A method, device and equipment for performance evaluation of a gas leakage detection system

Abstract

The present application relates to the technical field of safety production monitoring, and discloses a performance evaluation method, device and equipment of a gas leakage detection system, which comprises the following steps: in a standard gas environment and in a plurality of interference environments, a plurality of first gas concentration signals and a plurality of second gas concentration signals are collected in a target area by using a gas leakage detection system; the first gas concentration signals and the second gas concentration signals are respectively subjected to signal decomposition and reconstruction to obtain first target concentration signals and second target concentration signals; a sample database is constructed based on the first target concentration signals and the second target concentration signals; and the accuracy and the false alarm rate of the gas leakage detection system are determined based on the sample database. By collecting gas concentration signals in a standard gas environment and in an interference environment by using a gas leakage detection system, the performance in an ideal environment is considered, and the performance in a complex interference environment is also particularly focused on, so that the evaluation result is more suitable for actual application scenarios.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety production monitoring, and particularly relates to a performance evaluation method, device and equipment of a gas leakage detection system. BACKGROUND

[0002] Gas leakage not only has a serious impact on industrial production, but also may bring serious environmental and public safety risks. For example, gas leakage may cause fire, explosion accidents, and cause people to suffocate, etc. Therefore, it is crucial to detect and monitor gas leakage in a timely and accurate manner.

[0003] Existing gas leakage detection systems mainly focus on performance evaluation under standard laboratory conditions, although they can evaluate the basic functions and performance indicators of the gas leakage detection system, they cannot completely simulate various extreme environments that may be encountered in actual applications, resulting in incomplete performance evaluation. SUMMARY

[0004] Therefore, the present application provides a performance evaluation method, device and equipment of a gas leakage detection system to solve the problem that existing gas leakage detection systems focus on performance evaluation under standard laboratory conditions, resulting in incomplete performance evaluation.

[0005] In a first aspect, the present application provides a performance evaluation method of a gas leakage detection system, which comprises:

[0006] Under a standard gas environment, a gas leakage detection system is used to collect a plurality of first gas concentration signals in a target area;

[0007] Under a plurality of interference environments, a gas leakage detection system is used to collect a plurality of second gas concentration signals in a target area;

[0008] The plurality of first gas concentration signals and the plurality of second gas concentration signals are respectively subjected to signal decomposition and reconstruction to obtain a plurality of first target concentration signals and a plurality of second target concentration signals, the background noise and interference signals of the target concentration signals being less than those of the corresponding gas concentration signals;

[0009] Based on the plurality of first target concentration signals and the plurality of second target concentration signals, a sample database is constructed;

[0010] Based on the sample database, the accuracy and false alarm rate of the gas leakage detection system in gas detection are determined.

[0011] The performance evaluation method of the gas leakage detection system provided by the embodiment of the present application, by collecting a plurality of first gas concentration signals and a plurality of second gas concentration signals in the target area by using the gas leakage detection system in the standard gas environment and the interference environment respectively, not only considers the standard laboratory environment, but also introduces a variety of interference environments, can simulate various complex situations that may be encountered in actual application, so that the evaluation result is closer to the real scene, thereby improving the practicality and reliability of the evaluation, by decomposing and reconstructing the above-mentioned collected gas concentration signals, the background noise and interference signals in the signal can be effectively removed, the target concentration signal is more pure, the accuracy of subsequent data analysis is improved, a comprehensive and diversified sample database is constructed by the target concentration signals in different environments, which can better reflect the performance of the system under various environmental conditions, so as to calculate the accuracy and false alarm rate based on the sample database, so that the performance evaluation of the system is more scientific and rigorous, and the reliability and credibility of the system are enhanced, not only the performance in the ideal environment is considered, but also the performance in the complex interference environment is specially concerned, so that the evaluation result is more suitable for the actual application scene.

[0012] In an optional implementation, the plurality of first gas concentration signals and the plurality of second gas concentration signals are respectively decomposed and reconstructed to obtain a plurality of first target concentration signals and a plurality of second target concentration signals, comprising:

[0013] For any gas concentration signal, the gas concentration signal is decomposed into a residual component and a plurality of intrinsic mode functions;

[0014] The correlation coefficient between the gas concentration signal and each intrinsic mode function is calculated;

[0015] The intrinsic mode function with a correlation coefficient greater than a preset threshold is filtered out;

[0016] The intrinsic mode function that is not filtered out and the residual component are reconstructed to obtain a target concentration signal corresponding to the gas concentration signal.

[0017] The performance evaluation method of the gas leakage detection system provided by the embodiment of the present application, by decomposing the gas concentration signal and filtering out the intrinsic mode function with a correlation coefficient greater than a preset threshold, effectively eliminates the part with high noise and high interference, so that the noise and interference of the target concentration signal obtained by reconstruction are less, which helps to improve the evaluation accuracy.

