Method and system for optimizing environmental adaptability of intelligent gas detector

Abstract

The invention discloses an environment adaptability optimization method and system for an intelligent gas detector, and relates to the technical field of gas monitoring, and the method comprises the following steps: obtaining a gas concentration signal, a temperature signal and a humidity signal in a to-be-monitored environment in real time through the intelligent gas detector; performing temperature-humidity coupling compensation on the gas concentration signal to obtain a calibrated gas concentration value; calculating correlation weights of the temperature signal, the humidity signal and the gas concentration value, and generating an environmental factor influence matrix; and judging whether the change of the gas concentration value is caused by non-environmental factors or not through the environmental factor influence matrix. According to the method, the temperature and humidity space is divided into a plurality of areas, the temperature and humidity synergistic effect is accurately captured, and the influence of non-environmental factors is comprehensively judged in combination with time domain residual statistics, frequency domain principal component analysis and an environmental weight matrix.

Description

Technical Field

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

[0002] Gas detectors play an important role in industrial safety, environmental monitoring and other fields, but their detection accuracy is often affected by complex environmental factors (such as temperature and humidity fluctuations). The existing technology has the following shortcomings: Traditional gas detectors mostly use linear compensation models (such as fixed coefficient compensation), which makes it difficult to accurately characterize the nonlinear characteristics of temperature and humidity coupling. For example, in high temperature and high humidity environments, the cross-sensitivity effect of sensors is significant, resulting in increased compensation errors and higher false alarm rates.

[0003] Existing solutions usually judge concentration changes based on a single threshold or simple statistics (such as mean, variance), and cannot dynamically distinguish the impact of environmental factors (such as diurnal temperature drift) and non-environmental factors (such as gas leakage). For example, when the temperature and humidity fluctuate violently, environmental interference is mistakenly judged as a leakage event. Summary of the invention

[0004] In view of the problems existing in the existing intelligent gas detector environmental adaptability optimization method and system, the present invention is proposed.

[0005] Therefore, the problem to be solved by the present invention is that conventional gas detectors mostly use linear compensation models which are difficult to accurately characterize the nonlinear characteristics of the temperature and humidity coupling effect.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for optimizing environmental adaptability of an intelligent gas detector, which comprises the following steps: The gas concentration signal, temperature signal and humidity signal in the monitored environment are obtained in real time through the intelligent gas detector; Performing temperature-humidity coupling compensation on the gas concentration signal to obtain a calibrated gas concentration value; Calculating the correlation weights of the temperature signal, humidity signal and gas concentration value to generate an environmental factor impact matrix; Through the environmental factor impact matrix, it is determined whether the change in gas concentration value is caused by non-environmental factors.

[0007] As a preferred solution of the method for optimizing the environmental adaptability of the intelligent gas detector of the present invention, the method comprises the following steps: Preset multi-level concentration thresholds, compare the gas concentration value with the multi-level concentration thresholds, and generate an early warning signal based on the comparison results; The warning signal is matched with the corresponding control strategy and the operation is executed through the intelligent gas detector.

[0008] As a preferred solution of the method for optimizing the environmental adaptability of the intelligent gas detector of the present invention, the gas concentration value is obtained in the following manner: A temperature range interval and a humidity range interval are established respectively through the temperature signal and the humidity signal, and the temperature range interval and the humidity range interval are divided into a plurality of small areas respectively; The environmental status is monitored in real time, the membership of different small areas is calculated, and the top three small areas with the highest membership are selected for compensation result fusion to finally obtain the calibrated gas concentration value.

[0009] As a preferred solution of the method for optimizing the environmental adaptability of the intelligent gas detector of the present invention, the step of obtaining the environmental factor influence matrix includes: Define the time window length as L seconds, and generate a continuous historical data sequence stored in the window; Obtain temperature correlation calculation formula and humidity correlation calculation formula; Then the temperature correlation calculation formula and the humidity correlation calculation formula are processed, and finally the environmental factor influence matrix is ​​output.

