Railway ballast automatic blanking detection control method based on remote monitoring

Abstract

The invention discloses a railway ballast automatic blanking detection control method based on remote monitoring, and relates to the technical field of railway ballast laying, and the method comprises the following steps: collecting the operation speed of a conveying device, the screen vibration frequency of a screening device and the actual passing time of stone flowing through each key detection point in the railway ballast blanking process in real time; and all the original detection data are synchronously uploaded to the data processing module in a unified time label form. According to the method, a conveying beat curve is generated through multi-source data unified marking and time sequence reconstruction, dynamic offset and synchronous difference indexes are introduced, and quantitative evaluation and real-time adjustment of the sampling state are achieved. The sampling control unit dynamically optimizes the sampling starting point and frequency when deviation exceeds a threshold value, frame skipping and re-sampling are avoided, the particle size recognition accuracy is guaranteed, it is ensured that a screen is accurately controlled, the ballast screening quality and the ballast bed stability are improved, and the intelligence and safety of railway discharging operation are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of railway ballast laying, in particular to a railway ballast automatic unloading detection control method based on remote monitoring. BACKGROUND

[0002] The railway ballast automatic unloading detection control based on remote monitoring refers to that, in the process of railway ballast laying, integrated sensors, automatic control units and intelligent devices are used to automatically detect and accurately adjust the whole process of stone from the feeding hopper to the laying track, and the whole process is visually supervised and abnormally alarmed through a remote monitoring platform. This method can collect key parameters such as the flow, particle size, unloading speed and cleaning quality of the stone in real time, and automatically control the unloading rate and screening process to ensure that the ballast is uniform, clean and meets the particle size requirements. At the same time, the system also integrates airtight conveying and waste recycling mechanism to reduce dust pollution and realize resource recycling, and comprehensively improves the intelligentization, automation and green environmental protection level of railway ballast laying.

[0003] The prior art has the following disadvantages: In the existing railway ballast screening monitoring system based on laser particle size analysis, the laser sampling frequency is usually executed at a fixed period to continuously detect the particle size in the stone screening process. However, in actual unloading operation, the running speed of the conveyor belt is affected by many factors (such as load fluctuation, driving adjustment lag, operation beat switching, etc.), and short-time fluctuation or continuous deviation is easy to occur, and some systems lack real-time feedback mechanism for the running state of the conveyor belt, which causes the laser analysis module to be unable to adjust the sampling frequency synchronously. In such cases, the laser sampling rhythm and the actual beat of the stone passing through the screen cannot be dynamically matched, which is easy to cause the "frame skipping" (i.e. some stones are not scanned in time) or "re-sampling" (i.e. the same material is scanned multiple times) phenomenon of particle size identification data.

[0004] The above-mentioned abnormality will cause nonlinear distortion of the particle size identification result in data distribution, causing distortion of the screening state monitoring, system misjudgment of stone particle size compliance, and further affecting the accuracy of key control instructions such as subsequent screen vibration frequency and inclination adjustment, and in severe cases, it may cause unqualified ballast to mix into the laying process, thereby affecting the stability of the ballast bed and the safety of train operation.

[0005] The above information disclosed in the background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a railway ballast automatic unloading detection control method based on remote monitoring to solve the problems in the background.

[0007] In order to achieve the above object, the present application provides the following technical scheme: a railway ballast automatic unloading detection control method based on remote monitoring, comprising the following steps:

[0008] Real-time acquisition of the running speed of the conveying device, the screen vibration frequency of the screening device and the actual passing time of the stone at each key detection point during the railway ballast unloading process, and synchronous uploading of all original detection data to the data processing module in the form of a unified time label;

[0009] The data processing module reconstructs the time sequence of the stone passing time sequence data at each key detection point based on the unified time label, and generates a stone conveying tempo curve covering the whole process of the conveying device and the screening device;

[0010] Input the whole sampling time sequence of the laser particle size analysis unit during the detection process into the data processing module, and perform time registration on the current sampling time sequence and the constructed stone conveying tempo curve point by point, establish a point-by-point correspondence between the two time sequences, calculate the time offset of each corresponding point, and obtain the corresponding dynamic offset time sequence of the whole process;

[0011] Absolute value normalization is performed on the dynamic offset time sequence, and the fluctuation amplitude of the time offset is calculated in each detection period, and the beat synchronization difference index for quantifying the synchronization degree of the stone tempo and the laser sampling is generated based on the offset and the fluctuation amplitude;

[0012] Compare the beat synchronization difference index with the preset synchronization threshold value, if the beat synchronization difference index is greater than the threshold value, it is determined that there is a tempo mismatch state at present, and the sampling control unit is triggered to respond to the tempo difference information fed back by the detection point;

[0013] The sampling control unit dynamically adjusts the sampling start time and sampling interval parameters of the laser particle size analysis unit according to the tempo mismatch information, optimizes the start and end time and duration of the sampling window, and realizes the adaptive adjustment of the laser particle size analysis strategy through the real-time collaborative driving of multi-source monitoring data.

[0014] Preferably, the specific steps of real-time acquisition of the conveying device running speed, the screening device screen vibration frequency and the actual passing time of the stone at each key detection point are as follows:

[0015] The number of rotations of the conveying belt per unit time is collected by using a rotary encoder installed at the position of the conveying device drive shaft, and the current linear speed of the conveying device is calculated in real time according to the preset transmission ratio relationship, forming continuous conveying speed time sequence data;

[0016] The acceleration response values during the vibration of the screen mesh are recorded synchronously by the triaxial acceleration sensor deployed in the screening device frame structure, and the current main frequency value and harmonic characteristic parameter are extracted by combining the fast Fourier transform algorithm, which is used to reflect the vibration frequency of the screen mesh under the actual working state.

[0017] Based on the photoelectric identification sensors arranged at multiple key stone passing positions, the start and end times of the stone blocking the sensor light beam are detected, the actual passing time of the stone passing through each key detection point is determined by calculating the blocking duration and triggering time point, and the three types of original data of conveying speed, vibration frequency and stone passing time are encoded based on the unified timestamp and uploaded to the data processing module in time sequence.

[0018] Preferably, the specific steps of the data processing module for time series reconstruction of the stone passing time sequence data at each key detection point based on the unified time label are as follows:

[0019] The stone passing time data uploaded by each key detection point and encoded with a unified time label are arranged in time sequence, and an initial local time series is constructed for each detection point to reflect the instantaneous passing state of the stone at the respective detection point.

[0020] The time interval between consecutive time points in the local time series is calculated by difference operation, and the transmission time of the stone between different detection points is calculated, and the time interval is converted into spatial path distance by combining the running speed parameter of the conveying device, to realize the mapping between time and physical conveying path.

[0021] The time-distance mapping results of all detection points are concatenated in sequence according to the structure, and are smoothly connected by continuity interpolation and multi-point registration algorithm to form a unified stone conveying beat main curve.

