Remote management method and system for laser warning column equipment

Abstract

The invention relates to the technical field of remote management, in particular to a remote management method and system for laser warning column equipment, and the method comprises the following steps: based on the laser warning column equipment, collecting time sequence signals, sorting signal intensity and frequency deviation data of each frequency band, analyzing fluctuation characteristics and interval coverage fragments, and adjusting parameter weight distribution; and carrying out statistics on abnormal response conditions of the equipment in the space to obtain a regional linkage early warning identifier. According to the method, through full-time-period signal principal component and time sequence feature extraction and combination of hierarchical screening of interval coverage fragments, fine division of signal dynamic fluctuation rules and effective operation periods and multi-dimensional aggregation of state data such as energy consumption and temperature rise according to operation scenes are achieved, and the method is high in accuracy and high in reliability by adopting a mode that parameter weights are automatically adjusted along with key changes. Optimal mapping between operation parameters and operation states is dynamically established, remote monitoring is converted from static parameter interpretation into multi-factor self-adaptive evaluation, and the equipment abnormity identification accuracy and the group linkage response sensitivity are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of remote management technology, and in particular to a remote management method and system for laser warning post equipment. Background Technology

[0002] Remote management involves centralized monitoring, data acquisition, fault diagnosis, and parameter adjustment of various devices distributed across different geographical locations. It is primarily applied in scenarios such as IoT device management, smart city infrastructure operation and maintenance, and security equipment control. Traditional remote management methods for laser warning posts address the operational status monitoring, parameter setting, abnormal warning information collection, and device on / off control required during actual application. This typically involves establishing a data communication link between a wireless communication module and a central control system. The central control system issues commands and collects feedback information from the field devices to achieve remote data acquisition of the laser warning post's operational status, remote parameter adjustment, alarm reporting, and on / off control.

[0003] Existing management methods rely on parameters for a single time period and a limited number of data types. Monitoring signal changes are easily constrained by the acquisition frequency band and static thresholds. Energy consumption, temperature rise, and abnormal distribution during operation are not correlated in multiple dimensions. The operating status is judged solely based on data from the equipment feedback unit. There is a lack of effective linkage analysis when multiple devices on site issue regional abnormality warnings. The data correlation under different operating scenarios is weak, which makes it easy to make errors in the judgment of signal abnormalities. Multi-point collaborative management within a spatial range is difficult to form an organic response. The real-time performance and adaptability of intelligent remote monitoring of equipment are significantly limited. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a remote management method and system for laser warning post devices. The technical solution is as follows: On the one hand, a remote management method for laser warning post equipment is provided, including the following steps: S1: Based on the laser warning column device, collect the time series signal of the device operation, organize the signal strength and frequency offset data of each frequency band, compare the intensity and frequency change trends, determine the signal stability of each time period, and obtain the signal fluctuation characteristic parameter set; S2: Based on the set of signal fluctuation characteristic parameters, filter signal segments with limited fluctuation amplitude, calculate the signal strength of the sampling points of the segments, compare the relationship between the sampling points and the operating standard, determine the coverage of the continuous sequence, and obtain the coverage segment of the operating interval. S3: Based on the coverage segment of the operating range, organize the energy change and temperature rise parameters for each time period, count the operation duration and number of faults, and group and classify the parameters according to the operation type to obtain the operation parameter structure set; S4: Based on the set of operational parameter structures, compare the state parameter data sequences, calculate the change range of each parameter, screen for fluctuating parameters, adjust the parameter weight ratio, and obtain a dynamic weight distribution relationship group; S5: Based on the dynamic weight distribution relationship group, analyze the geographical location of each device's associated time, group them according to spatial proximity, retrieve the abnormal type and signal amplitude change within the group, determine the device response correlation, and obtain the regional linkage early warning identifier.

[0005] On the other hand, the signal fluctuation feature parameter set includes principal component identification code, feature fluctuation label, and time period clustering number; the operating interval coverage segment includes interval identification number, interval signal attribute label, and interval validity classification; the operation parameter structure set includes operating condition description label, energy consumption feature classification, and status hierarchical identifier; the dynamic weight distribution relationship group includes weight allocation identifier, grouping adjustment factor, and mode adaptation label; and the regional linkage early warning identifier includes linkage group number, regional anomaly classification, and group response feature code.

[0006] On the other hand, the specific steps for obtaining the signal fluctuation characteristic parameter set are as follows: S101: Based on the laser warning column device, analyze the time series data collected during operation, match the signal strength and frequency parameters at each time node, compare the differences in signal strength and corresponding frequency changes in continuous time periods, and combine the sequence content according to the synchronous change trend to obtain trend evolution data pairs. S102: Based on the trend evolution data pairs, compare the intensity and frequency change trends of each time period, analyze the signal-to-noise ratio parameter sequence within the same time period, screen the dense and scattered states of signal-to-noise ratio distribution in each group, judge the stability of each group of signals, and obtain the signal stability clustering results. S103: Based on the signal stability clustering results, select the time period of stable state, analyze the corresponding intensity and frequency change trends, calculate the fluctuation amplitude and change rate of each trend, and integrate all trend features to obtain the signal fluctuation feature parameter set.

[0007] On the other hand, the specific steps for obtaining the running interval coverage segment are as follows: S201: Based on the signal fluctuation characteristic parameter set, analyze each signal segment, identify signal segments with fluctuation amplitude within a set range, determine the distribution of signal intensity changes, screen continuous signal segments with stable fluctuation amplitude, and obtain a fluctuation amplitude screening set; S202: Based on the fluctuation amplitude filtering set, compare the signal strength of the sampling points of each signal segment with the reference range of the operating standard, determine the continuous coverage characteristics of the signal strength, identify the signal segments that meet the continuous coverage requirements, and obtain the signal coverage segment sequence. S203: Based on the time intervals and signal strength coverage characteristics marked in the signal coverage segment sequence, the duration and number of samples in each segment are statistically analyzed. After removing discontinuous segments, similar signals are aggregated to obtain the operating interval coverage segment.

