CN118425098B - Distributed laser methane detection method and system - Google Patents
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Abstract
The invention discloses a distributed laser methane detection method and system, in particular to the technical field of methane detection, which are used for solving the problem of inaccurate distributed laser methane detection; according to the invention, a plurality of detection nodes are arranged in a target monitoring area, and a wavelength modulation spectrometry is adopted, so that efficient and accurate methane concentration detection is realized, environmental data and laser detection data are synchronously collected at each node, the data are preprocessed to improve the accuracy and reliability of the data, the gas concentration is determined by analyzing the detected optical signals in real time, the laser wavelength and environmental parameters can be dynamically adjusted according to the detected gas concentration result, the detection performance is optimized and adapted to environmental changes, the matching degree of the data can be evaluated by comparing the historical data with the current data, so that appropriate control signals are generated, maintenance operation or adjustment measures are guided, and the continuity and accuracy of detection are ensured.
Description
Technical Field
The invention relates to the technical field of methane detection, in particular to a distributed laser methane detection method and system.
Background
Methane is an important natural gas component widely used in energy production and industrial feedstocks, and methane can form explosive mixtures when mixed with air over a range of concentrations, presenting a significant safety risk.
Conventional methane detection techniques such as catalytic combustion sensors, infrared absorption sensors, etc., although widely used, generally suffer from long response time, low sensitivity, susceptibility to environmental influences, etc., and these limitations have been reduced
Their application in large-scale or complex environments, so laser detection techniques are increasingly being applied to methane detection.
The prior art has the following defects:
The existing laser methane detection technology has high sensitivity and precision, but has obvious defects in environmental sensitivity, is extremely sensitive to environmental changes such as temperature fluctuation, humidity change and air pressure difference, can influence the propagation characteristics of laser and the absorption spectrum of gas, so as to interfere with the accuracy of detection results, for example, the temperature rise can lead to the widening of the line width of methane absorption, the increase of humidity can cause atomization or condensation of optical components of equipment, the stability of an optical path system is influenced, and in addition, the change of background gas can also overlap with the spectral characteristics of methane, the risk of false alarm is increased, and the robustness and the reliability of laser methane detection data are reduced under the interference of the environmental factors.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide a distributed laser methane detection method and system, which are configured to perform different control operations by arranging a plurality of laser methane detection nodes in a target monitoring area, collecting data, performing gas detection analysis, analyzing a detected optical signal, determining a gas concentration result, adjusting wavelength tuning and environmental parameters according to the gas concentration result, determining an environmental condition and a calibration result relationship based on historical data, performing dynamic comparison calibration according to the historical calibration data and current environmental data, and determining a matching condition of data in a comparison process, so as to solve the problems set forth in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A distributed laser methane detection method comprises the following steps:
arranging a plurality of laser methane detection nodes in a target monitoring area, synchronously collecting environment data and laser detection data, and preprocessing;
performing gas detection on the laser detection data by using a wavelength modulation spectrometry, analyzing the detected optical signals, and determining a gas concentration result;
Adjusting wavelength tuning and environmental parameters according to a gas concentration result, analyzing historical calibration data, determining the relation between the environmental conditions and the calibration result, and carrying out dynamic comparison calibration according to the historical calibration data and current environmental data;
Detecting influence information generated when historical calibration data and current environment data are compared is acquired, the matching condition of the data in the comparison process is determined, different control signals are generated according to the data matching condition, and different control operations are performed.
In a preferred embodiment, the environmental data and the laser detection data are collected and preprocessed synchronously, and the specific process is as follows:
Preprocessing sensor data, filtering and standardizing, and selecting a filter according to the characteristics of the data, wherein the filter comprises a low-pass filter, a high-pass filter, a band-pass filter or a band-stop filter;
for raw data collected from each sensor, calculating the mean and standard deviation thereof, converting the data to a standardized scale;
a time stamp is applied to each data point, each sensor is clocked, and all sensors are set to collect data at the same time.
In a preferred embodiment, the laser detection data is subjected to gas detection by using a wavelength modulation spectroscopy, the detected optical signal is analyzed to determine a gas concentration result, and wavelength tuning and environmental parameters are adjusted according to the detection result, which comprises the following steps:
Setting a basic wavelength of a laser, and adjusting the output wavelength of the laser to be close to the central wavelength of a target gas absorption line;
Performing wavelength tuning control on the generated low-frequency triangular wave and high-frequency sine wave, modulating the low-frequency triangular wave to high frequency by using the high-frequency sine wave, transmitting the modulated laser through the gas to be detected, determining the residual optical signal after the laser passes through the gas, and converting the optical signal into an electric signal through a photoelectric detector;
Processing the received electric signal by using a phase-locked amplifier to extract a harmonic signal under the modulation frequency;
And converting the harmonic signal into a gas concentration value according to the gas concentration information contained in the demodulated harmonic signal through a preset calibration curve and the actually measured harmonic amplitude.
