Quantum encryption handheld laser gas monitoring method and system

Abstract

The invention discloses a quantum encryption handheld laser gas monitoring method and system, and relates to the technical field of handheld laser gas monitoring. The quantum encryption handheld laser gas monitoring method and system comprise the following steps: S1, collecting environmental parameter information, storing environmental monitoring data, and establishing an environmental monitoring database; s2, performing laser spectrum monitoring on the target gas, and performing data quality evaluation based on a laser spectrum monitoring result; s3, executing quantum key analysis on the laser spectrum monitoring data qualified in data quality evaluation; and S4, performing environmental adaptability analysis by integrating the evaluation results of the environmental monitoring data and the laser spectrum monitoring data and the quantum key analysis result. The problems that an existing handheld gas monitoring device is unstable in operation under the complex environment conditions of high temperature, low temperature, high humidity, dust interference, air pressure fluctuation and the like, and the measurement precision is difficult to guarantee are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of handheld laser gas monitoring, and in particular to a quantum encryption handheld laser gas monitoring method and system. Background Art

[0002] As industrial requirements for gas leak detection continue to increase in response speed, accuracy, and information security, traditional laser gas detection systems have gradually exposed issues such as poor real-time performance, large errors, insufficient anti-interference capabilities, and a high risk of information leakage when faced with changing environments and wireless data transmission security issues. Furthermore, existing systems lack efficient adaptive analysis capabilities when facing large-scale data collection and processing, and are unable to automatically adjust parameters based on environmental changes or determine whether an abnormal state is present.

[0003] For example, the invention patent with publication number CN118377030B discloses a gas detection method and laser radar for laser radar, which relates to the field of laser radar and is used to solve the problem of interference caused by the influence of echo intensity introduced by different wavelengths in existing methods. The method includes: emitting a first light beam with a first wavelength as the center wavelength, a second light beam with a second wavelength as the center wavelength, and a third light beam with a third wavelength as the center wavelength into the atmosphere, wherein the first wavelength corresponds to the peak of the absorption spectrum of the gas to be measured, and the second and third wavelengths are located on both sides of the absorption spectrum peak; obtaining echoes from each light beam; separating from the echoes a first narrowband light with a first wavelength as the center wavelength, a second narrowband light with a second wavelength as the center wavelength, and a third narrowband light with a third wavelength as the center wavelength; performing preset statistics on the intensity of the second narrowband light and the intensity of the third narrowband light to obtain statistical intensity, and combining the intensity of the first narrowband light to obtain the content of the gas to be measured. This can improve the accuracy of detecting the gas to be measured in the atmosphere.

[0004] For example, the invention patent with announcement number CN107462900B discloses a gas composition detection lidar based on a wavelength tunable laser source, which includes: using a wavelength tunable laser to scan to obtain the absorption lines of the gas to be measured at different distances, and using a femtosecond optical comb to accurately calibrate the frequency of the emitted laser. By performing nonlinear fitting on the frequency-calibrated absorption lines of the gas to be measured and comparing them with a database, the concentration information of the gas to be measured is obtained. In order to measure the absorption lines of the gas to be measured, two schemes based on direct detection and coherent detection are provided. According to the optimized wavelength scanning range, the gas composition detection lidar based on the wavelength tunable laser source can realize the detection of multiple gas components, and has the characteristics of high detection accuracy, high distance resolution and high time resolution.

[0005] Existing laser monitoring equipment focuses primarily on improving the precision of gas absorption line matching and frequency calibration, primarily focusing on improving the accuracy of the optical measurement link itself. This lacks a systematic response mechanism to changes in external environmental variables that affect measurement stability. This can lead to problems such as measurement data drift, increased noise interference, and error accumulation in complex environments and dynamic field conditions. Furthermore, real-time dynamic adjustment mechanisms and intelligent adaptive algorithms have not yet been introduced to enhance data processing and system robustness. For example, there is no environmental adaptive feedback link, no signal reliability assessment or dynamic grading of measurement quality, and no data security design. Therefore, achieving stable monitoring in highly dynamic environments, improving the overall system's self-regulation capabilities, and ensuring the security and integrity of data during transmission remain prominent challenges in existing technology.

[0006] This application aims to overcome the above technical problems by innovatively designing a quantum encryption handheld laser gas monitoring method and system. Summary of the Invention

[0007] Technical problems solved

[0008] In response to the shortcomings of the existing technology, the present invention provides a quantum encryption handheld laser gas monitoring method and system, which solves the problems of unstable operation and difficult measurement accuracy of existing handheld gas monitoring equipment under complex environmental conditions such as high temperature, low temperature, high humidity, dust interference and air pressure fluctuations.

[0009] Technical Solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a quantum encryption handheld laser gas monitoring method and system, comprising the following steps: S1: collecting environmental parameter information, storing environmental monitoring data, establishing an environmental monitoring database, and preprocessing the data in the environmental monitoring database; S2: performing laser spectral monitoring on the target gas, performing data quality assessment based on the laser spectral monitoring results, judging whether it meets the measurement accuracy requirements according to the data quality assessment results, and then performing corresponding signal processing and calibration; S3: performing quantum key analysis on the laser spectral monitoring data that has passed the data quality assessment, and conducting key validity check, key update and encryption parameter adjustment according to the quantum key analysis results; S4: conducting environmental adaptability analysis based on the comprehensive environmental monitoring data, the evaluation results of the laser spectral monitoring data and the quantum key analysis results, and dynamically adjusting the laser power, sensor sensitivity and signal processing parameters based on the environmental adaptability analysis results.

[0011] Furthermore, the specific steps of collecting environmental parameter information, storing environmental monitoring data, and establishing an environmental monitoring database are as follows: environmental monitoring data includes real-time collection of environmental data through environmental sensors, where the environmental data includes: temperature, humidity, air pressure and dust concentration; real-time collection of external interference and equipment background noise through noise sensors, and recording as original environmental noise signals; real-time collection of gas concentration values through optical sensors, recording as actual gas concentration values, and marking the data collection timestamp to obtain the dynamic sampling period of gas concentration; storing environmental monitoring data and establishing an environmental monitoring database.

[0012] Furthermore, the specific steps of preprocessing the data in the environmental monitoring database are as follows: identifying and eliminating mutation values and abnormal data in the environmental monitoring data; standardizing and normalizing the environmental monitoring data of various units; performing trend extraction and smoothing on the processed continuous data stream; performing integrity check on the environmental monitoring data to ensure that the data format is correct and the fields are complete, and filling and marking missing values; performing data consistency verification on similar environmental parameters collected by multiple sensors, and making corrections when the data differences are too large; extracting key eigenvalues and eigenvectors based on the characteristics and correlation of the monitoring data, and performing dimensionality reduction processing using principal component analysis; filtering and denoising the original signal through the signal processing unit in the laser monitoring equipment to obtain the effective signal strength, which is recorded as the effective signal; taking the smallest positive number as the error correction term , to avoid zero and extremely small values in the calculation; record the relationship between the sensor's signal response and the known gas concentration, establish a calibration curve between different gases and the sensor's signal response, and based on the calibration curve, compare the signal measured by the sensor with the calibration curve in actual gas detection to obtain the corresponding reference concentration value; use Huffman coding and differential coding to compress the pre-processed environmental monitoring data, record the data volume before and after compression, and mark it as the gas monitoring original data volume and the gas monitoring compressed data volume; obtain the optimal environmental parameters from the environmental monitoring database, including the optimal temperature, optimal humidity, optimal air pressure and optimal dust concentration, and use these parameters as preset benchmark values for comparison and analysis of subsequent environmental monitoring data; store and manage the data through the microprocessor in the handheld laser monitoring device, and integrate various monitoring data into the environmental monitoring database.

[0013] Furthermore, the specific steps of performing laser spectral monitoring on the target gas and performing data quality assessment based on the laser spectral monitoring results are as follows: obtaining the effective signal, the original environmental noise signal, the error correction term, the measured gas concentration value and the reference concentration value; calculating the ratio of the effective signal to the original environmental noise signal, combining an extremely small error correction term to avoid zero and extremely small values in the calculation, recording it as the signal ratio, normalizing the difference between the measured gas concentration value and the reference concentration value and the reference concentration value to obtain the concentration error ratio, subtracting the concentration error ratio from 1 to obtain the adjustment factor, and multiplying it by the signal ratio to finally obtain the reliability assessment value.

