Intelligent Gas Detector Environmental Adaptability Optimization Method and System

Abstract

The present invention discloses an optimization method and system for the environmental adaptability of an intelligent gas detector, which relates to the technical field of gas monitoring. The method includes the following steps: obtaining the gas concentration signal, temperature signal, and humidity signal in the environment to be monitored in real time through the intelligent gas detector; performing temperature-humidity coupling compensation on the gas concentration signal to obtain the calibrated gas concentration value; calculating the correlation weights of the temperature signal, humidity signal, and gas concentration value to generate an environmental factor influence matrix; and determining whether the change in the gas concentration value is caused by non-environmental factors through the environmental factor influence matrix. The present invention divides the temperature-humidity space into multiple regions, accurately captures the temperature-humidity synergy effect, and combines time-domain residual statistics, frequency-domain principal component analysis, and environmental weight matrix to comprehensively determine the influence of non-environmental factors.

Description

Technical Field

[0001] The present invention relates to the technical field of gas monitoring, in particular to an intelligent gas detector environmental adaptability optimization method and system. Background Art

[0002] Gas detectors play an important role in the fields of industrial safety, environmental monitoring, etc., but their detection accuracy is often interfered by complex environmental factors (such as temperature and humidity fluctuations). The existing technologies have the following deficiencies:

[0003] Traditional gas detectors mostly adopt linear compensation models (such as fixed coefficient compensation), which are difficult to accurately characterize the non-linear characteristics of the temperature and humidity coupling effect. For example, in a high-temperature and high-humidity environment, the cross-sensitivity effect of the sensor is significant, resulting in an increase in compensation error and false alarm rate.

[0004] Existing solutions usually only judge the concentration change based on a single threshold or simple statistics (such as mean, variance), and cannot dynamically distinguish the influence of environmental factors (such as day-night temperature drift) from non-environmental factors (such as gas leakage). For example, when the temperature and humidity fluctuate violently, the environmental interference is misjudged as a leakage event. Summary of the Invention

[0005] In view of the problems existing in the existing intelligent gas detector environmental adaptability optimization methods and systems, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is that traditional gas detectors mostly adopt linear compensation models and are difficult to accurately characterize the non-linear characteristics of the temperature and humidity coupling effect.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides an intelligent gas detector environmental adaptability optimization method, which includes the following steps,

[0009] Real-time obtain gas concentration signals, temperature signals, and humidity signals in the environment to be monitored through an intelligent gas detector;

[0010] Perform temperature-humidity coupling compensation on the gas concentration signal to obtain a calibrated gas concentration value;

[0011] Calculate the correlation weights of the temperature signal, humidity signal, and gas concentration value, and generate an environmental factor influence matrix;

[0012] Judge whether the change in the gas concentration value is caused by non-environmental factors through the environmental factor influence matrix.

[0013] As a preferred embodiment of the environmental adaptability optimization method for the intelligent gas detector of the present invention, when determining whether the change in the gas concentration value is caused by non-environmental factors, the following steps are included:

[0014] Preset multiple levels of concentration thresholds, compare the gas concentration value with the multiple levels of concentration thresholds, and generate a warning signal based on the comparison result;

[0015] Match the warning signal with the corresponding control strategy and execute the operation through the intelligent gas detector.

[0016] As a preferred embodiment of the environmental adaptability optimization method for the intelligent gas detector of the present invention, the method for obtaining the gas concentration value includes:

[0017] Establish a temperature range interval and a humidity range interval through the temperature signal and the humidity signal respectively, and divide the temperature range interval and the humidity range interval into multiple small regions respectively;

[0018] Monitor the environmental state in real time, calculate the membership degrees belonging to different small regions, select the top three small regions with the highest membership degrees for compensation result fusion, and finally obtain the calibrated gas concentration value.

[0019] As a preferred embodiment of the environmental adaptability optimization method for the intelligent gas detector of the present invention, the steps for obtaining the environmental factor influence matrix include:

[0020] Define the time window length as L seconds and generate a continuous historical data sequence stored within the window;

[0021] Obtain the temperature correlation calculation formula and the humidity correlation calculation formula;

[0022] Then process the temperature correlation calculation formula and the humidity correlation calculation formula, and finally output the environmental factor influence matrix.