[0010] As a preferred solution of the method for optimizing the environmental adaptability of the intelligent gas detector of the present invention, the continuous historical data sequence stored in the window includes: , 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: ; In the formula, and They are respectively represented as temperature correlation and humidity correlation, and L represents the length of the defined time window. , and The average values ​​of temperature, humidity and concentration in the window are represented respectively; The temperature correlation calculation formula and humidity correlation calculation formula are processed and expressed as: ; ; In the formula, and They are respectively expressed as the weight of temperature influence and the weight of humidity influence; The output environmental factor impact matrix is ​​expressed as: ; Where M is represented as a 1×2 matrix.

[0011] As a preferred solution of the method for optimizing the environmental adaptability of the intelligent gas detector of the present invention, when the change of the gas concentration value is caused by non-environmental factors, a warning signal is generated by comparing the results; Among them, the warning signals include level one warning, level two warning and level three warning; The judgment condition for the first-level warning is that the change gradient of the gas concentration value is within the first-level interval; The judgment condition for the second-level warning is that the change gradient of the gas concentration value is within the second-level interval; The judgment condition for the third-level warning is that the change gradient of the gas concentration value is within the third-level interval.

[0012] As a preferred solution of the method for optimizing the environmental adaptability of the intelligent gas detector of the present invention, the method for setting the first-level interval, the second-level interval and the third-level interval includes: Preset standard concentration value Q; 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]; The adjacent intervals of the first-level interval are output as the second-level intervals, that is, the second-level intervals are expressed as: [Q-α-β, Q-α] and [Q+α, Q+α+β]; 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].

[0013] 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 impact judgment module; The signal acquisition module is responsible for acquiring the gas concentration signal, temperature signal and humidity signal in the monitored environment in real time through the 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 impact matrix according to these weights; The environmental factor impact judgment module is used to judge whether the change in gas concentration value is caused by non-environmental factors.

[0014] In a third aspect, an embodiment of the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, any step of the above-mentioned intelligent gas detector environmental adaptability optimization method is implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for optimizing the environmental adaptability of an intelligent gas detector is implemented.

[0016] The beneficial effects of the present invention are: The temperature and humidity space is divided into multiple areas to accurately capture the synergistic effect of temperature and humidity. Experiments show that within a wide temperature range of -40°C to 85°C, the compensation error is ≤±3%FS, which is 40% higher than the traditional linear model. Through fuzzy calculation, the adjacent areas are matched in real time to eliminate the mutation error caused by hard boundaries, and the error is reduced by 32% in the humidity mutation (ΔH>20%RH / min) scenario.

[0017] The influence of non-environmental factors is comprehensively determined by combining time domain residual statistics, frequency domain principal component analysis and environmental weight matrix. For example, when the frequency domain amplitude ratio is >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 and adaptively adapts to environmental baseline drift to avoid false alarms during the day and night caused by fixed thresholds. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 Flowchart of the method for optimizing the environmental adaptability of smart gas detectors. DETAILED DESCRIPTION

[0019] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0020] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0022] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0023] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are 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 direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0024] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" 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, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. Example 1

[0025] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for optimizing the environmental adaptability of an intelligent gas detector, comprising the following steps: S1. Obtain the gas concentration signal, temperature signal and humidity signal in the monitored environment in real time through the intelligent gas detector.

[0026] In an optional embodiment, in a chlorine storage tank area of ​​a chemical plant, the environmental parameter range is: Temperature: -20℃ to 50℃ (extreme temperatures from winter to summer); Humidity: 20%RH to 90%RH (dry environment to steam environment); Target gas: chlorine (Cl2), safety threshold 1ppm (OSHA standard).

[0027] S2. Perform temperature-humidity coupling compensation on the gas concentration signal to obtain a calibrated gas concentration value.

[0028] Gas concentration values ​​can be obtained by: A temperature range interval and a humidity range interval are established respectively through the temperature signal and the humidity signal, and the temperature range interval and the humidity range interval are divided into a plurality of small areas respectively; For example, the two-dimensional space of temperature and humidity is divided into N small areas, each of which is represented as: , where R represents a set of small regions, i represents the i-th small region, and Represent the temperature signal and humidity signal of the i-th small area respectively.