[0018] In an optional implementation, for any gas concentration signal, the gas concentration signal is decomposed into a residual component and a plurality of intrinsic mode functions, comprising:

[0019] The local maximum points and the local minimum points of the gas concentration signal are determined;

[0020] Based on the local maximum points and the local minimum points, a cubic spline interpolation method is used to fit the upper envelope line and the lower envelope line;

[0021] The average value of the upper envelope line and the lower envelope line is calculated to obtain the mean envelope;

[0022] The difference between the gas concentration signal and the mean envelope is determined, and in the case that the difference does not meet the decomposition requirement, the difference is taken as the gas concentration signal for the next decomposition, and the step of determining the local maximum points and the local minimum points of the gas concentration signal is returned to until the difference meets the decomposition requirement, and the difference is taken as the intrinsic mode function for this decomposition;

[0023] The difference between the gas concentration signal and all the intrinsic mode functions is taken as the residual component for this decomposition;

[0024] The residual component for this decomposition is taken as the gas concentration signal for the next decomposition, and the step of determining the local maximum points and the local minimum points of the gas concentration signal is returned to until the number of the local maximum points and the local minimum points of the residual component for this decomposition is less than the preset number threshold, and the final residual component is obtained.

[0025] The performance evaluation method of the gas leakage detection system provided by the embodiment of the application can decompose the complex gas concentration signal into multiple intrinsic mode functions and a residual component through signal decomposition, so that each component can be more clearly presented, facilitating further analysis, and gradually removing noise and other interference components in the iteration process, so that the final intrinsic mode function and the residual component can better reflect the real gas concentration change, and the quality and reliability of the signal are improved.

[0026] In an optional implementation, based on the plurality of first target concentration signals and the plurality of second target concentration signals, a sample database is constructed, including:

[0027] Based on the plurality of first target concentration signals, a plurality of positive samples are constructed;

[0028] Based on the plurality of second target concentration signals, a plurality of negative samples are constructed;

[0029] Based on the plurality of positive samples and the plurality of negative samples, a sample database is constructed.

[0030] The performance evaluation method of the gas leakage detection system provided by the embodiment of the application can accurately reflect the detection results of the gas leakage detection system under standard environmental conditions through the positive samples, providing a reliable basis for subsequent evaluation, and the negative samples can simulate various complex situations that may be encountered in actual applications, so that the evaluation results are closer to the real scene, and by combining the positive samples and the negative samples, a comprehensive and diversified sample database is constructed, covering detection results under different conditions, and the comprehensiveness and scientificity of the evaluation are improved.

[0031] In one alternative implementation, multiple positive samples are constructed based on multiple first target concentration signals, including:

[0032] For any first target concentration signal, if there is gas in the standard gas environment and the first target concentration signal indicates the presence of gas, the first target concentration signal is taken as a positive sample.

[0033] In the case where there is no gas in the standard gas environment and the first target concentration signal indicates that there is no gas, the first target concentration signal is used as a positive or negative sample.

[0034] Positive and negative samples corresponding to multiple first target concentration signals are used as positive samples.

[0035] The performance evaluation method for the gas leak detection system provided in this invention verifies whether the gas leak detection system can accurately detect the presence of gas when gas is present by using positive samples, ensuring the sensitivity and accuracy of the system. It simulates the presence of gas in a real environment and verifies whether the gas leak detection system can correctly determine the presence of gas when gas is not present by using positive and negative samples. Combining positive and negative samples helps to comprehensively evaluate the performance of the gas leak detection system in a standard gas environment.

[0036] In one alternative implementation, multiple negative samples are constructed based on multiple second target concentration signals, including:

[0037] For any second target concentration signal, if there is no gas in the interference environment corresponding to the second target concentration signal and the second target concentration signal indicates the presence of gas, the second target concentration signal will be regarded as a negative positive sample.

[0038] If the interference environment corresponding to the second target concentration signal contains gas but the second target concentration signal indicates that there is no gas, the second target concentration signal will be used as a negative sample.

[0039] The negative positive and negative negative samples corresponding to multiple second target concentration signals are used as negative samples.

[0040] The performance evaluation method for a gas leak detection system provided in this invention identifies false alarms (where the system incorrectly detects the presence of gas in the absence of actual gas leakage) by using negative positive samples. This simulates possible false alarms under various interference environments, making the evaluation results more closely resemble complex scenarios in real-world applications. Conversely, it identifies missed alarms (where the system fails to correctly detect the presence of gas in the presence of actual gas leakage) by using negative negative samples. Combining negative positive and negative samples helps to comprehensively evaluate the performance of the gas leak detection system under complex interference environments.

[0041] In one optional implementation, based on a sample database, the accuracy and false alarm rate of the gas leak detection system during gas detection are determined, including:

[0042] For any negative sample in the sample database, if the second target concentration signal corresponding to the negative sample satisfies the corresponding preset removal rule, the negative sample is removed from the sample database.

[0043] The ratio of the number of positive samples in the sample database to the total number of samples in the sample database is used as the accuracy of the gas leak detection system in gas detection.

[0044] The ratio of the number of positive and negative samples in the sample database to the total number of positive samples in the sample database is used as the false alarm rate of the gas leak detection system during gas detection.

[0045] The performance evaluation method for the gas leak detection system provided in this invention can ensure that the data in the sample database is valid and meets the evaluation criteria by removing negative samples that do not meet the preset removal rules. At the same time, it can reduce false alarms and false negatives caused by environmental or measurement errors, making the evaluation more accurate. The accuracy rate reflects the proportion of the system that correctly detects in the overall sample, while the false alarm rate evaluates the probability that the system will mistakenly detect the presence of gas when there is no gas present.

