CN119395251B - A rapid self-calibration method for on-site maintenance of water quality sensors - Google Patents
A rapid self-calibration method for on-site maintenance of water quality sensors Download PDFInfo
- Publication number
- CN119395251B CN119395251B CN202510006417.7A CN202510006417A CN119395251B CN 119395251 B CN119395251 B CN 119395251B CN 202510006417 A CN202510006417 A CN 202510006417A CN 119395251 B CN119395251 B CN 119395251B
- Authority
- CN
- China
- Prior art keywords
- water quality
- calibration
- quality sensor
- data
- measured
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A20/00—Water conservation; Efficient water supply; Efficient water use
- Y02A20/152—Water filtration
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Engineering & Computer Science (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Physics & Mathematics (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Investigating Or Analyzing Materials By The Use Of Electric Means (AREA)
Abstract
本发明的一种水质传感器现场维护快速自校准方法，属于水质监测技术领域，该方法包括启动水质传感器、记录初始数据、采集实测数据、计算自相关函数、校准调整、验证校准结果、记录与报告的步骤，本发明通过采用自相关函数分析实测数据，结合现场环境数据动态调整校准参数，并利用机器学习进行故障预判，实现了无需标准溶液的快速自校准，减少了对标准溶液的依赖，简化了校准过程，解决了水质传感器现场校准困难的问题，具有操作简便、无需标准溶液、适应现场环境、实时监测性能且具有自适应性的优点。
The invention discloses a method for rapid self-calibration of water quality sensor on-site maintenance, which belongs to the technical field of water quality monitoring. The method comprises the steps of starting the water quality sensor, recording initial data, collecting measured data, calculating autocorrelation function, calibrating and adjusting, verifying calibration results, and recording and reporting. The invention adopts autocorrelation function to analyze measured data, dynamically adjusts calibration parameters in combination with on-site environmental data, and uses machine learning to predict faults, thereby realizing rapid self-calibration without the need for standard solutions, reducing dependence on standard solutions, simplifying the calibration process, and solving the problem of difficult on-site calibration of water quality sensors. The method has the advantages of simple operation, no need for standard solutions, adaptability to on-site environments, real-time monitoring performance, and adaptability.
Description
Technical Field
The invention relates to the technical field of water quality monitoring, in particular to a rapid self-calibration method for on-site maintenance of a water quality sensor.
Background
Currently, the calibration process of the water quality sensor faces many challenges, and the conventional calibration method generally needs to use a standard solution for calibration, which is complex in operation and highly depends on the quality and accuracy of the standard solution, and in practical application, the transportation, storage and use of the standard solution may affect the accuracy of the standard solution, thereby affecting the reliability of the calibration result. Furthermore, the use of standard solutions also increases the cost and time of the calibration process.
In a field environment, calibration of water quality sensors is more difficult. The sensor performance may drift or deviate due to the influence of factors such as water temperature and flow rate, so that more frequent calibration is required, but the conventional calibration method is often not convenient and efficient to implement in a field environment, and another problem is that the conventional calibration method generally cannot reflect the performance change of the water quality sensor in real time, and the performance of the sensor may be gradually degraded due to various factors in the long-term monitoring process, but if the monitoring data is not found and corrected in time, inaccuracy of the monitoring data may be caused, and the scientificity of the water quality management decision is affected.
Furthermore, existing calibration methods often lack adaptivity. Different water environments may require different calibration strategies, but the traditional method is difficult to flexibly adjust the calibration parameters and the process according to specific conditions, and the stiff calibration mode may not meet the complex and changeable water quality monitoring requirements.
In view of the foregoing, it is desirable to provide a rapid self-calibration method for field maintenance of a water quality sensor.
Disclosure of Invention
Aiming at the defects of the technical background, the invention aims to provide a rapid self-calibration method for on-site maintenance of a water quality sensor.