[0014] Furthermore, the specific steps of judging whether the data quality assessment result meets the measurement accuracy requirement and then performing corresponding signal processing and calibration are as follows: real-time comparison of the reliability assessment value and the reliability threshold, the reliability threshold includes the first-level reliability threshold, the second-level reliability threshold and the third-level reliability threshold. According to the assessment results corresponding to different levels, the system automatically performs differentiated signal adjustment and calibration processing; when the reliability assessment value is greater than the third-level reliability threshold, the current filtering strategy and signal processing process are maintained, the signal gain is kept in the standard range, and redundant signal repeated sampling processing is disabled; when the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, the filtering strategy is automatically enhanced, switching to the enhanced filtering algorithm to improve the smoothing effect, and appropriately increasing the signal gain, At the same time, dynamic identification of noise spectrum and elimination of interference points are performed, and adaptive compensation adjustment is made to noise disturbance; when the reliability assessment value is greater than the first-level reliability threshold and less than or equal to the second-level reliability threshold, the signal enhancement mode is started, continuous multi-cycle signals are superimposed to enhance the intensity, and multi-level wavelet filtering and frequency band discrete algorithm are combined to suppress the main interference frequency, and dynamic gain enhancement and error modeling correction are performed at the same time; when the reliability assessment value is less than or equal to the first-level reliability threshold, the signal reconstruction mechanism is immediately started, and historical signal features are used for contrast extraction. At the same time, the filter parameters are adjusted to enhance the target feature dimension recognition capability, and multiple adjacent signals are used together for phase superposition to form a signal array, and the sampling time is extended. At the same time, the abnormal frequency band discrete elimination algorithm is enabled, and strong constrained low-pass processing is performed on the frequency points that exceed the noise upper limit.

[0015] Furthermore, the specific steps of performing quantum key analysis on the laser spectrum monitoring data that has passed the data quality assessment are as follows: obtaining the measured gas concentration value, the dynamic sampling period of the gas concentration, the amount of gas monitoring original data and the amount of gas monitoring compressed data, and obtaining the data optimization value through comprehensive analysis; the specific analysis method of the data optimization value is: summing the absolute values of the measured gas concentrations, taking the inverse of the sum and multiplying it by a measured balance coefficient, and calculating the data optimization value by calculating the ratio of the amount of gas monitoring original data to the amount of gas monitoring compressed data, and multiplying it by the compression balance coefficient.

[0016] Furthermore, the specific steps of conducting key validity check, key update and encryption parameter adjustment based on the quantum key analysis results are as follows: real-time comparison of data optimization value and optimization threshold, the optimization threshold includes first-level optimization threshold, second-level optimization threshold and third-level optimization threshold; when the data optimization value is greater than the third-level optimization threshold, the current power, filter strength and sensor sensitivity are kept unchanged, and the system enters low-power mode for continuous operation, and the quantum key synchronization check is started at the same time; when the data optimization value is greater than the second-level optimization threshold and less than or equal to the third-level optimization threshold, the environmental compensation mechanism is started, and parameters are corrected according to changes in temperature, humidity, air pressure and dust concentration, the quantum key update frequency is appropriately increased, and the data compression algorithm parameters are adjusted; when the data optimization value is greater than the first-level optimization threshold and less than or equal to the second-level optimization threshold, the adaptive Kalman filter mode is switched to, the filter parameters and encryption strength are adjusted according to data fluctuations, and bandwidth adaptation is performed to reduce packet loss; when the data optimization value is less than or equal to the first-level optimization threshold, the fault response mechanism is started, the laser power and sensor gain are increased, the signal is re-collected and the equipment is calibrated, and the quantum key is updated and the transmission link is reinitialized.

[0017] Furthermore, the specific steps of conducting environmental adaptability analysis based on the evaluation results of the comprehensive environmental monitoring data, laser spectrum monitoring data and quantum key analysis results are as follows: obtaining the current environmental temperature, humidity, air pressure, dust concentration, optimal temperature, optimal humidity and optimal air pressure, and comprehensively analyzing to obtain the environmental adaptation adjustment value; the analysis method of the environmental adaptation adjustment value is: comparing the temperature, humidity, air pressure and dust concentration data of the current environment with the preset optimal environmental parameters, calculating their deviation values, introducing the temperature error weight coefficient, humidity error weight coefficient, air pressure error weight coefficient and dust error weight coefficient respectively, and performing weighted adjustment on these deviation values, the temperature and humidity deviations are nonlinearly suppressed using an exponential attenuation function, while the air pressure and dust deviations are nonlinearly suppressed using a proportional suppression function; finally, through a multiplication operation, the comprehensive effect of these environmental factors is mapped to an environmental adaptation adjustment value.

[0018] Furthermore, the specific steps of dynamically adjusting the laser power, sensor sensitivity and signal processing parameters based on the environmental adaptability analysis results are as follows: real-time comparison of the environmental adaptability adjustment value and the adjustment threshold, the adjustment threshold includes a first-level adjustment threshold and a second-level adjustment threshold; the environmental adaptive adjustment strategy is based on the joint judgment of the environmental adaptability adjustment value and the reliability evaluation value; when the environmental adaptability adjustment value is greater than the second-level adjustment threshold and the reliability evaluation value is greater than the third-level reliability threshold, directly maintain the current settings of the laser wavelength, power and sensor sensitivity, enter the low power consumption mode, reduce the sampling and calculation frequency, improve the energy efficiency ratio, and do not need to perform additional compensation and adjustment operations; when the environmental adaptability adjustment value is greater than the second-level adjustment threshold and the reliability evaluation value is greater than the second-level reliability threshold and is less than or equal to the third-level reliability threshold, automatically enable the environmental compensation mechanism, adjust the laser wavelength and laser temperature control, fine-tune the sensor sensitivity and apply the enhanced filtering algorithm; when the environmental adaptability adjustment value is greater than the first-level adjustment threshold and is less than or equal to the second-level adjustment threshold and the reliability evaluation value is greater than the third-level reliability threshold, start the adaptive compensation mechanism, give priority to improving the sensor sensitivity and signal gain, and target Filter parameter optimization is implemented for the current environmental status; when the environmental adaptation adjustment value is greater than the first-level adjustment threshold and less than or equal to the second-level adjustment threshold and the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, it automatically switches to the enhanced processing mode, increases the laser power, uses multi-level wavelet denoising and adaptive filtering strategies to enhance interference suppression, and performs dynamic correction on the temperature, humidity and pressure environmental parameters; when the environmental adaptation adjustment value is greater than or equal to the first-level adjustment threshold and less than or equal to the second-level adjustment threshold and the reliability assessment value is greater than the first-level reliability threshold and less than or equal to the second-level reliability threshold, activates the comprehensive optimization path, including increasing laser power, maximizing sensor gain, and superimposing multiple sampling cycles to enhance signal strength, combining multi-level filtering and frequent key updates to ensure data integrity and transmission security; when the environmental adaptation adjustment value is less than the first-level adjustment threshold and the reliability assessment value is less than the first-level reliability threshold, enters the extreme protection mode, triggers the laser system self-calibration, self-cleaning and wavelength re-tuning processes, and simultaneously enables the full-performance operation mode to enhance signal reconstruction and encryption processing capabilities, and jointly optimizes the key system parameters based on historical monitoring data and the current environmental status.

[0019] A second aspect of the present invention provides a quantum encryption handheld laser gas monitoring system, which is applied to the above-mentioned quantum encryption handheld laser gas monitoring method, and includes an environmental monitoring and data acquisition module, a laser gas monitoring module, a quantum encryption and data processing module, and an environmental adaptation and fault detection module; the environmental monitoring and data acquisition module is used to collect environmental parameter information, store environmental monitoring data, establish an environmental monitoring database, and preprocess the data in the environmental monitoring database; the laser gas monitoring module is used to perform laser spectrum monitoring of the target gas, perform data quality assessment based on the laser spectrum monitoring results, determine whether the data meets the measurement accuracy requirements based on the data quality assessment results, and then perform corresponding signal processing and calibration; the quantum encryption and data processing module is used to perform quantum key analysis on laser spectrum monitoring data that passes the data quality assessment, conduct key validity checks, perform key updates, and adjust encryption parameters based on the quantum key analysis results; the environmental adaptation and fault detection module is used to integrate the environmental monitoring data, the evaluation results of the laser spectrum monitoring data, and the quantum key analysis results, perform environmental adaptability analysis, and dynamically adjust the laser power, sensor sensitivity, and signal processing parameters based on the environmental adaptability analysis results.

