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

Abstract

Embodiments of the present disclosure provide systems for greenhouse gas detection. The system includes a first detection part, a second detection part and a comprehensive processing part; the first detection part includes a simulation device and a first controller, the simulation device is configured to monitor the human body simulation sensory data in the monitoring area, and the first controller is configured to send a start instruction to the second detection part when the amount of change of the sensory data per unit time exceeds a threshold; the second detection part includes a detection device and a second controller, and the second controller is configured to After receiving the startup instruction, the detection device is controlled to detect the greenhouse gas concentration in the monitoring area to generate detection data, and the second controller is further configured to send the detection data to the comprehensive processing unit; the comprehensive processing unit includes A report module, the report module is configured to generate a greenhouse gas detection report according to the detection data. In this way, greenhouse gases can be detected in a timely manner.

Description

Greenhouse gas detection method, device, system and electronic device

technical field

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

Background technique

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

Taking the gas detection tube method as an example, it mainly transports the pretreated measurement gas into the measurement tube, and measures the concentration of the gas by measuring the optical probe (infrared or ultraviolet probe) installed at the pipe port.

However, because the environment is in a state of change, it is difficult for the simple gas detection tube method to adapt to the changing environment and detect the environment in time.

SUMMARY OF THE INVENTION

The present disclosure provides a method, device, system and electronic device for detecting greenhouse gases, so as to detect greenhouse gases in time.

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

The first detection part includes a simulation device and a first controller, the simulation device is configured to monitor the human body simulation sensory data in the monitoring area, and the first controller is configured to when the sensory data is within a unit time When the amount of change exceeds the threshold, send a start instruction to the second detection part;

The second detection part includes a detection device and a second controller, the second controller is configured to control the detection device to detect the greenhouse gas concentration in the monitoring area to generate detection data after receiving the activation instruction , the second controller is further configured to send the detection data to the integrated processing part;

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

According to the above aspect and any possible implementation, an implementation is further provided, wherein the detection data includes greenhouse gas content data and/or spectral detection data.

According to the above aspect and any possible implementation manner, an implementation manner is further provided, wherein the first controller is further configured to send the sensory data to the integrated processing unit;

The integrated processing unit is further configured to mark the sensory data according to the detection data to generate training samples, so as to train the greenhouse gas preset neural network model according to the training samples.

The above aspect and any possible implementation manner further provide an implementation manner, wherein 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 the pre-training The greenhouse gas preset neural network model is obtained to obtain the corresponding detection data.

Aspects as described above and any possible implementation manner, further provide an implementation manner, the simulation device includes a simulated respiratory system and a simulated sensor,

The simulated breathing system includes an intake channel, and the simulated sensor is installed on the inner wall of the intake channel,

The simulation sensor includes at least three of a temperature sensor, a humidity sensor, a pH sensor, and an air quality sensor with a human body simulation function.

According to a second aspect of the present disclosure, there is provided a method for detecting greenhouse gases, characterized by comprising:

calling the first detection unit to monitor the sensory data of the human simulation in the monitoring area;

When the amount of change of the sensory data per unit time exceeds a threshold, call the second detection unit to detect the greenhouse gas concentration in the monitoring area and generate detection data,

A greenhouse gas detection report is generated according to the detection data.

The above-mentioned aspect and any possible implementation manner further provide an implementation manner, and the method further includes:

The sensory data is marked according to the detection data to generate a training sample, and a preset neural network model of greenhouse gas is trained according to the training sample.

The above-mentioned aspect and any possible implementation manner further provide an implementation manner, and the method further includes:

Input the sensory data sent by the first detection unit into the 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 detecting greenhouse gases, comprising:

a calling unit for calling the sensory data of the human body simulation in the monitoring area of the first detection unit;

a processing unit, configured to call the second detection unit to detect the greenhouse gas concentration in the monitoring area and generate detection data when the variation of the sensory data per unit time exceeds a threshold;

A reporting unit, configured to generate 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 connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the at least one process The device is capable of performing the method of the second aspect of the present disclosure.

The solution of the present disclosure, on the basis of the gas detection tube method for detecting the content of greenhouse gases, introduces the human body simulation technology, and regulates the gas detection frequency based on the sensory data of the human body simulation, so that the generated detection data is consistent with the production and living environment of the human body. The correlation is higher, which can facilitate the management and control of greenhouse gas emissions. At the same time, this detection method improves the adaptability to the changing environment when the gas detection tube method is used for greenhouse gas detection.

It should be understood that the matters described in this Summary are not intended to limit key or critical 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.

Description of drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent when taken in conjunction with the accompanying drawings and with reference to the following detailed description. The accompanying drawings are used for a better understanding of the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals denote the same or similar elements, wherein:

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

FIG. 2 shows a flowchart of the method for greenhouse gas detection of the present disclosure;

FIG. 3 shows a flowchart of a method for generating a greenhouse gas detection report according to detection data;

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

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments These are some, but not all, embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

In addition, the term "and/or" in this article is only an association relationship to describe the associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, it can mean that A exists alone, A and B exist at the same time, There are three cases of B alone. In addition, the character "/" in this document generally indicates that the related objects are an "or" relationship.