[0008] On the other hand, the specific steps for obtaining the set of job parameter structures are as follows: S301: Based on the coverage segment of the operating section, statistically collect power energy change data and key component temperature rise parameters, compare the correspondence of each data in time, and sort out the energy fluctuations and temperature rise changes in different time periods to obtain the parameter set of the operating section. S302: Based on the parameter set of the work section, the work duration and number of fault records in each time period are counted, the work duration and number of faults in each time period are separated, and the work time and fault performance in each time period are integrated with the original collected parameters to obtain the work operation status information. S303: Based on the operation status information, analyze the energy consumption characteristics and status performance of each operation type, summarize the parameters of the same type of operation, and aggregate the working condition description and energy consumption classification parameters to obtain a set of operation parameter structures.

[0009] On the other hand, the specific steps for obtaining the dynamic weight distribution relationship group are as follows: S401: Based on the set of operation parameters, compare the variation range of each parameter within the interval, screen the parameters with variation range, determine the fluctuation characteristics of each state parameter, and collect the parameter sequence to obtain the fluctuation range parameter group. S402: Based on the fluctuation amplitude parameter group, arrange them in order of fluctuation amplitude, adjust the weight ratio of each parameter under the corresponding job type, optimize the weight allocation order, and organize the weight configuration relationship to obtain the weight configuration sequence; S403: Based on the weight configuration sequence, determine the matching relationship between each parameter weight and the job type, determine the combination structure of parameter weight and job type, and obtain the dynamic weight distribution relationship group.

[0010] On the other hand, the specific steps for obtaining the regional linkage early warning identifier are as follows: S501: Based on the dynamic weight distribution relationship group, determine the geographical location attributes of each device, calculate the distance between devices according to geospatial data, group them according to the principle of spatial proximity, integrate the device number and geographical information in the same group, and obtain the device spatial grouping; S502: Based on the device spatial grouping, retrieve the anomaly type and signal amplitude change of each group of devices within the same associated time period, statistically analyze the consistency of the anomaly type number of devices in the same group, determine the distribution characteristics of signal amplitude mutation, and integrate the anomaly category distribution and amplitude change data to obtain the anomaly response aggregation result; S503: Based on the abnormal response aggregation results, determine the correlation of abnormal responses of devices in each spatial group, compare the group response characteristics with geographical distribution, statistically analyze the range of correlated abnormal events, and obtain regional linkage early warning indicators.

[0011] On the other hand, the frequency band signal strength refers to the electromagnetic wave power value measured in each set frequency range for multiple sets of different frequency signals monitored or emitted by the device, and the frequency offset data refers to a set of binary data pairs formed by correlating the signal strength value collected at the same time point with the corresponding signal frequency.

[0012] On the other hand, the limited fluctuation range refers to the selected signal segments whose signal change range is within a certain range and do not exhibit extreme abrupt changes, after screening. The sampling point signal strength refers to the instantaneous intensity value of a single or group of signals collected at the target time.

[0013] On the other hand, a remote management system for laser warning post equipment is provided. This system is applied to a remote management method for laser warning post equipment, including: The signal feature extraction module is based on the laser warning column device. It collects the time series signal of the device operation, organizes the signal strength and frequency offset data of each frequency band, compares the intensity and frequency change trends, determines the signal stability of each time period, and obtains the signal fluctuation feature parameter set. Based on the set of signal fluctuation characteristic parameters, the interval filtering module filters signal segments with limited fluctuation amplitude, calculates the signal strength of the sampling points in the segment, compares the relationship between the sampling points and the operating standard, judges the coverage of the continuous sequence, and obtains the operating interval coverage segment. Based on the operating interval coverage segment, the parameter collection module organizes the energy change and temperature rise parameters for each time period, counts the operation duration and number of faults, and groups and classifies the parameters according to the operation type to obtain the operation parameter structure set. The weight allocation module compares the state parameter data sequence based on the set of operation parameters, calculates the change range of each parameter, screens fluctuating parameters, adjusts the parameter weight ratio, and obtains a dynamic weight distribution relationship group. Based on the dynamic weight distribution relationship group, the linkage early warning module analyzes the geographical location of each device's associated time, groups them according to spatial proximity, retrieves the abnormal type and signal amplitude changes within the group, determines the device response correlation, and obtains the regional linkage early warning identifier.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By extracting principal components and temporal features of signals throughout the entire time period, and combining this with hierarchical screening of coverage segments, the system achieves a fine division of dynamic signal fluctuation patterns and effective operating periods. Energy consumption and temperature rise data are aggregated in multiple dimensions according to the operating scenario. The system adopts a method of automatically adjusting parameter weights according to key changes to dynamically establish the optimal mapping between operating parameters and operating status. Anomaly information from multiple devices within the space is compared in groups and response features are collected. Remote monitoring is transformed from static parameter interpretation to multi-factor adaptive evaluation, effectively improving the accuracy of equipment anomaly identification and the sensitivity of group linkage response, and enhancing the efficient utilization of remote operation data and intelligent collaboration in multi-device management. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of the main steps of the present invention; Figure 2 This is a flowchart of steps S1 of the present invention; Figure 3 This is a flowchart of steps S2 of the present invention; Figure 4 This is a flowchart of steps S3 of the present invention; Figure 5 This is a flowchart of step S4 of the present invention; Figure 6 This is a flowchart of steps S5 of the present invention; Figure 7 This is a system block diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a remote management method for laser warning post devices, such as... Figure 1 As shown, it includes the following steps: S1: Based on the laser warning column device, collect time series data during operation, organize the paired data of signal strength and frequency offset of each frequency band, compare the trend of signal strength change and frequency change, analyze the time distribution of signal-to-noise ratio data, determine the stability of the signal in each time period, and obtain the signal fluctuation characteristic parameter set. S2: Based on the signal fluctuation characteristic parameter set, filter signal segments with limited fluctuation amplitude, calculate the signal intensity of all sampling points in the segment, judge the coverage of continuous signal sequence by comparing the relationship between sampling points and operating standards, and statistically analyze the range of intervals that meet the requirements to obtain the operating interval coverage segment. S3: Based on the operating interval coverage segment, organize the energy change data and component temperature rise parameters collected by the power supply in each time period, count the operating duration and number of fault records in that time period, group and classify the parameter data according to the operation type, and obtain the operation parameter structure set; S4: Based on the set of work parameter structures, compare the data sequences of each state parameter, calculate the change range of the parameter within the recorded interval, screen parameters with amplitude fluctuations, and adjust the weight ratio of each parameter according to the amplitude sorting to determine the combination structure of parameter weight and work type, and obtain the dynamic weight distribution relationship group. S5: Based on the dynamic weight distribution relationship group, analyze the geographical location of each device within the associated time period, group the devices according to spatial proximity, retrieve the abnormal types and signal amplitude changes of the devices within the group, count the consistency of abnormal type numbers, calculate the signal mutation amplitude distribution, and then determine the correlation of device responses within the spatial range to obtain the regional linkage early warning sign.

