CN114459523B - Calibration early warning method of online quality detection instrument - Google Patents
Calibration early warning method of online quality detection instrument Download PDFInfo
- Publication number
- CN114459523B CN114459523B CN202111513384.3A CN202111513384A CN114459523B CN 114459523 B CN114459523 B CN 114459523B CN 202111513384 A CN202111513384 A CN 202111513384A CN 114459523 B CN114459523 B CN 114459523B
- Authority
- CN
- China
- Prior art keywords
- calibration
- early warning
- detection instrument
- quality detection
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 82
- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000005259 measurement Methods 0.000 claims description 73
- 230000002159 abnormal effect Effects 0.000 claims description 20
- 235000019504 cigarettes Nutrition 0.000 claims description 17
- 238000012360 testing method Methods 0.000 claims description 5
- 238000005070 sampling Methods 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 abstract description 4
- 238000004364 calculation method Methods 0.000 abstract description 4
- 230000000694 effects Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 5
- 238000000105 evaporative light scattering detection Methods 0.000 description 4
- 230000002950 deficient Effects 0.000 description 3
- 238000009423 ventilation Methods 0.000 description 3
- 238000012790 confirmation Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000010801 machine learning Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000001960 triggered effect Effects 0.000 description 2
- 238000004164 analytical calibration Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
Abstract
The invention discloses a calibration early warning method of an online quality detection instrument, which belongs to the technical field of calibration methods of detection equipment. The calibration index updated in real time has good flexibility, and the calibration early warning false alarm is reduced. The normal interval is defined by calculating detection data in normal detection time, the data is close to an actual detection environment, and the reliability is high. The data judgment and calculation process is simple, the algorithm modeling is not needed to be carried out by depending on a large amount of data, the universality is high, and the implementation cost is low. And (3) carrying out calibration early warning judgment through abnormality of all the machines, realizing synchronization constraint and improving the stability of the calibration early warning.
Description
Technical Field
The invention belongs to the technical field of calibration methods of detection equipment, and particularly relates to a calibration early warning method of an online quality detection instrument.
Background
In the cigarette production process, the weight, circumference, suction resistance, ventilation and length data of the cigarettes are required to be detected through a quality detection instrument, but detection errors can be increased along with the increase of the detection times of the instrument, and detection results are easily influenced by the ambient temperature and humidity and the accuracy of the detection of the instrument is required to be ensured through calibration and correction.
The existing calibration method is to calibrate the detection instrument in a fixed time every day, delay cannot be avoided, if errors of equipment occur, error data cannot be judged, accuracy of the detection equipment is affected, invalid calibration is conducted for a large number of times when the equipment errors are within an allowable range, and time cost of workers is wasted. In order to improve the accuracy of calibration early warning, detection equipment in other fields adopts an inflection point method and a machine learning algorithm to predict the calibration time, the inflection point method identifies the wave crest and wave trough characteristics of a data curve and determines a calibration threshold value, and the early warning method can misreport when a cigarette is defective, so that the actual calibration requirement is difficult to meet. The machine learning algorithm needs to perform a large number of training, the error judgment of the quality detection instrument is difficult, the measurement results of cigarettes in the same batch in different time environments are different, the error cause is difficult to determine, a large number of accurate and effective training sets cannot be provided, and the reliability of the algorithm in application is difficult to guarantee.
Disclosure of Invention
The calibration index is calculated through normal detection time measurement data, the normal interval is determined based on the calibration index, the calibration index is updated along with the input of detection data, whether the measurement state is normal is judged through whether the updated calibration index falls into the normal interval, if all the machine stations connected with the quality detector are abnormal, the current quality detector is judged to need to be calibrated, the accuracy of calibration early warning is improved, the flexibility and pertinence of the calibration mode are improved, and the real-time index monitoring and early warning enable the instrument calibration operation to be more intelligent.