The aim of the invention can be achieved by the following technical scheme:
A water quality sensor field maintenance rapid self-calibration method comprises the following steps:
The method comprises the steps of S1, starting a water quality sensor, namely starting the water quality sensor and preheating the water quality sensor to check whether the water quality sensor is in a normal working state or not, wherein the water quality sensor is arranged on a buoy arranged in a measured water body, the buoy is connected with a shore treatment station through a water pipe to convey a water sample to the shore treatment station for water quality detection, the water quality sensor is electrically connected with a control component arranged in the shore treatment station to transmit water quality parameter data, and the control component calibrates the water quality sensor through a self-calibration device;
S2, recording initial data, namely stably placing the water quality sensor in a water body to be measured for waiting time t 1 so that the water quality sensor is suitable for the water body environment and achieves stable reading, and recording current displayed water quality parameter data and field environment data of the water quality sensor, wherein the field environment data comprise the water temperature and the flow rate of the water body to be measured as reference basis in the calibration process;
S3, acquiring actual measurement data, namely starting a data acquisition function of the water quality sensor, acquiring water quality parameter data at a specified time interval t 2, transmitting the water quality parameter data to the control assembly, continuously acquiring time t 3, and recording the data as an actual measurement data sequence x (N), wherein n=1, 2,3.
S4, calculating an autocorrelation function: the control component calculates its autocorrelation function Rxx (M) for the acquired measured data sequence x (N), where m=1, 2, 3..n-1, the control component analyzes its actual peak characteristics M from the autocorrelation function Rxx (M), the data value M 0 corresponding to the measured peak value represents the point where the repeatability of the measured data signal is strongest, and the autocorrelation function Rxx (M) satisfies the following relation:
wherein x (n+m) is a data sequence obtained by shifting the measured data sequence x (n) by m units in time;
S5, calibrating and adjusting, namely comparing the standard peak value characteristic K of the water quality sensor in a normal working state with the actually measured peak value M, and dynamically adjusting the calibrating parameters of the water quality sensor according to the deviation by the self-calibrating device when the deviation exceeds a threshold value, wherein the detecting precision of the water quality sensor is insufficient;
S6, verifying a calibration result, namely placing the water quality sensor in the measured water body again after completing adjustment of the calibration parameters, collecting the measured data again, calculating an autocorrelation function, and repeating the steps from S2 to S5 until the measured data of the water quality sensor meet the requirements;
s7, recording and reporting, namely recording a self-calibration process, wherein the self-calibration process comprises initial data, measured data, calibration parameters before and after adjustment and an autocorrelation analysis result, and generating a maintenance report according to the record for subsequent reference and audit.
As a preferable embodiment of the present invention, the measured peak characteristic includes a peak number and a peak size.
Further, the peak dimension includes a peak width and a peak relative height.
In step S5, based on the influence of the field environment data on the standard peak characteristic K, an adjustment factor a is introduced to dynamically adjust the standard peak characteristic K according to the field environment data, the adjusted standard peak characteristic is set to be K', and a relationship model is established between the adjustment factor a and the field environment data through multiple linear regression, so as to satisfy the following relationship expression:
Wherein T 'and V' are the water temperature and the flow rate corresponding to the standard peak characteristic K, T, V is the water temperature and the flow rate of the water body to be measured, and a 0、a1、a2 is the adjustment coefficient;
The self-calibration device dynamically adjusts the calibration parameters of the water quality sensor according to the deviation between the adjusted standard peak characteristic K' and the actually measured peak M.
As a preferable technical scheme of the invention, the control component constructs a fault pre-judging model based on machine learning, inputs the actually measured data sequence x (n) into the fault pre-judging model before each calibration adjustment, predicts whether the water quality sensor has potential fault risks, and if the risk exists, pertinently adjusts the calibration strategy in the calibration adjustment process, including increasing the calibration times and adjusting the adjustment amplitude of the calibration parameters.
As a preferable technical scheme of the invention, the control component adaptively adjusts the acquisition time interval t 2 and the acquisition time t 3 of the measured data according to a calibration adjustment strategy.
As a preferred technical solution of the present invention, the control component is specifically adapted to:
When the water quality sensor in a certain area is monitored to have the same kind of actual measurement peak characteristic deviation, the control component adaptively shortens the acquisition time interval t 2 of the actual measurement data to 0.5 times of the original acquisition time interval t 3 to 2 times of the original acquisition time interval t 2;
When the measured peak characteristic of the water quality sensor is monitored to be continuously stable, the control component adaptively increases the acquisition time interval t 2 of the measured data to 1.3 times of the original acquisition time interval t 3 to 0.8 times of the original acquisition time interval t 2.
As a preferable technical scheme of the invention, the control assembly self-calibration device is arranged in the water quality sensor or the shore treatment station.
