Methods, devices, systems, and electronic devices for greenhouse gas detection
By introducing human simulation technology and neural network models, the adaptability problem of greenhouse gas detection using the gas detector tube method under changing environments has been solved, achieving efficient and low-energy greenhouse gas detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- TERMINUSBEIJING TECH CO LTD
- Filing Date
- 2022-04-25
- Publication Date
- 2026-06-02
Smart Images
Figure CN114813598B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of greenhouse gas detection, and more particularly to the technical field of greenhouse gas detection using gas detection tubes. Background Technology
[0002] There are many methods for detecting greenhouse gases, such as semiconductor sensor detection, spectrometry, chemical analysis, electrochemical methods, and gas detection tube methods. Different methods are suitable for different environments and can solve some basic problems in the detection of greenhouse gases in the environment.
[0003] Taking the gas detection tube method as an example, it mainly involves delivering pretreated gas into the measurement tube and measuring the gas concentration by using an optical probe (infrared or ultraviolet probe) installed at the pipe port.
[0004] However, due to the changing environment, the simple gas detection tube method is difficult to adapt to the changing environment and to detect the environment in a timely manner. Summary of the Invention
[0005] This disclosure provides a method, apparatus, system, and electronic device for greenhouse gas detection, enabling timely detection of greenhouse gases.
[0006] According to a first aspect of this disclosure, a system for detecting greenhouse gases is provided. The system includes a first detection unit, a second detection unit, and a comprehensive processing unit;
[0007] The first detection unit includes a simulation device and a first controller. The simulation device is configured to monitor human simulated sensory data within the monitoring area. The first controller is configured to send a start command to the second detection unit when the change in the sensory data within a unit time exceeds a threshold.
[0008] The second detection unit includes a detection device and a second controller. The second controller is configured to, upon receiving the start command, control the detection device to detect the greenhouse gas concentration in the monitoring area and generate detection data. The second controller is also configured to send the detection data to the integrated processing unit.
[0009] The integrated processing unit includes a reporting module configured to generate a greenhouse gas detection report based on the detection data.
[0010] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the detection data includes greenhouse gas content data and / or spectral detection data.
[0011] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the first controller is also configured to send the sensory data to the integrated processing unit;
[0012] The integrated processing unit is also configured to label the sensory data based on the detection data to generate training samples, so as to train a pre-defined neural network model for greenhouse gases based on the training samples.
[0013] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the integrated processing unit further includes a prediction module, the prediction module being configured to input the sensory data sent by the first detection unit into a pre-trained greenhouse gas preset neural network model to obtain corresponding detection data.
[0014] In addition to the aspects and any possible implementations described above, a further implementation is provided in which the simulation device includes a simulated respiratory system and simulated sensors.
[0015] The simulated breathing system includes an air intake channel, and the simulated sensor is mounted on the inner wall of the air intake channel.
[0016] The simulation sensor includes at least three of the following: a temperature sensor, a humidity sensor, a pH sensor, and an air quality sensor, all of which have human body simulation capabilities.
[0017] According to a second aspect of this disclosure, a method for detecting greenhouse gases is provided, characterized in that it includes:
[0018] The sensory data of the human simulation within the monitoring area are invoked by the first detection unit;
[0019] When the change in the sensory data exceeds a threshold within a unit of time, the second detection unit is invoked to detect the greenhouse gas concentration within the monitored area and generate detection data.
[0020] A greenhouse gas detection report is generated based on the detection data.
[0021] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:
[0022] The sensory data is labeled based on the detection data to generate training samples, and a pre-defined neural network model for greenhouse gases is trained based on the training samples.
[0023] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:
[0024] The sensory data sent by the first detection unit is input into a pre-trained greenhouse gas preset neural network model to obtain the corresponding detection data.
