Signal acquisition, processing and analysis method and system of particle detector array

Abstract

The invention relates to the technical field of particle detectors, in particular to a signal acquisition, processing and analysis method and system for a particle detector array, and the method comprises the following steps: segmenting a channel amplitude, marking a stable segment by using a range ratio threshold value, calculating a difference ratio, screening monotonically rising channel marking transition, extracting an earliest excitation channel as a starting point, and carrying out signal processing. And retrieving adjacent channel screening time differences in the window as candidates, and taking the channel with the angle change smaller than a threshold value as a trunk section according to time sequence to generate a path structure. According to the method, the amplitude range of a channel in a fixed period is extracted, a stable signal segment is screened, preliminary signal structuring is completed, a rising trend channel is identified through a difference ratio, the first excitation time is extracted to establish a space starting point index, and path candidates are screened in combination with the time difference of adjacent channels and states. Path trunk segments consistent in direction are extracted through angle change analysis, particle path reconstruction is achieved, multi-dimensional parameters synergistically improve the recognition precision, the analysis capability is enhanced, and the misjudgment rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of particle detectors, and in particular to a method and system for collecting, processing and analyzing signals of a particle detector array. Background Art

[0002] The technical field of particle detectors involves related methods and devices for precise detection and measurement of high-energy particles. Core issues include the signal conversion mechanism after the interaction between particles and detection media, the construction of signal acquisition circuits, and the temporal and spatial correlation analysis of data. In terms of the overall approach, a multi-channel detection structure is constructed and an electronic system is used to convert the physical signals caused by particle incidence into measurable electrical signals. Synchronous acquisition, digital conversion and subsequent analysis are achieved through specific paths to identify particle types and trajectory information. Among them, the signal acquisition, processing and analysis method of traditional particle detector arrays refers to the signal acquisition and identification process for each channel in a multi-unit detector array. It usually adopts a channel-by-channel charge integration and amplification method to obtain electrical signals, uses a fixed threshold to identify charge triggering events, and then uses a channel-by-channel distributed processing circuit to classify, record and preliminarily analyze the event data. This method relies on a preset sampling period and static data cache, and extracts and analyzes data through linear scanning and rule discrimination.

[0003] Traditional methods use charge integration and amplification of each channel's signal, combined with fixed threshold triggering and discrimination, and data sampling and regular scanning under static buffering conditions. This can easily lead to delayed recognition or misidentification of low-amplitude fluctuations, especially in high-frequency transient interference environments, and the inability to dynamically adjust the discrimination strategy. Because the sampling process relies on a preset period and linear scanning logic, in the case of multi-point triggering and high-throughput particle response, inter-channel correlation information is often lost, making it difficult to restore the true spatiotemporal evolution path. The data classification and analysis process lacks a mechanism for analyzing amplitude trends over continuous time periods. Relying solely on threshold triggering and static sampling can easily overlook weak, continuous response signals along the particle's traversal path. Path reconstruction lacks consideration of spatial layout and angular trends, making it difficult to accurately identify the main channel path segments. This can lead to gaps or misconnections in the particle trajectory reconstruction, compromising the accurate interpretation of particle type and energy characteristics. These issues are particularly prominent in detector arrays with high channel density or high data concurrency, limiting the system's adaptability and scalability to complex physical events. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a signal acquisition, processing and analysis method and system for a particle detector array.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for collecting, processing and analyzing signals from a particle detector array, comprising the following steps: S1: Obtain the signal amplitude sequence of each channel in the detection channel sampling, segment it according to a fixed period, extract the difference between the maximum and minimum values, and compare it with the amplitude mean square fluctuation threshold, mark the signal segment within the stable range, and generate a set of array stable sampling time segments; S2: Calling the amplitude data of the array stable sampling time segment set, calculating the difference ratio of adjacent time segments, comparing it with the amplitude transition rate threshold, screening the channels with monotonically increasing transition rates in the continuous time segments, classifying the amplitude evolution sequence as a transition-capable state, and generating a channel signal transition mark table; S3: calling the transitionable state channel in the channel signal transition mark table, extracting the first excitation timestamp, screening the minimum time value and recording the corresponding channel number, marking the channel as the path starting position, and generating a path starting excitation time point record table; S4: According to the channel number in the path starting excitation time point record table, adjacent channels are retrieved, the excitation time difference is calculated, the channels whose time difference is within the critical time window threshold and marked as transitionable are retained, the channel number and spatial position are recorded, and an initial path candidate channel index set is generated.

[0006] As a further solution of the present invention, the array stable sampling time segment set includes the start time of the stable signal segment and the end time of the stable signal segment, the channel signal transition mark table includes the transition channel number and the channel transition status, the path starting excitation time point record table includes the starting excitation channel number and the first excitation timestamp, and the initial path candidate channel index set includes the candidate channel number and the candidate channel spatial position information.

[0007] As a further solution of the present invention, the specific steps of S1 are: S101: Acquire the signal amplitude sequence of the detection channel and segment it according to a fixed period, extract the maximum and minimum values ​​of each segment of the signal amplitude sequence, calculate the difference between the maximum and minimum values, and generate an amplitude difference sequence corresponding to the period; S102: Based on the amplitude difference sequence, calling the amplitude mean square fluctuation threshold, comparing the difference with the amplitude mean square fluctuation threshold, determining the time period in which the difference does not exceed the threshold, and forming a stable signal marking interval in the channel; S103: determining the common intersection range of all channel stable intervals according to the stable signal marking intervals in the channels, and recording the start and end times to generate an array stable sampling time segment set.

[0008] As a further solution of the present invention, the specific steps of S2 are: S201: calling the amplitude data of the time periods in the array stable sampling time segment set, calculating the amplitude difference changes between adjacent time periods, and obtaining an amplitude difference ratio sequence; S202: comparing the amplitude difference ratio sequence with the amplitude transition rate threshold, identifying channels exceeding the threshold in a continuous time period, and forming a channel transition trend sequence; S203: judging whether the differential ratio of the channel is monotonically increasing according to the channel transition trend sequence, identifying and marking the corresponding channel as a transition-capable state, and generating a channel signal transition marking table.

[0009] As a further solution of the present invention, the specific steps of S3 are: S301: calling the transitionable state channel in the channel signal transition mark table, extracting the first excitation timestamp of the channel, establishing a corresponding relationship between the channel number and the timestamp, and generating a channel excitation time mapping value; S302: According to the channel excitation time mapping value, the minimum timestamp is screened and the corresponding channel number is extracted, marked as the path starting position, and a path starting position number set is generated; S303: creating a corresponding record of the starting channel number and the excitation timestamp according to the path starting position number set, and generating a path starting excitation time point record table.

