CN117459188B - Electric Beidou communication system and communication method based on Beidou communication technology - Google Patents
Electric Beidou communication system and communication method based on Beidou communication technology Download PDFInfo
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Abstract
The invention provides an electric Beidou communication system and a communication method based on Beidou communication technology, and relates to the technical field of communication data transmission, wherein errors of data in the transmission process can be efficiently detected through Cyclic Redundancy Check (CRC), the calibration position of electric Beidou communication system equipment is determined by using Beidou positioning data, and then future positions P of the electric Beidou communication system equipment are predicted sequentially through a machine learning model future Obtaining predicted network quality Q through a network quality prediction model, and predicting future position P of the Beidou communication system equipment future And predicting the network quality Q to be substituted into the adjustment function so as to change the current increase and decrease of the transmission rate, so that the data packet loss and retransmission caused by network congestion can be reduced; meanwhile, a decision tree algorithm is adopted to effectively process a large amount of data, identify key information, reduce irrelevant data transmission, reduce the data size by adopting Huffman coding, further reduce the data transmission quantity and improve the transmission efficiency.
Description
Technical Field
The invention relates to the technical field of communication data transmission, in particular to an electric Beidou communication system and a communication method based on a Beidou communication technology.
Background
In the prior art, an electric Beidou communication system adopts an independent power taking mode, a block converts 220V of commercial power into weak power to supply power for other functional modules, an Ethernet communication unit module is mainly responsible for collecting concentrator data, processing data and storing data, a protocol conversion unit module is mainly responsible for mutual conversion of an electric power communication protocol and a Beidou communication protocol, a serial port communication module compresses, packetizes and packetizes the data, and the data is transmitted to a Beidou front-end server through a serial port through a Beidou communication machine, so that the independent power taking mode is beneficial to users in different areas, but the independent transmission mode can cause the problems of low resource utilization rate and high cost;
the existing short message communication transmission capacity based on the Beidou system is limited, and if the receiving party fails to receive, the data is easy to lose to form a second problem;
the technical scheme who solves problem one among the prior art is, and publication number is CN112260748A provides a power big dipper communication system and communication method based on big dipper communication technique, relates to big dipper communication technical field, includes: the field end module is used for arranging a plurality of different electric power acquisition devices on the ground so as to acquire electric power at a later stage, the electric power data is transmitted to the electric power Beidou communication system in a mode of collecting the electric power data in a plurality of places, the independent power taking mode is beneficial to users in different areas, solar energy and wind energy power generation are integrated, a storage battery can be provided for meeting self-powered operation, different application schemes can be formed by integrating a high-precision antenna, an inclination angle sensor, a remote switch, a water pressure meter, an ammeter and the like, the electric power acquisition device can be custom-developed according to different application scenes, the cost is low and the speed is high, the electric power data is summarized by the concentrator terminal module, independent transmission is avoided, the transmission cost is reduced, the Beidou communication technology and the concentrator terminal are innovatively combined, and the resource utilization rate is improved;
The technical scheme for solving the second problem in the prior art is that an electric power facility field data monitoring method, equipment and an electric power operation transmission system are provided by the publication No. CN109490660B, and belong to the technical field of data transmission. The method comprises the following steps: an acquisition step of acquiring state data of an electric power facility site; a processing step of performing data processing on the state data to obtain information data in a standard format; a transmitting step of transmitting the information data to a receiving terminal; judging whether the information data is successfully transmitted to a receiving end or not; if not, returning to the sending step; if yes, executing the data loss prevention step, solving the problem of power grid information transmission by utilizing the existing transmission bandwidth of the Beidou short message, and ensuring the success rate and the integrity of short message communication by establishing a data transmission receipt mechanism and a cloud data storage reissue mechanism based on the Beidou short message communication technology.
The technical scheme for solving the problems has the following defects:
in the technical scheme with the publication number of CN112260748A, the power data is summarized through a concentrator terminal module, so that independent transmission is avoided, but the data of an integrated high-precision antenna, an inclination angle sensor, a remote switch, a water pressure meter, an ammeter and the like are complicated to receive, so that a large amount of information containing irrelevant data needs to be processed; while the publication number CN109490660B guarantees the success rate and integrity of short message communication through a receipt mechanism and a cloud data storage and reissue mechanism, but cannot detect errors in the transmission process of data, and meanwhile cannot predict the network quality on the basis of the accurate prediction of the mobile path of the power beidou communication system, implement a change adjustment strategy for increasing and decreasing the transmission rate so as to reduce the loss and retransmission of data packets caused by network congestion, so that the processing mechanism of the publication number CN109490660B is relatively passive, accurately predicts the mobile path of the power beidou communication system equipment, can improve the accuracy of the corresponding network quality prediction result, can detect errors in the transmission process of data, and can predict the network quality on the basis of the accurate prediction of the mobile path of the power beidou communication system equipment, and implement a change adjustment strategy for increasing and decreasing the transmission rate so as to reduce the data transmission quantity, thereby improving the transmission efficiency.