[0020] Beneficial effects

[0021] The present invention has the following beneficial effects:

[0022] (1) The present invention realizes the standardized management and dynamic correction of multi-source environmental data by constructing an environmental monitoring database and preprocessing the environmental monitoring data. It not only effectively improves the integrity and consistency of the original data, but also provides a high-quality data foundation for subsequent signal processing and system adjustment, thereby solving the problems in traditional systems where environmental disturbance data cannot be used in real time and environmental changes affect system accuracy.

[0023] (2) The present invention introduces a multi-dimensional reliability evaluation model, combines the effective signal strength, noise level and concentration error ratio to calculate the trust index, and performs hierarchical signal calibration and filtering processing based on the evaluation results, so that the system can flexibly adjust the processing flow according to the real-time data quality, significantly improving the adaptability and measurement robustness under complex working conditions, and overcoming the shortcomings of traditional methods that the measurement process cannot be dynamically adjusted and is susceptible to interference and failure.

[0024] (3) The present invention integrates quantum key analysis and data optimization evaluation mechanism, establishes a data optimization value model based on the compression ratio and concentration accuracy of monitoring data, realizes adaptive adjustment of data encryption strength and compression strategy, effectively improves the security and transmission efficiency of monitoring data during remote transmission, and avoids the problems of untimely key updates and disconnection between encryption strategy and data quality in traditional systems.

[0025] (4) The present invention constructs a joint judgment mechanism of environmental adaptation adjustment value and reliability evaluation value. On the basis of realizing accurate identification of system operation status, it dynamically adjusts laser power, sensor sensitivity and filtering parameters to achieve full-process adaptive optimization, ensuring that the system can still maintain high-precision monitoring and stable operation in harsh environments, significantly improving the practicality, robustness and intelligence level of the system in multi-scenario applications.

[0026] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a quantum encryption handheld laser gas monitoring method and system of the present invention;

[0028] Figure 2 This is a diagram of the quantum encryption handheld laser gas monitoring method and system structure of the present invention;

[0029] Figure 3 This is a reliability evaluation value change trend diagram of the present invention;

[0030] Figure 4 This is a trend diagram of the data optimization value change of the present invention. DETAILED DESCRIPTION

[0031] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0032] See also Figure 1-Figure 4An embodiment of the present invention provides a technical solution: a quantum encryption handheld laser gas monitoring system, which applies a quantum encryption handheld laser gas monitoring method, including S1: collecting environmental parameter information, storing environmental monitoring data, establishing an environmental monitoring database, and preprocessing the data in the environmental monitoring database; S2: performing laser spectrum monitoring on the target gas, performing data quality assessment based on the laser spectrum monitoring results, judging whether it meets the measurement accuracy requirements according to the data quality assessment results, and then performing corresponding signal processing and calibration; S3: performing quantum key analysis on the laser spectrum monitoring data that has passed the data quality assessment, and conducting key validity check, key update and encryption parameter adjustment according to the quantum key analysis results; S4: performing environmental adaptability analysis based on the comprehensive environmental monitoring data, the evaluation results of the laser spectrum monitoring data and the quantum key analysis results, and dynamically adjusting the laser power, sensor sensitivity and signal processing parameters based on the environmental adaptability analysis results.

[0033] Specifically, the specific steps of collecting environmental parameter information, storing environmental monitoring data, and establishing an environmental monitoring database are as follows: Environmental monitoring data includes real-time collection of environmental data through environmental sensors, where the environmental data include: temperature, humidity, air pressure and dust concentration; the temperature sensor uses the DS18B20 model with an accuracy of up to ±0.5°C, the humidity and air pressure are measured by DHT22 and BMP280 sensors respectively, and the dust concentration is obtained by an infrared dust sensor, which has high detection sensitivity and can accurately respond to changes in particulate matter in the air; the noise sensor collects external interference and equipment background noise in real time and records them as the original environmental noise signal, which is used for subsequent reliability evaluation and dynamic filtering strategy selection. Selection; Real-time gas concentration values are collected through optical sensors and recorded as actual gas concentration values. Optical sensors can be constructed based on the principle of tunable laser absorption spectroscopy, with high selectivity and high sensitivity. At the same time, data collection timestamps are marked to identify the specific time node of each sampling, obtain the dynamic sampling period of gas concentration, and provide a time basis for subsequent optimization calculations and state predictions; the environmental monitoring data are stored. The storage medium can be on-chip RAM. The storage structure is automatically matched according to the data type and accuracy requirements to establish an environmental monitoring database. The database supports multi-dimensional indexing by time, sensor type and monitoring area, and has a data integrity verification mechanism and missing data completion function to ensure the accuracy and continuity of subsequent analysis.

[0034] In this implementation plan, this step comprehensively collects environmental parameters through multiple types of sensors, covering key information such as temperature, humidity, air pressure, dust concentration, external noise, and actual gas concentration values. It also combines timestamp records with dynamic sampling cycles of gas concentrations to complete standardized storage and structured management of data, and builds an environmental monitoring database with multi-dimensional indexing, integrity verification, and missing completion capabilities, thereby achieving accurate perception and continuous recording of the equipment operating environment, and providing stable, reliable, and traceable data support for subsequent data preprocessing, parameter adaptive adjustment, signal reliability assessment, and system optimization decisions.

[0035] Specifically, the specific steps for preprocessing the data in the environmental monitoring database are as follows: identify and eliminate the mutation values and abnormal data in the environmental monitoring data, and give priority to using sliding window statistics, z-score and IQR methods to implement multi-level anomaly detection to eliminate data distortion caused by sensor errors and sudden environmental disturbances; standardize and normalize the environmental monitoring data of various units to ensure unified dimensions and balanced feature weights in subsequent analysis, which is convenient for model input and horizontal comparison; extract trends and smooth the processed continuous data stream, and use exponentially weighted moving average and local weighted regression algorithms to improve data stability; perform integrity check on the environmental monitoring data to ensure that the data format is correct and the fields are complete, and fill and mark missing values. The filling strategy supports time series interpolation, historical mean completion and reference sensor replacement; for the same environmental parameters collected by multiple sensors, data consistency verification is carried out, and correction is performed when the data differences are too large. The automatic correction mechanism is realized through correlation analysis and deviation threshold judgment; according to the characteristics and correlation of the monitoring data, key eigenvalues and eigenvectors are extracted, and the principal component analysis is used for dimensionality reduction processing to reduce redundant information and improve the calculation efficiency of the model; the original signal is filtered and denoised by the signal processing unit in the laser monitoring equipment to obtain the effective signal strength. The filtering methods include Kalman filtering, wavelet denoising and other modes, and finally recorded as the effective signal; the minimum positive number is taken as the error correction term to avoid zero and extremely small values in the calculation. The error term is set to 10 -6The system records the relationship between the sensor's signal response and known gas concentrations, establishing calibration curves between different gases and the sensor's signal response. The calibration process is based on experimental data fitting results, using common methods such as linear fitting, polynomial regression, and support vector regression. Based on this calibration curve, in actual gas detection, the sensor's measured signal is compared with the calibration curve to obtain the corresponding baseline concentration value for subsequent error assessment and optimization calculations. The pre-processed environmental monitoring data is compressed using Huffman coding and differential coding. The data volumes before and after compression are recorded, labeled as the original gas monitoring data volume and the compressed gas monitoring data volume, and the compression ratio is used to evaluate system operating efficiency. The system obtains optimal environmental parameters from the environmental monitoring database, including optimal temperature, humidity, air pressure, and dust concentration. These parameters are used as preset baseline values for subsequent comparison and analysis of environmental monitoring data, and are dynamically updated to adapt to long-term environmental changes. The handheld laser monitoring device stores and manages data through a microprocessor, supporting both local caching and cloud synchronization. Various monitoring data are integrated into the environmental monitoring database, enabling structured storage and unified scheduling management of high-frequency data.

