CN114813598A

CN114813598A - Greenhouse gas detection method, device and system and electronic equipment

Info

Publication number: CN114813598A
Application number: CN202210442193.0A
Authority: CN
Inventors: 李强
Original assignee: Terminus Technology Group Co Ltd
Current assignee: Terminus Technology Group Co Ltd
Priority date: 2022-04-25
Filing date: 2022-04-25
Publication date: 2022-07-29

Abstract

Embodiments of the present disclosure provide systems for greenhouse gas detection. The system comprises a first detection part, a second detection part and a comprehensive processing part; the first detection part comprises a simulation device and a first controller, the simulation device is configured to monitor human body simulation sense data in a monitoring area, and the first controller is configured to send a starting instruction to the second detection part when the variation of the sense data in unit time exceeds a threshold value; the second detection part comprises a detection device and a second controller, the second controller is configured to control the detection device to detect the concentration of the greenhouse gas in the monitoring area to generate detection data after receiving the starting instruction, and the second controller is further configured to send the detection data to the comprehensive processing part; the integrated processing portion includes a reporting module configured to generate a greenhouse gas detection report based on the detection data. Therefore, the greenhouse gas can be detected in time.

Description

Greenhouse gas detection method, device and system and electronic equipment

Technical Field

The disclosure relates to the field of greenhouse gas detection, in particular to the technical field of detecting greenhouse gas by a gas detection tube method.

Background

There are many methods for detecting greenhouse gases, such as semiconductor sensor detection, spectroscopy, chemical analysis, electrochemical method, gas detection tube method, etc. different methods correspond to different environments, and can solve some basic problems of greenhouse gas detection in the environment.

Taking the gas detection tube method as an example, it is mainly to feed a pre-processed measurement gas into a measurement tube and measure the concentration of the gas by measuring an optical probe (infrared or ultraviolet probe) installed at a pipe port.

However, since the environment is in a changing state, the simple gas detection tube method is difficult to adapt to the changing environment and detect the environment in time.

Disclosure of Invention

The disclosure provides a greenhouse gas detection method, a greenhouse gas detection device, a greenhouse gas detection system and electronic equipment, so that greenhouse gas can be detected in time.

According to a first aspect of the present disclosure, a system for greenhouse gas detection is provided. The system comprises a first detection part, a second detection part and a comprehensive processing part;

the first detection part comprises a simulation device and a first controller, the simulation device is configured to monitor human body simulation sense data in a monitoring area, and the first controller is configured to send a starting instruction to the second detection part when the variation of the sense data in unit time exceeds a threshold value;

the second detection part comprises a detection device and a second controller, the second controller is configured to control the detection device to detect the concentration of the greenhouse gas in the monitoring area to generate detection data after receiving the starting instruction, and the second controller is further configured to send the detection data to the comprehensive processing part;

the integrated processing portion includes a reporting module configured to generate a greenhouse gas detection report based on the detection data.

The above aspects and any possible implementations further provide an implementation, wherein the detection data comprises greenhouse gas content data and/or spectral detection data.

The above-described aspects and any possible implementations further provide an implementation in which the first controller is further configured to send the sensory data to the integrated processing portion;

the comprehensive processing part is further configured to label the sensory data according to the detection data to generate a training sample, so as to train a greenhouse gas preset neural network model according to the training sample.

The above-mentioned aspect and any possible implementation manner further provide an implementation manner, where the comprehensive processing portion further includes a prediction module, and the prediction module is configured to input the sensory data sent by the first detection portion into a pre-trained greenhouse gas preset neural network model, so as to obtain corresponding detection data.

The aspect and any possible implementation described above, further provides an implementation, wherein the simulation apparatus includes a simulated respiratory system and a simulated sensor,

the simulated respiratory system comprises an air inlet channel, the simulated sensor is arranged on the inner wall of the air inlet channel,

the simulation sensor comprises at least three of a temperature sensor, a humidity sensor, a pH value sensor and an air quality sensor with human body simulation functions.

According to a second aspect of the present disclosure, there is provided a method for greenhouse gas detection, comprising:

calling the first detection part to monitor the simulated sensory data of the human body in the monitoring area;

calling the second detection part to detect the concentration of the greenhouse gas in the monitoring area and generating detection data when the variation of the sensory data in unit time exceeds a threshold value,

and generating a greenhouse gas detection report according to the detection data.

The above-described aspects and any possible implementations further provide an implementation, and the method further includes:

and marking the sensory data according to the detection data to generate a training sample, and training a greenhouse gas preset neural network model according to the training sample.

and inputting the sensory data sent by the first detection part into a pre-trained greenhouse gas preset neural network model to obtain corresponding detection data.

