CN107389878B - Self-checking method and device of sensor and computer readable storage medium - Google Patents
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Abstract
The invention provides a self-checking method and a self-checking device of a sensor and a computer readable storage medium, wherein the self-checking method of the sensor acquires target acquisition data and associated acquisition data associated with the target acquisition data; judging whether the target collected data is abnormal or not according to the associated collected data; and resetting the target sensor when the target acquisition data is abnormal. Through the mode, the target collected data are subjected to matching verification according to the associated sensor data. If a surge of smoke sensor data is detected, the PM2.5 sensor data should also rise, and if the PM2.5 sensor data falls, an anomaly in the PM2.5 sensor data value occurs. Therefore, one or more sensors which are abnormal in the data acquisition device are found in real time, the reliability of the sensor data is improved, and the technical problem that the abnormal sensor data cannot be found in time is solved.
Description
Technical Field
The invention relates to the field of household appliances, in particular to a self-checking method and device of a sensor and a computer readable storage medium.
Background
In order to acquire comprehensive target data, a plurality of sensors are usually arranged in a conventional data acquisition device, and the acquisition of sample data is completed through the mutual cooperation of the plurality of sensors. For example, in a conventional air quality detector, a plurality of built-in sensors, such as a smoke sensor, a fine particulate matter PM2.5 sensor, or a gas compound TVOC sensor, respectively acquire smoke data, fine particulate matter data, or total volatile organic compound data in the air, so as to generate air sample data of the current environment, thereby performing air quality detection of the current environment. However, the sensors in the device are generally susceptible to various interference factors such as driving current and voltage, working current and voltage, or working environment, which may cause abnormal operation of one or more sensors, thereby causing abnormal collected data of one or more sensors. If the data abnormality of the sensor is not found in time, the data acquisition device cannot work normally.
Disclosure of Invention
The invention mainly aims to provide a self-checking method of a sensor, a terminal and a computer readable storage medium, and aims to solve the technical problem that abnormal sensor data cannot be found in time.
In order to achieve the above object, the present invention provides a self-calibration method of a sensor, which is characterized by comprising the following steps:
acquiring target acquisition data of a target sensor and associated acquisition data of other sensors associated with the target sensor;
judging whether the target collected data is abnormal or not according to the associated collected data;
and resetting the target sensor when the target acquisition data is abnormal.
Optionally, the step of determining whether the target collected data is abnormal according to the associated collected data includes:
judging whether the associated collected data comprises a plurality of groups of data;
when the associated collected data includes a plurality of sets of data, determining whether there is at least one set of associated collected data matching the target collected data, wherein,
and if the associated collected data matched with the target collected data does not exist, judging that the target collected data is abnormal.
Optionally, after the step of determining whether the associated collected data includes multiple sets of data, the method further includes:
when the associated collected data comprises a group of data, acquiring third-party data associated with the target sensor in a server;
determining whether the target acquisition data matches the third party data, wherein,
and if the target collected data is not matched with the third-party data, judging that the target collected data is abnormal.
Optionally, the step of resetting the target sensor when the target acquisition data is abnormal includes:
when the target acquisition data is abnormal, acquiring standard data corresponding to all the associated acquisition data;
calculating a difference value between the standard data and the target acquisition data, and judging whether the difference value belongs to a preset threshold range;
and resetting the target sensor when the difference does not belong to a preset threshold value.
Optionally, after the step of calculating a difference between the standard data and the target collected data and determining whether the difference reaches a preset threshold, the method further includes:
when the difference value belongs to a preset threshold value, second target data of the target sensor and second related data of other sensors are obtained according to a preset time interval;
judging whether the second target data is abnormal or not according to the second related data;
resetting the target sensor when the second target data is abnormal.
Optionally, when the associated collected data includes a plurality of sets of data, the step of determining whether there is at least one set of associated collected data matching the target collected data includes:
acquiring a target change value of the target acquisition data and other change values of all associated acquisition data;
determining whether there is at least one set of other variance values that match the target variance value, wherein,
and if no other change value matched with the target change value exists, judging that no associated acquisition data matched with the target acquisition data exists.