In the present disclosure, based on the detection of greenhouse gas content by the gas detection tube method, the human body simulation technology is introduced, and the gas detection frequency is regulated based on the sensory data of the human body simulation, so that the generated detection data is related to the production and living environment of the human body. It has a higher degree of detection, which can facilitate the management and control of greenhouse gas emissions. At the same time, this detection method improves the adaptability to changing environments when using the gas detection tube method for greenhouse gas detection.

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

The first detection part 110 includes a simulation device 111 and a first controller 112, the simulation device 111 is configured to monitor the human body simulation sensory data in the monitoring area, and the first controller 112 is configured to When the amount of change of the data per unit time exceeds the threshold, send a start instruction to the second detection unit 120;

The second detection part 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 in the monitoring area after receiving the start-up instruction. Gas concentration generates detection data, the second controller 122 is further configured to send the detection data to the integrated processing unit 130;

The integrated processing section includes a 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 of the gas detection tube method for detecting the content of greenhouse gases, introduces the human body simulation technology, and regulates the gas detection frequency based on the sensory data of the human body simulation. The living environment is more relevant, which can facilitate the management and control of greenhouse gas emissions. At the same time, this detection method improves the adaptability to changing environments when using the gas detection tube method for greenhouse gas detection.

In this embodiment, the detection data includes greenhouse gas content data and/or spectral detection data.

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

The integrated processing unit is further configured to mark the sensory data according to the detection data to generate training samples, so as to train the greenhouse gas preset neural network model according to the training samples.

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 amount of change of sensory data per unit time exceeds a threshold , the corresponding detection data can be directly calculated from the current sensory data monitored and used as empirical data to generate a greenhouse gas detection report, which can effectively reduce the number of startups of the detection device 121, improve the work efficiency of greenhouse gas detection, and reduce energy consumption. consumption.

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 this embodiment, the simulation device includes a simulated breathing system and a simulated sensor,

The simulated breathing system includes an intake channel, and the simulated sensor is installed on the inner wall of the intake channel,

The simulation sensor includes at least three of a temperature sensor, a humidity sensor, a pH sensor, and an air quality sensor with a human body simulation function.

The simulation device 111 of this embodiment can simulate the human body's senses after inhaling the gas in the monitoring area by simulating the human body's breathing system 100 and arranging a simulation sensor on the inner wall of the air intake passage, and generate sensory data, which enables the senses The data is more in line with the feeling of the human body when breathing, and the gas detection frequency is regulated based on the sensory data simulated by the human body, so that the generated detection data has a higher correlation with the production and living environment of the human body, which can facilitate the analysis of greenhouse gas emissions. Control. When implemented, the number of sensors in the simulated sensor can be increased or decreased.

FIG. 2 shows a flowchart of a method for greenhouse gas detection corresponding to the above system. The greenhouse gas detection method includes:

S22: Invoke the first detection unit to monitor the sensory data of human simulation in the monitoring area.

S24, when the amount of change of the sensory data per unit time exceeds a threshold, call the second detection unit to detect the greenhouse gas concentration in the monitoring area and generate detection data.

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

The solution of the present disclosure, on the basis of the gas detection tube method for detecting the content of greenhouse gases, introduces the human body simulation technology, and regulates the gas detection frequency based on the sensory data of the human body simulation, so that the generated detection data is consistent with the production and living environment of the human body. The correlation is higher, which can facilitate the management and control of greenhouse gas emissions. At the same time, this detection method improves the adaptability to the changing environment when the gas detection tube method is used for greenhouse gas detection.

In this embodiment, the sensory data is marked according to the detection data to generate a training sample, and a preset neural network model of greenhouse gas 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 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 amount of change of sensory data per unit time exceeds a threshold , the corresponding detection data can be directly calculated from the current sensory data monitored and used as empirical data to generate a greenhouse gas detection report, which can effectively reduce the number of startups of the detection device, improve the work efficiency of greenhouse gas detection, and reduce energy consumption. .

It should be noted that, for the sake of simple description, the foregoing method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present disclosure is not limited by the described action sequences. Because certain steps may be performed in other orders or concurrently in accordance with the present disclosure. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present disclosure.

The above is an introduction to the method embodiments, and the solutions described in the present disclosure will be further described below through the device embodiments.

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 apparatus 300 for detecting greenhouse gases includes:

a calling unit 310, configured to call the sensory data of the human simulation in the monitoring area monitored by the first detection unit;

a processing unit 320, configured to call the second detection unit to detect the greenhouse gas concentration in the monitoring area and generate detection data when the variation of the sensory data per unit time exceeds a threshold;

The reporting unit 330 is configured to generate a greenhouse gas detection report according to the detection data.

In some embodiments, a training unit is further included, configured to mark the sensory data according to the detection data to generate a training sample, and train a preset neural network model of greenhouse gas according to the training sample.

In some embodiments, a detection unit is further included, 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.