The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide an electric Beidou communication system and a communication method based on a Beidou communication technology, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
electric power big dipper communication system based on big dipper communication technique is applicable to mobilizable electric power big dipper communication system equipment, include:
and a data acquisition module: the power data acquisition device is used for acquiring power data of power Beidou communication system equipment in different areas and transmitting the power data to the power Beidou communication system;
and a data processing module: the method comprises the steps of executing a data cleaning algorithm on collected data, removing invalid data, processing a large number of different types of power collected data by using a decision tree algorithm, identifying key information in the power collected data, reducing irrelevant data transmission, compressing the collected cleaned data by using Huffman coding to reduce the size of the data, and sending the processed data to a Beidou communication receiving end for data mutual transmission;
An error detection module: for detecting errors in the data during transmission by means of a cyclic redundancy check, CRC;
and a rate optimization module: the method comprises the steps of acquiring historical moving paths of power Beidou communication system equipment, acquiring network bandwidth B and delay D data corresponding to path positions of the power Beidou communication system equipment, processing the data based on the acquired data, and sequentially generating future positions P of the power Beidou communication system equipment future And predicting the network quality Q, acquiring and analyzing the ideal data transmission rate R and the current data transmission rate R current Generating a new data transfer rate R new And R is taken as new Comparing and analyzing with the ideal data transmission rate R, and then adjusting the dynamic data transmission rate;
position P future And a calibration module: the method comprises the steps of calibrating a process of predicting a moving path of the electric Beidou communication system device through a machine learning model LSTM, wherein the process of the moving path comprises the steps of acquiring position coordinates P= (x, y, z) of the electric Beidou communication system device in real time through a receiver in the Beidou system, wherein x, y, z represent coordinates of the electric Beidou communication system device in a three-dimensional space, and based on the real-time positioning data, pseudo-range equations of M satellites measured through the receiver are expressed as follows:
Wherein,representing estimated bias, x of approximated pseudoranges i′ Is the time offset of the receiver in the pseudorange bias, y i′ Is the initial coordinate information of the receiver in the pseudo-range deviation, h i′ A pseudo-range from a receiving point to each transmitting station is represented, ω' represents an unknown quantity in a pseudo-range equation, m represents a corresponding receiver class label, and an elevation or geocenter distance of the user is calculated from the barometric altitude measurement, with the following calculation formula:
t in 1 Representing the geocentric coordinates of the station, T 2 Express elevation, f 1 For plane reference, f 2 Representing elevation reference, f s Representing a uniform elevation reference, f k Representing the altitude of the user, wherein o and eta are f respectively 1 And f 2 Is a factor of adjustment of (2);
suppose (x) 0 ,y 0 ,z 0 ) Is the accurate position (x) of the electric Beidou communication system equipment 1 ,y 1 ,z 1 ) Is determined by the receiver (x 2 ,y 2 ,z 2 ) Is the estimated error, in the actual positioning scheme x 0 ＝x 1 +x 2 y 0 ＝y 1 +y 2 And is z 0 ＝z 1 +z 2 Substituting equation F (k), carrying out Taylor series expansion, linearizing, solving an equation of the calibration position of the electric Beidou communication system equipment, solving equation P (x, y, z) for a plurality of times by using an iteration method until the positioning result meets the precision requirement of solving, and inputting the positioning result meeting the precision requirement into real-time positioning data for calibration;
retransmission mechanism module: the receiving end uses the same CRC algorithm to detect the data integrity, and the receiving end sends the retransmission request without passing the check when detecting the error based on the CRC result; the sender retransmits the data after receiving the retransmission request;
And a data post-processing module: the cloud storage system is used for designing a cloud storage architecture, supporting large data volume storage and quick access, implementing a data encryption and backup mechanism, integrating a data mining algorithm, carrying out data analysis and prediction, and providing a data visualization interface.
The electric Beidou communication method based on the Beidou communication technology is used for executing the electric Beidou communication system and comprises the following steps of:
initializing an electric Beidou communication system, and adjusting the transmission rate according to the predicted path of the current electric Beidou communication system equipment and the corresponding predicted network quality;
step one: collecting power data of power Beidou communication system equipment in different areas and transmitting the power data to a power Beidou communication system;
step two: performing a data cleaning algorithm on the collected data to remove invalid data, processing a large number of different types of power collected data by using a decision tree algorithm, identifying key information in the power collected data, reducing irrelevant data transmission, compressing the collected and cleaned data by using Huffman coding to reduce the size of the data, and transmitting the processed data to a Beidou communication receiving end for data inter-transmission;
step three: detecting errors of data in the transmission process through Cyclic Redundancy Check (CRC);
Step four: acquiring historical moving paths of electric Beidou communication system equipment, network bandwidth B and delay D data corresponding to path positions of the electric Beidou communication system equipment, processing the data based on the acquired data, and sequentially generating future positions P of the electric Beidou communication system equipment future And predicting the network quality Q, acquiring and analyzing the ideal data transmission rate R and the current data transmission rate R current Generating a new data transfer rate R new And R is taken as new Comparing and analyzing with the ideal data transmission rate R, and then adjusting the dynamic data transmission rate;
step five: future position P future And a calibration module: calibrating a process for predicting a movement path of an electric Beidou communication system device by means of a machine learning model LSTM, wherein the movement path process comprises the steps of acquiring position coordinates P= (x, y, z) of the electric Beidou communication system device in real time by utilizing a receiver in the Beidou system, wherein x, y, z represent coordinates of the device in a three-dimensional space, and based on real-time positioning data, obtaining the position coordinates P= (x, y, z) of the electric Beidou communication system device by means of a jointThe pseudo-range equation of the M satellites measured by the receiver is expressed as follows:
wherein,representing estimated bias, x of approximated pseudoranges i′ Is the time offset of the receiver in the pseudorange bias, y i′ Is the initial coordinate information of the receiver in the pseudo-range deviation, h i′ A pseudo-range from a receiving point to each transmitting station is represented, ω' represents an unknown quantity in a pseudo-range equation, m represents a corresponding receiver class label, and an elevation or geocenter distance of the user is calculated from the barometric altitude measurement, with the following calculation formula:
t in 1 Representing the geocentric coordinates of the station, T 2 Express elevation, f 1 For plane reference, f 2 Representing elevation reference, f s Representing a uniform elevation reference, f k Representing the altitude of the user, wherein o and eta are f respectively 1 And f 2 Is a factor of adjustment of (2);
step six: realizing automatic repeat request ARQ protocol, the receiving end uses the same CRC algorithm to detect the data integrity, and the receiving end sends the repeat request without passing the check when detecting the error based on the CRC result; the sender retransmits the data after receiving the retransmission request;
step seven: the cloud storage architecture is designed, large data volume storage and quick access are supported, a data encryption and backup mechanism is implemented, a data mining algorithm is integrated, data analysis and prediction are performed, and a data visualization interface is provided.
Compared with the prior art, the invention has the beneficial effects that: errors of data in a transmission process can be efficiently detected through Cyclic Redundancy Check (CRC), and Beidou determination is utilized The calibration position of the device is determined by the bit data, and then the future position P of the power Beidou communication system device is predicted sequentially through a machine learning model future Obtaining predicted network quality Q through a network quality prediction model, and predicting future position P of the Beidou communication system equipment future And predicting the network quality Q to be substituted into the adjustment function so as to change the current increase and decrease of the transmission rate, so that the data packet loss and retransmission caused by network congestion can be reduced; meanwhile, a decision tree algorithm is adopted to effectively process a large amount of data, identify key information, reduce irrelevant data transmission, reduce the data size by adopting Huffman coding, further reduce the data transmission quantity and improve the transmission efficiency.
Drawings
FIG. 1 is a block diagram of the overall system flow of the present invention;
FIG. 2 is a schematic flow chart of the whole method of the invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "up", "down", "left", "right" and the like are used only to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed accordingly.