According to a third aspect of the present disclosure, there is provided an apparatus for greenhouse gas detection, comprising:

the calling unit is used for calling the sensory data of the human body simulation in the monitoring area monitored by the first detection part;

the processing unit is used for calling the second detection part to detect the concentration of the greenhouse gas in the monitoring area and generating detection data when the variation of the sensory data in unit time exceeds a threshold value;

and the reporting unit is used for generating a greenhouse gas detection report according to the detection data.

According to a fourth aspect of the present disclosure, an electronic device is provided. The electronic device includes: at least one processor and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the second aspect of the disclosure.

According to the scheme, the human body simulation technology is introduced on the basis of detecting the greenhouse gas content by the gas detection tube method, the gas detection frequency is regulated and controlled on the basis of the sensory data of human body simulation, the relevance of the generated detection data and the production and living environment of a human body is higher, the emission of greenhouse gas can be conveniently controlled, and meanwhile, the detection mode improves the adaptability of the greenhouse gas detection method to the changed environment.

It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. The accompanying drawings are included to provide a further understanding of the present disclosure, and are not intended to limit the disclosure thereto, and the same or similar reference numerals will be used to indicate the same or similar elements, where:

FIG. 1 shows a schematic diagram of a system for greenhouse gas detection of the present disclosure;

FIG. 2 shows a flow chart of a method of greenhouse gas detection of the present disclosure;

FIG. 3 shows a flow chart of a method of generating a greenhouse gas detection report from detection data;

FIG. 4 shows a block diagram of an electronic device used to implement the method of greenhouse gas detection of an embodiment of the present disclosure.

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without inventive step, are intended to be within the scope of the present disclosure.

In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.

In the method, on the basis of detecting the greenhouse gas content by using the gas detection tube method, a human body simulation technology is introduced, the gas detection frequency is regulated and controlled on the basis of the sensory data of human body simulation, the relevance of the generated detection data and the production and living environment of a human body is higher, the emission of the greenhouse gas can be conveniently controlled, and meanwhile, the detection mode improves the adaptability of the gas detection tube method to the changing environment during greenhouse gas detection.

Fig. 1 shows a schematic diagram of a greenhouse gas monitoring system 100 of the present disclosure and the interaction relationship among the first detection part 110, the second detection part 120 and the integrated processing part 130 in the system 100.

The first detection part 110 comprises a simulation device 111 and a first controller 112, the simulation device 111 is configured to monitor human body simulation sense data in a monitoring area, the first controller 112 is configured to send a start instruction to the second detection part 120 when the variation of the sense data in a unit time exceeds a threshold value;

the second detecting part 120 comprises a detecting device 121 and a second controller 122, the second controller 122 is configured to control the detecting device 121 to detect the concentration of the greenhouse gas in the monitored area to generate detection data after receiving the starting instruction, and the second controller 122 is further configured to send the detection data to the comprehensive processing part 130;

the integrated processing portion includes a reporting module 132, the reporting module 132 configured to generate a greenhouse gas detection report based on the detection data.

The system 100 of this embodiment, on the basis that the gas detection tube method detects greenhouse gas content, introduced human simulation technique, the sense organ data of using human simulation regulates and control gas detection frequency as the basis, the detection data that generate like this is higher with the production living environment relevance degree of human body, can be convenient for manage and control greenhouse gas's emission, this kind of detection mode has improved the adaptability to the environment of change with the gas detection tube method when carrying out greenhouse gas detection simultaneously.

In this embodiment, the detection data comprises greenhouse gas content data and/or spectroscopic detection data.

In this embodiment, the first controller is further configured to transmit the sensory data to the integrated processing section;

According to the embodiment, the neural network is used for deeply learning the corresponding relation between the sensory data and the detection data, a mathematical model corresponding to the sensory data and the detection data is established, and when the variation of the sensory data in unit time exceeds a threshold value, the corresponding detection data can be directly calculated through the monitored current sensory data and used as empirical data to generate a greenhouse gas detection report, so that the starting times of the detection device 121 can be effectively reduced, the working efficiency of greenhouse gas detection is improved, and the energy consumption is reduced.

In this embodiment, the comprehensive processing unit further includes a prediction module, and the prediction module 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.