In a preferred embodiment, wavelength tuning and environmental parameters are adjusted based on the gas concentration results as follows:
collecting laser detection data of laser detection output, including light intensity and absorption characteristics, and collecting environmental data from an environmental sensor, the environmental data including temperature, humidity and air pressure;
determining the alignment degree of the current laser wavelength and a target absorption peak, and determining whether wavelength adjustment is needed;
if the detected wavelength is not completely aligned with the absorption peak, adjusting the temperature and/or current of the laser to adjust the wavelength;
and analyzing the environmental data, and establishing a compensation model, wherein the model adjusts environmental parameters according to the air pressure, the temperature and the humidity.
In a preferred embodiment, detection influence information generated when comparing historical calibration data with current environmental data is acquired, and the matching condition of the data in the comparison process is determined, wherein the specific process is as follows:
acquiring detection influence information generated in the process of comparing historical calibration data with current environment data, wherein the detection influence information comprises calibration related information and trend identification information;
The calibration related information comprises a calibration related difference index, and the trend identification information comprises a dynamic trend stability index;
And carrying out simultaneous generation on the acquired calibration association difference index and the dynamic trend stability index to generate a detection state evaluation coefficient.
In a preferred embodiment, different control signals are generated according to the data matching condition, and different control operations are performed, and the specific process is as follows:
comparing the detection state evaluation coefficient with a detection threshold value;
If the detection state evaluation coefficient is greater than or equal to the detection threshold value, generating an early warning detection signal, and notifying maintenance personnel to perform inspection and intervention;
If the detection state evaluation coefficient is smaller than the detection threshold, a state stable signal is generated, and no additional calibration or maintenance is needed to continue normal operation.
A distributed laser methane detection system for the above-mentioned distributed laser methane detection method, comprising: the regional data acquisition module is used for arranging a plurality of laser methane detection nodes in the target monitoring region, synchronously acquiring environment data and laser detection data and preprocessing the environment data and the laser detection data;
The concentration analysis module is used for detecting the gas of the laser detection data by using a wavelength modulation spectrometry, analyzing the detected optical signals and determining a gas concentration result;
the calibration module is used for adjusting wavelength tuning and environmental parameters according to the gas concentration result, analyzing historical calibration data, determining the relation between the environmental condition and the calibration result, and carrying out dynamic contrast calibration according to the historical calibration data and the current environmental data;
the detection control module is used for acquiring detection influence information generated when the historical calibration data and the current environment data are compared, determining the matching condition of the data in the comparison process, generating different control signals according to the data matching condition, and performing different control operations.
The invention has the technical effects and advantages that:
According to the invention, by arranging a plurality of detection nodes in a target monitoring area and adopting a wavelength modulation spectrometry, high-efficiency and accurate methane concentration detection is realized, environmental data and laser detection data are synchronously collected at each node, the data are preprocessed to improve the accuracy and reliability of the data, the gas concentration is determined by analyzing the detected optical signals in real time, and the laser wavelength and environmental parameters can be dynamically adjusted according to the measured gas concentration result so as to optimize the detection performance and adapt to environmental changes;
In addition, the matching degree of the data can be evaluated by comparing the history and the current data, so that a proper control signal is generated, maintenance operation or adjustment measures are guided, and the continuity and accuracy of detection are ensured.
Drawings
FIG. 1 is a flow chart of a distributed laser methane detection method of the present invention.
Fig. 2 is a schematic structural diagram of a distributed laser methane detection system according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: as shown in fig. 1, a distributed laser methane detection method includes:
arranging a plurality of laser methane detection nodes in a target monitoring area, synchronously collecting environment data and laser detection data, and preprocessing;
performing gas detection on the laser detection data by using a wavelength modulation spectrometry, analyzing the detected optical signals, and determining a gas concentration result;
Adjusting wavelength tuning and environmental parameters according to a gas concentration result, analyzing historical calibration data, determining the relation between the environmental conditions and the calibration result, and carrying out dynamic comparison calibration according to the historical calibration data and current environmental data;
Detecting influence information generated when historical calibration data and current environment data are compared is acquired, the matching condition of the data in the comparison process is determined, different control signals are generated according to the data matching condition, and different control operations are performed.