[0022] Preferably, the specific steps of the data processing module for time registration of the entire sampling time sequence input by the laser particle size analysis unit with the constructed stone conveying beat curve are as follows:

[0023] The sampling time sequence generated by the laser particle size analysis unit during actual detection is standardized according to the time label, and the sampling points with abnormal intervals are removed to form a high-precision time sampling sequence.

[0024] The data processing module calls the stone conveying beat curve that has been constructed, selects the nearest beat time point on the curve with respect to each sampling time based on the unified time axis, establishes a one-to-one correspondence between the sampling time sequence and the beat curve time point, and marks the specific time difference between each pair of corresponding points.

[0025] The time difference values of all sampling points and their corresponding beat points are taken as basic data to form a new dynamic offset time sequence in time sequence.

[0026] Preferably, the time difference values of all sampling points and their corresponding beat points are taken as basic data to form a new dynamic offset time sequence in time sequence as follows:

[0027] The data processing module receives each sampling time collected by the laser particle size analysis unit, and positions the beat time point most adjacent to each sampling time on a unified time axis according to the established beat curve, establishes a one-to-one correspondence between them, and extracts the time difference value of each corresponding group;

[0028] The time difference values between all matched sampling time points and beat time points are subjected to absolute value processing to remove directional interference of the time difference, and are arranged in the order of laser sampling time to construct a strictly time-sequenced offset sequence;

[0029] The time-arranged offset sequence is input as a new dynamic offset time sequence to continuously reflect the synchronization error state of the laser sampling process with respect to the actual stone flow rhythm.

[0030] Preferably, the specific steps of processing the dynamic offset time sequence to generate a beat synchronization difference index for quantifying the synchronization degree of stone beats and laser sampling are as follows:

[0031] For each time offset in the obtained dynamic offset time sequence, take the absolute value and perform linear normalization processing according to a preset maximum allowable time offset range to standardize all offset values to continuous values between 0 and 1;

[0032] According to the defined detection period, divide the time window, extract all normalized offset values in the current period in each detection period, and calculate the range value and standard deviation value of the current group of data to form a statistical index reflecting the fluctuation amplitude of beat synchronization stability;

[0033] The average normalized offset value in each detection period and its corresponding fluctuation amplitude index are comprehensively calculated by a weighting function to generate a beat synchronization difference index representing the synchronization quality of the current period of beats and sampling.

[0034] Preferably, the sampling control unit compares and analyzes the beat synchronization difference index calculated in the current detection period with a preset beat synchronization difference index reference threshold to calculate a beat adaptation adjustment coefficient, and the calculation expression is as follows:

[0035]

[0036] In the formula, Δ sync(t) is a beat synchronization difference index, Δ ref is a beat synchronization difference index reference threshold, α is a sensitivity adjustment coefficient, and λ(t) is a beat adaptation adjustment coefficient.

[0037] After obtaining the beat adaptation adjustment coefficient λ(t), the sampling control unit calculates a new sampling start time of the current period based on the default sampling start time of the last period and the average offset between the laser sampling points and the beat points in the current period, and the calculation expression is as follows:

[0038]

[0039] In the formula, is a default sampling start time, μ offset (t) is a current period average sampling offset value, τ start (t+1) is a next period sampling start time.

[0040] In order to improve the sensitive response ability of the sampling strategy to the beat fluctuation, the sampling control unit dynamically calculates the sampling interval and the sampling window period according to the initial value of the sampling interval and the offset standard deviation in the current detection period, and the specific calculation formula is as follows:

[0041] In order to improve the adaptive ability to the synchronization fluctuation, after adjusting the sampling start point, the sampling control unit further calculates the sampling interval and the sampling window period based on the beat adaptation adjustment coefficient λ(t), the current period average sampling offset value μ offset (t), and the offset standard deviation, and the specific formula is as follows:

[0042]

[0043] In the formula, Δτ(t+1) is a next period sampling interval, Δτ 0 is a default sampling interval initialized and set, β is a sampling frequency adjustment sensitivity coefficient, σ offset (t) is a time offset standard deviation of all sampling points in the current period, N is a preset number of sampling points in each sampling period, and ω(t+1) is a duration of the entire laser particle size analysis process in the next sampling period.

[0044] In the above technical solution, the present application provides technical effects and advantages:

[0045] Through the above railway ballast automatic unloading detection control method based on remote monitoring, high-precision dynamic synchronization between laser particle size analysis sampling behavior and actual stone conveying rhythm is realized, and the accuracy of particle size identification and the stability of system control are significantly improved. The method reconstructs the complete conveying rhythm curve through unified time labeling and time sequence reconstruction of multi-source monitoring data, and introduces dynamic offset time sequence and rhythm synchronization difference index to continuously quantitatively evaluate the sampling state, so that the system can realize real-time sensing of the matching degree between sampling and rhythm. When the synchronization offset exceeds the threshold, the sampling control unit can respond in real time by adjusting the sampling start time and sampling frequency to realize closed-loop adaptive optimization of the sampling strategy. This mechanism effectively avoids sampling abnormalities such as "frame skipping" and "re-sampling", prevents particle size identification results from being distorted, ensures the accuracy of subsequent screen vibration frequency and inclination control instructions, and fundamentally guarantees the ballast screening quality, track bed structure stability and train operation safety, and comprehensively improves the intelligence, refinement and reliability level of the railway ballast unloading process. BRIEF DESCRIPTION OF DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art based on these drawings.

[0047] Figure 1 The method flowchart of the present application is based on the railway ballast automatic unloading detection control method based on remote monitoring. DETAILED DESCRIPTION

[0048] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations, however, can be implemented in many different ways and should not be construed as limited to the examples set forth herein; rather, these examples are provided so that this disclosure will be thorough and complete, and will fully convey the inventive concept to those skilled in the art.

[0049] The present application provides a railway ballast automatic unloading detection control method based on remote monitoring as shown in Figure 1 The method comprises the following steps:

[0050] The running speed of the conveying device, the screen vibration frequency of the screening device and the actual passing time of the stones flowing through each key detection point in the railway ballast unloading process are collected in real time, and all original detection data are uploaded to the data processing module in the form of unified time label;

[0051] The role of this step is to build a multi-source data basis with high precision and high consistency for the whole railway ballast automated unloading detection control system, and to realize dynamic perception and information synchronization management of the whole unloading process. In the actual unloading operation process, the stone material reaches the laying track from the feeding port through the conveying device and the screening device, and each link involves changes in different physical states and transmission speeds, especially the running speed of the conveying device and the vibration frequency of the screen mesh, which have a direct impact on the stone flow rhythm and screening behavior. Therefore, by deploying rotary encoders, three-axis acceleration sensors and photoelectric identification sensors at key positions, key physical quantities such as conveying speed, vibration frequency and stone passing time are collected, and are marked with a unified time label. This not only enables the alignment and integration of various heterogeneous sensor data in the time dimension, but also ensures the accurate reconstruction of the state of each link by the data processing module. This provides the necessary foundation for subsequent time series analysis, sampling window optimization, beat synchronization control and other core functions, solves the misjudgment and imbalance problems caused by chaotic sampling time sequence and isolated data processing in traditional systems, significantly improves the response capability and control precision of the unloading detection system, and is a prerequisite for realizing the "intelligent and closed-loop" control process in a true sense.

