Regional carbon dioxide concentration detection method and system based on sensor data

Abstract

The invention relates to the field of gas detection, in particular to a regional carbon dioxide concentration detection method and system based on sensor data. Acquiring gas temperature data in the surrounding area through a temperature sensor to obtain average gas temperature data; acquiring infrared signal data in the surrounding area by using a signal receiver to obtain carbon dioxide infrared signal data; decoding the carbon dioxide infrared signal data based on an NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data; a PSO-RBF neural network model is established; inputting the carbon dioxide infrared signal data and the average gas temperature data into a PSO-RBF neural network model for identification, judging whether the carbon dioxide gas concentration exceeds an early warning threshold value or not, and if the carbon dioxide gas concentration exceeds the early warning threshold value, transmitting the carbon dioxide gas concentration to a server for early warning. The carbon dioxide concentration in the area can be effectively and accurately detected, and the detection efficiency and accuracy of the carbon dioxide concentration are improved.

Description

Technical Field

[0001] The present invention relates to the field of gas detection, and in particular to a method and system for detecting regional carbon dioxide concentration based on sensor data. Background Art

[0002] There are many methods for detecting carbon dioxide concentration, such as electrochemical method, gas chromatography, spectral detection method, etc. The most widely used detection method in the prior art is the non-dispersive infrared detection method ND IR, which has the characteristics of a wide measurement range. However, the non-dispersive infrared gas detection device is easily affected by the ambient temperature and the measurement distance, resulting in deviations in the measurement results, and there are certain failures in the detection of carbon dioxide concentration data in the entire area. Therefore, how to improve the efficiency and accuracy of carbon dioxide concentration detection in the entire area is a technical problem that needs to be solved at this stage. Summary of the invention

[0003] The purpose of the present invention is to solve the above problems and to design a method and system for detecting regional carbon dioxide concentration based on sensor data.

[0004] To achieve the above object, the technical solution of the present invention is that, further, in the above-mentioned method for detecting regional carbon dioxide concentration based on sensor data, the method for detecting regional carbon dioxide concentration comprises the following steps:

[0005] Based on the ultrasonic sensor emitting a 41KHZ ultrasonic signal to the surrounding area, the temperature sensor acquires the gas temperature data in the surrounding area to obtain the average gas temperature data;

[0006] Using a signal receiver to acquire infrared signal data in the surrounding area, obtaining carbon dioxide infrared signal data;

[0007] Decoding the carbon dioxide infrared signal data based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data;

[0008] Establishing an RBF neural network model, optimizing the parameters of the RBF neural network model using a PSO particle swarm algorithm to obtain a PSO-RBF neural network model;

[0009] Inputting the carbon dioxide infrared signal data and the average gas temperature data into the PSO-RBF neural network model for identification to obtain the carbon dioxide gas concentration;

[0010] It is determined whether the carbon dioxide gas concentration exceeds a warning threshold. If the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to a server for warning.

[0011] Furthermore, in the above-mentioned regional carbon dioxide concentration detection method, the ultrasonic sensor transmits a 41KHZ ultrasonic signal to the surrounding area, and obtains the gas temperature data in the surrounding area through the temperature sensor, including:

[0012] Based on the ultrasonic transmitter emitting 41KHZ ultrasonic signals into the surrounding area, the ultrasonic transmitter is set at a density of 2 / 100m 2 ;

[0013] The gas temperature data of multiple areas are acquired through the air temperature sensor, and the gas temperature data are averaged to obtain average gas temperature data.

[0014] Furthermore, in the above-mentioned regional carbon dioxide concentration detection method, the use of a signal receiver to acquire infrared signal data in the surrounding area to obtain carbon dioxide infrared signal data includes:

[0015] Using a signal receiver to acquire infrared signal data in a surrounding area, filtering and amplifying the infrared signal data to obtain initial infrared signal data;

[0016] The initial infrared signal data is input into a shaping circuit, and the shaping circuit is used to convert the sinusoidal wave signal of the initial infrared signal data into a square wave signal to obtain carbon dioxide infrared signal data.

