CN114427902B - Flow automatic checking device based on Internet of things and steady-state curve algorithm and application thereof - Google Patents
Flow automatic checking device based on Internet of things and steady-state curve algorithm and application thereof Download PDFInfo
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- 238000007689 inspection Methods 0.000 claims abstract description 81
- 239000013618 particulate matter Substances 0.000 claims abstract description 16
- 238000001514 detection method Methods 0.000 claims abstract description 10
- 238000000034 method Methods 0.000 claims description 24
- 238000003908 quality control method Methods 0.000 claims description 19
- 239000002245 particle Substances 0.000 claims description 12
- 230000002159 abnormal effect Effects 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 description 19
- 238000012360 testing method Methods 0.000 description 12
- 238000005070 sampling Methods 0.000 description 10
- 239000003570 air Substances 0.000 description 8
- 238000012423 maintenance Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000005259 measurement Methods 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000012795 verification Methods 0.000 description 4
- 239000000428 dust Substances 0.000 description 2
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 239000012080 ambient air Substances 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000001595 flow curve Methods 0.000 description 1
- 238000009533 lab test Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000013011 mating Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000005086 pumping Methods 0.000 description 1
- 238000007430 reference method Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Abstract
The invention relates to the technical field of flow checking and calibrating of particulate matter measuring instruments, in particular to an automatic flow checking device based on the Internet of things, a steady-state curve algorithm and application thereof, wherein a data acquisition sub-end periodically acquires flow checking data monitored by a flow meter from a terminal, a plurality of flow checking data are taken as a group, standard deviation of each group of flow checking data is calculated by continuously sliding and taking values, a steady flow data group is determined, and a flow checking true value is obtained from the steady flow data group. The invention has short detection time, and can complete flow calibration and send out inspection reports by collecting three-minute data.
Description
Technical Field
The invention relates to the technical field of flow inspection and calibration of particulate matter measuring instruments, in particular to an automatic flow inspection device based on the Internet of things, a steady-state curve algorithm and application thereof.
Background
Particulate matter monitors are instruments for measuring the mass concentration of particulate matter suspended in air, and generally can measure TSP, PM2.5, PM10, and have been widely used for monitoring the mass concentration of particulate matter in air environments, workplaces, vehicles, and the like. The accuracy of the monitoring result is greatly influenced by the accuracy of flow measurement, and the average monitoring result is 21.9% higher when the sampling flow is lower than the set point flow by 5% in the monitoring process through the atmospheric monitoring laboratory test of the China environmental monitoring total station; when the sampled flow rate is 5% higher than the set point flow rate, the monitoring result is 8.3% lower on average. In the standards of JJG 846-2015 dust concentration determinator, JJG 520-2005 dust sampler, JJG 943-2011 total suspended particulate matter sampler, ambient air particulate matter (PM 10 and PM 2.5) sampler technical requirements and detection methods (HJ 93-2013), no flow checking and calibrating method of the particulate matter measuring instrument has been queried.
Fan Wei et al propose a flow check calibration method: and connecting a flow standard device or a standard device with a sampling port of the air inlet particulate matter measuring instrument, starting the instrument, respectively reading standard flow values and the indicating values of the instrument to be detected for 3 times after the instrument is stabilized, and calculating an instantaneous flow indicating value error according to a formula (1), wherein the error requirement is +/-5%.
E＝(q v －q vs )/q vs (1)
Wherein E is instantaneous flow error,%; q vs The average value of three measurement results of the flow standard is L/min; q v The average value of 3 measurement values of the flow of the instrument to be measured is L/min. The method has the advantages of small data sample size, simple calculation, short time consumption of the calibration process and the like, but the problem of poor data repeatability is easy to occur because the data sample size is too small and the data acquisition time is not specifically required.