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme: a calibration early warning method of an online quality detection instrument comprises the following steps:
Step 1, calibrating a quality detection instrument for the first time by using a standard component to enable measurement data to be consistent with the standard component, wherein the measurement time is T 0;
Step 2, setting a normal detection time threshold T 1 of the equipment, and storing measurement data X ij in the normal detection time threshold T 1 of the equipment;
Step 3, calculating a calibration index R i based on the stored measurement data X ij;
step 4, calculating a corresponding normal interval based on the calibration index R i;
step 5, after the measurement time of the quality detection instrument is greater than the normal detection time threshold T 1, recalculating the corresponding calibration index R 'i according to the new measurement data, judging whether the calibration index R' i falls into the normal interval range, if so, the measurement state is a normal state, and otherwise, the measurement state is a suspected abnormal state;
and step 6, updating the state queue until the machine platform connected with the current quality detection instrument completes one round of measurement data acquisition, if the measurement state of the current round is suspected to be abnormal, considering that the current quality detection instrument needs to be calibrated, and recording the measurement data time as early warning calibration time.
Preferably, the step 3 includes the following steps:
Step 31, calculating a mean value and a standard deviation:
mu＝mean(Xij)＝mean([x11,x12,…,xij])
s＝std(Xij)＝std([x11,x12,…,xij])
Wherein X ij＝[x11,x12,…,xji is measurement data, i represents the number of connected machines of the quality detection instrument, and j represents the number of products measured once:
Step 32, eliminating abnormal values to obtain a new X' ij＝[x′11,x′12,…,x′ji′:
Wherein x ij represents the measured value of the jth product of the ith machine in the current measured object; x -ij represents that the current value is an outlier and is removed from the dataset; x' ij represents the result value subjected to the outlier test; ij' is the length of the data set after the outlier is removed;
step 33, calculating a calibration index R i:
wherein C i is a technical standard value of the brand of cigarettes produced by the current machine.
Preferably, in the step 4, a 95% confidence interval is calculated based on the calibration index R i, and the 95% confidence interval is a normal interval.
Preferably, in the step 2, a joint density curve is drawn through historical data, and sampling frequency and rotation duty ratio of each machine in the measurement data X ij in the time threshold are optimized through the joint density curve.
The invention has the beneficial effects that:
Dividing a period of time after the calibration of the quality detection instrument into normal detection time, calculating a calibration index according to data detected in the normal detection time, calculating a normal interval according to the calibration index, updating the calibration index according to measurement data exceeding the normal detection time, judging whether the quality detection instrument is suspected to be abnormal according to the calibration index, and if all machines connected with the quality detection instrument are suspected to be abnormal, marking the measurement data time as early warning calibration time. The flexibility of the calibration index updated in real time is superior to that of a fixed threshold value adopted by an inflection point method, and the calibration early warning false alarm is reduced. The normal interval is defined by calculating detection data in normal detection time, the data is close to an actual detection environment, and the reliability is high. The data judgment and calculation process is simple, the algorithm modeling is not needed to be carried out by depending on a large amount of data, the universality is high, and the implementation cost is low. And (3) carrying out calibration early warning judgment through abnormality of all the machines, realizing synchronization constraint and improving the stability of the calibration early warning.
Drawings
FIG. 1 is a schematic flow chart of a calibration early warning method;
FIG. 2 is a schematic diagram of a quality inspection instrument and a machine layout;
FIG. 3 shows the effect of the mass detection instrument MTS-2004 on the early warning;
FIG. 4 shows the effect of the mass detection instrument MTS-2005;
FIG. 5 shows the effect of the mass detection instrument MTS-2008 on early warning;
FIG. 6 is a diagram of the effect of the warning of the quality control instrument MTS-2010;
And 7, a density curve of the combination of the measurement times and the calibration early warning times is shown in the figure.
Detailed Description
In order to facilitate the understanding and implementation of the present invention by those skilled in the art, the technical solution of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
Taking a quality detecting instrument, namely a Rui Tuo certain series of comprehensive test tables, which is most frequently used in a rolling workshop as an example, the measured data indexes comprise five weight, circumference, suction resistance, ventilation rate and length. The detection errors of the suction resistance and the ventilation rate can be increased along with the increase of the detection times of the instrument, are easily influenced by the temperature and the humidity in the environment, and are less interfered by human factors. The embodiment of the invention selects the resistance-to-suction measurement data as experimental data sources. Selecting a quality detection instrument: MTS-2004, MTS-2005, MTS-2008, MTS-2010 and two product brands: a and B, the corresponding production scene layout structure is shown in figure 2, and data detection is carried out.
