A method and system for monitoring residential gas

Abstract

This invention discloses a method and system for monitoring residential gas emissions. The method includes collecting environmental data from a gas monitoring scenario and aligning the environmental data temporally based on the collection period; performing coupled calculation and analysis on multi-parameter environmental data and gas concentration data to obtain environmental interference parameters for the current gas monitoring scenario; correcting the parameters of the gas concentration sensor based on edge parameters; re-judging the hazard level of the gas monitoring scenario; and adjusting compensation parameters according to the re-judgment results until the hazard level reaches the target level. This invention achieves multi-parameter coupled analysis and real-time calculation through edge computing, performs multi-dimensional adaptive compensation based on the calculation results, actively reduces the hazard level by combining environmental linkage intervention, and constructs a complete closed loop to achieve iterative optimization of the model. This solves the problems of false alarms, missed alarms, and hazard level distortion caused by environmental interference, improving the accuracy, adaptability, and safety of residential gas monitoring.

Description

Technical Field

[0001] This invention relates to the field of gas safety monitoring technology, specifically to a residential gas monitoring method and system. Background Technology

[0002] Residential gas (natural gas, liquefied petroleum gas, etc.) is one of the main energy sources for residential life, but it is flammable, explosive and toxic. Once a leak occurs, it can easily cause safety accidents such as fire, explosion and poisoning, threatening the lives and property of residents.

[0003] Currently, the main method for managing residential gas leaks is by installing combustible gas alarms. These alarms use sensors to detect the gas concentration in the environment and compare it with a pre-set threshold. When the gas concentration reaches the threshold, an alarm is triggered to alert residents of a gas leak.

[0004] However, the detection accuracy of gas concentration sensors is easily affected by environmental parameters such as temperature, humidity, atmospheric pressure, and ventilation efficiency. For example, high temperature and high humidity environments can cause sensor response delays and decreased sensitivity. Fixed thresholds cannot adapt to changes in sensor performance under different environments, which can easily lead to false alarms or missed alarms. They have poor environmental adaptability. At the same time, focusing only on the single indicator of gas concentration without combining environmental parameters (such as ventilation efficiency and air quality) and equipment status (such as valve opening and closing and gas meter measurement) for comprehensive risk assessment makes it impossible to identify safety hazards such as minor leaks and hidden leaks. They also cannot dynamically adjust according to environmental changes, resulting in resource waste when the environment is stable and insufficient monitoring accuracy when the environment fluctuates. Summary of the Invention

[0005] Based on this, the purpose of this invention is to provide a residential gas monitoring method and system, which aims to solve the problems of poor environmental adaptability and insufficient monitoring accuracy of current gas monitoring methods due to their single monitoring dimension.

[0006] To achieve the above objectives, the present invention proposes a residential gas monitoring method, the residential gas monitoring method comprising: Collect environmental data for gas monitoring scenarios, and perform time-series alignment of the environmental data based on the collection period. The environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data. The environmental multi-parameter data and the gas concentration data are coupled and calculated to obtain the environmental interference parameters under the current gas monitoring scenario. Based on the edge parameters, the parameters of the gas concentration sensor are corrected, the hazard level of the gas monitoring scenario is re-judged, and the compensation parameters are adjusted according to the re-judgment results until the hazard level reaches the target level.

[0007] According to one aspect of the above technical solution, in the step of collecting environmental data for a gas monitoring scenario and performing time-series alignment of the environmental data based on the collection period, wherein the environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data: The environmental multi-parameter data includes at least the current monitored environment's temperature, humidity, air pressure, ventilation status, and air quality; The device status data includes sensor lifespan, power supply data, and communication quality.

[0008] According to one aspect of the above technical solution, the step of performing coupled calculation and analysis on the environmental multi-parameter data and the gas concentration data to obtain the environmental interference parameters under the current gas monitoring scenario is as follows: The environmental multi-parameter data is mapped to the [0,1] interval to construct a multi-dimensional environmental feature vector, and the gas concentration data and equipment status data are normalized. The pre-built edge computing model is trained using normalized data from the current gas monitoring scenario to obtain the interference weights of each parameter in the environmental multi-parameter data. The environmental interference coefficient is then calculated by combining the interference weights with the environmental multi-parameter data.