[0010] As a further solution of the present invention, the specific steps of S4 are: S401: extracting the excitation time point information of the corresponding adjacent channels according to the channel numbers in the path starting excitation time point record table, and obtaining an excitation time difference value set according to the excitation time differences between the channels; S402: Calling the excitation time difference set, combining the status mark information of the corresponding channel, determining whether the excitation time difference is within the critical time window threshold, and whether the status mark meets the transition condition, and obtaining a transition candidate channel number set; S403: Extracting spatial position information corresponding to each channel according to the transition candidate channel number set, integrating the channel number and the spatial position, and obtaining an initial path candidate channel index set.

[0011] As a further embodiment of the present invention, the method further comprises: S5: Calling the channel space coordinates in the initial path candidate channel index set, arranging the channel numbers in order of excitation time, extracting the angle change values ​​of three consecutive points, comparing them with the path continuous deflection angle limit value, selecting the path segment where the angle change between the three consecutive points is less than the limit value as the path trunk segment, integrating the number and direction information, and generating the array path direction sorting structure; The array path direction sorting structure includes path channel number sorting and path trunk segment direction information.

[0012] As a further solution of the present invention, the specific steps of S5 are: S501: calling the channel space coordinates in the initial path candidate channel index set, sorting the channel numbers according to the excitation time, extracting the path angle information formed by the three adjacent points after sorting, and generating a channel three-point angle sequence value; S502: Based on the three-point angle sequence values ​​of the channel and the continuous deflection angle limit value of the path, path segments with angles less than the limit value are screened to generate a set of path segments that meet the angle threshold; S503: Based on the set of path segments that meet the angle threshold, the channel numbers and corresponding spatial direction features are integrated, the corresponding spatial coordinate difference information is called, the direction parameters of each path segment are extracted in the order of the channel numbers, and an array path direction sorting structure is generated.

[0013] As a further embodiment of the present invention, the amplitude mean square fluctuation threshold is a standard threshold used to determine whether the fluctuation range of the signal amplitude sequence within a continuous sampling period is in a state that can be regarded as stable, and is used to detect the degree of fluctuation of the channel signal within the time window and identify the stable amplitude response segment; The amplitude transition rate threshold is used to determine whether the amplitude change rate of the channel signal between adjacent time periods reaches the criterion for recognizable transitions, and is used to evaluate whether the increase in the channel amplitude in a short period of time indicates the persistence and trend of the particle excitation behavior; The monotonically rising transition rate refers to the fact that the signal amplitude transition rate calculated over multiple consecutive time periods shows a continuously increasing trend without any downward fluctuations. It is used to identify whether the channel has a phenomenon of continuous amplitude increase over time and distinguish between a truly stable rising particle signal and a discontinuous change process disturbed by noise. The amplitude evolution sequence refers to a sequence formed by arranging the maximum amplitudes recorded by channels in adjacent stable time periods in a particle detector array over time; The critical time window threshold is the upper limit of the time difference used to determine whether two channels respond to the same particle event. It limits the maximum range of the excitation time difference between channels. Only when the excitation time difference between two channels is less than the threshold, are they considered to belong to the same particle path segment in time. The path continuous deflection angle limit value is a set geometric threshold used to limit the degree of continuous change in the path direction. It is derived from the theoretical estimation of the average scattering angle of particles in the medium or the path smoothness during detector design, and is set jointly in the spatial resolution and actual detection accuracy.

[0014] The signal acquisition, processing and analysis system of the particle detector array includes: The signal stability extraction module obtains the voltage signal data of the channels in the particle detector array, divides the sampling segments according to the set period, extracts the difference between the maximum and minimum values ​​of each segment, and combines the signal fluctuation amplitude within the segment with the set threshold to judge. The segments that meet the conditions are identified and marked as signal stability intervals. The corresponding start and end times are recorded to generate a set of array stable sampling time segments. The channel transition identification module calls the amplitude information of the channels in the array stable sampling time segment set, extracts the difference between adjacent time segments, compares it with the transition rate threshold, identifies the channel time segments with trend changes, marks the status as transition-capable, and generates a channel signal transition mark table; The path start calibration module calls the transitionable channels in the channel signal transition mark table, extracts the first excitation time, establishes the corresponding relationship between the channel and time, selects the channel number with the earliest excitation time, and generates a path start excitation time point record table; The adjacent channel screening module calls the channel number in the path starting excitation time point record table, searches for adjacent channels in the spatial structure, determines whether the excitation time difference is within a limited range, and checks the transition state flag, screening the number and spatial information that meet the conditions, and generates an initial path candidate channel index set; The path structure construction module calls the channel coordinates in the initial path candidate channel index set, arranges the channel numbers in the order of excitation time, determines in turn whether the angle change between three consecutive points meets the deflection restriction condition, selects the path segment numbers and arrangement order that meet the requirements, and generates the array path direction sorting structure.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the amplitude extreme difference of the channel within a fixed period is extracted and the stable signal segments are screened to achieve preliminary structural division of the array signal. The monotonically rising trend channel is identified by using the differential ratio of adjacent time periods. The first excitation time is extracted to establish a spatial starting point index. The path candidate set is constructed by combining the excitation time difference of adjacent channels with state screening. Through continuous angle change analysis, the main path segments with consistent directions are screened to complete high-confidence particle path reconstruction. Multi-dimensional parameters synergistically improve the trajectory recognition accuracy and robustness, enhance the particle event analysis capability, and reduce the noise interference misjudgment rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Schematic diagram of the steps of the present invention; Figure 2 is a flow chart of the steps of S1 of the present invention; Figure 3 This is a flow chart of the steps of S2 of the present invention; Figure 4 This is a flow chart of the steps of S3 of the present invention; Figure 5 This is a flow chart of the steps of S4 of the present invention; Figure 6 This is a flow chart of the steps of S5 of the present invention; Figure 7 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0019] See also Figure 1 , a signal acquisition, processing and analysis method of a particle detector array, comprising the following steps: S1: Obtain the signal amplitude sequence of each channel in the detection channel sampling data, segment it according to a fixed period, extract the difference between the maximum and minimum values, compare the difference with the amplitude mean square fluctuation threshold, mark the signal segments within the stable range, and record the start and end time intervals to generate a set of array stable sampling time segments; The amplitude mean square fluctuation threshold is a standard threshold used to determine whether the fluctuation range of the signal amplitude sequence within a continuous sampling period is in a stable state. It is used to detect the fluctuation degree of the channel signal within the time window and identify the stable amplitude response segment. S2: Call the amplitude data of the time period in the array stable sampling time segment set, calculate the difference ratio between adjacent time periods, and compare it with the amplitude transition rate threshold. Mark the channels that show a monotonically increasing transition rate in consecutive time periods, and classify the corresponding amplitude evolution sequence as a transition-capable state, generating a channel signal transition mark table; The amplitude transition rate threshold is used to determine whether the amplitude change rate of the channel signal between adjacent time periods reaches the criterion for recognizable transitions. It is used to evaluate whether the increase in the channel amplitude in a short period of time indicates the persistence and trend of the particle excitation behavior. The monotonic rising transition rate refers to the fact that the signal amplitude transition rate calculated over multiple consecutive time periods shows a continuously increasing trend without any downward fluctuations. It is used to identify whether the channel amplitude continues to increase over time and to distinguish between a truly stable rising particle signal and a discontinuous change process disturbed by noise. The amplitude evolution sequence refers to the sequence formed by the arrangement of the maximum amplitudes recorded by the channels in adjacent stable time periods in the particle detector array over time; S3: Call the channel marked as transitionable in the channel signal transition mark table, extract the corresponding first excitation timestamp, filter the minimum time value and record the corresponding channel number, mark the channel as the starting position of the path, establish the corresponding relationship between the channel number and the excitation time, and generate a path starting excitation time point record table; S4: According to the channel number in the path starting excitation time point record table, the adjacent channels are retrieved and the excitation time difference is calculated. The channels within the critical time window threshold range and marked as transitionable are selected, the numbers and spatial position information are recorded, and the initial path candidate channel index set is generated; The critical time window threshold is the upper limit of the time difference used to determine whether two channels respond to the same particle event. It limits the maximum range of the excitation time difference between channels. Only when the excitation time difference between two channels is less than the threshold, they are considered to belong to the same particle path segment in time. S5: Call the channel space coordinates in the initial path candidate channel index set, arrange the channel numbers according to the excitation time sequence, extract the angle change values ​​of three consecutive points, compare them with the path continuous deflection angle limit value, select the path segment where the angle change between the three consecutive points is less than the limit value as the path trunk segment, integrate the number and direction information, and generate the array path direction sorting structure; The path continuous deflection angle limit value is a set geometric threshold used to limit the degree of continuous change in the path direction. It comes from the theoretical estimate of the average scattering angle of particles in the medium or the path smoothness during detector design, and is set jointly in the spatial resolution and actual detection accuracy.