[0025] According to a third aspect of this disclosure, an apparatus for detecting greenhouse gases is provided, comprising:
[0026] The calling unit is used to call the sensory data of the human simulation in the monitoring area of the first detection unit;
[0027] The processing unit is used to call the second detection unit to detect the greenhouse gas concentration in the monitored area and generate detection data when the change in the sensory data exceeds a threshold within a unit of time.
[0028] The reporting unit is used to generate a greenhouse gas detection report based on the detection data.
[0029] According to a fourth aspect of this disclosure, an electronic device is provided. The electronic device includes: at least one processor and a memory communicatively connected to said at least one processor; wherein the memory stores instructions executable by said at least one processor, the instructions being executed by said at least one processor to enable said at least one processor to perform the method of the second aspect of this disclosure.
[0030] The disclosed scheme, based on the gas detector tube method for detecting greenhouse gas content, introduces human simulation technology. The gas detection frequency is adjusted based on the sensory data of human simulation, so that the generated detection data is more relevant to the human production and living environment, which facilitates the control of greenhouse gas emissions. At the same time, this detection method improves the adaptability of greenhouse gas detection using the gas detector tube method to changing environments.
[0031] It should be understood that the description in the Summary of the Invention is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description
[0032] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:
[0033] Figure 1 A schematic diagram of a greenhouse gas detection system of this disclosure is shown;
[0034] Figure 2 A flowchart of the method for detecting greenhouse gases disclosed herein is shown;
[0035] Figure 3 A flowchart illustrating a method for generating greenhouse gas detection reports based on detection data is shown.
[0036] Figure 4 A block diagram of an electronic device for implementing the greenhouse gas detection method according to embodiments of the present disclosure is shown. Detailed Implementation
[0037] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.
[0038] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0039] In this disclosure, based on the gas detector tube method for detecting greenhouse gas content, human simulation technology is introduced. The gas detection frequency is adjusted based on the sensory data of human simulation. The generated detection data is more relevant to the human production and living environment, which facilitates the control of greenhouse gas emissions. At the same time, this detection method improves the adaptability of greenhouse gas detection using the gas detector tube method to changing environments.
[0040] Figure 1 A schematic diagram is shown of the greenhouse gas monitoring system 100 of this disclosure and the interaction between the first detection unit 110, the second detection unit 120 and the integrated processing unit 130 in the system 100.
[0041] The first detection unit 110 includes a simulation device 111 and a first controller 112. The simulation device 111 is configured to monitor human simulated sensory data within the monitoring area. The first controller 112 is configured to send a start command to the second detection unit 120 when the change in the sensory data within a unit time exceeds a threshold.
[0042] The second detection unit 120 includes a detection device 121 and a second controller 122. The second controller 122 is configured to control the detection device 121 to detect the greenhouse gas concentration in the monitoring area and generate detection data after receiving the start command. The second controller 122 is also configured to send the detection data to the integrated processing unit 130.
[0043] The integrated processing unit includes a reporting module 132, which is configured to generate a greenhouse gas detection report based on the detection data.
[0044] The system 100 in this embodiment, based on the gas detector tube method for detecting greenhouse gas content, introduces human simulation technology. The gas detection frequency is adjusted based on the sensory data of human simulation. The generated detection data is more relevant to the human production and living environment, which facilitates the control of greenhouse gas emissions. At the same time, this detection method improves the adaptability of greenhouse gas detection using the gas detector tube method to changing environments.
[0045] In this embodiment, the detection data includes greenhouse gas content data and / or spectral detection data.
[0046] In this embodiment, the first controller is further configured to send the sensory data to the integrated processing unit;
[0047] The integrated processing unit is also configured to label the sensory data based on the detection data to generate training samples, so as to train a pre-defined neural network model for greenhouse gases based on the training samples.
[0048] According to this embodiment, a neural network is used to perform deep learning on the correspondence between sensory data and detection data, and a mathematical model corresponding to sensory data and detection data is established. When the change in sensory data within a unit of time exceeds a threshold, the corresponding detection data can be directly calculated from the current sensory data and used as empirical data to generate a greenhouse gas detection report. This can effectively reduce the number of times the detection device 121 is started, improve the working efficiency of greenhouse gas detection, and reduce energy consumption.