Those skilled in the art can clearly understand that, for the convenience and brevity of the description, for the specific working process of the described modules, reference may be made to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

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

Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 4 , the device 400 includes a computing unit 401 that can be executed according to a computer program stored in a read only memory (ROM) 402 or loaded from a storage unit 408 into a random access memory (RAM) 403 Various appropriate actions and handling. In the RAM 403, various programs and data necessary for the operation of the device 400 can also be stored. The computing unit 401 , the ROM 402 , and the RAM 403 are connected to each other through a bus 404 . An input/output (I/O) interface 405 is also connected to bus 404 .

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

Computing unit 401 may be various general-purpose and/or special-purpose processing components with processing and computing capabilities. Some examples of computing units 401 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various specialized artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing 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 method of greenhouse gas detection may be implemented as a computer software program tangibly embodied on 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 on device 400 via ROM 402 and/or communication unit 409 . When a computer program is loaded into RAM 403 and executed by computing unit 401, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured by any other suitable means (eg, by means of firmware) to perform the method of greenhouse gas detection.

Various implementations of the systems and techniques described herein above may be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips system (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpretable on a programmable system including at least one programmable processor that The processor, which may be a special purpose or general-purpose programmable processor, may receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device an 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 codes 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, performs the functions/functions specified in the flowcharts and/or block diagrams. Action is implemented. The program code may execute entirely on the machine, partly on the machine, partly on the machine and partly on a remote machine as a stand-alone software package or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in connection with the instruction execution system, apparatus or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media may include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), fiber optics, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide interaction with a user, the systems and techniques described herein may be implemented on a computer having a display device (eg, a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user ); and a keyboard and pointing device (eg, a mouse or trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (eg, visual feedback, auditory feedback, or tactile feedback); and can be in any form (including acoustic input, voice input, or tactile input) to receive input from the user.

The systems and techniques described herein may be implemented on a computing system that includes back-end components (eg, as a data server), or a computing system that includes middleware components (eg, an application server), or a computing system that includes front-end components (eg, a user's computer having a graphical user interface or web browser through which a user may interact with implementations of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system may be interconnected by any form or medium of digital data communication (eg, a communication network). Examples of communication networks include: Local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

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

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, the steps described in the present disclosure can 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, there is no limitation herein.

The above-mentioned specific embodiments do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present disclosure should be included within the protection scope of the present disclosure.

Claims (10)

1. a system for greenhouse gas detection, characterized in that, Including a first detection part, a second detection part and a comprehensive processing part; The first detection part includes a simulation device and a first controller, the simulation device is configured to monitor the human body simulation sensory data in the monitoring area, and the first controller is configured to when the sensory data is within a unit time When the amount of change exceeds the threshold, send a start instruction to the second detection part; The second detection part includes a detection device and a second controller, the second controller is configured to control the detection device to detect the greenhouse gas concentration in the monitoring area to generate detection data after receiving the activation instruction , the second controller is further configured to send the detection data to the integrated processing part; The integrated processing section includes a reporting module 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 comprises greenhouse gas content data and/or spectral detection data. 3 . 3. The system for greenhouse gas detection according to claim 1, wherein, the first controller is further configured to send the sensory data to the integrated processing section; The integrated processing unit is further configured to mark the sensory data according to the detection data to generate training samples, so as to train the greenhouse gas preset neural network model according to the training samples. 4. The system for greenhouse gas detection according to claim 1, wherein, 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. 5. The system for greenhouse gas detection according to claim 3, wherein the simulation device comprises a simulation breathing system and a simulation sensor, The simulated breathing system includes an intake channel, and the simulated sensor is installed on the inner wall of the intake channel, The simulation sensor includes at least three of a temperature sensor, a humidity sensor, a pH sensor, and an air quality sensor with a human body simulation function. 6. A method for greenhouse gas detection implemented by the system according to any one of claims 1-5, characterized in that, comprising: calling the first detection unit to monitor the sensory data of the human simulation in the monitoring area; When the amount of change of the sensory data per unit time exceeds a threshold, the second detection unit is invoked to detect the greenhouse gas concentration in the monitoring area and generate detection data, A greenhouse gas detection report is generated according to the detection data. 7. The method according to claim 6, wherein the method further comprises: The sensory data is marked according to the detection data to generate a training sample, and a preset neural network model of greenhouse gas is trained according to the training sample. 8. The method according to claim 6, wherein the method further comprises: Input the sensory data sent by the first detection unit into the pre-trained greenhouse gas preset neural network model to obtain corresponding detection data. 9. A device for detecting greenhouse gases, comprising: a calling unit, used for calling the sensory data of the human body simulation in the monitoring area monitored by the first detection unit; a processing unit, configured to call the second detection unit to detect the greenhouse gas concentration in the monitoring area and generate detection data when the variation of the sensory data per unit time exceeds a threshold; A reporting unit, configured to generate a greenhouse gas detection report according to the detection data. 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, the instructions being executed by the at least one processor. The at least one processor executes to enable the at least one processor to perform the method of any of claims 6-8.

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