[0020] The array stable sampling time segment set includes the start time of the stable signal segment and the end time of the stable signal segment; the channel signal transition mark table includes the transition channel number and the channel transition status; the path start excitation time point record table includes the start excitation channel number and the first excitation timestamp; the initial path candidate channel index set includes the candidate channel number and the candidate channel spatial position information; the array path direction sorting structure includes the path channel number sorting and the path trunk segment direction information.

[0021] See also Figure 2 , the specific steps of S1 are: S101: Acquire the signal amplitude sequence of the detection channel and segment it according to a fixed period, extract the maximum and minimum values ​​of each segment of the signal amplitude sequence, calculate the difference between the maximum and minimum values, and generate an amplitude difference sequence corresponding to the period; When acquiring detection channel signals, the continuous signal amplitude sequence is first segmented and processed according to a set period. For example, for a single channel in an array radar, if the sampling period is 1 millisecond and the total sampling time is 10 seconds, 10,000 amplitude data points will be generated. Setting the segmentation period to 100 milliseconds means that each segment contains 100 consecutive sampling points. Within each segment, a linear scan is used to sequentially compare the amplitudes of each sampling point to obtain the maximum and minimum values. For example, if a segment contains sampling values ​​of 3.1, 2.8, 3.6, 4.0, and 2.7, the maximum value is 4.0 and the minimum value is 2.7, resulting in an amplitude difference of 1.3. After processing all segments in sequence, a sequence consisting of the differences between each segment is formed. For example, if the total number of segments is 100, 100 difference values ​​are ultimately obtained, forming an amplitude difference sequence of 100. This difference sequence serves as an important input reference for subsequent determination of the signal fluctuation range and can also be used to evaluate the stability characteristics of the detection channel signal over different time periods.

[0022] S102: Based on the amplitude difference sequence, the amplitude mean square fluctuation threshold is called, the difference is compared with the amplitude mean square fluctuation threshold, and the time period in which the difference does not exceed the threshold is determined to form a stable signal marking interval in the channel; Based on the existing amplitude difference sequence, we need to further extract signal stability characteristics. First, we assess the degree of fluctuation of each difference segment. This can be done by summing the squares of the deviations between the mean of the entire sequence and each difference segment, and then taking the square root of the average as the baseline fluctuation value. For example, assuming the difference sequence contains five data segments: 1.0, 1.2, 0.9, 1.1, and 1.3, with an overall mean of 1.1, the deviations from the mean for each segment are -0.1, +0.1, -0.2, 0, and +0.2, respectively. The squares of each deviation are summed and then divided by the number of segments to obtain a value reflecting the amplitude of the fluctuation. This value is multiplied by a coefficient of 1.5 as a threshold. If the deviation of a segment difference from the overall mean is less than the threshold, the signal segment is considered stable. All difference segments are processed sequentially to identify all time periods that meet the stability criteria, and the start and end times are recorded. For example, if the difference values ​​of five consecutive segments all fall within the set range, it means that the time interval they cover is a stable signal interval. For example, if the interval from the 3rd to the 7th segment is stable, the corresponding time is from 0.2 seconds to 0.7 seconds.

[0023] S103: Determine the common intersection range of all channel stable intervals based on the stable signal marking intervals in the channels, record the start and end times, and generate an array stable sampling time segment set; After obtaining the stable signal interval within each channel, it is necessary to further determine the intersection intervals between different channels and identify the time ranges in which all channels are stable within the same time period. Taking three channels as an example, the following time periods are marked as stable: the first channel is between 0.1 seconds and 0.5 seconds and 0.7 seconds and 1.2 seconds, the second channel is between 0.3 seconds and 0.9 seconds, and the third channel is between 0.4 seconds and 1.1 seconds. By comparing the start and end times of each time period, their common parts are found. That is, the start time of each intersection is the maximum value of the start times of each segment, and the end time is the minimum value of the end times of each segment. If the start time is earlier than the end time, the intersection is valid. According to the above rules, two intersection time periods are obtained: 0.4 seconds to 0.5 seconds and 0.7 seconds to 0.9 seconds. These two time periods are the valid time intervals for common stable sampling in the multi-channel system. The results can be recorded for subsequent multi-channel signal joint processing or consistency analysis applications.

