Broadband loss test and data synchronization method for transformer

Abstract

The invention relates to a broadband loss test and data synchronization method for a transformer, which belongs to the technical field of test data synchronization methods, and comprises the following steps of: setting a data transmission quantity threshold value by acquiring the data transmission quantity of a data synchronization network of a current timestamp; the method comprises the steps of obtaining a data transmission quantity of a data synchronization network of a current timestamp and a data transmission quantity threshold value, performing evaluation according to the data transmission quantity of the data synchronization network of the current timestamp, obtaining an evaluation analysis result, finally obtaining related early warning information or an information transmission plan according to the analysis result, and performing test data synchronization optimization on the information transmission plan through a genetic algorithm. According to the method, the information transmission quantity of the data synchronization network is evaluated according to the network fluctuation data, so that the test data transmitted in unit time is dynamically adjusted by searching the optimal solution through the genetic algorithm, the test data can be synchronized according to the preset mode, the test data is prevented from being lost, and the test data synchronization rate is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of test data synchronization methods, and in particular to a broadband loss test and data synchronization method for a transformer. Background Art

[0002] The increase in operating frequency will lead to an increase in the core loss of the high-frequency transformer, resulting in a serious temperature rise inside the core, which reduces the operating efficiency of the equipment. Therefore, broadband loss testing of transformers is one of the important experiments. Nowadays, due to the development of Internet of Things technology, after the test, the test data is transmitted to the test platform or remote terminal through data synchronization. However, since the transformer test may involve hundreds or even tens of thousands of units, the synchronization of data may be affected by network fluctuations, which may cause data synchronization abnormalities or even only synchronize partial data, making it impossible to upload the test data effectively, resulting in the need for retesting. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a broadband loss test and data synchronization method for a transformer.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a transformer broadband loss test and data synchronization method, comprising the following steps:

[0006] By performing broadband loss tests on transformers, obtaining test data, and building a data synchronization network, building timestamps, obtaining test data generated by each timestamp, and obtaining network fluctuation data of the data synchronization network;

[0007] Building a data transmission volume prediction model of the data synchronization network based on the network fluctuation data, predicting the data synchronization network based on the data transmission volume prediction model of the data synchronization network, and obtaining the data transmission volume of the data synchronization network at the current timestamp;

[0008] Setting a data transmission volume threshold, and evaluating the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation analysis result;

[0009] Relevant early warning information or information transmission plan is obtained according to the analysis result, and the information transmission plan is optimized synchronously with test data through a genetic algorithm.

[0010] Furthermore, in this method, by performing a broadband loss test on the transformer, obtaining test data, building a data synchronization network, building a timestamp, obtaining test data generated by each timestamp, and obtaining network fluctuation data of the data synchronization network, specifically:

[0011] Setting a number of operating frequencies, and performing a broadband loss test on the transformer based on the operating frequencies, obtaining test data, and collecting frequency information and file size information generated by the test data within a unit time;

[0012] Constructing a data synchronization network, and calculating the total file size information within the unit time according to the frequency information and file size information generated by the test data within the unit time;

[0013] According to the total file size information within the unit time, the data synchronization network is configured to transmit information within the unit time, thereby completing the construction of the data synchronization network;

[0014] Build timestamps, obtain test data generated by each timestamp, and obtain network fluctuation data of the data synchronization network.

[0015] Furthermore, in this method, a data transmission volume prediction model of the data synchronization network is constructed based on the network fluctuation data, specifically:

[0016] Collect the data transmission volume of the data synchronization network within a unit time under each network fluctuation data, and introduce the graph neural network, taking the network fluctuation data as the first node and the data transmission volume within a unit time as the second node;

[0017] Building a data transmission volume prediction model of the data synchronization network based on a deep neural network, building a directed description relationship, and connecting the first node and the second node based on the directed description relationship to form a topological structure diagram;

[0018] Acquire a relevant adjacency matrix based on the topological structure graph, and input the relevant adjacency matrix into a data transmission volume prediction model of the data synchronization network for training;

[0019] When the loss function of the data transmission volume prediction model of the data synchronization network converges to a preset value, the model parameters of the data transmission volume prediction model of the data synchronization network are saved, and the training of the data transmission volume prediction model of the data synchronization network is completed.

[0020] Furthermore, in this method, the data synchronization network is predicted based on the data transmission volume prediction model of the data synchronization network to obtain the data transmission volume of the data synchronization network at the current timestamp, specifically:

[0021] Inputting the network fluctuation data of the data synchronization network into a data transmission volume prediction model of the data synchronization network for prediction;

[0022] The data transmission volume of the data synchronization network at the current timestamp is obtained through prediction, and the data transmission volume of the data synchronization network at the current timestamp is output.

[0023] Furthermore, in this method, a data transmission volume threshold is set, and an evaluation is performed based on the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation analysis result, specifically:

[0024] Setting a data transmission volume threshold value to determine whether the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold value;

[0025] When the data transmission volume of the data synchronization network of the current timestamp is greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for normal data transmission;

[0026] When the data transmission volume of the data synchronization network at the current timestamp is not greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for abnormal data transmission.

[0027] Furthermore, in this method, relevant early warning information or information transmission plan is obtained according to the analysis result, specifically:

[0028] When the analysis result is a data synchronization network with abnormal data transmission, relevant warning information is generated according to the data synchronization network with abnormal data transmission, and displayed and prompted in a preset manner;

[0029] When the analysis result is a data synchronization network with normal data transmission, frequency information and file size information of test data generated within a unit time are obtained;

[0030] An information transmission plan is generated for a data synchronization network for normal data transmission according to the frequency information and file size information generated by the test data within the unit time, and the information transmission plan is output.

[0031] Furthermore, in this method, the information transmission plan is optimized for test data synchronization by using a genetic algorithm, specifically:

[0032] Obtain the data transmission volume of the data synchronization network at the current timestamp, introduce a genetic algorithm, and set a genetic generation based on the genetic algorithm to obtain the frequency information and file size information of the test data generated within a unit time in the information transmission plan, and sort the test data in the order of time of generation;

[0033] By sorting, test data sorted in chronological order is obtained, and data synchronization combinations within a unit time are initialized according to the test data sorted in chronological order, and a random number of test data are selected for data synchronization;

[0034] Obtain file size information of each test data in the data synchronization combination, count the file size information of each test data in the data synchronization combination, obtain total file size information, and determine whether the total file size information is greater than the data transmission volume of the data synchronization network at the current timestamp;

[0035] When the total file size information is larger than the data transmission volume of the data synchronization network at the current timestamp, inheritance is performed based on genetic algebra to readjust the data synchronization combination within a unit time until it is no larger than the data transmission volume of the data synchronization network at the current timestamp.

[0036] A second aspect of the present invention provides a wide-band loss test and data synchronization system for transformers, the system comprising a memory and a processor, the memory comprising a wide-band loss test and data synchronization method program for transformers, and when the wide-band loss test and data synchronization method program for transformers is executed by the processor, the steps of any one of the wide-band loss test and data synchronization methods for transformers are implemented.

[0037] A third aspect of the present invention provides a terminal device, including:

[0038] The data acquisition module is responsible for performing broadband loss tests on transformers, acquiring test data, building a data synchronization network, building timestamps, acquiring test data generated by each timestamp, and acquiring network fluctuation data of the data synchronization network;

[0039] A first data analysis module is responsible for building a data transmission volume prediction model of the data synchronization network based on the network fluctuation data, predicting the data synchronization network based on the data transmission volume prediction model of the data synchronization network, and obtaining the data transmission volume of the data synchronization network at a current timestamp;

[0040] The second data analysis module is responsible for setting a data transmission volume threshold, and performing evaluation according to the data transmission volume of the data synchronization network of the current timestamp and the data transmission volume threshold, to obtain the evaluation analysis result;

[0041] The data synchronization module is responsible for obtaining relevant early warning information or information transmission plan according to the analysis results, and performing test data synchronization optimization on the information transmission plan through a genetic algorithm.

[0042] A fourth aspect of the present invention provides a computer-readable storage medium, which includes a program for a wide-band loss test and data synchronization method for a transformer. When the program for a wide-band loss test and data synchronization method for a transformer is executed by a processor, the steps of any one of the methods for a wide-band loss test and data synchronization for a transformer are implemented.

[0043] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0044] The present invention performs a broadband loss test on the transformer to obtain test data, and constructs a data synchronization network, constructs a timestamp, obtains the test data generated by each timestamp, obtains the network fluctuation data of the data synchronization network, and then constructs a data transmission volume prediction model of the data synchronization network based on the network fluctuation data, predicts the data synchronization network based on the data transmission volume prediction model of the data synchronization network, obtains the data transmission volume of the data synchronization network at the current timestamp, thereby setting the data transmission volume threshold, and evaluates the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain the evaluation analysis results, and finally obtains relevant early warning information or information transmission plans according to the analysis results, and optimizes the information transmission plan for test data synchronization through a genetic algorithm. The present invention evaluates the information transmission volume of the data synchronization network according to the network fluctuation data, and thus finds the optimal solution through a genetic algorithm to dynamically adjust the test data transmitted within a unit time, so that the test data can be synchronized in a preset manner, avoid test data loss, and improve the test data synchronization rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0046] Figure 1 The overall flow chart of the broadband loss test and data synchronization method for transformers is shown;

[0047] Figure 2 A partial method flow chart of a method for broadband loss testing and data synchronization for transformers is shown;

[0048] Figure 3 A system block diagram of a broadband loss test and data synchronization system for transformers is shown;

[0049] Figure 4 A device schematic diagram of a terminal device is shown. DETAILED DESCRIPTION

[0050] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0052] like Figure 1 As shown, the first aspect of the present invention provides a transformer broadband loss test and data synchronization method, comprising the following steps:

[0053] S102: Perform a broadband loss test on the transformer to obtain test data, build a data synchronization network, build a timestamp, obtain test data generated by each timestamp, and obtain network fluctuation data of the data synchronization network;

[0054] S104: constructing a data transmission volume prediction model of the data synchronization network based on the network fluctuation data, predicting the data synchronization network based on the data transmission volume prediction model of the data synchronization network, and obtaining the data transmission volume of the data synchronization network at the current timestamp;

[0055] S106: Setting a data transmission volume threshold, and evaluating the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation analysis result;

[0056] S108: Obtain relevant early warning information or information transmission plan based on the analysis results, and perform test data synchronization optimization on the information transmission plan through a genetic algorithm.

[0057] It should be noted that the present invention evaluates the information transmission volume of the data synchronization network according to the network fluctuation data, and thus finds the optimal solution through a genetic algorithm to dynamically adjust the test data transmitted within a unit time, so that the test data can be synchronized in a preset manner, thereby avoiding test data loss and improving the test data synchronization rate.

[0058] Furthermore, in this method, by performing a broadband loss test on the transformer, obtaining test data, building a data synchronization network, building a timestamp, obtaining test data generated by each timestamp, and obtaining network fluctuation data of the data synchronization network, specifically:

[0059] Set several operating frequencies, and perform broadband loss tests on the transformer based on the operating frequencies, obtain test data, and collect frequency information and file size information generated by the test data within a unit time;

[0060] Build a data synchronization network to calculate the total file size information within a unit time based on the frequency information and file size information generated by the test data within a unit time;

[0061] According to the total file size information within a unit time, the data synchronization network is configured to transmit information within a unit time, thereby completing the construction of the data synchronization network;

[0062] Build timestamps, obtain test data generated by each timestamp, and obtain network fluctuation data of the data synchronization network.

[0063] It should be noted that network fluctuation data includes network delay, bit error rate, information transmission success rate and other data. This method can improve the rationality of building a data synchronization network.

[0064] Furthermore, in this method, a data transmission volume prediction model of the data synchronization network is constructed based on the network fluctuation data, specifically:

[0065] Collect the data transmission volume of the data synchronization network within a unit time under each network fluctuation data, and introduce the graph neural network, taking the network fluctuation data as the first node and the data transmission volume within a unit time as the second node;

[0066] A data transmission volume prediction model of a data synchronization network is constructed based on a deep neural network, a directed description relationship is constructed, and a first node and a second node are connected based on the directed description relationship to form a topological structure diagram;

[0067] Obtaining a relevant adjacency matrix based on the topological structure graph, and inputting the relevant adjacency matrix into a data transmission volume prediction model of a data synchronization network for training;

[0068] When the loss function of the data transmission volume prediction model of the data synchronization network converges to a preset value, the model parameters of the data transmission volume prediction model of the data synchronization network are saved, and the training of the data transmission volume prediction model of the data synchronization network is completed.

[0069] It should be noted that the data transmission volume of the data synchronization network within a unit time is an upper limit of data transmission, that is, the maximum data transmission volume threshold of the data transmission within a unit time, and the network fluctuation can affect the data transmission volume of the data synchronization network within a unit time. Through this method, a data transmission volume prediction model of the data synchronization network can be constructed, and the data transmission volume prediction model of the data synchronization network can be used to obtain the data transmission volume of the data synchronization network within a unit time under each network fluctuation data.

[0070] Furthermore, in this method, the data synchronization network is predicted based on the data transmission volume prediction model of the data synchronization network to obtain the data transmission volume of the data synchronization network at the current timestamp, specifically:

[0071] Inputting network fluctuation data of the data synchronization network into a data transmission volume prediction model of the data synchronization network for prediction;

[0072] The data transmission volume of the data synchronization network at the current timestamp is obtained through prediction, and the data transmission volume of the data synchronization network at the current timestamp is output.

[0073] Furthermore, in this method, a data transmission volume threshold is set, and an evaluation is performed based on the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation analysis result, specifically:

[0074] Set a data transmission volume threshold to determine whether the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold;

[0075] When the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for normal data transmission;

[0076] When the data transmission volume of the data synchronization network at the current timestamp is not greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for abnormal data transmission.

[0077] It should be noted that this method can be used to evaluate the data synchronization network and obtain a data synchronization network with normal data transmission. When the data transmission volume of the data synchronization network at the current timestamp is not greater than the data transmission volume threshold, it is a data synchronization network with abnormal data transmission, indicating that an abnormality has occurred in the data transmission.

[0078] Furthermore, in this method, relevant early warning information or information transmission plan is obtained according to the analysis results, specifically:

[0079] When the analysis result is a data synchronization network with abnormal data transmission, relevant warning information is generated according to the data synchronization network with abnormal data transmission, and displayed and prompted in a preset manner;

[0080] When the analysis result is a data synchronization network with normal data transmission, the frequency information and file size information of the test data generated within a unit time are obtained;

[0081] An information transmission plan is generated for the data synchronization network for normal data transmission according to the frequency information and file size information generated by the test data within a unit time, and the information transmission plan is output.

[0082] like Figure 2 As shown, further, in this method, the information transmission plan is optimized for test data synchronization by using a genetic algorithm, specifically:

[0083] S202: Obtain the data transmission volume of the data synchronization network at the current timestamp, introduce a genetic algorithm, and set a genetic generation based on the genetic algorithm to obtain the frequency information and file size information of the test data generated within a unit time in the information transmission plan, and sort the test data in the order of their generation time;

[0084] S204: acquiring the test data sorted in chronological order by sorting, initializing the data synchronization combination within a unit time according to the test data sorted in chronological order, and selecting a random number of test data for data synchronization;

[0085] S206: Obtain file size information of each test data in the data synchronization combination, store the file size information of each test data in the data synchronization combination, obtain total file size information, and determine whether the total file size information is greater than the data transmission volume of the data synchronization network at the current timestamp;

[0086] S208: When the total file size information is greater than the data transmission volume of the data synchronization network at the current timestamp, inheritance is performed based on genetic algebra to readjust the data synchronization combination within a unit time until it is no greater than the data transmission volume of the data synchronization network at the current timestamp.

[0087] It should be noted that, through the present invention, the test data transmitted within a unit time can be optimized according to the data transmission volume of the data synchronization network of the current timestamp.

[0088] like Figure 3 As shown, the second aspect of the present invention provides a wide-band loss test and data synchronization system 3 for transformers. The system 3 includes a memory 31 and a processor 32. The memory includes a wide-band loss test and data synchronization method program for transformers. When the wide-band loss test and data synchronization method program for transformers is executed by the processor 32, any step of the wide-band loss test and data synchronization method for transformers is implemented.

[0089] like Figure 4 As shown, the third aspect of the present invention provides a terminal device, including:

[0090] The data acquisition module 10 is responsible for performing broadband loss tests on the transformer, acquiring test data, building a data synchronization network, building a timestamp, acquiring test data generated by each timestamp, and acquiring network fluctuation data of the data synchronization network;

[0091] The first data analysis module 20 is responsible for building a data transmission volume prediction model of the data synchronization network based on the network fluctuation data, predicting the data synchronization network based on the data transmission volume prediction model of the data synchronization network, and obtaining the data transmission volume of the data synchronization network at the current timestamp;

[0092] The second data analysis module 30 is responsible for setting a data transmission volume threshold, and evaluating the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain the evaluation analysis result;

[0093] The data synchronization module 40 is responsible for obtaining relevant warning information or information transmission plan according to the analysis results, and performing test data synchronization optimization on the information transmission plan through a genetic algorithm.

[0094] Furthermore, in this device, by performing broadband loss test on the transformer, obtaining test data, building a data synchronization network, building a timestamp, obtaining test data generated by each timestamp, and obtaining network fluctuation data of the data synchronization network, specifically:

[0095] Set several operating frequencies, and perform broadband loss tests on the transformer based on the operating frequencies, obtain test data, and collect frequency information and file size information generated by the test data within a unit time;

[0096] Build a data synchronization network to calculate the total file size information within a unit time based on the frequency information and file size information generated by the test data within a unit time;

[0097] According to the total file size information within a unit time, the data synchronization network is configured to transmit information within a unit time, thereby completing the construction of the data synchronization network;

[0098] Build timestamps, obtain test data generated by each timestamp, and obtain network fluctuation data of the data synchronization network.

[0099] It should be noted that network fluctuation data includes network delay, bit error rate, information transmission success rate and other data. This method can improve the rationality of building a data synchronization network.

[0100] Furthermore, in this device, a data transmission volume prediction model of the data synchronization network is constructed based on the network fluctuation data, specifically:

[0101] Collect the data transmission volume of the data synchronization network within a unit time under each network fluctuation data, and introduce the graph neural network, taking the network fluctuation data as the first node and the data transmission volume within a unit time as the second node;

[0102] A data transmission volume prediction model of a data synchronization network is constructed based on a deep neural network, a directed description relationship is constructed, and a first node and a second node are connected based on the directed description relationship to form a topological structure diagram;

[0103] Obtaining a relevant adjacency matrix based on the topological structure graph, and inputting the relevant adjacency matrix into a data transmission volume prediction model of a data synchronization network for training;

[0104] When the loss function of the data transmission volume prediction model of the data synchronization network converges to a preset value, the model parameters of the data transmission volume prediction model of the data synchronization network are saved, and the training of the data transmission volume prediction model of the data synchronization network is completed.

[0105] It should be noted that the data transmission volume of the data synchronization network within a unit time is an upper limit of data transmission, that is, the maximum data transmission volume threshold of the data transmission within a unit time, and the network fluctuation can affect the data transmission volume of the data synchronization network within a unit time. Through this method, a data transmission volume prediction model of the data synchronization network can be constructed, and the data transmission volume prediction model of the data synchronization network can be used to obtain the data transmission volume of the data synchronization network within a unit time under each network fluctuation data.

[0106] Furthermore, in this device, the data synchronization network is predicted based on the data transmission volume prediction model of the data synchronization network to obtain the data transmission volume of the data synchronization network at the current timestamp, specifically:

[0107] Inputting network fluctuation data of the data synchronization network into a data transmission volume prediction model of the data synchronization network for prediction;

[0108] The data transmission volume of the data synchronization network at the current timestamp is obtained through prediction, and the data transmission volume of the data synchronization network at the current timestamp is output.

[0109] Furthermore, in this device, a data transmission volume threshold is set, and an evaluation is performed based on the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation analysis result, specifically:

[0110] Set a data transmission volume threshold to determine whether the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold;

[0111] When the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for normal data transmission;

[0112] When the data transmission volume of the data synchronization network at the current timestamp is not greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for abnormal data transmission.

[0113] It should be noted that this method can be used to evaluate the data synchronization network and obtain a data synchronization network with normal data transmission. When the data transmission volume of the data synchronization network at the current timestamp is not greater than the data transmission volume threshold, it is a data synchronization network with abnormal data transmission, indicating that an abnormality has occurred in the data transmission.

[0114] Furthermore, in this device, relevant early warning information or information transmission plan is obtained according to the analysis results, specifically:

[0115] When the analysis result is a data synchronization network with abnormal data transmission, relevant warning information is generated according to the data synchronization network with abnormal data transmission, and displayed and prompted in a preset manner;

[0116] When the analysis result is a data synchronization network with normal data transmission, the frequency information and file size information of the test data generated within a unit time are obtained;

[0117] An information transmission plan is generated for the data synchronization network for normal data transmission according to the frequency information and file size information generated by the test data within a unit time, and the information transmission plan is output.

[0118] Furthermore, in this device, the information transmission plan is optimized for test data synchronization by using a genetic algorithm, specifically:

[0119] Obtain the data transmission volume of the data synchronization network at the current timestamp, introduce a genetic algorithm, and set the genetic generation based on the genetic algorithm to obtain the frequency information and file size information of the test data generated within a unit time in the information transmission plan, and sort the test data in the order of their generation time;

[0120] By sorting, the test data sorted in chronological order is obtained, and the data synchronization combination within a unit time is initialized according to the test data sorted in chronological order, and a random number of test data are selected for data synchronization;

[0121] Obtain the file size information of each test data in the data synchronization combination, calculate the file size information of each test data in the data synchronization combination, obtain the total file size information, and determine whether the total file size information is greater than the data transmission volume of the data synchronization network at the current timestamp;

[0122] When the total file size information is larger than the data transmission volume of the data synchronization network at the current timestamp, inheritance is performed based on genetic algebra to readjust the data synchronization combination within a unit time until it is no larger than the data transmission volume of the data synchronization network at the current timestamp.

[0123] It should be noted that, through the present invention, the test data transmitted within a unit time can be optimized according to the data transmission volume of the data synchronization network of the current timestamp.

[0124] The fourth aspect of the present invention provides a computer-readable storage medium, which includes a program for a wide-band loss test and data synchronization method for a transformer. When the program for a wide-band loss test and data synchronization method for a transformer is executed by a processor, any step of the wide-band loss test and data synchronization method for a transformer is implemented.

[0125] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0126] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0127] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0128] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0129] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0130] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A broadband loss test and data synchronization method for transformers, characterized in that: The following steps are involved: By performing broadband loss tests on transformers, obtaining test data, and building a data synchronization network, building timestamps, obtaining test data generated by each timestamp, and obtaining network fluctuation data of the data synchronization network; Building a data transmission volume prediction model of the data synchronization network based on the network fluctuation data, predicting the data synchronization network based on the data transmission volume prediction model of the data synchronization network, and obtaining the data transmission volume of the data synchronization network at the current timestamp; Setting a data transmission volume threshold, and evaluating the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation analysis result; Relevant early warning information or information transmission plan is obtained according to the analysis result, and the information transmission plan is optimized synchronously with test data through a genetic algorithm.

2. A transformer broadband loss test and data synchronization method according to claim 1, characterized in that: By performing broadband loss tests on transformers, obtaining test data, building a data synchronization network, building timestamps, obtaining test data generated by each timestamp, and obtaining network fluctuation data of the data synchronization network, specifically: Setting a number of operating frequencies, and performing a broadband loss test on the transformer based on the operating frequencies, obtaining test data, and collecting frequency information and file size information generated by the test data within a unit time; Constructing a data synchronization network, and calculating the total file size information within the unit time according to the frequency information and file size information generated by the test data within the unit time; According to the total file size information within the unit time, the data synchronization network is configured to transmit information within the unit time, thereby completing the construction of the data synchronization network; Build timestamps, obtain test data generated by each timestamp, and obtain network fluctuation data of the data synchronization network.

3. A transformer broadband loss test and data synchronization method according to claim 1, characterized in that: A data transmission volume prediction model of a data synchronization network is constructed based on the network fluctuation data, specifically: Collect the data transmission volume of the data synchronization network within a unit time under each network fluctuation data, and introduce the graph neural network, taking the network fluctuation data as the first node and the data transmission volume within a unit time as the second node; Building a data transmission volume prediction model of the data synchronization network based on a deep neural network, building a directed description relationship, and connecting the first node and the second node based on the directed description relationship to form a topological structure diagram; Acquire a relevant adjacency matrix based on the topological structure graph, and input the relevant adjacency matrix into a data transmission volume prediction model of the data synchronization network for training; When the loss function of the data transmission volume prediction model of the data synchronization network converges to a preset value, the model parameters of the data transmission volume prediction model of the data synchronization network are saved, and the training of the data transmission volume prediction model of the data synchronization network is completed.

4. A transformer broadband loss test and data synchronization method according to claim 1, characterized in that: The data synchronization network is predicted based on the data transmission volume prediction model of the data synchronization network to obtain the data transmission volume of the data synchronization network at the current timestamp, specifically: Inputting the network fluctuation data of the data synchronization network into a data transmission volume prediction model of the data synchronization network for prediction; The data transmission volume of the data synchronization network at the current timestamp is obtained through prediction, and the data transmission volume of the data synchronization network at the current timestamp is output.

5. A transformer broadband loss test and data synchronization method according to claim 1, characterized in that: A data transmission volume threshold is set, and an evaluation is performed based on the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation analysis result, specifically: Setting a data transmission volume threshold value to determine whether the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold value; When the data transmission volume of the data synchronization network of the current timestamp is greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for normal data transmission; When the data transmission volume of the data synchronization network at the current timestamp is not greater than the data transmission volume threshold, the data transmission volume of the current data synchronization network is used as the data synchronization network for abnormal data transmission.

6. A transformer broadband loss test and data synchronization method according to claim 1, characterized in that: Obtain relevant early warning information or information transmission plan based on the analysis results, specifically: When the analysis result is a data synchronization network with abnormal data transmission, relevant warning information is generated according to the data synchronization network with abnormal data transmission, and displayed and prompted in a preset manner; When the analysis result is a data synchronization network with normal data transmission, frequency information and file size information of test data generated within a unit time are obtained; An information transmission plan is generated for a data synchronization network for normal data transmission according to the frequency information and file size information generated by the test data within the unit time, and the information transmission plan is output.

7. A transformer broadband loss test and data synchronization method according to claim 1, characterized in that: The information transmission plan is optimized for test data synchronization through genetic algorithms, specifically: Obtain the data transmission volume of the data synchronization network at the current timestamp, introduce a genetic algorithm, and set a genetic generation based on the genetic algorithm to obtain the frequency information and file size information of the test data generated within a unit time in the information transmission plan, and sort the test data in the order of time of generation; By sorting, test data sorted in chronological order is obtained, and data synchronization combinations within a unit time are initialized according to the test data sorted in chronological order, and a random number of test data are selected for data synchronization; Obtain file size information of each test data in the data synchronization combination, count the file size information of each test data in the data synchronization combination, obtain total file size information, and determine whether the total file size information is greater than the data transmission volume of the data synchronization network at the current timestamp; When the total file size information is larger than the data transmission volume of the data synchronization network at the current timestamp, inheritance is performed based on genetic algebra to readjust the data synchronization combination within a unit time until it is no larger than the data transmission volume of the data synchronization network at the current timestamp.

8. A broadband loss test and data synchronization system for transformers, characterized in that: The system includes a memory and a processor, wherein the memory includes a program for a wide-band loss test and data synchronization method for a transformer. When the program for a wide-band loss test and data synchronization method for a transformer is executed by the processor, the steps of the wide-band loss test and data synchronization method for a transformer as described in any one of claims 1 to 7 are implemented.

9. A terminal device, characterized in that: include: The data acquisition module is responsible for performing broadband loss tests on transformers, acquiring test data, building a data synchronization network, building timestamps, acquiring test data generated by each timestamp, and acquiring network fluctuation data of the data synchronization network; A first data analysis module is responsible for building a data transmission volume prediction model of the data synchronization network based on the network fluctuation data, predicting the data synchronization network based on the data transmission volume prediction model of the data synchronization network, and obtaining the data transmission volume of the data synchronization network at a current timestamp; The second data analysis module is responsible for setting a data transmission volume threshold, and performing evaluation according to the data transmission volume of the data synchronization network of the current timestamp and the data transmission volume threshold, to obtain the evaluation analysis result; The data synchronization module is responsible for obtaining relevant early warning information or information transmission plan according to the analysis results, and performing test data synchronization optimization on the information transmission plan through a genetic algorithm.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a transformer broadband loss test and data synchronization method program. When the transformer broadband loss test and data synchronization method program is executed by a processor, the steps of the transformer broadband loss test and data synchronization method as described in any one of claims 1 to 7 are implemented.

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