WO2024069919A1 - Dispositif d'apprentissage automatique et dispositif de mesure de concentration de dioxyde de carbone - Google Patents

Dispositif d'apprentissage automatique et dispositif de mesure de concentration de dioxyde de carbone Download PDF

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WO2024069919A1
WO2024069919A1 PCT/JP2022/036655 JP2022036655W WO2024069919A1 WO 2024069919 A1 WO2024069919 A1 WO 2024069919A1 JP 2022036655 W JP2022036655 W JP 2022036655W WO 2024069919 A1 WO2024069919 A1 WO 2024069919A1
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value
estimated value
correction
unit
machine learning
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PCT/JP2022/036655
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Japanese (ja)
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一紀 中田
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Tdk株式会社
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/59—Transmissivity
    • G01N21/61—Non-dispersive gas analysers

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  • the present invention relates to a machine learning machine and a carbon dioxide concentration measurement device.
  • Carbon dioxide concentration measuring devices that measure the carbon dioxide concentration in a space are used in various places.
  • the carbon dioxide concentration in a vinyl greenhouse is measured with a CO2 sensor, and the greenhouse is controlled to have an environment suitable for plant growth.
  • CO2 sensors By measuring the carbon dioxide concentration indoors with a CO2 sensor, it is possible to promote ventilation in the room and prevent infectious diseases.
  • air conditioning systems often measure the carbon dioxide concentration to control the indoor environment.
  • the non-dispersive infrared absorption (ND-IR) method is known, which calculates carbon dioxide concentration based on the amount of infrared radiation absorbed.
  • CO2 sensors using the ND-IR method can measure CO2 concentration with relatively high accuracy. Even with CO2 sensors using the ND-IR method, as described in Patent Documents 1 and 2, the reference value may shift due to long-term changes over time, and regular calibration is required.
  • CO2 sensors using the ND-IR method can measure CO2 concentration with relatively high accuracy, but are expensive.
  • CO2 sensors using the ND-IR method have many components such as infrared emitters, filters, and light receiving elements, and the package is large, which is a problem.
  • the present invention has been made in consideration of the above circumstances, and aims to provide a machine learning machine for realizing an inexpensive, highly accurate CO2 sensor.
  • a machine learning machine includes an estimation unit, a timing detection unit, and a correction unit.
  • the estimation unit estimates a carbon dioxide concentration based on an input signal.
  • the timing detection unit monitors an estimated value output from the estimation unit and determines the timing of correction of the estimated value.
  • the correction unit determines a correction value for correcting the estimated value each time a correction instruction is received from the timing detection unit.
  • the timing detection unit instructs the correction unit to make a correction when the change width between a first estimated value at a first time and a second estimated value at a second time subsequent to the first time is greater than the first change width and the second estimated value is equal to or smaller than a first specified value, or when the change width between the first estimated value and the second estimated value is greater than the second change width and the second estimated value is equal to or larger than a second specified value.
  • the timing detection unit may further instruct the correction unit to perform correction when the estimated value output from the estimation unit is equal to or less than a third specified value over a first period.
  • the timing detection unit may further instruct the correction unit to perform correction when the estimated value output from the estimation unit is equal to or greater than a fourth specified value over a second period.
  • the timing detection unit may further instruct the correction unit to perform correction when the change range between the first estimated value and the second estimated value becomes larger than a third change range.
  • the correction unit may add the correction value to the estimated value or subtract the correction value from the estimated value.
  • the correction unit may add a value to the estimated value or subtract a value from the estimated value. The value increases stepwise until it reaches the correction value.
  • the estimation unit may be a recurrent neural network.
  • the estimation unit may perform a learning process and an estimation process, and the learning process may be performed in an interval in which the timing detection unit does not determine that correction is necessary.
  • the carbon dioxide concentration measuring device according to the second aspect is equipped with the machine learning device according to the above aspect.
  • the machine learning machine and carbon dioxide concentration measuring device can measure carbon dioxide concentration with high accuracy even with an inexpensive sensor.
  • FIG. 1 is a schematic diagram of a carbon dioxide concentration measuring device according to a first embodiment.
  • FIG. FIG. 1 is a conceptual diagram of a reservoir network, which is an example of a recurrent neural network.
  • FIG. 11 is a schematic diagram for explaining a first condition.
