CN107765617B - Train axle temperature data processing method and device - Google Patents
Train axle temperature data processing method and device Download PDFInfo
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Abstract
The invention provides a method and a device for processing train axle temperature data, wherein the method comprises the steps of acquiring a real-time monitoring data set of a temperature sensor in a preset time subsection; calculating indexes of preset time subsections according to the real-time monitoring data set; and judging whether the indexes of the real-time monitoring data set belong to the stable subset of the shaft temperature state, if so, transmitting the average value of the monitoring data monitored by the temperature sensor in a preset time subsection to a driver control room as shaft temperature data. The method and the device simplify the transmitted shaft temperature data structure when the shaft temperature is in a normal state, reduce the sending and storing cost of the shaft temperature data, and the simplified shaft temperature data can not reduce the diagnosis correctness of the driver control room on the shaft temperature fault diagnosis and can also improve the diagnosis real-time property.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a device for processing train axle temperature data.
Background
In 1881, China first started running from a train imported abroad and having a speed per hour of more than 20 kilometers, and in 2007, China produced a train with a first speed per hour of 160km/h, and nowadays, research and development of high-speed trains with higher speed per hour has been gradually carried out in China. The role of the train in the domestic rail transit field is more and more important, and the safety of the train is increasingly emphasized. As an important part of affecting the safe operation performance of the train, the monitoring of the axle temperature of the train is always concerned. Because the train bearings are distributed in each carriage, the number of monitoring nodes of each carriage is huge, in the running process of a train, the axle temperature data needs to be transmitted to a driver control room, the axle temperature data is monitored in real time by the driver control room, then the transmission of a large amount of axle temperature data can lead to communication bandwidth pressure, meanwhile, the real-time monitoring and processing difficulty of an axle temperature detection system in the driver control room on the axle temperature data can be increased, when the axle temperature state is stable, the driver control room does not need to carry out fault diagnosis on all the axle temperature data, and therefore, the axle temperature data in a normal state does not need to be transmitted in real time. Therefore, a shaft temperature data processing method is needed to reduce the transmission amount of the shaft temperature data in a normal state and improve the real-time performance of the shaft temperature data processing.
Disclosure of Invention
The invention provides a train axle temperature data processing method and device, which are used for solving the technical problem that effective real-time monitoring of axle temperature data by a driver control room is influenced due to data redundancy caused by large axle temperature data processing amount in a normal state in the prior art.
The invention provides a train axle temperature data processing method on the one hand, which comprises the following steps:
102, calculating indexes of preset time subsections according to the real-time monitoring data set; the index includes a first variable and a second variable, wherein the first variable
Second variable
vari、ΔiAnd TiRespectively representing the variance, range and mean of the monitoring data of the preset time subsections, wherein n is the number of the temperature sensors;
and 104, transmitting the average value of the monitoring data monitored by the temperature sensor in a preset time subsection to a driver control room as shaft temperature data.
Further, before step 101, the method further includes:
step a, acquiring a historical axle temperature data set in a preset time period, and dividing the historical axle temperature data set into a plurality of historical axle temperature data subsets corresponding to continuous preset time subsections; the historical shaft temperature data set comprises monitoring data of a plurality of temperature sensors for monitoring the shaft temperature within a preset time period; the historical shaft temperature data subset comprises monitoring data in a preset time sub-segment;
b, calculating indexes of all preset time subsections according to the historical axle temperature data subsets to obtain an index set;
and c, carrying out cluster analysis on the index set according to the first variable and the second variable, and dividing the index set into an axle temperature state stable subset and an axle temperature state unstable subset.
Further, after step c, step 103 further includes:
and d, acquiring a center of the stable subset of the shaft temperature state and a center of the unstable subset of the shaft temperature state, wherein the center of the stable subset of the shaft temperature state is composed of the mode of the first variable and the second variable in the stable subset of the shaft temperature state, and the center of the unstable subset of the shaft temperature state is composed of the mode of the first variable and the second variable in the unstable subset of the shaft temperature state.
Further, step 103 specifically includes: and judging whether the distance between the index and the center of the stable subset of the shaft temperature state is not greater than the distance between the index and the center of the unstable subset of the shaft temperature state, if so, judging that the index belongs to the stable subset of the shaft temperature state, and executing the step 104.