[0023] The signal fluctuation characteristic parameter set includes principal component identification code, characteristic fluctuation label, and time period clustering number; the operating interval coverage segment includes interval identification number, interval signal attribute label, and interval validity level; the operation parameter structure set includes operating condition description label, energy consumption characteristic classification, and status hierarchical identifier; the dynamic weight distribution relationship group includes weight allocation identifier, grouping adjustment factor, and mode adaptation label; and the regional linkage early warning identifier includes linkage group number, regional anomaly level, and group response characteristic code.

[0024] In S1, time series data refers to a set of raw signal observations automatically recorded in chronological order during the operation of the laser warning column equipment, reflecting the evolution of various equipment states or external signals over time; frequency band signal strength refers to the electromagnetic wave (or optical signal) power values ​​measured in various set frequency ranges (bands) for multiple sets of different frequency signals monitored or emitted by the equipment; frequency offset pairing data refers to a set of binary data pairs formed by correlating the signal strength value collected at the same time point with the corresponding signal frequency, used to analyze the dynamic characteristics of the signal changing with frequency; signal strength change trend refers to the directionality, growth or attenuation regularity of the signal strength changing continuously over time, which can be used as a reference for evaluating the working status of the equipment; frequency change trend refers to the trend of the signal's main frequency, carrier frequency or spectrum center changing over a continuous time period, commonly seen in multi-frequency monitoring or interference discrimination scenarios; time distribution refers to the distribution pattern of a certain type of parameter (such as signal-to-noise ratio) on the entire time axis, such as uniform distribution, sudden changes, discontinuities, etc.; stability performance refers to whether the signal fluctuates drastically in different time periods. High stability means that the parameter curve fluctuates little, which is easy to use for judging the normal state of the equipment.

[0025] In S2, "limited fluctuation amplitude" refers to signal segments selected after screening whose signal variation range is within a certain range and does not exhibit extreme abrupt changes; "sampling point signal strength" refers to the instantaneous intensity value of a single or group of signals collected at a specific moment, which is the basic component of the signal data sequence; "operational standards" refers to the benchmark parameters used to determine whether the laser warning column equipment is working properly, either set at the factory or during actual use, such as normal signal strength and normal noise range; "continuous signal sequence coverage" refers to the interval coverage characteristics that can completely reflect the continuous operating status of the equipment when the selected signal segments are connected to each other on the time axis to form continuous segments; "satisfactory interval range" refers to the time period selected according to the operation standards, during which the signal parameters are continuously within the allowable range and can be considered as effective operating time.

[0026] In S3, the energy change data collected by the power supply refers to the energy change curve of the power module output or consumption recorded during the operation of the equipment, reflecting the energy consumption characteristics of the equipment; the component temperature rise parameter refers to the temperature change of each core component of the equipment (such as the main control unit and laser module) in different working periods, used to determine thermal stability and potential faults; the working time refers to the actual time taken for the equipment to continuously complete the task in a certain working mode, which is an important indicator for operation and maintenance management and life assessment; the number of fault records refers to the number of various faults or abnormal alarms automatically recorded during operation, used for operation and maintenance statistics and reliability analysis; the working type refers to the different working modes set by the equipment according to the task requirements, such as continuous warning, timed warning or intelligent response, etc.; the parameter data refers to the total amount of all original or classified data related to the above operations, including power, temperature, time, fault and other collected parameters.

[0027] In S4, the data sequence of status parameters refers to an ordered sequence composed of multiple sets of key parameters (such as energy, temperature, operating time, and number of faults) continuously collected within a certain time range, arranged in the sampling order; the amplitude of change refers to the difference between the maximum and minimum values ​​of a parameter within the observation interval, used to reflect the parameter fluctuation; parameters with amplitude variation refer to parameter items in the data sequence that are detected to have large fluctuations or key changes, usually meaning that the parameter is sensitive to changes in equipment status; amplitude sorting refers to arranging the amplitude of change of each parameter from largest to smallest, used for subsequent weight allocation and discrimination; parameter weight ratio refers to assigning a relative importance ratio to each parameter in the judgment process involving multiple parameters, used to dynamically adjust equipment operation decisions; the combined structure refers to the data structure that organizes each parameter and its weight, the type of operation to which it belongs, etc., in a one-to-one correspondence manner, facilitating subsequent discrimination or configuration distribution.

[0028] In S5, correlation time refers to the same or similar time corresponding to each device or data record participating in the discriminant analysis; device geographic location refers to the latitude and longitude information of the physical installation point of the laser warning column in the actual space, used for spatial correlation analysis; spatial proximity refers to grouping devices that are close to each other or located in the same management area by comparing device geographic locations; anomaly type refers to the classification number of different types of abnormal events detected by the device (such as signal mutation, power drop, temperature anomaly, etc.); signal amplitude change refers to the change in signal strength or amplitude before and after the occurrence of an abnormal event, which is an important parameter for judging whether the devices have common anomalies; anomaly type number refers to the parameter after encoding various types of abnormal events, which is beneficial for statistics, archiving and batch management; signal mutation amplitude distribution refers to the distribution pattern formed by statistically analyzing all abnormal signal amplitude mutations within a certain space or time range; device response correlation refers to determining whether multiple devices have a trend or phenomenon of jointly responding to the same anomaly by comprehensively considering factors such as space, time and signal mutation.