The nobility et al propose a flow check and calibration method: and (3) taking down the sampling cutter, and connecting the air outlet of the standard flowmeter to the air inlet of the monitor. And after the monitor shows that the flow is stable, starting measurement and recording. The continuous test time is 18min, and the instantaneous flow values (working conditions) of the standard flowmeter and the monitoring instrument are recorded every 3min respectively, and 6 groups of data are obtained. Calculating flow test related indexes by using formulas (2), (3), (4) and (5), wherein the test result meets the flow test requirement (delta Q) R ≤±5％，Q diff ≤±4％)。
If it does not meet, the flow rate must be calibrated
In the formula (2):the average flow value of the standard flowmeter is corrected during the test period, and L/min; q (Q) Ri L/min is the corrected standard flowmeter instantaneous flow value during the test; i is the sequence number of the record instant time point during the test(i=1 to n); n is the total number of recorded transient time points during the test, 6.
In the formula (3), Q C Displaying average flow value, L/min, for the monitor panel during the test period; q (Q) Ci The monitor panel was shown instantaneous flow values, L/min, during the test.
In the formula (4): ΔQ R Relative error between the value of the corrected standard flowmeter and the instrument flow set point,%; q (Q) S The sampling flow rate set for the monitor is 16.7L/min.
In formula (5): q (Q) diff Relative error between the instrument display value and the corrected standard flow count value,%. The method has moderate detection time, standard data sampling time requirement and delta Q R And Q diff The flow calibration judgment is carried out in two dimensions, so that the flow control calibration time can be better controlled; however, the method does not provide stable flow conditions, which may cause data sampling errors, and is difficult to synchronously record the readings of the flowmeter and the monitor panel in the manual operation process, and Q is easily caused diff Data errors, resulting in failure of flow check; ΔQ R Nor can it reflect real-time changes.
Provision of federal reference method for flow check calibration:
(1) The flow is 16.7L/min, 1 time is measured every 5min, the flow is continuously measured for 24 hours, the average value of the flow is required to be within 5% of a specified value, and 16.7L/min is equivalent to the air intake of a person for 1 hour;
(2) Within 24h sampling time, the relative standard deviation reading of 5min cannot exceed 2%;
(3) The sampler is transferred from place to place and must be corrected. Flow was reviewed every 3 months with a traceable to NIST analyzer; 25% of monitors in a region need parallel samplers; each sampler needs to be examined 1 time every 4 years.
The method has standard measurement requirements and large sampling data quantity, and can ensure the accuracy of results; however, the method is time-consuming to operate, can not guarantee the monitoring task on the same day as the flow calibration, and has obvious limitation on long-term monitoring of particulate matters.
The perfect flow inspection quality control system can effectively ensure the objectivity of the monitoring result, provide accurate and reliable air quality information for government related departments, and provide important references for decision making. At present, the method for checking the flow of the particulate matters in China has no unified standard, and all reported methods have certain defects and limitations, are manually operated, lack effective automatic monitoring means for monitoring the process, and are easy to cause intentional or unintentional artificial interference and errors, so that the data of monitoring the particulate matters are distorted. A more sophisticated automated solution is needed to address the algorithm deficiencies and human errors in the flow check calibration process.
Disclosure of Invention
Aiming at the problem that the current air quality monitoring station room can judge whether the operation condition of the station room and the operation state of equipment are abnormal or not only by checking a plurality of pieces of historical data manually, the invention provides the data quality guaranteeing system and method based on the operation environment of the station room and the operation state of the equipment, which are convenient for monitoring the temperature, humidity and atmospheric pressure of the station room, and can give an alarm in time to remind relevant responsible persons to carry out corresponding processing so as to guarantee the quality of monitored data.
In order to solve the technical problems, the invention adopts the following technical scheme:
the flow automatic checking steady-state curve algorithm based on the Internet of things comprises the steps that a data acquisition sub-end periodically acquires flow checking data monitored by a flow meter from a terminal, a plurality of flow checking data are taken as a group, standard deviation of each group of flow checking data is calculated through continuous sliding value taking, a stable flow data group is determined, and a flow checking true value is obtained from the stable flow data group, and the flow checking true value comprises the following situations:
(1) If the flow check data all meet the standard deviation less than the preset value within the set time, and the data are consistent (the same value is read every time), taking the last group as the stable flow data group, and taking the last flow check data of the stable flow data group as the flow check true value;
(2) If the flow check data all meet the standard deviation smaller than the preset value but the data are inconsistent within the set time, taking a group with the smallest variance among all groups in the set time as the stable flow data group, and taking the third flow check data of the stable flow data group as the flow check true value;
(3) If the standard deviation of each group of flow checking data shows a fluctuation reducing trend within a set time, until the flow starts to be stable after the standard deviation is smaller than a preset value, the group of data after the flow starts to be stable is taken as a stable flow data group, and the third data of the stable flow data group is taken as a flow checking true value;
(4) If the flow rate does not reach a steady state for a long time, it indicates that the pump is abnormal.