  • FIG. 11 is a schematic diagram for explaining a second condition.
  • FIG. 13 is a schematic diagram for explaining a fourth condition.
  • FIG. 13 is a schematic diagram for explaining a fifth condition.
  • 11A and 11B are diagrams illustrating a correction operation by a correction unit.
  • 13A and 13B are diagrams illustrating another example of the correction operation by the correction unit.
  • 4 shows an example of a measurement result of the carbon dioxide concentration measuring device according to the first embodiment. 4 shows an example of the measurement result of a carbon dioxide concentration measuring device that does not have a correction unit.
  • FIG. 11 is a schematic diagram of a carbon dioxide concentration measuring device according to a first modified example.
  • First embodiment 1 shows a carbon dioxide concentration measuring device 100 according to the first embodiment.
  • the carbon dioxide concentration measuring device 100 includes, for example, a machine learning machine 10 and a sensor 20.
  • the sensor 20 is any sensor.
  • the sensor 20 is, for example, a CO2 sensor.
  • the sensor 20 measures the carbon dioxide concentration in the environment in which the carbon dioxide concentration measuring device 100 is placed, for example, with reference to temperature, humidity, air pressure, etc.
  • the accuracy of the sensor 20 may be lower than that of a CO2 sensor using the ND-IR method.
  • the machine learning machine 10 is, for example, a microcontroller or a processor.
  • the machine learning machine 10 has, for example, a CPU that performs arithmetic processing, a ROM that stores programs, and a RAM that is a working memory when the CPU performs arithmetic processing.
  • the machine learning machine 10 operates by executing the stored programs.
  • the machine learning machine 10 outputs an output signal based on an input signal from the sensor 20. Even if the measurement accuracy of the carbon dioxide concentration of the sensor 20 is low, the measurement accuracy of the carbon dioxide concentration measuring device 100 is improved by using the machine learning machine 10.
  • the machine learning device 10 has, for example, an estimation unit 1, a timing detection unit 2, a correction unit 3, a comparison unit 4, and a memory unit 5.
  • the machine learning device 10 performs a learning process and an estimation process.
  • the learning process is performed by the estimation unit 1, the comparison unit 4, and the memory unit 5.
  • the estimation process is performed by the estimation unit 1, the timing detection unit 2, and the correction unit 3.
  • the machine learning device 10 improves the measurement accuracy of the carbon dioxide concentration measuring device 100 through the learning process.
  • the machine learning device 10 outputs an estimated value of the carbon dioxide concentration in the environment in which the carbon dioxide concentration measuring device 100 is placed.
  • the estimation unit 1 is, for example, a part of the microcontroller or processor of the machine learning device 10.
  • the estimation unit 1 includes, for example, a CPU, ROM, RAM, etc.
  • the estimation unit 1 estimates the carbon dioxide concentration of the environment in which the carbon dioxide concentration measuring device 100 is placed based on the input signal from the sensor 20.
  • the estimation unit 1 includes a neural network.
  • a neural network is a mathematical model that mimics the network of nerve cells in the brain.
  • the estimation unit 1 is, for example, a recurrent neural network.
  • a recurrent neural network can handle nonlinear time series data. Nonlinear time series data is data whose values change over time, and examples include stock prices and the number of influenza epidemics.
  • a recurrent neural network is suitable for processing time series data by returning the results of processing in neurons in a later layer to neurons in an earlier layer.
  • Fig. 2 is a conceptual diagram of a reservoir network, which is an example of a recurrent neural network.
  • the neural network NN shown in Fig. 2 has an input layer L in , a reservoir layer R, and an output layer L out .
  • the reservoir layer R includes a plurality of nodes n i .
  • the number of nodes n i is not particularly limited. Hereinafter, the number of nodes n i is set to N.
  • Each of the nodes n i may be replaced with, for example, a physical device.
  • the physical device is, for example, a device that can convert an input signal into vibration, an electromagnetic field, a magnetic field, a spin wave, or the like.
  • connection weights are defined between each node n i .
  • the number of connection weights defined is equal to the number of combinations of connections between nodes n i .
  • Each connection weight between nodes n i is defined in principle and does not change through learning.
  • Each connection weight between nodes n i is arbitrary and may be the same as or different from each other. Some of the connection weights between multiple nodes n i may change through learning.