And step 1, calibrating the quality detection instrument for the first time by using a standard component to enable the measurement data to be consistent with the standard component, wherein the measurement time is T 0. And correcting the error of the quality detection instrument through calibration, clearing the data before calibration, and taking the time as the initial quantity of the whole algorithm data input.
Step 2, a device normal detection time threshold T 1 is set, and measurement data X ij in the device normal detection time threshold T 1 is stored. According to historical past data, the measurement result of the quality detection instrument within 2 hours after calibration is stable, measurement data X ij in the time period is taken as basic data accurately, the data is close to the actual detection environment, and the reliability is high.
Step 3, calculating a calibration index R i based on the stored measurement data X ij; the difference between the defective cigarette data and the normal value is extremely large, so that disturbance can be caused to calculation of the calibration index R i, the accuracy of calibration early warning is affected, and the measurement data of the actual defective cigarette are firstly eliminated in the calculation process of the calibration index R i.
Preferably, outliers are rejected by the "3σ" rule.
Step 31, calculating a mean value and a standard deviation:
mu＝mean(Xij)＝mean([x11,x12,…,xij]) (1)
s＝std(Xij)＝std([x11,x12,…,xij]) (2)
(1) In the formula (2), X ij＝[x11,x12,…,xji is measurement data, i represents the number of connected machines of the quality detection instrument, and j represents the number of products measured in a single time:
Step 32, eliminating abnormal values to obtain a new X' ij＝[x′11,x′12,…,x′ji′:
(3) Wherein x ij represents the measured value of the jth product of the ith machine in the current measured object; x -ij represents that the current value is an outlier and is removed from the dataset; x' ij represents the result value subjected to the outlier test; ij' is the length of the data set after the outlier is removed;
Step 33, calculating a calibration index R i: each measuring time on the current quality detecting instrument is T i, the corresponding calibration index is R i, and the calibration index of each measuring time in the normal detecting time threshold T 1 is calculated successively.
(4) Wherein C i is a technical standard value of the brand of cigarettes produced by the current machine.
Step 4, calculating a corresponding normal interval based on the calibration index R i; preferably, a 95% confidence interval is calculated based on the calibration index R i, the 95% confidence interval being the normal interval. The 95% confidence interval is calculated as follows:
(5) In the middle of The standard deviation sigma is the standard deviation of the calibration index R i, and the total number n is the total number of the calibration index R i.
And 5, after the measurement time of the quality detection instrument is greater than the normal detection time threshold T 1, recalculating the corresponding calibration index R 'i according to the new measurement data, judging whether the calibration index R' i falls into a normal interval range, if so, determining that the current measurement state is a normal state, and otherwise, determining that the current measurement state is a suspected abnormal state.
When the measurement time T i of the quality detection instrument is greater than the normal detection time threshold T 1, the new measurement data X j+1i+1 is recorded into the stored measurement data X ij, at this time, X ij＝[x11,x12,…,xij,xi+1j+1 is repeated by the updated X ij to calculate the calibration index R 'i corresponding to each new measurement data, the corresponding calibration index R' i is substituted into the normal interval calculated in the step 4, if the corresponding calibration index R 'i falls into the normal interval, the current measurement state is the normal state, and if the corresponding calibration index R' i does not fall into the normal interval, the current measurement state is the suspected abnormal state.
And step 6, updating the state queue until the machine platform connected with the current quality detection instrument completes one round of measurement data acquisition, if the measurement state of the current round is suspected to be abnormal, considering that the current quality detection instrument needs to be calibrated, and recording the measurement data time as early warning calibration time.
(7) Wherein N i represents the state of each measured data, and is marked as 1 if the state is normal after the judgment is carried out according to the calibration index R' i; if the state is suspected of being abnormal, the state is marked as 0.