Optionally, when the associated collected data includes a plurality of sets of data, the step of determining whether there is at least one set of associated collected data matching the target collected data includes:
determining a range value of the target acquisition data according to prestored data and all associated acquisition data;
and judging whether at least one group of associated collected data matched with the target collected data exists or not according to the range value.
Optionally, after the step of resetting the target sensor when the target acquisition data is abnormal, the method further includes:
acquiring third target data of the target sensor and third related data of other sensors according to a preset time threshold;
judging whether the third target data is abnormal or not according to the third relevant data;
and when the third target data is abnormal, generating an abnormal reminding message and sending the abnormal reminding message to a corresponding terminal and/or server.
In addition, in order to achieve the above object, the present invention further provides a self-checking apparatus for a sensor, which is characterized in that the self-checking apparatus for a sensor includes a processor, a memory, and a self-checking program for a sensor stored in the memory and operable on the processor, wherein the self-checking program for a sensor implements the steps described above when executed by the processor.
In addition, to achieve the above object, the present invention further provides a computer readable storage medium, on which a self-verification program of a sensor is stored, and the self-verification program of the sensor, when executed by a processor, implements the steps of the self-verification method of the sensor described above.
The invention provides a self-checking method and a self-checking device of a sensor and a computer readable storage medium, wherein the self-checking method of the sensor acquires target acquisition data and associated acquisition data associated with a target sensor; judging whether the target collected data is abnormal or not according to the associated collected data; and resetting the target sensor when the target acquisition data is abnormal. Through the mode, the target collected data are subjected to matching verification according to the associated sensor data. If a surge of smoke sensor data is detected, the PM2.5 sensor data should also rise, and if the PM2.5 sensor data falls, an anomaly in the PM2.5 sensor data value occurs. Therefore, one or more sensors which are abnormal in the data acquisition device are found in real time, the reliability of the sensor data is improved, and the technical problem that the abnormal sensor data cannot be found in time is solved.
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Fig. 1 is a schematic terminal structure diagram of a hardware operating environment of a self-calibration apparatus of a sensor according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a self-calibration method of a sensor according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a self-calibration method of the sensor according to a second embodiment of the present invention;
FIG. 4 is a flow chart of a self-calibration method of a sensor according to a third embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main idea of the embodiment scheme of the invention is as follows: acquiring target acquisition data of a target sensor and associated acquisition data of other sensors associated with the target sensor; judging whether the target collected data is abnormal or not according to the associated collected data; when the target acquisition data is abnormal, the target sensor is reset, and the technical problem that abnormal sensor data cannot be found in time is solved.
Referring to fig. 1, fig. 1 is a schematic terminal structure diagram of a hardware operating environment of a self-calibration apparatus of a sensor according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a PC, and can also be a mobile terminal device with a display function, such as a smart phone, a tablet computer, an electronic book reader, an MP4(Moving Picture Experts Group Audio Layer IV) player, a portable computer and the like. As shown in fig. 1, the terminal may include a processor 1001 (e.g., CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used for realizing connection communication among the components; the user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface); the memory 1005 may be a high-speed RAM memory, or may be a non-volatile memory (e.g., a magnetic disk memory), and optionally, the memory 1005 may be a storage device independent of the processor 1001.
Those skilled in the art will appreciate that the terminal configuration of the sensor self-calibration device hardware operating environment shown in fig. 1 does not constitute a limitation of the sensor self-calibration device of the present invention, and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
With continued reference to FIG. 1, the memory 1005 of FIG. 1, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a self-verifying program for the sensor.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a cloud server and performing data communication with the cloud server; the user interface 1003 may be connected to a client (user side) and performs data communication with the client; and the processor 1001 may be configured to invoke a self-checking program of the sensor stored in the memory 1005 and perform the following operations:
acquiring target acquisition data of a target sensor and associated acquisition data of other sensors associated with the target sensor;
judging whether the target collected data is abnormal or not according to the associated collected data;
and resetting the target sensor when the target acquisition data is abnormal.
Further, the processor 1001 may also call a self-checking program of the sensor stored in the memory 1005 to perform the following operations:
judging whether the associated collected data comprises a plurality of groups of data;
when the associated collected data includes a plurality of sets of data, determining whether there is at least one set of associated collected data matching the target collected data, wherein,
and if the associated collected data matched with the target collected data does not exist, judging that the target collected data is abnormal.
