CN118097914A - Smoke sensor processing method and device and smoke sensor - Google Patents
Smoke sensor processing method and device and smoke sensor Download PDFInfo
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Abstract
The application provides a processing method and a device of a smoke sensor and the smoke sensor, and relates to a smoke recognition technology, wherein the method comprises the following steps: and in the operation process of the smoke sensor, acquiring smoke data in the current environment according to a preset first acquisition frequency. Performing smoke detection on the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment. And (3) recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value. The method can dynamically adjust the calibrated 0-point calibration value according to the change condition of the smoke concentration in the current environment, and solves the technical problem of lower accuracy of the smoke acquisition result of the smoke sensor.
Description
Technical Field
The present application relates to smoke recognition technology, and in particular, to a method and apparatus for processing a smoke sensor, and a smoke sensor.
Background
At present, the installed capacity of an electrochemical energy storage system in recent years is doubled every year, new safety conditions are brought while the electrochemical energy storage system is developed vigorously, and the electrochemical energy storage device has the characteristic of high power, and once the electrochemical energy storage device fails, fire accidents are easily caused, so that the life safety of equipment and personnel is threatened. Therefore, the energy storage fire-fighting system needs to be controlled in a plurality of modes for preventive control, for example, sensors such as temperature, smoke and current are adopted to monitor the energy storage equipment in real time, and once abnormal conditions are found, the fire-fighting system is started in time to extinguish fire, so that fire accidents are prevented.
In the prior art, an optical scheme is often adopted, that is, a light-emitting source emits light, and a receiving device is further provided for receiving the light of the light-emitting source and converting the received light into a voltage. The luminous power of the luminous device is constant, but in the air with different transparencies, the light intensity received by the receiving area can change along with the air transparencies, the light change can lead to the voltage change converted by the smoke sensor, and the fire detector judges the smoke concentration by means of the voltage.
However, in the prior art, as the smoke accumulates over time, the reflecting surface of the smoke sensor becomes no longer smooth, that is, diffuse reflection is enhanced, the reflectivity is increased, the acquisition result of the smoke sensor is increased, and finally, 0-point drift and false alarm are caused. Or the accumulated dust falls off due to aging, vibration and the like of the smoke sensor, the reflection of the smoke sensor is weakened, and the acquisition result is reduced, so that a fire alarm is missed. Therefore, the accuracy of the acquisition result of the smoke sensor may be low, thereby leading to false alarm.
Disclosure of Invention
The application provides a smoke sensor processing method and device and a smoke sensor, which are used for solving the technical problem of low accuracy of a smoke acquisition result of the smoke sensor.
In a first aspect, the application provides a method of processing a smoke sensor, comprising:
During the operation process of the smoke sensor, acquiring smoke data in the current environment according to a preset first acquisition frequency;
Performing smoke detection on the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment;
and recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value.
Further, the adaptive calibration algorithm includes an up 0 point adaptation algorithm and a down 0 point adaptation algorithm.
Further, the recognition result information is generated according to the downward 0-point adaptation algorithm;
And recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value, wherein the method comprises the following steps of:
If the identification result information represents that each smoke data is higher than the current 0 point calibration value and the change rate is smaller than the preset smoke concentration threshold value in a preset first time interval, acquiring first preset quantity of smoke data according to a preset first acquisition frequency;
And averaging the first preset number of smoke data, taking the generated average value as a target calibration value, and updating the current 0 point calibration value into the target calibration value.
Further, the identification result information is generated according to the upward 0 point adaptation algorithm;
And recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value, wherein the method comprises the following steps of:
if the identification result information represents that each smoke data is lower than the current 0-point calibration value, lower than the preset calibration threshold value and lower than the preset smoke concentration threshold value in the preset second time interval, acquiring a first preset number of second smoke data according to a preset first acquisition frequency;
And averaging the first preset number of smoke data, taking the generated average value as a target calibration value, and updating the current 0 point calibration value into the target calibration value.
Further, the method further comprises:
After the smoke sensor is electrified, acquiring smoke data in the current environment according to a preset second acquisition frequency, and performing automatic calibration of a 0-point calibration value according to a plurality of acquired smoke data to generate an initial 0-point calibration value;
During the operation of the smoke sensor, the smoke data in the current environment is collected according to a preset first collection frequency, and the method comprises the following steps:
And after generating an initial 0-point calibration value, acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor.