In summary, the beneficial effects of the invention are as follows:
According to the invention, the deviation and drift of the water quality sensor are rapidly identified by analyzing the measured data through the autocorrelation function, and the calibration parameters are dynamically adjusted by combining with the on-site environment data, so that the invention can adapt to different water quality and environment conditions, and the machine learning is utilized to conduct fault pre-judgment, so that maintenance personnel can take measures in advance, the probability of fault occurrence is reduced, and the stability and reliability of the system are improved. The method realizes the rapid self-calibration without standard solution, has the advantages of simple operation, no standard solution, adaptation to field environment, real-time monitoring performance and self-adaptability, solves the problems of high cost and poor stability of the standard solution in the traditional water quality sensor calibration method, reduces complexity and uncertainty in the calibration process, and improves the calibration efficiency and accuracy.
Drawings
FIG. 1 is a schematic illustration of a buoy and shore treatment station of the present invention;
FIG. 2 is a flow chart of a method of the present invention for rapid self-calibration of water quality sensor field maintenance;
wherein, the system comprises a 1-water quality sensor, a 2-buoy, a 3-water pipe and a 4-shore treatment station.
Detailed Description
Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the particular embodiments presented herein are illustrative and explanatory only and are not restrictive of the invention.
It should be noted that in the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but that other embodiments of the invention and variations thereof are possible, and therefore the scope of the invention is not limited by the specific examples disclosed below.
As shown in fig. 1 to 2, a method for rapidly self-calibrating on-site maintenance of a water quality sensor 1 comprises the following steps:
The method comprises the steps of S1, starting a water quality sensor 1, preheating the water quality sensor 1, checking whether the water quality sensor 1 is in a normal working state, wherein the water quality sensor 1 is arranged on a buoy 2 arranged in a tested water body, the buoy 2 is connected with a shore treatment station 4 through a water pipe 3 so as to convey a water sample to the shore treatment station 4 for water quality detection, the water quality sensor 1 is electrically connected with a control component arranged in the shore treatment station 4 so as to transmit water quality parameter data, and the control component calibrates the water quality sensor 1 through a self-calibration device;
S2, recording initial data, namely stably placing the water quality sensor 1 in a water body to be measured for waiting time t 1 so that the water quality sensor 1 is adapted to the water body environment and reaches stable reading, and recording current displayed water quality parameter data and field environment data of the water quality sensor 1, wherein the field environment data comprise the water temperature and the flow velocity of the water body to be measured as reference bases in a calibration process;
S3, acquiring actual measurement data, namely starting a data acquisition function of the water quality sensor 1, acquiring water quality parameter data at a specified time interval t 2, transmitting the water quality parameter data to the control assembly, continuously acquiring time t 3, and recording the data as an actual measurement data sequence x (N), wherein n=1, 2,3.
S4, calculating an autocorrelation function: the control component calculates its autocorrelation function Rxx (M) for the acquired measured data sequence x (N), where m=1, 2,3. The control component analyzes actual peak characteristics M according to the autocorrelation function Rxx (M), wherein the actual peak characteristics comprise the number of peaks and the size of the peaks, and the size of the peaks comprises the width of the peaks and the relative height of the peaks; the data value M 0 corresponding to the measured peak value represents the point where the repeatability of the measured data signal is strongest, and the autocorrelation function Rxx (M) satisfies the following relation:
wherein x (n+m) is a data sequence obtained by shifting the measured data sequence x (n) by m units in time;
The introduction of the actual measurement peak value features is to more accurately reflect the working state of the water quality sensor 1 in the actual environment, and the actual measurement peak value features comprise the number of wave peaks, the width of the wave peaks and the relative height of the wave peaks, so that the response characteristics of the water quality sensor 1 under different water quality conditions can be comprehensively reflected. Specifically, the number of peaks reflects the frequency of the water quality parameter change, the width of the peaks reflects the amplitude and the duration of the water quality parameter change, the relative heights of the peaks are used for comparing the relative change conditions among different peaks, and the comprehensive analysis of the characteristics is helpful for more accurately evaluating the working state of the water quality sensor 1, so that a more accurate adjustment basis is provided in the calibration process.
S5, calibrating and adjusting, namely comparing the standard peak characteristic K of the water quality sensor 1 in a normal working state with the actually measured peak M, and dynamically adjusting the calibrating parameters of the water quality sensor 1 according to the deviation by the self-calibrating device when the deviation exceeds a threshold value, wherein the detecting precision of the water quality sensor 1 is insufficient;
S6, verifying a calibration result, namely placing the water quality sensor 1 in the measured water body again after completing adjustment of the calibration parameters, collecting measured data again, calculating an autocorrelation function, and repeating the steps from S2 to S5 until the measured data of the water quality sensor 1 meet the requirements;
s7, recording and reporting, namely recording a self-calibration process, wherein the self-calibration process comprises initial data, measured data, calibration parameters before and after adjustment and an autocorrelation analysis result, and generating a maintenance report according to the record for subsequent reference and audit.
