CN111130890A - Network flow dynamic prediction system - Google Patents
Network flow dynamic prediction system Download PDFInfo
- Publication number
- CN111130890A CN111130890A CN201911369907.4A CN201911369907A CN111130890A CN 111130890 A CN111130890 A CN 111130890A CN 201911369907 A CN201911369907 A CN 201911369907A CN 111130890 A CN111130890 A CN 111130890A
- Authority
- CN
- China
- Prior art keywords
- data
- flow
- prediction
- network
- network flow
- 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.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Artificial Intelligence (AREA)
- Environmental & Geological Engineering (AREA)
- Data Exchanges In Wide-Area Networks (AREA)
Abstract
The invention discloses a network flow dynamic prediction system, which comprises: the system comprises a flow prediction center, the Internet, an intermediate server and a flow acquisition terminal; the flow prediction center predicts the possible situation of network flow in the future time period according to the existing flow data, the internet is used for timely releasing the relevant data information in the flow prediction center, the intermediate server is used for recording the network flow data collected by each flow collection terminal, and the flow collection terminal is used for collecting the network flow real-time data in each time period. In the invention, the network flow dynamic prediction system adopts a multi-node distributed network flow acquisition mode, can acquire network flow dynamic real-time data at different time intervals, accurately predicts the dynamic network flow data through a mathematical model, can acquire prediction data which most accords with the development trend of network flow, and provides a corresponding data basis for the network flow of a user.
Description
Technical Field
The invention relates to the technical field of network flow prediction, in particular to a network flow dynamic prediction system.
Background
The network flow is very important for network management, flow engineering, network monitoring, route optimization and network measurement activities, is particularly used for end-to-end network flow, represents behavior characteristics of network users and network equipment activities, establishes a combined prediction system capable of accurately depicting and predicting characteristics and trends of the network flow, provides a method with practical significance for analysis and evaluation of network performance, provides theoretical basis and practical guidance for anomaly detection and attack response peer-to-peer based on flow characteristics, and has good application prospect.
The existing network traffic prediction system can only analyze and simply predict network traffic data collected under a single specific node, and cannot comprehensively analyze and predict dynamic real-time network traffic under different time periods, so that the possible trend condition of the dynamic network traffic in a future time period cannot be timely and accurately acquired, and a user cannot make an optimal response measure at the first time.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a network flow dynamic prediction system.
In order to achieve the purpose, the invention adopts the following technical scheme: a network traffic dynamic prediction system, comprising: the system comprises a flow prediction center, the Internet, an intermediate server and a flow acquisition terminal;
the flow prediction center is used as a statistic and prediction background of network flow, and is used for analyzing the acquired network flow and dynamic real-time network flow and predicting the possible situation of the network flow in a future time period according to the existing flow data;
the internet is used as a transmission bridge connected with an external network and used for timely releasing relevant data information in the traffic prediction center outwards and providing the relevant data information for a third party to supervise and early warn network traffic in real time;
the intermediate server is used as a storage device of network flow and is used for recording the network flow data acquired by each flow acquisition terminal, systematically classifying the network flow data and forming a flow data packet for upward transmission;
the flow acquisition terminal is used as acquisition equipment of network flow data and is used for acquiring network flow real-time data at each time interval.
As a further description of the above technical solution:
the flow prediction center comprises a flow mining module, a flow analysis module and a flow prediction module;
the flow mining module is used for mining and collecting potential deep flow data in the network flow and acquiring all data information of the network flow;
the flow analysis module is used for analyzing the associated data link information in the network flow data and acquiring all node information of the network flow;
the flow prediction module is used for predicting the possible occurrence condition of the network flow in the future time period by adopting a model prediction mode according to the collected network flow data.
As a further description of the above technical solution:
and the prediction model of the flow prediction module adopts a model of a support vector machine to perform prediction, and the support vector machine model adopts a Gaussian kernel function to perform calculation prediction.
A network flow dynamic prediction method comprises the following steps:
s01: data preprocessing, namely preprocessing the originally recorded network traffic data to integrate the original data into a proper interval range;
s02: roughly selecting regression parameters, and roughly selecting the regression parameters of the original data by using a verification mode;
s03: selecting regression parameters, and performing fine selection on the original data on the basis of rough selection to obtain the optimal regression parameters;
s04: training a model, namely importing the optimal parameters into the established mathematical model of the support vector machine to train a mathematical prediction model;
s05: judging the training is finished, judging the real-time state of the mathematical prediction model training, and performing subsequent prediction processing of network flow;
s06: and (4) simulating and predicting, namely predicting by using a trained data prediction model, selecting a sample point as a check sample, and comparing the check sample with predicted data to obtain the optimal prediction result of the network flow.