Further, after the step a and before the step b, the method further comprises:
and e, calculating and obtaining the variance, range and mean of the monitoring data of each temperature sensor at each preset time subsection according to the historical shaft temperature data subset.
In another aspect, the present invention provides a train axle temperature data processing apparatus, including:
the real-time monitoring data set acquisition module is used for acquiring a real-time monitoring data set of the temperature sensor within a preset time sub-period;
the index calculation module is used for calculating the indexes of the preset time subsections according to the real-time monitoring data set; the index includes a first variable weighted _ var and a second variable weighted _ range, wherein,
vari、Δiand TiRespectively representing the variance, range and mean of the monitoring data of the preset time subsections, wherein n is the number of the temperature sensors;
the judging module is used for judging whether the indexes of the real-time monitoring data set belong to the stable axle temperature subset, and if so, the data transmission module is triggered, wherein the stable axle temperature subset is a subset of the index set obtained by calculating the indexes of all the preset time subsections according to the existing historical axle temperature data set;
and the data transmission module is used for transmitting the average value of the monitoring data monitored by the temperature sensor in a preset time subsection to the driver control room as the shaft temperature data.
Further, the above apparatus further comprises:
the historical shaft temperature data set acquisition module is used for acquiring a historical shaft temperature data set in the existing preset time period and dividing the historical shaft temperature data set into a plurality of historical shaft temperature data subsets corresponding to continuous preset time subsections; the historical shaft temperature data set comprises monitoring data of a plurality of temperature sensors for monitoring the shaft temperature within a preset time period; the historical shaft temperature data subset comprises monitoring data in a preset time sub-segment;
the index set calculation module is used for calculating indexes of all the preset time subsections according to the historical axle temperature data subsets so as to obtain an index set;
and the index set dividing module is used for carrying out cluster analysis on the index set according to the first variable and the second variable and dividing the index set into an axle temperature state stable subset and an axle temperature state unstable subset.
Further, the above apparatus further comprises:
the center acquisition module is used for acquiring a shaft temperature state stable subset center and a shaft temperature state unstable subset center, wherein the shaft temperature state stable subset center is composed of the mode of a first variable and a second variable in the shaft temperature state stable subset, and the shaft temperature state unstable subset center is composed of the mode of the first variable and the second variable in the shaft temperature state unstable subset.
Further, the judging module is specifically configured to: and judging whether the distance between the index and the center of the stable subset of the shaft temperature state is not greater than the distance between the index and the center of the unstable subset of the shaft temperature state, if so, judging that the index belongs to the stable subset of the shaft temperature state, and triggering the data transmission module.
Further, the above apparatus further comprises:
and the monitoring data calculation module is used for calculating and obtaining the variance, range and mean of the monitoring data of each temperature sensor at each preset time subsection according to the historical shaft temperature data subset.
The invention provides a train axle temperature data processing method and a train axle temperature data processing device, which are characterized in that whether indexes of a real-time monitoring data set belong to an axle temperature state stable subset or not is judged according to the real-time monitoring data set in a preset time subsection obtained by a temperature sensor, if so, the axle temperature state represented by the real-time monitoring data set obtained by the temperature sensor is stable, so that the average value of the monitoring data of the temperature sensor in the preset time subsection is taken as the axle temperature data to be transmitted to a driver control room.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
fig. 1 is a schematic flow chart of a train axle temperature data processing method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a train axle temperature data processing method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a train axle temperature data processing device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a train axle temperature data processing device according to a fourth embodiment of the present invention.
In the drawings, like parts are provided with like reference numerals. The figures are not drawn to scale.
Detailed Description
The invention will be further explained with reference to the drawings.
Example one
Fig. 1 is a schematic flow chart of a train axle temperature data processing method according to an embodiment of the present invention; as shown in fig. 1, the present embodiment provides a train axle temperature data processing method, including:
Specifically, the preset time sub-section may be set according to an actual situation, for example, 1 minute or 2 minutes, which is not limited herein, the real-time monitoring data set is a set of monitoring data of the temperature sensor for monitoring the shaft temperature, and if the number of times that the temperature sensor monitors the shaft temperature within 1 minute is 60, there are 60 data in the real-time monitoring data set of the temperature sensor within the preset time sub-section (1 minute).