[0052] In the railway ballast automated unloading detection control method, the key detection nodes refer to important positions in the conveying and screening process of stone materials that have physical state changes, parameter collection significance or control instruction triggering functions. These nodes are crucial for building a whole-process beat curve and realizing precise synchronization control. In this scheme, the key detection nodes mainly include the following positions: first, the feeding port of the conveying device, which is used to obtain the initial time mark of the stone material entering the system; second, the middle position of the conveying belt, which can be used to monitor the change in conveying rhythm and synchronize the evaluation of conveying speed fluctuations; third, the position above the laser particle size analysis unit, which is the core sampling node of the system, used to capture the particle size detection time and perform time registration; fourth, the entrance of the screening device, which is used to mark the time point of the stone material entering the screening process and provide a reference for screen mesh control; fifth, the middle or end of the screen mesh, which is used to judge the completion of stone material screening and the flow-out time, and assist in determining the screening efficiency; in addition, a node can also be set at the waste diversion channel to monitor the direction and processing time of unqualified stone materials. These key nodes upload raw data with a unified time label, realize precise modeling of the whole stone conveying and screening path, and are the basis for realizing multi-link closed-loop control and dynamic sampling optimization.

[0053] The specific steps of real-time collection of conveying device running speed, screen mesh vibration frequency of the screening device and actual passing time of stone materials through each key detection point are as follows:

[0054] The rotation number of the conveying belt per unit time is collected by a rotary encoder installed on the driving shaft of the conveying device, and the current linear speed of the conveying device is calculated in real time according to a preset transmission ratio relationship, thereby forming continuous conveying speed time sequence data.

[0055] In order to accurately monitor the discharging speed of the stone, a high-precision rotary encoder is installed on the driving shaft or driven shaft of the conveying device to record the number of shaft rotations per unit time in real time. The number of rotations is converted into linear speed by a preset transmission ratio and the diameter parameter of the pulley, thereby obtaining the running speed of the conveying belt at any time. The purpose of this step is to provide basic dynamic information of stone flow, which is the core parameter for evaluating the change of discharging rhythm, and provides a time reference for subsequent laser sampling time and frequency adjustment, ensuring that the laser analysis can adapt to the actual fluctuations of the conveying speed.

[0056] The three-axis acceleration sensor deployed in the frame structure of the screening device synchronously records the acceleration response value during the vibration of the screen, and extracts the current main frequency value and harmonic characteristic parameter by using the fast Fourier transform algorithm, which is used to reflect the vibration frequency of the screen under actual working condition.

[0057] The three-axis acceleration sensor is embedded in the structural frame of the screening device to capture the multi-dimensional vibration acceleration signal of the screen in real time. The original acceleration signal collected is sent to the embedded signal processing module, and the main frequency, harmonic frequency and amplitude are extracted by using the fast Fourier transform (FFT) algorithm to accurately calculate the current working frequency of the screen. The purpose of this step is to monitor whether the screen is in the preset resonance working state, and to provide supplementary basis for analyzing the stone distribution state and passing speed, which helps to identify the physical reasons for unstable screening and serves as a reference variable for dynamically adjusting the sampling window length.

[0058] Based on the photoelectric identification sensors arranged at multiple key stone passing positions, the start and end time of the stone blocking the sensor light beam is detected, the actual passing time of the stone passing through each key detection point is determined by calculating the blocking duration and triggering time point, and the conveying speed, vibration frequency and stone passing time are encoded based on the unified timestamp, and uploaded to the data processing module in time sequence, ensuring the high precision and consistency of subsequent time sequence reconstruction.

[0059] The time stamp of each stone passing through the position is accurately calculated by comparing the start and end time of the blocked sensor beam when the stone blocks the sensor beam. After the trigger time points collected by all sensors are synchronized through a unified time reference, the data processing module generates a complete time sequence of the stone passing through the path. The purpose of this step is to provide the accurate time of the stone particles passing through each link of the system as the time anchor point for laser sampling registration and offset correction, ensuring that the particle size identification process has sufficient real-time performance and spatial positioning accuracy.

[0060] The data processing module reconstructs the time sequence of the stone passing time sequence data at each key detection point based on the unified time label to generate a stone conveying rhythm curve covering the whole process of the conveying device and the screening device.

[0061] The purpose of this step is to build a complete, continuous, and quantifiable stone conveying rhythm main curve as the core reference for dynamic sampling control by the laser particle size analysis unit, achieving accurate alignment of the laser sampling frequency and the stone flow rhythm. In the process of automatic unloading of railway ballast, the stone gradually passes through multiple detection points from the feeding end of the conveying device and finally enters the screening device. Each detection point collects the time stamp of the stone passing through the sensor. Due to the relative independence of each sensor, there may be small errors and time sequence offsets in the sampling time. Direct use of raw data cannot reflect the true rhythm of the continuous movement of the stone. To solve this problem, the data processing module first standardizes the time sequence of each detection point using a unified time label to build their respective local time sequence curves. Then, based on the physical path length between detection points and the conveying speed parameters, the transmission time of the stone between detection points and the corresponding distance relationship are calculated. Finally, the continuity interpolation algorithm is used to fill in the time gaps between detection points, and the multi-point registration algorithm is used to calibrate the time deviation and shape difference between different data sequences, finally generating a rhythm main curve covering the entire conveying and screening process. This curve not only reflects the movement trajectory of the stone in space, but also truly reproduces its flow rhythm on the time axis, which is the basic condition for dynamic synchronization control of laser sampling and stone conveying.

[0062] The specific steps of the data processing module reconstructing the time sequence of the stone passing time sequence data at each key detection point based on the unified time label are as follows:

[0063] Arrange the stone passing time data uploaded by each key detection point in time sequence and build an initial local time sequence for each detection point to reflect the instantaneous passing state of the stone at each detection point.

[0064] The photoelectric sensor arranged at each key detection point collects specific time information of the stone passing through the detection point, and is uniformly attached with a time label generated by the system master clock. After receiving the original time data uploaded by each detection point, the data processing module first sorts the data of each detection point in time according to the time label to form a local time sequence reflecting the continuous passing of the stone at the point. The role of this step is to convert the stone passing information distributed in different spatial positions into structured time information, so that the stone flow state of each detection point has time continuity and traceability, laying a foundation for the analysis of the time relationship between different detection points.

[0065] The interval between the continuous time points in the local time sequence is differentiated to calculate the transmission time of the stone between different detection points, and the interval is converted into a spatial path distance by combining the running speed parameter of the conveying device, so as to realize the mapping between time and physical conveying path.