In distributed laser methane detection, sensor selection and integration are key steps for ensuring accurate detection and system stability, and can effectively monitor methane concentration in a wide area and collect and analyze data from a plurality of detection points;
Arranging a plurality of laser methane detection nodes in a target monitoring area, wherein each node comprises a laser emitter, an environment sensor, a photoelectric detector and necessary signal processing hardware, and each node is provided with a wireless communication module (such as Wi-Fi or LoRa) to allow each laser methane detection node to send collected data to a central processing unit or a cloud platform for data processing;
Selecting an environmental sensor, including selecting a temperature sensor with high precision and good stability, such as a platinum resistance temperature sensor (PT 100), for monitoring and controlling the temperature of the laser, thereby affecting its wavelength; a sensor capable of accurately measuring ambient humidity, such as a capacitive humidity sensor (e.g., DHT 22), is selected to monitor ambient humidity that may have an impact on laser propagation; monitoring the ambient air pressure using a high-precision air pressure sensor (such as BMP 280), which is necessary for high-precision air detection, particularly in applications where pressure changes have a significant impact on the detection result;
Testing the selected sensors to ensure that the sensors can provide stable and accurate readings under expected environmental conditions, checking the response time, accuracy, stability and long-term reliability of the sensors, and ensuring that they meet the performance requirements of the system;
under the environment conditions of practical application, all sensors are tested, so that the data of all sensors can be accurately processed by the system without interference, the sensors are calibrated in a necessary way, accurate readings are provided under the practical operation conditions, calibration equipment such as standard gas or a temperature source is used for calibration, and the accuracy and the repeatability of the calibration process are ensured.
Preprocessing sensor data, filtering and standardizing to ensure the real-time property and accuracy of the data, and synchronously collecting and processing the environmental sensor data and the laser detection data;
Selecting a proper filter such as a low-pass, high-pass, band-pass or band-stop filter according to the characteristics of data, removing high-frequency noise by using a low-pass filter for an environmental sensor and laser detection data, selecting a digital filtering algorithm such as Butterworth, chebyshev, elliptic or FIR, an IIR filter and the like, and designing and testing the performance of the filter by using tools such as MATLAB or Python;
The filtering algorithm is implemented in the data acquisition process, and can be implemented in software and hardware such as a microcontroller or data acquisition software;
for raw data collected from each sensor, the mean and standard deviation thereof were calculated, the data was converted to standardized dimensions, Where X is raw data, μ is mean, σ is standard deviation;
the normalization process is applied during the data preprocessing stage to ensure that all data is processed and analyzed with the same criteria.
The time stamp is applied to each data point (whether environmental data or laser detection data), so that the clock synchronization of each sensor is ensured, or time alignment is carried out in software after acquisition, a data acquisition program is designed, so that all sensors acquire data in almost the same time or according to a preset time sequence, in a real-time monitoring system, a real-time operating system (RTOS) or a similar mechanism can be utilized to ensure the synchronous execution of data acquisition tasks, the synchronous data is stored in a database or a data file, and the time label and the source label of the synchronous data are kept, so that the subsequent joint analysis is facilitated.
And the environmental data and the laser detection data are collected at the same time point or within the same time window, so that the relevance of the data and the accuracy of subsequent analysis are ensured.
The gas detection is carried out by using a wavelength modulation spectrometry, the precise modulation of the laser wavelength is carried out, analyzing the detected optical signal to determine the concentration of the gas, wherein the method comprises the following specific steps:
Setting a fundamental wavelength of a laser (a laser emitter or the like), and adjusting an output wavelength of the laser to be close to a center wavelength of a target gas absorption line Two signals generated by the microcontroller are applied on the laser: a low frequency triangle wave and a high frequency sine wave, which combine to modulate the injection current of the laser to at the fundamental wavelengthOscillations of small range of wavelengths are generated around;
The wavelength of the laser can be expressed as: In which, in the process, Representing the amplitude of the modulation,Representing modulation frequency, t representing time;
performing wavelength tuning control, namely generating two paths of signals, namely a low-frequency triangular wave and a high-frequency sinusoidal wave, modulating the triangular wave to high frequency by utilizing the sinusoidal wave, driving a tunable laser diode, and dynamically adjusting the output wavelength of the laser according to the environmental sensor data to enable the output wavelength to be near an absorption peak of the gas;
because the wavelength of the laser can change along with the change of the temperature, the temperature of the laser is controlled, the output wavelength of the laser is approximately aligned to a specific absorption line of the target gas, the output wavelength is dynamically adjusted to cover the absorption peak of the gas by adjusting the current injected into the laser, and the whole absorption line can be scanned;
The intensity and the wavelength of laser are changed in the signal scanning process, and a high-frequency sine wave is used for modulating a low-frequency triangular wave to a high frequency, so that light emitted by a laser is modulated on the wavelength, and then the absorption spectrum line of target gas is scanned;
the modulated laser light is passed through the gas containing the target gas, and the gas absorbs the laser light at the wave lambda (t), so that the light intensity is attenuated, and the expression of the light intensity attenuation is as follows: Wherein, the method comprises the steps of, wherein, Is the initial light intensity, α (λ (t)) is the absorption coefficient of the gas at wavelength λ (t), L is the optical path length, c is the gas concentration, exp represents an exponential function of the natural logarithm with e as the base;
When the modulated laser is transmitted through the gas to be detected, light with specific wavelength is absorbed by the gas, a photoelectric detector receives the residual light signal after passing through the gas, the signal contains information absorbed by the gas, and the light signal is received by the photoelectric detector and converted into an electric signal;
The phase-locked amplifier is used for processing the received electric signals, the device can improve the signal-to-noise ratio of the signals, and the phase-locked amplifier demodulates the electric signals to extract harmonic signals under the modulation frequency;
extracting a signal component at a specific frequency by using a phase-locked amplifier, wherein the second harmonic (2 f) is usually concerned with sensitivity to absorption characteristics because the second harmonic is insensitive to laser intensity variation, and the phase-locked amplifier demodulates the received modulated electric signal according to a reference frequency (which is the same as the laser modulation frequency) to extract a signal component related to the modulation frequency;
the second harmonic (2 f) is given by: Wherein, the method comprises the steps of, wherein, Is atAt the derivative of the absorption coefficient alpha,Is atSecond derivative of absorption coefficient alpha;
According to the gas concentration information contained in the demodulated harmonic signals, the signals can be converted into accurate gas concentration values through a preset calibration curve and actually measured harmonic amplitudes;
it should be noted that, for each gas sample of known concentration, the amplitude of the harmonic signal is measured and recorded, and the recorded signal amplitude is patterned with the corresponding gas concentration using statistical or data analysis software (e.g., excel, origin, matlab, etc.), and a calibration curve is fitted. Common fitting models include linear regression, polynomial regression, etc., and corresponding gas concentration values are found by inserting the actual measured harmonic signal amplitudes into the calibration curve. For example if a linear model is used.