102, calculating indexes of preset time subsections according to the real-time monitoring data set; the index includes a first variable and a second variable, wherein the first variable
Second variable
vari、ΔiAnd TiThe variance, range and mean of the monitoring data of the preset time subsections are respectively, and n is the number of the temperature sensors.
Specifically, because the train bearings are distributed in each carriage, and each carriage monitoring node is provided with a plurality of, the temperature sensors for monitoring the shaft temperature are provided with a plurality of temperature sensors, and for the plurality of temperature sensors, the real-time monitoring data sets of the same preset time subsection are obtained, and the indexes of the real-time monitoring data sets are calculated.
And 103, judging whether the indexes of the real-time monitoring data set belong to the stable axle temperature subset, if so, executing the step 104, wherein the stable axle temperature subset is the subset of the index set obtained by calculating the indexes of all the preset time subsections according to the existing historical axle temperature data set.
The real-time monitoring data set corresponding to the indexes belonging to the stable subset of the shaft temperature state indicates that the shaft temperature state is stable, the existing historical shaft temperature data set is a set formed by shaft temperature data acquired by temperature sensors, and the historical shaft temperature data set comprises monitoring data of a plurality of temperature sensors in a preset time period; the preset time period may be divided into a plurality of preset time sub-periods.
And 104, transmitting the average value of the monitoring data monitored by the temperature sensor in a preset time subsection to a driver control room as shaft temperature data.
When the indexes of the real-time monitoring data set belong to the stable subset of the shaft temperature state, the shaft temperature state represented by the real-time monitoring data set obtained by the temperature sensor is stable, and the average value of the monitoring data monitored by the temperature sensor in a preset time subsection is used as the shaft temperature data and is transmitted to a driver control room, so that the transmission quantity of the shaft temperature data is reduced.
In the embodiment, the axle temperature states are classified, so that the axle temperature data are pertinently sent in different modes to be provided for a fault diagnosis system of a driver control room to detect the axle temperature states, the axle temperature data redundancy is reduced, and a feasible solution is provided for solving network congestion in train wireless network communication.
According to the train axle temperature data processing method provided by the embodiment, when the real-time monitoring data set obtained by the temperature sensor is judged to represent that the axle temperature state is stable, the average value of the monitoring data of the temperature sensor in the preset time subsection is taken as the axle temperature data and is transmitted to the driver control room, so that the axle temperature data structure is simplified, the sending and storing cost of the axle temperature data is reduced, the diagnosis correctness of the driver control room on the axle temperature fault diagnosis can not be reduced by the simplified axle temperature data, and meanwhile, the diagnosis real-time performance can be improved.
Example two
This embodiment is a supplementary explanation based on the above embodiment.
Fig. 2 is a schematic flow chart of a train axle temperature data processing method according to a second embodiment of the present invention; as shown in fig. 2, the present embodiment provides a train axle temperature data processing method, including:
step a, acquiring a historical axle temperature data set in a preset time period, and dividing the historical axle temperature data set into a plurality of historical axle temperature data subsets corresponding to continuous preset time subsections; the historical shaft temperature data set comprises monitoring data of a plurality of temperature sensors for monitoring the shaft temperature within a preset time period; the historical axle temperature data subset includes monitoring data within a preset time sub-segment.
Specifically, the preset time period is divided into a plurality of continuous preset time subsections, and the historical axle temperature data subset is a data set corresponding to the preset time subsections in the historical axle temperature data set.
And e, calculating and obtaining the variance, range and mean of the monitoring data of each temperature sensor at each preset time subsection according to the historical shaft temperature data subset.
And b, calculating indexes of all the preset time subsections according to the historical shaft temperature data subsets to obtain an index set.
Specifically, the index of each preset time sub-segment may be calculated according to the following formula, where the index includes a first variable and a second variable, where the first variable is
Second variable
vari、ΔiAnd TiThe variance, range and mean of the monitoring data of the preset time subsections are respectively, and n is the number of the temperature sensors. The first variable represents the size of the shaft temperature, and the second variable represents the size of the shaft temperature fluctuation.
And c, carrying out cluster analysis on the index set according to the first variable and the second variable, and dividing the index set into an axle temperature state stable subset and an axle temperature state unstable subset.
