polarization direction parameters are preset before the start of each measurement cycle, causing significant differences in the reflection characteristics of the same object surface under different polarization directions, thereby weakening the periodic reflection coupling behavior caused by specific structures. During this operation, a real-time feedback control sequence provides the polarization direction selection logic and rotation timing, ensuring that the rotation behavior is synchronized with the signal reception window, sampling time structure, and main echo energy enhancement results. To ensure that the direction rotation operation does not affect the alignment accuracy of the true main wave propagation path, a phase synchronization calibration mechanism is introduced. After each direction switch, the starting point of the main wave phase is recalibrated, and trajectory correction is performed in subsequent sampling to ensure ranging continuity.

[0041] This invention constructs a nanosecond-level time audit benchmark to achieve precise acquisition and consistent modeling of incident phase, echo delay, and energy density. It further introduces a counterfactual playback mechanism and a shadow time series substitution strategy to accurately identify and eliminate abnormal echo trajectories. Based on this, phase conjugate registration decouples and separates overlapping pulse signals within a continuous sampling window, obtaining a clean trajectory representation of the true signal. Simultaneously, by combining Hamiltonian variational risk modeling and time-gated command generation, it achieves dynamic freezing of high-risk time segments and real-time feedback control of the response path. Finally, through collaborative mechanisms such as micro-period misalignment control, energy enhancement, time grid reconstruction, and dynamic polarization rotation, it completes fine modulation of the pulse signal propagation path and adaptive suppression of secondary echoes. This method significantly improves the measurement stability and anti-interference capability of multi-point ranging systems in high-speed motion and complex surface reflection environments, ensuring the continuity, accuracy, and reliability of ranging results and control response. It is widely applicable to critical applications such as online cutting, feeding control, and dimensional monitoring, and has significant engineering value for improving the operating efficiency and measurement reliability of automated production lines.

[0042] This invention provides, for example Figure 2 The multi-point object distance measurement device shown includes a time-series audit benchmark module, a pseudo-peak identification and deduction module, a time-series registration and de-echo module, a risk modeling and gating module, and a path reconstruction and control module. The timing audit benchmark module establishes a nanosecond-level timing audit benchmark, collects the phase information of the incident pulse, the time delay of the echo signal and the echo energy density, generates a timing consistency matrix, and constructs the reflection feature fingerprint of the object surface to provide a unified timing reference benchmark. The pseudo-peak identification and deduction module constructs a counterfactual playback sequence, uses reflection feature fingerprints to guide the generation of shadow time series, replaces suspicious signal segments in the sampled data, calculates the delay distribution trend, extracts abnormal echo trajectories, and generates a set of pseudo-echo pulse positions. The timing registration de-echo module establishes a gradient orthogonal registration process based on the location set of pseudo-echo pulses, performs phase conjugate registration on the time-delayed trajectory, realizes the timing separation of overlapping pulse signals within the continuous sampling window, and outputs a de-echo image containing the real signal trajectory. The risk modeling and gating module generates a dynamic time risk model based on the echo-de-echo image, constructs a Hamiltonian variational risk distribution field, uses the risk gradient to generate time gating commands for controlling the measurement window, freezes time intervals with high misjudgment risk, and constructs a real-time feedback control sequence for regulating the signal response path based on the time gating commands. The path reconstruction and control module executes a micro-periodic misalignment control process based on a real-time feedback control sequence. Through the combined operation of phase conjugate energy enhancement, time grid reversible reconstruction, and dynamic rotation of polarization direction, it reconstructs the pulse signal propagation path, suppresses secondary echo interference formed by reflected signals, and achieves stable closed-loop regulation of the distance measurement process.

[0043] The present invention provides a multi-point object distance measurement method, which is implemented by the above-mentioned multi-point object distance measurement device. For details of the specific method and process of the multi-point object distance measurement device, please refer to the above-mentioned embodiment of the multi-point object distance measurement method, which will not be repeated here.