[0009] in, This represents the environmental interference coefficient. , , , , These are the normalized values ​​for temperature, humidity, atmospheric pressure, ventilation efficiency, and air quality, respectively. , , , , The interference weights for each parameter are obtained after offline training of the edge computing model using historical data.

[0010] According to one aspect of the above technical solution, in the step of correcting the parameters of the gas concentration sensor based on the edge parameters: After obtaining the environmental interference coefficient, the gas concentration data is corrected using the environmental interference coefficient to eliminate environmental influences and obtain the true gas concentration.

[0011] in, To compensate for the actual concentration of the gas, For gas concentration data, and This is the compensation coefficient in the edge computing model.

[0012] According to one aspect of the above technical solution, after obtaining the actual gas concentration data under the current gas monitoring scenario, the initial danger level under the current gas monitoring scenario is obtained by combining the environmental interference coefficient and equipment status data. Based on the initial hazard level in the current gas monitoring scenario, the system performs concentration detection value compensation, alarm threshold adaptive adjustment, and sampling frequency and filtering adaptive compensation. If the initial hazard level reaches the warning level, environmental linkage compensation is triggered.

[0013] According to one aspect of the above technical solution, the compensated gas concentration data is obtained, the hazard level is reassessed, and the compensation parameters are adjusted according to the reassessment results until the hazard level reaches the target level.

[0014] The present invention also proposes a residential gas monitoring system, which is used to implement the above-mentioned residential gas monitoring method, the system comprising: The data acquisition module is used to acquire environmental data for gas monitoring scenarios and perform time-series alignment of the environmental data based on the acquisition period. The environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data. The analysis module is used to perform coupled calculation and analysis on the environmental multi-parameter data and the gas concentration data to obtain the environmental interference parameters under the current gas monitoring scenario; The compensation module is used to correct the parameters of the gas concentration sensor based on the edge parameters, re-determine the hazard level of the gas monitoring scenario, and adjust the compensation parameters according to the re-determination result until the hazard level reaches the target level.

[0015] The present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the residential gas monitoring method described above.

[0016] The present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the residential gas monitoring method as described above.

[0017] In summary, the residential gas monitoring method proposed in this invention achieves adaptive adjustment of gas monitoring parameters and proactive reduction of hazard levels through multi-parameter acquisition, edge computing analysis, and hazard level determination of the acquired parameters. Simultaneously, adaptive compensation is implemented, followed by hazard level reassessment. This complete process forms a closed-loop system, thoroughly resolving issues such as hazard level misjudgment and insufficient monitoring accuracy caused by environmental interference, ensuring low false alarms, high reliability, and adaptability in residential gas monitoring. By integrating multi-environmental parameter coupling calculations and employing triple software compensation (concentration calibration, threshold adjustment, and filter compensation) combined with hardware compensation linked to ventilation, the method achieves accurate reduction of false hazard levels, eliminating false alarms and missed alarms caused by environmental interference. Based on real-time risk data from edge computing, monitoring parameters are dynamically adjusted, improving adaptability. This invention achieves multi-parameter coupling analysis and real-time calculation through edge computing, performs multi-dimensional adaptive compensation based on the calculation results, proactively reduces hazard levels through environmental linkage intervention, and constructs a complete closed loop for model iterative optimization. This solves the problems of false alarms, missed alarms, and hazard level distortion caused by environmental interference, improving the accuracy, adaptability, and safety of residential gas monitoring.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] Figure 1 This is a flowchart of the residential gas monitoring method in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the residential gas monitoring system in Embodiment 2 of the present invention; Figure 3 This is a structural block diagram of the electronic device in Embodiment 4 of the present invention. Detailed Implementation

[0020] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that the disclosure of the present invention will be more thorough and complete.

[0021] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly on the other element or there may be an intervening element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," "upper," "lower," and similar expressions used herein are for illustrative purposes only and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0022] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. The term "and / or" as used herein includes any and all combinations of one or more of the related listed items.

