CN115238574A - Digital twin-based power transmission line data management method - Google Patents
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Abstract
The invention discloses a digital twin-based power transmission line data management method; the method comprises the following steps: s1, acquiring existing data information from a big data network; s2, inputting big data information into a digital model for simulation; s3, acquiring data information through actual physical equipment; s4, inputting the acquired data information into the digital model; s5, judging the system through data information calculated by the digital model; the invention provides a transmission line construction idea based on a digital twin technology by combining the current situation and development of the digital twin technology, designs a data processing and data management method of the transmission line digital twin, and analyzes the application scene of the transmission line digital twin in detail from the application level, thereby obtaining the conclusion that the transmission line digital twin can thoroughly change the operation management mode, equipment guarantee mode, organization command and other scenes of the transmission line.
Description
Technical Field
The invention belongs to the technical field of power transmission lines, and particularly relates to a digital twin-based power transmission line data management method.
Background
With the development of social economy, the scale of power grid equipment is greatly increased, the management work is heavy day by day, and the conventional management mode cannot adapt to the operation requirement of a modern large power grid, the problems that the conventional information technology needs to be improved in the aspects of cross-professional fusion, whole-process management of equipment and the like, so that the conventional management system cannot meet the requirements of power system innovation on efficiency improvement and operation and maintenance cost reduction.
Although the intelligent power internet of things inspection method and system based on the digital twin technology are based on the digital twin technology and utilize real-time interaction of data acquired by power equipment, the daily inspection work and the visual remote inspection task of 'unmanned inspection' can be realized, and the consumption of manpower and material resources is greatly reduced; the simulation inspection can be carried out through the virtual object, potential safety hazards of operation are eliminated, and inspection tasks are optimized, but the problems that digital twin simulation processing cannot be carried out on the power transmission line, safety calculation, line protection judgment cannot be achieved and the like in the prior art are not solved, and therefore a power transmission line data management method based on digital twin is provided.
Disclosure of Invention
The invention aims to provide a digital twin-based transmission line data management method to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: the transmission line data management method based on the digital twins comprises the following steps:
s1, acquiring existing data information from a big data network: acquiring data information through a communication network, acquiring big data from a cloud platform, and calculating the big data information;
s2, inputting big data information into a digital model for simulation: inputting big data information into a digital model, then performing simulation, and performing calculation processing through cloud computing to improve the accuracy of the data information;
s3, acquiring data information through actual physical equipment: the data information of the power transmission line is acquired through the data transmission network, and the data information is calculated, so that the accuracy of the data information is improved;
s4, inputting the acquired data information into the digital model: inputting the data information into the digital model to realize the simulation calculation processing of the digital model, and then comparing the acquired data information with the acquired big data information;
s5, judging the system through data information calculated by the digital model: and judging the data information obtained by the calculation of the digital model, taking the judged data information as a detection value of the line safety hazard early warning, returning the data information to maintain and maintain the system, storing and processing the data information, and displaying the result on the platform.
Preferably, the AI learning technology is adopted in the cloud platform in S1 to acquire the big data, and the data is selected and acquired through a neural network algorithm, and the electric transmission line is constructed through cloud big data information.
Preferably, the AI learning technique is used for combining a large amount of data with the ultra-strong operation processing capability and the intelligent algorithm, so as to realize identification and calculation of cloud big data and complete model construction, and the calculation process of the neural network algorithm is as follows:
s101, taking a sample from the sample set (A) i ,B i ) Wherein A is i Is an input, B i Is the desired output;
s102, calculating the actual output O of the network;
s103, calculating D = B i -O；
S104, adjusting the weight matrix W according to D;
and S105, repeating the process for each sample until the error does not exceed a fixed range for the whole sample set.
Preferably, the digital model in S2 is an MATLAB model, and the MATLAB model is obtained by multiple regression linear calculation using a regression () function;
f (x 1, x2, x 3) = a1+ a2 x1+ a3 x2+ a4 x3 is a multivariate linear regression function;
the solving method comprises the following steps:
x1＝[...]；x2＝[...]；x3＝[...]；
X＝[ones(n,1)x1x2x3]；
y＝[...]；
a = regress (y, X); a is the fitting coefficient of the multiple linear regression function, and x1, x2, and x3 are the input values of the multiple linear regression function.