Wavelength tuning and environmental parameters are adjusted in real time according to detection results, stability and accuracy of detection are ensured, and output wavelength of a laser and other relevant environmental parameters are adjusted according to the real-time detection results, wherein the method comprises the following specific steps:
collecting laser detection data output by laser detection, including light intensity and absorption characteristics, and collecting environmental data such as temperature, humidity, air pressure and the like from an environmental sensor;
Analyzing the alignment degree of the current laser wavelength and the target absorption peak, determining whether wavelength adjustment is needed, analyzing environmental data, and determining the influence of the environmental data on the laser propagation and absorption characteristics;
designing a feedback-based adjustment algorithm for adjusting the current and temperature of the laser according to real-time data, using PID control (proportional-integral-derivative control) or other suitable control algorithm to achieve accurate control;
If a wavelength is detected that is not perfectly aligned with the absorption peak, the temperature and/or current of the laser is adjusted to fine tune the wavelength. This can be achieved by increasing or decreasing the current of the laser, temperature adjustment is typically controlled using a thermoelectric cooler (TEC), and the adjustment is calculated using the formula: Δλ=ktΔt+kiΔi, where Δt and Δi are adjustment amounts of temperature and current, respectively, and kT and kI are adjustment coefficients determined experimentally or empirically;
according to the condition that the environmental parameters influence the absorption coefficient, a compensation model is established, the model adjusts light absorption calculation according to the factors such as air pressure, temperature, humidity and the like, and the compensation expression is as follows: Wherein, the method comprises the steps of, wherein, 、AndFor adjustment coefficients, Δt, Δp, and Δrh are deviations from temperature, air pressure, humidity in the baseline environmental conditions.
The output of the detection system is adjusted based on the environmental data, which may include adjustments to laser power, sensitivity of the detection window, and other relevant parameters, and the detection parameters are adjusted in real-time using real-time data processing software to compensate for the effects of environmental changes.
For methane detection, extracting useful features from spectrum data accurately and considering the influence of environmental parameters, and is particularly critical to establishing a robust prediction model, the feature engineering is to extract key features such as absorption peak value, peak width and the like from original spectrum data, and design composite features by combining the environmental parameters (temperature, humidity and air pressure) so as to enhance the environmental adaptability of the model, and the specific steps are as follows:
The entire spectrum is first scanned to determine the approximate location of the absorption peak. This can be done by looking for local minima points, as the absorption peaks usually appear as valleys in the spectrum;
Measuring the light intensity at the peak, measuring the width of the peak at half height, as a method for describing the peak width, and providing information about the shape of the peak, reflecting the concentration of the sample and the environmental conditions, the basic formula being: Wherein AndThe right and left wavelengths of the peak at half height, respectively;
Synchronously recording environmental parameters during measurement, such as temperature T, humidity RH and air pressure P, directly integrating the environmental parameters as features into a model, or constructing derivative features, designing interaction features related to spectrum features and the environmental parameters, for example, adjusting the values of the spectrum features by using the environmental parameters, and carrying out normalization processing on all the features to ensure that the model cannot deviate due to the difference of the dimensions of different features;
Selecting a proper machine learning model (such as a random forest, a support vector machine and a convolutional neural network), performing model training, and evaluating the performance of the model by using a cross verification method to ensure that the model has good generalization capability under various environmental conditions;
evaluating the data characteristics, and selecting a proper model according to the size of the data, the type of the features (such as time series, scalar features, images, etc.), and the nature of the prediction task (regression or classification);
The random forest is suitable for processing data with high-dimensional characteristics, can provide characteristic importance estimation, is easy to understand and implement, and is insensitive to abnormal values; the Support Vector Machine (SVM) is suitable for classification problems with distinct boundaries, especially if the amount of data is not very large; if the spectral data can be considered as an image, then a Convolutional Neural Network (CNN) can be used, which is suitable for extracting complex patterns of spatial hierarchy;
carrying out standardization or normalization processing on all input features, evaluating the generalization capability of the model by using a K-fold cross validation method, selecting optimal parameters, evaluating the performance of the model under different environmental conditions by using a cross validation result, focusing on indexes such as accuracy, recall, F1 score and the like, analyzing the situation of model prediction errors, and determining whether an over-fitting or under-fitting problem exists;
the trained model is deployed, real-time data processing and methane concentration prediction are realized, an online learning mechanism is designed, and the model can be continuously updated and optimized according to new data, so that high accuracy is maintained;
And the complex data is processed by using a machine learning model, so that the intelligent level and adaptability of the system are improved, and the influence of multi-gas interference on a detection result is reduced.
