CN117743404B - Storage tank servo sampling intelligent supervisory systems based on artificial intelligence - Google Patents
Storage tank servo sampling intelligent supervisory systems based on artificial intelligence Download PDFInfo
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Abstract
The invention provides an artificial intelligence-based storage tank servo sampling intelligent supervision system, which comprises a data monitoring platform and a sampling operation platform for data interaction; the data monitoring platform constructs a distributed search engine, performs historical data search according to the time index, and acquires the historical parameter data of the storage tank with the same specification; fitting the historical parameter data with a time line of the storage tank, adjusting the difference of the historical parameter data, establishing an error coefficient corresponding to the storage tank, and checking the error coefficient; establishing a data feedback interval according to the error coefficient, and sending a control instruction to a sampling operation platform by a data monitoring platform at regular time in the data feedback interval; the sampling operation platform controls the servo sampling device to sample the storage tank according to the control instruction, samples after sampling are detected after sample reserving, detected data are uploaded to the data monitoring platform after marking, and corresponding marking is carried out on the samples after sample reserving.
Description
Technical Field
The invention belongs to the field of intelligent supervision, and particularly relates to an artificial intelligence-based storage tank servo sampling intelligent supervision system.
Background
At present, a storage tank plays an important role in the industrial production process, but the storage tank has a severe internal environment and is easy to generate the risks of combustibility, explosiveness and the like, so that the storage tank is particularly important to sample and monitor regularly. The traditional storage tank sampling mode has the problems of inaccurate sampling, poor timeliness, potential safety hazard and the like. The storage tank servo sampling intelligent supervision system based on artificial intelligence adopts advanced technical means to solve the problems. Thus, there is a need for an artificial intelligence based storage tank servo sampling intelligent supervision system.
Disclosure of Invention
The invention provides an artificial intelligence-based storage tank servo sampling intelligent supervision system, which solves the problems of low working efficiency and poor safety caused by poor sampling precision and low automation degree when sampling is performed in the prior art.
The technical scheme of the invention is realized as follows: an artificial intelligence-based storage tank servo sampling intelligent supervision system comprises a data monitoring platform and a sampling operation platform for data interaction;
The data monitoring platform constructs a distributed search engine, performs historical data search according to the time index, and acquires the historical parameter data of the storage tank with the same specification;
Fitting the historical parameter data with a time line of the storage tank, adjusting the difference of the historical parameter data, establishing an error coefficient corresponding to the storage tank, and checking the error coefficient;
Establishing a data feedback interval according to the error coefficient, and sending a control instruction to a sampling operation platform by a data monitoring platform at regular time in the data feedback interval;
The sampling operation platform controls the servo sampling device to sample the storage tank according to the control instruction, samples after sampling are reserved and then detected, detected data are marked and then uploaded into the data monitoring platform, and corresponding marks are carried out on reserved samples;
and adjusting error coefficients through the fed-back detection data, and adjusting the data feedback interval in real time.
The system comprises a data monitoring platform and a sampling operation platform for data interaction. The data monitoring platform is a core component of the system and constructs a distributed search engine. The engine can search the historical data according to the time index to obtain the historical parameter data of the storage tank with the same specification. By fitting the historical parameter data with the time line of the storage tank, the historical parameter data can be subjected to difference adjustment, an error coefficient corresponding to the storage tank is established, and the error coefficient is checked.
According to the error coefficient, the system establishes a data feedback interval. In this interval, the data monitoring platform will send control instructions to the sampling operation platform at regular time. And the sampling operation platform controls the servo sampling device to sample the storage tank according to the instructions, and samples after sampling are reserved and then detected. The detection data is marked and then uploaded to a data monitoring platform, and corresponding marking is carried out on the reserved sample.
Through the detection data fed back, the system can adjust error coefficients and adjust the data feedback interval in real time so as to improve the accuracy and precision of sampling.
In constructing a distributed search engine, the system first establishes an index cluster. This cluster contains a plurality of nodes, each of which is responsible for the data retrieval of a segment. Then, the system gathers the data retrieved by all nodes and sorts them according to the time sequence.
The historical parameter data includes in-tank pressure data, in-tank temperature data, in-tank humidity data, and liquid level data. In processing these data, the system ranks them and performs a mean process to obtain a mean value of the historical parameters in the tank. The system then fits the in-tank historical parameter mean to the tank timeline to obtain more accurate parameter data.
When the fitting of the historical parameter data and the storage tank time line is carried out, the system correspondingly fits all the historical parameter data and carries out data difference adjustment according to the characteristic parameters in the current storage tank. The characteristic parameter is a set of differential parameters in the same gauge tank. Before the difference adjustment is performed, the system sets a weight coefficient for each element in the characteristic parameter set, and performs weight adjustment according to the configuration of different difference parameters. By integrating the weight ratio, the system can perform more accurate adjustment on the data.
The data feedback interval is a time interval, and the monitoring time range is selected through an error coefficient. The selected time interval is used as a feedback interval to be output to the sampling operation platform so as to carry out sampling analysis in the set time interval.