[0066] The data processing module processes adjacent detection points in the above local time sequence in pairs, calculates the difference between the theoretical transmission time and the actual transmission time of the stone between adjacent detection points according to the pre-set physical length of the conveying path in the system and the real-time collected running speed of the conveying device. Through the ratio analysis of the time difference and the path length, the change of the conveying speed of the stone at different time periods is deduced, and the mapping relationship between time and conveying path is established. The role of this step is to combine discrete time data with continuous physical path to construct a continuous conveying characteristic curve across detection points, which reflects the dynamic characteristics of stone flow and provides accurate parameter basis for subsequent beat curve splicing and interpolation.

[0067] The time-distance mapping results of all detection points are concatenated in sequence according to the structure, and a unified stone conveying beat main curve is formed by smooth connection through continuity interpolation and multi-point registration algorithm, which covers the complete path of the stone from the inlet of the conveying device to the outlet of the screening device, providing a continuous and full-process reference benchmark for sampling control and synchronization judgment.

[0068] The data processing module splices the time-path mapping results between each detection point in sequence according to the physical process order of stone transmission in the system, adjusts the possible time drift in the splicing process by using the multi-point registration algorithm, and corrects the abrupt nodes by using the curve continuity interpolation technology, finally generates a complete and continuous stone conveying beat main curve. The beat curve accurately describes the dynamic rhythm change of each batch of stone from the starting point of feeding to the end point of screening. The role of this step is to provide a continuous time reference benchmark for the laser sampling module, so that the sampling frequency and the stone flow rhythm can be accurately matched, thereby greatly improving the integrity of particle size identification and the coordination of system control.

[0069] "Continuity interpolation" and "multi-point registration algorithm" are mathematical processing methods for constructing continuous curves between time series and physical paths, which function to eliminate breakpoints, mutations or drifts generated in the splicing process of multi-source asynchronous data collection, and generate a unified beat curve with time coherence and physical interpretability. Continuity interpolation refers to estimating intermediate data using mathematical interpolation methods (such as linear interpolation, spline interpolation, etc.) when there is a time or distance gap between adjacent detection points, to ensure smooth transition of the curve in numerical and derivative values; while the multi-point registration algorithm aligns multiple sampling points on the time axis or spatial axis, and realizes the synchronous mapping of multiple data sequences by minimizing the time difference, shape difference or dynamic change difference. The reason for choosing these two algorithms is that in the process of railway ballast discharging, the sensors are distributed discretely, the triggering time has a slight difference, and the transmission path changes. Without effective interpolation and registration processing, the beat curve will present jumping, misplacement or discontinuity, affecting the accuracy and reliability of subsequent laser sampling control. Therefore, this combined method can effectively improve the continuity and reliability of data reconstruction, and provide a solid data foundation for the system to realize precise synchronous control and feedback regulation.

[0070] The laser particle size analysis unit inputs all the sampling time series of the collected data into the data processing module, and performs time registration with the stone conveying beat curve constructed at the current sampling time series point by point, establishes a point-by-point corresponding relationship between the two time series, calculates the time offset of each corresponding point, and obtains the corresponding dynamic offset time series of the whole process;

[0071] The purpose of this step is to achieve high-precision time synchronization between the sampling behavior of the laser particle size analysis unit and the actual stone conveying rhythm, ensure the dynamic consistency of the sampling opportunity and the real position of the stone, and thus improve the accuracy of particle size detection and the coordination of system control. In the process of automatic ballast unloading on the railway, the laser particle size analysis unit usually collects data at a fixed frequency, but in actual operation, the stone conveying speed and rhythm may change dynamically due to changes in load, drive lag, or fluctuations in screening state, resulting in a time deviation between the laser sampling rhythm and the stone flow rhythm. To solve this problem, the system first inputs all the sampling time sequences collected by the laser particle size analysis unit into the data processing module, and uniformly uses the system master clock standard for time labeling processing, excluding abnormal sampling points, and forming a standardized sampling time sequence. Subsequently, the data processing module calls the previously constructed stone conveying rhythm curve, and according to the principle of the smallest time difference on the time axis, each laser sampling time is matched with the closest time node on the rhythm curve one by one, establishing a one-to-one correspondence between the two time sequences. Finally, the system calculates the time difference for each paired point, and all the time differences are sequentially combined into a dynamic offset time sequence, which is used to quantify the synchronization offset state between the laser sampling time and the real rhythm of the stone. This offset sequence not only reflects abnormal situations such as sampling delay, frame skipping, or re-sampling, but also serves as an important input for subsequent sampling strategy dynamic adjustment and rhythm synchronization difference index calculation, providing data support and criteria for real-time sampling optimization and closed-loop control in the unloading process.

[0072] The specific steps of inputting all the sampling time sequences collected by the laser particle size analysis unit into the data processing module and matching them with the constructed stone conveying rhythm curve point by point are as follows:

[0073] The sampling time sequence generated by the laser particle size analysis unit in the actual detection process is standardized according to the time label, and after removing the sampling points with abnormal intervals, a high-precision time sampling sequence is formed, which is then input into the data processing module for subsequent matching processing;

[0074] When the laser particle size analysis unit performs particle size detection, each scan records the corresponding sampling time point, forming an original sampling time sequence. Due to vibration interference, system delay, or repeated data writing during sampling, some sampling points may have abnormal time intervals or dense overlaps. To ensure the accuracy of the matching process, the system first filters the original sampling time sequence, removes outliers, and performs time label standardization, aligning all sampling points to the system master clock time axis. The purpose of this step is to provide a set of high-precision sampling time sequences without interference, overlap, and clear time sequence for the subsequent time matching process, ensuring a stable one-to-one correspondence with the rhythm curve.

[0075] The data processing module calls the stone conveying rhythm curve that has been constructed, selects the most adjacent rhythm time point on the curve with each sampling time as the reference, establishes a one-to-one correspondence between the sampling time sequence and the rhythm curve time point, and marks the specific time difference between each corresponding point;

[0076] The data processing module calls the previously reconstructed stone conveying rhythm main curve, and matches each laser sampling time point with the closest time node on the rhythm curve based on the unified time axis as the reference. The matching principle can be based on the minimum time difference criterion to achieve the optimal pairing between each laser sampling time point and a real stone passing time point on the rhythm curve, thereby establishing a one-to-one correspondence time relationship table. The role of this step is to construct an accurate mapping channel between the laser sampling behavior and the physical movement of the stone, and to open up the data structure logical link for subsequent offset calculation and synchronization analysis.

[0077] The time difference between all sampling points and their corresponding rhythm points is used as the basis data to form a new dynamic offset time sequence in chronological order, which is used to reflect the degree of synchronization offset between the laser sampling process and the actual stone flow rhythm, and to provide dynamic quantitative indicators for subsequent sampling strategy adjustment and synchronization judgment.

