CN116112530A - Hydraulic engineering real-time acquisition data management method and system - Google Patents
Hydraulic engineering real-time acquisition data management method and system Download PDFInfo
- Publication number
- CN116112530A CN116112530A CN202310395206.8A CN202310395206A CN116112530A CN 116112530 A CN116112530 A CN 116112530A CN 202310395206 A CN202310395206 A CN 202310395206A CN 116112530 A CN116112530 A CN 116112530A
- Authority
- CN
- China
- Prior art keywords
- data
- real
- data set
- time
- filling
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000013523 data management Methods 0.000 title claims abstract description 44
- 238000005259 measurement Methods 0.000 claims abstract description 65
- 230000002159 abnormal effect Effects 0.000 claims abstract description 46
- 238000001514 detection method Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000005856 abnormality Effects 0.000 abstract description 7
- 238000012217 deletion Methods 0.000 abstract description 7
- 230000037430 deletion Effects 0.000 abstract description 7
- 238000007726 management method Methods 0.000 description 18
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 13
- 238000013461 design Methods 0.000 description 12
- 230000006870 function Effects 0.000 description 7
- 230000008859 change Effects 0.000 description 4
- 238000004590 computer program Methods 0.000 description 4
- 238000013480 data collection Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000007621 cluster analysis Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003646 Spearman's rank correlation coefficient Methods 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000011022 operating instruction Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000003204 osmotic effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
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
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
- G06F16/215—Improving data quality; Data cleansing, e.g. de-duplication, removing invalid entries or correcting typographical errors
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y10/00—Economic sectors
- G16Y10/35—Utilities, e.g. electricity, gas or water
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y20/00—Information sensed or collected by the things
- G16Y20/10—Information sensed or collected by the things relating to the environment, e.g. temperature; relating to location
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/10—Detection; Monitoring
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q2209/00—Arrangements in telecontrol or telemetry systems
- H04Q2209/80—Arrangements in the sub-station, i.e. sensing device
- H04Q2209/84—Measuring functions
-
- 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
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping
Abstract
The application provides a hydraulic engineering real-time acquisition data management method and system, wherein a real-time data set is obtained, and the real-time data set comprises a plurality of real-time acquisition data acquired by a plurality of acquisition units in a target hydraulic engineering; removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises real-time acquisition data acquired by at least one acquisition unit; calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance; and determining a filling data set corresponding to the real data set according to the preset filling radius and the measurement distance set, wherein the filling data in the filling data set is used for replacing the real-time acquisition data with the corresponding filling data when the real-time acquisition data is abnormal. The technical problem that abnormal or error function realization of a system is caused by abnormality or deletion of real-time acquisition data of hydraulic engineering is solved.
Description
Technical Field
The application relates to the technical field of hydraulic engineering, in particular to a hydraulic engineering real-time acquisition data management method and system.
Background
Along with the continuous development of the Internet of things technology, the communication technology and the big data processing technology, a plurality of intelligent management systems are gradually constructed in the field of hydraulic engineering in recent years, and the intelligent management systems become important technical supports of hydraulic engineering such as hydrologic monitoring, north-south water transfer, water resource allocation, flood control, irrigation, hydroelectric power generation, health and safety monitoring of reservoirs/dams and the like. The data related to hydraulic engineering includes three types:
static data-space geographic information, management unit information, personnel post information, hydraulic engineering attribute information, management system, data file, registration, safety identification and plan scheme.
Dynamic data-data in the engineering operation process, such as construction projects, danger elimination reinforcement, inspection information, engineering inspection information, maintenance and maintenance information, personnel work accounts, engineering operation accounts, personnel training accounts, safety production accounts, system logs and the like.
Data are collected in real time, namely, data which are automatically collected through a sensing system, such as rainfall, water level, water quantity, flow, osmotic pressure, deformation observation, water quality, gate opening and closing, video and the like.
In hydraulic engineering, a large amount of data can be involved, and the intelligent management system collects the data through a large amount of data collection units such as sensors or measuring instruments and realizes various management functions such as flood prediction, water resource scheduling, water quality regulation and control and the like through the processing of the data. However, the core control algorithm of the existing intelligent management system generally defaults or assumes that the data collected by the data collection unit is correct and complete, but in practical application, abnormality of the data caused by various interference factors in the data collection or data transmission process cannot be avoided, even the data loss caused by damage of the data collection unit or the transmission device, so that great potential safety hazard is brought to the functional implementation of the intelligent management system of the hydraulic engineering, and serious managers of the hydraulic engineering can make wrong decisions.
Disclosure of Invention
The application provides a hydraulic engineering real-time acquisition data management method and system, which are used for solving the technical problem that the system is abnormal in function realization or wrong due to the abnormality or the deletion of the hydraulic engineering real-time acquisition data.
In a first aspect, the present application provides a method for managing real-time collected data of hydraulic engineering, including:
acquiring a real-time data set, wherein the real-time data set comprises a plurality of real-time acquisition data acquired by a plurality of acquisition units in a target hydraulic engineering;
removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises real-time acquisition data acquired by at least one acquisition unit;
calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance;
determining a filling data set corresponding to the real data set according to a preset filling radius and a measurement distance set, wherein the filling data in the filling data set is used for: when the real-time acquisition data is abnormal, the real-time acquisition data is replaced by corresponding filling data.