[0029] Each small area corresponds to a dynamic domain compensation formula, which is expressed as: ; In the formula, and and are expressed as polynomial compensation coefficients in the i-th region, which are used to fit the linear and nonlinear effects of temperature and humidity on gas concentration; It is expressed as the amplitude coefficient of the exponential term, which controls the intensity of the temperature and humidity coupling effect; It is expressed as an exponential decay coefficient, which determines the influence range of the temperature and humidity product term (for example, the larger the λ, the faster the coupling effect decays with changes in temperature and humidity); and Respectively represent the calibration reference temperature and humidity; It is expressed as the calibrated gas concentration value in the i-th area; Expressed as raw concentration signal.

[0030] The environmental status is monitored in real time, the membership of different small areas is calculated, and the top three small areas with the highest membership are selected for compensation result fusion to finally obtain the calibrated gas concentration value.

[0031] Real-time detection of environmental status , calculate its membership to each region: ; In the formula, is the degree of membership of region i, is the centroid coordinate of region i, and All are shape factors; Select the top three regions of membership to fuse the compensation results: ; In the formula, is the calibrated gas concentration value.

[0032] In this embodiment, a situation experiment can be selected. First of all, it is necessary to know that the temperature range is -40℃ to 85℃, and the gradient test points include -40℃, -20℃, 0℃, 25℃ (benchmark), 50℃, 70℃, and 85℃; the humidity range is 10%RH to 90%RH, and the gradient test points include 10%, 30%, 50% (benchmark), 70%, and 90%RH. Therefore, there are 35 temperature and humidity combinations (7 temperature points × 5 humidity points).

[0033] The temperature and humidity space is divided into 9 areas (3×3 grid): the temperature zones are -40℃~15℃, 15℃~50℃, and 50℃~85℃; the humidity zones are 10%~40%RH, 40%~70%RH, and 70%~90%RH.

[0034] Taking the high temperature and high humidity area (50℃~85℃, 70%~90%RH) as an example, the compensation parameters after calibration are: =0.0023, =-0.00015, =1.2*10 -6 ; =0.0018, =-0.00008; =0.05; =0.012; =25℃, =50%RH.

[0035] Later, during the test, the detector was calibrated to output 100ppm (error ±0.5ppm) in a standard environment of 25°C and 50%RH; For the first scene of low temperature and dryness (-40℃, 10%RH), the original signal = 132.4ppm (significantly affected by low temperature), the matching area is the low temperature and low humidity area (membership 0.85) and the adjacent area (membership 0.12, 0.03), and the result after compensation is 100.8ppm, with an error of +0.8%FS.

[0036] For the second scenario of high temperature and high humidity (85°C, 90%RH), the original signal = 78.6ppm (high temperature and high humidity cause the sensor sensitivity to decrease), the matching area is the high temperature and high humidity area (membership 0.92) and the adjacent area (membership 0.06, 0.02). After compensation, the result is 99.2ppm, with an error of -0.8%FS.

[0037] For the sudden change of temperature and humidity in the third scenario (temperature rises suddenly from 25℃ to 70℃, and humidity drops from 50%RH to 20%RH), the original signal fluctuates between 85~115ppm. After dynamic fusion of membership and compensation, it stabilizes at 98~102ppm, with a maximum instantaneous error of ±2%FS.

[0038] Table 1: Experimental diagram of different test conditions

[0039] It can be seen from Table 1 that within the range of -40℃~85℃, the maximum error of this embodiment is ±2.4%FS, which is significantly better than the ±8.7%FS of the traditional linear model, and the error is reduced by 72.4%. In the extreme high temperature and high humidity scenario (85℃, 90%RH), the compensation error is only -0.8%FS, which is 94.7% higher than the traditional model (-15.2%FS).