[0023] Example 1 Please see Figure 1 Figure 1 shows a flowchart of a residential gas monitoring method according to Embodiment 1 of the present invention. The residential gas monitoring method includes the following steps S01-S03, wherein: S01. Collect environmental data for the gas monitoring scenario, and perform time-series alignment of the environmental data based on the collection period. The environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data. S02. Perform coupled calculation and analysis on the environmental multi-parameter data and the gas concentration data to obtain the environmental interference parameters under the current gas monitoring scenario; S03. Based on the edge parameters, the parameters of the gas concentration sensor are corrected, the hazard level of the gas monitoring scenario is re-judged, and the compensation parameters are adjusted according to the re-judgment result until the hazard level reaches the target level.

[0024] It collects gas concentration data (raw gas concentration data), environmental multi-parameter data, and equipment status data in the gas monitoring scenario. The environmental multi-parameter data includes the current monitoring environment's temperature, humidity, air pressure, ventilation status, and air quality. The equipment status data includes sensor lifespan, power supply data, and communication quality.

[0025] The gas concentration data (raw gas concentration data), environmental multi-parameter data, and equipment status data are timestamped and aligned.

[0026] Data such as temperature, humidity, air pressure, ventilation status, and air quality are mapped to the [0,1] interval to avoid the influence of dimensions.

[0027]

[0028]

[0029]

[0030] in, , , , These are the normalized values ​​of temperature, humidity, atmospheric pressure, and ventilation efficiency, respectively. Based on these, a multidimensional environmental feature vector is constructed: .

[0031] The pre-built edge computing model is trained using normalized data from the current gas monitoring scenario to obtain the interference weights of each parameter in the environmental multi-parameter data. The environmental interference coefficient is then calculated by combining the interference weights with the environmental multi-parameter data.

[0032] in, This represents the environmental interference coefficient. , , , , These are the normalized values ​​for temperature, humidity, atmospheric pressure, ventilation efficiency, and air quality, respectively. , , , , To obtain the interference weights for each parameter after offline training of the edge computing model using historical data, The value range is [0,1]. The larger the value, the more severe the environment of the current gas monitoring scenario and the lower the accuracy of the gas concentration sensor.

[0033] After obtaining the environmental interference coefficient, the gas concentration data is first corrected using the environmental interference coefficient to eliminate environmental influencing factors and obtain the true gas concentration.

[0034]

[0035]

[0036] in, To determine the true concentration of the calibrated fuel gas, For gas concentration data, For environmental gain coefficient, This is the environmental offset coefficient. and The compensation coefficients learned by the edge computing model.

[0037] After obtaining the actual gas concentration data under the current gas monitoring scenario, the initial hazard level under the current gas monitoring scenario is obtained by combining the environmental interference coefficient and equipment status data:

[0038]

[0039] in, To assess the overall risk level, ∈[0,1], This is the value after normalizing the actual gas concentration. This is the hazard amplification factor; the more severe the environment, the higher the hazard amplification factor. This refers to the equipment health coefficient.

[0040] In this embodiment, when ∈[0,0.25), indicating that the initial danger level of the current gas monitoring scenario is the safety level; when When ∈ [0.25, 0.5), it indicates that the initial danger level of the current gas monitoring scenario is the warning level; when When ∈ [0.5, 0.8), it indicates that the initial hazard level of the current gas monitoring scenario is Level 1 hazard; when When ∈[0.8,1], it indicates that the initial danger level of the current gas monitoring scenario is level two.

[0041] Based on the initial level of danger in the current gas monitoring scenario, the following functions are implemented: concentration detection value compensation, alarm threshold adaptive adjustment, and sampling frequency and filtering adaptive compensation.

[0042] The concentration detection value compensation process is as follows:

[0043] in, To compensate for the gas concentration, and The fixed compensation coefficients are obtained by training the edge computing model. This is a normalized value for ventilation efficiency. It should be noted that the rule for compensating for concentration detection values ​​is as follows: when... ≥0.3, and When the value is ≥0.5, concentration detection value compensation is triggered.

[0044] The alarm threshold adaptive adjustment process is as follows:

[0045] in, To adapt the alarm threshold, To meet the basic alarm threshold of the national standard, For environmental stability coefficient, This is the threshold adjustment coefficient.

[0046] The alarm threshold adaptive adjustment rule is as follows: when ≥0.7 and ≥5m 3 / h, meaning the current gas monitoring scenario is in a stable and well-ventilated environment, if ≥ Increase the alarm threshold; when <0.3 and <5m 3 / h, meaning the current gas monitoring scenario is in a harsh environment with poor ventilation, if < Lower the alarm threshold.