Preferably, the data acquisition sub-end acquires flow check data monitored by the flowmeter once every 5s from the terminal, and each 7 flow check data is a group, continuously slides to take value to calculate standard deviation of each group of flow check data, determines the stable flow data group, and acquires the flow check true value from the stable flow data group, including the following situations:
(1) If the flow check data all meet the standard deviation less than +/-2% within 1 minute, and the data are consistent, taking the last group as the stable flow data group, and taking the last flow check data of the stable flow data group as the flow check true value;
(2) If the flow check data all meet the standard deviation less than +/-2% but the data are inconsistent within 1 minute, taking one group with the smallest variance among all groups in the period of time as the stable flow data group, and taking the third flow check data of the stable flow data group as the flow check true value;
(3) If the standard deviation of each group of flow inspection data shows a fluctuation reducing trend within 3 minutes, the flow starts to be stable after the standard deviation is less than +/-2%, the group of data after the stable flow is taken as a stable flow data group, and the third data of the stable flow data group is taken as a flow inspection true value;
(4) If the flow rate does not stabilize within 5 minutes, it indicates that the pump is abnormal.
Depending on the situation, it is generally about 1 minute stable, and if it is unstable for more than 3 minutes, it is an inaccurate situation. And is unstable beyond 3 minutes, and is unstable after 3 minutes. If the stabilization is achieved only after 3 minutes, the data set is not used, and the data acquisition is carried out again after the inspection. The detection of the particulate matter flow depends on the pumping of the pump, which is abnormal if the flow is not stable for a long time.
Preferably, the standard deviation function of each set of flow check data is STDEVP, calculated by the following formula:
wherein n is the number of all flow check data of the group,the data were checked for average for all flows in the group.
Preferably, n=7,the average of the data was checked for the set of 7 flows.
A method for automatically checking a steady-state curve algorithm by using the flow based on the Internet of things comprises the following steps:
A. flow meter type selection: before quality control of particle flow inspection, selecting a flowmeter type, and performing flowmeter matching;
B. WiFi device protocol matching: connecting WiFi equipment, inserting a slave into the flowmeter, connecting a host with a data acquisition terminal, and displaying the ID of the flowmeter equipment after the slave is connected, so as to indicate that the master and the slave are ready to be connected;
C. flow meter information checking: checking flow meter information at the data acquisition sub-end to confirm that each parameter information is correct;
D. the flowmeter device is in place: taking down a cutting head of the particle analyzer, and placing a handheld flowmeter connected with the WiFi module slave machine at the cutting head;
E. and opening a quality control task: adding a quality control task to the data acquisition sub-end, and preparing for flow quality control inspection;
F. and starting to execute the quality control task: starting flow inspection, acquiring flow inspection data monitored by a flow meter by a data receiving unit by a data acquisition sub-end, and acquiring a flow inspection true value according to the flow automatic inspection steady-state curve algorithm based on the Internet of things;
G. uploading data by a data acquisition end: the flow inspection truth value is uploaded to a flow automatic inspection calibration platform in real time, an inspection curve is generated and displayed in real time by a platform end according to the flow inspection truth value, and an inspection report is generated according to the relative error between the flow inspection truth value and the reading of the particulate matter analyzer;
H. if the inspection result is qualified: if the relative error does not exceed the specified value, the inspection result is unqualified, and the flow inspection is finished;
I. if the inspection result is unqualified: if the relative error exceeds the specified value, the checking result is not qualified, the flow checking true value of the last checking is manually input, the automatic compensation flow calibration is carried out, the flow checking is restarted until the checking is qualified, and the semi-automatic flow checking calibration of the particulate matters can be completed, and the flow checking is finished.