In one possible design, removing part or all of the data in the real data set from the real data set to obtain at least one data subset includes:
One or more coincidence data are removed from the real-time data set and the real data set at the same time, the real-time data set with the coincidence data removed is used as a single data subset, and the coincidence data exist in the real-time data set and the real data set;
judging whether the real data set after the coincidence data is removed is empty or not;
if not, continuing to reject one or more coincident data from the real-time data set and the real data set at the same time, taking the real-time data set with the coincident data rejected as a single data subset, and judging whether the real data set with the coincident data rejected is empty or not;
if yes, determining to obtain one or more data subsets.
Optionally, the preset distance model includes a calculation model of multiple measurement distances, and each data subset corresponds to one kind or multiple kinds of measurement distances.
In one possible design, determining a padding data set corresponding to the real data set according to the preset padding radius and the metric distance set includes:
combining all the measurement distances smaller than or equal to a preset filling radius in the measurement distance set into a clustering neighborhood set corresponding to the real data set;
determining a filling coefficient according to the clustering neighborhood set by using an information entropy model;
And determining a filling data set according to the filling coefficient and the clustering neighborhood set.
In one possible design, using an information entropy model, determining fill factors from a set of clustered neighbors includes:
wherein ,
for the fill factor->
and />
And N is the total number of the clustering neighborhoods in the clustering neighborhood set.
In one possible design, determining the population data set from the population coefficients and the set of clustering neighbors includes:
wherein ,
for populating the dataset, +.>
and />
For the fill factor->
And N is the total number of the clustering neighborhoods in the clustering neighborhood set.
In one possible design, after acquiring the real-time dataset, further comprising:
performing anomaly detection on the real-time data set by using a preset anomaly detection model;
if abnormal real-time acquisition data exists in the real-time data set, replacing the abnormal real-time acquisition data by using the filling data set;
if no abnormal real-time acquisition data exists in the real-time data set, judging whether the filling data set is to be updated or not;
if yes, part or all of the data in the real data set is removed from the real data set, and at least one data subset is obtained.
In a second aspect, the present application provides a hydraulic engineering real-time collected data management system, including:
the system comprises a plurality of acquisition units, a data acquisition unit and a data transmission unit, wherein the acquisition units are used for acquiring real-time acquisition data in a target hydraulic engineering in real time;
a data management unit for:
acquiring a real-time data set, wherein the real-time data set comprises real-time acquisition data acquired by a plurality of acquisition units;
removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises real-time acquisition data acquired by at least one acquisition unit;
calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance;
determining a filling data set corresponding to the real data set according to a preset filling radius and a measurement distance set, wherein the filling data in the filling data set is used for: when the real-time acquisition data is abnormal, the real-time acquisition data is replaced by corresponding filling data.
In one possible design, the hydraulic engineering real-time collected data management system further includes: an anomaly monitoring unit configured to:
performing anomaly detection on the real-time data set by using a preset anomaly detection model;
If abnormal real-time acquisition data exists in the real-time data set, replacing the abnormal real-time acquisition data by using the filling data set;
if abnormal real-time acquisition data does not exist in the real-time data set, sending a data normal identifier to the data management unit;
the data management unit is further used for:
receiving a data normal identifier, and judging whether the filling data set is required to be updated according to the updating requirement identifier of the filling data set;
if yes, determining an updated filling data set according to the real-time data set, namely, realizing any one possible hydraulic engineering real-time acquisition data management method provided in the first aspect.
In a third aspect, the present application provides an electronic device, comprising: a processor, a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement any one of the possible hydraulic engineering real-time acquisition data management methods provided in the first aspect.
In a fourth aspect, the present application provides a storage medium, in which computer-executable instructions are stored, where the computer-executable instructions, when executed by a processor, are configured to implement any one of the possible hydraulic engineering real-time collected data management methods provided in the first aspect.
In a fifth aspect, the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements any one of the possible hydraulic engineering real-time acquisition data management methods provided in the first aspect.
The application provides a hydraulic engineering real-time acquisition data management method and system. Acquiring a real-time data set, wherein the real-time data set comprises a plurality of real-time acquisition data acquired by a plurality of acquisition units in a target hydraulic engineering; removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises real-time acquisition data acquired by at least one acquisition unit; calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance; and determining a filling data set corresponding to the real data set according to the preset filling radius and the measurement distance set, wherein the filling data in the filling data set is used for replacing the real-time acquisition data with the corresponding filling data when the real-time acquisition data is abnormal. The technical problem that abnormal or error function realization of a system is caused by abnormality or deletion of real-time acquisition data of hydraulic engineering is solved. The management personnel of the hydraulic engineering can accurately master the real-time state of the hydraulic engineering, and mislead of error information of a digital system is avoided, so that the management personnel is prevented from making an error decision.