[0046] In a second aspect, the present invention provides a performance evaluation device for a gas leak detection system, the device comprising:

[0047] The first acquisition module is used to acquire multiple first gas concentration signals in the target area under a standard gas environment using a gas leak detection system;

[0048] The second acquisition module is used to acquire multiple second gas concentration signals in the target area using a gas leak detection system under various interference environments.

[0049] The signal processing module is used to decompose and reconstruct multiple first gas concentration signals and multiple second gas concentration signals respectively to obtain multiple first target concentration signals and multiple second target concentration signals. The background noise and interference signals of the target concentration signals are less than those of the corresponding gas concentration signals.

[0050] The module is used to construct a sample database based on multiple first target concentration signals and multiple second target concentration signals;

[0051] The evaluation module is used to determine the accuracy and false alarm rate of the gas leak detection system when detecting gas, based on a sample database.

[0052] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the performance evaluation method of the gas leak detection system of the first aspect or any corresponding embodiment described above.

[0053] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the performance evaluation method of the gas leak detection system of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0054] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0055] Figure 1 This is a flowchart of a performance evaluation method for a gas leak detection system according to an embodiment of the present invention;

[0056] Figure 2 This is a structural block diagram of a performance evaluation device for a gas leak detection system according to an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Existing gas leak detection systems primarily focus on performance evaluation under standard laboratory conditions, resulting in an incomplete assessment. The performance evaluation method for gas leak detection systems provided in this invention not only considers performance under ideal conditions but also pays special attention to performance under complex interference environments, making the evaluation results more closely aligned with real-world application scenarios.

[0060] According to an embodiment of the present invention, a performance evaluation method for a gas leak detection system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0061] This embodiment provides a performance evaluation method for a gas leak detection system, which can be used in terminals. Figure 1 This is a flowchart of a performance evaluation method for a gas leak detection system according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0062] Step S101: Under a standard gas environment, a gas leak detection system is used to collect multiple first gas concentration signals within the target area. Specifically, the standard gas environment refers to the standard laboratory environment used in the evaluation of existing gas leak detection systems. The target area refers to the area within a certain range from the gas leak detection system. For example, gas containers with different methane concentrations can be placed sequentially at distances of 5m, 10m, 15m, 20m, 25m, 30m, 40m, and 50m from the installation location of the gas leak detection system. A gas leak detection system, such as a laser pan-tilt-zoom (FPZ) detection device, is used to collect the gas concentration of the gas containers at each location, obtaining multiple first gas concentration signals.

[0063] Step S102: Under various interference environments, a gas leak detection system is used to collect multiple second gas concentration signals within the target area. Specifically, the interference environment can be swaying, darkness, reflection, or rain / fog. For any interference environment, the gas leak detection system is used to collect signals at the gas containers at the multiple locations listed in step S101 to obtain multiple second gas concentration signals.

[0064] Step S103 involves decomposing and reconstructing multiple first gas concentration signals and multiple second gas concentration signals to obtain multiple first target concentration signals and multiple second target concentration signals. The background noise and interference signals of the target concentration signals are less than those of the corresponding gas concentration signals. Specifically, due to the existence of the etalon effect, the gas concentration signals collected by the gas leak detection system will cause background fluctuation interference. Therefore, EMD (Empirical Mode Decomposition) can be used to decompose the gas concentration signals and reconstruct them, which can improve the purity of the signals, make them closer to the actual gas concentration, and help improve the accuracy of the assessment.

[0065] Step S104: Construct a sample database based on multiple first target concentration signals and multiple second target concentration signals. Specifically, the optimized first target concentration signals and second target concentration signals are integrated into a sample database containing data from various environments, making the sample database richer and more diverse, better reflecting the actual performance of the system, and helping to comprehensively evaluate system performance.

[0066] Step S105: Based on the sample database, determine the accuracy and false alarm rate of the gas leak detection system during gas detection. Specifically, accuracy and false alarm rate are typically calculated when evaluating the system performance of a gas leak detection system. Accuracy reflects the proportion of correct detections in the overall sample, while the false alarm rate assesses the probability that the system incorrectly detects the presence of gas when it is not actually present. By calculating accuracy and false alarm rate, the system's performance during gas detection can be effectively evaluated.

[0067] The performance evaluation method for a gas leak detection system provided in this invention collects multiple first gas concentration signals and multiple second gas concentration signals within a target area under both standard gas and interference environments. This method not only considers the standard laboratory environment but also introduces various interference environments, simulating complex situations that may be encountered in practical applications. This makes the evaluation results closer to real-world scenarios, thereby improving the practicality and reliability of the evaluation. By decomposing and reconstructing the collected gas concentration signals, background noise and interference signals can be effectively removed, making the target concentration signal purer and improving the accuracy of subsequent data analysis. A comprehensive and diverse sample database is constructed using target concentration signals from different environments, better reflecting the system's performance under various conditions. Based on this sample database, the accuracy and false alarm rate are calculated, making the system performance evaluation more scientific and rigorous, enhancing the system's reliability and trustworthiness. This method considers not only performance under ideal conditions but also performance under complex interference environments, making the evaluation results more closely aligned with actual application scenarios.