Preferably, the particle analyzer reading defaults to 16.67L/min, and the relative error of the flow check truth value and the particle analyzer reading is obtained by the following formula:
(flow truth-particulate analyzer reading)/particulate analyzer reading ×100%.
Preferably, if the relative error between the flow inspection truth value and the particulate matter analyzer reading is not more than |±2% |, the inspection result is not qualified; if the detection result exceeds 2 percent, the detection result is unqualified.
Preferably, the inspection report further includes indoor temperature, humidity, start time and end time of the inspection curve, reference flowmeter model, factory number, last calibration time, next calibration time, slope, intercept, and correlation coefficient.
The device for automatically checking the steady-state curve algorithm by using the flow based on the Internet of things comprises a data acquisition sub-end, a data acquisition unit and a flowmeter; the data acquisition sub-end comprises a flow automatic checking and calibrating platform and a data receiving unit which are connected through a USB; the data acquisition unit comprises a WiFi module and a signal converter which are connected through TTL; the data receiving unit is matched with the WiFi module through a protocol; the flowmeter is connected with the signal converter through RS 232.
Compared with the prior art, the implementation of the invention has the following beneficial effects:
(1) The detection time is short, and the flow calibration can be completed and an inspection report can be provided by collecting three-minute data;
(2) The data continuity is high, the second-level sampling interval can be used for checking the data change condition in real time;
(3) Explicit data stability requirement, data stability threshold is set as: deviation of < | + -2% |;
(4) A more reasonable truth value calculation scheme is adopted, and data truth values are calculated for different data deviation conditions respectively;
(5) The flow checking result can be automatically judged and the compensation calibration can be automatically carried out, so that errors and labor cost caused by manual judgment and manual calibration are reduced;
(6) The compatibility to different flow meters is high, and the flow meter parameter management can be completed at the platform end.
Drawings
FIG. 1 is a flow check quality control flow chart;
FIG. 2 is an exemplary diagram of an additional flow meter interface;
FIG. 3 is an exemplary diagram of a flowmeter editing interface;
FIG. 4 is a schematic diagram of a flowmeter mating and slave connection interface;
FIG. 5 is an exemplary graph of flow check results;
FIG. 6 is a diagram of an example traffic inspection report;
FIG. 7 flow check steady state schematic;
FIG. 8 is a schematic diagram of a flow meter data acquisition system;
fig. 9 is a diagram of a data acquisition unit.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but embodiments of the present invention are not limited thereto.
It is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments, and that all other embodiments obtained by persons of ordinary skill in the art without making creative efforts based on the embodiments in the present invention are within the protection scope of the present invention.
Examples:
referring to fig. 1-9, an automatic flow checking and calibrating device based on internet of things and a system thereof, a basic flow implemented by the invention is shown in fig. 1, and the method comprises the following steps:
(1) Flow meter type selection: before the quality control of the particle flow inspection, an operation and maintenance worker opens a particle material control interface of a data acquisition end, and selects the type of a flowmeter to perform flowmeter matching.
(2) WiFi device protocol matching: and the slave is inserted into the flowmeter, and the host is connected with the data acquisition end. After the slave is connected, the flowmeter equipment ID can be displayed to indicate that the master and the slave are connected.
(3) Flow meter information checking: and (5) checking flow meter information at the data acquisition end to confirm that the information of each parameter is correct.
(4) The flowmeter device is in place: the particle analyzer cutting head was removed and a handheld flow meter, which had been plugged into the WiFi module slave, was placed there.
(5) And opening a quality control task: and adding a quality control task at the data acquisition end, clicking the start execution, and performing flow quality control inspection.
(6) And starting to execute the quality control task: and starting flow inspection, and acquiring flow meter inspection data by the data acquisition end.
(7) Uploading data by a data acquisition end: and uploading the flow inspection data to a flow automatic inspection calibration platform in real time, and displaying an inspection curve at the platform end in real time. And generates an inspection report from the inspection record.
(8) If the inspection result is unqualified: and manually inputting the flow truth value of the last inspection, performing automatic compensation flow calibration, restarting the flow inspection until the inspection is qualified, and finishing the semi-automatic flow inspection calibration of the particulate matters.