Referring to fig. 1 and 2, the present invention provides a technical solution:
embodiment one:
electric power big dipper communication system based on big dipper communication technique is applicable to mobilizable electric power big dipper communication system equipment, include:
and a data acquisition module: the power data acquisition device is used for acquiring power data of power Beidou communication system equipment in different areas and transmitting the power data to the power Beidou communication system;
and a data processing module: the method comprises the steps of executing a data cleaning algorithm on collected data, removing invalid data, processing a large number of different types of power collected data by using a decision tree algorithm, identifying key information in the power collected data, reducing irrelevant data transmission, compressing the collected cleaned data by using Huffman coding to reduce the size of the data, and sending the processed data to a Beidou communication receiving end for data mutual transmission;
an error detection module: for detecting errors in the data during transmission by means of a cyclic redundancy check, CRC;
and a rate optimization module: the method comprises the steps of acquiring historical moving paths of power Beidou communication system equipment, acquiring network bandwidth B and delay D data corresponding to path positions of the power Beidou communication system equipment, processing the data based on the acquired data, and sequentially generating future positions P of the power Beidou communication system equipment future And predicting the network quality Q, acquiring and analyzing the ideal data transmission rate R and the current data transmission rate R current Generating a new data transfer rate R new And R is taken as new Comparing and analyzing with the ideal data transmission rate R, and then adjusting the dynamic data transmission rate;
position P future And a calibration module: calibration of a process for predicting a movement path of an electric Beidou communication system device by means of a machine learning model LSTM, wherein the movement path process comprises acquiring position coordinates P= (x, y, z) of the electric Beidou communication system device in real time by means of a receiver in the Beidou system, wherein x, y, z represent coordinates of the electric Beidou communication system device in three-dimensional space, and based on real-time positioning data, M satellites are measured by the receiverThe pseudo-range equation is expressed as:
wherein,representing estimated bias, x of approximated pseudoranges i′ Is the time offset of the receiver in the pseudorange bias, y i′ Is the initial coordinate information of the receiver in the pseudo-range deviation, h i′ A pseudo-range from a receiving point to each transmitting station is represented, ω' represents an unknown quantity in a pseudo-range equation, m represents a corresponding receiver class label, and an elevation or geocenter distance of the user is calculated from the barometric altitude measurement, with the following calculation formula:
T in 1 Representing the geocentric coordinates of the station, T 2 Express elevation, f 1 For plane reference, f 2 Representing elevation reference, f s Representing a uniform elevation reference, f k Representing the altitude of the user, wherein o and eta are f respectively 1 And f 2 Is a factor of adjustment of (2);
suppose (x) 0 ,y 0 ,z 0 ) Is the accurate position (x) of the electric Beidou communication system equipment 1 ,y 1 ,z 1 ) Estimate of (x) 2 ,y 2 ,z 2 ) Is the receiver estimation error, x in the actual positioning scheme 0 ＝x 1 +x 2 y 0 ＝y 1 +y 2 And is z 0 ＝z 1 +z 2 Substituting equation F (k), carrying out Taylor series expansion, linearizing, solving an equation of the calibration position of the electric Beidou communication system equipment, solving equation P (x, y, z) for a plurality of times by using an iteration method until the positioning result meets the precision requirement of solving, and inputting the positioning result meeting the precision requirement into real-time positioning data for calibration;
retransmission mechanism module: the receiving end uses the same CRC algorithm to detect the data integrity, and the receiving end sends the retransmission request without passing the check when detecting the error based on the CRC result; the sender retransmits the data after receiving the retransmission request;
and a data post-processing module: the cloud storage system is used for designing a cloud storage architecture, supporting large data volume storage and quick access, implementing a data encryption and backup mechanism, integrating a data mining algorithm, carrying out data analysis and prediction, and providing a data visualization interface.
Embodiment two:
further described on the basis of the first embodiment, the method uses a decision tree algorithm to process a plurality of different types of power harvesting data and identify key information therein, reducing extraneous data transmissions includes,
decision tree algorithm formula:
where G represents information gain, T is training set, X is feature, and only data classified as important is transmitted.
The operation steps are as follows:
a. data preparation: preparing and cleaning a training data set for constructing a decision tree;
b. feature selection: selecting proper characteristics for constructing a decision tree;
c. constructing a decision tree:
constructing a decision tree according to training data by using an algorithm (such as ID3, C4.5 or CART);
selecting optimal features using information gain or other criteria at each node;
d. classification using decision trees: classifying the new data by using the constructed decision tree;
e. evaluation and optimization: the performance of the decision tree is evaluated and the necessary optimizations are performed.
The application of huffman coding to compress the acquired cleaned data to reduce the data size includes,
the application of huffman encoded compressed data is formulated as follows:
wherein p (x) i ) Representing character x i The probability of occurrence, according to the frequency, the codes with different lengths are distributed to different data, so that the frequently occurring data occupy less space;
The operation steps are as follows:
a. frequency statistics: calculating the occurrence frequency of each character in the data to be compressed;
b. constructing a Huffman tree: constructing a Huffman tree according to the occurrence frequency of the characters, wherein the characters with high frequency are allocated with shorter paths, and the characters with low frequency are allocated with longer paths;
c. generating an encoding table: generating a unique binary code for each character according to the Huffman tree;
d, encoding data: converting the original data into huffman coding using the generated coding table;
e transmitting or storing compressed data: and sending or storing the compressed data.
Embodiment III:
further to the description of the second embodiment, the error of the CRC detection data during transmission through the cyclic redundancy check includes,
the data frame is designed to include a Header and a data portion Payload.
Adding CRC check bits at the tail of the data packet, and introducing a cyclic redundancy check CRC formula for error detection:
CRC(x)＝x n +x n-1 +L+x k
where n represents the number of data bits and k is the number of check bits. The operation steps are as follows:
a. selecting an appropriate CRC formula: depending on the size of the data packet and the level of error detection required, a suitable CRC polynomial is selected, such as CRC-16, CRC-32, etc.
b. Calculating a CRC check value:
the data is treated as one long binary number (typically a multiple of 8 bits); modulo-2 division of this binary number with the selected CRC polynomial; the remainder obtained is the CRC check value.
c. Appending a CRC check value: a CRC check value is appended to the end of the packet.
d. Transmitting data: and transmitting the data packet with the CRC value to a receiving party.
Embodiment four:
on the basis of the third embodiment, further describing the step of acquiring the historical movement path of the power Beidou communication system equipment, the step of acquiring network bandwidth B and delay D data corresponding to the path position of the power Beidou communication system equipment, and sequentially generating future positions P of the power Beidou communication system equipment based on the acquired data future And predicting the network quality Q includes,
monitoring the bandwidth B and delay D of the current network in real time, wherein B represents the transmission capacity of the current network, the unit is Mbps or Gbps, D represents the transmission time of a data packet from a source to a destination, and the unit is usually millisecond ms;
the future position P future The acquisition steps of (a) are as follows:
input: historical location data p= { P1, P2,., pn }; and (3) outputting: predicted future position P future ；
In particular, the method comprises the steps of,
and (3) data collection: we first need to obtain historical position data of the power beidou communication system device from the beidou system, and typically, these data include the position coordinates p= (x, y, z) of the power beidou communication system device at each time point.