  • An input signal is input to the reservoir layer R from the input layer L in .
  • the input signal is input, for example, from an external sensor 20.
  • the input signal interacts with the nodes n i while propagating between them in the reservoir layer R. Signals interact with each other when a signal propagating to a node n i affects a signal propagating to another node n i . For example, when the input signal propagates between the nodes n i , a connection weight is applied to the input signal, and the input signal changes.
  • the reservoir layer R projects the input signal into a multidimensional nonlinear space.
  • the input signal input to the reservoir layer R is replaced with another signal. At least a portion of the information contained in the input signal is retained in a changed form.
  • One or more signals S i are sent to the output layer L out from the reservoir layer R.
  • a connection weight x i is applied to each of the signals S i output from the reservoir layer R.
  • the output layer L out performs a multiplication operation in which the connection weight x i is applied to the signal S i , and a summation operation in which the results of each product operation are added together.
  • the connection weight x i is updated in a learning process, and inference is performed based on the updated connection weight x i .
  • the comparison unit 4 is, for example, a part of a microcontroller or a processor.
  • the comparison unit 4 includes, for example, a CPU, a ROM, a RAM, etc.
  • the comparison unit 4 compares the teacher data stored in the storage unit 5 with the estimation result from the estimation unit 1.
  • the comparison unit 4 calculates the degree of agreement (correct answer rate) between the teacher data and the estimation result. If the accuracy rate of the comparison unit 4 is poor, the machine learning device 10 adjusts the connection weight x i of the estimation unit 1 so as to increase the accuracy rate.
  • the storage unit 5 is, for example, a memory.
  • the storage unit 5 may be an external server.
  • Teacher data is stored in the storage unit 5.
  • the teacher data is, for example, a measurement result obtained by a highly accurate carbon dioxide concentration measuring device.
  • the teacher data is, for example, a measurement result obtained by a CO2 sensor using the ND-IR method.
  • the comparison unit 4 compares the estimation result from the estimation unit 1 with the teacher data stored in the memory unit 5 to increase the degree of agreement between the estimation result and the teacher data.
  • the degree of agreement between the estimation result and the teacher data in the learning process, even when the carbon dioxide concentration is measured using a low-accuracy sensor 20, it is possible to measure the carbon dioxide concentration with the same accuracy as when the carbon dioxide concentration is measured using a highly accurate sensor that serves as teacher data.
  • the learning process is preferably performed in an interval in which the timing detection unit 2, described later, does not determine that correction is necessary.
  • the timing detection unit 2 is, for example, a part of a microcontroller or a processor.
  • the timing detection unit 2 includes, for example, a CPU, a ROM, a RAM, etc.
  • the timing detection unit 2 operates during the estimation process of the machine learning device 10. During the estimation process, the timing detection unit 2 monitors the estimated value output from the estimation unit 1 and determines the timing for correcting the estimated value.
  • the timing detection unit 2 instructs the correction unit 3 to correct the estimated value output from the estimation unit 1 when, for example, at least the first condition or the second condition is satisfied.
  • the timing detection unit 2 may instruct the correction unit 3 to correct the estimated value output from the estimation unit 1 when, for example, the first condition and the second condition are satisfied.
  • FIG. 3 is a schematic diagram for explaining the first condition.
  • the graph shown in FIG. 3 shows the change over time of the estimated value output from the estimation unit 1.
  • the horizontal axis of the graph in FIG. 3 is time t, and the vertical axis is the estimated value V of the carbon dioxide concentration output from the estimation unit 1.
  • the estimated value is output at regular intervals and changes over time.
  • the carbon dioxide concentration fluctuates based on a reference value Vb.
  • the reference value Vb can be set arbitrarily according to the environment in which the carbon dioxide concentration measuring device 100 is installed.
  • the reference value Vb is, for example, the CO2 concentration in the atmosphere, and is approximately 400 ppm.
  • the first condition is that the change range ⁇ V between the first estimated value V1 and the second estimated value V2 is greater than the first change range ⁇ V1, and the second estimated value V2 is equal to or smaller than the first specified value Vs1.
  • the first estimated value V1 is an estimated value at the first time t1.
  • the second estimated value V2 is an estimated value at the second time t2.