[0023] As a preferred embodiment of the environmental adaptability optimization method for the intelligent gas detector of the present invention, the continuous historical data sequence stored within the window includes , and wherein, T is the temperature sequence, H is the humidity sequence, and C is the gas concentration sequence;

[0024] The temperature correlation calculation formula is expressed as:

[0025] ;

[0026] The humidity correlation calculation formula is expressed as:

[0027] ;

[0028] Wherein, and respectively represent temperature correlation and humidity correlation, and L represents the defined time window length value. , and respectively represent the average values of temperature, humidity and concentration within the window.

[0029] After processing the temperature correlation calculation formula and the humidity correlation calculation formula, they are expressed as:

[0030] ;

[0031] ;

[0032] Wherein, and respectively represent the weight of temperature influence and the weight of humidity influence.

[0033] The output environmental factor influence matrix is expressed as:

[0034] ;

[0035] Wherein, M represents a 1×2 matrix.

[0036] As a preferred scheme of the environmental adaptability optimization method of the intelligent gas detector described in the present invention, wherein: when the change in the gas concentration value is caused by non-environmental factors, a warning signal is generated through the comparison result;

[0037] Among them, the warning signal includes a first-level warning, a second-level warning and a third-level warning;

[0038] The determination condition for the first-level warning is that the change gradient of the gas concentration value is within the first-level interval;

[0039] The determination condition for the second-level warning is that the change gradient of the gas concentration value is within the second-level interval;

[0040] The determination condition for the third-level warning is that the change gradient of the gas concentration value is within the third-level interval.

[0041] As a preferred scheme of the environmental adaptability optimization method of the intelligent gas detector described in the present invention, wherein: the setting method of the first-level interval, the second-level interval and the third-level interval includes,

[0042] Preset a standard concentration value Q;

[0043] Take the adjacent interval of the standard concentration value as the output of the first-level interval, that is, the first-level interval is expressed as: (Q-α, Q+α);

[0044] Output the adjacent intervals of the first-level interval as the second-level interval, that is, the second-level interval is expressed as: (Q - α - β, Q - α] and [Q + α, Q + α + β);

[0045] Output the adjacent intervals of the second-level interval as the third-level interval, that is, the third-level interval is expressed as: [Q - α - β - γ, Q - α - β] and [Q + α + β, Q + α + β + γ].

[0046] In a second aspect, an embodiment of the present invention provides an intelligent gas detector environmental adaptability optimization system, which includes a signal acquisition module, a coupling compensation module, a matrix generation module, and an environmental factor influence judgment module;

[0047] The signal acquisition module is responsible for obtaining the gas concentration signal, temperature signal, and humidity signal in the environment to be monitored in real time through the intelligent gas detector;

[0048] The coupling compensation module performs temperature-humidity coupling compensation on the collected gas concentration signal;

[0049] The matrix generation module calculates the correlation weights between the temperature signal, humidity signal, and gas concentration value, and generates an environmental factor influence matrix based on these weights;

[0050] The environmental factor influence judgment module is used to judge whether the change in the gas concentration value is caused by non-environmental factors.

[0051] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and: when the processor executes the computer program, any step of the above-mentioned intelligent gas detector environmental adaptability optimization method is implemented.

[0052] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, and: when the computer program is executed by a processor, any step of the above-mentioned intelligent gas detector environmental adaptability optimization method is implemented.

[0053] The beneficial effects of the present invention are:

[0054] The temperature and humidity space is divided into multiple regions to accurately capture the temperature and humidity synergistic effect. Experiments show that in the wide temperature range of -40°C to 85°C, the compensation error ≤ ±3%FS, which is 40% higher than the traditional linear model. By fuzzy calculation, the adjacent regions are matched in real time to eliminate the mutation error caused by the hard boundary, and the error is reduced by 32% in the humidity mutation (ΔH > 20%RH / min) scenario.

[0055] Combined with time-domain residual statistics, frequency-domain principal component analysis, and environmental weight matrix, non-environmental factor impacts are comprehensively determined. For example, when the frequency-domain amplitude ratio > 5:2:1, the high-frequency characteristics of sudden leakage can be specifically identified, and the false alarm rate is reduced from 25% to 8%. The threshold is calculated based on real-time data, adapting to environmental baseline drift to avoid day-night false alarms caused by fixed thresholds. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0057] Figure 1 It is a flowchart of the environmental adaptability optimization method for intelligent gas detectors. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] To make the above objects, features, and advantages of the present invention more obvious and understandable, the specific embodiments of the present invention will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0059] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0060] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that excludes other embodiments.