And c, performing cluster analysis on the index set according to the indexes of the preset time subsections calculated in the step b, and dividing the index set into a stable subset of the shaft temperature state and an unstable subset of the shaft temperature state, wherein the shaft temperature data corresponding to the indexes belonging to the stable subset of the shaft temperature state represents that the shaft temperature state is stable, and the shaft temperature data corresponding to the indexes belonging to the unstable subset of the shaft temperature state represents that the shaft temperature state is unstable.
The statistical classification of the train axle temperature states (the axle temperature state stable subset and the axle temperature state unstable subset) is realized by training historical axle temperature data (namely the historical axle temperature data subset) and through the cluster analysis of an index set of the historical axle temperature data.
And d, acquiring a center of the stable subset of the shaft temperature state and a center of the unstable subset of the shaft temperature state, wherein the center of the stable subset of the shaft temperature state is composed of the mode of the first variable and the second variable in the stable subset of the shaft temperature state, and the center of the unstable subset of the shaft temperature state is composed of the mode of the first variable and the second variable in the unstable subset of the shaft temperature state.
Specifically, the mode of a first variable and the mode of a second variable in the stable subset of the shaft temperature state are respectively used as a first value and a second value of the center of the stable subset of the shaft temperature state, when the mode of the first variable in the stable subset of the shaft temperature state has multiple values, the average value of the multiple values is used as the first value of the center of the stable subset of the shaft temperature state, and the center of the unstable subset of the shaft temperature state is obtained in the same mode.
102, calculating indexes of preset time subsections according to the real-time monitoring data set; the index includes a first variable and a second variable, wherein the first variable
Second variable
vari、ΔiAnd TiThe variance, range and mean of the monitoring data of the preset time subsections are respectively, and n is the number of the temperature sensors.
Step 103', judging whether the distance between the index and the center of the stable subset of the shaft temperature state is not greater than the distance between the index and the center of the unstable subset of the shaft temperature state, if so, judging that the index belongs to the stable subset of the shaft temperature state, and executing the step 104.
Further, the distance in step 103' can be measured by euclidean distance, absolute distance, weighted distance, etc.
And 104, transmitting the average value of the monitoring data monitored by the temperature sensor in a preset time subsection to a driver control room as shaft temperature data.
The average value of the monitoring data is adopted to replace the shaft temperature information in the preset time subsections, so that the redundant information of the shaft temperature data in the normal state is eliminated, and the working strength of the monitoring system is reduced.
The train axle temperature data processing method provided by this embodiment is to train according to an existing historical axle temperature data set to obtain an index set, then perform cluster analysis on the index set, divide the index set into a steady subset of an axle temperature state and an unstable subset of the axle temperature state, and when a real-time monitoring data set belongs to the steady subset of the axle temperature state, indicate that the axle temperature state represented by the axle temperature data obtained by a temperature sensor is stable at that time, so that an average value of monitoring data of the temperature sensor in a preset time subsection is transmitted to a driver control room as the axle temperature data, so as to simplify an axle temperature data structure, reduce transmission and storage costs of the axle temperature data, and meanwhile, the simplified axle temperature data does not reduce diagnosis efficiency of the driver control room on axle temperature fault diagnosis, and improve diagnosis real-time.
EXAMPLE III
The present embodiment is an apparatus embodiment, and is configured to perform the method in the first embodiment.
Fig. 3 is a schematic structural diagram of a train axle temperature data processing device according to a third embodiment of the present invention; as shown in fig. 3, the present embodiment provides a train axle temperature data processing apparatus, which includes a real-time monitoring data set obtaining module 201, an index calculating module 202, a determining module 203, and a data transmitting module 204.
The real-time monitoring data set acquisition module 201 is configured to acquire a real-time monitoring data set of the temperature sensor within a preset time sub-period;
the index calculation module 202 is configured to calculate an index of a preset time sub-segment according to the real-time monitoring data set; the index includes a first variable weighted _ var and a second variable weighted _ range, wherein,
vari、Δiand TiRespectively representing the variance, range and mean of the monitoring data of the preset time subsections, wherein n is the number of the temperature sensors;
the judging module 203 is used for judging whether the indexes of the real-time monitoring data set belong to the stable axle temperature subset, and if so, the data transmission module 204 is triggered, wherein the stable axle temperature subset is a subset of the index set obtained by calculating the indexes of all the preset time subsections according to the existing historical axle temperature data set;
and the data transmission module 204 is used for transmitting the average value of the monitoring data monitored by the temperature sensor in a preset time subsection to a driver control room as shaft temperature data.