[0036] In this implementation scheme, this step completes the whole process from outlier elimination, unit unification, trend smoothing, integrity verification to feature extraction and dimensionality reduction by systematically preprocessing the raw data in the environmental monitoring database. It further combines signal filtering, error correction and calibration curve matching to achieve the extraction of effective signals and accurate acquisition of benchmark concentration values. At the same time, the volume of the processed data is optimized through compression coding, and the optimal environmental parameters are introduced as a reference benchmark to improve the comparability and adaptability of the environmental data. Finally, the cleaned high-quality data is imported into the environmental monitoring database through the microprocessor, providing stable, standardized and directly callable data support for subsequent modules such as data quality assessment, system parameter optimization, and intelligent decision-making and scheduling.

[0037] Specifically, the target gas is monitored by laser spectroscopy, and the specific steps for performing data quality assessment based on the laser spectroscopy monitoring results are as follows: obtaining the effective signal, the original environmental noise signal, the error correction term, the measured gas concentration value, and the reference concentration value. Among them, the effective signal is collected by the photoelectric converter integrated in the laser receiving module and denoised by the Kalman filter. The original environmental noise signal is detected by the noise sensor in real time by external interference and equipment background fluctuation value. The error correction term is used to avoid zero division and numerical instability, and the value range is usually 10 -8 ~10 -6 , usually set to 10 -6The specific value is flexibly set according to the system accuracy and signal strength; the ratio of the effective signal to the original environmental noise signal is calculated to measure the change in the signal-to-noise ratio, and a very small error correction term is combined to avoid zero and very small values in the calculation, and recorded as the signal ratio. The higher the ratio, the more stable the measurement environment and the clearer the signal; the difference between the actual gas concentration value and the reference concentration value and the reference concentration value are normalized to obtain the concentration error ratio, which reflects the degree of deviation of the actual measurement value compared with the calibration model. The adjustment factor is obtained by subtracting the concentration error ratio from 1. This factor is used to correct the reliability degradation caused by measurement deviation; finally, the adjustment factor is multiplied by the signal ratio to form a comprehensive evaluation index of signal quality and concentration accuracy, and finally the reliability assessment value is obtained. This value is used as the key basis for subsequent measurement validity judgment, equipment status switching and system intelligent parameter adjustment.

[0038] The specific calculation formula for the reliability evaluation value is:

[0039]

[0040] In the formula, I represents the reliability evaluation value, S r represents the original environmental noise signal, S e represents the effective signal, ∈ represents the error correction term, C s Indicates the measured gas concentration value, C r Indicates the baseline concentration value.

[0041] As shown in Table 1, it is a statistical table of the reliability evaluation value changes provided by the embodiment of the present application. In this embodiment, the effective signal of sample 1 is set to 0.8, and the original environmental noise signal S rThe effective signal of sample 2 is set to 1.2, the original ambient noise signal is set to 0.6, the actual gas concentration is set to 102, and the reference concentration is set to 100; the effective signal of sample 3 is set to 1.5, the original ambient noise signal is set to 0.7, the actual gas concentration is set to 98, and the reference concentration is set to 100; the effective signal of sample 4 is set to 2.0, the original ambient noise signal is set to 0.8, the actual gas concentration is set to 105, and the reference concentration is set to 100; the effective signal of sample 5 is set to 2.5, the original ambient noise signal is set to 0.9, the actual gas concentration is set to 110, and the reference concentration is set to 100; the effective signal of sample 6 is set to 2 .8, the original environmental noise signal is set to 1.0, the measured gas concentration value is set to 115, and the reference concentration value is set to 100; the effective signal of sample 7 is set to 3.0, the original environmental noise signal is set to 1.1, the measured gas concentration value is set to 120, and the reference concentration value is set to 100; the effective signal of sample 8 is set to 3.2, the original environmental noise signal is set to 1.1, the measured gas concentration value is set to 122, and the reference concentration value is set to 100; the effective signal of sample 9 is set to 3.5, the original environmental noise signal is set to 1.2, the measured gas concentration value is set to 118, and the reference concentration value is set to 100; the effective signal of sample 10 is set to 4.0, the original environmental noise signal is set to 1.3, the measured gas concentration value is set to 125, and the reference concentration value is set to 100.

[0042] Table 1 Statistics of changes in reliability evaluation values

[0043]

[0044]

[0045] like Figure 3 As shown in Table 1, the reliability evaluation value variation trend diagram provided in the embodiment of the present application is shown in Table 1. Figure 3As can be seen, Sample 5 has the highest reliability assessment value, reaching 2.5000. This indicates that this sample performs optimally in terms of signal-to-noise ratio and concentration consistency. The system has the highest confidence in the data, directly using it for output and entering energy-saving mode. Sample 1 has a reliability assessment value of 1.6000, approaching the lower limit of the medium reliability threshold, indicating low signal strength and significant noise interference. The system may need to perform signal enhancement and calibration. The three reliability thresholds set in the figure are: Level 1 reliability threshold of 1.0, Level 2 reliability threshold of 1.5, and Level 3 reliability threshold of 2.0. Overall, the reliability assessment value increases steadily between Samples 1 and 5, then fluctuates slightly but remains above the Level 3 reliability threshold. This indicates that the system's overall monitoring status is stable and reliable, with most samples already in the high reliability range and exhibiting good measurement accuracy and interference resistance. Higher reliability assessment values indicate that the system is more accurate in identifying target gas concentrations and more reliable monitoring results.

[0046] In this implementation scheme, this step realizes the real-time quality assessment and quantitative expression of laser spectrum monitoring data by constructing a two-factor evaluation model based on signal-to-noise ratio and concentration error. By collecting effective signals, original environmental noise signals, measured gas concentration values and reference concentration values, the system constructs an adjustment factor with normalized concentration deviation, and combines it with the signal ratio to finally form a reliability assessment value as the core indicator for judging the credibility of the data. This indicator not only reflects the stability of the measurement environment and the accuracy of the data, but also provides a basis for the subsequent signal processing path, calibration strategy and energy consumption mode selection. Combining the formula calculation and sample data analysis, it can be seen that the reliability assessment value shows a stable and distinguishable change trend under different signal states and concentration deviations. Based on this, the system automatically determines whether the data meets the output conditions and whether it needs to enter compensation processing. By setting multi-level reliability thresholds, the system establishes a refined response mechanism for data of different quality levels, which significantly improves the judgment accuracy, application efficiency and overall robustness of the monitoring results.

[0047] Specifically, the specific steps of judging whether the data meets the measurement accuracy requirements based on the data quality assessment results and then performing the corresponding signal processing and calibration are as follows: real-time comparison of the reliability assessment value and the reliability threshold, the reliability threshold includes the first-level reliability threshold, the second-level reliability threshold and the third-level reliability threshold. The system automatically determines the data credibility level according to the matching results of the current assessment value and the thresholds at each level, and triggers the corresponding processing mechanism accordingly to achieve efficient data quality closed-loop control; according to the assessment results corresponding to different levels, the system automatically performs differentiated signal adjustment and calibration processing to ensure that the stability and responsiveness of the signal processing link are maintained under different quality conditions; when the reliability assessment value is greater than the third-level reliability threshold, the system determines that the current data quality is in a high-confidence state, maintains the current filtering strategy and signal processing process, keeps the signal gain in the standard range, and disables redundant signal repeated sampling processing to reduce energy consumption and improve measurement efficiency; when the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, the filtering strategy is automatically enhanced and switched to the enhanced filtering algorithm to improve the smoothing effect. The enhanced filtering is implemented based on adaptive Kalman filtering and sliding average convolution, and the signal gain is appropriately increased to enhance the signal perception capability. The system simultaneously performs dynamic noise spectrum identification and interference point elimination, marks abnormal frequency bands using a frequency domain feature recognition algorithm, and performs adaptive compensation adjustments for noise disturbances to improve data stability under medium reliability conditions. When the reliability assessment value is greater than the first-level reliability threshold and less than or equal to the second-level reliability threshold, the signal enhancement mode is activated, superimposing continuous multi-cycle signal strengths to form a time-domain enhancement sequence. Multi-stage wavelet filtering and a frequency band discretization algorithm are combined to suppress the main interference frequency, further removing low-frequency drift and high-frequency interference. Dynamic gain boosting and error modeling correction are also performed to enhance the response capability to weak signals. When the reliability assessment value is less than or equal to the first-level reliability threshold, the signal reconstruction mechanism is immediately activated. The system determines that the current data quality is unusable, uses historical signal features for contrast extraction, and performs template matching based on signal patterns under similar environmental conditions in the database. Filter parameters are also adjusted to enhance the recognition capability of target feature dimensions. Multiple adjacent signals are combined for phase superposition to form a signal array, and the sampling time is extended to increase data redundancy. The abnormal frequency band discretization algorithm is also activated, and strongly constrained low-pass processing is performed on frequencies exceeding the noise ceiling, ensuring data recovery and fault tolerance even in extreme situations.