[0029] like Figure 2 As shown, the specific steps for obtaining the signal fluctuation characteristic parameter set are as follows: S101: Based on the laser warning column device, analyze the time series data collected during operation, match the signal strength and frequency parameters at each time node, compare the differences in signal strength and corresponding frequency changes in continuous time periods, and combine the sequence content according to the synchronous change trend to obtain trend evolution data pairs. During operation, time-series signal data is automatically recorded. The signal strength and corresponding frequency values ​​at each time point are read sequentially according to the device's recording time. A signal strength-frequency pairing relationship is established for each time point. The paired data is then divided into several continuous time segments, for example, a time window of 10 seconds. The difference between signal strength and frequency between adjacent time points within each time segment is compared differentially to determine if their directions of change are consistent. If both strength and frequency increase, the trend is considered consistent; if one increases while the other decreases, the directions are considered inconsistent. Further comparisons of the synchronization of changes within time segments with consistent trends are then made to determine if the difference in the magnitude of the strength and frequency changes is within an acceptable range. If the relative error of the change difference is specified to be no more than 5%, and the condition is met, the data within that time period is marked as trend synchronization. The time window is continuously slid and the above operation is repeated to summarize and classify all segments with consistent trends and synchronized changes. For example, if a device records a signal strength decrease from 85 to 75 and a frequency increase from 2.45GHz to 2.46GHz between 08:00:00 and 08:00:30, the signal strength decreases while the frequency increases, so the directions are inconsistent and no effective pair is formed. However, in another time period, the signal strength increases from 70 to 80 and the frequency increases from 2.43GHz to 2.44GHz. The directions are consistent and the change difference is close, so it can be classified as an effective trend evolution segment. In this way, multiple synchronized evolution segments are extracted segment by segment and organized to form trend evolution data pairs.

[0030] S102: Based on trend evolution data pairs, compare the intensity and frequency change trends of each time period, analyze the signal-to-noise ratio parameter sequence within the same time period, screen the dense and scattered states of signal-to-noise ratio distribution in each group, judge the stability of each group of signals, and obtain the signal stability clustering results. The direction and amplitude of signal strength and frequency changes in each trend segment are analyzed for differences. By comparing the growth or decrease trends of the difference point by point within the sliding segment, segments with consistent trends are marked as trend-matching groups. Then, the signal-to-noise ratio (SNR) observation sequence is further extracted from the segment. That is, for each time point, the recorded signal strength and background noise values ​​are obtained, and the ratio of the two is calculated as the SNR value at that point. Subsequently, the SNR value sequence of the entire segment is analyzed for density according to statistical distribution, and the average value and fluctuation degree of the sequence are calculated. If the fluctuation value is less than the set stability threshold, such as 3dB, the SNR fluctuation in the segment is considered small, and it is a signal stable segment; otherwise, it is considered fluctuating. Large fluctuations indicate unstable segments. For example, within a certain continuous time period, the signal strength fluctuates slightly between 78 and 82 dBm, the corresponding noise value varies between 58 and 60 dBm, and the signal-to-noise ratio (SNR) fluctuates consistently between 19 and 22 dB. At this point, the average value is 20.5, and the fluctuation value is approximately 1.2 dB, which is below the 3 dB threshold and is therefore considered a stable state. In another segment, the signal strength jumps from 75 to 90 dBm, the noise changes from 58 to 65 dBm, and the SNR rises from 17 to 25 and then falls back to 20, with fluctuation values ​​exceeding 6 dB, thus classifying it as an unstable segment. Subsequently, all trend segments are divided into stable and unstable categories according to their SNR fluctuation degree, and their classification labels are recorded as the signal stability clustering results.

[0031] S103: Based on the signal stability clustering results, select the time period of stable state, analyze the corresponding intensity and frequency change trends, calculate the fluctuation amplitude and change rate of each trend, and integrate all trend features to obtain the signal fluctuation feature parameter set. Time periods classified as stable are selected. For each stable period, the difference between the signal strength and frequency sequences is extracted to obtain the fluctuation range of signal strength from maximum to minimum within that period. Simultaneously, the frequency variation range within that period is recorded. Then, based on the sampling time interval, the rate of change of each difference per unit time is calculated. This involves statistically analyzing the rates of change of signal strength and frequency within that time period. Finally, the fluctuation range and rate of change are combined as two independent features to form a feature vector, creating a trend feature set for that period. For example, within a certain time period, the signal strength increases from 76 to 82 and then decreases... 78. The frequency increases from 2.432GHz to 2.435GHz, the signal strength fluctuation is 6dB, and the frequency fluctuation is 0.003GHz. Sampling every 5 seconds, the strength change rate is 1.2dB / s, and the frequency change rate is 0.0006GHz / s. This segment characteristic is recorded as fluctuation amplitude of 6 and 0.003, and change rate of 1.2 and 0.0006. This feature is added to the parameter set. The above steps are repeated for all time intervals that are determined to be stable segments. After extracting the combination of fluctuation amplitude and change rate of all stable segments, the signal fluctuation feature parameter set is formed.

[0032] like Figure 3 As shown, the specific steps for obtaining the coverage segment of the running interval are as follows: S201: Based on the signal fluctuation characteristic parameter set, analyze each signal segment, identify signal segments with fluctuation amplitude within a set range, determine the distribution of signal intensity changes, screen continuous signal segments with stable fluctuation amplitude, and obtain a fluctuation amplitude screening set; The signal strength fluctuation amplitude value and its corresponding time index information recorded in each segment are extracted and read one by one. The fluctuation amplitude parameter is called and the amplitude filtering threshold range is set. For example, the acceptable fluctuation amplitude is set in the range of 3 to 7 dBm. Fluctuations within this range are considered to be in a normal fluctuation state. The judgment standard is based on the factory reference intensity fluctuation range set during equipment operation, and reasonable upper and lower limits are determined by comparing with historical operating data. Then, each segment of signal data is traversed and its fluctuation amplitude is compared with the set threshold. If the fluctuation amplitude is greater than 7 or less than 3, it is judged as an abnormal segment and is removed and does not enter the subsequent processing flow. If the fluctuation amplitude is between 3 and 7, the segment is retained. Then, the intensity change distribution of the retained signal segments is evaluated. A time sliding window method is used to divide each segment into groups of 10 seconds. Multiple sub-time slices are used to perform difference calculations on the sampled signal strength values ​​within each sub-slice. The degree of balance of signal variation in a segment is measured by the number of positive and negative changes in the difference. If six or more consecutive sampling points in a segment have a variation amplitude of less than 1 dBm, and the alternation between positive and negative fluctuations does not exceed two times, the fluctuation state is considered stable; otherwise, it is considered unstable fluctuation. For example, if a segment of signal strength is recorded as 70, 71, 69, 70.5, 70.8, 71.2, 70.7, then the difference values ​​are all within ±1 dBm, and the alternation is not drastic, so it can be classified as a stable fluctuation segment. If another segment is recorded as 70, 76, 65, 78, 60, then the fluctuation exceeds 10 dBm, and the direction of change reverses multiple times, so it is classified as an unstable fluctuation segment. All signal segments that meet the stable fluctuation conditions are selected and constructed into a fluctuation amplitude filter set.