[0044] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A multi-point object distance measurement method, characterized in that, Includes the following steps: S100 establishes a nanosecond-level time audit benchmark, collects the phase information of the incident pulse, the time delay of the echo signal and the echo energy density, generates a time consistency matrix, and constructs the reflection feature fingerprint of the object surface to provide a unified time reference benchmark. S200, construct counterfactual playback sequence, use reflection feature fingerprint to guide the generation of shadow time series, replace suspicious signal segments in the sampled data, infer the delay distribution trend, extract abnormal echo trajectories, and generate a set of pseudo echo pulse positions; S300 establishes a gradient orthogonal registration process based on the location set of pseudo-echo pulses, performs phase conjugate registration on the time-delayed trajectory, and outputs a de-echo image; S400 generates a dynamic time risk model based on echo-de-echo images, constructs a Hamiltonian variational risk distribution field, uses risk gradients to generate time gating commands for controlling the measurement window, freezes time intervals with high misjudgment risk, and constructs a real-time feedback control sequence for regulating the signal response path based on the time gating commands. The S500 performs a micro-periodic misalignment control process based on a real-time feedback control sequence. Through the combined operation of phase conjugate energy enhancement, time grid reversible reconstruction, and dynamic rotation of polarization direction, it reconstructs the pulse signal propagation path and suppresses secondary echo interference formed by reflected signals.

2. The multi-point object distance measurement method according to claim 1, characterized in that, Step S100 includes: The high-frequency pulse transmission operation is performed by using a narrow pulse source with an extremely short rise time to emit continuous excitation pulses. The sampling time base calibration is completed through delay line matching technology, and the initial phase information of each echo signal is collected to construct a time reference base. For each echo signal, a delay time measurement operation is performed. The propagation path is estimated using the difference between the time calibration value and the received timestamp. The maximum energy point is selected by sampling through a sliding window to determine the effective echo delay time. An energy density acquisition mechanism is introduced to integrate and normalize the power curve within the sampling window, and then register it with the phase and delay information on the time axis to form a three-dimensional feature structure of the reflected signal. A time consistency matrix is ​​constructed, and the measurement point parameters within multiple periods are normalized and expanded into a set of time vectors. The reflection behavior feature map is extracted through feature matching and induction as a unified time reference standard.

3. The multi-point object distance measurement method according to claim 1, characterized in that, Step S200 includes: Shadow time series are generated based on time consistency matrix and reflection feature fingerprint, and the time distribution of echo signal in the current measurement period is mapped to historical stable response structure to construct a control framework. The current echo signal is fitted using the shadow time series as a reference model, and the data segments that deviate from the shadow model in terms of time, amplitude and energy distribution are marked. The multipath interference interval is inferred based on the delay growth trend. The marked suspicious data segments are replaced with the corresponding segments in the shadow time series, and the phase continuity of the replaced waveform is checked to improve the trajectory smoothness and data reliability. Based on the time difference before and after the replacement, a delay change curve is constructed, and jump points that do not conform to the stable response trend are identified and marked as suspected points of pseudo-echo pulses. Finally, a set of pseudo-echo positions is formed for subsequent registration processing.

4. The multi-point object distance measurement method according to claim 3, characterized in that, During the shadow time series replacement process, the phase abruptness and energy jump amplitude of the replaced waveform are monitored synchronously, and the continuity check is performed after the replacement to ensure that the time coordinate and energy level maintain a smooth transition. When the phase difference exceeds the preset threshold, the replacement is immediately stopped and recorded as a high-risk pseudo-peak source to ensure the accuracy of pseudo-echo identification and the stability and consistency of the delay trajectory.

5. The multi-point object distance measurement method according to claim 1, characterized in that, Step S300 includes: Delay trajectory distribution map is constructed based on the location set of pseudo-echo pulses, the information of abrupt changes in time growth rate and delay slope is identified, and nonlinear distortion features in the delay trajectory are extracted by envelope filtering. Extract the main phase peak position of each echo signal and perform time axis sliding matching operation to align the waveform structure to the physical propagation response center, and use the phase conjugate structure to symmetrically repair the asymmetric section; Overlap rate analysis and amplitude suppression processing are performed on all registered signal sequences, and the slope trend of the residual reflection band at the tail is converged and adjusted through a time smoothing mechanism to improve the waveform separation effect. Based on the reconstructed signal sequence after registration, a real signal trajectory image is constructed, false echo trajectories are removed, and phase feature information is superimposed to generate a two-dimensional image with temporal structure and energy distribution characteristics for feedback analysis.