[0078] After the one-to-one matching of the sampling time and the rhythm time point is completed, the system calculates the time difference of each matched point and arranges all the time differences in chronological order to generate a new dynamic offset time sequence. This sequence quantifies the degree of offset of the laser sampling behavior relative to the actual stone flow rhythm, and can intuitively reflect whether the synchronization accuracy meets the standard. The role of this step is to provide real-time, continuous, and quantifiable synchronization deviation data to provide a basis for the system to determine whether there is a rhythm mismatch, and to provide necessary input parameters for dynamic adjustment of the sampling strategy, ultimately ensuring the accurate coordinated operation of the particle size detection system and the stone rhythm.

[0079] The steps of forming a new dynamic offset time sequence by using the time difference between all sampling points and their corresponding rhythm points as the basis data in chronological order are as follows:

[0080] The data processing module receives each sampling time collected by the laser particle size analysis unit, and locates the most adjacent rhythm time point on the unified time axis with each sampling time as the reference, establishes a one-to-one correspondence between the two, and extracts the time difference of each corresponding group;

[0081] In the data processing stage, after the system receives each sampling time point collected by the laser particle size analysis unit, the pre-constructed stone conveying beat curve is used to locate the beat time point closest to each sampling time on the unified time axis, and a one-to-one correspondence is established by using the minimum time difference principle. To ensure the accuracy of matching, the system uses a dynamic sliding window strategy to limit the maximum allowed registration error range and avoid misregistration. The role of this step is to determine the physical corresponding state of each laser sampling behavior in the stone conveying process, providing a basic pairing structure for judging whether the sampling deviates from the true stone flow rhythm.

[0082] The absolute value of the time difference between all matched sampling time points and beat time points is processed to remove the directional interference of the time difference, and the laser sampling time is arranged in order to construct a set of strictly time-ordered offset sequences;

[0083] After pairing, the system calculates the time difference between each group of sampling points and beat points, and uniformly uses absolute value processing to eliminate the directional factors of sampling advance or lag, and only the size of the offset is retained as the measurement standard of synchronization deviation. Subsequently, all time difference values are arranged in order of laser sampling time to construct a set of offset data sequences with clear time labels. The role of this step is to structure and time sequence a large amount of dispersed time difference information, so that the evolution trend of the offset situation can be identified and tracked by the system, providing basic data support for subsequent volatility analysis and adjustment strategy.

[0084] The time-arranged offset sequence is input as a new dynamic offset time sequence to continuously reflect the synchronization error state of the laser sampling process for the actual stone flow rhythm, providing continuous, dynamic, and quantifiable synchronization evaluation basis for subsequent beat synchronization difference index calculation and sampling control logic judgment.

[0085] The system uniformly names the time-arranged offset sequence as a dynamic offset time sequence and continuously inputs it as data for real-time storage in the data processing module for rolling analysis and dynamic evaluation. This sequence can continuously reflect the synchronization accuracy of each laser sampling behavior in the current system beat background, and is the core dynamic indicator for measuring system synchronization changes. The role of this step is to build a high-frequency, real-time updated offset evaluation channel, enabling the system to have the ability to monitor, detect abnormalities, and predict the beat matching state in real time, providing continuous and stable data input for subsequent beat synchronization difference index calculation and controller response decision.

[0086] The dynamic offset time sequence is subjected to absolute value normalization, and the fluctuation amplitude of the time offset is calculated within each detection period. Based on the offset and the fluctuation amplitude, the beat synchronization difference index is generated to quantify the synchronization degree of the stone beat and the laser sampling.

[0087] The role of this step is to digitize, standardize and quantitatively express the synchronization offset state between laser sampling and stone tempo, so as to construct a "tempo synchronization difference index" that can dynamically reflect the synchronization accuracy of the system, and provide clear decision basis for subsequent sampling strategy adjustment and closed-loop control. In the laser particle size detection system, the time point of laser sampling is often based on fixed frequency, while the real tempo of stone shows dynamic instability due to factors such as fluctuation of conveying speed, change of screen state, etc. There is often a time misalignment between the two. To solve this problem, the system first processes the absolute value of the time difference between each sampling point and the corresponding stone tempo point, eliminating directional interference, and then normalizes according to the maximum allowed offset range, converting different amplitudes of offset into standardized values, establishing a general evaluation basis for sampling deviation. On this basis, the system divides the time window according to the detection period, respectively calculates the fluctuation amplitude (such as range and standard deviation) of the normalized offset value in each period, and extracts the possible systematic instability factors in the process of tempo and sampling matching. Finally, the average value and fluctuation amplitude of the offset are calculated by a weighted function to generate a synchronization difference index that can quantify the quality of tempo-sampling matching. The larger the index value, the worse the sampling accuracy of the system, the more serious the matching disorder; on the contrary, the smaller the value, the better the synchronization state of the system. This process changes the traditional fuzzy sampling error judgment into a measurable, traceable and controllable digital index, which is the core support mechanism for realizing intelligent sampling control, adaptive strategy adjustment and stable operation of the system.

[0088] The specific steps of processing the dynamic offset time series to generate the tempo synchronization difference index for quantifying the synchronization degree of stone tempo and laser sampling are as follows:

[0089] For each time offset in the obtained dynamic offset time series, take the absolute value and perform linear normalization according to the preset maximum allowable time offset range to standardize all offset values to continuous values between 0 and 1, which are used to unify the synchronization deviation degree of each sampling time;

[0090] After completing the time registration between the laser sampling time and the stone tempo curve and generating the dynamic offset time series, the offset values need to be processed first. The absolute value is processed to eliminate the influence of positive and negative directions, and only the size of the time difference is retained. Then, the system performs linear normalization on all absolute value offsets according to the preset maximum allowable offset threshold, so that they are mapped to the standardized interval of 0 to 1. This normalization process can eliminate the evaluation bias caused by differences in sampling density or inconsistent fluctuation range between different detection periods, so that the offset values have a unified evaluation scale. The role of this step is to establish a standardized data basis for subsequent synchronization analysis, so that the offset values under different sampling periods or different operating conditions have comparability and statistical significance.

[0091] According to the definition of the detection cycle, the time window is divided, the normalized offset value in each detection cycle is extracted, and the range value and standard deviation value of the current group data are calculated to form a statistical index reflecting the fluctuation amplitude of the beat synchronization stability;

[0092] The normalized offset sequence is divided into time windows according to the preset detection cycle, the offset data points in each period are extracted, and the range value (difference between maximum and minimum) and standard deviation value are calculated to obtain a statistical quantity reflecting the fluctuation degree of the offset in the period. The range index reveals the upper limit of the range of the offset change, and the standard deviation index reflects the consistency and concentration of the offset fluctuation. The role of this step is to judge the stability and consistency of the laser sampling and stone beat synchronization process. Through quantifying the fluctuation amplitude, it can identify whether there are potential problems such as periodic mismatch, sampling drift or beat instability in the system, and provide supporting data for subsequent judgment of whether dynamic adjustment of sampling strategy is needed.

[0093] The average normalized offset value in each detection cycle and its corresponding fluctuation amplitude index are integrated by a weighted function to generate a beat synchronization difference index representing the quality of the current cycle beat and sampling synchronization, which is the core evaluation parameter for subsequent judgment of whether the synchronization is mismatched and whether the sampling adjustment control logic is triggered.