Preferably, the calculation processing includes data acquisition, data classification, data filtering, data conversion and data gain, the data acquisition is used for acquiring data of a big data network and data information of a power transmission line, the data classification is used for classifying the data information, the data filtering is used for filtering the data information, the data filtering adopts a limiting filtering algorithm, a median filtering algorithm, an arithmetic mean filtering algorithm, a low-pass filtering algorithm or an IIR digital filtering algorithm to filter out clutter, the data conversion is used for adaptively converting data types, and the data gain is used for amplifying the data information.
Preferably, the means for acquiring the data information of the power transmission line in S3 is to acquire data through various sensors or devices, and the sensors or the devices include a camera, an unmanned inspection machine, a voltage detection sensor, a current detection sensor, a temperature sensor, an environment sensor, a humidity sensor, a basic geographic data collector, a satellite image, an oblique photography data collector, a GIM digital power grid model data transmitter and a laser point cloud power grid engineering design data transmitter.
Preferably, the data information acquired in S4 and the acquired big data information are compared by using an LM339 comparator or an LM393 comparator, and the LM339 comparator or the LM393 comparator is used to compare the big data information and the acquired data information, determine an error of the data information, and implement protection of the computing power transmission system.
Preferably, the line safety risk early warning in S5 is a line safety risk early warning based on image recognition, and the specific method includes the following steps:
s501, obtaining the growth condition of trees around the tower through a data acquisition module;
s502, automatically identifying the conducting wire and the tree of the power transmission line in a machine learning mode according to the acquired picture information;
s503, marking the shortest distance point between the wire and the tree and calculating the distance;
and S504, if the shortest distance between the wire and the tree is smaller than the required safety distance, intelligently early warning.
Preferably, the storage processing in S5 is storage and management of multi-source heterogeneous data, and the data storage and management memory stores the acquired data, establishes a corresponding database, and manages and calls the database.
Preferably, the platform data of the platform in S5 has characteristics of multi-source heterogeneity, wide distribution, and dynamic growth, and the platform has a secure storage method, the secure storage method uses a homomorphic encryption manner to protect key service data or user privacy data, and the secure storage method uses a homomorphic encryption manner.
Compared with the prior art, the invention has the beneficial effects that:
aiming at the cloud design of the power transmission line, the current situation and the development of a digital twin technology are combined, a power transmission line construction thought based on the digital twin technology is provided, a data processing and data management method of the digital twin of the power transmission line is designed, and the application prospect is very wide; aiming at the power grid engineering design and the operation environment, the technology of power grid engineering design, operation environment data processing and fusion such as satellite images, oblique photography data, basic geographic data, BIM/CAD building model data, GIM digital power grid model, city street view data, laser point cloud and the like is combined, the cleaning, conversion, processing, storage and fusion of various data resources are realized, the holographic visualization of the power grid design process and the operation environment is realized, the incidence relation between the internet of things perception data and the power grid data model is established, a power grid holographic data platform is established, and the real-time and visualization of the full operation state of the power grid are realized.
Drawings
FIG. 1 is a schematic structural diagram of the steps of the method of the present invention;
FIG. 2 is a schematic diagram of the system of 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. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
referring to fig. 1, the present invention provides a technical solution: the transmission line data management method based on the digital twin comprises the following steps:
s1, acquiring existing data information from a big data network: acquiring data information through a communication network, acquiring big data from a cloud platform, and calculating the big data information;
s2, inputting big data information into a digital model for simulation: inputting big data information into a digital model, then performing simulation, and performing calculation processing through cloud computing to improve the accuracy of the data information;
s3, acquiring data information through actual physical equipment: the data information of the power transmission line is acquired through the data transmission network, and the data information is calculated, so that the accuracy of the data information is improved;
s4, inputting the acquired data information into the digital model: inputting the data information into the digital model to realize the simulation calculation processing of the digital model, and then comparing the acquired data information with the acquired big data information;
s5, judging the system through data information calculated by the digital model: and judging the data information obtained by the calculation of the digital model, taking the judged data information as a detection value of the line safety hazard early warning, returning the data information to maintain and maintain the system, storing and processing the data information, and displaying the result on the platform.
In order to acquire data information and realize AI learning to acquire the data information, in this embodiment, it is preferable that the cloud platform in S1 acquires big data by using an AI learning technology, and realizes selection and acquisition of the data through a neural network algorithm, and constructs a power transmission line through the cloud big data information.