[0068] This embodiment provides a performance evaluation method for a gas leak detection system, which can be used in the aforementioned terminal. The method specifically includes the following steps:

[0069] Step S201: Under a standard gas environment, a gas leak detection system is used to collect multiple first gas concentration signals within the target area. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0070] Step S202: Under various interference environments, a gas leak detection system is used to collect multiple second gas concentration signals within the target area. For details, please refer to [link to relevant documentation]. Figure 1Step S102 of the illustrated embodiment will not be described again here.

[0071] Step S203: Perform signal decomposition and reconstruction on multiple first gas concentration signals and multiple second gas concentration signals respectively to obtain multiple first target concentration signals and multiple second target concentration signals. The background noise and interference signals of the target concentration signals are less than those of the corresponding gas concentration signals.

[0072] Specifically, step S203 includes:

[0073] Step S2031: For any gas concentration signal, decompose the gas concentration signal into residual components and multiple intrinsic mode functions.

[0074] In some optional implementations, step S2031 above includes:

[0075] Step a1: Identify the local maxima and local minima of the gas concentration signal. Specifically, find all local maxima and local minima in the gas concentration signal. These points are used to construct the envelope later, thereby better capturing the oscillatory characteristics of the signal.

[0076] Step a2: Based on the local maxima and local minima, cubic spline interpolation is used to fit the upper and lower envelopes. Specifically, cubic spline interpolation connects all local maxima to form the upper envelope, and similarly connects all local minima to form the lower envelope. Cubic spline interpolation can generate smooth and continuous envelopes, accurately reflecting the upper and lower boundaries of the signal, and avoiding the discontinuities and errors that may be introduced by simple linear interpolation.

[0077] Step a3: Calculate the average value of the upper and lower envelopes to obtain the mean envelope. Specifically, calculate the average value of the upper and lower envelopes at each point to form an intermediate reference line, i.e., the mean envelope. The mean envelope provides the center position of signal oscillation, which helps in subsequent difference calculations and ensures that the decomposition result is closer to the eigenmode functions of the true signal.

[0078] Step a4: Determine the difference between the gas concentration signal and the mean envelope. If the difference does not meet the decomposition requirements, use the difference as the gas concentration signal for the next decomposition, returning to the step of determining the local maxima and local minima of the gas concentration signal, until the difference meets the decomposition requirements. Then, use the difference as the intrinsic mode function (EMF) for this decomposition. Specifically, calculate the difference between the gas concentration signal and the mean envelope. If the difference does not meet the preset decomposition requirements, use the difference as a new gas concentration signal and start the decomposition process again from step a1. Repeat this process until the difference meets the decomposition requirements, at which point the difference is used as an EMF. The decomposition requirements can be that the difference, at any given point, contains an equal number of extreme points in its upper and lower waveforms, and its average value is zero over the entire time range of the signal. By iteratively removing high-frequency components, the final EMF is ensured to have a single frequency characteristic. The iterative process makes the final EMF purer, reducing the influence of noise and other interference.

[0079] Step a5: The difference between the gas concentration signal and all intrinsic mode functions is taken as the residual component of this decomposition. Specifically, all extracted intrinsic mode functions are subtracted from the gas concentration signal to obtain the remaining part, i.e., the residual component. The residual component usually represents the low-frequency or trend part of the signal, preserving information about long-term changes.

[0080] Step a6: The residual component of this decomposition is used as the gas concentration signal for the next decomposition. The process returns to the step of determining the local maxima and local minima of the gas concentration signal until the sum of the number of local maxima and local minima of the residual component of this decomposition is less than a preset threshold, thus obtaining the final residual component. Specifically, the current residual component is regarded as a new gas concentration signal, and the process from step a1 to step a5 is repeated to continue the decomposition. When the sum of the number of local maxima and local minima of the residual component is less than the set threshold, the decomposition stops, and the residual component at this time is regarded as the final low-frequency component or trend term. Through multiple iterations, it is ensured that all frequency components of the signal are fully extracted, from high frequency to low frequency, and finally a complete signal decomposition is achieved. Optionally, any gas concentration signal can be represented by the following equation (1), which includes multiple intrinsic mode functions and a residual component.

[0081]

[0082] Where x(t) represents the gas concentration signal; m represents the number of intrinsic mode functions; IMF k r(t) represents the k-th intrinsic mode function; r(t) represents the residual component.

[0083] Step S2032: Calculate the correlation coefficient between the gas concentration signal and each intrinsic mode function. Specifically, the correlation coefficient measures the linear correlation between the original gas concentration signal and each intrinsic mode function. A larger correlation coefficient indicates a stronger correlation between the intrinsic mode function and certain significant features in the gas concentration signal. These significant features can be noise or interference. Optionally, the correlation coefficient can be calculated using the following formula (2).

[0084] ρk=corr(x(t),IMFk(t))(2)

[0085] Where ρk represents the correlation coefficient between the k-th intrinsic mode function and the gas concentration signal; corr() represents the function for calculating the correlation coefficient, and the embodiments of the present invention do not impose restrictions on the function used to calculate the correlation coefficient; x(t) represents the gas concentration signal; IMF k (t) represents the k-th intrinsic mode function.