The model is updated regularly according to the newly collected data so as to adapt to environmental changes, and fine adjustment is carried out on the model by utilizing the new data so as to save resources and time, and standard gas is used for calibration regularly, so that the accuracy of laser methane detection and the stability of equipment are ensured;
analyzing historical calibration data, extracting the relation between the environmental conditions and the calibration results, combining a self-learning calibration algorithm (a support vector machine, a decision tree, a random forest and the like), comparing according to the historical calibration data and the current environmental data, dynamically adjusting the calibration frequency and parameters, and improving the accuracy and stability of laser methane detection.
Acquiring detection influence information generated when historical calibration data and current environment data are compared, and determining the stability of the data in the comparison process, wherein the detection influence information comprises calibration related information and trend identification information;
The calibration related information comprises a calibration related difference index, wherein the calibration related difference index is used for representing the calibration accuracy condition of the methane detection equipment under different environment variables and is used for helping to identify the degree of the influence of environment factors on the equipment calibration, so that a calibration strategy can be adjusted more pertinently, the calibration related information can be used for evaluating the potential influence of environment changes on the equipment performance and helping to make a more effective calibration plan so as to ensure the accurate operation of the equipment under various environments;
along with the accumulation of new data and the change of environment, the calculation of the calibration association difference index is updated regularly to ensure that the latest environment influence condition is reflected, the calibration association difference index is used as the basis for the adjustment of the equipment calibration strategy, the calibration frequency is determined, the future maintenance requirement is predicted, and the running efficiency of the equipment is optimized;
The acquisition mode of the calibration association difference index is as follows:
collecting measurement data and contemporaneous environmental data in the process of calibrating equipment to obtain measurement values of equipment before calibration Post-calibration device measurementsCalculating calibration difference data: Establishing a calibration difference data set: Acquiring an actual measured environment data set: n represents the number of environmental data, and the average value of the calibration difference data set is obtained Mean value of environment data setCalculating a calibration correlation value, wherein the calculation expression is as follows:；
obtaining the difference values of adjacent data elements in the environment data set, and establishing an environment ranking difference value set: m is a positive integer, and calculating a spearman class value, wherein the calculation expression is as follows: calculating a calibration correlation difference index, wherein the calculation expression is as follows: 。
the environmental data includes actual readings of environmental parameters such as temperature, humidity, air pressure, etc.
The trend identification information comprises a dynamic trend stability index, wherein the dynamic trend stability index is used for representing the stability and consistency of the data trend under the time sequence, reflects the variation amplitude of trend components of the time sequence at different time points, namely the consistency and variation fluctuation of the trend components under different calibration periods and environmental conditions, and helps to identify the stability of the data trend or the variation fluctuation of the data trend. In particular, the dynamic trend stability index reveals the behavior pattern of the time series over a long period of time, whether there is a significant fluctuation or a sustained steady increase/decrease trend;
The dynamic trend stability index is mainly used for monitoring the stable development of equipment or environmental parameters along with time, helping technical teams and decision makers identify possible performance degradation or aggravation of environmental influence, and in calibration data analysis, the dynamic trend stability index can reveal whether the performance of the equipment is affected by continuous external environmental change or whether the equipment has problems of internal aging, abrasion and the like.
The dynamic trend stability index can measure the consistency of the time trend of the calibration parameters (such as the measured values after calibration of temperature, pressure and the like), if the trend is stable, the equipment or the environment shows consistent behavior under similar conditions, the future performance or condition is predicted to be more reliable, and the intensity of the trend and the fluctuation of the trend at different time points are reflected by the dynamic trend stability index through comparing the slope of the trend with the variation coefficient of the trend. The high dynamic trend stability index value indicates strong and stable trend, and the low dynamic trend stability index value may suggest that the trend is present but is changed greatly and has poor stability.
The dynamic trend stability index may be used to determine maintenance and calibration cycles for the device, particularly when the device exhibits a trend stability change, and in environmental monitoring, the dynamic trend stability index helps evaluate the stability of environmental parameters over time, particularly for environmentally sensitive or environmentally more affected operations.