[0033] S202: Based on the fluctuation amplitude screening set, compare the signal strength of the sampling points of each signal segment with the reference range of the operating standard, determine the continuous coverage characteristics of the signal strength, identify the signal segments that meet the continuous coverage requirements, and obtain the signal coverage segment sequence. The signal strength values ​​of all sampling points within each segment are extracted and compared point-by-point with the equipment's preset operating standard range. The operating standard range is set through experimental calibration; for example, the reference range for the signal strength of a laser warning column under normal operating conditions is set to 68dBm to 82dBm. If the signal strength of a sampling point is lower than 68dBm or higher than 82dBm, it is determined to exceed the operating standard. First, all sampling points within each signal segment are detected, and the number and continuous distribution of sampling points within the standard range are recorded. Determining the continuous coverage characteristic means determining whether there are continuous sampling segments with signal strength continuously within the standard range within a time period. If three or more such segments appear in a signal segment... If continuous sampling points meet the operating standards and the sampling time interval is within the set sampling period, such as 5 seconds between each point, then the signal segment is considered to meet the continuous coverage condition. Conversely, if the signal is intermittent or there are non-continuous points that meet the standards, then the segment is removed. For example, if a signal segment is recorded as: 69, 70, 71, 85, 66, 68, the first 3 points are within the operating standard range and are continuously collected, so they are determined to be a valid segment. However, the middle two points, 85 and 66, exceed the upper and lower limits respectively, causing a continuity interruption. Therefore, even if the subsequent points are within the range, they are no longer considered as continuous coverage segments. All signal segments that pass the continuous coverage determination are serialized, numbered, and archived to construct a signal coverage segment sequence.

[0034] S203: Based on the time intervals marked in the signal coverage segment sequence and the signal strength coverage characteristics, the duration and number of samples in each segment are statistically analyzed. After removing discontinuous segments, similar signals are aggregated to obtain the operating interval coverage segments. Based on the time intervals and coverage characteristics of each signal segment in the signal coverage segment sequence, the duration of each segment and the total number of sampling points are counted. The sampling time interval is set according to the default sampling period of the device, for example, sampling once every 5 seconds. If the coverage duration of a segment is 60 seconds, there should be 12 valid sampling points. If the actual recorded sampling points are less than 10, it indicates that there is an intermediate discontinuity problem, which is judged as a discontinuous segment and needs to be removed from the coverage sequence. Then, all non-discontinuous signal segments with standard sampling density are classified according to their fluctuation characteristics or signal strength levels. Segments with similar signal distribution and sampling structure are aggregated into the same type of signal segment and uniformly identified by their affiliation number. For example, if the signal records in three different time periods are 70, 73, and 70.5~72.5, the number of sampling points are 12, 13, and 11 respectively, and the fluctuation amplitude does not exceed 2dBm, they are judged to be the same type of signal and merged into the same operating coverage group. During the aggregation process, the overall time range and total number of sampling points after merging need to be recorded to obtain the operating interval coverage segment after removing discontinuous segments and aggregating.

[0035] like Figure 4As shown, the specific steps for obtaining the task parameter structure set are as follows: S301: Based on the coverage segment of the operating section, statistically collect power energy change data and key component temperature rise parameters, compare the correspondence of each data in time, and sort out the energy fluctuations and temperature rise changes in different time periods to obtain the parameter set of the operating section. The system extracts power module energy output data collected during equipment operation, segments the energy data for each time period along a time axis, records the energy readings at the beginning and end of each segment, and uses the difference to represent the energy change within that segment. Simultaneously, it extracts temperature change curves for key components within the same time period, breaking down the temperature rise rate, maximum temperature value, and change range for each component in the temperature data. Then, by comparing energy changes and temperature rise trends, it finds the correspondence between the two in the time dimension, mapping the starting point and peak point of energy changes to the corresponding moments of temperature changes, and recording the synchronicity of the changes in the two sets of data within each time period. For example, in a certain segment, the energy output increases from 100Wh to 120Wh. Simultaneously, the main control board temperature rises from 38℃ to 52℃. The time periods for both changes are consistent and the trend is upward, indicating a synchronous relationship. However, if the energy decreases while the temperature increases in another segment, it indicates a difference between the two and should be recorded as an asynchronous period. Then, data segments with significant asynchronous changes are counted in 5-minute intervals. Combined records of energy fluctuations greater than 10Wh or temperature changes greater than 10℃ within these segments are extracted and included in the differential time period set as differential change segments. Finally, all data segments with complete correspondence are structurally organized into two categories: energy-temperature rise synchronous segments and energy-temperature rise differential segments. Their timestamps, energy change values, temperature change values, start and end point positions are marked respectively, forming a working segment parameter set.

[0036] S302: Based on the parameter set of the work section, the work duration and number of fault records in each time period are counted, the work duration and number of faults in each time period are separated, and the work time and fault performance in each time period are integrated with the original collected parameters to obtain the work operation status information. The start and end times of each segment are read line by line from the equipment operation log. The actual operation time within that time period is calculated based on the timestamp. Sampling is performed every 10 seconds, so a 600-second segment should contain 60 sampling points. If more than 10% of the sampling points are missing, the segment is removed and not included in subsequent processing. While recording the operation time, the operation log is checked for any fault event identifiers. The number of fault alarms generated within that time period and their corresponding times are recorded and archived in chronological order. The operation time and the number of faults are organized in a structured format. During the data integration process, the energy data, temperature rise parameters, operation time, and fault records for each time period are summarized. If the operation duration of a certain segment is 12 minutes and there are 2 faults during that period, the operation time is recorded as 720 seconds and the number of faults is recorded as 2. This data is then concatenated with the energy change value and temperature rise change value of that segment in the previous operation segment parameters to establish a mapping structure between time periods and operation performance, forming the operation time and fault performance records for each operation segment, which constitute the operation status information.