[0086] Step S2033: Filter out intrinsic mode functions (IMFs) with correlation coefficients greater than a preset threshold. Specifically, a correlation coefficient threshold is set based on system requirements and experience. Typically, the preset threshold needs to consider the signal characteristics and noise levels under different environments. For each IMF, if its correlation coefficient is greater than the set threshold, it is considered that the IMF mainly contains noise or interference, and it is filtered out and no longer participates in subsequent signal reconstruction.

[0087] In some optional implementations, in actual industrial applications, the characteristics of the gas concentration signal will continuously change due to variations in operating cycles, meteorological conditions, installation location, and operating conditions. To ensure that the embodiments of the present invention maintain high accuracy and stability under changing environments, the following extensions have been made to parameter selection and optimization: First, adaptive adjustment of signal decomposition parameters: Based on historical data collected from industrial sites and environmental simulation data, statistical analysis and dynamic correction are performed on parameters such as the number of iterations, envelope fitting method, and stopping criteria for signal decomposition. When the system detects deviations in environmental parameters, such as temperature, humidity, illuminance, or vibration level, it automatically calls the corresponding parameter template or uses methods such as weighted moving averages to fine-tune the above parameters to better adapt to the current noise characteristics. Second, dynamic setting of correlation coefficient threshold: By analyzing the distribution characteristics of correlation coefficients under various interference environments, the preset threshold of the correlation coefficient is dynamically optimized using methods such as regression models or neural networks to more accurately filter out highly correlated noise components.

[0088] Step S2034: The unfiltered intrinsic mode functions and residual components are reconstructed to obtain the target concentration signal corresponding to the gas concentration signal. Specifically, the signal is reconstructed using the remaining intrinsic mode functions and residual components after filtering, and the reconstructed target concentration signal is obtained, which can be represented by the following formula (3). The reconstructed signal retains the useful information in the original gas concentration signal, while effectively reducing the influence of background noise and interference. By filtering out the intrinsic mode functions that are highly correlated with the background fluctuation, the amplitude of the system's background fluctuation is reduced, thereby reducing the interference of the background signal on the detection results. It can also effectively suppress the influence of interference signals such as optical interference fringes, improve the purity of the signal, and make the reconstructed signal closer to the real gas signal, reducing the possibility of false alarms and missed alarms, thereby significantly improving the detection accuracy of the system. At the same time, the system can still maintain high stability and reliability when facing complex and changeable environments, meeting the high-standard industrial safety monitoring requirements.

[0089]

[0090] in, denoted by ; K represents the set of unelectronic modes that were not filtered out; r(t) represents the residual component.

[0091] Step S204: Construct a sample database based on multiple first target concentration signals and multiple second target concentration signals.

[0092] Specifically, step S204 includes:

[0093] Step S2041: Construct multiple positive samples based on multiple first target concentration signals.

[0094] In some optional implementations, step S2041 above includes:

[0095] Step b1: For any first target concentration signal, if the presence of gas in the standard gas environment and the first target concentration signal indicates the presence of gas, the first target concentration signal is taken as a positive sample. Specifically, if gas is indeed present in the standard gas environment and the first target concentration signal also indicates the presence of gas, this first target concentration signal is marked as TP, i.e., a positive sample. This indicates that the system has correctly detected the presence of gas. Optionally, various methods can be used to determine whether the target concentration signal indicates the presence of gas. For example, the target concentration signal can be compared with a preset gas concentration; if the signal value exceeds the preset gas concentration, it indicates that gas may be present in the environment; a curve of gas concentration changing over time can be plotted to observe whether there are obvious peaks or situations where the concentration is consistently higher than the preset gas concentration; and the laser absorption spectrum can be analyzed to confirm whether there are gas leakage characteristics.

[0096] Step b2: If there is no gas in the standard gas environment and the first target concentration signal indicates the absence of gas, the first target concentration signal is treated as a positive or negative sample. Specifically, if there is indeed no gas in the standard gas environment and the first target concentration signal also indicates the absence of gas, this first target concentration signal is marked as TN, i.e., a positive or negative sample. This indicates that the system has correctly detected the absence of gas.

[0097] Step b3 involves collecting the positive and negative samples corresponding to multiple first target concentration signals as positive samples. Specifically, all first target concentration signals labeled as positive and negative samples are gathered together as the final set of positive samples, which helps in system evaluation.

[0098] Step S2042: Construct multiple negative samples based on multiple second target concentration signals.

[0099] In some optional implementations, step S2042 above includes:

[0100] Step c1: For any second target concentration signal, if there is no gas in the interfering environment corresponding to the second target concentration signal but the second target concentration signal indicates the presence of gas, then the second target concentration signal is treated as a negative positive sample. Specifically, if there is actually no gas in the interfering environment, but the second target concentration signal indicates the presence of gas, this second target concentration signal is marked as FP, i.e., a negative positive sample. This indicates that the system has incorrectly reported the presence of gas, i.e., a false alarm has occurred.

[0101] Step c2: If the interference environment corresponding to the second target concentration signal contains gas but the second target concentration signal indicates the absence of gas, then the second target concentration signal is treated as a negative negative sample. Specifically, if gas actually exists in the interference environment, but the second target concentration signal indicates its absence, this second target concentration signal is marked as FN, i.e., a negative negative sample. This indicates that the system missed the presence of gas under specific interference conditions, meaning a false negative occurred.