The dynamic trend stability index is obtained as follows:
Collecting data generated during comparison of historical calibration data and current environmental data, converting the generated data into time series data, and decomposing the time series data into trend components Seasonal ingredientAnd residual component；
For trend componentsDetermination of trend slope using linear regression analysisCalculating trend slope, wherein the calculation expression is: Wherein, the method comprises the steps of, wherein, Is the average value of the time points t,Is a trend componentIs calculated, the trend intercept is calculated as: calculating a trend slope error value, wherein the calculation expression is as follows: wherein N is the number of time points, the dynamic trend stability index is calculated, and the calculation expression is as follows: 。
It should be noted that, the trend component, the seasonal component, and the residual component are generally obtained by using a time series decomposition method, such as decomposition of a seasonal decomposition trend-seasonal-residual (STL), the trend component shows a long-term trend of the data with time, the residual component contains the remaining part after removing the trend and the season in the data, which is generally considered as random noise, the specific mathematical principle of the STL decomposition involves local weighted regression (Loess smoothening) of the cyclic subsequence, the trend component extracts the trend by applying Loess Smoothing to the whole time series, the seasonal component extracts the seasonal pattern from the trended data (i.e., the result of subtracting the trend component from the original data), and the slope of the trend component is determined by a linear regression model, and reflects the change rate of the trend with time.
The acquired calibration association difference index and dynamic trend stability index are combined to obtain a detection state evaluation coefficient, the state and performance of the equipment in an actual operation environment can be systematically evaluated and monitored through the detection state evaluation coefficient, in the distributed laser methane detection system, the performance and calibration state of each node in the whole detection network can be evaluated and monitored, and problems and performance degradation possibly occurring in the equipment can be predicted, so that predictive maintenance is implemented, unexpected shutdown and maintenance cost can be reduced, and the overall reliability and efficiency of the system are improved.
One form of computational expression for detecting the state evaluation coefficients is: In which, in the process, In order to detect the state-assessment coefficient,、For calibrating the preset proportionality coefficient of the associated difference index and the dynamic trend stability index, and、Are all greater than 0.
It should be noted that, the size of the preset scaling factor is a specific numerical value obtained by quantizing each parameter, and in order to facilitate the subsequent comparison, the size of the scaling factor depends on the number of sample data and the person skilled in the art to initially set a corresponding preset scaling factor for each group of sample data; and the method is not unique, and the proportional relation between the parameter and the quantized numerical value is not influenced, as is the inverse relation between the dynamic trend stability index and the detection state evaluation coefficient.
The larger the calibration association difference index is, the smaller the dynamic trend stability index is, namely the larger the performance value of the detection state evaluation coefficient is, the larger the difference exists between the historical calibration data and the current environment data, the equipment may not adapt to the current environment change well, or the calibration process has problems, so that the reliability and the accuracy of the data are reduced, the overall state of the equipment may be poor, the problems of calibration and trend stability are obvious, and the method is an important warning signal when the laser methane detection is carried out, and indicates that the detection result may not be reliable enough and measures need to be taken for correction or maintenance;
The smaller the calibration association difference index is, the larger the dynamic trend stability index is, namely the smaller the performance value of the detection state evaluation coefficient is, the smaller the difference between the historical calibration data and the current environment data is, the more consistent the calibration process is, the better adaptability of the device is shown between different calibration periods, the device has stable response to environment changes, the higher the stability of the device performance trend is, the device has a consistent and predictable performance trend, and no large fluctuation or sudden performance drop occurs.
Comparing the generated detection state evaluation coefficient with a detection threshold value to generate different control signals, and performing distributed laser methane detection maintenance according to the generated control signals to obtain more accurate methane related data, so that equipment can be managed and maintained systematically, and the accuracy and reliability of methane detection data are ensured;
after the detection state evaluation coefficient is obtained, comparing the detection state evaluation coefficient with a detection threshold value;
If the detection state evaluation coefficient is smaller than the detection threshold, a state stable signal is generated, and the state stable signal indicates that the equipment performs well in the current operation environment and the calibration state, which means that the equipment is accurately calibrated, the performance parameters are in a desired range, the functions of the equipment can be reliably executed, such as accurately detecting the methane concentration in the environment, and no additional calibration or maintenance is needed for the equipment in a short period of time. The current state of the device is sufficient to continue operation without intervention due to performance issues, which helps to reduce maintenance costs and avoid unnecessary operational interruptions.