[0061] The present invention is described in detail in conjunction with schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally not in accordance with the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0062] Meanwhile, in the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper, lower, inner, and outer" is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. In addition, the terms "first, second, or third" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0063] Unless otherwise clearly defined and limited in the present invention, the terms "installed, connected, and coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can also be a mechanical connection, an electrical connection, or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations. Embodiment

[0064] Refer to Figure 1 , which is the first embodiment of the present invention. This embodiment provides an intelligent gas detector environmental adaptability optimization method, including the following steps.

[0065] S1. Real-time obtain the gas concentration signal, temperature signal, and humidity signal in the environment to be monitored through the intelligent gas detector.

[0066] In an optional embodiment, in a chlorine gas storage tank area of a chemical plant, the environmental parameter range is:

[0067] Temperature: -20°C to 50°C (extreme temperatures from winter to summer); Humidity: 20%RH to 90%RH (from dry environment to steam environment); Target gas: chlorine gas (Cl2), safety threshold 1 ppm (OSHA standard).

[0068] S2. Perform temperature-humidity coupling compensation on the gas concentration signal to obtain the calibrated gas concentration value.

[0069] The way to obtain the gas concentration value includes:

[0070] Respectively establish a temperature range interval and a humidity range interval through the temperature signal and the humidity signal, and divide the temperature range interval and the humidity range interval into multiple small regions;

[0071] For example, divide the two-dimensional temperature-humidity space into N small regions, and each small region is expressed as:

[0072] , where R represents the set of small regions, and i represents the i-th small region. And respectively represent the temperature signal and humidity signal of the i-th small area.

[0073] Each small area corresponds to a dynamic sub-region compensation formula, and the dynamic sub-region compensation formula is expressed as:

[0074] ;

[0075] In the formula, and both represent the polynomial compensation coefficients in the i-th area, which are used to fit the linear and non-linear effects of temperature and humidity on gas concentration; represents the amplitude coefficient of the exponential term, which controls the intensity of the temperature and humidity coupling effect; represents the exponential decay coefficient, which determines the influence range of the temperature and humidity product term (for example, the larger λ is, the faster the coupling effect decays with the change of temperature and humidity); and respectively represent the calibrated reference temperature and humidity; represents the calibrated gas concentration value in the i-th area; represents the original concentration signal.

[0076] Real-time monitor the environmental state, calculate the membership degrees of different small areas it belongs to, select the top three small areas with the highest membership degrees for fusing the compensation results, and finally obtain the calibrated gas concentration value.

[0077] Real-time detect the environmental state , and calculate the membership degrees of each area it belongs to:

[0078] ;

[0079] In the formula, is the membership degree of area i, is the centroid coordinate of area i, and are both shape factors;

[0080] Select the top three areas with the highest membership degrees for fusing the compensation results:

[0081] ;

[0082] In the formula, is the calibrated gas concentration value.

[0083] In this embodiment, one case can be selected for experiment. First of all, it is necessary to know that the temperature range is from -40°C to 85°C, and the gradient test points include -40°C, -20°C, 0°C, 25°C (reference), 50°C, 70°C, 85°C; the humidity range is from 10%RH to 90%RH, and the gradient test points include 10%, 30%, 50% (reference), 70%, 90%RH. Therefore, there are a total of 35 groups of temperature and humidity combinations (7 temperature points × 5 humidity points).

[0084] The temperature and humidity space is divided into 9 regions (3×3 grid): among them, the temperature partitions are -40°C to 15°C, 15°C to 50°C, 50°C to 85°C; the humidity partitions are 10% to 40%RH, 40% to 70%RH, 70% to 90%RH.

[0085] Taking the high temperature and high humidity region (50°C to 85°C, 70% to 90%RH) as an example, the calibrated compensation parameters are:

[0086] = 0.0023, = -0.00015, = 1.2×10 -6 ; = 0.0018, = -0.00008; = 0.05; = 0.012; = 25°C, = 50%RH.

[0087] After that, during the test, in the standard environment of 25°C and 50%RH, the calibrated output of the detector is 100ppm (error ±0.5ppm);

[0088] For the first scenario of low temperature and dryness (-40°C, 10%RH), the original signal = 132.4ppm (significantly larger due to low temperature), the matching region is the low temperature and low humidity region (membership degree 0.85), and the adjacent regions (membership degrees 0.12, 0.03). After compensation, the obtained result is 100.8ppm, with an error of +0.8%FS.