Further, the automatic calibration of the 0 point calibration value is performed according to the collected multiple smoke data, and the initial 0 point calibration value is generated, which comprises the following steps:
if it is determined that the change rate of each of the plurality of smoke data is smaller than a preset smoke concentration threshold, acquiring a second preset number of smoke data according to a preset second acquisition frequency;
And averaging the second preset number of smoke data, and taking the generated average value as an initial 0-point calibration value of power-on.
In a second aspect, the present application provides a processing device for a smoke sensor, comprising:
The first acquisition module is used for acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor;
The detection module is used for detecting the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment;
And the calibration module is used for recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value.
Further, the adaptive calibration algorithm includes an up 0 point adaptation algorithm and a down 0 point adaptation algorithm.
Further, the recognition result information is generated according to the downward 0-point adaptation algorithm;
the calibration module comprises:
the first acquisition unit is used for acquiring first preset quantity of smoke data according to a preset first acquisition frequency if the identification result information represents that each piece of smoke data is higher than a current 0-point calibration value and the change rate is smaller than a preset smoke concentration threshold value in a preset first time interval;
And the first updating unit is used for averaging the first preset number of smoke data, taking the generated average value as a target calibration value and updating the current 0 point calibration value into the target calibration value.
Further, the identification result information is generated according to the upward 0 point adaptation algorithm;
the calibration module comprises:
The second acquisition unit is used for acquiring first preset quantity of second smoke data according to a preset first acquisition frequency if the identification result information represents that each smoke data is lower than a current 0-point calibration value, lower than a preset calibration threshold value and lower than a preset smoke concentration threshold value in a preset second time interval;
and the second updating unit is used for averaging the first preset number of smoke data, taking the generated average value as a target calibration value and updating the current 0 point calibration value into the target calibration value.
Further, the apparatus further comprises:
The second acquisition module is used for acquiring smoke data in the current environment according to a preset second acquisition frequency after the smoke sensor is electrified;
The calibration module is used for carrying out automatic calibration on the 0-point calibration value according to the collected multiple smoke data and generating an initial 0-point calibration value;
The first acquisition module is specifically configured to:
And after generating an initial 0-point calibration value, acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor.
Further, the calibration module includes:
The third acquisition unit is used for acquiring a second preset number of smoke data according to a preset second acquisition frequency if the change rate of each smoke data in the plurality of smoke data is smaller than a preset smoke concentration threshold value;
And the average unit is used for averaging the second preset number of smoke data and taking the generated average value as an initial 0-point calibration value for power-on.
In a third aspect, the application provides a smoke sensor comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, the processor implementing the method of the first aspect when executing the computer program.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for performing the method of the first aspect when executed by a processor.
In a fifth aspect, the application provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the first aspect.
The application provides a processing method and device of a smoke sensor and the smoke sensor, wherein smoke data in the current environment are collected according to a preset first collection frequency in the operation process of the smoke sensor. Performing smoke detection on the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment. And (3) recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value. In the scheme, smoke detection is carried out on a plurality of collected smoke data through a preset self-adaptive calibration algorithm, identification result information is generated, and the current 0-point calibration value is self-adaptively calibrated according to the identification result information. Therefore, the calibrated 0-point calibration value can be dynamically adjusted according to the change condition of the smoke concentration in the current environment, and the technical problem that the accuracy of the smoke acquisition result of the smoke sensor is low is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic flow chart of a method for processing a smoke sensor according to an embodiment of the present application;
Fig. 2 is a flow chart of another method for processing a smoke sensor according to an embodiment of the present application;
fig. 3 is a flow chart of a processing method of a smoke sensor according to another embodiment of the present application;
fig. 4 is a flow chart of a processing method of a smoke sensor according to another embodiment of the present application;
fig. 5 is a flow chart of another method for processing a smoke sensor according to an embodiment of the present application;
fig. 6 is a system block diagram of a method for processing a smoke sensor according to an embodiment of the present application;
Fig. 7 is a schematic structural diagram of a processing device of a smoke sensor according to an embodiment of the present application;
Fig. 8 is a schematic structural diagram of another processing device for a smoke sensor according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a smoke sensor according to an embodiment of the present application.