Drawings
Fig. 1 is a schematic flow chart of a hydraulic engineering real-time collected data management method according to an embodiment of the present application;
fig. 2 is a flow chart of another real-time collected data management method for hydraulic engineering according to the embodiment of the present application;
fig. 3 is a schematic structural diagram of a hydraulic engineering real-time collected data management system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, including but not limited to combinations of embodiments, which can be made by one of ordinary skill in the art without inventive faculty, are intended to be within the scope of the present application, based on the embodiments herein.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be capable of operation in sequences other than those illustrated or described herein, for example. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In order to solve the technical problem that the abnormal or missing of the real-time collected data of the hydraulic engineering causes abnormal or wrong function realization of the system, the invention concept of the application is as follows:
when all real-time acquisition data of the hydraulic engineering can be accurately acquired and are absent, one or more real-time acquisition data in the real-time acquisition data can be actively extracted to serve as the true values of the missing data or the abnormal data, and then the missing data set is obtained after the true values are deleted. By utilizing complex relevance among various data in hydraulic engineering, all real-time acquired data are assumed to be an information whole, relevance between a true value and a missing data set is found through a plurality of angles, and filling data which can be within an acceptable error range are reversely pushed out through the relevance so as to replace the true value. By periodically updating the filling data, when the missing data or the abnormal data appear in all the real-time collected data, the missing data or the abnormal data can be replaced by the filling data in time, so that the accuracy of information output by the whole intelligent management system is ensured, the situation that the digital twinning corresponding to the intelligent management system is too large in difference with hydraulic engineering in the real world is avoided, the intelligent management system outputs error information to management personnel, and the operation potential safety hazards or safety accidents of the hydraulic engineering are avoided.
Fig. 1 is a schematic flow chart of a hydraulic engineering real-time collected data management method according to an embodiment of the present application. As shown in fig. 1, the specific steps of the method include:
s101, acquiring a real-time data set.
In this step, the real-time data set includes a plurality of real-time collected data collected by a plurality of collection units in the target hydraulic engineering. The acquisition unit includes: various types of sensors (such as a water flow sensor, a water temperature sensor, a sluice opening and closing sensor and the like) and a data detection device.
Specifically, after the acquisition unit acquires the real-time acquisition data, the acquisition unit encrypts the codes, and then transmits the codes to the data management system in a wired or wireless mode, and the data management system decrypts and decodes the codes and stores the codes in the database. And then, the data detection unit reads all the real-time acquisition data from the database, and judges whether the real-time acquisition data are abnormal or not through a detection algorithm. For example, it is determined whether the amount of change between the real-time collected data and the history data at the latest history time is greater than a preset change threshold, or whether the value of the real-time collected data is greater than or less than a preset threshold. If all the real-time acquired data have no abnormal value and no missing, the real-time acquired data can be proved to be used for deducing filling data corresponding to the missing data or abnormal data in a preset period if some data are missing or abnormal.
S102, removing part or all data in the real data set from the real-time data set to obtain at least one data subset.
In this step, the real data set comprises real-time acquisition data acquired by at least one acquisition unit.
Specifically, the essence of this step is to delete one or more data from the real-time data set without outliers or missing values, thereby yielding a missing data set, i.e. the above-mentioned data subset, and to be able to know the true values of these missing or outliers.
In one possible design, a preset test table may be read, a target acquisition unit that is supposed to fail in the test is determined, and one or more real-time acquired data acquired by the target acquisition unit is deleted from the real-time data set, so as to obtain a data subset.
In one possible design, in order to more accurately obtain multiple fill data corresponding to multiple outliers or missing values at the same time, it is necessary to mine the association between the true value and the subset of data from multiple aspects. And if n missing values or abnormal values are assumed, at this time, m real-time acquired data can be deleted from the real-time data set each time, wherein m is less than n, and one data subset is obtained each time, so that after deletion for a plurality of times, the deletion of n real-time acquired data from the real-time data set is realized, and a plurality of data subsets are obtained.
S103, calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model.
In this step, the set of metric distances includes at least one metric distance. The metric distance is some cluster statistics defined for calculating the similarity between two objects, is a quantitative index of cluster analysis, and can be quantitatively subjected to cluster analysis.
In this embodiment, the preset distance model may include a calculation model of various measured distances, for example, the types of measured distances include: one or more of Spearman correlation coefficient (Spearman's Rank Correlation Coefficient), manhattan distance, euclidean distance, minkowski distance, hamming distance, chebyshev distance, distance correlation coefficient (Distance Correlation), jekade distance, and cosine distance, and each subset of data may correspond to a metric distance of one category or a plurality of categories.
Preferably, the present application finds by big data testing: the error between the filling data obtained by the spearman correlation coefficient, the manhattan distance, the euclidean distance and the minkowski distance and the true value is small, and the root mean square error is ranked as: spearman correlation coefficient < manhattan distance < euclidean distance = minkowski distance.