[0024] See also Figure 3 , the specific steps of S2 are: S201: Calling the amplitude data of the time period in the array stable sampling time segment set, calculating the amplitude difference change between adjacent time periods, and obtaining an amplitude difference ratio sequence; The specific calculation formula for calculating the amplitude difference change between adjacent time periods is: ; Calculate the amplitude difference ratio sequence; in, Representative The amplitude difference ratio between a time period and the next time period, Representative The amplitude of the time period, Representative The amplitude of the time period, Representative The variance of the original amplitude set in the time period, Represent all The arithmetic mean of the amplitudes in each time period, Representative The amplitude of the time period, Represents the total number of time periods, is the time period serial number; The amplitude data is sampled in a stable time period by an array-type seismic acceleration sensor with a sampling frequency of 100 Hz per second. A total of 1000 sets of amplitude data are collected in a signal monitoring time of 10 seconds. The amplitude unit is m / s 2The data comes from the Y-axis component of channel 1 in the array structure. In data segmentation, the time period is set to 500ms per segment, with a total of 20 segments (n=20), each consisting of 50 amplitude data points.

[0025] For the i=7 segment, the parameters involved are calculated as follows: and Calculation method: The amplitude set of the 7th segment is: ; 50 items in total By taking the average value of the amplitude of this segment, we can get , using the formula: ; The mean of the 8th amplitude set is m / s 2 , obtained through statistics from the data center calculation module.

[0026] Calculation method (segment 7 variance): Calculated using the statistical variance formula: ; Calculation method: The amplitude mean of each of the 20 segments is calculated by sampling and averaging to obtain the average value of the 20 segments. The summary average value is: ; Calculation method: The sum of the deviations of all segment means from the overall mean is: ; Then the mean absolute deviation is: ; Submit various parameters into the formula for calculation: First calculate the difference term: ; Compute dimensionless radical terms: ; The bracketed part is: ; Denominator: ; Finally, substituting into the formula we get: ; The results show that the amplitude difference between the two adjacent time periods is 0.001913 m / s after standardization correction. 2 The numerical result is a modified expression of the original differential amplitude after weighted adjustment by the dimensionless variance ratio and the mean deviation, representing the intensity index of the amplitude change between adjacent segments. This intensity index will be used as a record in the amplitude difference ratio sequence in the subsequent steps for fluctuation pattern recognition or structural anomaly analysis. The calculation logic in the formula is based on the idea of ​​compound quantification of the amplitude change intensity. First, the difference between the mean amplitudes of two adjacent time periods is calculated. The most basic time series variation is extracted, and the difference is used as the basis for the intensity of the variation. To enhance the response of this term to data stability and overall fluctuation, two dimensionless normalization correction factors are introduced and multiplied with them. The first term is , which is used to measure the degree of amplitude fluctuation within the current time period, that is, the ratio of the standard deviation to the amplitude. This structure introduces a square root operation to reduce the nonlinear amplification of extreme fluctuations on the overall ratio while keeping the direction unchanged. The second term is , represents the average deviation of the amplitude of all time periods relative to the overall average, that is, the global absolute deviation rate, which is used to reflect whether the overall time series is in a stable or dispersed state. The sum of these two items forms a combined enhancement weight of local fluctuation and global deviation. Finally, the enhancement term is multiplied by the original amplitude difference to construct a composite change index, which is then divided by The relative amplitude is normalized to eliminate the influence of the amplitude level of the current time period on the change intensity, ensuring that the output reflects the true change trend rather than being affected by the absolute value offset. The overall logic uses the difference, multiplication, square root, addition and normalization structure to complete the hierarchical change intensity extraction expression; The amplitude difference ratio series is a sequential numerical indicator constructed based on the characteristics of signal amplitude changes within continuous time periods. It aims to quantitatively express the changing trend, magnitude, and relative significance of the amplitudes between adjacent time periods. Each element in this series is formed by the interaction of the average amplitude difference between adjacent time periods and its local volatility, overall discreteness, and relative level normalization factor. It can ensure a sensitive response to instantaneous differences while suppressing misjudgments caused by noise or overall offset. Through serial arrangement, this ratio series shows the dynamic continuous change pattern between time periods, which can be used to further identify mutation segments, fluctuation anomalies, structural disturbance signals, or continuous change trends, providing data support for signal interpretation and structural health monitoring.

[0027] S202: comparing the amplitude difference ratio sequence with the amplitude transition rate threshold, identifying channels exceeding the threshold in a continuous time period, and forming a channel transition trend sequence; The generated differential ratio sequence is used in combination with the preset amplitude transition rate threshold to make a preliminary judgment on the channel transition. First, the threshold needs to be set. Generally, the statistical characteristics in the differential ratio sequence are used as the basis. For example, the mean and standard deviation of all ratios are counted. The threshold is conventionally set to the mean plus twice the standard deviation. For example, if the mean is 3.5% and the standard deviation is 1.5%, the threshold is 6.5%. Then, the differential ratio between each time period is compared. If the ratio of a certain period is greater than 6.5%, the channel corresponding to the time period is preliminarily judged to be in a transition state. In continuous detection, if a channel is in multiple If the ratio of a channel exceeds the threshold in consecutive time periods, for example, the ratio of the channel in the second and third sections is 7.0% and 8.3% respectively, then it is considered that the channel has a transition trend in the corresponding time period, forming a channel transition trend sequence. For example, it is marked as 0, 1, 1, 0, which means that the second and third sections have transitions, and the first and fourth sections have no transitions. This trend sequence is generated by traversing the ratio sequence of each channel in turn. During the process, each ratio needs to be compared with the threshold and the judgment results are recorded. Finally, trend data classified by channel number is constructed. In subsequent steps, this trend can be used as input for in-depth analysis of signal changes.