[0048] In this implementation scheme, this step establishes a signal processing and calibration response mechanism based on the data credibility level by comparing the reliability evaluation value calculated in real time with the preset multi-level reliability threshold, thereby realizing hierarchical judgment and dynamic regulation of the monitoring data quality. Based on the interval in which the reliability evaluation value is located, the system automatically determines the current data state and matches the corresponding processing path, from high-credibility energy-saving output, to medium-credibility filtering enhancement, to low-credibility signal compensation and reconstruction, forming a set of closed-loop control strategies that dynamically adapt to various signal quality states. This mechanism not only ensures the realization of an efficient and low-power operation mode when the data quality is good, but also ensures that when the quality degrades and anomalies occur, the signal availability and fault tolerance are improved through filter adjustment, multi-cycle superposition, template matching and other means, thereby effectively enhancing the robustness, flexibility and stability of the system under different environmental interference conditions.

[0049] Specifically, the steps for performing quantum key analysis on laser spectroscopy monitoring data that has passed the data quality assessment are as follows: The measured gas concentration value, the dynamic gas concentration sampling period, the volume of gas monitoring raw data, and the volume of gas monitoring compressed data are acquired in real time by an optical sensor and analyzed and integrated by a microprocessor. The dynamic gas concentration sampling period is set according to the monitoring strategy and can be adaptively adjusted. The volume of gas monitoring raw data and the volume of gas monitoring compressed data respectively record the change in sensor data volume before and after data acquisition. A comprehensive analysis is performed to obtain a data optimization value, which reflects the overall structural efficiency and concentration signal stability of the data and is used to guide subsequent quantum key validity judgment and encryption strategy switching. The specific analysis method for the data optimization value is as follows: the absolute values of the measured gas concentration values are summed to obtain the amplitude representing the overall concentration change. The inverse of this sum is then multiplied by a measured balance coefficient. This coefficient is used to adjust the influence of the concentration term on the optimization result. The measured balance coefficient λ1 is obtained by regression fitting the concentration error and system processing capacity under multiple environmental monitoring data. It is typically set between 0.2 and 0.5. A higher value indicates a greater sensitivity of the system to concentration error. At the same time, the compression ratio (the ratio of the raw gas monitoring data to the compressed gas monitoring data), or compression rate, is calculated and multiplied by the compression balance coefficient. This coefficient reflects the contribution of compression efficiency to data availability and transmission security. Based on transmission network stability testing and historical compression performance statistical evaluation, the compression balance coefficient λ2 is typically set between 0.3 and 0.7. A higher value indicates that the system prioritizes the impact of compression efficiency on the overall optimization result. Finally, the two weighted results are added together to calculate the data optimization value, which serves as the core reference for determining whether quantum keys should be updated or encryption strength adjusted.

[0050] The specific calculation formula for data optimization value is:

[0051]

[0052] Where D opt represents the data optimization value, T represents the dynamic sampling period of gas concentration, C r Indicates the measured gas concentration value, S c Indicates the amount of compressed data for gas monitoring, S r It represents the amount of raw data of gas monitoring, λ1 represents the measured balance coefficient, and λ2 represents the compression balance coefficient.

[0053] As shown in Table 2, it is a statistical table of changes in the data optimization value provided in the embodiment of the present application. In this embodiment, the gas concentration value of sample 1 is set to 100, the amount of gas monitoring raw data is set to 1000, and the amount of gas monitoring compressed data is set to 600; the gas concentration value of sample 2 is set to 105, the amount of gas monitoring raw data is set to 980, and the amount of gas monitoring compressed data is set to 590; the gas concentration value of sample 3 is set to 110, the amount of gas monitoring raw data is set to 1020, and the amount of gas monitoring compressed data is set to 620; the gas concentration value of sample 4 is set to 95, the amount of gas monitoring raw data is set to 1010, and the amount of gas monitoring compressed data is set to 610; the gas concentration value of sample 5 is set to 98, and the amount of gas monitoring raw data is set to 990, The gas monitoring compressed data volume is set to 605; the measured gas concentration value of sample 6 is set to 102, the gas monitoring raw data volume is set to 985, and the gas monitoring compressed data volume is set to 595; the measured gas concentration value of sample 7 is set to 100, the gas monitoring raw data volume is set to 1005, and the gas monitoring compressed data volume is set to 615; the measured gas concentration value of sample 8 is set to 107, the gas monitoring raw data volume is set to 1030, and the gas monitoring compressed data volume is set to 630; the measured gas concentration value of sample 9 is set to 115, the gas monitoring raw data volume is set to 1025, and the gas monitoring compressed data volume is set to 620; the measured gas concentration value of sample 10 is set to 120, the gas monitoring raw data volume is set to 995, and the gas monitoring compressed data volume is set to 600.

[0054] Table 2 Statistics of changes in data optimization values

[0055]

[0056]

[0057] like Figure 4 As shown in the figure, it is a trend diagram of the data optimization value provided in the embodiment of the present application. Figure 2As can be seen, sample 7 has the highest data optimization value, indicating that this sample achieves an optimal balance between data compression ratio and concentration error control. The system demonstrates strong data processing capabilities and compression efficiency under these environmental conditions, which can be considered the ideal state of optimization. Samples 1 and 10 have relatively low data optimization values. Although above the minimum optimization threshold, the optimization level is weak. Under these conditions, the system will initiate adjustments to improve compression performance and error compensation. The three optimization thresholds set in the figure are: 0.245 for the first optimization threshold, 0.250 for the second optimization threshold, and 0.255 for the third optimization threshold. The overall trend in the figure shows that the data optimization values for all samples are within an acceptable range, with some samples reaching a medium optimization level, reflecting the system's robustness and stability under various sampling conditions. Higher data optimization values indicate a greater ability of the system to achieve efficient compression while maintaining concentration measurement accuracy, and thus, stronger optimization performance.

[0058] In this implementation scheme, the effect of this step is to construct a data optimization value calculation model by extracting key parameters such as the measured gas concentration value, the dynamic sampling period of gas concentration, the amount of gas monitoring raw data and the amount of gas monitoring compressed data, and comprehensively evaluate the performance of the monitoring data in two dimensions: concentration stability and compression efficiency. The higher the data optimization value, the more it indicates that the system not only has good concentration measurement consistency under the current environmental conditions, but also achieves efficient data compression and structure optimization. As the core reference indicator of quantum key management, this value directly affects the system's dynamic adjustment of encryption strength, key update frequency and other strategies, helps to improve the security and resource utilization of data transmission, and ensures that the quantum encryption module continues to maintain efficient and stable operation in a changing environment.