Further, the processor 1001 may also call a self-checking program of the sensor stored in the memory 1005 to perform the following operations:
when the associated collected data comprises a group of data, acquiring third-party data associated with the target sensor in a server;
determining whether the target acquisition data matches the third party data, wherein,
and if the target sensor is not matched with the third-party data, judging that the target acquisition data is abnormal.
Further, the processor 1001 may also call a self-checking program of the sensor stored in the memory 1005 to perform the following operations:
when the target acquisition data is abnormal, acquiring standard data corresponding to all the associated acquisition data;
calculating a difference value between the standard data and the target acquisition data, and judging whether the difference value belongs to a preset threshold range;
and resetting the target sensor when the difference does not belong to a preset threshold value.
Further, the processor 1001 may also call a self-checking program of the sensor stored in the memory 1005 to perform the following operations:
when the difference value belongs to a preset threshold value, second target data of the target sensor and second related data of other sensors are obtained according to a preset time interval;
judging whether the second target data is abnormal or not according to the second related data;
resetting the target sensor when the second target data is abnormal.
Further, the processor 1001 may also call a self-checking program of the sensor stored in the memory 1005 to perform the following operations:
acquiring a target change value of the target acquisition data and other change values of all associated acquisition data;
determining whether there is at least one set of other variance values that match the target variance value, wherein,
and if no other change value matched with the target change value exists, judging that no associated acquisition data matched with the target acquisition data exists.
Further, the processor 1001 may also call a self-checking program of the sensor stored in the memory 1005 to perform the following operations:
determining a range value of the target acquisition data according to prestored data and all associated acquisition data;
and judging whether at least one group of associated collected data matched with the target collected data exists or not according to the range value.
Further, the processor 1001 may also call a self-checking program of the sensor stored in the memory 1005 to perform the following operations:
acquiring third target data of the target sensor and third related data of other sensors according to a preset time threshold;
judging whether the third target data is abnormal or not according to the third relevant data;
and when the third target data is abnormal, generating an abnormal reminding message and sending the abnormal reminding message to a corresponding terminal and/or server.
Based on the hardware structure, the embodiment of the self-checking method of the sensor is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a self-calibration method of a sensor according to a first embodiment of the present invention.
In this embodiment, the self-calibration method of the sensor includes the following steps:
step S10, acquiring target acquisition data of a target sensor and associated acquisition data of other sensors associated with the target sensor;
in this embodiment, in order to solve the technical problem that abnormal sensor data cannot be found in time, matching verification is performed on target acquisition data according to the associated sensor data, so that one or more sensors which are abnormal in the data acquisition device are found in real time, and the reliability of the sensor data is improved. The self-checking method of the sensor provided by the embodiment can be particularly applied to a data acquisition device, and the method can also be applied to sensor detection in other devices in the embodiment. Specifically, the self-calibration device of the sensor corresponding to the self-calibration method of the sensor provided in this embodiment may be a data acquisition device such as an embedded central coprocessor of an air quality detector, where the coprocessor further acquires associated acquired data associated with the target sensor in the data acquisition device when acquiring target acquired data. The other sensors associated with the target sensor may be a preset association list, for example, if the target sensor is a smoke sensor in an air quality detector, one or more of a PM2.5 sensor, a TVOC sensor, or a formaldehyde sensor in the air quality detector is added to the association list. Smoke sensors, PM2.5 sensors, TVOC sensors or formaldehyde sensors in the air quality detector may also be added to the association list, with one sensor being the target sensor and the remainder being the associated sensor. In a specific embodiment, a user can add the associated sensor according to the actual situation of the user, and add the corresponding sensor into the association list according to the adding operation of the user. In further embodiments, the associated collected data may be sensor data in the data collecting apparatus, or may be sensor data in other data collecting apparatuses communicatively connected to the data collecting apparatus.