In order to implement the data information acquisition and calculation processing by the neural network algorithm, in this embodiment, preferably, the AI learning technique is used to combine a large amount of data with the ultra-strong operation processing capability and the intelligent algorithm, so as to implement the identification and calculation of the cloud big data and complete the model construction, and the calculation process of the neural network algorithm is as follows:
s101, taking a sample from the sample set (A) i ,B i ) Wherein A is i Is an input, B i Is the desired output;
s102, calculating the actual output O of the network;
s103, calculating D = B i -O；
S104, adjusting the weight matrix W according to D;
s105, repeating the process for each sample until the error does not exceed a fixed range for the whole sample set;
in order to carry out weight processing on the output of the neural network algorithm, a Delta learning rule is adopted:
w ij (t+1)＝w ij (t)+α(d i -y i )x j (t)
in the formula:
w ij -connection weight of neuron j to neuron i;
d i -a desired output of neuron i;
y i -the actual output of neuron i;
x j -represents the state of neuron k, 1 in the activated state and 0 or-1 in the inhibited state;
α — constant of learning speed;
if the actual output of the neuron is greater than the desired output, the weight that all inputs are positively connected is reduced, and vice versa.
In order to create a digital model and perform calculation processing on information, in this embodiment, it is preferable that the digital model in S2 is an MATLAB model, and the MATLAB model is a multiple regression linear calculation method and is obtained by a regression () function;
f (x 1, x2, x 3) = a1+ a2 x1+ a3 x2+ a4 x3 is a multiple linear regression function;
the solving method comprises the following steps:
x1＝[...]；x2＝[...]；x3＝[...]；
X＝[ones(n,1)x1x2x3]；
y＝[...]；
a = regression (y, X); a is the fitting coefficient of the multiple linear regression function, and x1, x2, and x3 are the input values of the multiple linear regression function.
In order to implement data information processing and maintain the accuracy of data information, in this embodiment, it is preferable that the calculation processing process or content includes data acquisition, data classification, data filtering, data conversion and data gain, where the data acquisition is used to implement acquisition of data of a big data network and data information of a power transmission line, the data classification is used to implement classification processing of the data information, the data filtering is used to implement filtering processing of the data information, the data filtering employs a limiting filtering algorithm, a median filtering algorithm, an arithmetic mean filtering algorithm, a low-pass filtering algorithm or an IIR digital filtering algorithm to filter out clutter, the data conversion is used to implement adaptive conversion of data types, and the data gain is used to implement amplification processing of the data information.
In order to realize data information acquisition on the power transmission line, the data information acquisition is performed by using various physical devices, in this embodiment, preferably, the means for acquiring the data information of the power transmission line in S3 is to acquire data by using various sensors or devices, and the sensors or the devices include a camera, an unmanned inspection machine, a voltage detection sensor, a current detection sensor, a temperature sensor, an environment sensor, a humidity sensor, a basic geographic data collector, a satellite image, an oblique photography data collector, a GIM digital power grid model data transmitter and a laser point cloud power grid engineering design data transmitter.
In order to implement comparison of data information and determine the safety of the power transmission line, in this embodiment, it is preferable that, in S4, an LM339 comparator or an LM393 comparator is used for comparing the acquired data information with the acquired big data information, and the LM339 comparator or the LM393 comparator compares the input big data information with the acquired data information to determine an error of the data information, and the real-time safety protection of the power transmission system is implemented through calculation of the error.
In order to realize the detection of the safety hazard of the power transmission line, in this embodiment, preferably, the line safety hazard warning in S5 is a line safety hazard warning based on image recognition,
the method comprises the following steps:
s501, acquiring the growth condition of trees around a pole tower through a data acquisition module;
s502, automatically identifying the conducting wires and the trees of the power transmission line in a machine learning mode according to the acquired picture information;
s503, marking the shortest distance point between the wire and the tree and calculating the distance;
and S504, if the shortest distance between the wire and the tree is smaller than the required safety distance, intelligently early warning.
In order to implement storage of data information, in this embodiment, preferably, the storage manner in S5 is storage and management of multi-source heterogeneous data, the acquired data is stored by using a memory, a corresponding database is established, and management and calling are performed.