[0102] Step c3 involves collecting the negative positive and negative samples corresponding to multiple second target concentration signals as negative samples. Specifically, all second target concentration signals labeled as negative positive and negative samples are gathered together as the final negative sample set, which facilitates system evaluation.

[0103] Step S2043: Construct a sample database based on multiple positive and multiple negative samples. Specifically, all positive samples from step S2041 and all negative samples from step S2042 are merged into one database as the basis for subsequent system performance evaluation to achieve a comprehensive assessment.

[0104] Step S205: Based on the sample database, determine the accuracy and false alarm rate of the gas leak detection system during gas detection.

[0105] Specifically, step S205 includes:

[0106] Step S2051: For any negative sample in the sample database, if the second target concentration signal corresponding to the negative sample satisfies the corresponding preset rejection rule, the negative sample is removed from the sample database. Specifically, the preset rejection rule can be a concentration error, such as 3%. Since this embodiment of the invention is performed under known gas concentration conditions, that is, the gas concentration is known regardless of whether the concentration signal is collected in a standard gas environment or an interference environment. For any negative sample in the sample database, if the difference between the concentration value represented by its corresponding second target concentration signal and the known gas concentration exceeds 3%, it is removed. Removing these negative samples can reduce the impact on the evaluation results and improve the reliability of accuracy and false alarm rate calculations.

[0107] Step S2052: The ratio of the number of positive samples in the sample database to the total number of samples in the sample database is used as the accuracy of the gas leak detection system during gas detection. Specifically, the accuracy of the gas leak detection system is determined by the following formula (4), which reflects the system's ability to correctly identify gas leaks in the overall detection process. High accuracy means that the system can better distinguish between the presence and absence of gas.

[0108]

[0109] Where Accrtacy represents accuracy; TP represents the number of positive positive samples in the positive samples; TN represents the number of positive negative samples in the positive samples; FP represents the number of negative positive samples in the negative samples after removal; and FN represents the number of negative negative samples in the negative samples after removal.

[0110] Step S2053: The ratio of the number of positive and negative samples in the sample database to the total number of positive samples in the sample database is used as the false alarm rate of the gas leak detection system during gas detection. Specifically, the false alarm rate of the gas leak detection system can be determined by the following formula (5), which reflects the probability of false alarms when the system is without gas. A low false alarm rate means that the system produces fewer false alarms.

[0111]

[0112] Wherein, FPR represents the false alarm rate; TP represents the number of positive positive samples in the positive sample; and TN represents the number of negative positive samples in the positive sample.

[0113] In some optional implementations, when the false alarm rate of the system is detected to be consistently high or other trend problems exist, a parameter retraining and re-optimization process is triggered to update parameters such as the number of signal decomposition iterations and relevant thresholds, so as to ensure the long-term adaptability and stability of the system.

[0114] The performance evaluation method for a gas leak detection system provided in this invention collects multiple first gas concentration signals and multiple second gas concentration signals within a target area under both standard gas and interference environments. This method not only considers the standard laboratory environment but also introduces various interference environments, simulating complex situations that may be encountered in practical applications. This makes the evaluation results closer to real-world scenarios, thereby improving the practicality and reliability of the evaluation. By decomposing and reconstructing the collected gas concentration signals, background noise and interference signals can be effectively removed, making the target concentration signal purer and improving the accuracy of subsequent data analysis. A comprehensive and diverse sample database is constructed using target concentration signals from different environments, better reflecting the system's performance under various conditions. Based on this sample database, the accuracy and false alarm rate are calculated, making the system performance evaluation more scientific and rigorous, enhancing the system's reliability and trustworthiness. This method considers not only performance under ideal conditions but also performance under complex interference environments, making the evaluation results more closely aligned with actual application scenarios.

[0115] This embodiment also provides a performance evaluation device for a gas leak detection system, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0116] This embodiment provides a performance evaluation device for a gas leak detection system, such as... Figure 2 As shown, it includes:

[0117] The first acquisition module 201 is used to acquire multiple first gas concentration signals in a target area under a standard gas environment using a gas leak detection system.

[0118] The second acquisition module 202 is used to acquire multiple second gas concentration signals in the target area using a gas leak detection system under various interference environments.

[0119] The signal processing module 203 is used to decompose and reconstruct multiple first gas concentration signals and multiple second gas concentration signals respectively to obtain multiple first target concentration signals and multiple second target concentration signals. The background noise and interference signals of the target concentration signals are less than those of the corresponding gas concentration signals.

[0120] Module 204 is used to construct a sample database based on multiple first target concentration signals and multiple second target concentration signals.

[0121] Evaluation module 205 is used to determine the accuracy and false alarm rate of the gas leak detection system when detecting gas, based on a sample database.

[0122] In some alternative implementations, the signal processing module 203 includes:

[0123] The decomposition unit is used to decompose any gas concentration signal into residual components and multiple intrinsic mode functions.

[0124] The calculation unit is used to calculate the correlation coefficient between the gas concentration signal and each intrinsic mode function.

[0125] The filtering unit is used to filter out intrinsic mode functions whose correlation coefficient is greater than a preset threshold.

[0126] The reconstruction unit is used to reconstruct the unfiltered intrinsic mode functions and residual components to obtain the target concentration signal corresponding to the gas concentration signal.