(9) Ending: the flow inspection is finished, an inspection report can be generated each time, inspection details (including three states in the process of being completed, not started and being executed) can be checked, and a flow inspection curve can be checked in real time.
1 flowmeter management
Before flow inspection, flow meter management is needed, and the flow automatic calibration platform provides flow meter management functions including adding and editing flow meter equipment: information such as brands, models, numbers, operation and maintenance personnel, operation and maintenance units, last verification time, effective date, slope, intercept and the like can also support verification report uploading. If the platform end does not have the corresponding flowmeter equipment information, a flowmeter needs to be newly added/edited, as shown in fig. 2 and 3.
2 flowmeter connection
And after the platform end selects the flowmeter, performing flowmeter connection operation. Connecting the data receiving unit with the platform unit and setting the data receiving unit as a host; the data acquisition unit is connected with the calibration flowmeter and is set as a slave machine, and the master machine and the flowmeter power switch are respectively turned on; after the master-slave machine is successfully connected, the data of the flowmeter can be read at the flow automatic checking and calibrating platform, as shown in fig. 4.
3 flow check
And the operation and maintenance personnel adds a flow checking quality control task at the data acquisition end, and can click the start of execution by checking that the flow meter parameters are accurate. The operation and maintenance personnel take down the cutting head of the particle monitor, take up the handheld flowmeter, start to carry out flow quality control inspection, upload flow data to the platform end, and the platform end shows steady-state curves in real time. After the flow inspection is finished, the details of the inspection process can be checked, and the inspection record is used for generating an inspection report. If the inspection result is not qualified, the flow truth value of the last inspection can be manually input, automatic flow supplement calibration can be performed, and flow inspection can be restarted until the inspection is qualified, as shown in fig. 5.
As shown in fig. 6, the flow check report data illustrates:
the indoor temperature and humidity are uploaded to the platform end through the indoor temperature and humidity sensor by the data acquisition;
the start time and end time are the inspection time periods of the flow inspection curve;
the model number of the reference flowmeter, the factory number, the last calibration time and the next calibration time are matched by the data acquisition end; the slope, the intercept and the correlation coefficient are calculated according to the verification report;
the flow meter uploading data in the flow test result is a flow stability true value obtained by a test curve, and the flow stability true value is calculated according to a steady-state curve algorithm; the particle analyzer reading defaults to 16.67L/min; the relative error is calculated from the relative error formula: (flow truth-particulate analyzer reading)/particulate analyzer reading 100%; whether calibration is carried out or not is judged according to the relative error threshold value: when the error is 2 percent, the flow calibration can be carried out;
the flow curve is obtained according to the data acquisition port interface
Flow checking steady-state curve algorithm
4 algorithm:
the data acquisition sub-end acquires flow meter monitoring data once every 5s from the terminal, acquires flow data within 3 minutes, continuously slides and takes a value to calculate the standard deviation of each group of data every 7 data until the flow starts to be stable after the standard deviation is less than +/-2%, and takes the third data of the group of data after the flow reaches the stability as a flow check true value. If stabilization is not achieved within 5 minutes, this indicates pump abnormality.
If the flow check data all meet the standard deviation less than +/-2% within 1 minute, the data are consistent, and the last data are taken as flow check true values.
If the flow check data all meet the standard deviation less than +/-2% within 1 minute, a group with the smallest variance among all groups within 1 minute is taken as a stable flow data group, and the third data of the stable group is taken as a flow check true value.
Note that: the standard deviation function is STDEVP, and the calculation formula is as follows:where n=7, < >>The average value for the 7 data of this group.
Advantages are:
compared with the existing flow checking algorithm, the flow checking steady-state algorithm has the following advantages:
(1) The detection time is short, and the flow calibration can be completed and an inspection report can be provided by collecting three-minute data;
(2) The data continuity is high, the second-level sampling interval can be used for checking the data change condition in real time;
(3) Explicit data stability requirement, data stability threshold is set as: deviation of < | + -2% |;
(4) A more reasonable truth value calculation scheme is adopted, and data truth values are calculated for different data deviation conditions respectively;
(5) The flow checking result can be automatically judged and the compensation calibration can be automatically carried out, so that errors and labor cost caused by manual judgment and manual calibration are reduced;
(6) The compatibility to different flow meters is high, and the flow meter parameter management can be completed at the platform end.