[0040] The compensation parameters of each area are calibrated offline through the quantum particle swarm algorithm (QPSO), and the training data covers the full temperature and humidity range.

[0041] S3. Calculate the correlation weights of the temperature signal, humidity signal and gas concentration value to generate an environmental factor impact matrix.

[0042] The steps to obtain the environmental factor impact matrix include: Define the time window length as L seconds, and generate a continuous historical data sequence stored in the window; Among them, the continuous historical data sequence stored in the window includes , and , where T is the temperature sequence, H is the humidity sequence, and C is the gas concentration sequence; Obtain temperature correlation calculation formula and humidity correlation calculation formula; The temperature correlation calculation formula is expressed as: ; The humidity correlation calculation formula is expressed as: ; in, and They are respectively represented as temperature correlation and humidity correlation, and L represents the length of the defined time window. , and The average values ​​of temperature, humidity and concentration in the window are represented respectively; Then the temperature correlation calculation formula and the humidity correlation calculation formula are processed, and finally the environmental factor influence matrix is ​​output.

[0043] The temperature correlation calculation formula and humidity correlation calculation formula are processed and expressed as: ; ; In the formula, and They are respectively expressed as the weight of temperature influence and the weight of humidity influence; The output environmental factor impact matrix is ​​expressed as: ; Where M is represented as a 1×2 matrix.

[0044] S4. Determine whether the change in gas concentration value is caused by non-environmental factors through the environmental factor impact matrix.

[0045] When judging whether the change in gas concentration value is caused by non-environmental factors, the following steps are included: Preset multi-level concentration thresholds, compare the gas concentration value with the multi-level concentration thresholds, and generate an early warning signal based on the comparison results; The warning signal is matched with the corresponding control strategy and the operation is executed through the intelligent gas detector.

[0046] When the change in gas concentration value is caused by non-environmental factors, an early warning signal is generated by comparing the results; Among them, the warning signals include level one warning, level two warning and level three warning; The judgment condition for the first-level warning is that the change gradient of the gas concentration value is within the first-level interval; The judgment condition for the second-level warning is that the change gradient of the gas concentration value is within the second-level interval; The judgment condition for the third-level warning is that the change gradient of the gas concentration value is within the third-level interval.

[0047] The setting methods of the first-level interval, the second-level interval and the third-level interval include: Preset standard concentration value Q; The adjacent intervals of the standard concentration values ​​are output as the first-level intervals, that is, the first-level intervals are expressed as: (Q-α, Q+α); 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); 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].

[0048] 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]; 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.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] In summary, the temperature and humidity space is divided into multiple areas to accurately capture the synergistic effect of temperature and humidity. Experiments show that within a wide temperature range of -40°C to 85°C, the compensation error is ≤±3%FS, which is 40% higher than the traditional linear model. Through fuzzy calculation, the adjacent areas are matched in real time to eliminate the mutation error caused by hard boundaries, and the error is reduced by 32% in the humidity mutation (ΔH>20%RH / min) scenario.

[0053] The influence of non-environmental factors is comprehensively determined by combining time domain residual statistics, frequency domain principal component analysis and environmental weight matrix. For example, when the frequency domain amplitude ratio is >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 and adaptively adapts to environmental baseline drift to avoid false alarms during the day and night caused by fixed thresholds.

[0054] Example 2 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 impact judgment module; The signal acquisition module is responsible for acquiring the gas concentration signal, temperature signal and humidity signal in the monitored environment in real time through the 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 impact matrix according to these weights; The environmental factor impact judgment module is used to judge whether the change in gas concentration value is caused by non-environmental factors.

[0055] This embodiment also provides a computer device, which is suitable for the case of the environmental adaptability optimization method of the intelligent gas detector, including a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the environmental adaptability optimization method of the intelligent gas detector proposed in the above embodiment.

[0056] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. 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 a computer program. The internal memory provides an environment for the operation of the operating system and the computer program 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, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0057] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for optimizing the environmental adaptability of an intelligent gas detector as proposed in the above embodiment is implemented.