And (3) deleting data: the deleted or anomalous data is removed and considered for performing other data cleaning tasks, and for LSTM range gravity, the data may be normalized to scale their values to a range of 0 to 1.
Segmenting the dataset: the cleaned data set is divided into a training set and a testing set, wherein the training set is used for training a model, and the testing set is used for evaluating the performance of the model.
And (3) constructing a model: we use LSTM (long short term memory network) as a predictive model, which is a special RNN that can efficiently process time series data due to its special design structure.
Training a model: input data (historical position data) is input into the model, and parameters of the model are adjusted to minimize the predictive tray.
Test model: the performance of the model is evaluated using the test set, and deviations in the model are made based on the performance of the model on the test set.
And (3) applying a model: and predicting the future position of the power Beidou communication system equipment by using the trained model, wherein the input is historical position data of the power Beidou communication system equipment, and the output is the predicted future position.
The step of obtaining the predicted network quality Q is as follows: a random forest algorithm is used to construct a network quality prediction model,
b1, input: position coordinates P and historical network quality data n= { N1, N2, nm }, where Ni contains bandwidth B and delay D metrics; and (3) outputting: predicted network quality Q; the specific operation steps resemble future positions P future ；
b2, a model formula: random forests predict Q through the integrated learning of multiple decision trees, q=f (N, P) representing a function based on location and historical network data.
c1, policy adjustment is performed based on an adjustment function: adjusting a data transmission strategy according to the predicted value of Q, and if the predicted network condition of Q is bad, reducing the data transmission rate or switching to a more stable transmission protocol; if the Q prediction network condition is good, the data transmission rate is increased to increase the efficiency;
obtaining the ideal data transmission rate R includes calculating the ideal data transmission rate R using the formula;
alpha is an adjustment factor for balancing the effects of bandwidth and delay.
Fifth embodiment:
further describing the fourth embodiment, the acquiring and analyzing of the ideal data transmission rate R and the current data transmission rate R current Generating a new data transfer rate R new Comprising the steps of (a) a step of,
the adjustment function formula is as follows:
R new ＝g(Q,R current )
wherein R is new Is a new data transmission rate, R current Is the current data transmission rate, g is a Q-based adjustment function;
Said and will R new Dynamic data transmission rate adjustment after comparison with the desired data transmission rate R includes,
according to the calculated R new Adjusting the data transmission rate;
when R is new Lower than the current data transmission rate R current Reducing the data transmission rate when the data is transmitted;
when R is new Higher than the current data transmission rate R current When the data transmission rate is increased.
Example six:
further describing the fifth embodiment, in the satellite navigation system, the pseudo range (pseudo range) refers to a value obtained by measuring the propagation time between the user received signal and the satellite transmitted signal and multiplying it by the speed of light to be converted into a range, which contains a plurality of error sources and is called pseudo range;
the basic principle of pseudo-range measurement is that the receiver estimates the range by measuring the time of arrival of a signal transmitted from a satellite at the receiver, which time can be converted into range, since the propagation speed of the signal in air is known, usually taking the speed of light;
however, in practical engineering applications, there are a number of uncontrollable factors and sources of error, resulting in differences between the measured values of the pseudoranges and the true range, some of the major sources of error include:
atmospheric delay: the signal is delayed as it traverses the atmosphere, which is related to the humidity and temperature in the atmosphere;
Clock difference: inaccuracy of the satellite clock and the receiver clock introduces errors, which can occur due to the time of signal propagation and the receiver clock not being synchronized, even if the satellite clock is accurately adjusted;
ionospheric delay: the signals can be affected by the ionosphere when passing through the ionosphere, and extra delay is introduced;
multipath effects: the signals may reflect, refract, or otherwise interact with the surrounding environment during propagation, causing the receiver to receive signals on multiple paths, which may cause the pseudorange measurements to deviate from true range;
receiver hardware error: inaccuracy and errors in the receiver hardware may also affect the accuracy of the pseudorange measurements;
the M satellites are at least three, the Taylor series expansion is performed, after linearization, the equation for solving the calibration position of the electric Beidou communication system equipment comprises,
taylor series expansion:
wherein P is g Parameters representing latitude, P h A parameter representing longitude, J being the position of the satellite on the two-dimensional orbital plane;
equation of calibration position of electric Beidou communication system equipment:
where Δkl, Δgl represent the coordinates and index, respectively, of the satellite's position in the two-dimensional orbital plane.
Embodiment seven:
On the basis of the sixth embodiment, the method further includes that the receiving end uses the same CRC algorithm to detect data integrity, and when the receiving end detects an error based on the CRC result, the checking is failed, and the sending retransmission request includes designing a data packet structure including check bits, where the processing steps include, in order, receiving data, calculating a CRC check value, checking data integrity, requesting retransmission, and sending retransmission;
the operation steps are as follows:
a. receiving data: the receiving party receives the data packet sent by the sending party;
b. calculating a CRC check value: the receiving side calculates a CRC check value of the received data (excluding CRC check bits) using the same CRC algorithm as the transmitting side;
c. checking data integrity: comparing the calculated CRC check value with the received CRC check value;
if the two are the same, the data is considered to be error-free;
if the data are different, the data are considered to have errors in the transmission process;
d. request retransmission: if the data check fails, the receiving party sends a retransmission request to the sending party;
e. and (5) retransmitting by a sender: and after receiving the retransmission request, the sender retransmits the data packet.
The sender performs data retransmission after receiving the retransmission request, and comprises the processing steps of data packetizing, adding a serial number, sending a data packet, receiving and checking, confirming receiving, processing retransmission and completing transmission in sequence;
The specific operation steps are as follows:
a. data packetizing: the sending end divides big data into a plurality of small data packets;
b. adding a serial number: appending a sequence number to each data packet for tracking and validation;
c. and (5) transmitting a data packet: the transmitting end transmits the data packets in sequence;
d. receiving and checking: the receiving end receives the data packet and performs data integrity check by using methods such as CRC;
e. confirmation of receipt:
if the data packet is good, the receiving end sends an acknowledgement ACK receipt containing the serial number of the data packet;
if the data packet is damaged or not received, the receiving end sends negative acknowledgement NACK or does not send ACK;
f. processing retransmission:
the sending end retransmits the corresponding data packet after overtime or receiving NACK;
the sending end sends the next data packet after receiving the ACK;
g. and (3) completing transmission: after all the data packets are confirmed to be received, the transmission process is completed.