[0017] Further, in the above-mentioned regional carbon dioxide concentration detection method, the carbon dioxide infrared signal data is decoded based on the NEC infrared remote control protocol to obtain the decoded carbon dioxide infrared signal data, including:

[0018] Acquire carbon dioxide infrared signal data, calculate the pulse width of the infrared signal data based on the NEC infrared remote control protocol, and obtain the infrared signal pulse width;

[0019] The NEC infrared remote control protocol at least includes a guide code, an address code, an address inverse code, a command code, and a command inverse code;

[0020] Determine whether the pulse is a head pulse according to the pulse width of the infrared signal. If so, clear the receiving counter and output the infrared signal data; if not, determine whether the pulse is "1", and set the corresponding bit of the pulse;

[0021] Add "1" to the receiving counter to determine whether the reception is completed. If not, output the infrared signal data; if so, check whether it is correct;

[0022] If the verification is correct, the key value is saved and the infrared signal data is output to obtain the decoded carbon dioxide infrared signal data; the logic "0" is 1ms, the logic "1" is 2ms, and the pulse time is 500us.

[0023] Furthermore, in the above-mentioned regional carbon dioxide concentration detection method, the RBF neural network model is established, and the parameters of the RBF neural network model are optimized using the PSO particle swarm algorithm to obtain the PSO-RBF neural network model, including:

[0024] Establishing an RBF neural network model, determining the topological structure of the RBF neural network model, initializing the RBF neural network model, standardizing constants and weighting coefficients;

[0025] Determine the parameters that need to be mapped and initialize the particle speed and particle position in the PSO particle swarm algorithm;

[0026] Calculate the particle fitness and update the optimal Pbest and Gbest; the Pbest is the individual extreme value, indicating the position with the best fitness among all the positions experienced by a certain particle individual; the Gbest is the group extreme value, indicating the position with the best fitness searched by all particles;

[0027] The inertia weight is updated to determine whether the conditions are met, and if so, the parameters of the RBF neural network model are determined to obtain the PSO-RBF neural network model.

[0028] Furthermore, in the above-mentioned regional carbon dioxide concentration detection method, the carbon dioxide infrared signal data and the average gas temperature data are input into the PSO-RBF neural network model for identification to obtain the carbon dioxide gas concentration, including:

[0029] Inputting carbon dioxide infrared signal data and average gas temperature data into the PSO-RBF neural network model;

[0030] Performing standardization processing on the carbon dioxide infrared signal data and the average gas temperature data;

[0031] Performing dimensionality reduction processing on the carbon dioxide infrared signal data and the average gas temperature data by using FA analysis method;

[0032] The main factors are determined, and the scores of the main factors are calculated. The scores of the main factors are used as input data of the PSO-RBF neural network model, and the carbon dioxide gas concentration is obtained after training.

[0033] Further, in the above-mentioned regional carbon dioxide concentration detection method, the determining whether the carbon dioxide gas concentration exceeds the warning threshold, if the carbon dioxide gas concentration exceeds the warning threshold, transmitting the carbon dioxide gas concentration to the server for warning, includes:

[0034] Determine whether the carbon dioxide gas concentration exceeds the warning threshold, if the carbon dioxide gas concentration exceeds the warning threshold, the warning threshold at least includes the carbon dioxide gas concentration being greater than 0.1%;

[0035] The carbon dioxide gas concentration is transmitted to the server for early warning, and early warning feedback is obtained every 5 minutes;

[0036] If there is no early warning feedback within 20 minutes, the carbon dioxide in the area will be filtered and absorbed by the carbon dioxide absorption device;

[0037] And obtain the target carbon dioxide infrared signal data and real-time average gas temperature data in the area in real time;

[0038] The target carbon dioxide infrared signal data and the real-time average gas temperature data are input into the PSO-RBF neural network model to obtain the target carbon dioxide gas concentration.

[0039] If the target carbon dioxide gas concentration is less than the warning threshold, the carbon dioxide absorption device is stopped from filtering and absorbing the carbon dioxide in the area.