[0127] In some alternative implementations, the decomposition unit includes:

[0128] The first determining sub-unit is used to determine the local maxima and local minima of the gas concentration signal.

[0129] The fitting sub-unit is used to fit the upper and lower envelopes based on local maxima and local minima using cubic spline interpolation.

[0130] The calculation sub-unit is used to calculate the average value of the upper and lower envelopes to obtain the mean envelope.

[0131] The second determining subunit is used to determine the difference between the gas concentration signal and the mean envelope. If the difference does not meet the decomposition requirements, the difference is used as the gas concentration signal for the next decomposition. The process returns to the step of determining the local maximum and local minimum points of the gas concentration signal until the difference meets the decomposition requirements. The difference is then used as the eigenmode function of this decomposition.

[0132] The third determining sub-unit is used to take the difference between the gas concentration signal and all intrinsic mode functions as the residual component of this decomposition.

[0133] The fourth determination subunit is used to take the residual component of this decomposition as the gas concentration signal for the next decomposition and return to the step of determining the local maximum and local minimum points of the gas concentration signal until the sum of the number of local maximum and local minimum points of the residual component of this decomposition is less than a preset number threshold, and the final residual component is obtained.

[0134] In some alternative implementations, building module 204 includes:

[0135] The first building unit is used to construct multiple positive samples based on multiple first target concentration signals.

[0136] The second building unit is used to construct multiple negative samples based on multiple second target concentration signals.

[0137] The third building unit is used to construct a sample database based on multiple positive samples and multiple negative samples.

[0138] In some alternative implementations, the first building unit includes:

[0139] The fifth determining subunit is used to take any first target concentration signal as a positive sample when there is gas in the standard gas environment and the first target concentration signal indicates the presence of gas.

[0140] The sixth determining subunit is used to treat the first target concentration signal as a positive or negative sample when there is no gas in the standard gas environment and the first target concentration signal indicates that there is no gas.

[0141] The first construction subunit is used to take the positive and negative samples corresponding to multiple first target concentration signals as positive samples.

[0142] In some alternative implementations, the second building unit includes:

[0143] The seventh determining subunit is used to treat any second target concentration signal as a negative positive sample when there is no gas in the interference environment corresponding to the second target concentration signal and the second target concentration signal indicates the presence of gas.

[0144] The eighth determining subunit is used to treat the second target concentration signal as a negative sample when the interference environment corresponding to the second target concentration signal contains gas and the second target concentration signal indicates that there is no gas.

[0145] The second construction subunit is used to treat the negative positive samples and negative negative samples corresponding to multiple second target concentration signals as negative samples.

[0146] In some alternative implementations, the evaluation module 205 includes:

[0147] The elimination unit is used to eliminate any negative sample from the sample database if the second target concentration signal corresponding to the negative sample meets the corresponding preset elimination rule.

[0148] The first evaluation unit is used to measure the ratio of the number of positive samples in the sample database to the total number of samples in the sample database as the accuracy of the gas leak detection system in gas detection.

[0149] The second evaluation unit is used to take the ratio of the number of positive and negative samples in the sample database to the number of all positive samples in the sample database as the false alarm rate of the gas leak detection system when detecting gas.

[0150] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0151] In this embodiment, the performance evaluation device for the gas leak detection system is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0152] This invention also provides a computer device having the above-described features. Figure 2 The device shown is a performance evaluation device for a gas leak detection system.

[0153] Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 3 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 3Take a processor 10 as an example.

[0154] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0155] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0156] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0157] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0158] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0159] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0160] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0161] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0162] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A performance evaluation method for a gas leak detection system, characterized in that, The method includes: In a standard gas environment, a gas leak detection system is used to collect multiple primary gas concentration signals within the target area; Under various interference environments, the gas leak detection system described above collects multiple second gas concentration signals within the target area. The plurality of first gas concentration signals and the plurality of second gas concentration signals are respectively decomposed and reconstructed to obtain a plurality of first target concentration signals and a plurality of second target concentration signals, wherein the background noise and interference signals of the target concentration signals are less than those of the corresponding gas concentration signals; A sample database is constructed based on the plurality of first target concentration signals and the plurality of second target concentration signals; Based on the sample database, the accuracy and false alarm rate of the gas leak detection system during gas detection are determined; The step of performing signal decomposition and reconstruction on the plurality of first gas concentration signals and the plurality of second gas concentration signals to obtain a plurality of first target concentration signals and a plurality of second target concentration signals includes: For any gas concentration signal, the gas concentration signal is decomposed into residual components and multiple intrinsic mode functions; Calculate the correlation coefficient between the gas concentration signal and each intrinsic mode function; Filter out intrinsic mode functions with a correlation coefficient greater than a preset threshold; The unfiltered intrinsic mode functions and the residual components are reconstructed to obtain the target concentration signal corresponding to the gas concentration signal; For any given gas concentration signal, the gas concentration signal is decomposed into a residual component and multiple intrinsic mode functions, including: Determine the local maxima and local minima of the gas concentration signal; Based on the local maxima and local minima, the upper and lower envelopes are obtained by fitting using cubic spline interpolation. Calculate the average value of the upper envelope and the lower envelope to obtain the mean envelope; The difference between the gas concentration signal and the mean envelope is determined. If the difference does not meet the decomposition requirements, the difference is used as the gas concentration signal for the next decomposition. The process returns to the step of determining the local maximum and local minimum points of the gas concentration signal until the difference meets the decomposition requirements. The difference is then used as the intrinsic mode function for this decomposition. The difference between the gas concentration signal and all intrinsic mode functions is taken as the residual component of this decomposition. The residual component of this decomposition is used as the gas concentration signal for the next decomposition. The process returns to the step of determining the local maxima and local minima of the gas concentration signal until the sum of the number of local maxima and local minima of the residual component of this decomposition is less than a preset threshold, thus obtaining the final residual component. The step of constructing a sample database based on the plurality of first target concentration signals and the plurality of second target concentration signals includes: Based on the multiple first target concentration signals, multiple positive samples are constructed; Based on the multiple second target concentration signals, multiple negative samples are constructed; The sample database is constructed based on the multiple positive samples and the multiple negative samples.