Step S20, judging whether the target collected data is abnormal or not according to the associated collected data;
specifically, the associated collected data may be one set of data or multiple sets of data. If the target acquisition data is one of formaldehyde sensor data, PM2.5 sensor data, or TVOC sensor data, the associated acquisition data may include one or more of these. The target sensor parameter anomaly may be a mismatch with the set of target acquisition data or a mismatch with none of the sets of target acquisition data. Acquiring the target collectionWhen the data and the associated collected data are in the same state, according to a preset matching rule, when the associated collected data change rises, the target collected data change correspondingly rises. Or associating the collected data with the target collected data in a certain functional relationship, etc. In a specific embodiment, the associated collected data may correspond to target collected data within a fixed range, and the corresponding relationship is stored in a corresponding table. For example, the formaldehyde sensor data is 0.2mg/m3When the data is below, the TVOC sensor data should be at 0.6mg/m3The PM2.5 sensor data should be 75 μ g/m3The following, etc. And judging whether the target collected data conforms to the value range corresponding to the associated collected data or not according to the corresponding table. Or judging whether the change trend of the associated collected data is consistent with the change trend of the target collected data.
Step S30, if the target collected data does not match the associated collected data, the target sensor is reset.
Specifically, when the variation trend of the target collected data is inconsistent with the variation trend of the associated collected data, or the standard data range of the target sensor corresponding to the associated collected data is obtained according to the correspondence table, and the target collected data is inconsistent with the associated collected data, it is determined that the target collected data is not matched with the associated collected data. And when the target collected data is judged not to be matched with the associated collected data, namely the target collected data is abnormal, namely the target sensor is abnormal, sending a reset instruction to the target sensor so as to reset the target sensor.
The embodiment provides a self-checking method and a self-checking device for a sensor and a computer readable storage medium, wherein the self-checking method for the sensor acquires target acquisition data of a target sensor and associated acquisition data of other sensors associated with the target sensor; judging whether the target collected data is abnormal or not according to the associated collected data; and resetting the target sensor when the target acquisition data is abnormal. Through the mode, the target collected data are subjected to matching verification according to the associated sensor data. If a surge of smoke sensor data is detected, the PM2.5 sensor data should also rise, and if the PM2.5 sensor data falls, an anomaly in the PM2.5 sensor data value occurs. Therefore, one or more sensors which are abnormal in the data acquisition device are found in real time, the reliability of the sensor data is improved, and the technical problem that the abnormal sensor data cannot be found in time is solved.
Referring to fig. 3, fig. 3 is a schematic flow chart of a self-calibration method of a sensor according to a second embodiment of the present invention.
In this embodiment, based on the above embodiment shown in fig. 2, further in this embodiment, step S20 specifically includes:
step S21, judging whether the associated collected data comprises a plurality of groups of data;
specifically, the associated collected data may be one or more groups, and the condition of the associated collected data is further determined. And judging that the acquired associated collected data comprises one or more groups of formaldehyde sensor data, PM2.5 sensor data or TVOC sensor data according to the type or the quantity of the associated collected data. One group is an average value or a median value of the sensor data of the same type, and the like, and can be set according to the actual situation of a user.
Step S22, when the associated collected data includes multiple sets of data, determining whether there is at least one set of associated collected data matching the target collected data.
Specifically, when it is determined that the associated collected data includes at least two of formaldehyde sensor data, PM2.5 sensor data, or TVOC sensor data, it is further determined whether the target collected data is not matched with all of the associated collected data. And when the target collected data are not matched with all the associated collected data, the target collected data are obviously abnormal. When the associated collected data has multiple groups, namely the probability of the associated collected data being abnormal is very small, the target collected data is abnormal, and a reset instruction is sent to the target sensor so as to reset the target sensor.
Further, in this embodiment, step S22 includes:
step S221, acquiring a target change value of the target acquisition data and other change values of all associated acquisition data;
specifically, a target variation value of the target collected data within a preset time, that is, within a period of time, and other variation values of the associated collected data are obtained. For example, within 30 minutes from 8 points to 8 points, the formaldehyde sensor of the target sensor corresponds to a target change value of +0.05mg/m3Other sensor PM2.5 sensor, corresponding other variation value +5 μ g/m3Other TVOC sensor, corresponding other variation value +0.02mg/m3And the like.
Step S222, determining whether there is at least one group of other variation values matching the target variation value, wherein,
and if no other change value matched with the target change value exists, judging that no associated acquisition data matched with the target acquisition data exists.