6. The multi-point object distance measurement method according to claim 5, characterized in that, When constructing a real signal trajectory image, the phase change rate is used as the color dimension. The effective echo signal within the continuous sampling window is displayed as intensity strips across the time axis. The consistency of the response trajectory is compared and analyzed by superimposing a reference shadow sequence.

7. The multi-point object distance measurement method according to claim 5, characterized in that, Step S400 includes: A point-by-point risk assessment of the full-time domain signal trajectory is performed based on the echo image. A risk scoring sequence is constructed based on the phase evolution rate, delay fluctuations, and energy density fluctuations, and an initial time risk curve is formed. Based on the initial risk curve, a Hamiltonian variational risk distribution field is established. The risk density vector flow field is generated by calculating the risk gradient direction and the rate of change, and the continuous risk growth interval is calibrated. Based on the high-risk intervals identified by the risk distribution field, a time gating instruction is generated. During the high-risk time period, a shielding control is applied to the sampling window and a time buffer offset is set to construct an envelope-type freeze zone. A real-time feedback control sequence is generated based on the dynamic response relationship between time-gated commands and echo images, and the sampling frequency, response intensity, and signal weight allocation are dynamically adjusted according to risk changes to achieve stable ranging control.

8. The multi-point object distance measurement method according to claim 7, characterized in that, A local gain adjustment mechanism is introduced into the real-time feedback control sequence to improve the energy recognition sensitivity of the sampling window when the ranging signal strength is insufficient or the trajectory boundary is blurred, thereby enhancing the accuracy and stability of trajectory judgment.

9. A multi-point object distance measurement method according to claim 7, characterized in that, Step S500 includes: The micro-periodic misalignment control operation is performed based on the real-time feedback control sequence. The emission time points of adjacent periodic pulses are randomly arranged by nanosecond-level time offset to break the time domain overlap between the secondary echo and the main echo, and the phase change is recorded as a reference for dynamic parameter adjustment. After completing the micro-periodic misalignment, a phase conjugate energy enhancement operation is performed based on the energy difference between the main signal and the secondary signal. The main echo energy is amplified by phase inversion and superposition with the reference waveform, and the secondary echo of phase mismatch is weakened. By combining the energy-enhanced signal structure with a time-grid reversible reconstruction operation, the sampling accuracy is improved in the main echo interval by adjusting the sampling density and maintaining the time structure reversibility to maintain the ranging continuity. After time rearrangement is completed, a dynamic polarization direction rotation operation is performed. The difference in reflection response is controlled by changing the pulse vibration direction, and a phase synchronization calibration mechanism is introduced to ensure the precise alignment of the main wave propagation path.

10. A multi-point object distance measuring device, used to implement the multi-point object distance measuring method according to any one of claims 1-9, characterized in that, It includes a time-series audit baseline module, a pseudo-peak identification and deduction module, a time-series registration and echo cancellation module, a risk modeling and gating module, and a path reconstruction and control module. The timing audit benchmark module establishes a nanosecond-level timing audit benchmark, collects the phase information of the incident pulse, the time delay of the echo signal and the echo energy density, generates a timing consistency matrix, and constructs the reflection feature fingerprint of the object surface to provide a unified timing reference benchmark. The pseudo-peak identification and deduction module constructs a counterfactual playback sequence, uses reflection feature fingerprints to guide the generation of shadow time series, replaces suspicious signal segments in the sampled data, calculates the delay distribution trend, extracts abnormal echo trajectories, and generates a set of pseudo-echo pulse positions. The temporal registration and de-echo module establishes a gradient orthogonal registration process based on the location set of pseudo-echo pulses, performs phase conjugate registration on the time-delayed trajectory, and outputs the de-echo image. The risk modeling and gating module generates a dynamic time risk model based on the echo-de-echo image, constructs a Hamiltonian variational risk distribution field, uses the risk gradient to generate time gating commands for controlling the measurement window, freezes time intervals with high misjudgment risk, and constructs a real-time feedback control sequence for regulating the signal response path based on the time gating commands. The path reconstruction control module executes a micro-periodic misalignment control process based on a real-time feedback control sequence. Through the combined operation of phase conjugate energy enhancement, time grid reversible reconstruction, and dynamic rotation of polarization direction, it reconstructs the pulse signal propagation path and suppresses secondary echo interference formed by reflected signals.

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