[0047] The adaptive compensation process for filtering is as follows: The filtered gas concentration can be calculated using the first-order inertial filter formula:

[0048] in, To determine the concentration of the filtered fuel gas, These are the filter coefficients. To compensate for the gas concentration, This represents the historical filtered gas concentration.

[0049] The rules for selecting the filter coefficients are as follows: when When <0.25, For values ​​∈ [0.2, 0.3], strong filtering is used; When 0.25≤ When <0.5, For values ​​∈ [0.4, 0.5], use medium filtering; when When ≥0.5, ∈[0.7, 0., 8], weak filtering is used.

[0050] At the same time, when The higher the value, the higher the sampling frequency.

[0051] Furthermore, when the initial danger level reaches the warning level, environmental linkage compensation is triggered. Specifically, this can be done by turning on the exhaust equipment to improve ventilation efficiency, and calculating the environmental interference coefficient and comprehensive danger value of the gas monitoring scenario after the linkage compensation is activated, thereby further reducing the danger level. The execution time of the environmental linkage compensation can be preset, and the positive monitoring mode can be restored after the execution is completed.

[0052] Furthermore, if the initial hazard level is Level 1 hazard level, and after reassessment it becomes a warning level or a safety level, then the linkage operations corresponding to Level 1 hazard level (such as closing gas valves and starting ventilation equipment) will be shut down, and the response measures corresponding to the warning level or safety level will be implemented.

[0053] If the initial hazard level is level two, and after reassessment it becomes level one, a warning level, or a safety level, then the linkage operation corresponding to level two hazard level (such as starting up intelligent security equipment) will be shut down, and the corresponding response measures will be executed.

[0054] If the risk level is lower than the initial risk level after reassessment for N consecutive monitoring cycles, N can be set to 5, and the risk level remains stable at the safety / warning level. In this case, the current compensation parameter is locked and compensation adjustment is stopped.

[0055] In addition, the data obtained from the compensation and risk reduction processes can be used for iterative updates of the edge computing model to continuously optimize compensation parameters and calculation logic.

[0056] In summary, the residential gas monitoring method proposed in this invention achieves adaptive adjustment of gas monitoring parameters and proactive reduction of hazard levels through multi-parameter acquisition, edge computing analysis, and hazard level determination of the acquired parameters. Simultaneously, adaptive compensation is implemented, followed by hazard level reassessment. This complete process forms a closed-loop system, thoroughly resolving issues such as hazard level misjudgment and insufficient monitoring accuracy caused by environmental interference, ensuring low false alarms, high reliability, and adaptability in residential gas monitoring. By integrating multi-environmental parameter coupling calculations and employing triple software compensation (concentration calibration, threshold adjustment, and filter compensation) combined with hardware compensation linked to ventilation, the method achieves accurate reduction of false hazard levels, eliminating false alarms and missed alarms caused by environmental interference. Based on real-time risk data from edge computing, monitoring parameters are dynamically adjusted, improving adaptability. This invention achieves multi-parameter coupling analysis and real-time calculation through edge computing, performs multi-dimensional adaptive compensation based on the calculation results, proactively reduces hazard levels through environmental linkage intervention, and constructs a complete closed loop for model iterative optimization. This solves the problems of false alarms, missed alarms, and hazard level distortion caused by environmental interference, improving the accuracy, adaptability, and safety of residential gas monitoring.

[0057] Example 2 In another aspect, this invention also provides a residential gas monitoring system, please refer to [link / reference needed]. Figure 2The diagram shown is a structural schematic of the residential gas monitoring system in Embodiment 2 of the present invention. The residential gas monitoring system includes: The data acquisition module is used to acquire environmental data for gas monitoring scenarios and perform time-series alignment of the environmental data based on the acquisition period. The environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data. The analysis module is used to perform coupled calculation and analysis on the environmental multi-parameter data and the gas concentration data to obtain the environmental interference parameters under the current gas monitoring scenario; The compensation module is used to correct the parameters of the gas concentration sensor based on the edge parameters, re-determine the hazard level of the gas monitoring scenario, and adjust the compensation parameters according to the re-determination result until the hazard level reaches the target level.