If the detection state evaluation coefficient is greater than or equal to the detection threshold, generating an early warning detection signal, wherein the early warning detection signal indicates that the equipment can not work according to expectations possibly caused by calibration deviation, performance degradation or environmental condition change of the equipment, and under the condition, equipment operators and maintenance teams should pay attention to information provided by the indexes, and timely check and intervene to prevent potential false readings and avoid potential safety hazards caused by the potential false readings;
The generated early warning detection signal is an operation prompt, which indicates that the equipment needs to be immediately checked, calibrated or maintained so as to ensure the accuracy of detection data and the reliable operation of the equipment, and the timely intervention can prevent the development of potential faults into more serious problems, thereby reducing the maintenance cost and avoiding possible safety risks;
after receiving the early warning detection signal, corresponding measures should be taken in time to correct abnormal conditions and ensure the accuracy and reliability of laser methane detection, and the following possible specific steps are adopted:
If the threshold is exceeded frequently, this may indicate that existing detection and maintenance strategies need to be adjusted, and that the usage conditions of the device, the impact of environmental factors, or the period of detection and maintenance may need to be re-evaluated to accommodate the changing operating environment;
According to the actual running data and maintenance history of the equipment, the detection threshold value is periodically checked and adjusted to ensure that the latest equipment state and operation environment are reflected, and the data from the distributed laser methane detection equipment and the environment monitoring data are collected and analyzed by utilizing the data management system, so that the health condition of the equipment can be comprehensively evaluated; an emergency response framework and precautionary action plan is developed that can quickly take action to calibrate, maintain or otherwise perform necessary actions when a detection threshold is exceeded.
It should be noted that, the setting of the detection threshold may be determined according to a specific scenario and requirement, and is generally adjusted and optimized according to factors such as historical data, regional characteristics, environment, and the like.
According to the invention, by arranging a plurality of detection nodes in a target monitoring area and adopting a wavelength modulation spectrometry, high-efficiency and accurate methane concentration detection is realized, environmental data and laser detection data are synchronously collected at each node, the data are preprocessed to improve the accuracy and reliability of the data, the gas concentration is determined by analyzing the detected optical signals in real time, and the laser wavelength and environmental parameters can be dynamically adjusted according to the measured gas concentration result so as to optimize the detection performance and adapt to environmental changes;
In addition, the matching degree of the data can be evaluated by comparing the history and the current data, so that a proper control signal is generated, maintenance operation or adjustment measures are guided, and the continuity and accuracy of detection are ensured.
Embodiment 2 is an embodiment of a system of embodiment 1, configured to implement a distributed laser methane detection method described in embodiment 1, as shown in fig. 2, and specifically includes:
the regional data acquisition module is used for arranging a plurality of laser methane detection nodes in the target monitoring region, synchronously acquiring environment data and laser detection data and preprocessing the environment data and the laser detection data;
The concentration analysis module is used for detecting the gas of the laser detection data by using a wavelength modulation spectrometry, analyzing the detected optical signals and determining a gas concentration result;
the calibration module is used for adjusting wavelength tuning and environmental parameters according to the gas concentration result, analyzing historical calibration data, determining the relation between the environmental condition and the calibration result, and carrying out dynamic contrast calibration according to the historical calibration data and the current environmental data;
the detection control module is used for acquiring detection influence information generated when the historical calibration data and the current environment data are compared, determining the matching condition of the data in the comparison process, generating different control signals according to the data matching condition, and performing different control operations.
The above formulas are all formulas for removing dimensions and taking numerical calculation, and specific dimensions can be removed by adopting various means such as standardization, and the like, which are not described in detail herein, wherein the formulas are formulas for acquiring a large amount of data and performing software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, ATA hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state ATA hard disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (5)
1. The distributed laser methane detection method is characterized by comprising the following steps of:
arranging a plurality of laser methane detection nodes in a target monitoring area, synchronously collecting environment data and laser detection data, and preprocessing;
performing gas detection on the laser detection data by using a wavelength modulation spectrometry, analyzing the detected optical signals, and determining a gas concentration result;
Adjusting wavelength tuning and environmental parameters according to a gas concentration result, analyzing historical calibration data, determining the relation between the environmental conditions and the calibration result, and carrying out dynamic comparison calibration according to the historical calibration data and current environmental data;
Acquiring detection influence information generated when historical calibration data and current environment data are compared, determining the matching condition of the data in the comparison process, generating different control signals according to the data matching condition, and performing different control operations;
the method comprises the steps of obtaining detection influence information generated when historical calibration data and current environment data are compared, and determining the matching condition of the data in the comparison process, wherein the specific process is as follows:
acquiring detection influence information generated in the process of comparing historical calibration data with current environment data, wherein the detection influence information comprises calibration related information and trend identification information;
The calibration related information comprises a calibration related difference index, and the trend identification information comprises a dynamic trend stability index;
the acquired calibration association difference index and dynamic trend stability index are combined to generate a detection state evaluation coefficient;