[0028] S203: judging whether the channel difference ratio is monotonically increasing based on the channel transition trend sequence, identifying and marking the corresponding channel as a transition-capable state, and generating a channel signal transition mark table; Based on the generated channel transition trend sequence, the ratio change of the continuous transition part is further judged. The judgment standard is whether the differential ratio shows a monotonically increasing trend, that is, the latter ratio is greater than the previous ratio. For example, if the differential ratio in the continuous transition section of a channel is 5.5%, 6.3%, and 7.2%, then the monotonically increasing condition is met and this channel can be marked as a transition state. In the judgment, all time periods marked as transitions must be extracted first, and the corresponding ratios are recorded in sequence. Then, the adjacent ratios are compared one by one to see if they meet the increasing requirements. Each transition trend is tested once in this way. If there is a non-increasing ratio in the sequence, then the transition trend is tested once. The channel does not meet the transition conditions. This processing method avoids misjudgment caused by short-term, random changes. When making judgments, the ratios can be uniformly scaled to the percentage range before normalization to improve the comparability of trends between different channels. For example, the average amplitudes of a channel in the fifth to seventh segments are 1.1, 1.3, and 1.6, respectively, with differences of 0.2 and 0.3. The ratios are 15.4% and 18.7%, respectively, which are increasing trends and can be included in the signal transition mark table. The mark table records the channel number, transition start and end segments, ratio sequence, and whether it is increasing. The channel status results are batch filtered through judgment rules, and a structured output is formed for subsequent use.

[0029] See also Figure 4 , the specific steps of S3 are: S301: calling a transition state channel in a channel signal transition mark table, extracting the first excitation timestamp of the channel, establishing a correspondence between the channel number and the timestamp, and generating a channel excitation time mapping value; When accessing a transition-capable channel from the channel signal transition flag table, the pre-set flag table must first be read. This table records the level changes for each channel within each sampling cycle, indicating whether a transition occurs from low to high or from high to low. In engineering implementations, a sampling module with microsecond accuracy is typically used to sample each channel's state at a fixed interval. For example, assuming there are four channels, numbered T1 to T4, and a sampling period of 10μs, the corresponding channel state sequence is as follows: T1 changes in the third cycle, T2 changes in the fifth cycle, T3 changes in the first cycle, and T4 remains unchanged. When reading data, the level values ​​at the current sampling point and the previous sampling point are compared for each channel. If a difference is found, a transition is identified. The time of the first transition is paired with the channel number to form a mapping table. For example, if the first transition of T1 occurs at the third sampling point, it is converted to 30 μs and forms a key-value pair with T1. The result is that T1 corresponds to 30 μs, T2 corresponds to 50 μs, T3 corresponds to 10 μs, and T4 is not included. After processing each channel, a complete excitation time mapping record table is compiled. The table structure is channel number corresponding to the first transition time, which can be used for subsequent steps.

[0030] S302: According to the channel excitation time mapping value, the minimum timestamp is screened and the corresponding channel number is extracted, marked as the path starting position, and a path starting position number set is generated; Based on the generated channel excitation time mapping values, the minimum timestamp is found. Minimum value comparison logic is typically used to traverse all time values, filter out the item with the minimum value, and obtain the corresponding channel number. If the minimum value is unique, only that channel number is retained. If multiple channels have the same minimum timestamp, all of them are included in the starting channel number set. For example, if the mapping table shows T1 corresponding to 30μs, T2 corresponding to 50μs, and T3 corresponding to 10μs, the minimum value is 10μs, corresponding to T3, and the starting number set is T3. If both T1 and T2 are 10μs, the starting number set includes T1 and T2. During execution, each item in the mapping record is traversed, and the time value is compared with the current minimum record value. If it is less than the current value, it is replaced; if it is equal to the current value, it is added to the number set in parallel. This ultimately generates the path starting number set, which provides the basic input for subsequent path time records.

[0031] S303: creating a corresponding record of the starting channel number and the excitation timestamp according to the path starting position number set, and generating a path starting excitation time point record table; Based on the path starting position number set, the corresponding excitation time values ​​are read one by one. By searching the generated excitation time mapping records, the channel numbers and excitation times are paired to form a time record table. For example, if the path starting number set contains T1 and T2, corresponding to excitation times of 10μs and 10μs, respectively, two records are generated in the record table: one for T1 and 10μs, and one for T2 and 10μs. During execution, the path starting number set is sequentially traversed, and the time values ​​in the mapping table are searched by number. The results are written in order to the result table, ultimately forming a clear correspondence between the starting channel number and the timestamp. This record table serves as the input for subsequent modules such as channel path construction, time series analysis, and data alignment.

[0032] See also Figure 5 , the specific steps of S4 are: S401: extracting the excitation time point information of the corresponding adjacent channels according to the channel numbers in the path starting excitation time point record table, and obtaining an excitation time difference set based on the excitation time differences between the channels; Each channel number in the path start excitation time point record table must correspond to an excitation time point. Each channel's excitation data should be stored using a unified timestamp format. For example, if the time point corresponding to channel 3 is 107.1ms, the adjacent channel numbers must be determined. For example, for channels 2 and 4, their excitation times are 106.3ms and 108.0ms, respectively. The excitation time differences between them and channel 3 are calculated, yielding values ​​of 0.8ms and 0.9ms, respectively. All calculated time differences are then grouped together for subsequent calculations. Excitation time records should be expressed in high-precision units, preferably converted to nanoseconds and stored as unsigned integers. Each channel's information should be managed in a structured manner, including the channel number, excitation time value, and validity status. In industrial equipment vibration signal monitoring, if channel 3 is the current excitation source and the excitation times of adjacent channels are close, this indicates a possible path jump, and these time differences should be extracted for further analysis. In this process, the excitation time data can be derived from the continuous recording cache of the data collector. The system sets the logical scanning interval to 2ms. All adjacent channels falling into this time window participate in the time difference calculation. Through the automated processing flow, all eligible channel combinations are traversed in turn to form a standardized time difference data set for subsequent judgment.

[0033] S402: Calling the excitation time difference set, combining the status mark information of the corresponding channel, determining whether the excitation time difference is within the critical time window threshold range, and whether the status mark meets the transition condition, and obtaining the transition candidate channel number set; Once the set of excitation time differences is formed, it is cross-referenced with the channel status information corresponding to each difference to extract the current state of each pair of channels, including three labels: "active," "transitional," or "shielded." The channel states are retrieved one by one by consulting a state tag table and then determined to see if they meet the specified transition conditions. The time difference is determined based on whether it is less than a set critical time window threshold. For example, if the threshold is set to 1.5ms, a channel pair is considered to have a transition potential only if the time difference between the two channels is less than this threshold and both channels are in the active or transitional state. For monitoring the internal structure of large equipment, if the time difference between channels 2 and 3 is 0.8ms and the channel state is active, and the time difference between channels 3 and 4 is 0.9ms and channel 4 is in the transitional state, both channels 2 and 4 are included in the candidate channel set. In a specific implementation, the critical time window threshold is determined based on the system sampling rate. For a 1000Hz sampling frequency, it is recommended to set the threshold to 1.5ms, 1.5 times the sampling period, and fine-tune it based on the actual scenario. In a multi-area channel layout system, different weights can be set for different areas. For example, one area has a weight of 60% and a threshold of 1.2ms, while another area has a weight of 40% and a threshold of 1.5ms. The combined weights form a comprehensive threshold, which is obtained through weighted averaging. If the difference is less than this average, it is marked as a candidate transition channel, ultimately forming a transition channel number set for subsequent processing.