Examples:
one meter monitoring reading is taken every 5s starting from 10:30:00, 10:30:00-10:30:30: as a first set of data, the STDEVP function calculates the standard deviation of the first set of data, and the result 0.406342572 > |+ -0.02| does not meet the standards of the algorithms (2) and (3), and is executed according to the algorithm (1). Sliding and taking a value to calculate standard deviation of a second group of data (10:30:05-10:30:35 data), and similarly calculating to obtain standard deviation of all data within 3 minutes until the standard deviation is less than 0.02, wherein the standard deviation of 10:30:50 is 0.018516402 less than 0.02, the standard deviation corresponds to a data group of 10:30:50-10:31:20, and selecting third data in the group as true value of the data, namely the true value time of the flow measurement is 10:31:00, and measuring the flow value: 16.61L/min as shown in Table 1:
TABLE 1 flow check steady state algorithm calculation
| Sequence number | Time | flow/(L/min) | Standard deviation of |
| 1 | 10:30:00 | 14.91 | 0.406342572 |
| 2 | 10:30:05 | 15.22 | 0.375298521 |
| 3 | 10:30:10 | 15.43 | 0.361109402 |
| 4 | 10:30:15 | 15.62 | 0.330429529 |
| 5 | 10:30:20 | 15.79 | 0.28264061 |
| 6 | 10:30:25 | 16.01 | 0.216389671 |
| 7 | 10:30:30 | 16.15 | 0.157428312 |
| 8 | 10:30:35 | 16.36 | 0.086849624 |
| 9 | 10:30:40 | 16.5 | 0.04257047 |
| 10 | 10:30:45 | 16.56 | 0.025872529 |
| 11 | 10:30:50 | 16.59 | 0.018516402 |
| 12 | 10:30:55 | 16.6 | 0.01484615 |
| 13 | 10:31:00 | 16.61 | 0.011780302 |
| 14 | 10:31:05 | 16.63 | 0.007559289 |
| 15 | 10:31:10 | 16.63 | 0.006998542 |
| 16 | 10:31:15 | 16.64 | 0.004948717 |
| 17 | 10:31:20 | 16.64 | 0.006388766 |
| 18 | 10:31:25 | 16.64 | 0.006388766 |
| 19 | 10:31:30 | 16.65 | 0.004948717 |
| 20 | 10:31:35 | 16.65 | 0.004948717 |
| 21 | 10:31:40 | 16.65 | 0.00451754 |
| 22 | 10:31:45 | 16.65 | 0.003499271 |
| 23 | 10:31:50 | 16.66 | 0.003499271 |
| 24 | 10:31:55 | 16.66 | 0.00451754 |
| 25 | 10:32:00 | 16.66 | 0.004948717 |
| 26 | 10:32:05 | 16.66 | 0.004948717 |
| 27 | 10:32:10 | 16.66 | 0.006388766 |
| 28 | 10:32:15 | 16.66 | 0.006388766 |
| 29 | 10:32:20 | 16.67 | 0.004948717 |
| 30 | 10:32:25 | 16.67 | 0.004948717 |
| 31 | 10:32:30 | 16.67 | 0.004714045 |
| 32 | 10:32:35 | 16.67 | 0.004 |
| 33 | 10:32:40 | 16.68 | |
| 34 | 10:32:45 | 16.68 | |
| 35 | 10:32:50 | 16.68 | |
| 36 | 10:32:55 | 16.68 | |
From table 1, a steady state schematic of the flow check as shown in fig. 7 can be obtained.
The invention provides a flow automatic checking and calibrating device based on the Internet of things, which comprises a calibrating flowmeter, a flow data acquisition unit, a data receiving unit and a flow automatic checking and calibrating platform unit. Calibrating a flowmeter to measure the flow of the atmospheric particulates; the flow data acquisition unit directly acquires the reading of the flowmeter; the data receiving unit transmits the data to the automatic flow checking and calibrating platform unit in real time; the platform unit receives the flowmeter data, generates a calibration reference value, monitors a flow calibration coefficient and automatically generates a flow inspection result report according to the template.