[0040] Furthermore, in the above-mentioned regional carbon dioxide concentration detection system based on sensor data, the regional carbon dioxide concentration detection system includes:

[0041] A data acquisition module is used to transmit a 41KHZ ultrasonic signal to the surrounding area based on an ultrasonic sensor, obtain gas temperature data in the surrounding area through a temperature sensor, and obtain average gas temperature data;

[0042] A signal receiving module, used to obtain infrared signal data in the surrounding area using a signal receiver to obtain carbon dioxide infrared signal data;

[0043] A data decoding module, used for decoding the carbon dioxide infrared signal data based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data;

[0044] A model building module is used to build an RBF neural network model, and optimize the parameters of the RBF neural network model using a PSO particle swarm algorithm to obtain a PSO-RBF neural network model;

[0045] A gas detection module, used for inputting carbon dioxide infrared signal data and average gas temperature data into the PSO-RBF neural network model for identification to obtain carbon dioxide gas concentration;

[0046] The detection and warning module is used to determine whether the carbon dioxide gas concentration exceeds the warning threshold. If the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to the server for warning.

[0047] Furthermore, in the above-mentioned regional carbon dioxide concentration detection system based on sensor data, the data decoding module includes the following submodules:

[0048] An acquisition submodule, used to acquire carbon dioxide infrared signal data, calculate the pulse width of the infrared signal data based on the NEC infrared remote control protocol, and obtain the infrared signal pulse width;

[0049] The protocol submodule is used to determine that the NEC infrared remote control protocol at least includes a guide code, an address code, an address inverse code, a command code, and a command inverse code;

[0050] The pulse submodule is used to determine whether the pulse is a head pulse according to the pulse width of the infrared signal. If so, the receiving counter is cleared and the infrared signal data is output; if not, it is determined whether the pulse is "1", and the corresponding bit of the pulse is set;

[0051] The judgment submodule is used to add "1" to the receiving counter to determine whether the reception is completed. If not, the infrared signal data is output; if yes, it is checked whether it is correct;

[0052] The check submodule is used to judge whether the key value is saved if the check is correct, and output the infrared signal data to obtain the decoded carbon dioxide infrared signal data; the logic "0" is 1ms, the logic "1" is 2ms, and the pulse time is 500us.

[0053] Furthermore, in the above-mentioned regional carbon dioxide concentration detection system based on sensor data, the model building module includes the following submodules:

[0054] Establishing a submodule for establishing an RBF neural network model, determining the topological structure of the RBF neural network model, initializing the RBF neural network model, standardizing constants and weighting coefficients;

[0055] The parameter submodule is used to determine the parameters that need to be mapped and initialize the particle speed and particle position in the PSO particle swarm algorithm;

[0056] The calculation submodule is used to calculate the fitness of particles and update the optimal Pbest and Gbest; the Pbest is the individual extreme value, indicating the position with the best fitness among all the positions experienced by a certain particle individual; the Gbest is the group extreme value, indicating the position with the best fitness searched by all particles;

[0057] The weight submodule is used to update the inertia weight and determine whether the conditions are met. If so, the parameters of the RBF neural network model are determined to obtain the PSO-RBF neural network model.

[0058] Its beneficial effects are that, by emitting 41KHZ ultrasonic signals to the surrounding area based on ultrasonic sensors, the gas temperature data in the surrounding area is obtained through temperature sensors to obtain average gas temperature data; infrared signal data in the surrounding area is obtained by using signal receivers to obtain carbon dioxide infrared signal data; the carbon dioxide infrared signal data is decoded based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data; an RBF neural network model is established, and the parameters of the RBF neural network model are optimized using the PSO particle swarm algorithm to obtain a PSO-RBF neural network model; the carbon dioxide infrared signal data and the average gas temperature data are input into the PSO-RBF neural network model for identification to obtain carbon dioxide gas concentration; it is determined whether the carbon dioxide gas concentration exceeds the warning threshold, and if the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to the server for warning. It can effectively detect the carbon dioxide concentration in the area accurately, reduce the influence of temperature and distance on carbon dioxide detection, improve the detection efficiency and accuracy of carbon dioxide concentration, detect and warn of excessive carbon dioxide concentration, and improve the safety of regional carbon dioxide concentration. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the following detailed description of the preferred embodiment.The drawings are only for the purpose of illustrating the preferred embodiments and are not to be construed as limiting the invention.