Example eight:
further describing the seventh embodiment, the integrating data mining algorithm performs data analysis and prediction, providing a data visualization interface includes selecting a decision tree algorithm to construct a data mining model according to requirements,
the formula of the information gain is:
wherein: IG (D, a) is the information gain of attribute a to dataset D; entropy (D) is the Entropy of dataset D; value (A) is all possible Values for attribute A; d (D) v Is the data subset when the value of the attribute A is v; the data sets D and the subsets D are the |D| and the |D_v| respectively v Is of a size of (a) and (b).
Training and testing the model: training the model using the training dataset and evaluating the model performance using the test dataset;
data prediction and analysis: predicting and analyzing the new data by using the model and generating a visual interface;
specific: a user-friendly data visualization interface design,
demand analysis: determining which data visualizations, such as map displays, charts, etc., are needed by the user;
tool selection: selecting a suitable visualization tool or library according to the requirements, for example using d3.Js, plotly, tableau, etc.;
prototype design: designing an interface prototype, including layout, color scheme, chart type and the like;
implementation and test: and according to the design development interface, testing is carried out, so that good user experience is ensured.
Optimizing data flow: the real-time stream processing capability of the data is realized,
in particular, the data stream optimization,
data source definition: explicit data sources such as databases, ioT devices, etc.;
real-time stream processing: selecting a proper real-time processing frame, such as Apache Kafka, apache Flink and the like;
designing a data stream topology structure;
implementing data processing logic such as data filtering, aggregation, etc.;
Batch processing: using a batch framework, such as Apache Hadoop or Spark;
batch processing operations are designed and implemented.
Providing an API interface, realizing seamless integration with other systems, in particular,
determining which functions the API needs to provide, such as data querying, data submission, etc.;
API design: defining RESTful interface specifications;
design resources and methods such as GET, POST, etc.;
the realization is as follows: the API is implemented using a framework such as Spring Boot or express. Js.
Authentication and authorization are realized, and interface safety is ensured;
and (3) testing: performing API unit test and integration test;
writing a document: and an API document is provided, so that integration is convenient.
Example nine:
the electric Beidou communication method based on the Beidou communication technology is used for executing the electric Beidou communication system and comprises the following steps of:
step one: collecting power data of power Beidou communication system equipment in different areas and transmitting the power data to a power Beidou communication system;
step two: performing a data cleaning algorithm on the collected data to remove invalid data, processing a large number of different types of power collected data by using a decision tree algorithm, identifying key information in the power collected data, reducing irrelevant data transmission, compressing the collected and cleaned data by using Huffman coding to reduce the size of the data, and transmitting the processed data to a Beidou communication receiving end for data inter-transmission;
Step three: detecting errors of data in the transmission process through Cyclic Redundancy Check (CRC);
step four: acquiring historical moving paths of electric Beidou communication system equipment, network bandwidth B and delay D data corresponding to path positions of the electric Beidou communication system equipment, processing the data based on the acquired data, and sequentially generating future positions P of the electric Beidou communication system equipment future And predicting the network quality Q, acquiring and analyzing the ideal data transmission rate R and the current data transmission rate R current Generating a new data transfer rate R new And R is taken as new Comparing and analyzing with the ideal data transmission rate R, and then adjusting the dynamic data transmission rate;
step five: future position P future And a calibration module: the method comprises the steps of calibrating a process for predicting a moving path of the electric Beidou communication system device through a machine learning model LSTM, wherein the moving path process comprises the steps of acquiring position coordinates P= (x, y, z) of the electric Beidou communication system device in real time through a receiver in the Beidou system, wherein x, y and z represent coordinates of the electric Beidou communication system device in a three-dimensional space, and based on real-time positioning data, pseudo-range equations of M satellites measured through the receiver are expressed as follows:
Wherein,representing estimated bias, x of approximated pseudoranges i′ Is the time offset of the receiver in the pseudorange bias, y i′ Is the initial coordinate information of the receiver in the pseudo-range deviation, h i′ Representing the point of receptionThe pseudoranges to each transmitting station, ω', represent the unknowns in the pseudorange equation, m represents the corresponding receiver class label, and calculate the elevation or geocentric distance of the user from the barometric altitude measurement as follows:
t in 1 Representing the geocentric coordinates of the station, T 2 Express elevation, f 1 For plane reference, f 2 Representing elevation reference, f s Representing a uniform elevation reference, f k Representing the altitude of the user, wherein o and eta are f respectively 1 And f 2 Is a factor of adjustment of (2);
step six: realizing automatic repeat request ARQ protocol, the receiving end uses the same CRC algorithm to detect the data integrity, and the receiving end sends the repeat request without passing the check when detecting the error based on the CRC result; the sender retransmits the data after receiving the retransmission request;
step seven: the cloud storage architecture is designed, large data volume storage and quick access are supported, a data encryption and backup mechanism is implemented, a data mining algorithm is integrated, data analysis and prediction are performed, and a data visualization interface is provided.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application.
Claims (6)
1. Electric power big dipper communication system based on big dipper communication technique is applicable to mobilizable electric power big dipper communication system equipment, its characterized in that includes:
and a data acquisition module: the power data acquisition device is used for acquiring power data of power Beidou communication system equipment in different areas and transmitting the power data to the power Beidou communication system;
and a data processing module: the method comprises the steps of executing a data cleaning algorithm on collected data, removing invalid data, processing a large number of different types of power collected data by using a decision tree algorithm, identifying key information in the power collected data, reducing irrelevant data transmission, compressing the collected cleaned data by using Huffman coding to reduce the size of the data, and sending the processed data to a Beidou communication receiving end for data mutual transmission;
an error detection module: for detecting errors in the data during transmission by means of a cyclic redundancy check, CRC;
and a rate optimization module: the method comprises the steps of acquiring historical moving paths of power Beidou communication system equipment, acquiring network bandwidth B and delay D data corresponding to path positions of the power Beidou communication system equipment, processing the data based on the acquired data, and sequentially generating future positions P of the power Beidou communication system equipment future And a prediction of the quality Q of the network,
specifically, the method comprises the steps of monitoring the bandwidth B and the delay D of the current network in real time, wherein B represents the transmission capability of the current network, D represents the transmission time of a data packet from a source to a destination,
the future position P future The acquisition steps of (a) are as follows:
input: historical location data p= { P1, P2,., pn }; and (3) outputting: predicted future position P future ；
The step of obtaining the predicted network quality Q is as follows: a random forest algorithm is used to construct a network quality prediction model,