In order to realize platform management and data encryption processing, the platform is provided with a secure storage method, and the secure storage method adopts a homomorphic encryption mode to protect key service data or user privacy data. Homomorphic encryption is an encryption method that can be used that allows a particular algebraic operation on the ciphertext to obtain a result that is still encrypted, as is the same operation on the plaintext that is then encrypted. Homomorphic encryption allows operations such as retrieval, comparison, etc. to be performed on the encrypted data to produce correct results without the need to decrypt the data throughout the process. Another method for realizing storage safety is ACL control of file reading, writing and execution of Hadoop, and the file authority control is realized by combining a user-defined user group strategy.
The authority management can adopt a mainstream user authentication and authorization mechanism to carry out authority management on the big data user. The unified authority platform of the company can be adopted to carry out unified authentication and authorization management on the users.
The working principle and the using process of the invention are as follows:
the method comprises the following steps of firstly, acquiring the existing data information from a big data network: acquiring data information through a communication network, acquiring big data from a cloud platform, and calculating the big data information;
secondly, inputting the big data information into a digital model for simulation: inputting big data information into a digital model, then performing simulation, and performing calculation processing through cloud computing to improve the accuracy of the data information;
thirdly, acquiring data information through actual physical equipment: the data information of the power transmission line is acquired through the data transmission network, and the data information is calculated, so that the accuracy of the data information is improved;
fourthly, inputting the acquired data information into the digital model: inputting the data information into the digital model to realize the simulation calculation processing of the digital model, and then comparing the acquired data information with the acquired big data information;
and fifthly, judging the system through the data information calculated by the digital model: and judging the data information obtained by the calculation of the digital model, taking the judged data information as a detection value of the line safety hazard early warning, returning the data information to maintain and maintain the system, storing and processing the data information, and displaying the result on the platform.
Example two:
referring to fig. 2, the platform is electrically connected with a communicator, the communicator is in communication connection with a big data network through an intelligent gateway, the communicator is used for acquiring data information on the network, a calculation processing module is electrically connected between the communicator and the platform, the calculation processing module includes data acquisition, data classification, data filtering, data conversion and data gain, the data acquisition is used for acquiring data of the big data network and data information of the power transmission line, the data classification is used for classifying the data information, the data filtering is used for filtering the data information, the data conversion is used for adaptively converting data types, and the data gain is used for amplifying the data information, the platform is electrically connected with a display for displaying data information and a control key for controlling and adjusting the data information, and is electrically connected with an indicator light for indicating the running state and an alarm for alarming data failure, the communicator is also used for being connected with a cloud calculator and physical equipment in a communication way, so as to realize high-speed calculation of the data information and acquire the detection data information of the physical equipment, the physical equipment comprises a camera, an unmanned inspection machine, a voltage detection sensor, a current detection sensor, a temperature sensor, an environment sensor, a humidity sensor, a basic geographic data collector, a satellite image, an oblique photography data collector, a GIM digital power grid model data transmitter and a laser point cloud power grid engineering design data transmitter, and the platform is also electrically connected with a memory for storing the data information, the memory adopts a storage device for storing and managing multi-source heterogeneous data, and is also provided with a data encryption processing module, so that a ciphertext is kept to be subjected to specific algebraic operation to obtain a result which is still encrypted, and the operation is the same as that of a plaintext and then the result is encrypted. Homomorphic encryption allows operations such as retrieval, comparison, etc. to be performed on the encrypted data to produce correct results without the need to decrypt the data throughout the process. The other method for the storage safety is ACL control of file reading, writing and execution of Hadoop, and the file authority control is realized by combining a user-defined user group strategy; the platform further comprises an LM339 comparator or an LM393 comparator, wherein the LM339 comparator or the LM393 comparator is used for comparing big data information with acquired data information, judging errors of the data information and achieving implementation protection of the calculation power transmission system.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The transmission line data management method based on the digital twin is characterized by comprising the following steps:
s1, acquiring existing data information from a big data network: acquiring data information through a communication network, acquiring big data from a cloud platform, and calculating the big data information;
s2, inputting big data information into a digital model for simulation: inputting big data information into a digital model, then performing simulation, and performing calculation processing through cloud computing to improve the accuracy of the data information;
s3, acquiring data information through actual physical equipment: the data information of the power transmission line is acquired through the data transmission network, and the data information is calculated, so that the accuracy of the data information is improved;
s4, inputting the acquired data information into the digital model: inputting the data information into the digital model to realize the simulation calculation processing of the digital model, and then comparing the acquired data information with the acquired big data information;
s5, judging the system through data information calculated by a digital model: and judging the data information obtained by the calculation of the digital model, taking the judged data information as a detection value of the line safety hazard early warning, returning the data information to maintain and repair the system, storing and processing the data information, and displaying the result on a platform.