[0037] S303: Based on the operation status information, analyze the energy consumption characteristics and status performance of each operation type, summarize the parameters of the same type of operation, and aggregate the working condition description and energy consumption classification parameters to obtain the operation parameter structure set; The tasks were categorized by type, and four parameters were extracted for each type: total energy consumption, temperature rise range, operating duration, and failure frequency. Data within each task group was statistically analyzed to determine the distribution of energy consumption characteristics for each task type. For example, continuous alert tasks had an average energy consumption of 25Wh, a temperature rise range of 12℃, and a failure frequency of 0.2 times per hour, while timed response tasks had an average energy consumption of 10Wh, a temperature rise range of 6℃, and a failure frequency of 0.05 times per hour. Therefore, continuous alert tasks can be categorized as high-energy-consuming, medium-temperature-rise, and high-frequency-failure tasks, while timed response tasks are classified as low-energy-consuming and high-temperature-rise, while timed response tasks are classified as high-frequency-failure. The operation should be classified as low-energy consumption, low-temperature rise, and stable. Then, the operating condition characteristics of each type of operation are extracted and summarized. The operating condition description is defined based on the combination of energy change, temperature rise amplitude, and number of failures. For example, when the energy consumption of a certain operation segment is above 30Wh and the temperature rise exceeds 15℃, it is marked as a high-load operation. If the energy consumption is below 15Wh and the temperature rise does not exceed 8℃, it is marked as a light-load operation. Then, all operation segments are classified and grouped according to the operating condition type. The energy consumption-related statistical indicators in each category are extracted and marked as energy consumption classification parameters. The operating condition description of each operation segment is matched and aggregated with the energy consumption classification to construct a set of operation parameter structures.

[0038] like Figure 5 As shown, the specific steps for obtaining the dynamic weight distribution relationship group are as follows: S401: Based on the set of operational parameter structures, compare the variation range of each parameter within the interval, screen parameters with variation range, determine the fluctuation characteristics of each state parameter, and collect the parameter sequence to obtain the fluctuation range parameter group. The parameter sequences are extracted sequentially by time period. Complete sampling sequences of key parameters such as energy consumption, main control unit temperature change, operation duration, and number of failures are read for each operation time segment. Then, the difference between the maximum and minimum values ​​of each parameter within the current segment is calculated to represent the parameter's variation range within that segment. A threshold range for amplitude judgment is then set; for example, a lower limit for fluctuation range is set at 10Wh for energy consumption, 8℃ for temperature rise, 300 seconds for duration, and 1 for the number of failures. If the variation range of a parameter within a certain time period is lower than the corresponding lower limit, it is considered... Parameters with insignificant fluctuations are excluded from subsequent calculations. If the amplitude criteria are met, the parameter is considered to have valid change characteristics in that time period and is retained for further processing. For example, if the energy change is 32Wh, the temperature change is 9.5℃, the operating time is 720 seconds, and the number of failures is 2 in a certain operating segment, all of which meet the preset amplitude threshold standard, then all four parameters are considered valid. If the energy consumption in another segment is only 8Wh, then the energy item is considered to have insufficient fluctuation amplitude and is no longer included in the summary. All parameters that meet the amplitude criteria are organized into a parameter sequence set, and their time period number and parameter type label are recorded to form a fluctuation amplitude parameter group.

[0039] S402: Based on the fluctuation amplitude parameter group, arrange them in order of fluctuation amplitude, adjust the weight ratio of each parameter under the corresponding job type, optimize the weight allocation order, and organize the weight configuration relationship to obtain the weight configuration sequence; The parameters are sorted according to their fluctuation range over different time periods, arranged from largest to smallest, to create a parameter ranking table. Then, using fluctuation range as a reference indicator, the analytical weight of each parameter under the corresponding job type is adjusted. The adjustment principle is that the larger the fluctuation range, the higher the weight assigned, and the smaller the fluctuation range, the lower the weight assigned. For example, in the "Continuous Alert" job type, if the energy consumption fluctuation range is 30Wh, the temperature rise is 6℃, the number of failures is 3, and the job time is 600 seconds, the corresponding initial weight ratios are 0.3 and 0.2, respectively. The values ​​were initially set to 0.25 and 0.25. A comparison revealed that the change in the number of faults was greater than that in energy consumption. Therefore, the weight of the fault item was increased to 0.35, and energy consumption decreased to 0.25. Temperature rise and time were adjusted to 0.2 and 0.2 respectively. After these adjustments, the weights of the parameters corresponding to each job type were numbered and recorded, and the weight values ​​were associated with their respective parameter items. These were then organized into a table structure for storage. Each record included a job type label, parameter name, adjusted weight value, and sorting position. After uniform processing of all job types, a weight configuration relationship set covering all job types was formed, generating a weight configuration sequence.

[0040] S403: Based on the weight configuration sequence, determine the matching relationship between each parameter weight and the job type, determine the combination structure of parameter weight and job type, and obtain the dynamic weight distribution relationship group; Extract the corresponding job type label and weight value, and determine whether the performance of the parameter category under the job type matches the weight ranking. For example, in the "timed response" job type, if the temperature rise fluctuation is only 4℃ but it is still in the top three of the weight allocation, its matching with the actual performance needs to be re-evaluated. If there is a discrepancy between the parameter weight ranking and the actual fluctuation value of the parameter, the matching relationship is deemed unreasonable and needs to be re-ranked and corrected. In the judgment process, the ranking of all parameters under each job type is compared with the ranking of their actual fluctuation values. If the two are completely consistent, the matching relationship is completely consistent. If the ranking positions differ by two or more, it is judged as low matching degree. If they differ by only one, it is marked as medium matching degree. After summarizing all the judgment results, a four-column structure table is established, consisting of parameter name, job type, weight value, and matching level. After completing this process for all job types, all parameter-weight pairs that meet the matching criteria are combined and stored with their respective job types to obtain a dynamic weight distribution relationship group.

[0041] like Figure 6 As shown, the specific steps for obtaining the regional linkage early warning indicator are as follows: S501: Based on the dynamic weight distribution relationship group, determine the geographical location attributes of each device, calculate the distance between devices according to geospatial data, group them according to the principle of spatial proximity, integrate the device number and geographical information in the same group, and obtain the device spatial grouping; For each device, extract its registered geographic location data from its configuration, reading its longitude and latitude information as spatial coordinates. Then, compare any two devices one by one, calculating their straight-line distance using coordinate difference. Compare this distance result with a set spatial proximity threshold, which can be set to 100 meters. This means that if the actual distance between two devices is less than or equal to 100 meters, they are considered similar devices and grouped into the same initial group. After calculating the pairwise distances of all devices, further integration is performed based on the affiliation of overlapping devices. If the same device is determined to be less than the threshold distance to multiple devices, these multiple devices must be grouped into the same group to complete the merging operation. For example, the distance between devices A and B... The distance between B and C is 70 meters, and the distance between A and C is 120 meters. Although A and C do not directly meet the distance threshold, A and B, and B and C both meet the conditions. Therefore, A, B, and C are grouped together. For each spatial group, record the list of equipment numbers, the latitude and longitude coordinates of each equipment, the group number, and the coordinates of the group center point. The group center point can be calculated based on the average position of the equipment within the group. For example, if the latitudes of the three equipment in the group are 31.2011, 31.2014, and 31.2009, and the longitudes are 121.4567, 121.4564, and 121.4569, then the center point coordinates are 31.2011 and 121.4567. Summarize all groups to form equipment spatial groups.