2. The method according to claim 1, characterized in that, The construction of multiple positive samples based on the multiple first target concentration signals includes: For any first target concentration signal, if there is gas in the standard gas environment and the first target concentration signal indicates the presence of gas, the first target concentration signal is taken as a positive sample. In the case where there is no gas in the standard gas environment and the first target concentration signal indicates that there is no gas, the first target concentration signal is used as a positive or negative sample. The positive and negative samples corresponding to the plurality of first target concentration signals are taken as the positive samples.

3. The method according to claim 2, characterized in that, The construction of multiple negative samples based on the multiple second target concentration signals includes: For any second target concentration signal, if there is no gas in the interference environment corresponding to the second target concentration signal and the second target concentration signal indicates the presence of gas, the second target concentration signal is taken as a negative positive sample. If the interference environment corresponding to the second target concentration signal contains gas and the second target concentration signal indicates that there is no gas, the second target concentration signal will be used as a negative sample. The negative positive samples and negative negative samples corresponding to the plurality of second target concentration signals are used as the negative samples.

4. The method according to claim 3, characterized in that, The determination of the accuracy and false alarm rate of the gas leak detection system during gas detection based on the sample database includes: For any negative sample in the sample database, if the second target concentration signal corresponding to the negative sample satisfies the corresponding preset removal rule, the negative sample is removed from the sample database. The ratio of the number of positive samples in the sample database to the total number of samples in the sample database is used as the accuracy of the gas leak detection system in gas detection. The ratio of the number of positive and negative samples in the sample database to the total number of positive samples in the sample database is used as the false alarm rate of the gas leak detection system during gas detection.

5. A performance evaluation device for a gas leak detection system, characterized in that, The device includes: The first acquisition module is used to acquire multiple first gas concentration signals in the target area under a standard gas environment using a gas leak detection system; The second acquisition module is used to acquire multiple second gas concentration signals in the target area using the gas leak detection system under various interference environments. The signal processing module is used to decompose and reconstruct the plurality of first gas concentration signals and the plurality of second gas concentration signals respectively to obtain a plurality of first target concentration signals and a plurality of second target concentration signals, wherein the background noise and interference signals of the target concentration signals are less than those of the corresponding gas concentration signals; A construction module is used to construct a sample database based on the plurality of first target concentration signals and the plurality of second target concentration signals; An evaluation module is used to determine the accuracy and false alarm rate of the gas leak detection system when detecting gas, based on the sample database. Specifically, the signal processing module is used for: For any gas concentration signal, the gas concentration signal is decomposed into residual components and multiple intrinsic mode functions; Calculate the correlation coefficient between the gas concentration signal and each intrinsic mode function; Filter out intrinsic mode functions with a correlation coefficient greater than a preset threshold; The unfiltered intrinsic mode functions and the residual components are reconstructed to obtain the target concentration signal corresponding to the gas concentration signal; For any given gas concentration signal, the gas concentration signal is decomposed into a residual component and multiple intrinsic mode functions, including: Determine the local maxima and local minima of the gas concentration signal; Based on the local maxima and local minima, the upper and lower envelopes are obtained by fitting using cubic spline interpolation. Calculate the average value of the upper envelope and the lower envelope to obtain the mean envelope; The difference between the gas concentration signal and the mean envelope is determined. If the difference does not meet the decomposition requirements, the difference is used as the gas concentration signal for the next decomposition. The process returns to the step of determining the local maximum and local minimum points of the gas concentration signal until the difference meets the decomposition requirements. The difference is then used as the intrinsic mode function for this decomposition. The difference between the gas concentration signal and all intrinsic mode functions is taken as the residual component of this decomposition. The residual component of this decomposition is used as the gas concentration signal for the next decomposition. The process returns to the step of determining the local maxima and local minima of the gas concentration signal until the sum of the number of local maxima and local minima of the residual component of this decomposition is less than a preset threshold, thus obtaining the final residual component. The building module is specifically used for: Based on the multiple first target concentration signals, multiple positive samples are constructed; Based on the multiple second target concentration signals, multiple negative samples are constructed; The sample database is constructed based on the multiple positive samples and the multiple negative samples.

6. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the performance evaluation method of the gas leak detection system according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the performance evaluation method of the gas leak detection system according to any one of claims 1 to 4.

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