Further, in step S5, based on the influence of the field environment data on the standard peak feature K, an adjustment factor a is introduced to dynamically adjust the standard peak feature K according to the field environment data, the adjusted standard peak feature is set to K', and the adjustment factor a and the field environment data establish a relationship model through multiple linear regression, so as to satisfy the following relationship:
Wherein T 'and V' are the water temperature and the flow rate corresponding to the standard peak characteristic K, T, V is the water temperature and the flow rate of the water body to be measured, and a 0、a1、a2 is the adjustment coefficient;
The self-calibration device dynamically adjusts the calibration parameters of the water quality sensor 1 according to the deviation between the adjusted standard peak characteristic K' and the actually measured peak M.
In practice, the readings of the water quality sensor 1 are affected by on-site environmental data such as water temperature and flow rate. In order to improve the accuracy of calibration, the application introduces an adjustment factor A and dynamically adjusts the standard peak value characteristic K according to the field environment data. The adjustment factor A establishes a relation with on-site environment data through a multiple linear regression model, so that the adjusted standard peak characteristic K' is more in line with actual environment conditions, the problem of influence of environment conditions on the calibration accuracy in the calibration process of the water quality sensor 1 is solved, different environment conditions can be self-adapted in the calibration process, and the detection accuracy and reliability of the water quality sensor 1 are further improved. The water temperature change affects the reaction speed and sensitivity of the water quality sensor 1, the water temperature increase causes the sensitivity of the water quality sensor 1 to chemical reaction or biological reaction to increase, and the reading is larger, so the adjustment coefficient a 1 is a negative number, the flow rate change affects the sampling frequency and measurement accuracy of the water quality sensor 1, and the high flow rate causes the reading of the water quality sensor 1 to be smaller, so the adjustment coefficient a 2 is a positive number.
In this embodiment, the standard peak characteristic K of the water quality sensor 1 is measured when the water temperature T 'of the measured water body is 20 ℃ and the flow velocity V' is 1 m/s, the adjustment factor a is 1.0, and the field environment data is that the water temperature T of the measured water body is 25 ℃ and the flow velocity V is 1.5 m/s, and the adjustment coefficients a 0、a1、a2 are respectively 1, -0.04 and 0.2 through multiple linear regression, so that the adjustment factor a and the field environment data satisfy the following relation:
based on the on-site environment data, the water temperature T of the water body to be measured is 25 ℃ and the flow velocity V is 1.5 m/s, the adjustment factor A can be obtained to be 0.9, namely the following formula is adopted:
i.e. the standard peak feature K should be scaled down based on the field environment of the present embodiment.
As a preferred embodiment of the present invention, the control component constructs a failure prediction model based on machine learning, inputs the actually measured data sequence x (n) into the failure prediction model before each calibration adjustment, predicts whether the water quality sensor 1 has potential failure risk, and if so, pertinently adjusts the calibration strategy in the calibration adjustment process, including increasing the number of calibration times and adjusting the adjustment amplitude of the calibration parameters.
The fault pre-judging model based on machine learning can predict the fault risk of the water quality sensor 1 by analyzing the actually measured data sequence x (n), training is performed by utilizing historical data, the relation between actually measured data and the fault risk is established, the current actually measured data sequence is input into the fault pre-judging model before each calibration and adjustment, and the model can output the prediction result of the fault risk. If the prediction result shows that the fault risk exists, the control component dynamically adjusts the calibration strategy according to the size and type of the risk, including increasing the number of calibration times and adjusting the adjustment amplitude of the calibration parameters so as to ensure the effectiveness of the calibration.
The construction of the fault pre-judging model comprises the following steps:
1) Collecting a plurality of groups of field environment data, initial water quality actual measurement data and water quality sensor 1 fault data, integrating the field environment data, the initial water quality actual measurement data and the water quality sensor 1 fault data into a data set, performing noise and abnormal value removal processing on the data set, including deleting data points with obvious errors and data points with overlong communication link interruption time, and performing normalization processing on data with different magnitudes;
2) Model training, namely dividing the preprocessed data set into a training set and a verification set, wherein the training set is used for learning the parameters of the fault pre-judging model, and the verification set is used for evaluating the performance of the fault pre-judging model and optimizing the parameters of the model through a gradient descent method;
3) And (3) model evaluation and optimization, namely, evaluating the proportion of the fault predicted by the fault pre-judging model correctly by adopting an accuracy rate alpha, and evaluating the proportion of the fault predicted by the fault pre-judging model to the actual fault by adopting a recall rate beta, wherein the harmonic mean value of the accuracy rate and the recall rate is an F1 value, which is used for reflecting the comprehensive performance of the fault pre-judging model, and optimizing the fault pre-judging model according to the expected requirement and the evaluation result.