The acquisition mode of the calibration association difference index is as follows:
Collecting measurement data and contemporaneous environmental data in the device calibration process, acquiring a device measurement value C pre before calibration, a device measurement value C post after calibration, and calculating calibration difference data: Δc= |c post-Cpre | to establish a calibration difference data set: c= { C 1,c2,…,cn }, obtain the actual measured environmental data set: e= { E 1,e2,…,en }, n represents the number of environmental data, and the average value of the calibration difference data set is obtained Mean value of environment data setCalculating a calibration correlation value, wherein the calculation expression is as follows:
Obtaining the difference values of adjacent data elements in the environment data set, and establishing an environment ranking difference value set, wherein D= { D 1,d2,…,dm }, m is a positive integer, and calculating a spearman grade value, and the calculation expression is as follows: calculating a calibration association difference index, wherein the calculation expression is as follows:
the dynamic trend stability index is obtained as follows:
Collecting data generated in the process of comparing the historical calibration data with the current environment data, converting the generated data into time series data, and decomposing the time series data into a trend component T t, a seasonal component S t and a residual component R t;
The trend slope is calculated by applying linear regression analysis to the trend component T t, and the calculation expression is: Wherein, Is the average value of the time points t,Is the average value of the trend component T t, calculates the trend intercept, and calculates the expression as follows: calculating a trend slope error value, wherein the calculation expression is as follows: wherein N is the number of time points, the dynamic trend stability index is calculated, and the calculation expression is as follows:
the calculation expression of the detection state evaluation coefficient is: Wherein J x is a detection state evaluation coefficient, r 1、r2 is a preset proportionality coefficient for calibrating an associated difference index and a dynamic trend stability index, and r 1、r2 is greater than 0;
Different control signals are generated according to the data matching condition, and different control operations are performed, and the specific process is as follows:
comparing the detection state evaluation coefficient with a detection threshold value;
If the detection state evaluation coefficient is greater than or equal to the detection threshold value, generating an early warning detection signal, and notifying maintenance personnel to perform inspection and intervention;
If the detection state evaluation coefficient is smaller than the detection threshold, a state stable signal is generated, and no additional calibration or maintenance is needed to continue normal operation.
2. The distributed laser methane detection method according to claim 1, wherein: the environment data and the laser detection data are synchronously collected and preprocessed, and the specific process is as follows:
Preprocessing sensor data, filtering and standardizing, and selecting a filter according to the characteristics of the data, wherein the filter comprises a low-pass filter, a high-pass filter, a band-pass filter or a band-stop filter;
Calculating an average value and a standard deviation for raw data collected from each sensor, and converting the data to a standardized scale;
a time stamp is applied to each data point, each sensor is clocked, and all sensors are set to collect data at the same time.
3. The distributed laser methane detection method according to claim 2, wherein: the method comprises the steps of performing gas detection on laser detection data by using a wavelength modulation spectrometry, analyzing a detected optical signal, determining a gas concentration result, and adjusting wavelength tuning, environmental parameters and environmental parameters according to the detection result, wherein the specific process is as follows:
Setting a basic wavelength of a laser, and adjusting the output wavelength of the laser to be close to the central wavelength of a target gas absorption line;
Performing wavelength tuning control on the generated low-frequency triangular wave and high-frequency sine wave, modulating the low-frequency triangular wave to high frequency by using the high-frequency sine wave, transmitting the modulated laser through the gas to be detected, determining the residual optical signal after the laser passes through the gas, and converting the optical signal into an electric signal through a photoelectric detector;
Processing the received electric signal by using a phase-locked amplifier to extract a harmonic signal under the modulation frequency;
And comparing a preset calibration curve with the actually measured harmonic amplitude according to gas concentration information contained in the demodulated harmonic signal, and converting the harmonic signal into a gas concentration value.
4. A distributed laser methane detection method according to claim 3, wherein: wavelength tuning and environmental parameters are adjusted according to the gas concentration result, and the specific process is as follows:
collecting laser detection data of laser detection output, including light intensity and absorption characteristics, and collecting environmental data from an environmental sensor, the environmental data including temperature, humidity and air pressure;
determining the alignment degree of the current laser wavelength and a target absorption peak, and determining whether wavelength adjustment is needed;
if the detected wavelength is not completely aligned with the absorption peak, adjusting the temperature and/or current of the laser to adjust the wavelength;
And analyzing the environmental data, and establishing a compensation model which adjusts environmental parameters according to the air pressure, the temperature and the humidity.
5. A distributed laser methane detection system for implementing a distributed laser methane detection method according to any of claims 1-4, comprising:
the regional data acquisition module is used for arranging a plurality of laser methane detection nodes in the target monitoring region, synchronously acquiring environment data and laser detection data and preprocessing the environment data and the laser detection data;
The concentration analysis module is used for detecting the gas of the laser detection data by using a wavelength modulation spectrometry, analyzing the detected optical signals and determining a gas concentration result;
the calibration module is used for adjusting wavelength tuning and environmental parameters according to the gas concentration result, analyzing historical calibration data, determining the relation between the environmental condition and the calibration result, and carrying out dynamic contrast calibration according to the historical calibration data and the current environmental data;
the detection control module is used for acquiring detection influence information generated when the historical calibration data and the current environment data are compared, determining the matching condition of the data in the comparison process, generating different control signals according to the data matching condition, and performing different control operations.
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