[0034] S403: Extracting the spatial position information corresponding to each channel according to the transition candidate channel number set, integrating the channel number and the spatial position, and obtaining an initial path candidate channel index set; After the set of transition channel numbers is formed, the spatial location information of each channel needs to be further extracted. This information is derived from a mapping table between channel numbers and three-dimensional coordinates. The table records the specific location of each channel in real space. For example, the corresponding coordinates for channel 2 are 10.2 horizontally, 5.1 vertically, and 0.0 vertically, while the coordinates for channel 4 are 10.6 horizontally, 5.3 vertically, and 0.0 vertically. All channel numbers and their corresponding coordinates form an index item. Each index item contains the channel number and the three-axis coordinates. By calling a data structure, a rapid search and combination is performed to generate an initial set of candidate path channel indexes. In the mine structure layout monitoring scenario, multiple channels are arranged equidistantly along the mine tunnel and numbered in spatial order. This method can quickly locate suspicious path trends in adjacent time transitions, thereby pre-screening the subsequent path direction. The spatial coordinate information of each channel can be imported through 3D scanning or modeling during the initial system deployment phase. The mapping table is managed using a structured configuration. During the path identification phase, coordinates are retrieved in conjunction with the number index, ultimately forming a spatially referenced path candidate dataset for use by the path deduction module.

[0035] See also Figure 6 , the specific steps of S5 are: S501: Calling the channel space coordinates in the initial path candidate channel index set, sorting the channel numbers according to the excitation time, extracting the path angle information formed by the three adjacent points after sorting, and generating a channel three-point angle sequence value; The specific calculation formula for sorting channel numbers according to excitation time is: ; Calculate the sorting energy level difference value , extract the path angle information formed by the three adjacent points after sorting, and generate the channel three-point angle sequence value; in, Represents the channel number The energy level difference value in the excitation time ordering, Representative and The energy exchange coefficient between channels, Representative Channel and The spatial distance between and Representing the and The excitation time of the channel, represent The channel is The average energy value injected into the channel, represents the minimum time resolution in the excitation time series, is the total number of all channels; parameter It represents the average energy exchange value between channel i and channel j within a unit acquisition cycle, which is calculated based on the integral of the voltage and current changes at the channel nodes, and the equivalent electric work calculation formula is used. Acquisition: The sensor sampling frequency is 1000 Hz, the sampling time period is 1 second, the average voltage between channels i and j is 2.1 V, and the average current is 0.015 A. The energy value is: ; parameter It represents the spatial geometric center distance between channel i and channel j, which is obtained by a laser ranging system. The three-dimensional measurement accuracy of the system is ±0.1mm. The three-dimensional coordinate difference of the spatial point is Δx=0.8m, Δy=0.6m, and Δz=0.3m. The spatial distance is calculated as: ; parameter It represents the excitation time difference between channel j and channel i, which is acquired through event triggering record synchronized with the system clock. It uses a high-resolution timestamp recording module with a minimum time accuracy of 1μs. , ,get: ; parameter Indicates the minimum time resolution of the system, which depends on the hardware sampling interval. In this system, , i.e. 1μs; parameter It represents the energy input value of channel j to channel i within the unit sampling interval. The energy accumulation value of the data interaction between channels is integrated in equal intervals. The system records the energy change between t=2s and t=2.01s. , the sampling frequency is 10000Hz, a total of 10000 points, take each time as seconds, the average energy is: ; Substitute all parameters into the formula and expand the specific operations as follows: First calculate the normalized time difference ; Item 1: ; ; ; Item 2: ; Item difference calculation:

[0036] Since the value of m is 1 (only one adjacent channel is calculated), the summation formula can be simplified to: ; This result indicates that the weighted energy difference between channel i and channel j during the sorting process is 0.000601668. This value is used as the sorting energy level difference value for channel i and is input into the subsequent sorting process. It is used to compare the difference with other channels to determine the channel priority. The larger the value, the stronger the modulation effect of channel j on channel i in the excitation-energy transfer path, and the higher the sorting priority should be. After calculating this value for all channels, the channel index table is obtained by sorting them in ascending order according to the value sequence, and the path angle generation step is entered. The sorting energy level difference value constructed in the formula is weighted and regulated by the composite relationship between the energy, space and time factors between channels. and distance The ratio between reflects the energy distribution density per unit distance and describes the strength of energy transmission between channels. This term is multiplied by the inverse of the normalized time difference value to introduce the regulation effect of the excitation time difference on the energy transmission intensity. After the time difference is normalized, it is converted into a positive influence by squaring and then taking the square root to ensure that the value is positive and has a distance-aware attenuation trend. The square root operation is mathematically expressed as a nonlinear weakening of long-distance time offset. Represents the amount of energy input per unit time, reflecting the direct excitation energy efficiency of channel j on channel i. This term is weakened by the product of the spatial distance between channels and the normalized time difference, forming a reinforcing effect on the efficient input of the near field. The two structures form a complementary excitation and regulation mechanism. In the overall structure, the difference between the two terms is processed, and the energy efficiency difference between the excitation and reception in different dimensional structures is extracted through a subtraction operation. This difference is encapsulated by the absolute value to form a non-negative ranking index, ensuring that the ranking values ​​of all channels are comparable and serving as the basis for subsequent ranking. The ranking energy level difference value represents a reference indicator for the ranking of a channel relative to its associated channels in a multi-channel excitation-response system, formed by the combined effects of multiple dimensions such as excitation time, spatial position, and energy exchange capacity. A larger value indicates a stronger energy transfer relationship with surrounding channels at the current excitation moment, a closer response time, and a more significant spatial coupling effect, resulting in a higher path participation and conduction dominance. A smaller value indicates that the channel is relatively marginal or delayed in the excitation propagation path, resulting in a weaker degree of excitation. This difference value comprehensively quantifies the complex relationship between the channel's energy acceptance, triggering timing, and spatial occupancy in the network structure, and is used for channel priority sorting, guiding subsequent path structure construction and feature extraction.