[0060] Figure 1 This is a schematic diagram of a first embodiment of a method for detecting regional carbon dioxide concentration based on sensor data in an embodiment of the present invention;

[0061] Figure 2 It is a schematic diagram of a second embodiment of a method for detecting regional carbon dioxide concentration based on sensor data in an embodiment of the present invention;

[0062] Figure 3 It is a schematic diagram of a third embodiment of a method for detecting regional carbon dioxide concentration based on sensor data in an embodiment of the present invention;

[0063] Figure 4 Schematic diagram of a first embodiment of a regional carbon dioxide concentration detection system based on sensor data in an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0066] The present invention will be described in detail below in conjunction with the accompanying drawings. Figure 1 As shown, a method for detecting regional carbon dioxide concentration based on sensor data, the method for detecting regional carbon dioxide concentration includes the following steps:

[0067] Step 101: The ultrasonic sensor transmits a 41KHZ ultrasonic signal to the surrounding area, and the temperature sensor obtains the gas temperature data in the surrounding area to obtain the average gas temperature data;

[0068] Specifically, in this embodiment, the ultrasonic transmitter transmits a 41KHZ ultrasonic signal to the surrounding area, and the setting density of the ultrasonic transmitter is 2 / 100m 2 ; Obtain gas temperature data of multiple areas through the air temperature sensor, average the gas temperature data, and obtain the average gas temperature data. The ultrasonic sensor receiver converts the received ultrasonic signal into a sinusoidal signal. The sinusoidal signal needs to be filtered and amplified before the next signal processing. This process requires two circuits: a filter circuit and a differential amplifier circuit. The sinusoidal signal converted by the ultrasonic sensor receiver first passes through the filter circuit and then enters the differential amplifier circuit. The ultrasonic sensor receiver receives the ultrasonic signal and converts it into a 41kHz sinusoidal signal. In order to obtain an effective 41kHz sinusoidal signal, a suitable filter amplifier circuit needs to be designed. Here, a suitable filter is needed to eliminate interference signals, filter the passing sinusoidal signal, leave the sinusoidal signal with a suitable frequency, and attenuate the sinusoidal signal with a frequency higher or lower than the center frequency.

[0069] Step 102: using a signal receiver to acquire infrared signal data in the surrounding area to obtain carbon dioxide infrared signal data;

[0070] Specifically, in this embodiment, a signal receiver is used to obtain infrared signal data in the surrounding area, and the infrared signal data is filtered and amplified to obtain initial infrared signal data; the initial infrared signal data is input into a shaping circuit, and the sine wave signal of the initial infrared signal data is converted into a square wave signal by the shaping circuit to obtain carbon dioxide infrared signal data. After the sine wave signal passes through the filtering circuit, it will enter the amplification circuit. The shaping circuit can usually be built with a zero-crossing comparator or a hysteresis comparator. When the shaping circuit chooses to use a zero-crossing comparator for design, if a weak interference is generated at the input end of the comparator, then the signal output at the output end will oscillate. Therefore, a zero-crossing comparator is generally not selected in the shaping circuit. Because the hysteresis comparator can well resist external interference signals, the output end will not produce a voltage jump due to slight changes in the input end and external interference signals.

[0071] Step 103: Decode the carbon dioxide infrared signal data based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data;

[0072] Specifically, in this embodiment, carbon dioxide infrared signal data is obtained, and the pulse width of the infrared signal data is calculated based on the NEC infrared remote control protocol to obtain the infrared signal pulse width; the NEC infrared remote control protocol at least includes a guide code, an address code, an address inverse code, a command code, and a command inverse code; according to the infrared signal pulse width, it is determined whether the pulse is a head pulse, if so, the receiving counter is cleared and the infrared signal data is output; if not, it is determined whether the pulse is "1", and the corresponding bit of the pulse is set; "1" is added to the receiving counter to determine whether the reception is completed, if not, the infrared signal data is output; if so, it is verified whether it is correct; if the verification is correct, the key value is saved, and the infrared signal data is output to obtain the decoded carbon dioxide infrared signal data; the logic "0" is 1ms, the logic "1" is 2ms, and the pulse time is 500us.