[0049] In this embodiment, the integrated processing unit further includes a prediction module, which is configured to input the sensory data sent by the first detection unit into a pre-trained greenhouse gas preset neural network model to obtain corresponding detection data.
[0050] In this embodiment, the simulation device includes a simulated respiratory system and simulated sensors.
[0051] The simulated breathing system includes an air intake channel, and the simulated sensor is mounted on the inner wall of the air intake channel.
[0052] The simulation sensor includes at least three of the following: a temperature sensor, a humidity sensor, a pH sensor, and an air quality sensor, all of which have human body simulation capabilities.
[0053] The simulation device 111 in this embodiment simulates the human respiratory system 100 and sets up simulation sensors on the inner wall of the air intake channel to simulate the sensory experience of a human inhaling gas within the monitoring area and generate sensory data. This makes the sensory data more closely resemble the feeling of human breathing. Based on the human simulated sensory data, the gas detection frequency is adjusted, resulting in detection data that is more relevant to the human's production and living environment, facilitating the control of greenhouse gas emissions. In implementation, the number of sensors in the simulation sensor can be increased or decreased.
[0054] Figure 2 A flowchart of a method for greenhouse gas detection corresponding to the above system is shown. This greenhouse gas detection method includes:
[0055] S22, invoke the sensory data of the human simulation in the monitoring area monitored by the first detection unit.
[0056] S24, when the change in the sensory data within a unit of time exceeds a threshold, the second detection unit is invoked to detect the greenhouse gas concentration in the monitored area and generate detection data.
[0057] S26, Generate a greenhouse gas detection report based on the detection data.
[0058] The disclosed scheme, based on the gas detector tube method for detecting greenhouse gas content, introduces human simulation technology. The gas detection frequency is adjusted based on the sensory data of human simulation, so that the generated detection data is more relevant to the human production and living environment, which facilitates the control of greenhouse gas emissions. At the same time, this detection method improves the adaptability of greenhouse gas detection using the gas detector tube method to changing environments.
[0059] In this embodiment, the sensory data is labeled based on the detection data to generate training samples, and the greenhouse gas preset neural network model is trained based on the training samples.
[0060] In this embodiment, the sensory data sent by the first detection unit is input into a pre-trained greenhouse gas preset neural network model to obtain the corresponding detection data.
[0061] According to this embodiment, a neural network is used to perform deep learning on the correspondence between sensory data and detection data, and a mathematical model corresponding to sensory data and detection data is established. When the change in sensory data within a unit of time exceeds a threshold, the corresponding detection data can be directly calculated from the current sensory data and used as empirical data to generate a greenhouse gas detection report. This can effectively reduce the number of times the detection device is started, improve the efficiency of greenhouse gas detection, and reduce energy consumption.
[0062] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.
[0063] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.
[0064] Figure 3 A block diagram of a greenhouse gas detection apparatus 400 according to an embodiment of the present disclosure is shown. The apparatus may be included in... Figure 1 In the system.
[0065] like Figure 3 As shown, the greenhouse gas detection device 300 includes:
[0066] Calling unit 310 is used to call the sensory data of the human simulation in the monitoring area of the first detection unit;
[0067] Processing unit 320 is used to call the second detection unit to detect the greenhouse gas concentration in the monitored area and generate detection data when the change in the sensory data exceeds a threshold within a unit time.
[0068] Reporting unit 330 is used to generate a greenhouse gas detection report based on the detection data.
[0069] In some embodiments, a training unit is further included, which is used to label the sensory data according to the detection data to generate training samples, and to train a pre-defined neural network model for greenhouse gases according to the training samples.
[0070] In some embodiments, a detection unit is further included, which is used to input the sensory data sent by the first detection unit into a pre-trained greenhouse gas preset neural network model to obtain corresponding detection data.