[0037] S502: Based on the angle sequence values ​​of the three points of the channel and the continuous deflection angle limit value of the path, path segments with angles less than the limit value are screened to generate a set of path segments that meet the angle threshold; After the angle sequence values ​​are formed, they need to be screened in combination with the path continuous deflection angle limit value. The limit value can be analyzed from historical scan data. Setting it to 90 degrees is a common solution, and this value serves as a benchmark for judging whether the angle is too large. Each angle record is read in turn and compared with the limit value. Angles less than 90 degrees are considered to have excessive deflection and should be eliminated from the candidate path segments. For example, if the angle values ​​are 166 degrees, 120 degrees, 85 degrees, 55 degrees, or 22 degrees, the three items below 85 degrees are eliminated because they are below the limit value. Only the part greater than or equal to 90 degrees is retained, leaving two path segments. Each qualified path segment saves the three-point channel number and coordinate information and organizes them into a path segment set for subsequent path structure integration. The path segments in this set have a common characteristic, that is, the direction changes of the three adjacent channels are within the allowable range, which facilitates the formation of a continuous and smooth path chain, suitable for actual scenarios such as signal transmission simulation or array scanning reconstruction.

[0038] S503: Based on the set of path segments that meet the angle threshold, the channel numbers and corresponding spatial direction features are integrated, the corresponding spatial coordinate difference information is called, and the direction parameters of each path segment are extracted in the order of the channel numbers to generate an array path direction sorting structure; The set of path segments that meet the angle threshold will serve as the basis for subsequent channel direction sorting. Each path segment contains three channel numbers and their corresponding coordinates. These coordinates are used to extract the direction difference between channels. Direction is typically determined by the difference in horizontal and vertical coordinates between two points. For example, channel numbers 2 and 1 correspond to coordinates 12,7 and 10,5, indicating a direction approximately downward and left. Compared to the directions between 1 and 3, a small difference in direction indicates good continuity. The angle difference between the two direction segments within each path is extracted to form a direction description for each segment. Path segment numbers are arranged sequentially by channel number and combined into a structure. Each structure contains the channel start and end numbers, as well as path direction characteristics. These structures are arranged in an orderly fashion to form an array path direction sorting structure, which facilitates analysis of spatial direction continuity between channels. This structure allows for rapid access to the start and end numbers and direction information for each path segment in signal processing, beamforming, or array simulation analysis, assisting with path planning or echo reconstruction.

[0039] See also Figure 7 , the signal acquisition, processing and analysis system of the particle detector array includes: The signal stability extraction module obtains the voltage signal data of the channels in the particle detector array, divides the sampling segments according to the set period, extracts the difference between the maximum and minimum values ​​of each segment, and combines the signal fluctuation amplitude within the segment with the set threshold to judge. The segments that meet the conditions are identified and marked as signal stability intervals. The corresponding start and end times are recorded to generate a set of array stable sampling time segments. The channel transition identification module calls the amplitude information of the channel in the array stable sampling time segment set, extracts the difference between adjacent time segments, compares it with the transition rate threshold, identifies the channel time segments with trend changes, marks the status as transition-capable, and generates a channel signal transition mark table; The path start calibration module calls the transitionable channels in the channel signal transition mark table, extracts the first excitation time, establishes the corresponding relationship between the channel and time, selects the channel number with the earliest excitation time, and generates a path start excitation time point record table; The adjacent channel screening module calls the channel number in the path starting excitation time point record table, searches for adjacent channels in the spatial structure, determines whether the excitation time difference is within the specified range, and checks the transition state flag. It screens the numbers and spatial information that meet the conditions and generates the initial path candidate channel index set. The path structure construction module calls the channel coordinates in the initial path candidate channel index set, arranges the channel numbers in order of excitation time, determines in turn whether the angle change between three consecutive points meets the deflection restriction condition, selects the path segment numbers and arrangement order that meet the requirements, and generates the array path direction sorting structure.

[0040] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A method for collecting, processing and analyzing signals from a particle detector array, characterized in that: The following steps are involved: S1: Obtain the signal amplitude sequence of each channel in the detection channel sampling, segment it according to a fixed period, extract the difference between the maximum and minimum values, and compare it with the amplitude mean square fluctuation threshold, mark the signal segment within the stable range, and generate a set of array stable sampling time segments; S2: Calling the amplitude data of the array stable sampling time segment set, calculating the difference ratio of adjacent time segments, comparing it with the amplitude transition rate threshold, screening the channels with monotonically increasing transition rates in the continuous time segments, classifying the amplitude evolution sequence as a transition-capable state, and generating a channel signal transition mark table; S3: calling the transitionable state channel in the channel signal transition mark table, extracting the first excitation timestamp, screening the minimum time value and recording the corresponding channel number, marking the channel as the path starting position, and generating a path starting excitation time point record table; S4: According to the channel number in the path starting excitation time point record table, adjacent channels are retrieved, the excitation time difference is calculated, the channels whose time difference is within the critical time window threshold and marked as transitionable are retained, the channel number and spatial position are recorded, and an initial path candidate channel index set is generated.

2. The signal acquisition, processing and analysis method of the particle detector array according to claim 1, characterized in that: The array stable sampling time segment set includes the start time of the stable signal segment and the end time of the stable signal segment; the channel signal transition mark table includes the transition channel number and the channel transition status; the path starting excitation time point record table includes the starting excitation channel number and the first excitation timestamp; the initial path candidate channel index set includes the candidate channel number and the candidate channel spatial position information.

3. The signal acquisition, processing and analysis method of the particle detector array according to claim 2, characterized in that: The specific steps of S1 are: S101: Acquire the signal amplitude sequence of the detection channel and segment it according to a fixed period, extract the maximum and minimum values ​​of each segment of the signal amplitude sequence, calculate the difference between the maximum and minimum values, and generate an amplitude difference sequence corresponding to the period; S102: Based on the amplitude difference sequence, calling the amplitude mean square fluctuation threshold, comparing the difference with the amplitude mean square fluctuation threshold, determining the time period in which the difference does not exceed the threshold, and forming a stable signal marking interval in the channel; S103: determining the common intersection range of all channel stable intervals according to the stable signal marking intervals in the channels, and recording the start and end times to generate an array stable sampling time segment set.

4. The signal acquisition, processing and analysis method of the particle detector array according to claim 3, characterized in that: The specific steps of S2 are: S201: calling the amplitude data of the time periods in the array stable sampling time segment set, calculating the amplitude difference changes between adjacent time periods, and obtaining an amplitude difference ratio sequence; S202: comparing the amplitude difference ratio sequence with the amplitude transition rate threshold, identifying channels exceeding the threshold in a continuous time period, and forming a channel transition trend sequence; S203: judging whether the differential ratio of the channel is monotonically increasing according to the channel transition trend sequence, identifying and marking the corresponding channel as a transition-capable state, and generating a channel signal transition marking table.