The state queue is K, K= [ N 1,N2,…,Ni ], the default length of the state queue is consistent with the number i of the machines connected with the quality detection instrument, but in the actual measurement process, the assumed machine wheel flow measurement mechanism is broken, the length of the state queue K is increased until the state queue K contains at least one detection data of all the machines, if the data of the state queue K are all 0, the measurement state of the current round is suspected abnormal, the error of the current quality detection instrument is considered to be calibrated, and the time of the measurement data is recorded as the early warning calibration time. And judging and inputting the detection data of the early warning calibration time, stopping early warning if the measurement data state N i is in a normal state, and continuously early warning if the measurement data state N i is in a suspected abnormal state.
Specific embodiments are that certain package workshop production data, quality detecting instruments and machine stations and brand layout structures are shown in fig. 2, A brand cigarettes are produced through 1# machine stations, 2# machine stations, 7# machine stations, 8# machine stations and 9# machine stations, B brand cigarettes are produced through 4# machine stations, 5# machine stations, 6# machine stations, 10# machine stations, 11# machine stations and 12# machine stations, MTS-2004 quality detecting instruments are responsible for detecting cigarettes produced by 1# machine stations and 2# machine stations, MTS-2005 quality detecting instruments are responsible for detecting cigarettes produced by 4# machine stations, 5# machine stations and 6# machine stations, MTS-2008 quality detecting instruments are responsible for detecting cigarettes produced by 7# machine stations, 8# machine stations and 9# machine stations, MTS2005 quality detecting instruments are responsible for detecting cigarettes produced by 10# machine stations, 11# machine stations and 12# machine stations
As shown in FIG. 3, after the first calibration is performed by the standard component, the detection time of the first 4 times of measured data is within the normal detection time threshold T 1, the normal detection interval is defined by the first 4 times of detected data, the 5 th measured result is suspected abnormality, but the state queue K is only recorded, the 1 time of data, and the length of the state queue K is less than the number of the machine stations connected with the MTS-2004 quality detection instrument by 2, so that the calibration pre-warning is not triggered at the current point. The 6 th measurement result is also suspected abnormal, 1# state and 2# state of each machine are recorded in the state queue K, the calibration early warning judgment standard is met, and the time of the measurement data is recorded as early warning calibration time. And next 3 times of measurement are all of the machine stations No. 2, the state is suspected abnormal, and the corresponding state sequence is updated. And because the last measured value of the No. 1 machine in the state sequence is in a suspected abnormal state, calibration early warning is continuously sent out. After actual calibration is carried out on the same day, a new early warning is started. And no calibration early warning occurs again in the rest measurement process.
The MTS-2005 quality detecting instrument early warning effect diagram is shown in fig. 4, the machine wheel flow test is more standard, and no calibration early warning occurs. In the detection process, the cigarette parameters produced by the No. 4 machine are generally deviated from the existing technical standard values but not exceed the abnormal value rejection interval, the data of the problematic cigarettes are reserved, and the detection data of the No. 4 machine are confirmed on site to be in fit with the actual production.
The early warning effect diagram of the MTS-2008 quality detecting instrument is shown in fig. 5, the 13 th measured value is obviously mutated, but the value meets the existing brand technical range and is not taken as an outlier to be removed. In the actual detection process, 15: and the temperature is reduced by sudden heavy rain after 00 minutes, the ambient temperature and humidity are changed, calibration early warning is continuously sent out after 15:50, and after on-site confirmation, the quality detection instrument is really influenced by the ambient temperature and humidity to generate errors, so that the calibration early warning result is accurate and effective.
The MTS-2010 mass detection instrument pre-warning effect diagram is shown in FIG. 6. And 19:06 triggering one calibration early warning. After that, although the corresponding measurement result and the calibration index on the 11# machine are out of standard, the calibration indexes corresponding to the other two machines are normal, so that the early warning is not triggered again, and after the on-site confirmation and 19:00 time sharing, the 11# machine equipment is debugged, the error of the quality detection instrument is caused by manual operation, and the calibration early warning result is accurate and effective.