input: position coordinates P and historical network quality data n= { N1, N2, nm }, where Ni contains bandwidth B and delay D metrics; and (3) outputting: predicted network quality Q;
model formula: predicting Q, q=f (N, P) by ensemble learning of a plurality of decision trees by a random forest, the q=f (N, P) representing a function based on location and historical network data;
policy adjustment is performed based on the adjustment function: adjusting a data transmission strategy according to the predicted value of Q, and if the predicted network condition of Q is bad, reducing the data transmission rate or switching to a more stable transmission protocol; if the Q prediction network condition is good, the data transmission rate is increased to increase the efficiency;
obtaining the ideal data transmission rate R includes calculating the ideal data transmission rate R using the following formula;
α is an adjustment factor for balancing the effects of bandwidth and delay;
acquiring and analyzing an ideal data transmission rate R and a current data transmission rate R current Generating a new data transfer rate R new And R is taken as new Comparing and analyzing with the ideal data transmission rate R, and then adjusting the dynamic data transmission rate;
the adjustment function formula is as follows:
R new ＝g(Q,R current )
wherein R is new Is newData transmission rate, R current Is the current data transmission rate, g is a Q-based adjustment function;
said and will R new Dynamic data transmission rate adjustment after comparison with the desired data transmission rate R includes,
according to the calculated R new Adjusting the data transmission rate;
when R is new Lower than the current data transmission rate R current Reducing the data transmission rate when the data is transmitted;
when R is new Higher than the current data transmission rate R current Increasing the data transmission rate;
position P future And a calibration module: the method comprises the steps of calibrating a process for predicting a moving path of the electric Beidou communication system device through a machine learning model LSTM, wherein the moving path process comprises the steps of acquiring position coordinates P= (x, y, z) of the electric Beidou communication system device in real time through a receiver in the Beidou system, wherein x, y and z represent coordinates of the electric Beidou communication system device in a three-dimensional space, and based on real-time positioning data, pseudo-range equations of M satellites measured through the receiver are expressed as follows:
Wherein,representing estimated bias, x of approximated pseudoranges i′ Is the time offset of the receiver in the pseudorange bias, y i′ Is the initial coordinate information of the receiver in the pseudo-range deviation, h i′ A pseudo-range from a receiving point to each transmitting station is represented, ω' represents an unknown quantity in a pseudo-range equation, m represents a corresponding receiver class label, and an elevation or geocenter distance of the user is calculated from the barometric altitude measurement, with the following calculation formula:
t in 1 Representing the geocentric coordinates of the station, T 2 Express elevation, f 1 For plane reference, f 2 Representing elevation reference, f s Representing a uniform elevation reference, f k Representing the altitude of the user, wherein o and eta are f respectively 1 And f 2 Is a factor of adjustment of (2);
suppose (x) 0 ,y 0 ,z 0 ) Is the accurate position (x) of the electric Beidou communication system equipment 1 ,y 1 ,z 1 ) Estimate of (x) 2 ,y 2 ,z 2 ) Is the estimated error, in the actual positioning scheme x 0 ＝x 1 +x 2 y 0 ＝y 1 +y 2 And is z 0 ＝z 1 +z 2 Substituting equation F (k), carrying out Taylor series expansion, linearizing, solving an equation of the calibration position of the electric Beidou communication system equipment, solving equation P (x, y, z) for a plurality of times by using an iteration method until the positioning result meets the precision requirement of solving, and inputting the positioning result meeting the precision requirement into real-time positioning data for calibration;
the M satellites are at least three, the Taylor series expansion is performed, after linearization, the equation for solving the calibration position of the electric Beidou communication system equipment comprises,
Taylor series expansion:
wherein P is g Parameters representing latitude, P h A parameter representing longitude, J being the position of the satellite on the two-dimensional orbital plane;
equation of calibration position of electric Beidou communication system equipment:
wherein Δkl and Δgl represent the coordinates and index of the satellite's position on the two-dimensional orbital plane, respectively;
retransmission mechanism module: the receiving end uses the same CRC algorithm to detect the data integrity, and the receiving end sends the retransmission request without passing the check when detecting the error based on the CRC result; the sender retransmits the data after receiving the retransmission request;
and a data post-processing module: the cloud storage system is used for designing a cloud storage architecture, supporting large data volume storage and quick access, implementing a data encryption and backup mechanism, integrating a data mining algorithm, carrying out data analysis and prediction, and providing a data visualization interface.
2. The Beidou communication technology-based power Beidou communication system of claim 1, wherein: the use of decision tree algorithms to process a large number of different categories of power harvesting data and identify key information therein, reducing extraneous data transmissions including,
decision tree algorithm formula:
Wherein G represents information gain, T is training set, X is characteristic, and only data classified as important is transmitted;
the application of huffman coding to compress the acquired cleaned data to reduce the data size includes,
the application of huffman encoded compressed data is formulated as follows:
wherein p (x) i ) Representing character x i The probability of occurrence, different data are allocated codes of different lengths according to the frequency, so that the frequently occurring data occupy less space.
3. The Beidou communication technology-based power Beidou communication system of claim 1, wherein: the error of the CRC detection data during transmission by means of a cyclic redundancy check includes,
designing a data frame comprising a Header and a data portion Payload;
adding CRC check bits at the tail of the data packet, and introducing a cyclic redundancy check CRC formula for error detection:
CRC(x)＝x n +x n-1 +L+x k
where n represents the number of data bits and k is the number of check bits.
4. The Beidou communication technology-based power Beidou communication system of claim 1, wherein: the method comprises the steps that a receiving end uses the same CRC algorithm to detect data integrity, a receiving end does not pass the check when detecting errors based on a CRC check result, a data packet structure is designed to send a retransmission request, the data packet structure comprises check bits, and the processing steps are sequentially that the receiving end receives data, calculates a CRC check value, checks the data integrity, requests retransmission and a sender to retransmit;
The data retransmission of the sender after receiving the retransmission request comprises the processing steps of data packetizing, attaching a serial number, sending the data packet, receiving and checking, confirming the receiving, processing the retransmission and completing the transmission.
5. The Beidou communication technology-based power Beidou communication system of claim 1, wherein: the integrated data mining algorithm performs data analysis and prediction, providing a data visualization interface includes,
and selecting a decision tree algorithm according to requirements to construct a data mining model, wherein the formula of the information gain is as follows:
wherein: IG (D, a) is the information gain of attribute a to dataset D; entropy (D) is the Entropy of dataset D; value (A) is all possible for attribute AA value; d (D) v Is the data subset when the value of the attribute A is v; the data sets D and the subsets D are the |D| and the |D_v| respectively v Is of a size of (2);
training and testing the model: training the model using the training dataset and evaluating the model performance using the test dataset;
data prediction and analysis: predicting and analyzing the new data by using the model and generating a visual interface;
optimizing data flow: the real-time data stream processing capability is realized, an API interface is provided, and seamless integration with other systems is realized.