As a preferred embodiment of the present invention, the control component adaptively adjusts the acquisition time interval t 2 and the acquisition time t 3 of the measured data according to a calibration adjustment strategy.
During the calibration process, if the fluctuation of the measured data is found to be larger, the control component can shorten the acquisition time interval t 2 so as to increase the data sampling frequency, thereby capturing the change condition of the water quality parameter more finely. Meanwhile, the control component can also prolong the acquisition time t 3, through the self-adaptive adjustment mechanism, the control component can flexibly cope with different calibration requirements, the calibration efficiency and the accuracy of the water quality sensor 1 are improved, the trouble of manual intervention and reliance on standard solutions is reduced to acquire measured data in a longer time period, the accuracy and the reliability of a calibration result are ensured, and the acquired data can be ensured to reflect the real condition of the water quality more accurately under different environmental conditions and sensor states.
Further, the control component is specifically adapted to:
When the water quality sensor 1 in a certain area is monitored to have the same kind of actual measurement peak characteristic deviation, the control component adaptively shortens the acquisition time interval t 2 of the actual measurement data to 0.5 times of the original acquisition time interval t 3 to 2 times of the original acquisition time interval t 2;
When the measured peak characteristic of the water quality sensor 1 is monitored to be stable continuously, the control component adaptively increases the acquisition time interval t 2 of the measured data to 1.3 times of the original acquisition time interval t 3 to 0.8 times of the original acquisition time interval t 2.
If it is found that a certain peak characteristic deviation exists in the water quality sensor 1 in a certain area, the control component sends an instruction to adjust the collection time interval t 2 and the collection time t 3 of the water quality sensor 1 in the whole area, so that the peak characteristic of the water quality sensor 1 is captured more accurately, the calibration efficiency and accuracy are improved, if the collection frequency is increased in the time period when the peak appears, the peak data can be acquired more accurately, the calibration accuracy is further improved, the collection time interval is prolonged appropriately in the time period when the peak characteristic is stable, the unnecessary data collection frequency and the burden of a system can be reduced, and the workload and the data transmission quantity of the sensor are reduced.
As a preferred embodiment of the invention, the control assembly self-calibration means are installed in the water quality sensor 1 or in the shore treatment station 4.
The self-calibration device comprises a data acquisition module, a calculation module and an adjustment module, wherein the data acquisition module is used for acquiring actual measurement data of the water quality sensor 1 in real time and transmitting the data to the calculation module, the calculation module calculates an autocorrelation function according to the actual measurement data and analyzes actual measurement peak characteristics, and the adjustment module dynamically adjusts calibration parameters of the water quality sensor 1 according to a calculation result. When the water quality sensor 1 is installed on the buoy 2, the self-calibration device can be installed in the buoy 2 and integrated with the water quality sensor 1, so that real-time calibration of the water quality sensor 1 can be realized, delay of data transmission is reduced, and calibration accuracy is improved, the self-calibration device can be installed in the shore processing station 4 and connected with the water quality sensor 1 on the buoy 2 through the water pipe 3, data of the water quality sensor 1 are collected in real time, calibration adjustment is performed, and calculation resources of the shore processing station 4 can be utilized, so that calibration efficiency and calibration accuracy are improved.
It should be understood that the foregoing embodiments are merely illustrative of one or more embodiments of the present invention, and that many other embodiments and variations thereof may be made by those skilled in the art without departing from the scope of the invention.