[0058] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the environmental adaptability of an intelligent gas detector, characterized in that: The following steps are included: The gas concentration signal, temperature signal and humidity signal in the monitored environment are obtained in real time through the intelligent gas detector; Performing temperature-humidity coupling compensation on the gas concentration signal to obtain a calibrated gas concentration value; Calculating the correlation weights of the temperature signal, humidity signal and gas concentration value to generate an environmental factor impact matrix; Through the environmental factor impact matrix, it is determined whether the change in gas concentration value is caused by non-environmental factors.

2. The method for optimizing the environmental adaptability of an intelligent gas detector according to claim 1, characterized in that: The method of determining whether the change in gas concentration is caused by non-environmental factors comprises the following steps: Preset multi-level concentration thresholds, compare the gas concentration value with the multi-level concentration thresholds, and generate an early warning signal based on the comparison results; The warning signal is matched with the corresponding control strategy and the operation is executed through the intelligent gas detector.

3. The method for optimizing the environmental adaptability of an intelligent gas detector according to claim 2, characterized in that: The gas concentration value is obtained by: A temperature range interval and a humidity range interval are established respectively through the temperature signal and the humidity signal, and the temperature range interval and the humidity range interval are divided into a plurality of small areas respectively; The environmental status is monitored in real time, the membership of different small areas is calculated, and the top three small areas with the highest membership are selected for compensation result fusion to finally obtain the calibrated gas concentration value.

4. The method for optimizing the environmental adaptability of an intelligent gas detector according to claim 3, characterized in that: The step of obtaining the environmental factor impact matrix includes: Define the time window length as L seconds, and generate a continuous historical data sequence stored in the window; Obtain temperature correlation calculation formula and humidity correlation calculation formula; Then the temperature correlation calculation formula and the humidity correlation calculation formula are processed, and finally the environmental factor influence matrix is ​​output.

5. The method for optimizing the environmental adaptability of an intelligent gas detector according to claim 4, characterized in that: The continuous historical data sequence stored in the window includes , and Among them, 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: ； In the formula, and They are respectively represented as temperature correlation and humidity correlation, and L represents the length of the defined time window. , and The average values ​​of temperature, humidity and concentration in the window are represented respectively; The temperature correlation calculation formula and humidity correlation calculation formula are processed and expressed as: ； ； In the formula, and They are respectively expressed as the weight of temperature influence and the weight of humidity influence; The output environmental factor impact matrix is ​​expressed as: ； Where M is represented as a 1×2 matrix.

6. The method for optimizing the environmental adaptability of an intelligent gas detector according to claim 5, characterized in that: When the change in gas concentration value is caused by non-environmental factors, an early warning signal is generated by comparing the results; Among them, the warning signals include level one warning, level two warning and level three warning; The judgment condition for the first-level warning is that the change gradient of the gas concentration value is within the first-level interval; The judgment condition for the second-level warning is that the change gradient of the gas concentration value is within the second-level interval; The judgment condition for the third-level warning is that the change gradient of the gas concentration value is within the third-level interval.

7. The method for optimizing the environmental adaptability of an intelligent gas detector according to claim 6, characterized in that: The method for setting the first-level interval, the second-level interval and the third-level interval includes: Preset standard concentration value Q; 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]; The adjacent intervals of the first-level interval are output as the second-level intervals, that is, the second-level intervals are expressed as: [Q-α-β, Q-α] and [Q+α, Q+α+β]; 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].

8. 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 7, characterized in that: It includes a signal acquisition module, a coupling compensation module, a matrix generation module, and an environmental factor impact judgment module; The signal acquisition module is responsible for acquiring the gas concentration signal, temperature signal and humidity signal in the monitored environment in real time through the 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 impact matrix according to these weights; The environmental factor impact judgment module is used to judge whether the change in gas concentration value is caused by non-environmental factors.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for optimizing the environmental adaptability of an intelligent gas detector as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the environmental adaptability of an intelligent gas detector according to any one of claims 1 to 7 are implemented.

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