[0058] It collects gas concentration data (raw gas concentration data), environmental multi-parameter data, and equipment status data in the gas monitoring scenario. The environmental multi-parameter data includes the current monitoring environment's temperature, humidity, air pressure, ventilation status, and air quality. The equipment status data includes sensor lifespan, power supply data, and communication quality.

[0059] The gas concentration data (raw gas concentration data), environmental multi-parameter data, and equipment status data are timestamped and aligned.

[0060] Data such as temperature, humidity, air pressure, ventilation status, and air quality are mapped to the [0,1] interval to avoid the influence of dimensions.

[0061]

[0062]

[0063]

[0064] in, , , , These are the normalized values ​​of temperature, humidity, atmospheric pressure, and ventilation efficiency, respectively. Based on these, a multidimensional environmental feature vector is constructed: .

[0065] The pre-built edge computing model is trained using normalized data from the current gas monitoring scenario to obtain the interference weights of each parameter in the environmental multi-parameter data. The environmental interference coefficient is then calculated by combining the interference weights with the environmental multi-parameter data.

[0066] in, This represents the environmental interference coefficient. , , , , These are the normalized values ​​for temperature, humidity, atmospheric pressure, ventilation efficiency, and air quality, respectively. , , , , To obtain the interference weights for each parameter after offline training of the edge computing model using historical data, The value range is [0,1]. The larger the value, the more severe the environment of the current gas monitoring scenario and the lower the accuracy of the gas concentration sensor.

[0067] After obtaining the environmental interference coefficient, the gas concentration data is first corrected using the environmental interference coefficient to eliminate environmental influencing factors and obtain the true gas concentration.

[0068]

[0069]

[0070] in, To determine the true concentration of the calibrated fuel gas, For gas concentration data, For environmental gain coefficient, This is the environmental offset coefficient. and The compensation coefficients learned by the edge computing model.

[0071] After obtaining the actual gas concentration data under the current gas monitoring scenario, the initial hazard level under the current gas monitoring scenario is obtained by combining the environmental interference coefficient and equipment status data:

[0072]

[0073] in, To assess the overall risk level, ∈[0,1], This is the value after normalizing the actual gas concentration. This is the hazard amplification factor; the more severe the environment, the higher the hazard amplification factor. This refers to the equipment health coefficient.

[0074] In this embodiment, when ∈[0,0.25), indicating that the initial danger level of the current gas monitoring scenario is the safety level; when When ∈ [0.25, 0.5), it indicates that the initial danger level of the current gas monitoring scenario is the warning level; when When ∈ [0.5, 0.8), it indicates that the initial hazard level of the current gas monitoring scenario is Level 1 hazard; when When ∈[0.8,1], it indicates that the initial danger level of the current gas monitoring scenario is level two.

[0075] Based on the initial level of danger in the current gas monitoring scenario, the following functions are implemented: concentration detection value compensation, alarm threshold adaptive adjustment, and sampling frequency and filtering adaptive compensation.

[0076] The concentration detection value compensation process is as follows:

[0077] in, To compensate for the gas concentration, and The fixed compensation coefficients are obtained by training the edge computing model. This is a normalized value for ventilation efficiency. It should be noted that the rule for compensating for concentration detection values ​​is as follows: when... ≥0.3, and When the value is ≥0.5, concentration detection value compensation is triggered.

[0078] The alarm threshold adaptive adjustment process is as follows:

[0079] in, To adapt the alarm threshold, To meet the basic alarm threshold of the national standard, For environmental stability coefficient, This is the threshold adjustment coefficient.

[0080] The alarm threshold adaptive adjustment rule is as follows: when ≥0.7 and ≥5m 3 / h, meaning the current gas monitoring scenario is in a stable and well-ventilated environment, if ≥ Increase the alarm threshold; when <0.3 and <5m 3 / h, meaning the current gas monitoring scenario is in a harsh environment with poor ventilation, if < Lower the alarm threshold.

[0081] The adaptive compensation process for filtering is as follows: The filtered gas concentration can be calculated using the first-order inertial filter formula:

[0082] in, To determine the concentration of the filtered fuel gas, These are the filter coefficients. To compensate for the gas concentration, This represents the historical filtered gas concentration.