Specific:
the spearman correlation coefficient is a measure of the degree of dependence of two variables. For sample volume ofnIs used for the measurement of the sample of (a),nthe raw data are converted into level data, the Szellman correlation coefficientsρCan be calculated by the formula (1):
wherein ,
collecting data for any one of the real data sets in real time, is #>
For the average of all data in the real dataset, +.>
Collecting data for any one of a subset of data in real time,/for each data subset>
The average of all data in the data subset.
Alternatively, one real data (i.e., real-time acquisition data in a real data set) may correspond to multiple different subsets of data, thereby obtaining multiple metric distances using equation (1).
Manhattan distance (Manhattan Distance) is a geometric term used to designate the sum of absolute wheelbases of two points in a standard coordinate system. Can be calculated by the formula (2):
wherein ,
is Manhattan distance, < >>
For any one of a first set of data, the first set of data comprising: the real data set or a subset of the real data set (i.e. the subset of the real data set may also be referred to as the first data set),
for any one of a second set of data, the second set of data comprising: the above-mentioned data subset or subset of data subsets (i.e. the subset of data subset may also be referred to as the second data set), and the first data set and the second data set have the same total data An amount k.
Optionally, one real data (i.e. real-time collected data in the real data set) may also correspond to a plurality of different data subsets, so that more measurement distances are obtained by using the formula (2), so as to find out association relations among more data, and improve accuracy of filling data.
Euclidean distance (Euclidean Distance) is thatpThe true distance between two points in dimensional space or the natural length of the vector. Can be calculated by the formula (3):
wherein ,
is European distance, ++>
For any one of the third data sets, the third data set comprising: real data set or subset of real data set, +.>
For any one of the fourth data sets, the fourth data set comprising: the above-mentioned data subset or subset of data subsets, and the third data set and the fourth data set have the same total data amount k.
Optionally, one real data (i.e. real-time collected data in the real data set) may also correspond to a plurality of different data subsets, so that more measurement distances are obtained by using the formula (3), so as to find out association relations among more data, and improve accuracy of filling data.
The Minkowski distance (Minkowski Distance) is a generalization of the Euclidean distance. At the position of n In dimensional space, real data sets are used as variablesAWith subsets of data as variablesB，The minkowski distance between the two can be calculated by equation (4):
wherein ,
for any one of the fifth data sets, the fifth data set comprising: real data set or subset of real data set, +.>
For any one of the sixth data set, the sixth data set comprising: the above data subset or subset of data subsets, and the fifth data set and the sixth data set have the same total data amount k, p being a constant.
Optionally, one real data (i.e. real-time collected data in the real data set) may also correspond to a plurality of different data subsets, so that more measurement distances are obtained by using the formula (4), so as to find out association relations among more data, and improve accuracy of filling data.
It should be noted that, for the hamming distance, chebyshev distance, distance correlation coefficient (Distance Correlation), jaccard distance and cosine distance (also referred to as cosine similarity coefficient Cosine Similarity), those skilled in the art can refer to the application modes of the above formulas (1) - (4) and the actual application scenario for performing specific calculation, which is not described herein again.
Finally, all the measured distances are combined into a set of measured distances. Notably, the metric distance set includes at least one distance, such as: the measurement distance sets are all spearman correlation coefficients, and each spearman correlation coefficient corresponds to the measurement distance between one real data set and one data subset; also for example: the set of metric distances comprises a spearman correlation coefficient, and/or a euclidean distance, and/or a minkowski distance, i.e. the metric distance between one real data set and one data subset corresponds to the spearman correlation coefficient, and/or a euclidean distance, and/or a minkowski distance.
And S104, determining a filling data set corresponding to the real data set according to the preset filling radius and the measurement distance set.
In this step, the padding data in the padding data set is used to: when the real-time acquisition data is abnormal, the real-time acquisition data is replaced by corresponding filling data.
Specifically, in one possible implementation manner, each target measurement distance smaller than the preset filling radius is selected from the measurement distance set, and the average value of each target measurement distance plus the value corresponding to the clustering center of the data subset is taken as filling data corresponding to one real data (i.e. one value element in the real data set), where the type of the average value includes: weighted average and arithmetic mean.
In another possible implementation manner, the preset filling radius represents a ranking threshold, each measurement distance in the measurement distance set is arranged from small to large, the measurement distance with the ranking smaller than the preset filling radius is taken as a target measurement distance, and a value corresponding to the cluster center of the data subset is added to an average value of each target measurement distance as filling data corresponding to one real data (i.e. one numerical element in the real data set), where the type of the average value includes: weighted average and arithmetic mean.
It is noted that the effective time of the filling data set is a preset period, and when the next preset period starts, the new filling data set determined in steps S101 to S104 needs to be repeated, so that the filling data can be ensured to be dynamically updated according to the real-time state of the hydraulic engineering, and the accuracy of the filling data is improved.