[0089] For the second scenario of high temperature and high humidity (85°C, 90%RH), the original signal = 78.6ppm (the sensitivity of the sensor decreases due to high temperature and high humidity), the matching region is the high temperature and high humidity region (membership degree 0.92), and the adjacent regions (membership degrees 0.06, 0.02). After compensation, the obtained result is 99.2ppm, with an error of -0.8%FS.

[0090] For the third scenario of sudden changes in temperature and humidity (temperature suddenly rises from 25°C to 70°C, humidity drops from 50%RH to 20%RH), the fluctuation range of the original signal is 85 - 115 ppm. After membership dynamic fusion, it stabilizes at 98 - 102 ppm after compensation, with a maximum instantaneous error of ±2%FS.

[0091] Table 1: Experimental schematic table under different test conditions

[0092] Test Conditions Error of Traditional Linear Model (±%FS) Error of Nonlinear Model in This Embodiment (±%FS) Degree of Error Reduction -40°C, 10%RH +12.5% +0.8% 93.6% 85°C, 90%RH -15.2% -0.8% 94.7% Temperature and Humidity Sudden Change Scenario ±18% ±2% 88.9% Average Error (Full Range) ±8.7% ±2.4% 72.4%

[0093] As can be seen from Table 1, in the range of -40°C to 85°C, the maximum error of this embodiment is ±2.4%FS, significantly better than the ±8.7%FS of the traditional linear model, with the error reduced by 72.4%. In the extreme high temperature and high humidity (85°C, 90%RH) scenario, the compensation error is only -0.8%FS, an improvement of 94.7% compared to the traditional model (-15.2%FS).

[0094] The compensation parameters for each region are calibrated offline by the Quantum Particle Swarm Optimization (QPSO), and the training data covers the full temperature and humidity range.

[0095] S3. Calculate the correlation weights of the temperature signal, humidity signal, and gas concentration value to generate an environmental factor influence matrix.

[0096] The steps to obtain the environmental factor influence matrix include

[0097] Define the time window length as L seconds to generate a continuous historical data sequence stored within the window;

[0098] Among them, the continuous historical data sequence stored within the window includes 、 and Among them, T is the temperature sequence, H is the humidity sequence, and C is the gas concentration sequence;

[0099] Obtain the temperature correlation calculation formula and the humidity correlation calculation formula;

[0100] The temperature correlation calculation formula is expressed as:

[0101] ;

[0102] The humidity correlation calculation formula is expressed as:

[0103] ;

[0104] In the formula, and respectively represent the temperature correlation and the humidity correlation, L represents the defined time window length value, 、 and The average values of temperature, humidity, and concentration within the window represented separately;

[0105] Then process the temperature correlation calculation formula and the humidity correlation calculation formula, and finally output the environmental factor influence matrix.

[0106] The temperature correlation calculation formula and the humidity correlation calculation formula after processing are expressed as:

[0107] ;

[0108] ;

[0109] In the formula, and respectively represent the weight of the temperature influence and the weight of the humidity influence;

[0110] The output environmental factor influence matrix is expressed as:

[0111] ;

[0112] In the formula, M represents a 1×2 matrix.

[0113] S4. Through the environmental factor influence matrix, determine whether the change in the gas concentration value is caused by non-environmental factors.

[0114] When determining whether the change in the gas concentration value is caused by non-environmental factors, the following steps are included,

[0115] Preset multi-level concentration thresholds, compare the gas concentration value with the multi-level concentration thresholds, and generate a warning signal based on the comparison result;

[0116] Match the warning signal with the corresponding control strategy and execute the operation through the intelligent gas detector.

[0117] When the change in the gas concentration value is caused by non-environmental factors, generate a warning signal based on the comparison result;

[0118] Among them, the warning signals include a first-level warning, a second-level warning, and a third-level warning;

[0119] The determination condition for the first-level warning is that the change gradient of the gas concentration value is within the first-level interval;

[0120] The determination condition for the second-level warning is that the change gradient of the gas concentration value is within the second-level interval;

[0121] The determination condition for the third-level warning is that the change gradient of the gas concentration value is within the third-level interval.