The calibration flowmeter is used for detecting the flow of the atmospheric particles, and information such as the brand, model, number, operation and maintenance personnel, operation and maintenance units, last verification time, effective date, slope, intercept and the like of the flowmeter can be edited and stored in the flow calibration platform unit.
An automatic flow checking device based on the Internet of things, as shown in fig. 8, comprises a data acquisition sub-end, a data acquisition unit and a flowmeter; the data acquisition sub-end comprises a flow automatic checking and calibrating platform and a data receiving unit which are connected through a USB; the data acquisition unit comprises a WiFi module and a signal converter which are connected through TTL; the data receiving unit is matched with the WiFi module through a protocol; the flowmeter is connected with the signal converter through RS 232.
The data acquisition unit is integrally packaged by using a plastic shell, and as shown in fig. 9, the shell comprises a power control switch, a signal converter, a WiFi module and a matched electronic circuit. The power switch controls the data acquisition unit to be turned on/off; one end of the signal converter is directly inserted into the flowmeter, and the other end of the signal converter is connected with the WiFi module; the WiFi module directly reads the flowmeter data through the TTL/RS232 signal converter and wirelessly transmits the flowmeter data to the data receiving unit.
The data receiving unit is integrally packaged by using a plastic shell, and the shell comprises a power control switch, a WiFi module, a USB interface and a matched electronic circuit. The power switch controls the data acquisition unit to be turned on/off; the WiFi module wirelessly receives the flowmeter data acquired by the data acquisition unit; the data receiving unit is communicated with the flow calibration platform through USB.
The flow automatic checking and calibrating platform unit is communicated with the data receiving unit through a USB, acquires flow meter monitoring data in real time, generates a calibration reference value and monitors a flow calibration coefficient; the platform has a flowmeter management function, can check flow calibration details in real time, and automatically generates a flow check result report after flow calibration is finished.
The foregoing disclosure is merely illustrative of the preferred embodiments of the present invention and is not intended to limit the scope of the claims herein, as equivalent changes may be made in the claims herein without departing from the scope of the invention.
Claims (9)
1. The flow automatic checking steady-state curve algorithm based on the Internet of things is characterized in that a data acquisition sub-end periodically acquires flow checking data monitored by a flow meter from a terminal, takes a plurality of flow checking data as a group, continuously slides to take a value to calculate the standard deviation of each group of flow checking data, determines a steady flow data group, and obtains a flow checking true value from the steady flow data group, wherein the flow checking true value comprises the following conditions:
(1) If the flow checking data all meet the standard deviation smaller than the preset value and the data are consistent within the set time, taking the last group as the stable flow data group and taking the last flow checking data of the stable flow data group as the flow checking true value;
(2) If the flow check data all meet the standard deviation smaller than the preset value but the data are inconsistent within the set time, taking a group with the smallest variance among all groups in the set time as the stable flow data group, and taking the third flow check data of the stable flow data group as the flow check true value;
(3) If the standard deviation of each group of flow checking data shows a fluctuation reducing trend within a set time, until the flow starts to be stable after the standard deviation is smaller than a preset value, the group of data after the flow starts to be stable is taken as a stable flow data group, and the third data of the stable flow data group is taken as a flow checking true value;
(4) If the flow rate does not reach a steady state for a long time, it indicates that the pump is abnormal.
2. The algorithm for automatically checking a steady-state curve of flow based on the internet of things according to claim 1, wherein the data acquisition sub-end acquires flow check data monitored by the flow meter from the terminal every 5s, each 7 flow check data is a group, continuously slides to take a value to calculate standard deviation of each group of flow check data, determines the steady flow data group, and obtains the flow check true value from the steady flow data group, wherein the method comprises the following steps:
(1) If the flow check data all meet the standard deviation less than +/-2% within 1 minute, and the data are consistent, taking the last group as the stable flow data group, and taking the last flow check data of the stable flow data group as the flow check true value;
(2) If the flow check data all meet the standard deviation less than +/-2% but the data are inconsistent within 1 minute, taking one group with the smallest variance among all groups in the period of time as the stable flow data group, and taking the third flow check data of the stable flow data group as the flow check true value;
(3) If the standard deviation of each group of flow inspection data shows a fluctuation reducing trend within 3 minutes, the flow starts to be stable after the standard deviation is less than +/-2%, the group of data after the stable flow is taken as a stable flow data group, and the third data of the stable flow data group is taken as a flow inspection true value;
(4) If the flow rate does not stabilize within 5 minutes, it indicates that the pump is abnormal.