Specifically, the change corresponding relationship between the target sensor and the other associated sensors is stored in a change table in advance, and if the data of the other sensors PM2.5 or TVOC sensor rises, the data of the target sensor formaldehyde sensor should rise correspondingly. When the PM2.5 sensor data or the TVOC sensor data of the other sensors decreases, the target sensor formaldehyde sensor data should correspondingly decrease. And judging whether the other change values are matched with the target change value or not according to the change relation table.
Further, in this embodiment, step S20 further includes:
step S223, determining a range value of the target acquisition data according to prestored data and all associated acquisition data;
specifically, according to a pre-stored data correspondence table between the associated collected data and the target collected data, that is, the value of the associated collected data is within a certain range, and the value of the target sensor is also within a certain range. If the associated collected data PM2.5 sensor data is 0-75 mu g/m3, the TVOC sensor data is 0-0.6 mg/m3Then, the corresponding target acquisition data formaldehyde is sensedThe data of the device should be 0-0.2 mg/m3. And after the associated collected data is obtained, obtaining a target collected data range value corresponding to the associated collected data according to the data corresponding table.
Step S224, determining whether there is at least one group of the associated collected data matched with the target collected data according to the range value.
Specifically, according to the range values, whether the target acquisition data is between the range values is judged, that is, whether the target sensor is matched with the associated acquisition data is judged.
The present invention provides a self-calibration method and apparatus for a sensor, and a computer-readable storage medium, and performs matching calibration on target collected data according to associated sensor data. If a surge of smoke sensor data is detected, the PM2.5 sensor data should also rise, and if the PM2.5 sensor data falls, an anomaly in the PM2.5 sensor data value occurs. Therefore, one or more sensors which are abnormal in the data acquisition device are found in real time, the reliability of the sensor data is improved, and the technical problem that the abnormal sensor data cannot be found in time is solved.
Referring to fig. 4, fig. 4 is a schematic flow chart of a self-calibration method of a sensor according to a third embodiment of the present invention.
In this embodiment, based on the above embodiment shown in fig. 2, step S20 specifically further includes:
step S23, if there is no associated collected data matching the target collected data, it is determined that the target collected data is abnormal.
When the associated collected data only comprises one of formaldehyde sensor data, PM2.5 sensor data or TVOC sensor data, the abnormality of the associated collected data and the target collected data is possibly generated. And when the target acquisition data and the associated acquisition data do not match, the target acquisition data and the associated acquisition data indicate that one or both of the target sensor and the other sensors are abnormal. At this time, a reset instruction may be sent to the target sensor and the other sensors to reset the target sensor and the sensors thereof.
Step S24, when the associated collected data includes a group of data, acquiring third party data associated with the target sensor in a server;
specifically, when the associated collected data has only one set of data, other sensors may be abnormal. In order to avoid matching with abnormal associated collected data, after matching with a group of associated collected data, third party data in the network is further acquired for matching again. Wherein, the third party data can be air environment data uploaded by a national weather station.
Step S25, determining whether the target collected data matches the third party data, wherein,
specifically, when third-party data on a network or in a server is acquired, the third-party data is compared with the target acquisition data.
Step S26, if the target sensor is not matched with the third party data, it is determined that the target collected data is abnormal.
Specifically, when the target collected data does not match the acquired third-party data, it is determined that the target collected data is abnormal.
The present invention provides a self-calibration method and apparatus for a sensor, and a computer-readable storage medium, and performs matching calibration on target collected data according to associated sensor data. If a surge of smoke sensor data is detected, the PM2.5 sensor data should also rise, and if the PM2.5 sensor data falls, an anomaly in the PM2.5 sensor data value occurs. Therefore, one or more sensors which are abnormal in the data acquisition device are found in real time, the reliability of the sensor data is improved, and the technical problem that the abnormal sensor data cannot be found in time is solved.
Further, in this embodiment, step S30 specifically includes:
step S31, when the target acquisition data is abnormal, acquiring standard data corresponding to all the associated acquisition data;
specifically, when it is determined that the associated collected data includes at least two of formaldehyde sensor data, PM2.5 sensor data, or TVOC sensor data, standard data corresponding to the associated collected data is obtained, where the standard data is normal target collected data corresponding to other sensors, the standard data may be a single data or a range value, and the range value may include an upper limit and a lower limit.