  • the first time t1 and the second time t2 are consecutive in time, and the second time t2 is the time at which the estimation unit 1 outputs an estimated value after the first time t1.
  • the first change width ⁇ V1 can be set arbitrarily in accordance with the measurement environment.
  • the first specified value Vs1 can be set arbitrarily in accordance with the measurement environment.
  • the first condition may be met.
  • the reference value Vb is set to the CO2 concentration of the outside air, and the estimated value should approach the reference value Vb by opening the window for ventilation or the like. If the estimated value is equal to or less than the first specified value Vs1 that is lower than the reference value Vb, it is highly likely that the accuracy rate of the estimated value has decreased.
  • the machine learning device 10 can maintain a high accuracy rate when the data changes continuously over time, but the accuracy rate tends to decrease when the data changes suddenly. Therefore, when the first condition is met, correcting the estimated value increases the accuracy of the carbon dioxide concentration output from the carbon dioxide concentration measuring device 100.
  • FIG. 4 is a schematic diagram for explaining the second condition.
  • the graph shown in FIG. 4 shows the change over time of the estimated value output from the estimation unit 1.
  • the horizontal axis of the graph in FIG. 4 is time t, and the vertical axis is the estimated value V of the carbon dioxide concentration output from the estimation unit 1.
  • the second condition is that the change range ⁇ V' between the first estimated value V1' and the second estimated value V2' is greater than the second change range ⁇ V2, and the second estimated value V2' is greater than or equal to the second specified value Vs2.
  • the first estimated value V1' is an estimated value at the first time t1'.
  • the second estimated value V2' is an estimated value at the second time t2'.
  • the first time t1' and the second time t2' are consecutive in time, and the second time t2' is the time at which the estimation unit 1 outputs an estimated value after the first time t1'.
  • the second change width ⁇ V2 can be set arbitrarily in accordance with the measurement environment.
  • the second change width ⁇ V2 may be the same as the first change width V1.
  • the second specified value Vs2 can be set arbitrarily in accordance with the measurement environment.
  • the absolute value of the difference between the second specified value Vs2 and the reference value Vb may be the same as the absolute value of the difference between the first specified value Vs1 and the reference value Vb.
  • the second condition may be met. If the CO2 concentration that is unlikely to be reached in the environment in which the carbon dioxide concentration measuring device 100 is installed is set as the second specified value Vs2, the fact that the estimated value is equal to or greater than the second specified value Vs2 means that the accuracy rate of the estimated value is likely to be low. In addition, as described above, the machine learning device 10 is not good at responding to sudden changes in data. When the second condition is met, correcting the estimated value increases the accuracy of the carbon dioxide concentration output from the carbon dioxide concentration measuring device 100.
  • the timing detection unit 2 may also instruct the correction unit 3 to make a correction when the third condition is satisfied.
  • the third condition is that the change range between the first estimated value and the second estimated value is greater than the third change range.
  • the third condition is a condition that focuses only on the change range and is unrelated to the first specified value Vs1 and the second specified value Vs2.
  • the third change range may be the same as or different from the first change range ⁇ V1 or the second change range ⁇ V2. As described above, the machine learning device 10 has difficulty responding to sudden changes in data. If the estimated value is corrected when the third condition is satisfied, the accuracy of the carbon dioxide concentration output from the carbon dioxide concentration measuring device 100 is improved.
  • the timing detection unit 2 may also instruct the correction unit 3 to make a correction when the fourth condition is satisfied.
  • FIG. 5 is a schematic diagram for explaining the fourth condition.
  • the graph shown in FIG. 5 shows the change over time in the estimated value output from the estimation unit 1.
  • the horizontal axis of the graph in FIG. 5 is time t, and the vertical axis is the estimated value V of the carbon dioxide concentration output from the estimation unit 1.
  • the fourth condition is that the estimated value output from the estimation unit 1 is equal to or less than the third specified value Vs3 over the first period ⁇ t1.
  • the first period ⁇ t1 can be set arbitrarily according to the measurement environment.
  • the third specified value Vs3 can be set arbitrarily according to the measurement environment.
  • the third specified value Vs3 may be the same as the first specified value Vs1.