[0083] The rules for selecting the filter coefficients are as follows: when When <0.25, For values ​​∈ [0.2, 0.3], strong filtering is used; When 0.25≤ When <0.5, For values ​​∈ [0.4, 0.5], use medium filtering; when When ≥0.5, ∈[0.7, 0., 8], weak filtering is used.

[0084] At the same time, when The higher the value, the higher the sampling frequency.

[0085] Furthermore, when the initial danger level reaches the warning level, environmental linkage compensation is triggered. Specifically, this can be done by turning on the exhaust equipment to improve ventilation efficiency, and calculating the environmental interference coefficient and comprehensive danger value of the gas monitoring scenario after the linkage compensation is activated, thereby further reducing the danger level. The execution time of the environmental linkage compensation can be preset, and the positive monitoring mode can be restored after the execution is completed.

[0086] Furthermore, if the initial hazard level is Level 1 hazard level, and after reassessment it becomes a warning level or a safety level, then the linkage operations corresponding to Level 1 hazard level (such as closing gas valves and starting ventilation equipment) will be shut down, and the response measures corresponding to the warning level or safety level will be implemented.

[0087] If the initial hazard level is level two, and after reassessment it becomes level one, a warning level, or a safety level, then the linkage operation corresponding to level two hazard level (such as starting up intelligent security equipment) will be shut down, and the corresponding response measures will be executed.

[0088] If the risk level is lower than the initial risk level after reassessment for N consecutive monitoring cycles, N can be set to 5, and the risk level remains stable at the safety / warning level. In this case, the current compensation parameter is locked and compensation adjustment is stopped.

[0089] In addition, the data obtained from the compensation and risk reduction processes can be used for iterative updates of the edge computing model to continuously optimize compensation parameters and calculation logic.

[0090] In summary, the residential gas monitoring system proposed in this invention achieves adaptive adjustment of gas monitoring parameters and proactive reduction of hazard levels through multi-parameter acquisition, edge computing analysis, and hazard level determination of the acquired parameters. It also performs adaptive compensation and hazard level reassessment after compensation, forming a closed-loop system that thoroughly solves problems such as hazard level misjudgment and insufficient monitoring accuracy caused by environmental interference, ensuring low false alarms, high reliability, and adaptability in residential gas monitoring. By integrating multi-environmental parameter coupling calculations and employing triple software compensation (concentration calibration, threshold adjustment, and filter compensation) combined with hardware compensation linked to ventilation, it achieves accurate reduction of false hazard levels, eliminating false alarms and missed alarms caused by environmental interference. Based on real-time risk data from edge computing, it dynamically adjusts monitoring parameters, improving adaptability. This invention achieves multi-parameter coupling analysis and real-time calculation through edge computing, performs multi-dimensional adaptive compensation based on the calculation results, proactively reduces hazard levels through environmental linkage intervention, and constructs a complete closed loop to achieve iterative model optimization. This solves the problems of false alarms, missed alarms, and hazard level distortion caused by environmental interference, improving the accuracy, adaptability, and safety of residential gas monitoring.

[0091] Example 3 In another aspect, the present invention also proposes a computer-readable storage medium having stored thereon one or more computer programs that, when executed by a processor, implement the aforementioned residential gas monitoring method.

[0092] Those skilled in the art will understand that the logic or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable storage medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0093] More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable storage media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0094] Example 4 Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 4. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the residential gas monitoring method described in the above embodiments. Figure 3 The electronic device 30 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0095] like Figure 3 As shown, the electronic device 30 can be manifested as a general-purpose computing device, such as a server device. The components of the electronic device 30 may include, but are not limited to: at least one processor 31, at least one memory 32, and a bus 33 connecting different system components (including memory 32 and processor 31).

[0096] Bus 33 includes a data bus, an address bus, and a control bus.

[0097] The memory 32 may include volatile memory, such as RAM 321 (random access memory), and / or cache memory 322, and may further include ROM 323 (read-only memory).

[0098] The memory 32 may also include a program tool 325 having a set (at least one) of program modules 324, including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0099] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32, such as the residential gas monitoring method of the present invention as described above.