Claims (7)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510006417.7A CN119395251B (en) | 2025-01-03 | 2025-01-03 | A rapid self-calibration method for on-site maintenance of water quality sensors |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202510006417.7A CN119395251B (en) | 2025-01-03 | 2025-01-03 | A rapid self-calibration method for on-site maintenance of water quality sensors |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN119395251A CN119395251A (en) | 2025-02-07 |
| CN119395251B true CN119395251B (en) | 2025-04-22 |
Family
ID=94426823
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202510006417.7A Active CN119395251B (en) | 2025-01-03 | 2025-01-03 | A rapid self-calibration method for on-site maintenance of water quality sensors |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN119395251B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120507493B (en) * | 2025-07-22 | 2025-10-03 | 中交三航局第八工程(湖南)有限公司 | Self-adaptive water quality measurement method for ecological floating island |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108889A (en) * | 2017-12-18 | 2018-06-01 | 杭州电子科技大学 | A kind of water monitoring data on-line processing method and device |
| CN116451142A (en) * | 2023-06-09 | 2023-07-18 | 山东云泷水务环境科技有限公司 | Water quality sensor fault detection method based on machine learning algorithm |
| CN117235511A (en) * | 2023-11-13 | 2023-12-15 | 北京市计量检测科学研究院 | Secondary instrument calibration method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10982959B2 (en) * | 2016-09-06 | 2021-04-20 | The Charles Stark Draper Laboratory, Inc. | Fused sensor ensemble for navigation and calibration process therefor |
| CN114894252A (en) * | 2022-05-13 | 2022-08-12 | 宁波水表(集团)股份有限公司 | Online water quality measuring device capability test system |
| CN115754194A (en) * | 2022-11-07 | 2023-03-07 | 广东化一环境科技有限公司 | Processing method, system, device and storage medium for water quality sensor calibration |
| CN118962273A (en) * | 2024-06-27 | 2024-11-15 | 国网江苏省电力有限公司淮安供电分公司 | Electrical equipment leakage detection system of transformer substation |
-
2025
- 2025-01-03 CN CN202510006417.7A patent/CN119395251B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108108889A (en) * | 2017-12-18 | 2018-06-01 | 杭州电子科技大学 | A kind of water monitoring data on-line processing method and device |
| CN116451142A (en) * | 2023-06-09 | 2023-07-18 | 山东云泷水务环境科技有限公司 | Water quality sensor fault detection method based on machine learning algorithm |
| CN117235511A (en) * | 2023-11-13 | 2023-12-15 | 北京市计量检测科学研究院 | Secondary instrument calibration method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN119395251A (en) | 2025-02-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN119395251B (en) | A rapid self-calibration method for on-site maintenance of water quality sensors | |
| CN102144158B (en) | Method for operating a measuring station and measuring station | |
| US20230324234A1 (en) | Method for Locating Abnormal Temperature Event of Distributed Optical Fiber | |
| KR20220093597A (en) | Quality inspection system of IoT-based urban meteorological data | |
| CN113029242B (en) | Fiber bragg grating sensor abnormity diagnosis method in structural health monitoring system | |
| CN119402083A (en) | Backbone network line loss analysis and early warning method and system based on deep learning | |
| CN114608678A (en) | Water meter calibration method and device based on pulse method | |
| CN120008671B (en) | Absolute hollow encoder performance evaluation method and system based on real-time feedback | |
| CN119738536A (en) | Host remote control terminal | |
| CN112418517A (en) | Prediction method for short-term and temporary early warning of freezing injury | |
| CN119984455A (en) | A self-diagnosis method for gas ultrasonic flowmeter | |
| CN119064820A (en) | Laser power supply component performance detection method and system | |
| CN214538177U (en) | Flow computer for automatic timing and automatic diagnosis of flowmeter | |
| CN116738859B (en) | Online nondestructive life assessment method and system for copper pipe | |
| CN119149916A (en) | Intelligent monitoring and analyzing method and system for heat exchanger faults | |
| EP1644833B1 (en) | Method to snapshot and playback raw data in an ultrasonic meter | |
| CN118534051B (en) | A carbon emission quantification platform and a carbon emission quantification system | |
| CN116817700B (en) | Caliper quality detection method and system | |
| CN119309955B (en) | Photovoltaic frame compressive strength test system | |
| CN119935286A (en) | A measurement and calibration method and system for ultrasonic water meters based on the Internet of Things | |
| CN119043396A (en) | Instrument state monitoring method, system and storage device | |
| CN120482110A (en) | A railway point rail ultrasonic guided wave nondestructive testing method and testing device | |
| CN120769289A (en) | 5G base station signal dynamic optimization method and system | |
| CN120628212A (en) | Temperature and pressure integrated transmission measurement method and system based on nonlinear compensation | |
| CN119469247A (en) | A high-precision air duct and water duct sensor detection system |
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 | ||
| GR01 | Patent grant |