Preferably, in the step 2, a joint density curve is drawn through historical data, and sampling frequency and rotation duty ratio of each machine in the measurement data X ij in the time threshold are optimized through the joint density curve.
As shown in FIG. 7, the high line area in the graph is a joint density curve between the measurement times and the calibration early warning times in the step 2; if the actual measurement times within 2 hours of calibration are less than 5 times, the normal interval range of the generated calibration index is narrowed, the sensitivity of calibration early warning is increased, and the times of the algorithm for identifying the need of calibration are increased. When the actual measurement times are greater than or equal to 10 times, the calibration early-warning times predicted by the method are generally smaller, the concentration degree of the density curve is better, and the algorithm calibration early-warning effect is more stable.
The inner circle points in fig. 7 represent the rotation duty ratios, and the darker the dot colors represent the higher the rotation duty ratios, the higher the satisfaction of the rotation measurements. And when the rotation occupation is higher, the calibration early warning times are less, and the rationality is higher. It can be seen from FIG. 7 that the rotation ratio of the detection apparatus MTS-2005, MTS-2008 and MTS-2010, which connects the three stations, is significantly lower than that of MTS-2004, which connects the two stations. If the rotation occupancy is relatively low, the index distribution characteristics of each machine extracted in the normal interval of the calibration index will be biased, and even the index distribution characteristics of the individual machines will be lost, as shown in (1) to (2) of fig. 7. Subgraphs (1) - (3), step 2 measurement times less than 10 and lower rotation duty cycle are the main reasons for the extra protruding area on the left side of the way; in the subgraph (4), since measurement does not occur within 2 hours after calibration, a normal interval of the calibration index cannot be defined, and the algorithm automatically processes the subsequent measured value as suspected abnormality and triggers calibration early warning.
Therefore, in summary, the measurement times and the rotation duty ratio of the step 2 are improved, and the calibration early warning precision and the calibration early warning stability are obviously improved. The two problems can be effectively avoided through a standardized operation flow, for example, a quality detection instrument connected with 2 machine stations is used for measuring each machine station every 25-30 minutes within 2 hours after actual calibration, and the quality detection instrument connected with 3 machine stations is used for measuring every 30-40 minutes. The time interval after 2 hours of calibration can be adjusted according to actual production requirements and expectations.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. The calibration early warning method of the online quality detection instrument is characterized by comprising the following steps of:
Step 1, calibrating a quality detection instrument for the first time by using a standard component to enable measurement data to be consistent with the standard component, wherein the measurement time is T 0;
Step 2, setting a normal detection time threshold T 1 of the equipment, and storing measurement data X ij in the normal detection time threshold T 1 of the equipment;
Step 3, calculating a calibration index R i based on the stored measurement data X ij;
step 4, calculating a corresponding normal interval based on the calibration index R i;
step 5, after the measurement time of the quality detection instrument is greater than the normal detection time threshold T 1, recalculating the corresponding calibration index R 'i according to the new measurement data, judging whether the calibration index R' i falls into the normal interval range, if so, the measurement state is a normal state, and otherwise, the measurement state is a suspected abnormal state;
and step 6, updating the state queue until the machine platform connected with the current quality detection instrument completes one round of measurement data acquisition, if the measurement state of the current round is suspected to be abnormal, considering that the current quality detection instrument needs to be calibrated, and recording the measurement data time as early warning calibration time.
2. The method for calibrating and pre-warning an on-line quality detecting instrument according to claim 1, wherein the step 3 comprises the following steps: step 31, calculating a mean value and a standard deviation:
mu＝mean(Xij)＝mean([x11,x12,...,xij])
s＝std(Xij)＝std([x11,x12,...,xij])
Wherein X ij＝[x11,x12,…xij is measurement data, i represents the number of connected machines of the quality detection instrument, and j represents the number of products measured once:
step 32, eliminating abnormal values to obtain a new X' ij＝[x'11,x'12,…x'ij':
Wherein x ij represents the measured value of the jth product of the ith machine in the current measured object; x -ij represents that the current value is an outlier and is removed from the dataset; x' ij represents the result value subjected to the outlier test; ij' is the length of the data set after the outlier is removed;
step 33, calculating a calibration index R i:
wherein C i is a technical standard value of the brand of cigarettes produced by the current machine.