6. An electric Beidou communication method based on Beidou communication technology, the method is used for executing the electric Beidou communication system of any one of claims 1-5, and is characterized in that: the method comprises the following steps:
step one: collecting power data of power Beidou communication system equipment in different areas and transmitting the power data to a power Beidou communication system;
step two: performing a data cleaning algorithm on the collected data to remove invalid data, processing a large number of different types of power collected data by using a decision tree algorithm, identifying key information in the power collected data, reducing irrelevant data transmission, compressing the collected and cleaned data by using Huffman coding to reduce the size of the data, and transmitting the processed data to a Beidou communication receiving end for data inter-transmission;
step three: detecting errors of data in the transmission process through Cyclic Redundancy Check (CRC);
step four: acquiring historical moving paths of electric Beidou communication system equipment, network bandwidth B and delay D data corresponding to path positions of the electric Beidou communication system equipment, processing the data based on the acquired data, and sequentially generating future positions P of the electric Beidou communication system equipment future And predicting the network quality Q, acquiring and analyzing the ideal data transmission rate R and the current data transmission rate R current Generating a new data transfer rate R new And R is taken as new Comparing and analyzing with ideal data transmission rate RDynamic data transmission rate adjustment;
step five: calibrating a process of predicting a moving path of the electric Beidou communication system device through a machine learning model LSTM, wherein the process of the moving path comprises the steps of acquiring position coordinates P= (x, y, z) of the electric Beidou communication system device in real time through a receiver in the Beidou system, wherein x, y, z represent coordinates of the electric Beidou communication system device in a three-dimensional space, and based on real-time positioning data, pseudo-range equations of M satellites measured through the receiver are expressed as follows:
wherein,representing estimated bias, x of approximated pseudoranges i′ Is the time offset of the receiver in the pseudorange bias, y i′ Is the initial coordinate information of the receiver in the pseudo-range deviation, h i′ A pseudo-range from a receiving point to each transmitting station is represented, ω' represents an unknown quantity in a pseudo-range equation, m represents a corresponding receiver class label, and an elevation or geocenter distance of the user is calculated from the barometric altitude measurement, with the following calculation formula:
T in 1 Representing the geocentric coordinates of the station, T 2 Express elevation, f 1 For plane reference, f 2 Representing elevation reference, f s Representing a uniform elevation reference, f k Representing the altitude of the user, wherein o and eta are f respectively 1 And f 2 Is a factor of adjustment of (2);
step six: realizing automatic repeat request ARQ protocol, the receiving end uses the same CRC algorithm to detect the data integrity, and the receiving end sends the repeat request without passing the check when detecting the error based on the CRC result; the sender retransmits the data after receiving the retransmission request;
step seven: the cloud storage architecture is designed, large data volume storage and quick access are supported, a data encryption and backup mechanism is implemented, a data mining algorithm is integrated, data analysis and prediction are performed, and a data visualization interface is provided.
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Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5720003A (en) * | 1994-10-27 | 1998-02-17 | Lucent Technologies Inc. | Method and apparatus for determining the accuracy limit of a learning machine for predicting path performance degradation in a communications network |
| WO1999018677A1 (en) * | 1997-10-02 | 1999-04-15 | Airsys Atm, Inc. | Gps augmentation system |
| US6418549B1 (en) * | 1998-10-30 | 2002-07-09 | Merunetworks, Inc. | Data transmission using arithmetic coding based continuous error detection |
| CN101819609A (en) * | 2001-09-21 | 2010-09-01 | 无线谷通讯有限公司 | Be used to design, follow the tracks of, measure, predict and optimize the system and method for data communication network |
| CN102868224A (en) * | 2012-09-20 | 2013-01-09 | 湖北省电力公司电力科学研究院 | Secondary network measurement and multimode decision-making method and device for intelligent substation |
| CN105024861A (en) * | 2015-07-28 | 2015-11-04 | 天津大学 | Beidou Satellite-based reliable remote communication method and communication device |
| JP2017103684A (en) * | 2015-12-03 | 2017-06-08 | 日本電信電話株式会社 | Network quality predictor, network quality prediction method, and program |
| CN109711715A (en) * | 2018-12-25 | 2019-05-03 | 国家电网有限公司 | Electric power telecommunication network risk assessment method |
| CN109756911A (en) * | 2019-01-31 | 2019-05-14 | 腾讯科技（深圳）有限公司 | Network quality prediction technique, business reorganization method, relevant device and storage medium |
| KR20200000090A (en) * | 2018-06-22 | 2020-01-02 | (주)아틀라스네트웍스 | Method for transmitting and receiving foward error correction according to best nework quality using machine learning |
| WO2020062193A1 (en) * | 2018-09-29 | 2020-04-02 | 福建网能科技开发有限责任公司 | Electric power data acquisition system based on beidou communication technology |
| CN111615134A (en) * | 2020-04-27 | 2020-09-01 | 深圳壹账通智能科技有限公司 | Method and device for processing mobile network access data and storage medium |
| KR20200119111A (en) * | 2019-04-09 | 2020-10-19 | 애니파이 주식회사 | A wireless terminal for providing dynamic changing connections to wireless networks |
| CN112491627A (en) * | 2020-12-02 | 2021-03-12 | 中国建设银行股份有限公司 | Network quality real-time analysis method and device |
| CN112954765A (en) * | 2021-02-24 | 2021-06-11 | 四川速宝网络科技有限公司 | Dynamic operator routing method based on network state monitoring |
| CN113783716A (en) * | 2021-07-27 | 2021-12-10 | 国网冀北电力有限公司信息通信分公司 | Flow prediction method and device based on cloud edge collaborative framework |
| CN113886592A (en) * | 2021-10-29 | 2022-01-04 | 国网辽宁省电力有限公司信息通信分公司 | Quality detection method for operation and maintenance data of power information communication system |