The present embodiment is a device embodiment corresponding to the method embodiment, and specific reference may be made to the description in the first embodiment, which is not described herein again.
Example four
This embodiment is a supplementary description made on the basis of the third embodiment, and is used for executing the method in the second embodiment.
Fig. 4 is a schematic structural diagram of a train axle temperature data processing device according to a fourth embodiment of the present invention; as shown in fig. 4, the apparatus further includes a historical shaft temperature data set obtaining module 205, an index set calculating module 206, an index set dividing module 207, a center obtaining module 208, and a monitoring data calculating module 209.
The historical shaft temperature data set acquisition module 205 is configured to acquire a historical shaft temperature data set in an existing preset time period, and divide the historical shaft temperature data set into historical shaft temperature data subsets corresponding to a plurality of consecutive preset time subsections; the historical shaft temperature data set comprises monitoring data of a plurality of temperature sensors for monitoring the shaft temperature within a preset time period; the historical shaft temperature data subset comprises monitoring data in a preset time sub-segment;
the index set calculation module 206 is configured to calculate an index of each preset time sub-segment according to the historical axle temperature data subset to obtain an index set;
and the index set dividing module 207 is used for performing cluster analysis on the index set according to the first variable and the second variable, and dividing the index set into an axle temperature state stable subset and an axle temperature state unstable subset.
The center obtaining module 208 is configured to obtain a shaft temperature state stationary subset center and a shaft temperature state unstable subset center, where the shaft temperature state stationary subset center is composed of a mode of a first variable and a second variable in the shaft temperature state stationary subset, and the shaft temperature state unstable subset center is composed of a mode of the first variable and the second variable in the shaft temperature state unstable subset.
And the monitoring data calculation module 209 is configured to calculate and obtain a variance, a range and a mean of the monitoring data of each temperature sensor at each preset time sub-segment according to the historical shaft temperature data subset.
Further, the determining module 203 is specifically configured to: and judging whether the distance between the index and the center of the stable axle temperature state subset is not greater than the distance between the index and the center of the unstable axle temperature state subset, if so, judging that the index belongs to the stable axle temperature state subset, and triggering the data transmission module 204.
The present embodiment is an embodiment of an apparatus corresponding to the method embodiment, and specific reference may be made to the description in embodiment two, which is not described herein again.
While the invention has been described with reference to a preferred embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the technical features mentioned in the embodiments can be combined in any way as long as there is no structural conflict. It is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (8)
1. A train axle temperature data processing method is characterized by comprising the following steps:
step 101, acquiring a real-time monitoring data set of a temperature sensor in a preset time sub-period;
102, calculating indexes of preset time subsections according to the real-time monitoring data set; the index includes a first variable and a second variable, wherein the first variable
Second variable
vari、ΔiAnd TiRespectively representing the variance, range and mean of the monitoring data of the preset time subsections, wherein n is the number of the temperature sensors;
step 103, judging whether the indexes of the real-time monitoring data set belong to the stable axle temperature subset, if so, executing the step 104, wherein the stable axle temperature subset is the subset of the index set obtained by calculating the indexes of all the preset time subsections according to the existing historical axle temperature data set;
which comprises the following steps: judging whether the distance between the index and the center of the stable subset of the shaft temperature state is not greater than the distance between the index and the center of the unstable subset of the shaft temperature state, if so, judging that the index belongs to the stable subset of the shaft temperature state, and turning to step 104 for execution;
and 104, taking the average value of the monitoring data monitored by the temperature sensor in the preset time subsections as the axle temperature data and transmitting the axle temperature data to a driver control room so as to simplify an axle temperature data structure.