[0071] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0072] According to embodiments of this disclosure, this disclosure also provides an electronic device, a non-transitory computer-readable storage medium storing computer instructions, and a computer program product.
[0073] Electronic devices are intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.
[0074] like Figure 4 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 402 or a computer program loaded from storage unit 408 into random access memory (RAM) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. Input / output (I / O) interface 405 is also connected to bus 404.
[0075] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0076] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above. For example, in some embodiments, the greenhouse gas detection method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 401 can be configured to perform the greenhouse gas detection method by any other suitable means (e.g., by means of firmware).
[0077] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0078] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0079] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0080] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0081] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.
[0082] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.
[0083] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.
[0084] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.
Claims
1. A system for detecting greenhouse gases, characterized in that, It includes the first testing department, the second testing department, and the comprehensive processing department; The first detection unit includes a simulation device and a first controller. The simulation device includes a simulated breathing system and simulated sensors. The simulated breathing system includes an air intake channel. The simulated sensors are installed on the inner wall of the air intake channel. The simulated sensors include at least three of the following: a temperature sensor, a humidity sensor, a pH sensor, and an air quality sensor, all with human body simulation functions. The simulation device is configured to monitor simulated human sensory data within a monitored area. The first controller is configured to send a start command to the second detection unit when the change in the sensory data within a unit of time exceeds a threshold. The second detection unit includes a detection device and a second controller. The second controller is configured to, upon receiving the start command, control the detection device to detect the greenhouse gas concentration in the monitoring area and generate detection data. The second controller is also configured to send the detection data to the integrated processing unit. The first controller is also configured to send the sensory data to the integrated processing unit; The integrated processing unit is further configured to label the sensory data based on the detection data to generate training samples, so as to train a pre-trained greenhouse gas neural network model based on the training samples; the integrated processing unit includes a prediction module and a reporting module, the prediction module is configured to input the sensory data sent by the first detection unit into the pre-trained greenhouse gas neural network model to obtain corresponding detection data, and the reporting module is configured to generate a greenhouse gas detection report based on the detection data.
2. The system for detecting greenhouse gases according to claim 1, wherein, The detection data includes greenhouse gas content data and / or spectral detection data.
3. A method of greenhouse gas detection implemented by the system of any one of claims 1-2, wherein, include: The sensory data of the human simulation within the monitoring area are invoked by the first detection unit; When the change in the sensory data exceeds a threshold within a unit of time, the second detection unit is invoked to detect the greenhouse gas concentration in the monitored area and generate detection data. The sensory data is labeled based on the detection data to generate training samples, and a pre-defined neural network model for greenhouse gases is trained based on the training samples. The sensory data sent by the first detection unit is input into a pre-trained greenhouse gas preset neural network model to obtain corresponding detection data; a greenhouse gas detection report is generated based on the detection data.
4. A device for detecting greenhouse gases, characterized in that include: The calling unit is used to call the sensory data of the human simulation in the monitoring area of the first detection unit; The human simulation includes a simulation device, which includes a simulated breathing system and simulated sensors. The simulated breathing system includes an air intake channel, and the simulated sensors are installed on the inner wall of the air intake channel. The simulated sensors include at least three of the following: a temperature sensor, a humidity sensor, a pH sensor, and an air quality sensor, all with human simulation functions. The simulation device is configured to monitor simulated human sensory data within a monitored area. The processing unit is configured to invoke the second detection unit to detect the concentration of the greenhouse gas in the monitoring area and generate detection data when the amount of change of the sensory data in a unit of time exceeds a threshold value; The training sample is generated by marking the sensory data according to the detection data, and the greenhouse gas preset neural network model is trained according to the training sample; The sensory data sent by the first detection unit is input into the greenhouse gas preset neural network model which is trained in advance, and corresponding detection data is obtained; and the reporting unit is configured to generate a greenhouse gas detection report according to the detection data.
5. An electronic device comprising: At least one processor; and a memory connected in communication with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of claim 3.