[0100] Electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). This communication can be performed via I / O interface 35 (input / output interface). Furthermore, electronic device 30 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public network, such as the Internet) via network adapter 36. Figure 3 As shown, network adapter 36 communicates with other modules of the model-generated electronic device 30 via bus 33. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the model-generated electronic device 30, including but not limited to: microcode, device drivers, redundant processors, disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems.

[0101] It should be noted that although several units / modules or sub-units / modules of the electronic device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0102] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0103] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method of monitoring residential gas, characterized by, The residential gas monitoring method includes: Collect environmental data for gas monitoring scenarios, and perform time-series alignment of the environmental data based on the collection period. The environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data. The environmental multi-parameter data and the gas concentration data are coupled and calculated to obtain the environmental interference parameters under the current gas monitoring scenario. Based on the edge parameters, the parameters of the gas concentration sensor are corrected, the hazard level of the gas monitoring scenario is re-judged, and the compensation parameters are adjusted according to the re-judgment results until the hazard level reaches the target level.

2. The method of claim 1, wherein, The step of collecting environmental data for the gas monitoring scenario and aligning the environmental data according to the collection period, wherein the environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data, is as follows: The environmental multi-parameter data includes at least the current monitored environment's temperature, humidity, air pressure, ventilation status, and air quality; The device status data includes sensor lifespan, power supply data, and communication quality.

3. The method of claim 1, wherein, The step of performing coupled calculation and analysis on the environmental multi-parameter data and the gas concentration data to obtain the environmental interference parameters under the current gas monitoring scenario is as follows: The environmental multi-parameter data is mapped to the [0,1] interval to construct a multi-dimensional environmental feature vector, and the gas concentration data and equipment status data are normalized. The pre-built edge computing model is trained using normalized data from the current gas monitoring scenario to obtain the interference weights of each parameter in the environmental multi-parameter data. The environmental interference coefficient is then calculated by combining the interference weights with the environmental multi-parameter data. in, This represents the environmental interference coefficient. , , , , These are the normalized values ​​for temperature, humidity, atmospheric pressure, ventilation efficiency, and air quality, respectively. , , , , The interference weights for each parameter are obtained after offline training of the edge computing model using historical data.

4. The method of claim 1, wherein, In the step of correcting the parameters of the gas concentration sensor based on the edge parameters: After obtaining the environmental interference coefficient, the gas concentration data is corrected using the environmental interference coefficient to eliminate environmental influences and obtain the true gas concentration. in, To compensate for the actual concentration of the gas, For gas concentration data, and This is the compensation coefficient in the edge computing model.

5. The method of resident gas monitoring of claim 5, wherein, After obtaining the actual gas concentration data under the current gas monitoring scenario, the initial hazard level under the current gas monitoring scenario is obtained by combining the environmental interference coefficient and equipment status data. Based on the initial hazard level in the current gas monitoring scenario, the system performs concentration detection value compensation, alarm threshold adaptive adjustment, and sampling frequency and filtering adaptive compensation. If the initial hazard level reaches the warning level, environmental linkage compensation is triggered.

6. The method of resident gas monitoring of claim 5, wherein, Obtain the compensated gas concentration data, reassess the hazard level, and adjust the compensation parameters based on the reassessment results until the hazard level reaches the target level.

7. A residential gas monitoring system characterized by, The residential gas monitoring system is used to implement the residential gas monitoring method according to any one of claims 1-6, and the system includes: The data acquisition module is used to acquire environmental data for gas monitoring scenarios and perform time-series alignment of the environmental data based on the acquisition period. The environmental data includes at least gas concentration data, environmental multi-parameter data, and equipment status data. The analysis module is used to perform coupled calculation and analysis on the environmental multi-parameter data and the gas concentration data to obtain the environmental interference parameters under the current gas monitoring scenario; A compensation module is configured to correct parameters of the gas concentration sensor based on the edge parameters, rejudge a danger level of the gas monitoring scene, and adjust compensation parameters according to a rejudging result until the danger level reaches a target level.

8. A computer readable storage medium having stored thereon a computer program, characterized in that, The program, when executed by the processor, implements the resident gas monitoring method according to any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor, when executing the computer program, implements the resident gas monitoring method according to any one of claims 1-6.

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