2. The train axle temperature data processing method according to claim 1, further comprising, before step 101:
step a, acquiring a historical axle temperature data set in a preset time period, and dividing the historical axle temperature data set into a plurality of historical axle temperature data subsets corresponding to continuous preset time subsections; the historical shaft temperature data set comprises monitoring data of a plurality of temperature sensors for monitoring the shaft temperature within a preset time period; the historical shaft temperature data subset comprises monitoring data in a preset time sub-segment;
b, calculating indexes of all preset time subsections according to the historical axle temperature data subsets to obtain an index set;
and c, carrying out cluster analysis on the index set according to the first variable and the second variable, and dividing the index set into an axle temperature state stable subset and an axle temperature state unstable subset.
3. The train axle temperature data processing method according to claim 2, wherein after step c, before step 103, further comprising:
and d, acquiring a center of the stable subset of the shaft temperature state and a center of the unstable subset of the shaft temperature state, wherein the center of the stable subset of the shaft temperature state is composed of the mode of the first variable and the second variable in the stable subset of the shaft temperature state, and the center of the unstable subset of the shaft temperature state is composed of the mode of the first variable and the second variable in the unstable subset of the shaft temperature state.
4. The train axle temperature data processing method according to claim 1, wherein after the step a and before the step b, the method further comprises:
and e, calculating and obtaining the variance, range and mean of the monitoring data of each temperature sensor at each preset time subsection according to the historical shaft temperature data subset.
5. The utility model provides a train axle temperature data processing device which characterized in that includes:
the real-time monitoring data set acquisition module is used for acquiring a real-time monitoring data set of the temperature sensor within a preset time sub-period;
the index calculation module is used for calculating the indexes of the preset time subsections according to the real-time monitoring data set; the index includes a first variable weighted _ var and a second variable weighted _ range, wherein,
vari、Δiand TiRespectively representing the variance, range and mean of the monitoring data of the preset time subsections, wherein n is the number of the temperature sensors;
the judging module is used for judging whether the indexes of the real-time monitoring data set belong to the stable axle temperature subset, and if so, the data transmission module is triggered, wherein the stable axle temperature subset is a subset of the index set obtained by calculating the indexes of all the preset time subsections according to the existing historical axle temperature data set;
the judgment module is specifically configured to: judging whether the distance between the index and the center of the stable subset of the axle temperature state is not greater than the distance between the index and the center of the unstable subset of the axle temperature state, if so, the index belongs to the stable subset of the axle temperature state, and triggering a data transmission module;
and the data transmission module is used for transmitting the average value of the monitoring data monitored by the temperature sensor in the preset time subsection to the driver control room as the axle temperature data so as to simplify the axle temperature data structure.
6. The train axle temperature data processing device according to claim 5, further comprising:
the historical shaft temperature data set acquisition module is used for acquiring a historical shaft temperature data set in the existing preset time period and dividing the historical shaft temperature data set into a plurality of historical shaft temperature data subsets corresponding to continuous preset time subsections; the historical shaft temperature data set comprises monitoring data of a plurality of temperature sensors for monitoring the shaft temperature within a preset time period; the historical shaft temperature data subset comprises monitoring data in a preset time sub-segment;
the index set calculation module is used for calculating indexes of all the preset time subsections according to the historical axle temperature data subsets so as to obtain an index set;
and the index set dividing module is used for carrying out cluster analysis on the index set according to the first variable and the second variable and dividing the index set into an axle temperature state stable subset and an axle temperature state unstable subset.
7. The train axle temperature data processing device according to claim 6, further comprising:
the center acquisition module is used for acquiring a shaft temperature state stable subset center and a shaft temperature state unstable subset center, wherein the shaft temperature state stable subset center is composed of the mode of a first variable and a second variable in the shaft temperature state stable subset, and the shaft temperature state unstable subset center is composed of the mode of the first variable and the second variable in the shaft temperature state unstable subset.
8. The train axle temperature data processing device according to claim 5, further comprising:
and the monitoring data calculation module is used for calculating and obtaining the variance, range and mean of the monitoring data of each temperature sensor at each preset time subsection according to the historical shaft temperature data subset.
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| CN110987232A (en) * | 2019-11-22 | 2020-04-10 | 西安思后网络科技有限公司 | Temperature measuring method of electronic thermometer |
| CN111383428B (en) * | 2020-05-29 | 2020-09-11 | 成都千嘉科技有限公司 | Online meter state monitoring and early warning method |
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