The embodiment provides a hydraulic engineering real-time acquisition data management method, which comprises the steps of acquiring a real-time data set, wherein the real-time data set comprises a plurality of real-time acquisition data acquired by a plurality of acquisition units in a target hydraulic engineering; removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises real-time acquisition data acquired by at least one acquisition unit; calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance; and determining a filling data set corresponding to the real data set according to the preset filling radius and the measurement distance set, wherein the filling data in the filling data set is used for replacing the real-time acquisition data with the corresponding filling data when the real-time acquisition data is abnormal. The technical problem that abnormal or error function realization of a system is caused by abnormality or deletion of real-time acquisition data of hydraulic engineering is solved. The management personnel of the hydraulic engineering can accurately master the real-time state of the hydraulic engineering, and mislead of error information of a digital system is avoided, so that the management personnel is prevented from making an error decision.
Fig. 2 is a flow chart of another hydraulic engineering real-time collected data management method according to the embodiment of the present application. As shown in fig. 2, the specific steps of the method include:
s201, acquiring a real-time data set.
In this step, the real-time data set includes a plurality of real-time collected data collected by a plurality of collection units in the target hydraulic engineering. The acquisition unit includes: various types of sensors (such as a water flow sensor, a water temperature sensor, a sluice opening and closing sensor and the like) and a data detection device.
S202, performing anomaly detection on the real-time data set by using a preset anomaly detection model.
In this step, the preset anomaly detection model performs comparison and discrimination of multiple aspects on each real-time collected data, including:
on the one hand: comparing each real-time collected data with the latest historical data, determining the corresponding variation of the real-time collected data, judging whether the variation is larger than a preset variation threshold, for example, the water temperature generally does not change greatly in a short time, the water flow generally does not change sharply in a non-flood period, and if the variation is larger than the preset variation threshold for the data, proving that the real-time collected data is abnormal.
On the other hand: judging whether the real-time acquisition data is missing or not. The sensor fails or the sensor is powered off due to the factors such as earthquake, so that the loss of the real-time acquired data is caused, and the abnormality of the real-time acquired data is determined.
If there is abnormal real-time acquisition data in the real-time data set, step S203 is executed, otherwise step S204 is executed.
S203, replacing abnormal real-time acquired data by using the filling data set.
In this step, the abnormal real-time acquisition data is replaced with the latest padding data in the padding data set. Specifically, each filling data in the filling data set corresponds to one missing real-time collected data, so that the abnormal real-time collected data is replaced by the corresponding filling data, the functions of the whole system can not be affected, or wrong information can not be output to misguide a manager. Optionally, after replacement, a prompt message may be output on a display interface of the system, so as to prompt a manager to check the abnormal cause as soon as possible, and eliminate potential safety hazards.
S204, judging whether the filling data set is needed to be updated.
In this step, whether the filling data set needs to be updated is indicated by an update flag, for example, "0" indicates that no update is needed, and "1" indicates that no update is needed.
If no update is required, the present flow is ended, and if update is required, step S205 is executed.
S205, removing part or all of the data in the real data set from the real data set to obtain at least one data subset.
In this embodiment, a plurality of data subsets are obtained in a plurality of loops, each loop having the following steps:
s2051, simultaneously eliminating one or more coincident data from the real-time data set and the real data set.
In this step, the coincidence data exists in both the real-time data set and the real data set. Namely, any data in the real data set is deleted from the real data set, and simultaneously, any data in the real data set is deleted from the real data set.
S2052, taking the real-time data set with the coincident data removed as a single data subset.
S2053, judging whether the real data set after the coincident data is removed is empty.
In this step, if empty, it is verified that all culling has been completed, ending the cycle, resulting in one or more subsets of data. Otherwise, the process returns to S2051 to start the next cycle.
S206, calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model.
In this step, the set of metric distances includes at least one metric distance.
It should be noted that, the specific real-time manner of this step may refer to step S103, which is not described herein.
S207, combining all the measurement distances smaller than or equal to a preset filling radius in the measurement distance set into a clustering neighborhood set corresponding to the real data set.
In this step, the metric distance less than or equal to the preset filling radius is taken as a clustering neighborhood, i.e. the nearest neighborhood.
S208, determining a filling coefficient according to the clustering neighborhood set by using the information entropy model.
In this step, the definition of the entropy of the reference information is referred to, and a calculation formula of the filling coefficient is obtained, as shown in formula (5):
wherein ,
for the fill factor->
and />
Is a clustering neighborhood in a clustering neighborhood set, N is a clustering neighborhood in the clustering neighborhood setTotal number.
It should be noted that, the filling coefficient borrows the definition of information entropy, and extracts information among a plurality of clustering neighborhoods in the clustering neighborhoods, thereby obtaining the association between the real data set and the data subset. And further uses this association to obtain padding data.
S209, determining a filling data set according to the filling coefficient and the clustering neighborhood set.
In this step, a padding data set can be obtained according to formula (6):
wherein ,
for populating the dataset, +.>
and />
For the fill factor->
And N is the total number of the clustering neighborhoods in the clustering neighborhood set.