As a further description of the above technical solution:
in step S01, the raw data is normalized by preprocessing the raw data
Within the interval.
As a further description of the above technical solution:
in step S02, the verification mode is a cross-verification mode, the preprocessed data are divided into three parts, i.e., a training set, an evaluation set, and a test set, and regression parameters C and g of the data are roughly selected.
As a further description of the above technical solution:
in step S03, when the raw data is selected finely, the regression optimal parameters C and g should be obtained based on the contour map selected roughly.
As a further description of the above technical solution:
in the step S05, when it is determined that the training of the mathematical model is not finished, returning the regression parameters C and g of the data obtained by the training to the step S03 again, and obtaining the regression optimal parameters C and g based on the roughly selected contour map again;
when it is judged that the mathematical model training is finished, the process of step S06 is performed.
As a further description of the above technical solution:
in step S06, the originally included network traffic data is selected and imported into the training model, real-time training of the known network data is performed, prediction is performed, and a sufficient number of sample points in the continuous time period of the original network data are selected and compared with the network data traffic result at the prediction position of the training model, so as to obtain the optimal prediction result of the network traffic.
Advantageous effects
The invention provides a dynamic network flow prediction system. The method has the following beneficial effects:
(1): the network flow dynamic prediction system adopts a multi-node distributed network flow acquisition mode, can acquire network flow dynamic real-time data at different time intervals, accurately predicts the dynamic network flow data through a mathematical model, can acquire prediction data which most accords with the development trend of network flow, and provides a corresponding data basis for the network flow of a user.
(2): the network flow dynamic prediction system utilizes the model architecture of the support vector machine, can improve the learning capability of the model as much as possible, seeks the optimal break point between the learning capability and the complexity, and maximally reduces the operation risk of the model architecture, thereby making up the defect of poor network popularization capability.
Drawings
Fig. 1 is a schematic overall architecture diagram of a network traffic dynamic prediction system according to the present invention;
FIG. 2 is a schematic diagram of a flow prediction center according to the present invention;
fig. 3 is a schematic flow chart of a dynamic network traffic prediction method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1 to fig. 3, a network traffic dynamic prediction system includes: the system comprises a flow prediction center, the Internet, an intermediate server and a flow acquisition terminal;
the flow prediction center is used as a statistic and prediction background of the network flow, and is used for analyzing the acquired network flow and the dynamic real-time network flow and predicting the possible situation of the network flow in the future time period according to the existing flow data;
the internet is used as a transmission bridge connected with an external network and used for timely releasing relevant data information in the traffic prediction center outwards and providing the relevant data information for a third party to carry out real-time supervision and early warning on network traffic;
the intermediate server is used as a storage device of network flow and is used for recording the network flow data acquired by each flow acquisition terminal, systematically classifying the network flow data and forming a flow data packet for upward transmission;
and the flow acquisition terminal is used as acquisition equipment of the network flow data and is used for acquiring the network flow real-time data at each time interval.
The flow prediction center comprises a flow mining module, a flow analysis module and a flow prediction module;
the flow mining module is used for mining and collecting potential deep flow data in the network flow and acquiring all data information of the network flow;
the process of data mining is divided into three steps: data preparation, data mining and data representation;
preparing data: the data integration is to extract and integrate data from an operation type environment, so that the semantic ambiguity problem is solved, dirty data are eliminated, the data selection and the pre-analysis can further narrow the data range, and the data mining quality is improved;
data mining: the actual mining work at this stage includes determining how to generate assumptions, whether for the data mining system to generate assumptions for the user, or to make assumptions for the indications that may be contained in the database;
data expression: the obtained information is reflected to the user in a mode convenient for the user to understand and observe, and the visualization tool can be used for operation for further analysis and comparison in the future.
The flow analysis module is used for analyzing the associated data link information in the network flow data and acquiring all node information of the network flow;
the flow prediction module is used for predicting the possible occurrence condition of the network flow in the future time period by adopting a model prediction mode according to the collected network flow data.