  • the lower limit of the CO2 concentration that may be reached in the environment in which the carbon dioxide concentration measuring device 100 is installed is set as the third specified value Vs3
  • the fact that the estimated value is maintained at or below the third specified value Vs3 for a certain period of time means that the accuracy rate of the estimated value is likely to have decreased. Therefore, if the estimated value is corrected when the fourth condition is satisfied, the accuracy of the carbon dioxide concentration output from the carbon dioxide concentration measuring device 100 is improved.
  • the timing detection unit 2 may also instruct the correction unit 3 to make a correction when the fifth condition is satisfied.
  • FIG. 6 is a schematic diagram for explaining the fifth condition.
  • the graph shown in FIG. 6 shows the change over time in the estimated value output from the estimation unit 1.
  • the horizontal axis of the graph in FIG. 6 is time t, and the vertical axis is the estimated value V of the carbon dioxide concentration output from the estimation unit 1.
  • the fifth condition is that the estimated value output from the estimation unit 1 is equal to or greater than the fourth specified value Vs4 over the second period ⁇ t2.
  • the second period ⁇ t2 can be set arbitrarily to suit the measurement environment.
  • the second period ⁇ t2 may be the same as or different from the first period ⁇ t1.
  • the fourth specified value Vs4 can be set arbitrarily to suit the measurement environment.
  • the fourth specified value Vs4 may be the same as the second specified value Vs2.
  • the upper limit of the CO2 concentration that may be reached in the environment in which the carbon dioxide concentration measuring device 100 is installed is set as the fourth specified value Vs4
  • the fact that the estimated value is maintained at or above the fourth specified value Vs4 for a certain period of time means that the accuracy rate of the estimated value is likely to have decreased. Therefore, if the estimated value is corrected when the fifth condition is satisfied, the accuracy of the carbon dioxide concentration output from the carbon dioxide concentration measuring device 100 is improved.
  • the correction unit 3 is, for example, a part of a microcontroller or a processor.
  • the correction unit 3 includes, for example, a CPU, a ROM, a RAM, etc.
  • the correction unit 3 operates during the estimation process of the machine learning device 10. During the estimation process, the correction unit 3 determines a correction value for correcting the estimated value each time a correction instruction is received from the timing detection unit 2. The correction value is rewritten, for example, each time a correction instruction is received from the timing detection unit 2.
  • FIG. 7 is a diagram showing the correction operation by the correction unit 3. As shown in FIG. 7, for example, when the first condition is satisfied, the absolute value of the difference between the reference value Vb and the second estimated value V2 is set as the correction value. For example, the correction unit 3 adds the correction value to the estimated value output at or after the third time t3, when the estimated value is output next after the second time t2.
  • FIG. 8 is a diagram showing another example of the correction operation by the correction unit 3.
  • the correction unit 3 may gradually increase the value added to the estimated value until it reaches the correction value.
  • the correction unit 3 adds gradually larger values to the estimated value during a third period ⁇ t3 from the third time t3 to the fourth time t4. Then, the correction value is added to the estimated value that is output after the fourth time t4. By gradually increasing the value added to the estimated value until it reaches the correction value, it is possible to prevent the output estimated value from changing suddenly.
  • the absolute value of the difference between the reference value Vb and the second estimated value V2' is set as the correction value.
  • the correction unit 3 subtracts the correction value from the estimated value that is output, for example, at or after the third time t3 when the estimated value is output next after the second time t2.
  • the correction unit 3 may gradually increase the value that is subtracted from the estimated value until the value that is subtracted from the estimated value becomes the correction value.
  • the absolute value of the reference value Vb and the second estimated value is set as the correction value.
  • the correction unit 3 adds the correction value to the estimated value output at or after the third time t3 when the estimated value is output next after the second time t2, or subtracts the correction value from the estimated value. If the second estimated value is smaller than the reference value Vb, the correction value is added to the estimated value, and if the second estimated value is larger than the reference value Vb, the correction value is subtracted from the estimated value. The value added to or subtracted from the estimated value may be increased stepwise until it reaches the correction value.
  • the absolute value of the difference between the reference value Vb and the estimated value that is first output after the first period ⁇ t1 has elapsed is set as the correction value.
  • the correction unit 3 adds the correction value to the estimated value that is output after the first period ⁇ t1 has elapsed.
  • the value added by the correction unit 3 may be increased stepwise until it reaches the correction value.