The embodiment provides a hydraulic engineering real-time acquisition data management method, which comprises the steps of acquiring a real-time data set, wherein the real-time data set comprises a plurality of real-time acquisition data acquired by a plurality of acquisition units in a target hydraulic engineering; removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises real-time acquisition data acquired by at least one acquisition unit; calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance; and determining a filling data set corresponding to the real data set according to the preset filling radius and the measurement distance set, wherein the filling data in the filling data set is used for replacing the real-time acquisition data with the corresponding filling data when the real-time acquisition data is abnormal. The technical problem that abnormal or error function realization of a system is caused by abnormality or deletion of real-time acquisition data of hydraulic engineering is solved. The management personnel of the hydraulic engineering can accurately master the real-time state of the hydraulic engineering, and mislead of error information of a digital system is avoided, so that the management personnel is prevented from making an error decision.
Fig. 3 is a schematic structural diagram of a hydraulic engineering real-time collected data management system according to an embodiment of the present application. The hydraulic engineering real-time acquisition data management system 300 can be realized by software, hardware or a combination of the two.
As shown in fig. 3, the hydraulic engineering real-time acquisition data management system 300 includes:
the plurality of acquisition units 301 are used for acquiring real-time acquisition data in the target hydraulic engineering in real time;
a data management unit 302 for:
acquiring a real-time data set, wherein the real-time data set comprises real-time acquisition data acquired by a plurality of acquisition units;
removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises real-time acquisition data acquired by at least one acquisition unit;
calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance;
determining a filling data set corresponding to the real data set according to a preset filling radius and a measurement distance set, wherein the filling data in the filling data set is used for: when the real-time acquisition data is abnormal, the real-time acquisition data is replaced by corresponding filling data.
Optionally, the preset distance model includes a calculation model of multiple measurement distances, and each data subset corresponds to one kind or multiple kinds of measurement distances.
In one possible design, the data management unit 302 is configured to execute a loop procedure to determine the subset of data, the loop procedure including:
one or more coincidence data are removed from the real-time data set and the real data set at the same time, the real-time data set with the coincidence data removed is used as a single data subset, and the coincidence data exist in the real-time data set and the real data set;
judging whether the real data set after the coincidence data is removed is empty or not;
if not, continuously and simultaneously eliminating one or more coincidence data from the real-time data set and the real data set, taking the real-time data set with the coincidence data eliminated as a single data subset, judging whether the real data set with the coincidence data eliminated is empty, namely starting to execute the next cycle;
if yes, determining to obtain one or more data subsets, namely ending the cycle.
In one possible design, the data management unit 302 is configured to:
combining all the measurement distances smaller than or equal to a preset filling radius in the measurement distance set into a clustering neighborhood set corresponding to the real data set;
Determining a filling coefficient according to the clustering neighborhood set by using an information entropy model;
and determining a filling data set according to the filling coefficient and the clustering neighborhood set.
In one possible design, the data management unit 302 is configured to calculate:
wherein ,
for the fill factor->
and />
And N is the total number of the clustering neighborhoods in the clustering neighborhood set.
In one possible design, the data management unit 302 is configured to calculate:
wherein ,
for populating the dataset, +.>
and />
For the fill factor->
And N is the total number of the clustering neighborhoods in the clustering neighborhood set.
This hydraulic engineering gathers data management system in real time still includes: an anomaly monitoring unit 303 for:
performing anomaly detection on the real-time data set by using a preset anomaly detection model;
if abnormal real-time acquisition data exists in the real-time data set, replacing the abnormal real-time acquisition data by using the filling data set;
if abnormal real-time acquisition data does not exist in the real-time data set, sending a data normal identifier to the data management unit;
the data management unit 302 is further configured to:
receiving a data normal identifier, and judging whether the filling data set is required to be updated according to the updating requirement identifier of the filling data set;
If yes, determining an updated filling data set according to the real-time data set, namely, realizing any one possible hydraulic engineering real-time acquisition data management method provided by the method embodiments.
It should be noted that, the system provided in the embodiment shown in fig. 3 may perform the method provided in any of the above method embodiments, and the specific implementation principles, technical features, explanation of terms, and technical effects are similar, and are not repeated herein.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, the electronic device 400 may include: at least one processor 401 and a memory 402. Fig. 4 shows an apparatus for example a processor.
A memory 402 for storing a program. In particular, the program may include program code including computer-operating instructions.
The processor 401 is configured to execute computer-executable instructions stored in the memory 402 to implement the methods described in the above method embodiments.
The processor 401 may be a central processing unit (central processing unit, abbreviated as CPU), or an application specific integrated circuit (application specific integrated circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
Alternatively, the memory 402 may be separate or integrated with the processor 401. When the memory 402 is a device independent from the processor 401, the electronic apparatus 400 may further include:
a bus 403 for connecting the processor 401 and the memory 402. The bus may be an industry standard architecture (industry standard architecture, abbreviated ISA) bus, an external device interconnect (peripheral component, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. Buses may be divided into address buses, data buses, control buses, etc., but do not represent only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 402 and the processor 401 are integrated on a chip, the memory 402 and the processor 401 may complete communication through an internal interface.