2. The digital twin-based transmission line data management method according to claim 1, characterized in that: the AI learning technology is adopted for acquiring the big data in the cloud platform in the S1, the data are selected and acquired through a neural network algorithm, and the power transmission line is constructed through the cloud big data information.
3. The digital twin-based transmission line data management method according to claim 2, characterized in that: the AI learning technology is used for combining a large amount of data with ultra-strong operation processing capacity and an intelligent algorithm, so that the cloud big data is identified and calculated, model construction is completed, and the calculation process of the neural network algorithm is as follows:
s101, taking a sample from the sample set (A) i ,B i ) Wherein A is i Is an input, B i Is the desired output;
s102, calculating the actual output O of the network;
s103, calculating D = B i -O；
S104, adjusting the weight matrix W according to D;
and S105, repeating the process for each sample until the error does not exceed a fixed range for the whole sample set.
4. The digital twin-based transmission line data management method according to claim 1, characterized in that: the digital model in the S2 adopts an MATLAB model, the MATLAB model adopts multiple regression linear calculation, and the multiple regression linear calculation is carried out by using a regression () function;
f (x 1, x2, x 3) = a1+ a2 x1+ a3 x2+ a4 x3 is a multivariate linear regression function;
the solving method comprises the following steps:
x1＝[...]；x2＝[...]；x3＝[...]；
X＝[ones(n,1)x1x2x3]；
y＝[...]；
a = regress (y, X); a is the fitting coefficient of the multiple linear regression function, and x1, x2, and x3 are the input values of the multiple linear regression function.
5. The digital twin-based transmission line data management method according to claim 1, characterized in that: the calculation processing comprises data acquisition, data classification, data filtering, data conversion and data gain, wherein the data acquisition is used for acquiring data of a big data network and data information of a power transmission line, the data classification is used for classifying the data information, the data filtering is used for filtering the data information, the data filtering adopts an amplitude limiting filtering algorithm, a median filtering algorithm, an arithmetic mean filtering algorithm, a low-pass filtering algorithm or an IIR (infinite impulse response) digital filtering algorithm to filter clutter, the data conversion is used for adaptively converting data types, and the data gain is used for amplifying the data information.
6. The digital twin-based transmission line data management method according to claim 1, characterized in that: the data information of the power transmission line in the S3 is acquired through various sensors or equipment, and the sensors or the equipment comprise a camera, an unmanned patrol inspection machine, a voltage detection sensor, a current detection sensor, a temperature sensor, an environment sensor, a humidity sensor, a basic geographic data acquisition unit, a satellite image, an oblique photography data acquisition unit, a GIM digital power grid model data transmitter and a laser point cloud power grid engineering design data transmitter.
7. The digital twin-based transmission line data management method according to claim 1, characterized in that: the data information collected in the step S4 is compared with the acquired big data information by using an LM339 comparator or an LM393 comparator, and the LM339 comparator or the LM393 comparator is used for comparing the big data information with the collected data information, determining an error of the data information, and implementing protection of the computing power transmission system.
8. The digital twin-based transmission line data management method according to claim 1, characterized in that: the line safety risk early warning in the S5 is based on image recognition, and the specific method comprises the following steps:
s501, obtaining the growth condition of trees around the tower through a data acquisition module;
s502, automatically identifying the conducting wire and the tree of the power transmission line in a machine learning mode according to the acquired picture information;
s503, marking the shortest distance point between the wire and the tree and calculating the distance;
and S504, if the shortest distance between the wire and the tree is smaller than the required safety distance, intelligently early warning.
9. The digital twin-based transmission line data management method according to claim 1, characterized in that: the storage processing in the S5 adopts the storage and management of multi-source heterogeneous data, and the data storage and management memory stores the acquired data, establishes a corresponding database, and manages and calls the database.
10. The digital twin-based transmission line data management method according to claim 1, characterized in that: the platform data of the platform in the S5 has the characteristics of multisource isomerism, wide distribution and dynamic growth, the platform has a safe storage method, and the safe storage method adopts a homomorphic encryption mode to protect key business data or user privacy data.
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