[0073] Step 104: Establish an RBF neural network model, and use a PSO particle swarm algorithm to optimize the parameters of the RBF neural network model to obtain a PSO-RBF neural network model;

[0074] Specifically, in this embodiment, an RBF neural network model is established, the topological structure of the RBF neural network model is determined, the RBF neural network model, the normalized constant and the weighted coefficient are initialized; the parameters to be mapped are determined, and the particle speed and particle position in the PSO particle swarm algorithm are initialized; the particle fitness is calculated, and the optimal Pbest and Gbest are updated; Pbest is an individual extreme value, indicating the position with the best fitness among all positions experienced by a certain individual particle; Gbest is a group extreme value, indicating the position with the best fitness searched by all particles; the inertia weight is updated to determine whether the conditions are met, and if so, the parameters of the RBF neural network model are determined to obtain a PSO-RBF neural network model.

[0075] Step 105: input the carbon dioxide infrared signal data and the average gas temperature data into the PSO-RBF neural network model for identification to obtain the carbon dioxide gas concentration;

[0076] Specifically, in this embodiment, the carbon dioxide infrared signal data and the average gas temperature data are input into the PSO-RBF neural network model; the carbon dioxide infrared signal data and the average gas temperature data are standardized; the carbon dioxide infrared signal data and the average gas temperature data are reduced in dimension using the FA analysis method; the main factors are determined, and the scores of the main factors are calculated, and the main factor scores are used as the input data of the PSO-RBF neural network model, and the carbon dioxide gas concentration is obtained after training.

[0077] Step 106: determine whether the carbon dioxide gas concentration exceeds the warning threshold. If the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to the server for warning.

[0078] Specifically, in this embodiment, it is determined whether the carbon dioxide gas concentration exceeds the warning threshold. If the carbon dioxide gas concentration exceeds the warning threshold, the warning threshold at least includes that the carbon dioxide gas concentration is greater than 0.1%; the carbon dioxide gas concentration is transmitted to the server for early warning, and an early warning feedback is obtained every 5 minutes; if there is no early warning feedback within 20 minutes, the carbon dioxide in the area is filtered and absorbed by the carbon dioxide absorption device; and the target carbon dioxide infrared signal data and the real-time average gas temperature data in the area are obtained in real time; the target carbon dioxide infrared signal data and the real-time average gas temperature data are input into the PSO-RBF neural network model to obtain the target carbon dioxide gas concentration. If the target carbon dioxide gas concentration is less than the warning threshold, the carbon dioxide absorption device is stopped from filtering and absorbing the carbon dioxide in the area.

[0079] Its beneficial effects are that, by emitting 41KHZ ultrasonic signals to the surrounding area based on ultrasonic sensors, the gas temperature data in the surrounding area is obtained through temperature sensors to obtain average gas temperature data; infrared signal data in the surrounding area is obtained by using signal receivers to obtain carbon dioxide infrared signal data; carbon dioxide infrared signal data is decoded based on NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data; an RBF neural network model is established, and the parameters of the RBF neural network model are optimized using the PSO particle swarm algorithm to obtain a PSO-RBF neural network model; carbon dioxide infrared signal data and average gas temperature data are input into the PSO-RBF neural network model for identification to obtain carbon dioxide gas concentration; it is determined whether the carbon dioxide gas concentration exceeds the warning threshold, and if the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to the server for warning. It can effectively detect the carbon dioxide concentration in the area accurately, reduce the influence of temperature and distance on carbon dioxide detection, improve the detection efficiency and accuracy of carbon dioxide concentration, detect and warn of excessively high carbon dioxide concentration, and improve the safety of regional carbon dioxide concentration.