Specific embodiments of the present disclosure have been shown by way of the above drawings and will be described in more detail below. These drawings and the written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the disclosed concepts to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure.
At present, the installed capacity of an electrochemical energy storage system in recent years is doubled every year, new safety conditions are brought while the electrochemical energy storage system is developed vigorously, and the electrochemical energy storage device has the characteristic of high power, and once the electrochemical energy storage device fails, fire accidents are easily caused, so that the life safety of equipment and personnel is threatened. Therefore, the energy storage fire-fighting system needs to be controlled in a plurality of modes for preventive control, for example, sensors such as temperature, smoke and current are adopted to monitor the energy storage equipment in real time, and once abnormal conditions are found, the fire-fighting system is started in time to extinguish fire, so that fire accidents are prevented.
In one example, an optical scheme is generally adopted, that is, a light-emitting source emits light, and a receiving device is further provided for receiving the light from the light-emitting source and converting the received light into a voltage. The luminous power of the luminous device is constant, but in the air with different transparencies, the light intensity received by the receiving area can change along with the air transparencies, the light change can lead to the voltage change converted by the smoke sensor, and the fire detector judges the smoke concentration by means of the voltage. However, in the prior art, as the smoke accumulates over time, the reflecting surface of the smoke sensor becomes no longer smooth, that is, diffuse reflection is enhanced, the reflectivity is increased, the acquisition result of the smoke sensor is increased, and finally, 0-point drift and false alarm are caused. Or the accumulated dust falls off due to aging, vibration and the like of the smoke sensor, the reflection of the smoke sensor is weakened, and the acquisition result is reduced, so that a fire alarm is missed. Therefore, the accuracy of the acquisition result of the smoke sensor may be low, thereby leading to false alarm.
In one example, the optical monitoring precision is very high, under the smoke concentration of the same concentration, the light-emitting source or the detector receiving the light source in the sensor has tiny difference, so that the smoke concentration detected by the sensor is inconsistent, the collected voltages are different, and the problem of inconsistent collection is caused, so that when the smoke sensor equipment leaves the factory, calibration is required, when the smoke concentration is 0, zero calibration is performed on the smoke sensor, and because a dust-free environment is required for calibrating the 0 point, the longer working time of product leaving the factory is increased.
The application provides a smoke sensor processing method and device and a smoke sensor, and aims to solve the technical problems in the prior art.
The following is an explanation of the terms of art to which the present application relates:
fire control detector: the fire-fighting detection device can detect information such as smoke concentration, temperature, carbon monoxide and the like.
Smoke sensor: the device dedicated to measuring the smoke value in air, referred to herein as photoelectric, i.e. having a light emitting source and receiving means, is such that smoke particles in air affect the light transmission and thus the current of the receiving means.
DB/m: sensitivity of smoke and unit of alarm.
Digital communication: the communication mode for transmitting the digital signal can be various, such as serial port, I2C, SPI, private protocol, etc., and can transmit data in two directions. The digital communication referred to herein generally refers to a chained communication scheme, i.e., communication in hand, which is essentially different from the bus mode of CAN communication.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a method for processing a smoke sensor according to an embodiment of the present application, as shown in fig. 1, the method includes:
step 101, acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor.
The execution subject of the present embodiment may be, for example, a smoke sensor, or a terminal device, or a processing apparatus or device of a smoke sensor, or other apparatus or device that may execute the present embodiment, which is not limited thereto. In this embodiment, the smoke sensor is described as an execution body.
Firstly, in the operation process of the smoke sensor, smoke data in the current environment are collected according to a preset first collection frequency. For example, the first acquisition frequency may be 200Hz, and a plurality of smoke data in the current environment may be acquired at 200 Hz.
102, Performing smoke detection on a plurality of collected smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment.
Illustratively, the adaptive calibration algorithm includes an up 0 point adaptation algorithm and a down 0 point adaptation algorithm. The upward 0-point adaptation algorithm mainly identifies whether the environment is changed or not and whether the 0-point is required to be automatically adapted or not and adjusts upward in normal operation. The downward 0-point adaptation algorithm mainly identifies whether the environment is changed or not and whether the 0-point needs to be automatically adapted and adjusted downward in the running process.