Step S32, calculating a difference value between the standard data and the target acquisition data, and judging whether the difference value belongs to a preset threshold range;
specifically, a difference value between the standard data and the target acquisition data is calculated, wherein when the standard data is a range value, a middle value of the range value is taken. And judging whether the difference value between the standard data and the target acquisition data exceeds a preset threshold range. The preset threshold range is a data threshold range set according to the sensor data acquisition error. Thereby judging whether the target acquisition data has an abnormality within an allowable error range.
And step S33, resetting the target sensor when the difference does not belong to a preset threshold.
Specifically, when the difference value between the standard data and the target collected data is out of the preset data threshold range, that is, the target collected data is abnormal out of an allowable error range, it is determined that the target sensor is abnormal, and a reset instruction is sent to the target sensor to reset the target sensor.
Step S34, when the difference value belongs to a preset threshold value, second target data of the target sensor and second related data of other sensors are obtained according to a preset time interval;
specifically, when the difference value between the standard data and the target collected data is within the preset data threshold range, it indicates that the target collected data has an abnormality within an allowable error range. And after a preset time interval, acquiring the target acquisition data and the associated acquisition data again, namely acquiring the second target data and the second associated data.
Step S35, determining whether the second target data is abnormal according to the second related data.
Specifically, whether the second target data is matched with the second related data is judged again, and if the second target data is still not matched with the second related data, it is judged that the target sensor is abnormal.
Step S36 is to reset the target sensor when the second target data is abnormal.
Specifically, when it is determined that the second target collected data is abnormal, it is determined that the target sensor is not an error but an abnormality has occurred. And sending a reset finger to the target sensor, and resetting the target sensor.
Further, in this embodiment, the self-calibration method of the sensor further includes:
step 40, when the target sensor is reset, acquiring third target data of the target sensor and third related data of other sensors according to a preset time threshold;
specifically, in order to confirm that the target sensor after being reset works normally, after the target sensor is reset, that is, the preset time threshold may be the time after the sensor starts working normally, the target collected data and the associated collected data, that is, the third target data and the third associated data, are acquired again.
Step 50, judging whether the third target data is abnormal or not according to the third relevant data;
specifically, if the third target data does not match the third related data, it is determined that the target collected data is abnormal.
And step 60, when the third target data is abnormal, generating an abnormal reminding message and sending the abnormal reminding message to a corresponding terminal and/or server.
Specifically, if the third target data does not match the third related data, that is, the target collected data is abnormal, the target sensor is still abnormal in storage after being reset. And generating an abnormal reminding message, and sending the abnormal reminding message to a corresponding terminal and/or server so as to remind a user that the target sensor has an abnormal problem which cannot be solved by resetting.
The invention also provides a computer readable storage medium.
The computer readable storage medium of the present invention stores a self-checking program of a sensor, which when executed by a processor implements the steps of the above-described intelligent method.
The method for implementing the self-calibration program of the sensor when executed may refer to various embodiments of the self-calibration method of the sensor of the present invention, and details thereof are not repeated herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (8)
1. A self-checking method of a sensor, characterized by comprising the following steps:
acquiring target acquisition data of a target sensor and associated acquisition data of other sensors associated with the target sensor; wherein the associated other sensors comprise one or more of a smoke sensor, a PM2.5 sensor, a TVOC sensor, or a formaldehyde sensor;
judging whether the target collected data is abnormal or not according to the associated collected data;
resetting the target sensor when the target acquisition data is abnormal;
wherein, the step of judging whether the target collected data is abnormal according to the associated collected data comprises the following steps:
judging whether the associated collected data comprises a plurality of groups of data;
when the associated collected data comprises a plurality of groups of data, judging whether at least one group of associated collected data matched with the target collected data exists, wherein if the associated collected data matched with the target collected data does not exist, judging that the target collected data is abnormal;
when the associated collected data comprises a group of data, acquiring third-party data associated with the target sensor in a server;
determining whether the target acquisition data matches the third party data, wherein,
and if the target collected data is not matched with the third-party data, judging that the target collected data is abnormal.