[0080] In this embodiment, please refer to Figure 2 In a second embodiment of a method for detecting regional carbon dioxide concentration based on sensor data in an embodiment of the present invention, an RBF neural network model is established, and the parameters of the RBF neural network model are optimized using a PSO particle swarm algorithm to obtain a PSO-RBF neural network model, including the following steps:

[0081] Step 201, establish an RBF neural network model, determine the topological structure of the RBF neural network model, initialize the RBF neural network model, standardize constants and weighting coefficients;

[0082] Step 202: Determine the parameters that need to be mapped and initialize the particle speed and particle position in the PSO particle swarm algorithm;

[0083] Step 203, calculate the particle fitness, and update the optimal Pbest and Gbest; Pbest is the individual extreme value, indicating the position with the best fitness among all the positions experienced by a certain particle individual; Gbest is the group extreme value, indicating the position with the best fitness searched by all particles;

[0084] Step 204: Update the inertia weight and determine whether the conditions are met. If so, determine the parameters of the RBF neural network model to obtain the PSO-RBF neural network model.

[0085] In this embodiment, please refer to Figure 3In a third embodiment of a method for detecting regional carbon dioxide concentration based on sensor data in an embodiment of the present invention, determining whether the carbon dioxide gas concentration exceeds a warning threshold, and if the carbon dioxide gas concentration exceeds the warning threshold, transmitting the carbon dioxide gas concentration to a server for warning includes the following steps:

[0086] Step 301, determining whether the carbon dioxide gas concentration exceeds the warning threshold, if the carbon dioxide gas concentration exceeds the warning threshold, the warning threshold at least includes the carbon dioxide gas concentration being greater than 0.1%;

[0087] Step 302: Transmit the carbon dioxide gas concentration to the server for early warning, and obtain early warning feedback once every 5 minutes;

[0088] Step 303: If there is no early warning feedback within 20 minutes, the carbon dioxide in the area is filtered and absorbed by a carbon dioxide absorption device;

[0089] Step 304, and obtaining the target carbon dioxide infrared signal data and real-time average gas temperature data in the area in real time;

[0090] Step 305: Input the target carbon dioxide infrared signal data and the real-time average gas temperature data into the PSO-RBF neural network model to obtain the target carbon dioxide gas concentration.

[0091] Step 306: If the target carbon dioxide gas concentration is less than the warning threshold, stop using the carbon dioxide absorption device to filter and absorb carbon dioxide in the area.

[0092] The above describes a method for detecting regional carbon dioxide concentration based on sensor data provided by an embodiment of the present invention. The following describes a system for detecting regional carbon dioxide concentration based on sensor data provided by an embodiment of the present invention. Figure 4 In one embodiment of the present invention, a regional carbon dioxide concentration detection system includes:

[0093] A data acquisition module is used to transmit a 41KHZ ultrasonic signal to the surrounding area based on an ultrasonic sensor, obtain gas temperature data in the surrounding area through a temperature sensor, and obtain average gas temperature data;

[0094] A signal receiving module, used to obtain infrared signal data in the surrounding area using a signal receiver to obtain carbon dioxide infrared signal data;

[0095] A data decoding module, used for decoding the carbon dioxide infrared signal data based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data;

[0096] The model building module is used to build the RBF neural network model, and optimize the parameters of the RBF neural network model using the PSO particle swarm algorithm to obtain the PSO-RBF neural network model;

[0097] The gas detection module is used to input the carbon dioxide infrared signal data and the average gas temperature data into the PSO-RBF neural network model for identification to obtain the carbon dioxide gas concentration;

[0098] The detection and warning module is used to determine whether the carbon dioxide gas concentration exceeds the warning threshold. If the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to the server for warning.

[0099] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for detecting regional carbon dioxide concentration based on sensor data, characterized in that: The regional carbon dioxide concentration detection method comprises the following steps: Based on the ultrasonic sensor emitting a 41KHZ ultrasonic signal to the surrounding area, the temperature sensor acquires the gas temperature data in the surrounding area to obtain the average gas temperature data; Using a signal receiver to acquire infrared signal data in the surrounding area, obtaining carbon dioxide infrared signal data; Decoding the carbon dioxide infrared signal data based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data; Establishing an RBF neural network model, optimizing the parameters of the RBF neural network model using a PSO particle swarm algorithm to obtain a PSO-RBF neural network model; Inputting the carbon dioxide infrared signal data and the average gas temperature data into the PSO-RBF neural network model for identification to obtain the carbon dioxide gas concentration; It is determined whether the carbon dioxide gas concentration exceeds a warning threshold. If the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to a server for warning.