In the step, smoke detection is carried out on a plurality of collected smoke data according to an upward 0-point adaptation algorithm and a downward 0-point adaptation algorithm, and identification result information is generated; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment.
And 103, recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value.
For example, if the identification result information is generated according to the downward 0-point adaptation algorithm, continuing to collect smoke data based on the downward 0-point adaptation algorithm, and calibrating the current 0-point calibration value of the smoke sensor downward again according to the collected smoke data to generate a target calibration value, wherein the target calibration value is smaller than the current 0-point calibration value. Or if the identification result information is generated according to the upward 0-point adaptation algorithm, continuing to collect smoke data based on the upward 0-point adaptation algorithm, and calibrating the current 0-point calibration value of the smoke sensor upwards again according to the collected smoke data to generate a target calibration value, wherein the target calibration value is larger than the current 0-point calibration value.
In the embodiment of the application, in the operation process of the smoke sensor, the smoke data in the current environment are acquired according to the preset first acquisition frequency. Performing smoke detection on the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment. And (3) recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value. In the scheme, smoke detection is carried out on a plurality of collected smoke data through a preset self-adaptive calibration algorithm, identification result information is generated, and the current 0-point calibration value is self-adaptively calibrated according to the identification result information. Therefore, the calibrated 0-point calibration value can be dynamically adjusted according to the change condition of the smoke concentration in the current environment, and the technical problem that the accuracy of the smoke acquisition result of the smoke sensor is low is solved.
Fig. 2 is a flow chart of another method for processing a smoke sensor according to an embodiment of the present application, as shown in fig. 2, the method includes:
step 201, after the smoke sensor is powered on, acquiring smoke data in the current environment according to a preset second acquisition frequency.
Illustratively, the smoke sensor needs to be calibrated by an initial calibration value of 0 point in the power-on stage, so that after the smoke sensor is powered on, the smoke data in the current environment are acquired according to a preset second acquisition frequency. For example, the second acquisition frequency is 100Hz.
Step 202, performing automatic calibration of a 0-point calibration value according to a plurality of collected smoke data, and generating an initial 0-point calibration value.
In one example, step 202 includes: if the change rate of each smoke data in the plurality of smoke data is smaller than the preset smoke concentration threshold value, acquiring a second preset number of smoke data according to a preset second acquisition frequency; and averaging the second preset number of smoke data, and taking the generated average value as an initial 0-point calibration value of power-on.
Fig. 3 is a schematic flow chart of a processing method of a smoke sensor according to another embodiment of the present application, as shown in fig. 3, after the smoke sensor is powered on, smoke data collection starts with a period of 100Hz, and the collection result (i.e. collected smoke data) is put into 500 caches. And judging whether the change rate of the smoke data acquired for 500 times continuously is lower than a preset smoke concentration threshold value of 5%, and if not, continuing to acquire. If yes, the smoke result of the current environment is stable, and 0-point calibration is started. The calibration mode is to continuously collect the smoke data of a second preset number of 100 times in a period of 100Hz, average the smoke data of 100 times, and store the generated average value as an initial 0-point calibration value of power-on.
Therefore, the application can automatically calibrate the 0-point calibration value when being electrified, and does not need to carry out 0-point calibration in a dust-free environment in factory time, so that the factory calibration time of products can be saved.
Step 203, after generating an initial calibration value of 0 point, acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor.
Illustratively, this step may refer to step 101 in fig. 1, and will not be described in detail.
204, Performing smoke detection on the collected multiple pieces of smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment.
In one example, the adaptive calibration algorithm includes an up 0 point adaptation algorithm and a down 0 point adaptation algorithm.
Illustratively, this step may refer to step 102 in fig. 1, and will not be described in detail.
And 205, recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value.
Step 205 includes two implementations:
The first implementation of step 205: the identification result information is generated according to a downward 0-point adaptation algorithm; if the identification result information represents that each smoke data is higher than the current 0 point calibration value and the change rate is smaller than the preset smoke concentration threshold value in a preset first time interval, acquiring first preset quantity of smoke data according to a preset first acquisition frequency; and averaging the first preset number of smoke data, taking the generated average value as a target calibration value, and updating the current 0 point calibration value into the target calibration value.