A prediction model of the flow prediction module adopts a model of a support vector machine for prediction, and the support vector machine adopts a Gaussian kernel function for calculation prediction;
the support vector machine model gives a new input sample x, deduces the number of times of output y corresponding to the x according to the given data sample, and the output y is a real number;
calculation formula of gaussian kernel function:
。
a network flow dynamic prediction method comprises the following steps:
s01: data preprocessing, namely preprocessing the originally recorded network traffic data to integrate the original data into a proper interval range;
s02: roughly selecting regression parameters, and roughly selecting the regression parameters of the original data by using a verification mode;
s03: selecting regression parameters, and performing fine selection on the original data on the basis of rough selection to obtain the optimal regression parameters;
s04: training a model, namely importing the optimal parameters into the established mathematical model of the support vector machine to train a mathematical prediction model;
s05: judging the training is finished, judging the real-time state of the mathematical prediction model training, and performing subsequent prediction processing of network flow;
s06: and (4) simulating and predicting, namely predicting by using a trained data prediction model, selecting a sample point as a check sample, and comparing the check sample with predicted data to obtain the optimal prediction result of the network flow.
In step S01, the raw data is normalized by preprocessing the raw data
Within the interval range;
after the normalization processing of the raw data, the accuracy of friction prediction can be improved, and the time series of the obtained information, Y = (x 1, x 2.. xn), is assumed that the time series of the ideograms, Y (t), consists of a normal trend, z (t), and a noise signal, n (t), i.e., Y (t) = z (t) + n (t), and the timing of the data preprocessing purpose generates a signal, z (t), reflecting the general trend, by noise filtering.
In step S02, the verification method adopts a cross-verification method, the preprocessed data are divided into three parts, i.e., a training set, an evaluation set, and a test set, and regression parameters C and g of the data are roughly selected.
In step S03, when the raw data is selected finely, the regression optimal parameters C and g should be obtained based on the contour map selected roughly.
In step S05, when it is determined that the training of the mathematical model is not completed, returning the regression parameters C and g of the data obtained by the training to step S03 again, and obtaining the optimal regression parameters C and g based on the roughly selected contour map again;
when it is judged that the mathematical model training is finished, the process of step S06 is performed.
In step S06, the originally included network traffic data is selected and imported into a training model, real-time training of known network data is performed, prediction is performed, and a sufficient number of sample points in a continuous time period of the original network data are selected and compared with the network data traffic result at the prediction position of the training model, so that the optimal prediction result of the network traffic can be obtained.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (9)
1. A system for dynamic prediction of network traffic, comprising: the system comprises a flow prediction center, the Internet, an intermediate server and a flow acquisition terminal;
the flow prediction center is used as a statistic and prediction background of network flow, and is used for analyzing the acquired network flow and dynamic real-time network flow and predicting the possible situation of the network flow in a future time period according to the existing flow data;
the internet is used as a transmission bridge connected with an external network and used for timely releasing relevant data information in the traffic prediction center outwards and providing the relevant data information for a third party to supervise and early warn network traffic in real time;
the intermediate server is used as a storage device of network flow and is used for recording the network flow data acquired by each flow acquisition terminal, systematically classifying the network flow data and forming a flow data packet for upward transmission;
the flow acquisition terminal is used as acquisition equipment of network flow data and is used for acquiring network flow real-time data at each time interval.
2. The system according to claim 1, wherein the traffic prediction center comprises a traffic mining module, a traffic analyzing module and a traffic predicting module;
the flow mining module is used for mining and collecting potential deep flow data in the network flow and acquiring all data information of the network flow;
the flow analysis module is used for analyzing the associated data link information in the network flow data and acquiring all node information of the network flow;
the flow prediction module is used for predicting the possible occurrence condition of the network flow in the future time period by adopting a model prediction mode according to the collected network flow data.
3. The system according to claim 2, wherein the prediction model of the traffic prediction module performs prediction using a model of a support vector machine, and the model of the support vector machine performs computational prediction using a gaussian kernel function.
4. A network flow dynamic prediction method is characterized by comprising the following steps:
s01: data preprocessing, namely preprocessing the originally recorded network traffic data to integrate the original data into a proper interval range;
s02: roughly selecting regression parameters, and roughly selecting the regression parameters of the original data by using a verification mode;
s03: selecting regression parameters, and performing fine selection on the original data on the basis of rough selection to obtain the optimal regression parameters;
s04: training a model, namely importing the optimal parameters into the established mathematical model of the support vector machine to train a mathematical prediction model;
s05: judging the training is finished, judging the real-time state of the mathematical prediction model training, and performing subsequent prediction processing of network flow;
s06: and (4) simulating and predicting, namely predicting by using a trained data prediction model, selecting a sample point as a check sample, and comparing the check sample with predicted data to obtain the optimal prediction result of the network flow.