[0042] S502: Based on device spatial grouping, retrieve the anomaly type and signal amplitude change of each group of devices within the same associated time period, statistically analyze the consistency of anomaly type numbering within the same group of devices, determine the distribution characteristics of signal amplitude mutations, and integrate anomaly category distribution and amplitude change data to obtain anomaly response aggregation results; After extracting all device numbers within each group, read the abnormal event data recorded by the corresponding device within a unified associated time period. Locate whether each device recorded an anomaly within that time period. If so, extract the type number and occurrence time of the abnormal event, and simultaneously extract the corresponding signal amplitude change value at that time point. The amplitude change value consists of the signal strength difference between two sampling points before and after the anomaly occurrence point. For example, if device A records an anomaly at 09:00:00, with a signal strength of 75dBm at the previous sampling point and 60dBm at the next sampling point, the sudden change amplitude is 15dBm. Then, this amplitude value... Record the current anomaly entry for the device in the field. After performing the same processing on all devices in the same group, count whether there are cases where the same anomaly type number appears in the group. If at least two devices in the group have the same anomaly number, record it as a consistent number. Further, perform numerical analysis on the anomaly mutation amplitude of all devices in the group and classify them according to the size of the fluctuation amplitude. For example, the amplitude above 10dBm is marked as a large mutation, between 5 and 10dBm is a medium mutation, and below 5dBm is a small mutation. At the same time, count and record the proportion of each type of mutation in the group, and output the aggregated result of the anomaly response of the group.

[0043] S503: Based on the abnormal response aggregation results, determine the correlation of abnormal responses of devices in each spatial group, compare the group response characteristics with geographical distribution, statistically analyze the range of correlated abnormal events, and obtain regional linkage early warning indicators. The abnormal response status of all devices within a group is compared group by group to determine whether there is an abnormal response correlation. The judgment condition is that three or more devices in the same group show the same type of abnormality with the same number in the same time period, and their signal change amplitude belongs to the same level classification. For example, if three devices all record an abnormality with type number 05 between 09:15 and 09:20, and the change amplitude is all above 12dBm, it is classified as a large change level. After meeting the correlation response condition, the group is recorded as having group response characteristics. The geographical boundary range of the devices in the group is calculated based on their latitude and longitude information, that is, the maximum latitude, minimum latitude, maximum longitude, and minimum longitude are recorded to identify the spatial range affected by the event. Then, all groups with response correlation are numbered, and each number is accompanied by the spatial range, number of devices, abnormality type number, response start and end time. A unified table is established to record the data, forming a regional response event summary. All groups marked with correlation response are constructed into a regional linkage early warning identifier.

[0044] like Figure 7 As shown, a remote management system for a laser warning post device includes: The signal feature extraction module is based on the laser warning column device. It collects the time series signal of the device operation, organizes the signal strength and frequency offset data of each frequency band, compares the intensity and frequency change trends, determines the signal stability of each time period, and obtains the signal fluctuation feature parameter set. The interval filtering module filters signal segments with limited fluctuation amplitude based on the signal fluctuation characteristic parameter set, calculates the signal intensity of the sampling points in the segment, compares the relationship between the sampling points and the operating standard, judges the coverage of the continuous sequence, and obtains the coverage segment of the operating interval. The parameter collection module organizes energy changes and temperature rise parameters for each time period based on the coverage segment of the operating range, counts the operation duration and number of faults, and groups and classifies the parameters according to the operation type to obtain a set of operation parameter structures. The weight allocation module is based on the set of job parameter structures. It compares the state parameter data sequences, calculates the change range of each parameter, screens fluctuating parameters, adjusts the parameter weight ratio, and obtains a dynamic weight distribution relationship group. The linkage early warning module analyzes the geographical location of each device based on the dynamic weight distribution relationship group, groups them according to spatial proximity, retrieves the abnormal type and signal amplitude changes within the group, determines the device response correlation, and obtains the regional linkage early warning identifier.

[0045] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A remote management method for a laser warning column device, characterized in that, The method includes: S1: Based on the laser warning column device, collect the time series signal of the device operation, organize the signal strength and frequency offset data of each frequency band, compare the intensity and frequency change trends, determine the signal stability of each time period, and obtain the signal fluctuation characteristic parameter set; S2: Based on the set of signal fluctuation characteristic parameters, filter signal segments with limited fluctuation amplitude, calculate the signal strength of the sampling points of the segments, compare the relationship between the sampling points and the operating standard, determine the coverage of the continuous sequence, and obtain the coverage segment of the operating interval. S3: Based on the coverage segment of the operating range, organize the energy change and temperature rise parameters for each time period, count the operation duration and number of faults, and group and classify the parameters according to the operation type to obtain the operation parameter structure set; S4: Based on the set of operational parameter structures, compare the state parameter data sequences, calculate the change range of each parameter, screen for fluctuating parameters, adjust the parameter weight ratio, and obtain a dynamic weight distribution relationship group; S5: Based on the dynamic weight distribution relationship group, analyze the geographical location of each device's associated time, group them according to spatial proximity, retrieve the abnormal type and signal amplitude change within the group, determine the device response correlation, and obtain the regional linkage early warning identifier.

2. The remote management method for the laser warning column device according to claim 1, characterized in that, The signal fluctuation feature parameter set includes principal component identification code, feature fluctuation label, and time period clustering number; the operating interval coverage segment includes interval identification number, interval signal attribute label, and interval validity level; the operation parameter structure set includes operating condition description label, energy consumption feature classification, and status hierarchical identifier; the dynamic weight distribution relationship group includes weight allocation identifier, grouping adjustment factor, and mode adaptation label; and the regional linkage early warning identifier includes linkage group number, regional anomaly level, and group response feature code.