[0122] The setting method for the first-level interval, the second-level interval, and the third-level interval includes,

[0123] Preset standard concentration value Q;

[0124] The adjacent interval of the standard concentration value is output as the first-level interval, that is, the first-level interval is expressed as: (Q-α, Q+α);

[0125] The adjacent intervals of the first-level interval are output as the second-level intervals, that is, the second-level intervals are represented as: (Q-α-β, Q-α] and [Q+α, Q+α+β);

[0126] The adjacent intervals of the second-level interval are output as the third-level intervals, that is, the third-level intervals are expressed as: [Q-α-β-γ, Q-α-β] and [Q+α+β, Q+α+β+γ].

[0127] In this embodiment, for example, the preset standard concentration value Q is 1ppm, and α=0.2ppm, β=0.3ppm, and γ=0.5ppm are set for the gas. Therefore, the first-level interval output by setting is expressed as: (0.8, 1.2), the second-level interval is expressed as: (0.5, 0.8] and [1.2, 1.5), and the third-level interval is expressed as: [0, 0.5] and [1.5, 2];

[0128] Then, tiny pipeline leakage (slow diffusion) and valve rupture (sudden leakage) were simulated, and the detector was configured with a sampling frequency of 1 time / second and an early warning response delay of ≤0.5 seconds.

[0129] In one scenario, a slow leak (level 1 warning), i.e., a leakage rate of 0.05ppm / s, a concentration change of t=8s: 1.4ppm, enters the upper limit of the level 1 interval of 1.2ppm, triggering a level 1 warning, activating the local sound and light alarm, and displaying a yellow warning sign in the central control room.

[0130] In another scenario, a moderate leak (secondary warning), that is, the leakage rate is 0.25ppm / s, the concentration change t=3s: 1.75ppm, entering the upper limit of the secondary interval of 1.5ppm, triggers the secondary warning, activates the ventilation system (air volume increased to 80%), and sends a text message to the security administrator.

[0131] In the third scenario, a sudden leak (level three warning), that is, the leakage rate is 0.8ppm / s, the concentration change t=1.25s: 2ppm, entering the upper limit of the level three interval 2ppm, triggering the level three warning, emergency shut-off of the upstream valve (response time 0.3 seconds), starting the spray suppression system, and uploading the alarm information to the emergency management platform.

[0132] In summary, the temperature and humidity space is divided into multiple regions to accurately capture the temperature and humidity synergy effect. Experiments show that in the wide temperature range of -40°C to 85°C, the compensation error ≤ ±3%FS, which is 40% higher than that of the traditional linear model. By fuzzy calculation, neighboring regions are matched in real time to eliminate the mutation error caused by the hard boundary, and the error is reduced by 32% in the case of humidity mutation (ΔH>20%RH / min).

[0133] Combined with time-domain residual statistics, frequency-domain principal component analysis, and environmental weight matrix, non-environmental factors are comprehensively judged. For example, when the frequency-domain amplitude ratio > 5:2:1, the high-frequency characteristics of sudden leakage can be specifically identified, and the false alarm rate is reduced from 25% to 8%. The threshold is calculated based on real-time data, adapting to the environmental baseline drift, and avoiding false alarms during day and night caused by fixed thresholds.

[0134] Embodiment 2

[0135] On the basis of the first embodiment, this embodiment further provides an intelligent gas detector environmental adaptability optimization system, including a signal acquisition module, a coupling compensation module, a matrix generation module, and an environmental factor influence judgment module;

[0136] The signal acquisition module is responsible for obtaining gas concentration signals, temperature signals, and humidity signals in the environment to be monitored in real time through an intelligent gas detector;

[0137] The coupling compensation module performs temperature-humidity coupling compensation on the collected gas concentration signals;

[0138] The matrix generation module calculates the correlation weights between the temperature signal, humidity signal, and gas concentration value, and generates an environmental factor influence matrix based on these weights;

[0139] The environmental factor influence judgment module is used to judge whether the change in the gas concentration value is caused by non-environmental factors.

[0140] This embodiment also provides a computer device, which is applicable to the case of the intelligent gas detector environmental adaptability optimization method, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the intelligent gas detector environmental adaptability optimization method proposed in the above embodiment.

[0141] The computer device may be a terminal, which includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0142] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the method for optimizing the environmental adaptability of an intelligent gas detector as proposed in the above embodiment.