The second implementation of step 205: the identification result information is generated according to an upward 0-point adaptation algorithm; if the identification result information represents that each smoke data is lower than the current 0 point calibration value, lower than the preset calibration threshold value and lower than the preset smoke concentration threshold value in the preset second time interval, acquiring a first preset number of second smoke data according to a preset first acquisition frequency; and averaging the first preset number of smoke data, taking the generated average value as a target calibration value, and updating the current 0 point calibration value into the target calibration value.
Illustratively, in a first implementation, the recognition result information is generated according to a downward 0-point adaptation algorithm. Fig. 4 is a schematic flow chart of a processing method of a smoke sensor according to an embodiment of the present application, and as shown in fig. 4, after the operation of the smoke sensor is started, smoke results are collected at a frequency of 200 Hz. And (3) carrying out smoke detection on the acquisition result (namely acquired smoke data), judging whether the acquisition result is lower than the current 0-point calibration value within a first time interval of 'continuous 10 seconds', wherein the change rate is smaller than a preset smoke concentration threshold value '5%', if not, continuing judging, if the identification result information represents that each smoke data is higher than the current 0-point calibration value within the continuous 10 seconds and the change rate is smaller than the preset smoke concentration threshold value '5%', indicating that the current environment is changed, updating the previously stored 0-point calibration value, wherein the updating mode is to continuously acquire the first preset number of '200' smoke data according to the preset first acquisition frequency of 200Hz, average the 200-time smoke data, store the generated average value as a target calibration value, and update the current 0-point calibration value as the target calibration value.
Therefore, the problem of 0-point calibration value reduction caused by wind blowing/vibration/artificial dust cleaning and the like in the running process can be solved according to the 0-point downward automatic adaptation function in the self-adaptive calibration algorithm. Specifically, when dust suddenly drops due to some external force, such as wind blowing, artificial dust removal and the like, the real collected 0-point calibration value is greatly reduced, and the problem of fire alarm false alarm can be caused. The application can automatically identify the process of suddenly disappearing dust on the smoke sensor, carry out algorithm adaptation, and carry out 0-point automatic downward adaptation in a very short time so as to avoid false alarm.
In a second implementation, the recognition result information is generated according to an upward 0-point adaptation algorithm. Fig. 5 is a flow chart of another processing method of a smoke sensor according to an embodiment of the present application, as shown in fig. 5, after the operation of the smoke sensor is started, smoke results are collected at a frequency of 200 Hz. And (3) carrying out smoke detection on the acquisition result (namely acquired smoke data) to judge whether the acquired smoke data are higher than the current 0 point calibration value but not higher than the preset calibration threshold value of 0.1dB/m within a preset second time interval of 24 continuous hours, wherein the change rate is smaller than the preset smoke concentration threshold value of 5%, if the acquired smoke data are not satisfied, continuing judging, if the identification result information represents that each smoke data are lower than the current 0 point calibration value, lower than the preset calibration threshold value and lower than the preset smoke concentration threshold value within the preset second time interval, indicating that the environment is changed, dust is gradually accumulated, and the previously stored 0 point calibration value needs to be updated, wherein the updating mode is to continuously acquire the first preset quantity of second smoke data of 200 times according to the preset first acquisition frequency of 200Hz, average value is calculated for 200 times, the generated average value is used as a target calibration value, and the current 0 point calibration value is updated as the target calibration value.
Therefore, the problem of 0-point calibration value floating caused by long-time operation and dust accumulation can be solved according to the 0-point automatic floating adaptation function in the self-adaptive calibration algorithm. Specifically, the dust accumulation process on the smoke sensor can be automatically identified, algorithm filtering is performed, and the problems that dust accumulation or sensor aging exists on the reflecting surface of the sensor when the smoke sensor runs for a long time, the reflecting surface becomes no longer smooth, namely the diffuse reflection effect is enhanced, the acquisition result is slowly increased, and finally the smoke acquisition result is inaccurate are solved. The application can automatically identify the problem, carry out algorithm adaptation, and carry out 0-point automatic upward adaptation in a very short time so as to avoid false alarm.