5. The method according to claim 4, wherein in step S01, the raw data is normalized by preprocessing the raw data
Within the interval.
6. The method according to claim 4, wherein in the step S02, the verification mode is a cross-verification mode, the preprocessed data are divided into three parts, namely a training set, an evaluation set and a test set, and regression parameters C and g of the data are roughly selected.
7. The method according to claim 4, wherein in step S03, when the raw data is selected finely, the regression optimal parameters C and g should be obtained based on the roughly selected contour map.
8. The method according to claim 4, wherein in step S05, when it is determined that the training of the mathematical model is not completed, the regression parameters C and g of the data obtained by the training are returned to step S03 again, and the regression optimal parameters C and g are obtained based on the roughly selected contour map again;
when it is judged that the mathematical model training is finished, the process of step S06 is performed.
9. The method according to claim 4, wherein in step S06, the originally included network traffic data is selected and imported into a training model, real-time training of known network data is performed, and prediction is performed, and a sufficient number of sample points in a continuous time period of the original network data are selected and compared with the network data traffic result predicted by the training model, so as to obtain the optimal prediction result of the network traffic.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911369907.4A CN111130890A (en) | 2019-12-26 | 2019-12-26 | Network flow dynamic prediction system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911369907.4A CN111130890A (en) | 2019-12-26 | 2019-12-26 | Network flow dynamic prediction system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111130890A true CN111130890A (en) | 2020-05-08 |
Family
ID=70503346
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911369907.4A Pending CN111130890A (en) | 2019-12-26 | 2019-12-26 | Network flow dynamic prediction system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111130890A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111800311A (en) * | 2020-06-22 | 2020-10-20 | 中科边缘智慧信息科技（苏州）有限公司 | Real-time sensing method for decentralized computing state |
| CN111913849A (en) * | 2020-07-29 | 2020-11-10 | 厦门大学 | Unsupervised anomaly detection and robust trend prediction method for operation and maintenance data |
| CN112637118A (en) * | 2020-11-10 | 2021-04-09 | 国网浙江省电力有限公司双创中心 | Flow analysis implementation method based on internal and external network drainage abnormity |
| CN113132415A (en) * | 2021-05-10 | 2021-07-16 | 安徽思珀特信息科技有限公司 | Threat discovery system based on network flow analysis |
| CN115499317A (en) * | 2022-11-15 | 2022-12-20 | 阿里云计算有限公司 | Gray scale verification method, electronic device and readable storage medium |
| CN117155864A (en) * | 2023-11-01 | 2023-12-01 | 南京市微驰数字科技有限公司 | Flow management system and method based on Internet |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102957579A (en) * | 2012-09-29 | 2013-03-06 | 北京邮电大学 | Network anomaly traffic monitoring method and device |
| CN106100922A (en) * | 2016-06-12 | 2016-11-09 | 中车青岛四方机车车辆股份有限公司 | The Forecasting Methodology of the network traffics of TCN and device |
| CN106779198A (en) * | 2016-12-06 | 2017-05-31 | 广州市科恩电脑有限公司 | A kind of congestion in road situation analysis method |
| CN106789297A (en) * | 2016-12-29 | 2017-05-31 | 淮海工学院 | Predicting network flow system and its method for predicting based on neutral net |
| CN109409587A (en) * | 2018-10-09 | 2019-03-01 | 南京航空航天大学 | A kind of airport excavated based on weather data is into traffic flow forecasting method of leaving the theatre |
| CN110310474A (en) * | 2018-05-14 | 2019-10-08 | 桂林远望智能通信科技有限公司 | A kind of vehicle flowrate prediction technique and device based on space-time residual error network |
| CN110855519A (en) * | 2019-11-07 | 2020-02-28 | 深圳市高德信通信股份有限公司 | Network flow prediction method |
-
2019
- 2019-12-26 CN CN201911369907.4A patent/CN111130890A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102957579A (en) * | 2012-09-29 | 2013-03-06 | 北京邮电大学 | Network anomaly traffic monitoring method and device |
| CN106100922A (en) * | 2016-06-12 | 2016-11-09 | 中车青岛四方机车车辆股份有限公司 | The Forecasting Methodology of the network traffics of TCN and device |
| CN106779198A (en) * | 2016-12-06 | 2017-05-31 | 广州市科恩电脑有限公司 | A kind of congestion in road situation analysis method |
| CN106789297A (en) * | 2016-12-29 | 2017-05-31 | 淮海工学院 | Predicting network flow system and its method for predicting based on neutral net |