3. The method for calibrating and pre-warning an online quality detection instrument according to claim 1, wherein in the step 4, a 95% confidence interval is calculated based on a calibration index R i, and the 95% confidence interval is a normal interval.
4. The method for calibrating and pre-warning an online quality detection instrument according to claim 1, wherein in the step 2, a joint density curve is drawn through historical data, and sampling frequency and rotation duty ratio of each machine in measurement data X ij in a time threshold are optimized through the joint density curve.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111513384.3A CN114459523B (en) | 2021-12-10 | 2021-12-10 | Calibration early warning method of online quality detection instrument |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111513384.3A CN114459523B (en) | 2021-12-10 | 2021-12-10 | Calibration early warning method of online quality detection instrument |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114459523A CN114459523A (en) | 2022-05-10 |
| CN114459523B true CN114459523B (en) | 2024-04-30 |
Family
ID=81405479
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111513384.3A Active CN114459523B (en) | 2021-12-10 | 2021-12-10 | Calibration early warning method of online quality detection instrument |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114459523B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115540954B (en) * | 2022-11-27 | 2023-04-07 | 四川易景智能终端有限公司 | Quality detection method, device and system for smart watch production |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0712598A (en) * | 1993-04-23 | 1995-01-17 | Ade Corp | Device and method for calibrating measuring system |
| CN102009474A (en) * | 2010-08-31 | 2011-04-13 | 浙江大学 | Method for testing welding quality of electro-fusion joint and realizing automatic evaluation |
| CN103557807A (en) * | 2013-11-08 | 2014-02-05 | 湖南中烟工业有限责任公司 | Method and device for detecting quality of cigarette filter stick end face shape |
| CN104332990A (en) * | 2014-10-28 | 2015-02-04 | 华东电网有限公司 | Method for acquiring real-time standby standardization quantity of regional power grid |
| CN105823713A (en) * | 2016-05-24 | 2016-08-03 | 深圳市蜂联科技有限公司 | Method for improving measuring precision of air quality detection device through iteration optimal calibration |
| CN107807199A (en) * | 2017-10-31 | 2018-03-16 | 华东理工大学 | A kind of electronic nose instrument and tobacco and tobacco product aesthetic quality's detection method |
| CN108629793A (en) * | 2018-03-22 | 2018-10-09 | 中国科学院自动化研究所 | The vision inertia odometry and equipment demarcated using line duration |
| CN109167818A (en) * | 2018-08-06 | 2019-01-08 | 长安大学 | Road evenness detection system based on smart phone crowdsourcing acquisition |
| CN109936164A (en) * | 2019-03-31 | 2019-06-25 | 东北电力大学 | Multiple-energy-source electric power system optimization operation method based on the analysis of power supply complementary characteristic |
| CN110516960A (en) * | 2019-08-23 | 2019-11-29 | 国网河北省电力有限公司保定供电分公司 | A kind of reliability index quantitative calculation method of substation relay protection device |
| CN110601900A (en) * | 2019-09-23 | 2019-12-20 | 中盈优创资讯科技有限公司 | Network fault early warning method and device |
| CN112304847A (en) * | 2020-11-02 | 2021-02-02 | 江西中烟工业有限责任公司 | Data comparison and automatic early warning method for cigarette filter stick physical index detection instrument |
-
2021
- 2021-12-10 CN CN202111513384.3A patent/CN114459523B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0712598A (en) * | 1993-04-23 | 1995-01-17 | Ade Corp | Device and method for calibrating measuring system |
| CN102009474A (en) * | 2010-08-31 | 2011-04-13 | 浙江大学 | Method for testing welding quality of electro-fusion joint and realizing automatic evaluation |
| CN103557807A (en) * | 2013-11-08 | 2014-02-05 | 湖南中烟工业有限责任公司 | Method and device for detecting quality of cigarette filter stick end face shape |
| CN104332990A (en) * | 2014-10-28 | 2015-02-04 | 华东电网有限公司 | Method for acquiring real-time standby standardization quantity of regional power grid |