In the embodiment of the application, after the smoke sensor is electrified, the smoke data in the current environment are acquired according to the preset second acquisition frequency. And (3) carrying out automatic calibration on the 0-point calibration value according to the collected multiple smoke data to generate an initial 0-point calibration value. And after generating an initial 0-point calibration value, acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor. Performing smoke detection on the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment. And (3) recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value. Therefore, the calibrated 0-point calibration value can be dynamically adjusted according to the change condition of the smoke concentration in the current environment, and the technical problem that the accuracy of the smoke acquisition result of the smoke sensor is low is solved.
Fig. 6 is a system block diagram of a processing method of a smoke sensor according to an embodiment of the present application, and as shown in fig. 6, the processing method is divided into 2 stages: the power-on stage and the operation process. In the power-on stage, the system is powered on for 0 point calibration; the operation stage has an upward 0-point adaptation algorithm and a downward 0-point adaptation algorithm, and the 2 adaptation methods of the upward 0-point adaptation algorithm and the downward 0-point adaptation algorithm are not completely the same.
Specifically, power-on 0 point calibration: the method mainly comprises the steps of calibrating a smoke sensor at a0 point in a power-on stage, and acquiring a smoke 0 point value of the environment during power-on. Upward 0-point adaptation algorithm: mainly in normal operation, whether the environment is changed or not is identified, and whether automatic adaptation and upward adjustment are needed to be carried out on the 0-point calibration value or not is judged. Downward 0-point adaptation algorithm: mainly in the operation process, whether the environment is changed or not is identified, and whether automatic adaptation and downward adjustment are needed to be carried out on the 0-point calibration value or not is judged.
Fig. 7 is a schematic structural diagram of a processing device of a smoke sensor according to an embodiment of the present application, as shown in fig. 7, the device includes:
The first acquisition module 31 is configured to acquire smoke data in a current environment according to a preset first acquisition frequency during an operation process of the smoke sensor.
The detection module 32 is configured to perform smoke detection on the collected multiple pieces of smoke data according to a preset adaptive calibration algorithm, so as to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment.
And the calibration module 33 is used for recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value.
The device of the embodiment may execute the technical scheme in the above method, and the specific implementation process and the technical principle are the same and are not described herein again.
Fig. 8 is a schematic structural diagram of another processing device for a smoke sensor according to an embodiment of the present application, and based on the embodiment shown in fig. 7, as shown in fig. 8, the adaptive calibration algorithm includes an upward 0-point adaptive algorithm and a downward 0-point adaptive algorithm.
In one example, the recognition result information is generated according to a downward 0-point adaptation algorithm.
Calibration module 33, comprising:
The first collection unit 331 is configured to collect a first preset number of smoke data according to a preset first collection frequency if the identification result information characterizes that each smoke data is higher than a current 0 point calibration value and the change rate is smaller than a preset smoke concentration threshold in a preset first time interval.
The first updating unit 332 is configured to average the first preset number of smoke data, take the generated average value as a target calibration value, and update the current 0 point calibration value as the target calibration value.
In one example, the recognition result information is generated according to an upward 0-point adaptation algorithm.
Calibration module 33, comprising:
the second collection unit 333 is configured to collect a first preset number of second smoke data according to a preset first collection frequency if the identification result information indicates that each smoke data is lower than a current 0 point calibration value, lower than a preset calibration threshold, and lower than a preset smoke concentration threshold in a preset second time interval.
The second updating unit 334 is configured to average the first preset number of smoke data, take the generated average value as a target calibration value, and update the current 0 point calibration value as the target calibration value.
In one example, the apparatus further comprises:
The second acquisition module 41 is configured to acquire, after the smoke sensor is powered on, smoke data in the current environment according to a preset second acquisition frequency.
The calibration module 42 is configured to perform automatic calibration of the 0 point calibration value according to the collected plurality of smoke data, and generate an initial 0 point calibration value.
The first acquisition module 31 is specifically configured to:
And after generating an initial 0-point calibration value, acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor.
In one example, the calibration module 42 includes:
The third collection unit 421 is configured to collect a second preset number of smoke data according to a preset second collection frequency if it is determined that the rate of change of each of the plurality of smoke data is less than the preset smoke concentration threshold.
And an average unit 422, configured to average the second preset number of smoke data, and take the generated average value as an initial 0-point calibration value for power-up.
The device of the embodiment may execute the technical scheme in the above method, and the specific implementation process and the technical principle are the same and are not described herein again.