[0059] Specifically, the specific steps for conducting key validity checks, key updates and encryption parameter adjustments based on the results of quantum key analysis are as follows: real-time comparison of data optimization values and optimization thresholds. The optimization thresholds include first-level optimization thresholds, second-level optimization thresholds and third-level optimization thresholds. The system automatically matches the corresponding threshold level according to the current data optimization level and triggers the corresponding key management strategy; when the data optimization value is greater than the third-level optimization threshold, it indicates that the current monitoring data has high precision and high compression efficiency. The system judges that the overall operating status is good, maintains the current power, filter strength and sensor sensitivity unchanged, enters low-power mode for continuous operation to reduce energy consumption and extend equipment life, and starts the quantum key synchronization check mechanism to quickly verify the consistency of the distributed keys to ensure the stability of the quantum encryption link; when the data optimization value is greater than the second-level optimization threshold and is less than or equal to the third-level optimization threshold, the system is in a suboptimal operating state, and the environmental compensation mechanism is started to correct the encryption parameters according to the real-time changes in current temperature, humidity, air pressure and dust concentration. Combined with the compensation The compensation model dynamically adjusts the key distribution frequency, appropriately increases the quantum key update frequency, and optimizes parameters such as the window length and encoding method in the data compression algorithm to improve data utilization in complex environments; when the data optimization value is greater than the first-level optimization threshold and less than or equal to the second-level optimization threshold, it indicates that the data quality is in the critical interval, and the system automatically switches to the adaptive Kalman filter mode, models the fluctuation characteristics in the signal and dynamically adjusts the filter parameters, adjusts the encryption strength, enhances the anti-interference ability, and executes the bandwidth adaptation strategy according to the stability of data transmission to prevent packet loss caused by key updates and excessive data packets; when the data optimization value is less than or equal to the first-level optimization threshold, the system determines that the current monitoring data quality has seriously deteriorated, and immediately activates the fault response mechanism, increases the laser power and sensor gain to enhance the signal strength, re-collects the signal and performs hardware-level calibration, and at the same time performs a forced key update operation and reinitializes the transmission link to ensure data security and communication effectiveness in extreme cases.

[0060] In this implementation, the optimized data value calculated in real time is compared against a preset optimization threshold, triggering the corresponding quantum key management strategy to dynamically adjust and optimize key validity, encryption strength, and related parameters. This mechanism automatically determines the current operating status based on data quality and adopts different processing paths, including low-power maintenance, high-frequency key updates, encryption parameter correction, and emergency fault response. This ensures the reliability and security of the quantum communication link under stable, complex, and extreme environmental conditions, while maximizing data transmission efficiency and overall system operational robustness.

[0061] Specifically, the specific steps for conducting environmental adaptability analysis based on the evaluation results of comprehensive environmental monitoring data, laser spectrum monitoring data, and quantum key analysis results are as follows: obtain the current ambient temperature, humidity, air pressure, dust concentration, optimal temperature, optimal humidity, and optimal air pressure. The current environmental data is collected in real time by multiple types of sensors deployed throughout the equipment. The optimal environmental parameters come from the system benchmark model and historical operation statistics, reflecting the environmental characteristics of the equipment under the most stable operating conditions; comprehensive analysis obtains the environmental adaptation adjustment value to measure the impact of the current environmental status on system performance; the analysis method of the environmental adaptation adjustment value is: compare the current environmental temperature, humidity, air pressure, and dust concentration data with the preset optimal environmental parameters, calculate their deviation values, and the absolute size of the deviation value represents the environmental The temperature error weight coefficient is obtained by quantifying the effect of temperature changes on measurement error during constant temperature control experiments, and is typically set between 0.2 and 0.4. The humidity error weight coefficient is determined by fitting the signal attenuation rate under different humidity conditions and is typically set between 0.1 and 0.3. The pressure error weight coefficient is obtained by using a regression model that models the effect of pressure changes on absorption line shifts and concentration errors, with a recommended range of 0.2 to 0.4. The dust concentration error weight coefficient is statistically extracted by the attenuation of the laser reception signal due to changes in particulate matter concentration, and is typically set between 0.3 and 0.5. These coefficients are fitted with historical environmental and performance data and can also be flexibly set based on actual application scenarios. These deviation values are weighted and adjusted. The temperature and humidity deviations are nonlinearly suppressed using an exponential attenuation function, thereby weakening the drastic impact of extreme environmental values on the system evaluation results. The air pressure and dust deviations are suppressed using a proportional suppression function to more accurately reflect their possible impact on the optical path stability and sensor contamination. Finally, through a multiplication operation, the combined effect of these environmental factors is mapped into an environmental adaptation adjustment value. This value serves as an important input parameter for the subsequent system to dynamically adjust the laser power, sensor sensitivity, and signal processing strategy, thereby achieving a quantitative description and control basis for the system stability and responsiveness in complex environments.

[0062] The specific calculation formula for the environmental adaptation adjustment value is:

[0063]

[0064] Where EAT is the environmental adaptation adjustment value, T is the temperature, H is the humidity, P is the air pressure, D is the dust concentration, β1 is the temperature error weight coefficient, β2 is the humidity error weight coefficient, β3 is the air pressure error weight coefficient, β4 is the dust concentration error weight coefficient, T opt represents the optimal temperature, H optrepresents the optimal humidity, P opt Indicates the optimal air pressure.

[0065] In this implementation, this step establishes an environmental adaptation adjustment value model by introducing environmental variables such as temperature, humidity, air pressure, and dust concentration, along with their corresponding weight coefficients. This allows for a quantitative assessment of the deviation between the current environmental state and the system's optimal operating conditions. This mechanism not only improves the system's sensitivity and accuracy to external environmental disturbances but also provides a reliable basis for the subsequent dynamic adjustment of laser power, sensor sensitivity, and signal processing strategies, thereby enhancing the device's adaptability and operational stability under complex operating conditions.

[0066] Specifically, the specific steps for dynamically adjusting laser power, sensor sensitivity, and signal processing parameters based on the environmental adaptability analysis results are as follows: real-time comparison of the environmental adaptability adjustment value and the adjustment threshold, which includes the first-level adjustment threshold and the second-level adjustment threshold. The system determines the required adjustment level by dynamically evaluating the degree of deviation between the current environmental state and the optimal operating conditions. The environmental adaptive adjustment strategy is based on the joint judgment of the environmental adaptability adjustment value and the reliability assessment value, ensuring that the adjustment logic not only considers external environmental disturbances but also integrates sensor measurement stability and signal quality, thereby realizing a dual-factor decision-making mechanism. When the environmental adaptability adjustment value is greater than the second-level adjustment threshold and the reliability assessment value is greater than the third-level reliability threshold, the system determines that the current operating state is good and directly maintains the current settings of laser wavelength, power, and sensor sensitivity, enters a low-power mode, reduces sampling and calculation frequency, and improves energy efficiency without performing additional compensation and adjustment operations. When the environmental adaptability adjustment value is greater than the second-level adjustment threshold and the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, the environmental compensation mechanism is automatically enabled to adjust the laser wavelength and laser temperature control parameters to make them more consistent with the actual absorption line position, fine-tune the sensor sensitivity to adapt to weak signals, and apply an enhanced filtering algorithm (such as a multi-level sliding average). and adaptive Kalman filtering) to improve signal stability; when the environmental adaptation adjustment value is greater than the first-level adjustment threshold and less than or equal to the second-level adjustment threshold and the reliability assessment value is greater than the third-level reliability threshold, the adaptive compensation mechanism is activated, giving priority to improving sensor sensitivity and signal gain, dynamically adjusting the front-end amplifier circuit parameters, and implementing filter parameter optimization based on the current environmental state, including cutoff frequency adjustment and window function reconstruction, to improve the responsiveness to target signal characteristics; when the environmental adaptation adjustment value is greater than the first-level adjustment threshold and less than or equal to the second-level adjustment threshold and the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, it automatically switches to enhanced processing mode, increasing laser power to improve gas absorption signal intensity, using multi-level wavelet denoising and adaptive filtering strategies to enhance interference suppression, and dynamically correcting temperature, humidity, and pressure environmental parameters to further suppress the impact of error interference sources; when the environmental adaptation adjustment value is greater than or equal to the first-level adjustment threshold and less than or equal to the second-level adjustment threshold and the reliability assessment value is greater than the first-level reliability threshold and less than or equal to the second-level reliability threshold, a comprehensive optimization path is activated, including increasing laser power, maximizing sensor gain, and superimposing multiple sampling cycles to improve signal strength, combining multi-level filtering with frequent key updates to ensure data integrity and transmission security;When the environmental adaptation adjustment value is less than the first-level adjustment threshold and the reliability assessment value is less than the first-level reliability threshold, the system enters extreme protection mode, triggering the laser system's self-calibration, self-cleaning, and wavelength retuning processes. Multi-parameter compensation is performed based on historical environmental change trends and abnormal signal records. The full-performance operation mode is simultaneously enabled to enhance signal reconstruction and encryption processing capabilities. Key system parameters are optimized based on historical monitoring data and current environmental conditions to ensure the system maintains basic operation and secure communication capabilities in extreme environments.