In a word, the intelligent monitoring system for the storage tank servo sampling based on artificial intelligence realizes intelligent monitoring and management of the storage tank sampling process through data interaction between a data monitoring platform and a sampling operation platform. The system utilizes artificial intelligence technology to analyze and adjust the historical parameter data, and improves the accuracy and precision of sampling.
As a preferred embodiment, when constructing the distributed search engine, firstly, an index cluster is established, and data search is performed simultaneously through a plurality of nodes, wherein any node is responsible for one section search, and then all node search data are collected and then are ordered according to a time sequence.
As a preferred implementation mode, the historical parameter data comprises in-tank pressure data, in-tank temperature data, in-tank humidity data and liquid level height data, the historical parameter data are arranged and then subjected to mean value processing, in-tank historical parameter mean values are obtained, and in-tank historical parameter mean values are fitted with a storage tank time line.
As a preferred embodiment, when the historical parameter data is fitted with the time line of the storage tank, all the historical parameter data are correspondingly fitted, and data difference adjustment is carried out according to the characteristic parameters in the current storage tank.
As a preferred embodiment, the characteristic parameter is a difference parameter set in the storage tank with the same specification, before the difference adjustment, a weight coefficient is set for elements in the characteristic parameter set, and after the corresponding weights are configured for different difference parameters, the difference adjustment is performed through the comprehensive weight ratio.
In a preferred embodiment, the data feedback interval is a time interval, the monitoring time range is selected by an error coefficient, the selected time interval is used as the feedback interval to be output to the sampling operation platform, and sampling analysis is performed within the set time interval.
After the technical scheme is adopted, the invention has the beneficial effects that: and (3) improving the sampling precision: through fitting and difference adjustment processing of historical parameter data, an error coefficient corresponding to the storage tank is established, and the sampling process is more accurate.
Realizing automatic control: the system realizes automatic control of sampling operation by sending control instructions at regular time, and reduces risk of manual operation.
Work efficiency is improved: the automatic sampling operation of the system reduces the manual participation and improves the working efficiency to a certain extent.
And the safety is improved: because the whole sampling process is automatic, the chance that personnel contact dangerous storage tank environment is reduced, and the operation safety is improved.
Realize data feedback and adjustment: by adjusting the error coefficient of the feedback detection data, the operation effect of the system can be optimized in real time, and an accurate sampling result can be maintained.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a block diagram of a system of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Examples:
As shown in FIG. 1, the storage tank servo sampling intelligent supervision system based on artificial intelligence comprises a data monitoring platform and a sampling operation platform for data interaction;
The data monitoring platform constructs a distributed search engine, performs historical data search according to the time index, and acquires the historical parameter data of the storage tank with the same specification;
Fitting the historical parameter data with a time line of the storage tank, adjusting the difference of the historical parameter data, establishing an error coefficient corresponding to the storage tank, and checking the error coefficient;
Establishing a data feedback interval according to the error coefficient, and sending a control instruction to a sampling operation platform by a data monitoring platform at regular time in the data feedback interval;
The sampling operation platform controls the servo sampling device to sample the storage tank according to the control instruction, samples after sampling are reserved and then detected, detected data are marked and then uploaded into the data monitoring platform, and corresponding marks are carried out on reserved samples;
and adjusting error coefficients through the fed-back detection data, and adjusting the data feedback interval in real time.
The working principle of the system mainly comprises two steps: data monitoring and sampling operations. And (3) data monitoring: the data monitoring platform constructs a distributed search engine, performs historical data search according to the time index, and obtains the historical parameter data of the storage tanks with the same specification. And fitting the historical parameter data with a time line of the storage tank, adjusting the difference of the historical parameter data, and establishing an error coefficient corresponding to the storage tank.
Sampling operation: and establishing a data feedback interval according to the error coefficient, and sending a control instruction to the sampling operation platform by the data monitoring platform at regular time in the data feedback interval. The sampling operation platform controls the servo sampling device to sample the storage tank according to the control instruction, and samples after sampling are reserved and then detected. And uploading the detection data to a data monitoring platform after marking, and carrying out corresponding marking on the reserved sample. And adjusting error coefficients through the fed-back detection data, and adjusting the data feedback interval in real time.
The working flow of the storage tank servo sampling intelligent supervision system based on artificial intelligence is as follows: the data monitoring platform acquires historical parameter data of the storage tank through the distributed search engine, performs fitting and difference adjustment processing, and establishes an error coefficient. And establishing a data feedback interval, and sending a control instruction to the sampling operation platform by the data monitoring platform at regular time. After the sampling operation platform receives the control instruction, the storage tank is sampled at regular time through the servo sampling device. And (5) detecting the sampled sample after sample retention, and marking the detection data. Uploading the marked detection data to a data monitoring platform, and carrying out corresponding marking on the reserved sample. The data monitoring platform adjusts error coefficients according to the fed-back detection data and adjusts the data feedback interval in real time.