3. The flow automatic checking steady state curve algorithm based on the internet of things as set forth in claim 1, wherein the standard deviation function of each set of flow checking data is STDEVP, calculated by the following formula:
STDEVP
wherein n is the number of all flow check data of the group,the data were checked for average for all flows in the group.
4. The flow automatic checking steady state curve algorithm based on the internet of things as claimed in claim 3, wherein n=7,the average of the data was checked for the set of 7 flows.
5. A method for applying the flow automatic checking steady state curve algorithm based on the internet of things as claimed in claim 1, comprising the following steps:
A. flow meter type selection: before quality control of particle flow inspection, selecting a flowmeter type, and performing flowmeter matching;
B. WiFi device protocol matching: connecting WiFi equipment, inserting a slave into the flowmeter, connecting a host with a data acquisition terminal, and displaying the ID of the flowmeter equipment after the slave is connected, so as to indicate that the master and the slave are ready to be connected;
C. flow meter information checking: checking flow meter information at the data acquisition sub-end to confirm that each parameter information is correct;
D. the flowmeter device is in place: taking down a cutting head of the particle analyzer, and placing a handheld flowmeter connected with the WiFi module slave machine at the cutting head;
E. and opening a quality control task: adding a quality control task to the data acquisition sub-end, and preparing for flow quality control inspection;
F. and starting to execute the quality control task: starting flow inspection, acquiring flow inspection data monitored by a flow meter by a data receiving unit by a data acquisition sub-end, and acquiring a flow inspection true value according to the flow automatic inspection steady-state curve algorithm based on the Internet of things;
G. uploading data by a data acquisition sub-end: the flow inspection truth value is uploaded to a flow automatic inspection calibration platform in real time, an inspection curve is generated and displayed in real time by a platform end according to the flow inspection truth value, and an inspection report is generated according to the relative error between the flow inspection truth value and the reading of the particulate matter analyzer;
H. if the inspection result is qualified: if the relative error does not exceed the specified value, the inspection result is qualified, and the flow inspection is finished;
I. if the inspection result is unqualified: if the relative error exceeds the specified value, the checking result is not qualified, the flow checking true value of the last checking is manually input, the automatic compensation flow calibration is carried out, the flow checking is restarted until the checking is qualified, and the semi-automatic flow checking calibration of the particulate matters can be completed, and the flow checking is finished.
6. The method of claim 5, wherein the particulate matter analyzer reading defaults to 16.67L/min, and the relative error of the flow check truth and the particulate matter analyzer reading is obtained by the following equation:
(flow truth-particulate analyzer reading)/particulate analyzer reading ×100%.
7. The method of claim 5, wherein the relative error of the flow check truth value and the particulate matter analyzer reading is not more than |±2% | and the check result is acceptable; if the detection result exceeds 2 percent, the detection result is unqualified.
8. The method of claim 5, wherein the inspection report further includes room temperature, humidity, start time and end time of inspection curve, reference flowmeter model, factory number, last calibration time, next calibration time, slope, intercept, correlation coefficient.
9. A device for automatically checking a steady-state curve algorithm by using the flow based on the internet of things according to claim 1, which is characterized by comprising a data acquisition sub-end, a data acquisition unit and a flowmeter; the data acquisition sub-end comprises a flow automatic checking and calibrating platform and a data receiving unit which are connected through a USB; the data acquisition unit comprises a WiFi module and a signal converter which are connected through TTL; the data receiving unit is matched with the WiFi module through a protocol; the flowmeter is connected with the signal converter through RS 232.
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