3. The remote management method for the laser warning column device according to claim 1, characterized in that, The specific steps for obtaining the signal fluctuation characteristic parameter set are as follows: S101: Based on the laser warning column device, analyze the time series data collected during operation, match the signal strength and frequency parameters at each time node, compare the differences in signal strength and corresponding frequency changes in continuous time periods, and combine the sequence content according to the synchronous change trend to obtain trend evolution data pairs. S102: Based on the trend evolution data pairs, compare the intensity and frequency change trends of each time period, analyze the signal-to-noise ratio parameter sequence within the same time period, screen the dense and scattered states of signal-to-noise ratio distribution in each group, judge the stability of each group of signals, and obtain the signal stability clustering results. S103: Based on the signal stability clustering results, select the time period of stable state, analyze the corresponding intensity and frequency change trends, calculate the fluctuation amplitude and change rate of each trend, and integrate all trend features to obtain the signal fluctuation feature parameter set.

4. The remote management method for the laser warning column device according to claim 1, characterized in that, The specific steps for obtaining the coverage segment of the running interval are as follows: S201: Based on the signal fluctuation characteristic parameter set, analyze each signal segment, identify signal segments with fluctuation amplitude within a set range, determine the distribution of signal intensity changes, screen continuous signal segments with stable fluctuation amplitude, and obtain a fluctuation amplitude screening set; S202: Based on the fluctuation amplitude filtering set, compare the signal strength of the sampling points of each signal segment with the reference range of the operating standard, determine the continuous coverage characteristics of the signal strength, identify the signal segments that meet the continuous coverage requirements, and obtain the signal coverage segment sequence. S203: Based on the time intervals and signal strength coverage characteristics marked in the signal coverage segment sequence, the duration and number of samples in each segment are statistically analyzed. After removing discontinuous segments, similar signals are aggregated to obtain the operating interval coverage segment.

5. The remote management method for the laser warning column device according to claim 1, characterized in that, The specific steps for obtaining the set of job parameter structures are as follows: S301: Based on the coverage segment of the operating section, statistically collect power energy change data and key component temperature rise parameters, compare the correspondence of each data in time, and sort out the energy fluctuations and temperature rise changes in different time periods to obtain the parameter set of the operating section. S302: Based on the parameter set of the work section, the work duration and number of fault records in each time period are counted, the work duration and number of faults in each time period are separated, and the work time and fault performance in each time period are integrated with the original collected parameters to obtain the work operation status information. S303: Based on the operation status information, analyze the energy consumption characteristics and status performance of each operation type, summarize the parameters of the same type of operation, and aggregate the working condition description and energy consumption classification parameters to obtain a set of operation parameter structures.

6. The remote management method for the laser warning column device according to claim 1, characterized in that, The specific steps for obtaining the dynamic weight distribution relationship group are as follows: S401: Based on the set of operation parameters, compare the variation range of each parameter within the interval, screen the parameters with variation range, determine the fluctuation characteristics of each state parameter, and collect the parameter sequence to obtain the fluctuation range parameter group. S402: Based on the fluctuation amplitude parameter group, arrange them in order of fluctuation amplitude, adjust the weight ratio of each parameter under the corresponding job type, optimize the weight allocation order, and organize the weight configuration relationship to obtain the weight configuration sequence; S403: Based on the weight configuration sequence, determine the matching relationship between each parameter weight and the job type, determine the combination structure of parameter weight and job type, and obtain the dynamic weight distribution relationship group.

7. The remote management method for the laser warning column device according to claim 1, characterized in that, The specific steps for obtaining the regional linkage early warning identifier are as follows: S501: Based on the dynamic weight distribution relationship group, determine the geographical location attributes of each device, calculate the distance between devices according to geospatial data, group them according to the principle of spatial proximity, integrate the device number and geographical information in the same group, and obtain the device spatial grouping; S502: Based on the device spatial grouping, retrieve the anomaly type and signal amplitude change of each group of devices within the same associated time period, statistically analyze the consistency of the anomaly type number of devices in the same group, determine the distribution characteristics of signal amplitude mutation, and integrate the anomaly category distribution and amplitude change data to obtain the anomaly response aggregation result; S503: Based on the abnormal response aggregation results, determine the correlation of abnormal responses of devices in each spatial group, compare the group response characteristics with geographical distribution, statistically analyze the range of correlated abnormal events, and obtain regional linkage early warning indicators.

8. The remote management method for the laser warning column device according to claim 1, characterized in that, The frequency band signal strength refers to the electromagnetic wave power value measured in each set frequency range for multiple sets of different frequency signals monitored or emitted by the device. The frequency offset data refers to a set of binary data pairs formed by correlating the signal strength value collected at the same time point with the corresponding signal frequency.

9. The remote management method for the laser warning column device according to claim 1, characterized in that, The limited fluctuation range refers to signal segments selected after screening whose signal variation range is within a certain range and do not exhibit extreme abrupt changes. The sampling point signal strength refers to the instantaneous intensity value of a single or group of signals collected at the target time.

10. A remote management system for a laser warning post device, the system being used to implement the remote management method for the laser warning post device as described in any one of claims 1-9, characterized in that, The system includes: The signal feature extraction module is based on the laser warning column device. It collects the time series signal of the device operation, organizes the signal strength and frequency offset data of each frequency band, compares the intensity and frequency change trends, determines the signal stability of each time period, and obtains the signal fluctuation feature parameter set. Based on the set of signal fluctuation characteristic parameters, the interval filtering module filters signal segments with limited fluctuation amplitude, calculates the signal strength of the sampling points in the segment, compares the relationship between the sampling points and the operating standard, judges the coverage of the continuous sequence, and obtains the operating interval coverage segment. Based on the operating interval coverage segment, the parameter collection module organizes the energy change and temperature rise parameters for each time period, counts the operation duration and number of faults, and groups and classifies the parameters according to the operation type to obtain the operation parameter structure set. The weight allocation module compares the state parameter data sequence based on the set of operation parameters, calculates the change range of each parameter, screens fluctuating parameters, adjusts the parameter weight ratio, and obtains a dynamic weight distribution relationship group. Based on the dynamic weight distribution relationship group, the linkage early warning module analyzes the geographical location of each device's associated time, groups them according to spatial proximity, retrieves the abnormal type and signal amplitude changes within the group, determines the device response correlation, and obtains the regional linkage early warning identifier.

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