| CN110310474A (en) * | 2018-05-14 | 2019-10-08 | 桂林远望智能通信科技有限公司 | A kind of vehicle flowrate prediction technique and device based on space-time residual error network |
| CN109409587A (en) * | 2018-10-09 | 2019-03-01 | 南京航空航天大学 | A kind of airport excavated based on weather data is into traffic flow forecasting method of leaving the theatre |
| CN110855519A (en) * | 2019-11-07 | 2020-02-28 | 深圳市高德信通信股份有限公司 | Network flow prediction method |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111800311A (en) * | 2020-06-22 | 2020-10-20 | 中科边缘智慧信息科技（苏州）有限公司 | Real-time sensing method for decentralized computing state |
| CN111800311B (en) * | 2020-06-22 | 2021-10-08 | 中科边缘智慧信息科技（苏州）有限公司 | Real-time sensing method for decentralized computing state |
| CN111913849A (en) * | 2020-07-29 | 2020-11-10 | 厦门大学 | Unsupervised anomaly detection and robust trend prediction method for operation and maintenance data |
| CN111913849B (en) * | 2020-07-29 | 2022-02-11 | 厦门大学 | Unsupervised anomaly detection and robust trend prediction method for operation and maintenance data |
| CN112637118A (en) * | 2020-11-10 | 2021-04-09 | 国网浙江省电力有限公司双创中心 | Flow analysis implementation method based on internal and external network drainage abnormity |
| CN113132415A (en) * | 2021-05-10 | 2021-07-16 | 安徽思珀特信息科技有限公司 | Threat discovery system based on network flow analysis |
| CN115499317A (en) * | 2022-11-15 | 2022-12-20 | 阿里云计算有限公司 | Gray scale verification method, electronic device and readable storage medium |
| CN115499317B (en) * | 2022-11-15 | 2023-04-07 | 阿里云计算有限公司 | Gray scale verification method, electronic device and readable storage medium |
| CN117155864A (en) * | 2023-11-01 | 2023-12-01 | 南京市微驰数字科技有限公司 | Flow management system and method based on Internet |
| CN117155864B (en) * | 2023-11-01 | 2024-01-30 | 南京市微驰数字科技有限公司 | Flow management system and method based on Internet |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111130890A (en) | Network flow dynamic prediction system | |
| CN111459700B (en) | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium | |
| CN111104981B (en) | Hydrological prediction precision evaluation method and system based on machine learning | |
| CN106951984B (en) | Dynamic analysis and prediction method and device for system health degree | |
| CN111143097B (en) | GNSS positioning service-oriented fault management system and method | |
| CN113010389B (en) | Training method, fault prediction method, related device and equipment | |
| CN108985279A (en) | The method for diagnosing faults and device of double-unit traction controller waveform | |
| CN111796957B (en) | Transaction abnormal root cause analysis method and system based on application log | |
| CN110162445A (en) | The host health assessment method and device of Intrusion Detection based on host log and performance indicator | |
| CN112702224A (en) | Method and device for analyzing quality difference of home broadband user | |
| CN113822366A (en) | Service index abnormality detection method and device, electronic equipment and storage medium | |
| CN109063885A (en) | A kind of substation's exception metric data prediction technique | |
| CN109002996A (en) | Methods of risk assessment and system based on water rate | |
| CN114202243A (en) | Engineering project management risk early warning method and system based on random forest | |
| CN112183906A (en) | Machine room environment prediction method and system based on multi-model combined model | |
| CN109740890A (en) | A kind of influencing factors of quality correlation analysis | |
| CN111949496B (en) | Data detection method and device | |
| CN111598700A (en) | Financial wind control system and method | |
| CN110855519A (en) | Network flow prediction method | |
| WO2021262344A1 (en) | Method and apparatus to detect scripted network traffic | |
| CN111314113B (en) | Internet of things node fault detection method and device, storage medium and computer equipment | |
| CN112948262A (en) | System test method, device, computer equipment and storage medium | |
| CN115469585B (en) | Hydropower unit state monitoring method and system based on big data | |
| Heydarian et al. | Automated benchmarking and monitoring of an earthmoving operation's carbon footprint using video cameras and a greenhouse gas estimation model | |
| CN115795325A (en) | Automatic-inspection power transmission tower modal parameter identification method and 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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200508 |
|
| RJ01 | Rejection of invention patent application after publication |