2. The sensor self-verification method of claim 1, wherein the step of resetting the target sensor when the target acquisition data is abnormal comprises:
when the target acquisition data is abnormal, acquiring standard data corresponding to all the associated acquisition data;
calculating a difference value between the standard data and the target acquisition data, and judging whether the difference value belongs to a preset threshold range;
and resetting the target sensor when the difference does not belong to a preset threshold value.
3. The self-verification method of a sensor according to claim 2, wherein the step of calculating the difference between the standard data and the target collected data and determining whether the difference reaches a preset threshold value further comprises:
when the difference value belongs to a preset threshold value, second target data of the target sensor and second related data of other sensors are obtained according to a preset time interval;
judging whether the second target data is abnormal or not according to the second related data;
resetting the target sensor when the second target data is abnormal.
4. The sensor self-verification method of claim 1, wherein the determining whether there is at least one set of associated collected data that matches the target collected data when the associated collected data includes multiple sets of data comprises:
acquiring a target change value of the target acquisition data and other change values of all associated acquisition data;
determining whether there is at least one set of other variance values that match the target variance value, wherein,
and if no other change value matched with the target change value exists, judging that no associated acquisition data matched with the target acquisition data exists.
5. The sensor self-verification method of claim 1, wherein the determining whether there is at least one set of associated collected data that matches the target collected data when the associated collected data includes multiple sets of data comprises:
determining a range value of the target acquisition data according to prestored data and all associated acquisition data;
and judging whether at least one group of associated collected data matched with the target collected data exists or not according to the range value.
6. The self-verification method of a sensor according to any one of claims 1 to 5, wherein after the step of resetting the target sensor when the target acquisition data is abnormal, the method further comprises:
acquiring third target data of the target sensor and third related data of other sensors according to a preset time threshold;
judging whether the third target data is abnormal or not according to the third relevant data;
and when the third target data is abnormal, generating an abnormal reminding message and sending the abnormal reminding message to a corresponding terminal and/or server.
7. A self-verifying unit for a sensor, characterized in that it comprises a processor, a memory and a self-verifying program for a sensor stored in said memory and operable on said processor, wherein said self-verifying program for a sensor, when executed by said processor, implements the steps of the self-verifying method for a sensor according to any one of claims 1 to 6.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a self-verification program of a sensor, which when executed by a processor implements the steps of the self-verification method of a sensor according to any one of claims 1 to 6.
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| CN110095568A (en) * | 2018-01-31 | 2019-08-06 | 智能城市网络有限公司 | For detecting the device and method of atmospheric environment sensor failure |
| CN111855914B (en) * | 2020-07-30 | 2022-08-02 | 广州交信投科技股份有限公司 | Air monitoring system based on mobile vehicle |
| CN113985935A (en) * | 2021-05-29 | 2022-01-28 | 苏州芯默科技有限公司 | Method for real-time monitoring process state of rapid heating furnace by current value |
| CN117250576B (en) * | 2023-11-16 | 2024-01-26 | 苏芯物联技术(南京)有限公司 | Current sensor real-time abnormality detection method based on multidimensional sensing data |
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| US5394934A (en) * | 1994-04-15 | 1995-03-07 | American Standard Inc. | Indoor air quality sensor and method |
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| US9207670B2 (en) * | 2011-03-21 | 2015-12-08 | Rosemount Inc. | Degrading sensor detection implemented within a transmitter |
| DE102011084264A1 (en) * | 2011-10-11 | 2013-04-11 | Robert Bosch Gmbh | Method and device for calibrating an environmental sensor |
| CN102928484B (en) * | 2012-11-09 | 2018-06-01 | 无锡市尚沃医疗电子股份有限公司 | A kind of gas sensor self-calibrating method |
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| CN106405007B (en) * | 2016-08-30 | 2018-10-26 | 河北先河环保科技股份有限公司 | The new calibration method of gas sensor, particulate matter sensors |
| CN106872650A (en) * | 2017-03-08 | 2017-06-20 | 国睿集团有限公司 | A kind of gas concentration lwevel calibration correction device and calibration correction method |
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