[0143] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. Technical details not described in detail in this embodiment can be referred to in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0144] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An optimization method for the environmental adaptability of an intelligent gas detector, characterized in that: Including the following steps, Obtain the gas concentration signal, temperature signal, and humidity signal in the environment to be monitored in real time through an intelligent gas detector; Perform temperature-humidity coupling compensation on the gas concentration signal to obtain the calibrated gas concentration value; Calculate the correlation weights of the temperature signal, humidity signal, and gas concentration value, and generate an environmental factor influence matrix; Judge whether the change in the gas concentration value is caused by non-environmental factors through the environmental factor influence matrix; When judging whether the change in the gas concentration value is caused by non-environmental factors, it includes the following steps, Preset multiple levels of concentration thresholds, compare the gas concentration value with the multiple levels of concentration thresholds, and generate a warning signal through the comparison result; Match the warning signal with the corresponding control strategy and execute the operation through the intelligent gas detector; The obtaining method of the gas concentration value includes, Establish a temperature range interval and a humidity range interval respectively through the temperature signal and the humidity signal, and divide the temperature range interval and the humidity range interval into multiple small regions respectively; Monitor the environmental state in real time, calculate the membership degrees belonging to different small regions, select the top three small regions with the highest membership degrees for compensation result fusion, and finally obtain the calibrated gas concentration value; The obtaining steps of the environmental factor influence matrix include, Define the time window length as L seconds and generate a continuous historical data sequence stored within the window; Obtain the temperature correlation calculation formula and the humidity correlation calculation formula; Then process the temperature correlation calculation formula and the humidity correlation calculation formula, and finally output the environmental factor influence matrix.

2. The method for optimizing the environmental adaptability of the intelligent gas detector according to claim 1, wherein: The window stores a continuous sequence of historical data, including , and , where T is the temperature sequence, H is the humidity sequence, and C is the gas concentration sequence; The temperature correlation calculation formula is expressed as: ； The humidity correlation calculation formula is expressed as: ； Wherein, and represent temperature correlation and humidity correlation respectively, and L represents the defined time window length value, , and represent the average values of temperature, humidity and concentration within the window respectively; After processing the temperature correlation calculation formula and the humidity correlation calculation formula, it is expressed as: ； ； In the formula, and respectively represent the weight of the temperature influence and the weight of the humidity influence; The output environmental factor influence matrix is expressed as: ； In the formula, M represents a 1×2 matrix.

3. The method for optimizing the environmental adaptability of the intelligent gas detector according to claim 2, characterized in that: When the change in the gas concentration value is caused by non-environmental factors, generate a warning signal through the comparison result; Among them, the warning signal includes a first-level warning, a second-level warning, and a third-level warning; The determination condition for the first-level warning is that the change gradient of the gas concentration value is within the first-level interval; The determination condition for the second-level warning is that the change gradient of the gas concentration value is within the second-level interval; The determination condition for the third-level warning is that the change gradient of the gas concentration value is within the third-level interval.

4. The intelligent gas detector environmental adaptability optimization method according to claim 3, characterized in that: The setting method of the first-level interval, second-level interval, and third-level interval includes, Preset a standard concentration value Q; Take the adjacent interval of the standard concentration value as the output of the first-level interval, that is, the first-level interval is expressed as: (Q-α, Q+α); Take the adjacent interval of the first-level interval as the output of the second-level interval, that is, the second-level interval is expressed as: (Q-α-β, Q-α] and [Q+α, Q+α+β); Take the adjacent interval of the second-level interval as the output of the third-level interval, that is, the third-level interval is expressed as: [Q-α-β-γ, Q-α-β] and [Q+α+β, Q+α+β+γ].

5. An intelligent gas detector environmental adaptability optimization system, based on the intelligent gas detector environmental adaptability optimization method according to any one of claims 1 to 4, characterized in that: Including a signal acquisition module, a coupling compensation module, a matrix generation module, and an environmental factor influence judgment module; The signal acquisition module is responsible for obtaining the gas concentration signal, temperature signal, and humidity signal in the environment to be monitored in real time through an intelligent gas detector; The coupling compensation module performs temperature-humidity coupling compensation on the collected gas concentration signal; The matrix generation module calculates the correlation weights between the temperature signal, the humidity signal and the gas concentration value, and generates an environmental factor influence matrix according to these weights; The environmental factor influence judgment module is used to judge whether the change of the gas concentration value is caused by non-environmental factors.

6. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the intelligent gas detector environmental adaptability optimization method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the intelligent gas detector environmental adaptability optimization method according to any one of claims 1 to 4.

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