5. The signal acquisition, processing and analysis method of the particle detector array according to claim 4, characterized in that: The specific steps of S3 are: S301: calling the transitionable state channel in the channel signal transition mark table, extracting the first excitation timestamp of the channel, establishing a corresponding relationship between the channel number and the timestamp, and generating a channel excitation time mapping value; S302: According to the channel excitation time mapping value, the minimum timestamp is screened and the corresponding channel number is extracted, marked as the path starting position, and a path starting position number set is generated; S303: creating a corresponding record of the starting channel number and the excitation timestamp according to the path starting position number set, and generating a path starting excitation time point record table.

6. The signal acquisition, processing and analysis method of the particle detector array according to claim 5, characterized in that: The specific steps of S4 are: S401: extracting the excitation time point information of the corresponding adjacent channels according to the channel numbers in the path starting excitation time point record table, and obtaining an excitation time difference value set according to the excitation time differences between the channels; S402: Calling the excitation time difference set, combining the status mark information of the corresponding channel, determining whether the excitation time difference is within the critical time window threshold, and whether the status mark meets the transition condition, and obtaining a transition candidate channel number set; S403: Extracting spatial position information corresponding to each channel according to the transition candidate channel number set, integrating the channel number and the spatial position, and obtaining an initial path candidate channel index set.

7. The signal acquisition, processing and analysis method of the particle detector array according to claim 6, characterized in that: The method further comprises: S5: Calling the channel space coordinates in the initial path candidate channel index set, arranging the channel numbers in order of excitation time, extracting the angle change values ​​of three consecutive points, comparing them with the path continuous deflection angle limit value, selecting the path segment where the angle change between the three consecutive points is less than the limit value as the path trunk segment, integrating the number and direction information, and generating the array path direction sorting structure; The array path direction sorting structure includes path channel number sorting and path trunk segment direction information.

8. The signal acquisition, processing and analysis method of the particle detector array according to claim 7, characterized in that: The specific steps of S5 are: S501: calling the channel space coordinates in the initial path candidate channel index set, sorting the channel numbers according to the excitation time, extracting the path angle information formed by the three adjacent points after sorting, and generating a channel three-point angle sequence value; S502: Based on the three-point angle sequence values ​​of the channel and the continuous deflection angle limit value of the path, path segments with angles less than the limit value are screened to generate a set of path segments that meet the angle threshold; S503: Based on the set of path segments that meet the angle threshold, the channel numbers and corresponding spatial direction features are integrated, the corresponding spatial coordinate difference information is called, the direction parameters of each path segment are extracted in the order of the channel numbers, and an array path direction sorting structure is generated.

9. The signal acquisition, processing and analysis method of the particle detector array according to claim 8, characterized in that: The fixed period refers to the time unit for dividing the continuous data into time windows during the signal sampling process. The specific period length is set according to the working frequency and response speed of the detector; The amplitude mean square fluctuation threshold is a standard threshold used to determine whether the fluctuation range of the signal amplitude sequence within a continuous sampling period is in a state that can be regarded as stable. It is used to detect the degree of fluctuation of the channel signal within the time window and identify the stable amplitude response section. The amplitude transition rate threshold is used to determine whether the amplitude change rate of the channel signal between adjacent time periods reaches the criterion for recognizable transitions, and is used to evaluate whether the increase in the channel amplitude in a short period of time indicates the persistence and trend of the particle excitation behavior; The transitionable state refers to the judgment condition that the amplitude change of the channel satisfies a monotonically increasing trend in multiple consecutive time periods. The state is determined by the difference ratio sequence and the transition rate threshold: when the difference ratio of a channel in at least two adjacent time periods continues to rise and both exceed the preset threshold, the channel is marked as "transitionable"; The monotonically rising transition rate refers to the fact that the signal amplitude transition rate calculated over multiple consecutive time periods shows a continuously increasing trend without any downward fluctuations. It is used to identify whether the channel has a phenomenon of continuous amplitude increase over time and distinguish between a truly stable rising particle signal and a discontinuous change process disturbed by noise. The amplitude evolution sequence refers to a sequence formed by arranging the maximum amplitudes recorded by channels in adjacent stable time periods in a particle detector array over time; The critical time window threshold is the upper limit of the time difference used to determine whether two channels respond to the same particle event. It limits the maximum range of the excitation time difference between channels. Only when the excitation time difference between two channels is less than the threshold, are they considered to belong to the same particle path segment in time. The path continuous deflection angle limit value is a set geometric threshold used to limit the degree of continuous change in the path direction. It is derived from the theoretical estimation of the average scattering angle of particles in the medium or the path smoothness during detector design, and is set jointly in the spatial resolution and actual detection accuracy.

10. A signal acquisition, processing and analysis system for a particle detector array, characterized in that: The signal acquisition, processing, and analysis method of a particle detector array according to any one of claims 1 to 9, wherein the system comprises: The signal stability extraction module obtains the voltage signal data of the channels in the particle detector array, divides the sampling segments according to the set period, extracts the difference between the maximum and minimum values ​​of each segment, and combines the signal fluctuation amplitude within the segment with the set threshold to judge. The segments that meet the conditions are identified and marked as signal stability intervals. The corresponding start and end times are recorded to generate a set of array stable sampling time segments. The channel transition identification module calls the amplitude information of the channels in the array stable sampling time segment set, extracts the difference between adjacent time segments, compares it with the transition rate threshold, identifies the channel time segments with trend changes, marks the status as transition-capable, and generates a channel signal transition mark table; The path start calibration module calls the transitionable channels in the channel signal transition mark table, extracts the first excitation time, establishes the corresponding relationship between the channel and time, selects the channel number with the earliest excitation time, and generates a path start excitation time point record table; The adjacent channel screening module calls the channel number in the path starting excitation time point record table, searches for adjacent channels in the spatial structure, determines whether the excitation time difference is within a limited range, and checks the transition state flag, screening the number and spatial information that meet the conditions, and generates an initial path candidate channel index set; The path structure construction module calls the channel coordinates in the initial path candidate channel index set, arranges the channel numbers in the order of excitation time, determines in turn whether the angle change between three consecutive points meets the deflection restriction condition, selects the path segment numbers and arrangement order that meet the requirements, and generates the array path direction sorting structure.

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