2. A method for detecting regional carbon dioxide concentration based on sensor data as claimed in claim 1, characterized in that: The method of transmitting a 41KHZ ultrasonic signal to the surrounding area based on the ultrasonic sensor and acquiring the gas temperature data in the surrounding area through the temperature sensor includes: Based on the ultrasonic transmitter emitting 41KHZ ultrasonic signals into the surrounding area, the ultrasonic transmitter is set at a density of 2 / 100m 2 ; The gas temperature data of multiple areas are acquired through the air temperature sensor, and the gas temperature data are averaged to obtain average gas temperature data.

3. A method for detecting regional carbon dioxide concentration based on sensor data as claimed in claim 1, characterized in that: The method of acquiring infrared signal data in the surrounding area by using a signal receiver to obtain carbon dioxide infrared signal data includes: Using a signal receiver to acquire infrared signal data in a surrounding area, filtering and amplifying the infrared signal data to obtain initial infrared signal data; The initial infrared signal data is input into a shaping circuit, and the shaping circuit is used to convert the sinusoidal wave signal of the initial infrared signal data into a square wave signal to obtain carbon dioxide infrared signal data.

4. A method for detecting regional carbon dioxide concentration based on sensor data as claimed in claim 1, characterized in that: The method of decoding the carbon dioxide infrared signal data based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data includes: Acquire carbon dioxide infrared signal data, calculate the pulse width of the infrared signal data based on the NEC infrared remote control protocol, and obtain the infrared signal pulse width; The NEC infrared remote control protocol at least includes a guide code, an address code, an address inverse code, a command code, and a command inverse code; Determine whether the pulse is a head pulse according to the pulse width of the infrared signal. If so, the receiving counter is cleared and the infrared signal data is output; if not, determine whether the pulse is "1", and set the corresponding bit of the pulse; Add "1" to the receiving counter to determine whether the reception is completed. If not, output the infrared signal data; if so, check whether it is correct; If the verification is correct, the key value is saved and the infrared signal data is output to obtain the decoded carbon dioxide infrared signal data; logic "0" is 1ms, logic "1" is 2ms, and the pulse time is 500us.

5. A method for detecting regional carbon dioxide concentration based on sensor data as claimed in claim 1, characterized in that: The RBF neural network model is established, and the parameters of the RBF neural network model are optimized by using a PSO particle swarm algorithm to obtain a PSO-RBF neural network model, including: Establishing an RBF neural network model, determining the topological structure of the RBF neural network model, initializing the RBF neural network model, standardizing constants and weighting coefficients; Determine the parameters that need to be mapped and initialize the particle speed and particle position in the PSO particle swarm algorithm; Calculate the particle fitness and update the optimal Pbest and Gbest; the Pbest is the individual extreme value, indicating the position with the best fitness among all the positions experienced by a certain particle individual; the Gbest is the group extreme value, indicating the position with the best fitness searched by all particles; The inertia weight is updated to determine whether the conditions are met, and if so, the parameters of the RBF neural network model are determined to obtain the PSO-RBF neural network model.

6. A method for detecting regional carbon dioxide concentration based on sensor data as claimed in claim 1, characterized in that: The carbon dioxide infrared signal data and the average gas temperature data are input into the PSO-RBF neural network model for identification to obtain the carbon dioxide gas concentration, including: Inputting carbon dioxide infrared signal data and average gas temperature data into the PSO-RBF neural network model; Performing standardization processing on the carbon dioxide infrared signal data and the average gas temperature data; Performing dimensionality reduction processing on the carbon dioxide infrared signal data and the average gas temperature data by using FA analysis method; The main factors are determined, and the scores of the main factors are calculated. The scores of the main factors are used as input data of the PSO-RBF neural network model, and the carbon dioxide gas concentration is obtained after training.

7. A method for detecting regional carbon dioxide concentration based on sensor data as claimed in claim 1, characterized in that: The determining whether the carbon dioxide gas concentration exceeds the warning threshold, and if the carbon dioxide gas concentration exceeds the warning threshold, transmitting the carbon dioxide gas concentration to a server for warning, includes: Determine whether the carbon dioxide gas concentration exceeds the warning threshold, if the carbon dioxide gas concentration exceeds the warning threshold, the warning threshold at least includes the carbon dioxide gas concentration being greater than 0.1%; The carbon dioxide gas concentration is transmitted to the server for early warning, and early warning feedback is obtained every 5 minutes; If there is no early warning feedback within 20 minutes, the carbon dioxide in the area will be filtered and absorbed by the carbon dioxide absorption device; And obtain the target carbon dioxide infrared signal data and real-time average gas temperature data in the area in real time; The target carbon dioxide infrared signal data and the real-time average gas temperature data are input into the PSO-RBF neural network model to obtain the target carbon dioxide gas concentration. If the target carbon dioxide gas concentration is less than the warning threshold, the carbon dioxide absorption device is stopped from filtering and absorbing the carbon dioxide in the area.

8. A regional carbon dioxide concentration detection system based on sensor data, characterized in that: The regional carbon dioxide concentration detection system includes the following modules: A data acquisition module is used to transmit a 41KHZ ultrasonic signal to the surrounding area based on an ultrasonic sensor, obtain gas temperature data in the surrounding area through a temperature sensor, and obtain average gas temperature data; A signal receiving module, used to obtain infrared signal data in the surrounding area using a signal receiver to obtain carbon dioxide infrared signal data; A data decoding module, used for decoding the carbon dioxide infrared signal data based on the NEC infrared remote control protocol to obtain decoded carbon dioxide infrared signal data; A model building module is used to build an RBF neural network model, and optimize the parameters of the RBF neural network model using a PSO particle swarm algorithm to obtain a PSO-RBF neural network model; A gas detection module, used for inputting carbon dioxide infrared signal data and average gas temperature data into the PSO-RBF neural network model for identification to obtain carbon dioxide gas concentration; The detection and warning module is used to determine whether the carbon dioxide gas concentration exceeds the warning threshold. If the carbon dioxide gas concentration exceeds the warning threshold, the carbon dioxide gas concentration is transmitted to the server for warning.

9. A regional carbon dioxide concentration detection system based on sensor data as claimed in claim 8, characterized in that: The data decoding module includes the following submodules: An acquisition submodule, used to acquire carbon dioxide infrared signal data, calculate the pulse width of the infrared signal data based on the NEC infrared remote control protocol, and obtain the infrared signal pulse width; The protocol submodule is used to determine that the NEC infrared remote control protocol at least includes a guide code, an address code, an address inverse code, a command code, and a command inverse code; The pulse submodule is used to determine whether the pulse is a head pulse according to the pulse width of the infrared signal. If so, the receiving counter is cleared and the infrared signal data is output; if not, it is determined whether the pulse is "1", and the corresponding bit of the pulse is set; The judgment submodule is used to add "1" to the receiving counter to determine whether the reception is completed. If not, the infrared signal data is output; if so, whether it is correct is verified; The check submodule is used to judge whether the check is correct, save the key value, and output the infrared signal data to obtain the decoded carbon dioxide infrared signal data; the logic "0" is 1ms, the logic "1" is 2ms, and the pulse time is 500us.

10. A regional carbon dioxide concentration detection system based on sensor data as claimed in claim 8, characterized in that: The model building module includes the following submodules: Establishing a submodule for establishing an RBF neural network model, determining the topological structure of the RBF neural network model, initializing the RBF neural network model, standardizing constants and weighting coefficients; The parameter submodule is used to determine the parameters that need to be mapped and initialize the particle speed and particle position in the PSO particle swarm algorithm; The calculation submodule is used to calculate the fitness of particles and update the optimal Pbest and Gbest; the Pbest is the individual extreme value, indicating the position with the best fitness among all the positions experienced by a certain particle individual; the Gbest is the group extreme value, indicating the position with the best fitness searched by all particles; The weight submodule is used to update the inertia weight and determine whether the conditions are met. If so, the parameters of the RBF neural network model are determined to obtain the PSO-RBF neural network model.

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