Fig. 9 is a schematic structural diagram of a smoke sensor according to an embodiment of the present application, where, as shown in fig. 9, the smoke sensor includes: a memory 51, and a processor 52.
The memory 51 stores a computer program executable on the processor 52.
The processor 52 is configured to perform the method as provided by the above-described embodiments.
The smoke sensor further comprises a receiver 53 and a transmitter 54. The receiver 53 is for receiving instructions and data transmitted from an external device, and the transmitter 54 is for transmitting instructions and data to the external device.
Embodiments of the present application also provide a non-transitory computer readable storage medium, which when executed by a processor of a smoke sensor, causes the smoke sensor to perform the method provided by the above embodiments.
The embodiment of the application also provides a computer program product, which comprises: a computer program stored in a readable storage medium, from which the computer program can be read by at least one processor of the smoke sensor, the at least one processor executing the computer program causing the smoke sensor to perform the scheme provided by any one of the embodiments described above.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method of processing a smoke sensor, comprising:
During the operation process of the smoke sensor, acquiring smoke data in the current environment according to a preset first acquisition frequency;
Performing smoke detection on the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment;
and recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value.
2. The method of claim 1, wherein the adaptive calibration algorithm comprises an up 0 point adaptation algorithm and a down 0 point adaptation algorithm.
3. The method of claim 2, wherein the recognition result information is generated according to the downward 0-point adaptation algorithm;
And recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value, wherein the method comprises the following steps of:
If the identification result information represents that each smoke data is higher than the current 0 point calibration value and the change rate is smaller than the preset smoke concentration threshold value in a preset first time interval, acquiring first preset quantity of smoke data according to a preset first acquisition frequency;
And averaging the first preset number of smoke data, taking the generated average value as a target calibration value, and updating the current 0 point calibration value into the target calibration value.
4. The method of claim 2, wherein the recognition result information is generated according to the upward 0-point adaptation algorithm;
And recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value, wherein the method comprises the following steps of:
if the identification result information represents that each smoke data is lower than the current 0-point calibration value, lower than the preset calibration threshold value and lower than the preset smoke concentration threshold value in the preset second time interval, acquiring a first preset number of second smoke data according to a preset first acquisition frequency;
And averaging the first preset number of smoke data, taking the generated average value as a target calibration value, and updating the current 0 point calibration value into the target calibration value.
5. The method according to any one of claims 1-4, further comprising:
After the smoke sensor is electrified, acquiring smoke data in the current environment according to a preset second acquisition frequency, and performing automatic calibration of a 0-point calibration value according to a plurality of acquired smoke data to generate an initial 0-point calibration value;
During the operation of the smoke sensor, the smoke data in the current environment is collected according to a preset first collection frequency, and the method comprises the following steps:
And after generating an initial 0-point calibration value, acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor.
6. The method of claim 5, wherein the automatically calibrating the 0-point calibration value based on the collected plurality of smoke data, generating an initial 0-point calibration value, comprises:
if it is determined that the change rate of each of the plurality of smoke data is smaller than a preset smoke concentration threshold, acquiring a second preset number of smoke data according to a preset second acquisition frequency;
And averaging the second preset number of smoke data, and taking the generated average value as an initial 0-point calibration value of power-on.
7. A smoke sensor processing apparatus, comprising:
The first acquisition module is used for acquiring smoke data in the current environment according to a preset first acquisition frequency in the operation process of the smoke sensor;
The detection module is used for detecting the collected multiple smoke data according to a preset self-adaptive calibration algorithm to generate identification result information; wherein the recognition result information characterizes the change condition of the smoke concentration of the current environment;
And the calibration module is used for recalibrating the current 0-point calibration value of the smoke sensor according to the identification result information to generate a target calibration value.
8. A smoke sensor comprising a memory, a processor, the memory having stored thereon a computer program executable on the processor, when executing the computer program, implementing the method of any of claims 1-6.
9. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1-6.
10. A computer program product comprising a computer program which, when executed by a processor, implements the method of any of claims 1-6.
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| CN202410211076.2A CN118097914A (en) | 2024-02-26 | 2024-02-26 | Smoke sensor processing method and device and smoke sensor |
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| CN202410211076.2A CN118097914A (en) | 2024-02-26 | 2024-02-26 | Smoke sensor processing method and device and smoke sensor |
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