  • the absolute value of the difference between the reference value Vb and the estimated value that is first output after the second period ⁇ t2 has elapsed is set as the correction value.
  • the correction unit 3 subtracts the correction value from the estimated value that is output after the second period ⁇ t2 has elapsed.
  • the value that the correction unit 3 subtracts may be increased stepwise until it reaches the correction value.
  • the estimation unit 1 has a higher degree of agreement with the teacher data through a learning process, and the accuracy of the estimated value output from the estimation unit 1 is higher than that of the raw data output by the sensor 20.
  • the machine learning device 10 outputs the estimated value from the estimation unit 1 as a predicted value of the carbon dioxide concentration.
  • the correction unit 3 corrects the estimated value.
  • the carbon dioxide concentration measuring device 100 according to this embodiment can improve the measurement accuracy of the carbon dioxide concentration by using the machine learning device 10. Furthermore, the carbon dioxide concentration measuring device 100 according to this embodiment can further improve the measurement accuracy of the carbon dioxide concentration by correcting the estimated value from the estimation unit 1 using the correction unit 3.
  • Fig. 9 shows an example of the measurement result of the carbon dioxide concentration measuring device according to the first embodiment.
  • Fig. 10 shows an example of the measurement result of the carbon dioxide concentration measuring device without the correction unit 3.
  • the reference in Fig. 9 and Fig. 10 is teacher data, which is the output value of a CO2 sensor using the ND-IR method.
  • the carbon dioxide concentration measuring device shown in Figures 9 and 10 performs learning processing in the first stage and estimation processing in the second stage.
  • the learning processing is performed in an interval where the timing detection unit 2 does not determine that correction is necessary.
  • the timing detection unit 2 and the correction unit 3 are turned off.
  • the estimated value using the output value from the less accurate sensor 20 has a high degree of agreement with the reference, and it can be confirmed that the measurement accuracy of the carbon dioxide concentration of the carbon dioxide concentration measuring device of the first embodiment is as high as that of a CO2 sensor using the ND-IR method.
  • Figure 9 shows a higher degree of agreement between the estimated value and the reference. This is because a correction process that satisfies the first condition was performed in the area surrounded by the dotted line. In other words, it can be confirmed that the functioning of correction unit 3 improves the measurement accuracy of the carbon dioxide concentration by the carbon dioxide concentration measuring device 100.
  • FIG. 11 is a schematic diagram of a carbon dioxide concentration measuring device 101 according to the first embodiment.
  • the comparison unit 4 and memory unit 5 are arranged outside the machine learning device 11. In other words, the comparison unit 4 and memory unit 5 are not installed in the edge terminal.
  • the learning process can be performed in advance at the time of shipment, or can be removed from the edge terminal.
  • the comparison unit 4 and memory unit 5 can also be stored in an external server, and the edge terminal and the external server can communicate to update the learning process.

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Abstract

Le dispositif d'apprentissage automatique de l'invention comprend : une unité d'estimation qui estime une concentration de dioxyde de carbone sur la base d'un signal d'entrée ; une unité de détection de synchronisation qui surveille une valeur estimée émise par l'unité d'estimation et qui détermine le moment de la correction de la valeur estimée ; et une unité de correction qui détermine une valeur de correction pour corriger la valeur estimée chaque fois qu'une instruction de correction est reçue de l'unité de détection de synchronisation. L'unité de détection de synchronisation envoie une instruction de correction à l'unité de correction si une largeur de variation entre une première valeur estimée à un premier instant et une seconde valeur estimée à un second instant qui est continu avec le premier instant est supérieure à une première largeur de variation et que la seconde valeur estimée est égale ou inférieure à une première valeur définie, ou si une largeur de variation entre la première valeur estimée et la seconde valeur estimée est supérieure à une seconde largeur de variation et que la seconde valeur estimée est égale ou supérieure à une seconde valeur définie.
PCT/JP2022/036655 2022-09-30 2022-09-30 Dispositif d'apprentissage automatique et dispositif de mesure de concentration de dioxyde de carbone WO2024069919A1 (fr)

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CN112683836A (zh) * 2021-01-12 2021-04-20 杭州麦乐克科技股份有限公司 基于bp神经网络的二氧化碳传感器的标定方法及其系统

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