| CN105823713A (en) * | 2016-05-24 | 2016-08-03 | 深圳市蜂联科技有限公司 | Method for improving measuring precision of air quality detection device through iteration optimal calibration |
| CN107807199A (en) * | 2017-10-31 | 2018-03-16 | 华东理工大学 | A kind of electronic nose instrument and tobacco and tobacco product aesthetic quality's detection method |
| CN108629793A (en) * | 2018-03-22 | 2018-10-09 | 中国科学院自动化研究所 | The vision inertia odometry and equipment demarcated using line duration |
| CN109167818A (en) * | 2018-08-06 | 2019-01-08 | 长安大学 | Road evenness detection system based on smart phone crowdsourcing acquisition |
| CN109936164A (en) * | 2019-03-31 | 2019-06-25 | 东北电力大学 | Multiple-energy-source electric power system optimization operation method based on the analysis of power supply complementary characteristic |
| CN110516960A (en) * | 2019-08-23 | 2019-11-29 | 国网河北省电力有限公司保定供电分公司 | A kind of reliability index quantitative calculation method of substation relay protection device |
| CN110601900A (en) * | 2019-09-23 | 2019-12-20 | 中盈优创资讯科技有限公司 | Network fault early warning method and device |
| CN112304847A (en) * | 2020-11-02 | 2021-02-02 | 江西中烟工业有限责任公司 | Data comparison and automatic early warning method for cigarette filter stick physical index detection instrument |
Non-Patent Citations (1)
| Title |
|---|
| 微观交通仿真模型参数标定结果取值方法研究;周晨静;《系统仿真学报》;第31卷(第12期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114459523A (en) | 2022-05-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108414682A (en) | A kind of air quality monitoring data quick calibration method based on wireless sensor network | |
| CN110531033B (en) | Method and device for monitoring online moisture measurement accuracy and moisture measurement system | |
| CN114459523B (en) | Calibration early warning method of online quality detection instrument | |
| CN116227752B (en) | Park facility management system based on Internet of things | |
| CN112418517B (en) | Prediction method for freeze injury short-term early warning | |
| CN107063955A (en) | air particle detector calibration method and management system | |
| CN110926571B (en) | System and method for evaluating and correcting accuracy of fuel level sensor | |
| CN114608678A (en) | Water meter calibration method and device based on pulse method | |
| CN113191678A (en) | Safe production index abnormity rapid sensing method based on Internet of things and artificial intelligence | |
| CN111735501B (en) | Temperature and humidity measuring method for environmental test facility equipment | |
| CN113959511B (en) | Flow metering method, equipment, medium and product based on jet flow water meter | |
| CN115752800A (en) | Medical wireless body temperature monitoring system based on internet | |
| CN114441469A (en) | Calibration method and device of moisture meter and computer equipment | |
| CN110020000A (en) | Determine the method and apparatus of abnormal air speed data | |
| JP3035089B2 (en) | Process characteristic estimation method and apparatus, process monitoring method and process control method using the estimation method | |
| FI129721B (en) | Method and arrangement for long term drift analysis | |
| CN114383646A (en) | Method and equipment for detecting resolution of continuously-changing type measured sensor | |
| CN113468693A (en) | Pressure sensor acquisition system based on big data | |
| Chernysheva et al. | The main principles of development of an intelligent multi-channel radiation thermometer | |
| CN107430396A (en) | Product inspection device, product inspection method and computer program | |
| CN114487283B (en) | Remote intelligent diagnosis and operation and maintenance method and system for air quality monitoring system | |
| CN115183805B (en) | Instrument automatic metrological verification method and system based on artificial intelligence | |
| Klevtsov | Application of the Hurst index to evaluate the testing of information gathering system components | |
| CN107607182A (en) | A kind of truck weighing system and Weighing method | |
| CN117131926A (en) | Electric energy metering error analysis method based on data storage |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |