CN117291554B - Cloud network collaborative operation method and system in power industry - Google Patents
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Abstract
The invention provides a cloud network collaborative operation method and a cloud network collaborative operation system in the power industry, wherein the method comprises the following steps: receiving an instruction for adding a target power transmission line into a cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer; the cloud network collaborative management platform receives basic information and extension information of a target power transmission line; uploading basic information to a first cloud resource pool, and obtaining physical trust through calculation in the first cloud resource pool; uploading the expansion information to a second cloud resource pool, and obtaining external trust through calculation in the second cloud resource pool; inputting the physical trust degree and the external trust degree into a data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of a target power transmission line; and comparing the operation data with a preset threshold value, and if the operation data is larger than the preset threshold value, adding the target power transmission line into the cloud network collaborative management platform. The method and the device can improve the accuracy of operation evaluation of the power transmission line.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a cloud network collaborative operation method and system in the power industry.
Background
The expansion of the grid results in the addition of transmission lines, which are distributed in complex and remote areas and are susceptible to natural disasters, thus causing faults and electrical accidents. In order to improve the operation service capability of the power grid company, the urgent problem of how to accurately operate and evaluate the power transmission line needs to be solved before the power transmission line is brought into the cloud network collaborative management platform.
Therefore, a cloud network collaborative operation method and a cloud network collaborative operation system in the power industry need to be provided to solve the technical problem.
Disclosure of Invention
The embodiment of the invention aims to provide a cloud network collaborative operation method and a cloud network collaborative operation system in the power industry, which can improve the accuracy of operation evaluation on a power transmission line, and specifically adopts the following technical scheme:
in a first aspect of the embodiment of the present invention, a cloud network collaborative operation method in a power industry is provided, where the method includes:
Receiving an instruction for adding a target power transmission line into a cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer;
The cloud network collaborative management platform receives basic information and extension information of the target power transmission line;
uploading the basic information to a first cloud resource pool, and obtaining physical trust through calculation in the first cloud resource pool;
Uploading the expansion information to a second cloud resource pool, and obtaining external trust through calculation in the second cloud resource pool;
Inputting the physical trust degree and the external trust degree into the data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of the target transmission line;
And comparing the operation data with a preset threshold, and if the operation data is larger than the preset threshold, adding the target power transmission line into the cloud network collaborative management platform.
Further, the basic information includes: target tower coordinates, target tower height, and target tower structure.
Further, the extension information includes: meteorological parameters, air humidity, wind and geographical region type.
Further, the uploading the basic information to a first cloud resource pool, and obtaining the physical trust degree in the first cloud resource pool through calculation includes:
acquiring an existing power transmission line similar to the target tower structure in the first cloud resource pool according to the target tower structure;
inquiring historical operation data of the existing transmission line;
Screening out high-quality existing power transmission lines according to the historical operation data, and inquiring average tower coordinates and average tower heights corresponding to the high-quality existing power transmission lines;
and obtaining the physical trust degree according to the numerical relation among the target tower coordinates and the target tower height, the average tower coordinates and the average tower height.
Further, the obtaining, according to the target tower structure, an existing power transmission line similar to the target tower structure in the first cloud resource pool includes:
acquiring a first structural image of the target tower;
acquiring a second structural image of any one of the existing power transmission lines;
Comparing the similarity between the first structural image and the second structural image by adopting an SSIM algorithm;
And if the similarity is higher than a similarity threshold, the corresponding existing transmission line is an existing transmission line similar to the target tower structure.
Further, the obtaining the physical trust according to the numerical relation between the target tower coordinates and the target tower heights, the average tower coordinates and the average tower heights includes:
The physical confidence level Q1 is calculated according to the following formula:
；
Wherein, the method comprises the following steps of , ) The coordinates of the target tower are represented,Representing the height of a target tower, ) The average tower coordinates are represented as such,And the average tower height is represented, and alpha and beta are weight parameters.
Further, the uploading the extension information to a second cloud resource pool, and obtaining the external trust degree in the second cloud resource pool through calculation includes:
Acquiring an existing power transmission line corresponding to the geographic region type in the second cloud resource pool according to the geographic region type;
querying historical fault data of the existing transmission line; the historical fault data comprises fault types and fault frequencies;
And inputting the fault type, the fault frequency and the meteorological parameters into a neural network model to obtain external trust.
Further, the inputting the fault type, the fault frequency and the meteorological parameter into a neural network model to obtain external trust degree includes:
inputting the meteorological parameters into a deep learning model to obtain a model output result;
And inputting the fault type, the fault frequency and the model output result into a multi-layer perceptron to obtain external trust.
Further, the inputting the physical trust and the external trust to the data operation layer, fusing the physical trust and the external trust, obtaining the operation data of the target transmission line includes:
the operation data S is calculated according to the following formula:
S=Q1·Q2；
Where Q1 represents physical confidence and Q2 represents external confidence.
In yet another aspect of the embodiments of the present invention, there is provided a cloud network co-operation system in the power industry, the system including:
the instruction acquisition module is used for receiving an instruction for adding the target transmission line into the cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer;
the information receiving module is used for receiving basic information and extension information of the target power transmission line by the cloud network collaborative management platform;
the first calculation module is used for uploading the basic information to a first cloud resource pool, and calculating the physical trust degree in the first cloud resource pool;
The second calculation module is used for uploading the expansion information to a second cloud resource pool, and calculating the external trust in the second cloud resource pool;
The third calculation module is used for inputting the physical trust degree and the external trust degree into the data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of the target power transmission line;
And the data processing module is used for comparing the operation data with a preset threshold value, and if the operation data is larger than the preset threshold value, adding the target power transmission line into the cloud network collaborative management platform.
From the above, the implementation of the invention brings at least the following beneficial effects:
(1) The method comprises the steps of receiving an instruction for adding a target power transmission line into a cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer; the cloud network collaborative management platform receives basic information and extension information of the target power transmission line; respectively uploading basic information and extension information to a first cloud resource pool and a second cloud resource pool, and obtaining external trust and external trust through calculation; inputting the physical trust and the external trust into the data operation layer for fusion to obtain operation data of the target transmission line; and determining whether to add the target transmission line to the cloud network collaborative management platform according to the operation data. And the cloud network collaborative operation management efficiency and the power grid operation safety are improved through fusion calculation of the target power transmission line related data.
(2) And acquiring the high-quality existing power transmission line similar to the target tower structure, and calculating the physical trust degree of the target power transmission line according to the average tower coordinate and the numerical relation between the average tower height corresponding to the high-quality existing power transmission line and the target tower coordinate and the height. Thereby improving the accuracy of the calculation of the operational data.
(3) External data of the target power transmission line and historical fault data of the existing power transmission line are obtained, a deep learning model and a multi-layer perceptron are introduced, and external trust of the target power transmission line is calculated. Thereby improving the accuracy of the calculation of the operational data.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an application scenario schematic diagram of a cloud network co-operation system in the power industry provided by the embodiment of the invention;
Fig. 2 is a schematic flow chart of a cloud network cooperative operation method in the power industry according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a cloud network co-operation system in the power industry according to the embodiment of the present invention;
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a cloud network collaborative operation method and a cloud network collaborative operation system in the power industry. Referring to fig. 1, fig. 1 is a schematic application scenario diagram of a cloud network co-operation system in the power industry according to an embodiment of the present invention, where the system may include a terminal and a server. The cloud network collaborative operation method in the power industry can be realized through a terminal or a server.
As shown in fig. 1, the terminal and the server are connected through a network, for example, a wired or wireless network connection. The terminal may include, but is not limited to, mobile terminals such as mobile phones and tablets, and fixed terminals such as computers, inquiry machines and advertising machines, where applications of various network platforms are installed. The server provides various business services for the user, including a service push server, a user recommendation server and the like.
It should be noted that, the schematic view of the application scenario of the cloud network co-operation system in the power industry shown in fig. 1 is only an example, and the terminal, the server and the application scenario described in the embodiment of the present invention are for more clearly describing the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention, and as a person of ordinary skill in the art can know that, with the evolution of the system and the appearance of a new service scenario, the technical solution provided by the embodiment of the present invention is applicable to similar technical problems.
Wherein the terminal may be configured to:
Receiving an instruction for adding a target power transmission line into a cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer;
The cloud network collaborative management platform receives basic information and extension information of the target power transmission line;
uploading the basic information to a first cloud resource pool, and obtaining physical trust through calculation in the first cloud resource pool;
Uploading the expansion information to a second cloud resource pool, and obtaining external trust through calculation in the second cloud resource pool;
Inputting the physical trust degree and the external trust degree into the data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of the target transmission line;
And comparing the operation data with a preset threshold, and if the operation data is larger than the preset threshold, adding the target power transmission line into the cloud network collaborative management platform.
It should be noted that, the steps of the cloud network collaborative operation method in the power industry may be executed by the server by the terminal.
Fig. 2 is a schematic flow chart of a cloud network collaborative operation method and a system in the power industry, which are provided by the embodiment of the invention, as shown in fig. 2, and the cloud network collaborative operation method and the system in the power industry include the following steps:
step 201, receiving an instruction of adding a target transmission line to a cloud network collaborative management platform.
The cloud network collaborative management platform may include a first cloud resource pool, a second cloud resource pool, and a data operation layer.
In some embodiments, the system or operator may receive commands or instructions from outside for the purpose of incorporating a particular transmission line into the power operation management system of the cloud network collaborative management platform.
In some embodiments, the cloud network collaborative management platform is divided into three main components:
A first cloud resource pool: the cloud computing resource pool is used for processing basic information of a target power transmission line and executing computation to acquire physical trust. In the first cloud resource pool, data processing and analysis for the power transmission line are performed to evaluate the physical reliability of the power transmission line.
And a second cloud resource pool: the cloud computing resource pool is used for processing the expansion information of the target power transmission line and executing computation to acquire external trust. The second cloud resource pool may involve processing of meteorological data, geographic information, and other external factors to assess the external environmental conditions and trustworthiness of the transmission line.
Data operation layer: is the core part of the whole collaborative management platform, and receives and fuses the trust data from the first cloud resource pool and the second cloud resource pool. In the data operation layer, the physical trust level and the external trust level are combined together to generate comprehensive operation data about the target transmission line. The above data may be used to make decisions such as whether to incorporate the transmission line into a cloud network collaborative management platform.
In summary, receiving an instruction to add a target transmission line to a cloud network collaborative management platform, a series of calculation and data processing processes are initiated to evaluate and manage the operational status and reliability of a particular transmission line. The operation efficiency of the power industry and the reliability of the power grid are improved.
Step 202, the cloud network collaborative management platform receives basic information and extension information of the target power transmission line.
The base information may include, among other things, target tower coordinates, target tower height, and target tower structure. In some embodiments, the core information about the target transmission line is generally used to evaluate the physical state and reliability of the line. The target tower coordinates are geographic coordinates of the tower or support structure on the power line, typically expressed in terms of longitude and latitude. This information can be used to determine the location of the tower. The target tower height represents the height of the tower or support structure on the transmission line, which is important when considering the physical confidence of the line. The target tower structure describes the type, shape and material of the tower or support structure, which is critical to determining the physical reliability and load carrying capacity of the line.
The extended information may include, among other things, meteorological parameters, air humidity, wind power, and geographic region type. In some embodiments, the extended information is typically used to consider external environmental factors of the transmission line to more fully evaluate the reliability of the line. In some embodiments, the weather parameters include weather conditions such as temperature, humidity, air pressure, etc., which may affect the performance of the transmission line, especially in severe weather conditions. Air humidity can affect electrical equipment on the power line, such as insulators. The strength and direction of the wind can affect the stability of the towers and wires of the transmission line. The geographical area type represents a geographical environment of the place where the transmission line is located, such as a city, a mountain area, a desert, etc. Different geographical areas have different impact on the maintenance and reliability of the line.
Step 203, uploading the basic information to a first cloud resource pool, and obtaining the physical trust degree in the first cloud resource pool through calculation.
Optionally, step 203 may further include:
acquiring an existing power transmission line similar to the target tower structure in the first cloud resource pool according to the target tower structure;
inquiring historical operation data of the existing transmission line;
Screening out high-quality existing power transmission lines according to the historical operation data, and inquiring average tower coordinates and average tower heights corresponding to the high-quality existing power transmission lines;
and obtaining the physical trust degree according to the numerical relation among the target tower coordinates and the target tower height, the average tower coordinates and the average tower height.
In some embodiments, base information about the target transmission line (e.g., target tower coordinates, target tower height, and target tower structure) may be uploaded to the first cloud resource pool. This resource pool is a cloud computing environment for storing and processing data related to the transmission line. Once the underlying information is uploaded to the first cloud resource pool, the next operation involves calculating a degree of physical trust. This confidence reflects the physical reliability or health of the target transmission line. The calculation of the physical confidence level may be based on different factors, depending on the target tower structure.
In some embodiments, the system may attempt to find an existing transmission line that is similar to the target tower structure. This may help the system to better understand the physical characteristics of the target line. The system may also query historical operational data with existing transmission lines, including past performance of the lines, fault conditions, etc. These data help to assess the health of the line. The system screens out high quality lines from existing transmission lines that have performed well or are better maintained in the past. Finally, the system calculates the physical confidence level of the target transmission line using the target tower coordinates, the target tower height, and the numerical relationship between the average tower coordinates and the average tower height. This confidence level may help the decision maker to understand the health of the line in order to make the corresponding operational decision. By the mode, the operation safety and efficiency of the power grid can be improved.
Optionally, the step of obtaining, according to the target tower structure, an existing transmission line similar to the target tower structure in the first cloud resource pool includes:
acquiring a first structural image of the target tower;
acquiring a second structural image of any one of the existing power transmission lines;
Comparing the similarity between the first structural image and the second structural image by adopting an SSIM algorithm;
And if the similarity is higher than a similarity threshold, the corresponding existing transmission line is an existing transmission line similar to the target tower structure.
In some embodiments, a structural image in the target transmission line may be acquired with respect to the target tower. This image is a photograph or drawing showing the appearance and structural details of the tower. Next, it is necessary to select a transmission line among the existing transmission lines and acquire a structural image of the line. This line is considered similar to the target tower structure, but not necessarily exactly the same line.
In some embodiments, a Structural Similarity Index (SSIM) algorithm may be used to compare the first structural image (target tower) and the second structural image (existing transmission line). SSIM is an image quality assessment method for measuring the degree of similarity between two images. If the SSIM value is high enough, it is stated that the two images are very similar in structure.
In some embodiments, a similarity threshold may be set that is used to determine when two structural images are considered sufficiently similar. If the SSIM value is above this threshold, the structure of the target tower may be considered very similar to the structure of the selected existing transmission line.
And finally, comparing the calculated SSIM value with a similarity threshold value to judge whether the selected existing transmission line is similar to the structure of the target tower. If the similarity is above the threshold, it may be concluded that the selected existing transmission line is similar in structure to the target tower.
By the method, whether the existing transmission line has the characteristics similar to the structure of the target tower or not is determined, the physical trust degree of the target transmission line is evaluated, and the physical trust degree can be used as an index for decision making, so that the reliability of the power system is improved.
Optionally, the step of obtaining the physical trust level according to the numerical relation between the target tower coordinates and the target tower heights, the average tower coordinates and the average tower heights includes:
The physical confidence level Q1 is calculated according to the following formula:
；
Wherein, The physical reliability of the target transmission line is expressed by the physical trust degree, (-), ) The coordinates of the target tower are represented,Representing the height of a target tower, ) The average tower coordinates are represented as such,The average tower height is represented, and alpha and beta are weight parameters for balancing the importance of coordinates and height in the calculation. They determine how much the coordinates and height contribute to the physical confidence. Typically, the values of these parameters will be determined according to the needs of a particular application.
The purpose of this formula is to calculate the physical confidence level from the numerical differences between the coordinates and altitude of the target tower and the average coordinates and altitude of the known existing transmission lines. Specifically:
The difference between the target tower coordinates and the known average coordinates is measured. This is represented by the euclidean distance, which takes into account the deviation between longitude and latitude. The difference between the target tower height and the known average height is measured.
It will be appreciated that a combination of these two terms is used to comprehensively consider the deviations of coordinates and altitude to calculate the physical confidence level of the target transmission line. If the value of Q1 is higher, it is indicated that the physical characteristics of the target transmission line are closer to the known average characteristics, so that more reliable operation and maintenance can be performed. The selection of the weight parameters α and β may be adjusted according to the requirements of the specific application, so as to reflect the relative importance of different factors, and the expansion information is uploaded to a second cloud resource pool, where the external trust is obtained through calculation.
Optionally, step 204 may include:
Acquiring an existing power transmission line corresponding to the geographic region type in the second cloud resource pool according to the geographic region type;
querying historical fault data of the existing transmission line; the historical fault data comprises fault types and fault frequencies;
And inputting the fault type, the fault frequency and the meteorological parameters into a neural network model to obtain external trust.
In some embodiments, the system may obtain, from the second cloud resource pool, an existing transmission line corresponding to the geographic region type according to the geographic region type. This means that the system will select transmission lines that are already present in the geographical area, which lines will operate under similar environmental conditions.
In some embodiments, the system may query the selected existing transmission line for historical fault data. The historical fault data includes the type of fault and the frequency of faults that occurred in the past on the lines. This information helps to understand the past performance and reliability of these lines.
In some embodiments, the system inputs the obtained fault type, fault frequency, and meteorological parameters into a neural network model. This neural network model is a computational model that can be used to analyze and process complex data relationships.
In some embodiments, the neural network model will process the input data and output a value of external confidence. This value represents the reliability of the target transmission line under the influence of historical fault data and meteorological parameters under a specific geographical region type. The value of the external confidence level may be determined based on the output of the model, with higher values generally representing higher reliability.
Optionally, the step of inputting the fault type, the fault frequency and the meteorological parameter into a neural network model to obtain external trust degree includes:
inputting the meteorological parameters into a deep learning model to obtain a model output result;
And inputting the fault type, the fault frequency and the model output result into a multi-layer perceptron to obtain external trust.
In some embodiments, the system may input the collected weather parameters into a deep learning model. The deep learning model is an artificial neural network for learning and understanding complex data relationships. In this context, the task of the deep learning model is to predict some relevant outcome from the entered meteorological parameters, which may be information about the reliability of the transmission line.
In some embodiments, the deep learning model will process the input meteorological parameters and generate a model output. The result is a numerical value representing a prediction based on the weather parameter, or a probability distribution representing the probability of the different results. This output reflects the impact of meteorological conditions on the reliability of the target transmission line.
In some embodiments, the fault type, fault frequency, and model output results may be input to a multi-layer perceptron (MLP): next, the system inputs the fault type, the fault frequency, and the output result from the previous deep learning model in the historical fault data together into a multi-layer perceptron (MLP). MLP is a common neural network architecture for processing and fusing different types of data.
In some embodiments, the MLP will process the incoming data and generate results of external confidence. This external confidence reflects the combined impact of a variety of factors, including historical fault data, weather conditions, and predictions of the deep learning model. The value of the external confidence level may be determined based on the output of the MLP, with higher values generally representing higher reliability.
In summary, the process calculates the external confidence level by comprehensively considering the meteorological parameters, the historical fault data and the prediction result of the model through the deep learning model and the MLP. The value of the external confidence level is helpful to evaluate the reliability of the target transmission line under external environmental conditions.
Step 205, inputting the physical trust degree and the external trust degree to the data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of the target transmission line.
Optionally, step 205 may include:
the operation data S is calculated according to the following formula:
S=Q1·Q2；
Where Q1 represents physical confidence and Q2 represents external confidence.
In some embodiments, the system may pass the previously calculated physical confidence level (Q1) and external confidence level (Q2) to the data operation layer. The data operation layer is a computing and decision-making environment for integrating various information to support operation decisions.
In some embodiments, physical and external trust levels will be fused together in the data operation layer. The fusion process can be designed by adopting different methods according to specific service requirements and algorithms. The method aims to comprehensively consider the influence of physical characteristics and external environmental factors on a target power transmission line so as to more comprehensively know the operation condition of the line.
Finally, by fusing the physical trust and the external trust, the system can obtain the operation data of the target transmission line. This operational data reflects the line status and reliability after a combination of physical and external factors.
In some embodiments, in the calculation formula of the operation data S, S represents operation data, Q1 represents physical trust, and Q2 represents external trust. This formula is used to multiply the physical confidence level with the external confidence level to obtain the final operational data. This formula can be designed according to specific business requirements to determine the weight and extent of impact of physical and external factors in the operational data.
In summary, by the above manner, the physical trust degree and the external trust degree can be comprehensively considered to calculate the operation data of the target transmission line, so that the power company is helped to better know the state and the reliability of the line, and the operation decision is supported.
And 206, comparing the operation data with a preset threshold, and if the operation data is larger than the preset threshold, adding the target power transmission line into the cloud network collaborative management platform.
In some embodiments, the system may obtain operation data, and reflect the operation state and reliability of the target transmission line by fusing comprehensive data obtained by the physical trust level and the external trust level. The system then compares this operational data to a pre-set threshold.
In some embodiments, during the comparison, the system checks whether the operational data is greater than a preset threshold. This preset threshold is set according to specific traffic requirements and operating criteria and typically represents a lower limit on the acceptable reliability level. If the operational data exceeds this threshold, it is indicative that the operational status of the target transmission line meets or exceeds the expected reliability criteria.
In some embodiments, if the operational data is greater than a preset threshold, the system will decide to add the target transmission line to the cloud network collaborative management platform. This means that the line will be incorporated into a management and monitoring system so that the utility can track and manage its operating status in real time and take necessary maintenance and repair measures.
In summary, the operation data obtained through calculation is compared with the preset threshold value in the above manner, so that the power company is assisted in determining the reliability state of the target transmission line. If the reliability of the line meets or exceeds the expected criteria, it will be incorporated into a collaborative management platform to ensure stable and reliable operation of the power system.
Comprehensively, the cloud network collaborative operation management efficiency and the safety of the power grid operation can be improved, and the operation state of the target power transmission line can be accurately estimated.
In order to implement the above method embodiments, the present invention further provides a cloud network co-operation system in the power industry, and fig. 3 shows a schematic structural diagram of the cloud network co-operation system in the power industry provided by the present invention, where the system includes:
The instruction acquisition module 301 is configured to receive an instruction for adding a target power transmission line to the cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer;
the information receiving module 302 is configured to receive basic information and extension information of the target power transmission line by using the cloud network collaborative management platform;
the first calculation module 303 is configured to upload the basic information to a first cloud resource pool, where the physical trust degree is obtained through calculation;
The second computing module 304 is configured to upload the extension information to a second cloud resource pool, where external trust is obtained through computing;
A third calculation module 305, configured to input the physical trust level and the external trust level to the data operation layer, and fuse the physical trust level and the external trust level to obtain operation data of the target transmission line;
And the data processing module 306 is configured to compare the operation data with a preset threshold, and if the operation data is greater than the preset threshold, add the target transmission line to the cloud network collaborative management platform.
In some embodiments, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing relevant data of the image acquisition device. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor is used for realizing a cloud network cooperative operation method and system in the power industry.
In some embodiments, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input system connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a cloud network co-operation method and system for the power industry. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input system of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In some embodiments, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In some embodiments, a computer readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and for brevity of description, all of the possible combinations of the technical features of the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
In summary, the cloud network collaborative operation method in the power industry provided by the invention comprises the following steps:
Receiving an instruction for adding a target power transmission line into a cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer;
The cloud network collaborative management platform receives basic information and extension information of the target power transmission line;
uploading the basic information to a first cloud resource pool, and obtaining physical trust through calculation in the first cloud resource pool;
Uploading the expansion information to a second cloud resource pool, and obtaining external trust through calculation in the second cloud resource pool;
Inputting the physical trust degree and the external trust degree into the data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of the target transmission line;
And comparing the operation data with a preset threshold, and if the operation data is larger than the preset threshold, adding the target power transmission line into the cloud network collaborative management platform.
Claims (4)
1. The cloud network collaborative operation method in the power industry is characterized by comprising the following steps of:
Receiving an instruction for adding a target power transmission line into a cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer;
the cloud network collaborative management platform receives basic information and extension information of the target power transmission line; the basic information includes: target tower coordinates, target tower height, and target tower structure; the extension information includes: meteorological parameters, air humidity, wind and geographic region type;
Uploading the basic information to a first cloud resource pool, and obtaining the physical trust degree in the first cloud resource pool through calculation, wherein the method comprises the following steps: acquiring an existing power transmission line similar to the target tower structure in the first cloud resource pool according to the target tower structure; inquiring historical operation data of the existing transmission line; screening out high-quality existing power transmission lines according to the historical operation data, and inquiring average tower coordinates and average tower heights corresponding to the high-quality existing power transmission lines; obtaining the physical trust degree according to the numerical relation between the target tower coordinates and the target tower heights, the average tower coordinates and the average tower heights, wherein the physical trust degree comprises the following steps: the physical confidence level Q1 is calculated according to the following formula: Where (x D,yD) represents the target tower coordinates, h D represents the target tower height,/> Representing average tower coordinates,/>Representing the average tower height, wherein alpha and beta are weight parameters;
Uploading the expansion information to a second cloud resource pool, and obtaining external trust through calculation in the second cloud resource pool, wherein the method comprises the following steps: acquiring an existing power transmission line corresponding to the geographic region type in the second cloud resource pool according to the geographic region type; querying historical fault data of the existing transmission line; the historical fault data comprises fault types and fault frequencies; inputting the fault type, the fault frequency and the meteorological parameters into a neural network model to obtain external trust, wherein the method comprises the following steps of: inputting the meteorological parameters into a deep learning model to obtain a model output result; inputting the fault type, the fault frequency and the model output result into a multi-layer perceptron to obtain external trust;
Inputting the physical trust degree and the external trust degree into the data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of the target transmission line;
And comparing the operation data with a preset threshold, and if the operation data is larger than the preset threshold, adding the target power transmission line into the cloud network collaborative management platform.
2. The method for cloud network co-operation in the power industry according to claim 1, wherein the obtaining, according to the target tower structure, an existing power transmission line in the first cloud resource pool similar to the target tower structure includes:
acquiring a first structural image of the target tower;
acquiring a second structural image of any one of the existing power transmission lines;
Comparing the similarity between the first structural image and the second structural image by adopting an SSIM algorithm;
And if the similarity is higher than a similarity threshold, the corresponding existing transmission line is an existing transmission line similar to the target tower structure.
3. The cloud network collaborative operation method of the power industry according to claim 1, wherein the inputting the physical trust and the external trust to the data operation layer, fusing the physical trust and the external trust, obtaining operation data of the target transmission line, includes:
the operation data S is calculated according to the following formula:
S＝Q1·Q2
Where Q1 represents physical confidence and Q2 represents external confidence.
4. The cloud network collaborative operation system of the power industry is characterized in that the system comprises:
the instruction acquisition module is used for receiving an instruction for adding the target transmission line into the cloud network collaborative management platform; the cloud network collaborative management platform comprises a first cloud resource pool, a second cloud resource pool and a data operation layer;
The information receiving module is used for receiving basic information and extension information of the target power transmission line by the cloud network collaborative management platform; the basic information includes: target tower coordinates, target tower height, and target tower structure; the extension information includes: meteorological parameters, air humidity, wind and geographic region type;
The first computing module is used for uploading the basic information to a first cloud resource pool, obtaining physical trust through computing in the first cloud resource pool, and is also used for: acquiring an existing power transmission line similar to the target tower structure in the first cloud resource pool according to the target tower structure; inquiring historical operation data of the existing transmission line; screening out high-quality existing power transmission lines according to the historical operation data, and inquiring average tower coordinates and average tower heights corresponding to the high-quality existing power transmission lines; obtaining the physical trust degree according to the numerical relation between the target tower coordinates and the target tower heights, the average tower coordinates and the average tower heights, wherein the physical trust degree comprises the following steps: the physical confidence level Q1 is calculated according to the following formula: Where (x D,yD) represents the target tower coordinates, h D represents the target tower height,/> Representing average tower coordinates,/>Representing the average tower height, wherein alpha and beta are weight parameters;
The second computing module is used for uploading the extension information to a second cloud resource pool, and obtaining external trust through computing in the second cloud resource pool, and is also used for: acquiring an existing power transmission line corresponding to the geographic region type in the second cloud resource pool according to the geographic region type; querying historical fault data of the existing transmission line; the historical fault data comprises fault types and fault frequencies; inputting the fault type, the fault frequency and the meteorological parameters into a neural network model to obtain external trust, wherein the method comprises the following steps of: inputting the meteorological parameters into a deep learning model to obtain a model output result; inputting the fault type, the fault frequency and the model output result into a multi-layer perceptron to obtain external trust;
The third calculation module is used for inputting the physical trust degree and the external trust degree into the data operation layer, and fusing the physical trust degree and the external trust degree to obtain operation data of the target power transmission line;
And the data processing module is used for comparing the operation data with a preset threshold value, and if the operation data is larger than the preset threshold value, adding the target power transmission line into the cloud network collaborative management platform.
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