[0094] After obtaining the average normalized offset value of each detection cycle and the fluctuation statistical index of the cycle, the system integrates the two types of indexes by a weighted function to generate a single beat synchronization difference index. This index comprehensively reflects the overall performance of "sampling offset degree" (i.e. offset value mean) and "sampling stability degree" (i.e. fluctuation amplitude), and the higher the value, the worse the synchronization accuracy. The role of this step is to compress the multi-dimensional synchronization offset information into a quantifiable and judgeable control signal, so that the system can judge whether there is a serious beat deviation based on a single index, thereby driving the subsequent sampling control logic to respond, realizing the precise matching and intelligent adaptive adjustment of laser particle size analysis and stone beat.

[0095] The weighting function refers to a mathematical method of multi-dimensional data fusion by assigning each index a corresponding weight coefficient when multiple indexes with different physical meanings or units of measurement are integrated to make them reflect different importance contributions in the final result. In the process of generating the beat synchronization difference index, the normalized offset value reflects the average synchronization deviation between the sampling and the stone beat, while the fluctuation amplitude index reflects the stability and consistency of the sampling process. Although both are related to synchronization quality, their sensitivity and impact in system error judgment are different. If they are directly added, it will lead to distorted evaluation results. By introducing the weighting function, the appropriate weight proportion can be set according to the actual control requirements or historical experience, for example, increasing the weight of the offset mean if more attention is paid to the offset degree, or increasing the weight of the fluctuation index if more attention is paid to the system stability, so as to flexibly adjust the evaluation strategy and generate a comprehensive index that meets the engineering logic and has practical control significance. Therefore, selecting the weighting function as the fusion means can effectively improve the discrimination ability and adaptability of the beat synchronization difference index, so that the system can more accurately identify the synchronization mismatch state and achieve precise control response accordingly.

[0096] The beat synchronization difference index is compared with the preset synchronization threshold value. If the beat synchronization difference index is greater than the threshold value, it is determined that there is a beat mismatch state at present, and the sampling control unit is triggered to respond to the beat difference information fed back by the detection point;

[0097] The step is to realize real-time judgment and abnormal identification of the synchronization state between laser sampling and stone conveying rhythm, and by setting a preset synchronization threshold, an intelligent monitoring logic with judgment standard and response mechanism is constructed, so as to ensure that the sampling behavior of the system always keeps consistent with the actual material flow rhythm. In the process of railway ballast automatic unloading, the stone flow rate is affected by many factors and exists dynamic fluctuation, and if the laser sampling is not synchronized with it, it will lead to problems such as decrease of particle size identification accuracy, data jumping or re-sampling, and then affect the downstream screening control and paving quality. Therefore, after the system generates the rhythm synchronization difference index in the previous step, it needs to set a synchronization threshold reflecting the maximum acceptable error range as the judgment basis for identifying whether the system is in the state of "synchronization normal" or "synchronization mismatch". When the difference index is lower than the threshold, it means that the sampling and rhythm match well and no intervention is needed; but once the difference index exceeds the threshold, it is judged that there is obvious rhythm mismatch phenomenon at present. At this time, the system will immediately trigger the sampling control unit to read the rhythm difference information fed back by each key detection point, and start the adaptive response process. The core function of this mechanism is to break through the "identification-judgment-response" synchronization control closed loop, and change the system from passive sampling mode to active sensing and real-time adjustment intelligent control mode. Through this step, the system can make accurate response in the early stage of stone rhythm deviation, maximize the accumulation and transmission of detection error, and effectively ensure the accuracy of particle size analysis results and the stability and safety of the entire paving process.

[0098] The sampling control unit dynamically adjusts the sampling start time and sampling interval parameters of the laser particle size analysis unit according to the rhythm mismatch information, optimizes the start and end time and duration of the sampling window, and realizes the adaptive adjustment of the laser particle size analysis strategy through the real-time collaborative driving of multi-source monitoring data, so as to realize the precise dynamic synchronization of laser sampling and stone conveying rhythm in the whole unloading process.

[0099] The purpose of this step is to establish an adaptive control mechanism based on feedback loop, so that the sampling behavior of the laser particle size analysis unit is accurately aligned with the actual stone conveying rhythm in the time dimension, thereby improving the representativeness, real-time and accuracy of particle size identification. In the process of railway ballast unloading, due to the influence of factors such as conveying speed, screen state, load change, etc., the stone rhythm will produce nonlinear fluctuation. If the laser particle size analysis unit always samples at a fixed frequency, it is easy to cause "frame skipping" or "re-sampling" and other problems, which will lead to particle size data distortion and ultimately affect the quality of ballast screening, classification and laying. Therefore, the sampling control unit identifies whether the sampling and rhythm are out of sync in real time through the key parameters such as the rhythm synchronization difference index, offset mean and fluctuation amplitude established in the early stage. When the system detects synchronization mismatch, the sampling control unit will dynamically adjust the starting time of laser sampling based on the offset information, so that it is more consistent with the real time of stone passing through the monitoring point; at the same time, further adjust the sampling interval and sampling window period to realize the flexible matching of sampling frequency and duration. In this process, the sampling control logic not only integrates multi-source monitoring data such as conveying speed, particle size distribution, vibration frequency, etc., but also takes the rhythm adjustment coefficient as the core drive to realize multi-dimensional linkage optimization of sampling parameters. Through this mechanism, the system can adaptively respond to the real-time fluctuation of the rhythm, realize the transformation of laser sampling behavior from "fixed passive" to "dynamic active", and continuously maintain the consistency with the actual material flow rhythm in the whole unloading process, ensuring the reliability and stability of particle size identification results in accuracy, integrity and real-time.

[0100] The specific steps of dynamically adjusting the sampling starting time and sampling interval parameters of the laser particle size analysis unit to optimize the start and end time and duration period of the sampling window are as follows:

[0101] The sampling control unit compares and analyzes the rhythm synchronization difference index calculated in the current detection period with the preset rhythm synchronization difference index reference threshold, calculates the rhythm adaptation adjustment coefficient, and the calculation expression is as follows:

[0102]

[0103] In the formula, Δ sync (t) is the rhythm synchronization difference index, which represents the average synchronization offset degree between the laser sampling rhythm and the actual stone rhythm in the current detection period t, and is used to reflect the "error intensity" of the system sampling behavior in the rhythm dimension. Δ refis the reference threshold of the beat synchronization difference index, which serves as a standardized benchmark for mapping the actual offset to the relative adjustment strength. α is the sensitivity adjustment coefficient, which is used to adjust the weight coefficient of the system's response to the synchronization difference amplitude. The value range is 0<α<1. λ(t) is the beat adaptation adjustment coefficient, which represents the adjustment amplification factor required to correct the laser sampling parameters within the current detection period t. If λ(t) = 1, it means that the sampling system maintains the default sampling rhythm without any adjustment. If λ(t) > 1, it means that the synchronization offset is increasing and the sampling correction strength needs to be increased.