When the distributed search engine is constructed, firstly, an index cluster is established, data search is carried out through a plurality of nodes at the same time, wherein any node is responsible for one section search, and then, the search data of all nodes are collected and then are ordered according to a time sequence.
The historical parameter data comprise in-tank pressure data, in-tank temperature data, in-tank humidity data and liquid level height data, the historical parameter data are arranged and then subjected to mean value processing, the in-tank historical parameter mean value is obtained, and the in-tank historical parameter mean value is fitted with a storage tank time line.
When the historical parameter data are fitted with the time line of the storage tank, all the historical parameter data are correspondingly fitted, and data difference adjustment is carried out according to the characteristic parameters in the current storage tank.
The characteristic parameters are difference parameter sets in the storage tanks with the same specification, before difference adjustment is carried out, weight coefficients are set for elements in the characteristic parameter sets, and after corresponding weights are configured for different difference parameters, difference adjustment is carried out through comprehensive weight ratios.
The data feedback interval is a time interval, the monitoring time range is selected through an error coefficient, the selected time interval is used as a feedback interval to be output to a sampling operation platform, and sampling analysis is performed in the set time interval.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (3)
1. The storage tank servo sampling intelligent supervision system based on artificial intelligence is characterized by comprising a data monitoring platform and a sampling operation platform for data interaction;
The data monitoring platform constructs a distributed search engine, performs historical data search according to the time index, and acquires the historical parameter data of the storage tank with the same specification;
Fitting the historical parameter data with a time line of the storage tank, adjusting the difference of the historical parameter data, establishing an error coefficient corresponding to the storage tank, and checking the error coefficient;
Establishing a data feedback interval according to the error coefficient, and sending a control instruction to a sampling operation platform by a data monitoring platform at regular time in the data feedback interval;
The sampling operation platform controls the servo sampling device to sample the storage tank according to the control instruction, samples after sampling are reserved and then detected, detected data are marked and then uploaded into the data monitoring platform, and corresponding marks are carried out on reserved samples;
Adjusting error coefficients through the fed-back detection data, and adjusting the data feedback interval in real time;
The historical parameter data comprise in-tank pressure data, in-tank temperature data, in-tank humidity data and liquid level height data, the historical parameter data are arranged and then subjected to mean value processing, the in-tank historical parameter mean value is obtained, and the in-tank historical parameter mean value is fitted with a storage tank time line; when the distributed search engine is constructed, firstly, an index cluster is established, data search is carried out through a plurality of nodes at the same time, wherein any node is responsible for one section search, and then, the search data of all nodes are collected and then are sequenced according to a time sequence; when the historical parameter data are fitted with the time line of the storage tank, all the historical parameter data are correspondingly fitted, and data difference adjustment is carried out according to the characteristic parameters in the current storage tank.
2. The artificial intelligence based storage tank servo sampling intelligent supervision system according to claim 1, wherein: the characteristic parameters are difference parameter sets in the storage tanks with the same specification, before difference adjustment is carried out, weight coefficients are set for elements in the characteristic parameter sets, and after corresponding weights are configured for different difference parameters, difference adjustment is carried out through comprehensive weight ratios.
3. The artificial intelligence based storage tank servo sampling intelligent supervision system according to claim 1, wherein: the data feedback interval is a time interval, the monitoring time range is selected through an error coefficient, the selected time interval is used as a feedback interval to be output to a sampling operation platform, and sampling analysis is performed in the set time interval.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108733006A (en) * | 2017-04-21 | 2018-11-02 | 上海明勃电气自动化有限公司 | Automatic control system monitor supervision platform |
| CN113420940A (en) * | 2021-07-15 | 2021-09-21 | 泗县汉和智能装备科技有限公司 | Safe operation supervision system for orchard weeding robot |
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| JP2012108748A (en) * | 2010-11-18 | 2012-06-07 | Sony Corp | Data processing device, data processing method, and program |
| TWI521360B (en) * | 2014-03-26 | 2016-02-11 | 國立成功大學 | Metrology sampling method and computer program product thereof |
| CN107788994B (en) * | 2017-10-12 | 2019-10-01 | 微泰医疗器械(杭州)有限公司 | A kind of real-time dynamic blood sugar monitoring system of intelligence based on cloud big data and method |
| WO2021038598A1 (en) * | 2019-08-29 | 2021-03-04 | Vemuri Ramana Somasurya | Smart, portable device to sample, measure, analyse, report & stabilise parameters |
| CN117219223A (en) * | 2023-08-28 | 2023-12-12 | 北京市永康药业有限公司 | Medicament monitoring method and system for medical storage cabinet |
| CN117240597A (en) * | 2023-11-06 | 2023-12-15 | 深圳市万特网络科技有限公司 | Internet-based application monitoring and early warning data management system and method |
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| CN108733006A (en) * | 2017-04-21 | 2018-11-02 | 上海明勃电气自动化有限公司 | Automatic control system monitor supervision platform |
| CN113420940A (en) * | 2021-07-15 | 2021-09-21 | 泗县汉和智能装备科技有限公司 | Safe operation supervision system for orchard weeding robot |
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