| CN114827951A (en) * | 2022-04-24 | 2022-07-29 | 重庆长安汽车股份有限公司 | Vehicle network quality analysis method and system based on vehicle terminal and storage medium |
| CN115278811A (en) * | 2022-07-28 | 2022-11-01 | 中国科学院计算技术研究所 | MPTCP connection path selection method based on decision tree model |
| CN115348638A (en) * | 2022-08-17 | 2022-11-15 | 广东电网有限责任公司 | Data communication path optimization method and system for BD3 short message |
| CN115866787A (en) * | 2022-10-28 | 2023-03-28 | 国网吉林省电力有限公司 | Network resource allocation method integrating terminal direct transmission communication and multi-access edge calculation |
| CN115884213A (en) * | 2021-09-28 | 2023-03-31 | 中国移动通信集团贵州有限公司 | Resource scheduling method and device and electronic equipment |
| CN116016244A (en) * | 2021-10-20 | 2023-04-25 | 腾讯科技（深圳）有限公司 | Method, device and equipment for monitoring network fault processing process |
| CN116528274A (en) * | 2023-07-05 | 2023-08-01 | 腾讯科技（深圳）有限公司 | Network quality regulation and control method and related equipment |
| CN117119332A (en) * | 2023-08-28 | 2023-11-24 | 国电南瑞科技股份有限公司 | Power distribution network data acquisition method and system based on Beidou short message communication |
| CN117135090A (en) * | 2023-08-28 | 2023-11-28 | 西安电子科技大学 | Satellite internet flow prediction method, system, equipment and medium based on federal learning framework |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2005322044A1 (en) * | 2004-12-24 | 2006-07-06 | Aspera, Inc. | Bulk data transfer |
| US20220302997A1 (en) * | 2021-03-18 | 2022-09-22 | Envistacom, Llc | Intelligent roaming for mobile and nomadic communications systems architecture and methods |
-
2023
- 2023-12-25 CN CN202311785937.XA patent/CN117459188B/en active Active
Patent Citations (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5720003A (en) * | 1994-10-27 | 1998-02-17 | Lucent Technologies Inc. | Method and apparatus for determining the accuracy limit of a learning machine for predicting path performance degradation in a communications network |
| WO1999018677A1 (en) * | 1997-10-02 | 1999-04-15 | Airsys Atm, Inc. | Gps augmentation system |
| US6418549B1 (en) * | 1998-10-30 | 2002-07-09 | Merunetworks, Inc. | Data transmission using arithmetic coding based continuous error detection |
| CN101819609A (en) * | 2001-09-21 | 2010-09-01 | 无线谷通讯有限公司 | Be used to design, follow the tracks of, measure, predict and optimize the system and method for data communication network |
| CN102868224A (en) * | 2012-09-20 | 2013-01-09 | 湖北省电力公司电力科学研究院 | Secondary network measurement and multimode decision-making method and device for intelligent substation |
| CN105024861A (en) * | 2015-07-28 | 2015-11-04 | 天津大学 | Beidou Satellite-based reliable remote communication method and communication device |
| JP2017103684A (en) * | 2015-12-03 | 2017-06-08 | 日本電信電話株式会社 | Network quality predictor, network quality prediction method, and program |
| KR20200000090A (en) * | 2018-06-22 | 2020-01-02 | (주)아틀라스네트웍스 | Method for transmitting and receiving foward error correction according to best nework quality using machine learning |
| WO2020062193A1 (en) * | 2018-09-29 | 2020-04-02 | 福建网能科技开发有限责任公司 | Electric power data acquisition system based on beidou communication technology |
| CN109711715A (en) * | 2018-12-25 | 2019-05-03 | 国家电网有限公司 | Electric power telecommunication network risk assessment method |
| CN109756911A (en) * | 2019-01-31 | 2019-05-14 | 腾讯科技（深圳）有限公司 | Network quality prediction technique, business reorganization method, relevant device and storage medium |
| KR20200119111A (en) * | 2019-04-09 | 2020-10-19 | 애니파이 주식회사 | A wireless terminal for providing dynamic changing connections to wireless networks |
| CN111615134A (en) * | 2020-04-27 | 2020-09-01 | 深圳壹账通智能科技有限公司 | Method and device for processing mobile network access data and storage medium |
| CN112491627A (en) * | 2020-12-02 | 2021-03-12 | 中国建设银行股份有限公司 | Network quality real-time analysis method and device |
| CN112954765A (en) * | 2021-02-24 | 2021-06-11 | 四川速宝网络科技有限公司 | Dynamic operator routing method based on network state monitoring |
| CN113783716A (en) * | 2021-07-27 | 2021-12-10 | 国网冀北电力有限公司信息通信分公司 | Flow prediction method and device based on cloud edge collaborative framework |
| CN115884213A (en) * | 2021-09-28 | 2023-03-31 | 中国移动通信集团贵州有限公司 | Resource scheduling method and device and electronic equipment |
| CN116016244A (en) * | 2021-10-20 | 2023-04-25 | 腾讯科技（深圳）有限公司 | Method, device and equipment for monitoring network fault processing process |
| CN113886592A (en) * | 2021-10-29 | 2022-01-04 | 国网辽宁省电力有限公司信息通信分公司 | Quality detection method for operation and maintenance data of power information communication system |
| CN114827951A (en) * | 2022-04-24 | 2022-07-29 | 重庆长安汽车股份有限公司 | Vehicle network quality analysis method and system based on vehicle terminal and storage medium |
| CN115278811A (en) * | 2022-07-28 | 2022-11-01 | 中国科学院计算技术研究所 | MPTCP connection path selection method based on decision tree model |
| CN115348638A (en) * | 2022-08-17 | 2022-11-15 | 广东电网有限责任公司 | Data communication path optimization method and system for BD3 short message |
| CN115866787A (en) * | 2022-10-28 | 2023-03-28 | 国网吉林省电力有限公司 | Network resource allocation method integrating terminal direct transmission communication and multi-access edge calculation |
| CN116528274A (en) * | 2023-07-05 | 2023-08-01 | 腾讯科技（深圳）有限公司 | Network quality regulation and control method and related equipment |
| CN117119332A (en) * | 2023-08-28 | 2023-11-24 | 国电南瑞科技股份有限公司 | Power distribution network data acquisition method and system based on Beidou short message communication |
| CN117135090A (en) * | 2023-08-28 | 2023-11-28 | 西安电子科技大学 | Satellite internet flow prediction method, system, equipment and medium based on federal learning framework |
Non-Patent Citations (9)
| Title |
|---|
| 一种基于压缩感知的重叠网络路径性能评价方法;李广野;雷为民;胡丹丹;张伟;;小型微型计算机系统;20170115(第01期);全文 * |
| 一种新的流媒体网络质量分析系统;钟开文;;电信快报;20100410(第04期);全文 * |
| 优化网络设备维护提高数据通信传输质量;陈宏伟;;中国新通信;20150620(第12期);全文 * |
| 信道质量对网络性能的影响;孙邵海;潘韵;;池州学院学报;20081028(05);全文 * |
| 基于SDN架构的电力通信网络质量感知技术研究;谢小军;潘子春;夏同飞;;计算机测量与控制;20171025(第10期);全文 * |
| 基于北斗RTK技术的电力铁塔变形监测技术研究;丛犁;杜秋实;窦增;王圣达;李俪修;任智源;;电力信息与通信技术;20151215(第12期);全文 * |
| 基于大数据分析的无线通信技术研究;徐全盛;葛林强;邹勤宜;;通信技术;20161210(第12期);全文 * |
| 基于大数据应用的无线网络优化研究;刘晓琴;;安阳师范学院学报;20190415(第02期);全文 * |
| 基于覆盖组播网络的实时视频传输方案的设计;程德强;钱建生;田隽;;电信科学;20060815(第08期);全文 * |
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