[0104] The beat adaptation adjustment coefficient λ(t) will serve as the core factor for the subsequent dynamic correction of sampling parameters, realizing the nonlinear mapping between the strength of synchronization offset and the sampling adjustment amplitude.

[0105] After obtaining the beat adaptation adjustment coefficient λ(t), the sampling control unit calculates the new sampling start time of this cycle based on the default sampling start time of the previous cycle and the average offset between the laser sampling point and the beat point in the current cycle. The calculation expression is as follows:

[0106] Where, It is the default sampling start time, indicating the theoretical time point at which laser sampling should start in each detection cycle under the initial setting. It is usually set to a fixed value by the program as the reference point for adjusting the start time. When the synchronization state is good (the offset is very small), the sampling start point is close to the default value; when the synchronization is out of adjustment, the offset is corrected according to the feedback result. offset (t) is the average sampling offset value of the current cycle, which represents the absolute average of the time difference between each laser sampling point and its corresponding stone beat point in the current detection cycle t. It is used to reflect the overall time offset trend of the sampling behavior relative to the stone beat in the entire sampling cycle. A positive offset value represents a general lag, and a negative offset value represents a general advance. The larger the absolute value, the greater the error. This parameter is an important basis for whether the sampling starting point needs to be corrected. start (t+1) is the starting time of the next sampling cycle, which means the starting time of the laser particle size analysis unit sampling operation in the next detection cycle t+1. By feedback adjusting the synchronization offset of the previous cycle, it ensures that the start time of the next sampling cycle can better align with the current stone conveying beat, reducing the particle size identification error caused by sampling delay or advance;

[0107] By linking the adjustment coefficient λ(t) with the average offset, the system can dynamically advance or delay the sampling starting point, thereby accurately aligning the changing trend of the stone beat under different operating conditions.

[0108] To improve the sensitivity of the sampling strategy to the beat fluctuation, the sampling control unit dynamically calculates the sampling interval and the sampling window period according to the initial value of the sampling interval and the standard deviation of the offset in the current detection period, and the specific calculation formula is as follows:

[0109] To improve the adaptive ability to the synchronization fluctuation, the sampling control unit further calculates the sampling interval and the sampling window period based on the beat adaptation adjustment coefficient λ(t), the current period average sampling offset value μ offset (t) and the offset standard deviation after adjusting the sampling starting point, and the specific formula is as follows:

[0110]

[0111] In the formula, Δτ(t+1) is the sampling interval of the next period, i.e. the time interval between two consecutive laser samplings, which determines the time accuracy of laser sampling. The smaller the value is, the higher the sampling frequency is, which is used to adjust the sampling density and match the fast or slow changes of the actual beat of the stone. Δτ 0 is the default sampling interval initialized and set as the basic reference value for dynamic adjustment. β is the sampling frequency adjustment sensitivity coefficient, and the value range is 0<β<1, which controls the influence of the offset fluctuation on the adjustment amplitude of the sampling interval. The larger the value is, the stronger the response is. σ offset (t) is the time offset standard deviation of all sampling points in the current period, which reflects the degree of system synchronization fluctuation. The larger the fluctuation is, the less stable the sampling accuracy is, which is used to determine whether the sampling interval should be reduced to improve the granularity and enhance the response ability to the beat fluctuation. N is the number of preset sampling points in each sampling period, which is used to fix the sampling frequency per period to ensure that the information amount in the overall analysis period is consistent even if the sampling density changes. ω(t+1) is the duration of the entire laser particle size analysis process in the next sampling period, which controls the span of each laser sampling process on the time axis and automatically expands or compresses with the change of the sampling interval to ensure the continuity of the granularity accuracy and system response.

[0112] Through the above steps, when the offset fluctuation increases, the system automatically shortens the sampling interval and increases the sampling frequency, thereby realizing the fast closed-loop matching between the laser sampling and the stone beat, and improving the timeliness and accuracy of the detection data.

[0113] Through the above railway ballast automatic unloading detection control method based on remote monitoring, high-precision dynamic synchronization between laser particle size analysis sampling behavior and actual stone conveying rhythm is realized, and the accuracy of particle size identification and the stability of system control are significantly improved. The method reconstructs the complete conveying rhythm curve through unified time marking and time sequence reconstruction of multi-source monitoring data, introduces dynamic offset time sequence and rhythm synchronization difference index to continuously quantitatively evaluate the sampling state, so that the system can realize real-time sensing of the matching degree between sampling and rhythm. When the synchronization offset exceeds the threshold, the sampling control unit can respond in real time by adjusting the sampling start time and sampling frequency to realize closed-loop adaptive optimization of the sampling strategy. This mechanism effectively avoids sampling abnormalities such as "frame skipping" and "re-sampling", prevents particle size identification results from being distorted, ensures the accuracy of subsequent screen vibration frequency and inclination control instructions, and fundamentally guarantees the quality of ballast screening, the stability of the track bed structure and the safety of train operation, and comprehensively improves the intelligence, refinement and reliability level of the railway ballast unloading process.

[0114] The above formulas are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the most recent real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0115] The above only describes some exemplary embodiments of the present application by way of illustration, and it is self-evident that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present application. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present application.

[0116] It should be noted that in this paper, if there are relationship terms such as first and second, they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0117] It should be understood that the size of the sequence number of the above processes does not mean the order of execution in various embodiments of the present application, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0118] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0119] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0120] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment according to actual needs.

[0121] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit.

[0122] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can easily think of changes or replacements within the technical scope disclosed in the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0123] The above only describes some exemplary embodiments of the present application by way of illustration, and it is needless to say that those skilled in the art can modify the described embodiments in various ways without deviating from the spirit and scope of the present application. Therefore, the above figures and descriptions are illustrative in nature and should not be understood as limiting the scope of protection of the claims of the present application.

Claims

1. A railway ballast automatic unloading detection and control method based on remote monitoring, characterized in that: The following steps are involved: Real-time data collection of the conveyor speed, screen vibration frequency, and actual passage time of the ballast at each key inspection point during the unloading process of railway ballast is collected, and all raw inspection data is synchronously uploaded to the data processing module in the form of unified time tags. The data processing module reconstructs the time series of the stone passing time series data at each key detection point based on a unified time tag, and generates a stone conveying beat curve covering the entire process of the conveying device and the screening device; All sampling time series collected by the laser particle size analysis unit during the detection process are input into the data processing module. The current sampling time series is time-aligned with the constructed stone conveying beat curve point by point, and a point-by-point correspondence is established between the two sets of time series. The time offset of each corresponding point is calculated to obtain the dynamic offset time series corresponding to the entire process. The absolute value of the dynamic offset time series is normalized, and the fluctuation amplitude of the time offset is calculated within each detection cycle. Based on the offset and fluctuation amplitude, a beat synchronization difference index is generated to quantify the degree of synchronization between the stone beat and the laser sampling. The beat synchronization difference index is compared with the preset synchronization threshold. If the beat synchronization difference index is greater than the threshold, it is determined that there is a beat mismatch state, and the sampling control unit is triggered to respond to the beat difference information fed back by the detection point; The sampling control unit dynamically adjusts the sampling start time and sampling interval parameters of the laser particle size analysis unit according to the beat mismatch information, optimizes the start and end time and duration of the sampling window, and completes the adaptive adjustment of the laser particle size analysis strategy through real-time collaborative driving of multi-source monitoring data.