In the embodiment, the simulation device comprises a simulation respiratory system and a simulation sensor,

The simulation device 111 of this embodiment, through the respiratory system 100 of simulation human body and set up the sense organ after the emulation sensor can simulate out the human body inhales the gas in the monitoring area on the inner wall of inlet channel, and generate the sense organ data, can make the sense organ data more laminate the impression when human body breathes like this, adjust and control gas detection frequency on the basis of the sense organ data of human body emulation, the detection data that generate like this is higher with the production living environment degree of relevance of human body, can be convenient for manage and control greenhouse gas's emission. In implementation, the number of sensors in the simulation sensor can be increased or decreased.

Fig. 2 shows a flow chart of a method for greenhouse gas detection corresponding to the system described above. The greenhouse gas detection method comprises the following steps:

and S22, calling the first detection part to monitor the simulated sensory data of the human body in the monitoring area.

And S24, when the variation of the sensory data in unit time exceeds a threshold value, calling the second detection unit to detect the greenhouse gas concentration in the monitoring area and generating detection data.

And S26, generating a greenhouse gas detection report according to the detection data.

In this embodiment, the sensory data are labeled according to the detection data to generate a training sample, and a preset neural network model of greenhouse gases is trained according to the training sample.

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 corresponding detection data.

According to the embodiment, the neural network is used for deeply learning the corresponding relation between the sensory data and the detection data, the mathematical model corresponding to the sensory data and the detection data is established, when the variation of the sensory data in unit time exceeds the threshold value, the corresponding detection data can be directly calculated through the monitored current sensory data and used as empirical data to generate the greenhouse gas detection report, so that the starting times of the detection device can be effectively reduced, the working efficiency of greenhouse gas detection is improved, and the energy consumption is reduced.

It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.

The above is a description of embodiments of the method, and the embodiments of the apparatus are described below to further illustrate the aspects of the disclosure.

Fig. 3 shows a block diagram of an apparatus 400 for greenhouse gas detection according to an embodiment of the present disclosure. The apparatus may be included in the system of fig. 1.

As shown in fig. 3, the greenhouse gas detection apparatus 300 includes:

the calling unit 310 is used for calling the sensory data of the human body simulation in the monitoring area monitored by the first detection part;

a processing unit 320, configured to invoke the second detecting portion to detect the concentration of the greenhouse gas in the monitored area and generate detection data when the variation of the sensory data in unit time exceeds a threshold;

and a reporting unit 330 for generating a greenhouse gas detection report according to the detection data.

In some embodiments, the device further includes a training unit, configured to label the sensory data according to the detection data to generate a training sample, and train a greenhouse gas preset neural network model according to the training sample.

In some embodiments, the device further comprises a detection unit, configured to input the sensory data sent by the first detection portion into a pre-trained greenhouse gas preset neural network model, so as to obtain corresponding detection data.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.

The present disclosure also provides an electronic device, a non-transitory computer readable storage medium storing computer instructions, and a computer program product according to embodiments of the present disclosure.

Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 4, the apparatus 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)402 or a computer program loaded from a storage unit 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data required for the operation of the device 400 can also be stored. The computing unit 401, ROM402, and RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

A number of components in device 400 are connected to I/O interface 405, including: an input unit 406 such as a keyboard, a mouse, or the like; an output unit 407 such as various types of displays, speakers, and the like; a storage unit 408 such as a magnetic disk, optical disk, or the like; and a communication unit 409 such as a network card, modem, wireless communication transceiver, etc. The communication unit 409 allows the device 400 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

Computing unit 401 may be a variety of general 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 dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 401 executes the respective methods and processes described above. For example, in some embodiments, the method of greenhouse gas detection may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 400 via the ROM402 and/or the communication unit 409. When the computer program is loaded into RAM403 and executed by computing unit 401, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method of greenhouse gas detection by any other suitable means (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These 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 the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a 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 may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A system for detecting greenhouse gases is characterized in that,

comprises a first detection part, a second detection part and a comprehensive processing part;

2. The system for greenhouse gas detection of claim 1, wherein the detection data comprises greenhouse gas content data and/or spectroscopic detection data.

3. The system for greenhouse gas detection of claim 1,

the first controller is further configured to transmit the sensory data to the integrated processing section;

4. The system for greenhouse gas detection of claim 1,

the comprehensive processing part further comprises a prediction module, and the prediction module is configured to input the sensory data sent by the first detection part into a pre-trained greenhouse gas preset neural network model to obtain corresponding detection data.

5. Greenhouse gas detection system according to claim 3, wherein the simulation means comprises a simulated respiratory system and a simulated sensor,

6. A method for greenhouse gas detection implemented according to the system of any one of claims 1-5, comprising:

7. The method of claim 6, further comprising:

8. The method of claim 6, further comprising:

9. A greenhouse gas detection device, comprising:

10. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 6-8.