Embodiments of the present application also provide a computer-readable storage medium, which may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, and specifically, the computer readable storage medium stores program instructions for the methods in the above method embodiments.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the method of the above-described method embodiments.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The hydraulic engineering real-time acquisition data management method is characterized by comprising the following steps of:
acquiring a real-time data set, wherein the real-time data set comprises a plurality of real-time acquisition data acquired by a plurality of acquisition units in a target hydraulic engineering;
removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises the real-time acquired data acquired by at least one acquisition unit;
calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance;
determining a filling data set corresponding to the real data set according to a preset filling radius and the measurement distance set, wherein filling data in the filling data set is used for: and when the real-time acquisition data is abnormal, replacing the real-time acquisition data with the corresponding filling data.
2. The method for managing real-time collected data of hydraulic engineering according to claim 1, wherein the step of removing part or all of the data in the real data set from the real data set to obtain at least one data subset comprises the steps of:
Simultaneously removing one or more coincidence data from the real-time data set and the real data set, and taking the real-time data set with the coincidence data removed as a single data subset, wherein the coincidence data exists in the real-time data set and the real data set;
judging whether the real data set after the coincidence data is removed is empty or not;
if not, continuing to reject one or more coincident data from the real-time data set and the real data set at the same time, taking the real-time data set with the coincident data rejected as a single data subset, and judging whether the real data set with the coincident data rejected is empty or not;
if yes, determining to obtain one or more data subsets.
3. The method for managing real-time collected data of hydraulic engineering according to claim 1, wherein the preset distance model comprises a plurality of calculation models of the measured distances, and each data subset corresponds to one kind or a plurality of kinds of measured distances.
4. A method for managing real-time collected data of hydraulic engineering according to any one of claims 1 to 3, wherein the determining a filling data set corresponding to the real data set according to a preset filling radius and the metric distance set includes:
Combining all the measurement distances smaller than or equal to the preset filling radius in the measurement distance set into a clustering neighborhood set corresponding to the real data set;
determining a filling coefficient according to the clustering neighborhood set by using an information entropy model;
and determining the filling data set according to the filling coefficient and the clustering neighborhood set.
5. The method for managing real-time collected data of hydraulic engineering according to claim 4, wherein determining the filling coefficient according to the clustering neighborhood set by using the information entropy model comprises:
wherein ,
for the filling factor, +.>
and />
And N is the total number of the clustering neighborhoods in the clustering neighborhood set.
6. The method of claim 4, wherein determining the filling data set from the filling coefficient and the clustering neighborhood set comprises:
wherein ,
for the filling data set,/a->
and />
For the filling factor, +.>
And N is the total number of the clustering neighborhoods in the clustering neighborhood set.
7. The hydraulic engineering real-time acquisition data management method according to claim 1, further comprising, after the acquiring of the real-time data set:
Performing anomaly detection on the real-time data set by using a preset anomaly detection model;
if the abnormal real-time acquired data exists in the real-time data set, replacing the abnormal real-time acquired data by using the filling data set;
if the real-time data set does not have abnormal real-time acquisition data, judging whether the filling data set is required to be updated or not;
if yes, part or all of the data in the real data set is removed from the real data set, and at least one data subset is obtained.
8. The utility model provides a hydraulic engineering gathers data management system in real time which characterized in that includes:
the system comprises a plurality of acquisition units, a data acquisition unit and a data transmission unit, wherein the acquisition units are used for acquiring real-time acquisition data in a target hydraulic engineering in real time;
a data management unit for:
acquiring a real-time data set, wherein the real-time data set comprises the real-time acquisition data acquired by a plurality of acquisition units;
removing part or all of data in a real data set from the real data set to obtain at least one data subset, wherein the real data set comprises the real-time acquired data acquired by at least one acquisition unit;
calculating a measurement distance set of the real data set and at least one data subset by using a preset distance model, wherein the measurement distance set comprises at least one measurement distance;
Determining a filling data set corresponding to the real data set according to a preset filling radius and the measurement distance set, wherein filling data in the filling data set is used for: and when the real-time acquisition data is abnormal, replacing the real-time acquisition data with the corresponding filling data.
9. The hydraulic engineering real-time acquisition data management system according to claim 8, comprising: an anomaly monitoring unit configured to:
performing anomaly detection on the real-time data set by using a preset anomaly detection model;
if the abnormal real-time acquired data exists in the real-time data set, replacing the abnormal real-time acquired data by using the filling data set;
if the real-time data set does not have abnormal real-time collected data, sending a data normal identifier to the data management unit;
the data management unit is further configured to:
receiving the data normal identifier, and judging whether the filling data set is required to be updated according to the updating requirement identifier of the filling data set;
if yes, the updated filling data set is determined according to the real-time data set.
10. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
The memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored in the memory to implement the hydraulic engineering real-time acquisition data management method as set forth in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310395206.8A CN116112530B (en) | 2023-04-14 | 2023-04-14 | Hydraulic engineering real-time acquisition data management method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310395206.8A CN116112530B (en) | 2023-04-14 | 2023-04-14 | Hydraulic engineering real-time acquisition data management method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116112530A true CN116112530A (en) | 2023-05-12 |
| CN116112530B CN116112530B (en) | 2023-06-23 |
Family
ID=86265905
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310395206.8A Active CN116112530B (en) | 2023-04-14 | 2023-04-14 | Hydraulic engineering real-time acquisition data management method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116112530B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104133866A (en) * | 2014-07-18 | 2014-11-05 | 国家电网公司 | Intelligent-power-grid-oriented missing data filling method |
| CN109460775A (en) * | 2018-09-20 | 2019-03-12 | 国家计算机网络与信息安全管理中心 | A kind of data filling method and device based on comentropy |
| CN110287179A (en) * | 2019-06-25 | 2019-09-27 | 广东工业大学 | A kind of filling equipment of shortage of data attribute value, device and method |
| US20200160147A1 (en) * | 2017-08-02 | 2020-05-21 | National Institute For Materials Science | Human brain like intelligent decision-making machine |
| CN113327136A (en) * | 2021-06-23 | 2021-08-31 | 中国平安财产保险股份有限公司 | Attribution analysis method and device, electronic equipment and storage medium |
| CN114091559A (en) * | 2020-07-31 | 2022-02-25 | 中移(苏州)软件技术有限公司 | Data filling method and device, equipment and storage medium |
-
2023
- 2023-04-14 CN CN202310395206.8A patent/CN116112530B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104133866A (en) * | 2014-07-18 | 2014-11-05 | 国家电网公司 | Intelligent-power-grid-oriented missing data filling method |
| US20200160147A1 (en) * | 2017-08-02 | 2020-05-21 | National Institute For Materials Science | Human brain like intelligent decision-making machine |
| CN109460775A (en) * | 2018-09-20 | 2019-03-12 | 国家计算机网络与信息安全管理中心 | A kind of data filling method and device based on comentropy |
| CN110287179A (en) * | 2019-06-25 | 2019-09-27 | 广东工业大学 | A kind of filling equipment of shortage of data attribute value, device and method |
| CN114091559A (en) * | 2020-07-31 | 2022-02-25 | 中移(苏州)软件技术有限公司 | Data filling method and device, equipment and storage medium |
| CN113327136A (en) * | 2021-06-23 | 2021-08-31 | 中国平安财产保险股份有限公司 | Attribution analysis method and device, electronic equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| XIAOFEI GONG 等: ""Research on Data filling Algorithm Based on Improved k-means and Information Entropy"", 《2018 IEEE 4TH INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS》 * |
| YU GU 等: ""Order-Sensitive Imputation for Clustered Missing Values"", 《IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING》, vol. 31, no. 1, XP011704056, DOI: 10.1109/TKDE.2018.2822662 * |
| 赵春霞 等: ""基于多元回归ＫＮＮ 的网络数据库不完整信息填充"", 《计算机仿真》, vol. 38, no. 8 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116112530B (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109472004B (en) | Comprehensive evaluation method, device and system for influences of climate change and human activities on hydrology and drought | |
| CN110008301B (en) | Regional geological disaster susceptibility prediction method and device based on machine learning | |
| CN108257121B (en) | Method, apparatus, storage medium and the terminal device that product defects detection model updates | |
| CN115034600A (en) | Early warning method and system for geological disaster monitoring | |
| CN115700636A (en) | Equipment inspection and report generation method, device, equipment and medium based on digital twin | |
| JP2020035407A (en) | Abnormal sign diagnostic device and abnormal sign diagnostic method | |
| CN112990579A (en) | Agricultural meteorological disaster forecasting method, device, equipment and storage medium | |
| CN111767192B (en) | Business data detection method, device, equipment and medium based on artificial intelligence | |
| CN110826689A (en) | Method for predicting county-level unit time sequence GDP based on deep learning | |
| CN113962320A (en) | Underground water monitoring data processing method and device | |
| CN114265001B (en) | Smart electric meter metering error evaluation method | |
| CN115370973A (en) | Water supply leakage monitoring method and device, storage medium and electronic equipment | |
| KR101960755B1 (en) | Method and apparatus of generating unacquired power data | |
| Guo et al. | Automatic data quality control of observations in wireless sensor network | |
| CN116112530B (en) | Hydraulic engineering real-time acquisition data management method and system | |
| CN114202179A (en) | Target enterprise identification method and device | |
| CN112949697A (en) | Method and device for confirming pipeline abnormity and computer readable storage medium | |
| CN115129706A (en) | Soil moisture observation data quality evaluation method considering periodic characteristics | |
| CN114384885A (en) | Process parameter adjusting method, device, equipment and medium based on abnormal working conditions | |
| CN114004138A (en) | Building monitoring method and system based on big data artificial intelligence and storage medium | |
| CN112632469A (en) | Method and device for detecting abnormity of business transaction data and computer equipment | |
| CN112926656A (en) | Method, system and equipment for predicting state of circulating water pump of nuclear power plant | |
| CN113537693A (en) | Personnel risk level obtaining method, terminal and storage device | |
| CN117669394B (en) | Mountain canyon bridge long-term performance comprehensive evaluation method and system | |
| CN116050120B (en) | Landslide hidden danger activity remote sensing evaluation modeling method, system and storage medium |
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 |