2. The method for detecting and controlling railway ballast automatic unloading based on remote monitoring according to claim 1 is characterized in that: The specific steps for real-time collection of the conveyor speed, the vibration frequency of the screening device screen, and the actual time it takes for the stone to pass through each key detection point are as follows: The rotary encoder installed on the drive shaft of the conveyor device collects the number of revolutions of the conveyor belt per unit time, and converts the current linear speed of the conveyor device in real time according to the preset transmission ratio relationship to form continuous conveyor speed time series data; The three-axis acceleration sensor deployed in the frame structure of the screening device synchronously records the acceleration response value of the screen during vibration, and combines the fast Fourier transform algorithm to extract the current main frequency value and harmonic characteristic parameters to reflect the vibration frequency of the screen in the actual working state; Based on the photoelectric identification sensors installed at multiple key stone passing positions, the start and end times of the stone blocking the sensor light beam are detected. By calculating the blocking duration and trigger time point, the actual passing time of the stone through each key detection point is determined. The three types of raw data, namely the conveying speed, vibration frequency and stone passing time, are encoded based on a unified timestamp and uploaded to the data processing module synchronously in chronological order.

3. The method for detecting and controlling railway ballast automatic unloading based on remote monitoring according to claim 1 is characterized in that: In the data processing module, based on the unified time tag, the specific steps for reconstructing the time series of the stone at each key detection point through the time series data are as follows: The stone passing time data encoded with a unified time tag uploaded by each key detection point are arranged in chronological order, and an initial local time series is constructed for each detection point to reflect the instantaneous passing status of the stone at each detection point; Perform difference calculation on the intervals between consecutive time points in the local time series to calculate the transmission time of the stone between different detection points. At the same time, combined with the operating speed parameters of the conveying device, the time interval is converted into the spatial path distance to achieve the mapping between time and physical conveying path. The time-distance mapping results of all detection points are cascaded and spliced ​​in structural order, and smoothly connected through continuity interpolation and multi-point registration algorithm to form a unified stone transportation rhythm master curve.

4. The method for detecting and controlling railway ballast automatic unloading based on remote monitoring according to claim 1, characterized in that: The specific steps for inputting all sampling time sequences collected by the laser particle size analysis unit into the data processing module and performing time registration point by point with the constructed stone conveying beat curve are as follows: The sampling time sequence generated by the laser particle size analysis unit during the actual detection process is standardized according to the time label, and sampling points with abnormal intervals are eliminated to form a high-precision time sampling sequence; The data processing module calls the constructed stone conveying beat curve, takes the unified time axis as the benchmark, selects the beat time point closest to each sampling moment on the curve, establishes a one-to-one correspondence between the sampling moment sequence and the beat curve time point, and marks the specific time difference between each pair of corresponding points; The time differences between all sampling points and their corresponding beat points are used as basic data to form a new dynamic offset time series in chronological order.

5. The method for detecting and controlling railway ballast automatic unloading based on remote monitoring according to claim 4 is characterized in that: The steps to use the time differences between all sampling points and their corresponding beat points as basic data and compose a new dynamic offset time series in chronological order are as follows: The data processing module receives each sampling moment collected by the laser particle size analysis unit and locates the beat time point closest to each sampling moment on the unified time axis based on the established beat curve, establishes a one-to-one correspondence between the two, and extracts the time difference of each corresponding group; Perform absolute value processing on the time differences between all matched sampling time points and beat time points to remove the directional interference of the time differences, and arrange them in the order of laser sampling time to construct a set of offset sequences with strict time sequence order; The time-arranged offset sequence is input as a new dynamic offset time series to continuously reflect the synchronization error state of the laser sampling process with respect to the actual stone flow rhythm.

6. The method for detecting and controlling railway ballast automatic unloading based on remote monitoring according to claim 1, characterized in that: The specific steps for processing the dynamic offset time series to generate a beat synchronization difference index for quantifying the degree of synchronization between the stone beat and the laser sampling are as follows: For each time offset in the obtained dynamic offset time series, the absolute value is taken and linear normalization is performed according to the preset maximum allowable time offset range, so that all offset values ​​are standardized to continuous values ​​between 0 and 1; Divide the time window according to the defined detection period, extract all the normalized offset values ​​in the current period in each detection period, and calculate the range and standard deviation of the current group of data to form a statistical indicator reflecting the fluctuation amplitude of beat synchronization stability; The average normalized offset value in each detection cycle and its corresponding fluctuation amplitude index are comprehensively calculated through a weighted function to generate a beat synchronization difference index representing the synchronization quality between the current cycle beat and sampling.

7. The method for detecting and controlling railway ballast automatic unloading based on remote monitoring according to claim 1, characterized in that: The sampling control unit compares and analyzes the beat synchronization difference index calculated in the current detection cycle with the preset beat synchronization difference index reference threshold, and calculates the beat adaptation adjustment coefficient. The calculation expression is as follows: Where, Δ sync (t) is the beat synchronization difference index, Δ ref is the reference threshold of the beat synchronization difference index, α is the sensitivity adjustment coefficient, and λ(t) is the beat adaptation adjustment coefficient; After obtaining the beat adaptation adjustment coefficient λ(t), the sampling control unit calculates the new sampling start time of this cycle based on the default sampling start time of the previous cycle and the average offset between the laser sampling point and the beat point in the current cycle. The calculation expression is as follows: Where, is the default sampling start time, μ offset (t) is the average sampling offset value of the current period, τ start (t+1) is the starting time of the next cycle sampling; To improve the sampling strategy's sensitivity to beat fluctuations, the sampling control unit dynamically calculates the sampling interval and sampling window period based on the initial value of the sampling interval and the standard deviation of the offset within the current detection period. The specific calculation formula is as follows: In order to improve the adaptive ability to synchronization fluctuations, the sampling control unit further adjusts the sampling starting point based on the beat adaptation adjustment coefficient λ(t) and the average sampling offset value μ of the current cycle. offset (t) and the offset standard deviation, calculate the sampling interval and sampling window period. The specific formula is as follows: Where Δτ(t+1) is the next cycle sampling interval, Δτ 0 is the default sampling interval set by initialization, β is the sampling frequency adjustment sensitivity coefficient, σ offset (t) is the time offset standard deviation of all sampling points in the current cycle, N is the preset number of sampling points in each sampling cycle, and ω(t+1) is the duration of the entire laser particle size analysis process in the next sampling cycle.

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