[0067] In this implementation scheme, this step constructs a dynamic adjustment system for system parameters by introducing a joint judgment mechanism of environmental adaptation adjustment values and reliability assessment values, achieving hierarchical response and precise control of laser power, sensor sensitivity, and signal processing strategies. Based on the degree of environmental change and data quality level, the system automatically compares the set multi-level adjustment thresholds, identifies the current operating status, and matches the corresponding control mode, from low-power maintenance, environmental compensation, adaptive enhancement, to comprehensive optimization and even extreme protection, to ensure that the system can maintain stable performance, reasonable energy efficiency, and reliable output under various types of environmental interference. This mechanism not only enhances the system's ability to quickly respond to environmental dynamics, but also improves the flexibility and robustness of the signal processing path, effectively ensuring the continuous operation, safe transmission, and high-quality data acquisition of the equipment under complex and even harsh conditions.

[0068] like Figure 2 As shown, a schematic diagram of the structure of a quantum encryption handheld laser gas monitoring system provided in an embodiment of the present application is provided. The quantum encryption handheld laser gas monitoring system provided in an embodiment of the present application applies a quantum encryption handheld laser gas monitoring method, including: an environmental monitoring and data acquisition module, a laser gas monitoring module, a quantum encryption and data processing module, and an environmental adaptation and fault detection module; the environmental monitoring and data acquisition module is used to collect environmental parameter information, store environmental monitoring data, establish an environmental monitoring database, and preprocess the data in the environmental monitoring database; the laser gas monitoring module is used to perform laser spectrum monitoring of the target gas, perform data quality assessment based on the laser spectrum monitoring results, determine whether the data meets the measurement accuracy requirements based on the data quality assessment results, and then perform corresponding signal processing and calibration; the quantum encryption and data processing module is used to perform quantum key analysis on the laser spectrum monitoring data that passes the data quality assessment, conduct key validity checks, perform key updates, and adjust encryption parameters based on the quantum key analysis results; the environmental adaptation and fault detection module is used to integrate the environmental monitoring data, the evaluation results of the laser spectrum monitoring data, and the quantum key analysis results, perform environmental adaptability analysis, and dynamically adjust the laser power, sensor sensitivity, and signal processing parameters based on the environmental adaptability analysis results.

[0069] In this implementation, this step realizes the modular division of labor and collaborative operation of quantum encryption handheld laser gas monitoring functions by building an integrated system architecture. Figure 2 As shown in Figure 1, the system consists of an environmental monitoring and data acquisition module, a laser gas monitoring module, a quantum encryption and data processing module, and an environmental adaptation and fault detection module. Each module operates collaboratively according to its functional division, forming a complete closed-loop detection and control mechanism. The environmental monitoring and data acquisition module is responsible for real-time acquisition and storage of environmental parameters such as temperature, humidity, air pressure, and dust concentration. It also preprocesses and extracts features from the environmental monitoring database to provide high-quality input for subsequent analysis. The laser gas monitoring module detects target gases based on high-precision laser spectroscopy technology. It assesses the reliability of the monitored data to determine whether it meets measurement accuracy requirements and triggers appropriate signal processing and calibration strategies accordingly. The quantum encryption and data processing module performs quantum key analysis on qualified data and, based on the analysis results, determines key validity, updates keys, and adaptively adjusts encryption strength to ensure secure and efficient data transmission. The environmental adaptation and fault detection module integrates the outputs of the aforementioned environmental monitoring, spectral analysis, and quantum encryption to construct an environmental adaptability model. Based on this model, it dynamically adjusts laser power, sensor sensitivity, and signal processing parameters. It also has abnormal state recognition and self-recovery capabilities. The system structure realizes closed-loop control of the entire process from environmental perception, gas detection, data encryption to operation adjustment, which not only improves detection accuracy and data security, but also enhances the equipment's adaptability and fault response capabilities in complex environments.

[0070] It should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities and operations. Moreover, the terms "include," "comprise," and any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0071] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and effectiveness.

Claims

1. A quantum encryption handheld laser gas monitoring method, characterized in that: The following steps are involved: S1: Collect environmental parameter information, store environmental monitoring data, establish an environmental monitoring database, and pre-process the data in the environmental monitoring database; S2: Perform laser spectroscopy monitoring on the target gas, perform data quality assessment based on the laser spectroscopy monitoring results, determine whether the data quality assessment results meet the measurement accuracy requirements, and then perform corresponding signal processing and calibration; S3: Perform quantum key analysis on the laser spectrum monitoring data that has passed the data quality assessment, and conduct key validity checks, key updates, and encryption parameter adjustments based on the quantum key analysis results; S4: Conduct environmental adaptability analysis based on the evaluation results of environmental monitoring data, laser spectrum monitoring data, and quantum key analysis results, and dynamically adjust laser power, sensor sensitivity, and signal processing parameters based on the environmental adaptability analysis results.

2. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps of collecting environmental parameter information, storing environmental monitoring data, and establishing an environmental monitoring database are as follows: Environmental monitoring data includes: environmental data, original environmental noise signals, measured gas concentration values and gas concentration dynamic sampling period; Environmental sensors collect real-time environmental data, including temperature, humidity, air pressure, and dust concentration; The noise sensor collects external interference and equipment background noise in real time and records them as the original environmental noise signal; The optical sensor collects gas concentration values in real time and records them as measured gas concentration values. The data collection timestamp is also marked to obtain the dynamic sampling period of gas concentration. Store environmental monitoring data and establish an environmental monitoring database.

3. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps of preprocessing the data in the environmental monitoring database are as follows: Identify and eliminate mutation values and abnormal data in environmental monitoring data; Standardize and normalize environmental monitoring data from various units; Perform trend extraction and smoothing on the processed continuous data stream; Conduct integrity checks on environmental monitoring data to ensure that the data format is correct and the fields are complete, and fill in and mark missing values; Verify the data consistency of similar environmental parameters collected by multiple sensors, and make corrections if the data differences are too large; According to the characteristics and correlation of monitoring data, key eigenvalues and eigenvectors are extracted, and principal component analysis is used to perform dimensionality reduction; The original signal is filtered and denoised by the signal processing unit in the laser monitoring device to obtain the effective signal strength, which is recorded as the effective signal; Take the smallest positive number as the error correction term to avoid zero and extremely small values in the calculation; Record the relationship between the sensor's signal response and known gas concentrations, and establish a calibration curve between different gases and the sensor's signal response. Based on this calibration curve, in actual gas detection, compare the sensor's measured signal with the calibration curve to obtain the corresponding reference concentration value; The pre-processed environmental monitoring data is compressed using Huffman coding and differential coding, and the data volumes before and after compression are recorded and marked as gas monitoring original data volume and gas monitoring compressed data volume; Obtain optimal environmental parameters from the environmental monitoring database, including optimal temperature, optimal humidity, optimal air pressure, and optimal dust concentration, and use these parameters as preset benchmark values for comparison and analysis of subsequent environmental monitoring data; The data is stored and managed by the microprocessor in the handheld laser monitoring device, and various monitoring data are integrated into the environmental monitoring database.

4. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps of performing laser spectrum monitoring on the target gas and performing data quality assessment based on the laser spectrum monitoring results are as follows: Obtain effective signal, original environmental noise signal, error correction item, measured gas concentration value and reference concentration value; Calculate the ratio of the effective signal to the original ambient noise signal, combine it with a very small error correction term to avoid zero and very small values in the calculation, and record it as the signal ratio. Normalize the difference between the measured gas concentration value and the reference concentration value and the reference concentration value to obtain the concentration error ratio. Subtract the concentration error ratio from 1 to obtain the adjustment factor, and multiply it by the signal ratio to finally obtain the reliability assessment value.

5. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps of judging whether the data quality assessment result meets the measurement accuracy requirement and then performing corresponding signal processing and calibration are as follows: Real-time comparison of reliability assessment values and reliability thresholds, including level 1, level 2, and level 3 reliability thresholds. Based on the assessment results corresponding to different levels, the system automatically performs differentiated signal conditioning and calibration processing. When the reliability evaluation value is greater than the third-level reliability threshold, the current filtering strategy and signal processing process are maintained, the signal gain is kept in the standard range, and redundant signal resampling processing is disabled; When the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, the filtering strategy is automatically enhanced, switching to the enhanced filtering algorithm to improve the smoothing effect and appropriately increase the signal gain. At the same time, dynamic noise spectrum identification and interference point elimination are performed, and adaptive compensation adjustments are made to the noise disturbance. When the reliability assessment value is greater than the first-level reliability threshold and less than or equal to the second-level reliability threshold, the signal enhancement mode is activated, the continuous multi-cycle signal strength is superimposed, the main interference frequency is suppressed by combining multi-level wavelet filtering and frequency band discretization algorithm, and dynamic gain enhancement and error modeling correction are performed at the same time; When the reliability assessment value is less than or equal to the first-level reliability threshold, the signal reconstruction mechanism is immediately started, and historical signal features are used for contrast extraction. At the same time, the filter parameters are adjusted to enhance the target feature dimension recognition capability. Multiple adjacent signals are combined for phase superposition to form a signal array, and the sampling time is extended. At the same time, the abnormal frequency band discrete elimination algorithm is enabled, and strongly constrained low-pass processing is performed on the frequency points that exceed the noise upper limit.

6. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps of performing quantum key analysis on the laser spectrum monitoring data that has passed the data quality assessment are as follows: Obtain the measured gas concentration value, gas concentration dynamic sampling period, gas monitoring raw data volume and gas monitoring compressed data volume, and obtain the data optimization value through comprehensive analysis; The specific analysis method of the data optimization value is: sum the absolute values of the measured gas concentrations, take the inverse of the sum and multiply it by a measured balance coefficient, calculate the data optimization value by calculating the ratio of the gas monitoring original data volume to the gas monitoring compressed data volume, and multiply it by the compression balance coefficient.

7. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps of carrying out key validity check, key update and encryption parameter adjustment based on the quantum key analysis results are as follows: Real-time comparison of data optimization values and optimization thresholds. The optimization thresholds include first-level optimization thresholds, second-level optimization thresholds, and third-level optimization thresholds. When the data optimization value is greater than the third-level optimization threshold, the current power, filter intensity, and sensor sensitivity are kept unchanged, and the system enters low-power mode for continuous operation, while starting the quantum key synchronization check. When the data optimization value is greater than the second-level optimization threshold and less than or equal to the third-level optimization threshold, the environmental compensation mechanism is activated to correct parameters according to changes in temperature, humidity, air pressure, and dust concentration, appropriately increase the frequency of quantum key updates, and adjust the data compression algorithm parameters; When the data optimization value is greater than the first-level optimization threshold and less than or equal to the second-level optimization threshold, it switches to the adaptive Kalman filter mode, adjusts the filter parameters and encryption strength according to data fluctuations, and performs bandwidth adaptation to reduce packet loss; When the data optimization value is less than or equal to the first-level optimization threshold, the fault response mechanism is activated to increase the laser power and sensor gain, re-collect the signal and calibrate the equipment, and at the same time update the quantum key and reinitialize the transmission link.

8. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps for performing environmental adaptability analysis based on the comprehensive environmental monitoring data, the evaluation results of the laser spectrum monitoring data, and the quantum key analysis results are as follows: Obtain the current ambient temperature, humidity, air pressure, dust concentration, optimal temperature, optimal humidity and optimal air pressure, and conduct comprehensive analysis to obtain the environmental adaptation adjustment value; The analysis method of the environmental adaptation adjustment value is as follows: the temperature, humidity, air pressure and dust concentration data of the current environment are compared with the preset optimal environmental parameters, and their deviation values are calculated. The temperature error weight coefficient, humidity error weight coefficient, air pressure error weight coefficient and dust error weight coefficient are introduced respectively, and these deviation values are weightedly adjusted. The temperature and humidity deviations are nonlinearly suppressed using an exponential attenuation function, while the air pressure and dust deviations are nonlinearly suppressed using a proportional suppression function. Finally, through a product operation, the combined effect of these environmental factors is mapped to an environmental adaptation adjustment value.

9. The quantum encryption handheld laser gas monitoring method according to claim 1, characterized in that: The specific steps of dynamically adjusting laser power, sensor sensitivity, and signal processing parameters based on the environmental adaptability analysis results are as follows: Real-time comparison of environmental adaptation adjustment values and adjustment thresholds, where the adjustment thresholds include primary adjustment thresholds and secondary adjustment thresholds; The environmental adaptive adjustment strategy is based on the joint judgment of the environmental adaptive adjustment value and the reliability evaluation value; When the environmental adaptation adjustment value is greater than the second-level adjustment threshold and the reliability assessment value is greater than the third-level reliability threshold, the current settings of the laser wavelength, power, and sensor sensitivity are directly maintained, and the system enters low-power mode, reducing the sampling and calculation frequency and improving energy efficiency without performing additional compensation and adjustment operations; When the environmental adaptation adjustment value is greater than the second-level adjustment threshold and the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, the environmental compensation mechanism is automatically enabled to adjust the laser wavelength and laser temperature control, fine-tune the sensor sensitivity and apply the enhanced filtering algorithm; When the environmental adaptation adjustment value is greater than the first-level adjustment threshold and less than or equal to the second-level adjustment threshold, and the reliability assessment value is greater than the third-level reliability threshold, the adaptive compensation mechanism is activated, giving priority to improving sensor sensitivity and signal gain, and implementing filter parameter optimization based on the current environmental conditions; When the environmental adaptation adjustment value is greater than the first-level adjustment threshold and less than or equal to the second-level adjustment threshold, and the reliability assessment value is greater than the second-level reliability threshold and less than or equal to the third-level reliability threshold, it automatically switches to the enhanced processing mode, increases the laser power, uses multi-level wavelet denoising and adaptive filtering strategies to enhance interference suppression, and performs dynamic correction on the temperature, humidity and pressure environmental parameters. When the environmental adaptation adjustment value is greater than or equal to the first-level adjustment threshold and less than or equal to the second-level adjustment threshold, and the reliability assessment value is greater than the first-level reliability threshold and less than or equal to the second-level reliability threshold, the comprehensive optimization path is activated, including increasing laser power, maximizing sensor gain, superimposing multiple sampling cycles to improve signal strength, and combining multi-level filtering and frequent key updates to ensure data integrity and transmission security; When the environmental adaptation adjustment value is less than the first-level adjustment threshold and the reliability assessment value is less than the first-level reliability threshold, the system enters extreme protection mode, triggering the laser system self-calibration, self-cleaning and wavelength readjustment processes, and simultaneously enabling the full-performance operation mode to improve signal reconstruction and encryption processing capabilities, and jointly optimize the system's key parameters based on historical monitoring data and current environmental conditions.

10. A quantum encryption handheld laser gas monitoring system, characterized by: Including environmental monitoring and data acquisition module, laser gas monitoring module, quantum encryption and data processing module, environmental adaptation and fault detection module; The environmental monitoring and data acquisition module is used to collect environmental parameter information, store environmental monitoring data, establish an environmental monitoring database, and pre-process the data in the environmental monitoring database; The laser gas monitoring module is used to perform laser spectrum monitoring on the target gas, perform data quality assessment based on the laser spectrum monitoring results, determine whether the data quality assessment results meet the measurement accuracy requirements, and then perform corresponding signal processing and calibration; The quantum encryption and data processing module is used to perform quantum key analysis on the laser spectrum monitoring data that has passed the data quality assessment, and to carry out key validity checks, key updates, and encryption parameter adjustments based on the quantum key analysis results; The environmental adaptation and fault detection module is used to integrate environmental monitoring data, evaluation results of laser spectrum monitoring data, and quantum key analysis results to perform environmental adaptability analysis, and dynamically adjust laser power, sensor sensitivity, and signal processing parameters based on the environmental adaptability analysis results.

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