A broadband loss test and data synchronization method for transformers

Abstract

The present invention relates to a broadband loss test and data synchronization method for transformers, belonging to the technical field of test data synchronization methods. The present invention obtains the data transmission volume of the data synchronization network at the current timestamp, thereby setting a 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 an evaluation analysis result. Finally, based on the analysis result, relevant early warning information or an information transmission plan is obtained, and the information transmission plan is optimized for test data synchronization using a genetic algorithm. The present invention evaluates the information transmission volume of the data synchronization network based on network fluctuation data, and then uses a genetic algorithm to find the optimal solution to dynamically adjust the test data transmitted within a unit time, so that the test data can be synchronized according to a preset method, avoid test data loss, and improve the test data synchronization rate.

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 transformers. Background Art

[0002] Increasing the operating frequency increases the core loss of high-frequency transformers, leading to a significant temperature rise within the core and reduced equipment efficiency. Therefore, performing broadband loss testing on transformers is a crucial experiment. Nowadays, with the advancement of IoT technology, test data is transmitted to a test platform or remote terminal through data synchronization after testing. However, since hundreds or even tens of thousands of transformers may be tested, data synchronization can be affected by network fluctuations, potentially leading to data synchronization anomalies or even partial data synchronization, preventing effective data upload and necessitating 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 transformers.

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

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

[0006] By performing broadband loss tests on transformers, obtaining test data, building a data synchronization network, establishing 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 for 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 the 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] Building a data synchronization network, and calculating the total file size information within the unit time based on the frequency information and file size information generated by the test data within the unit time;

[0013] configuring the data synchronization network for information transmission volume per unit time according to the total file size information per 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 for 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 topology diagram;

[0018] 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 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, and determining whether the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold;

[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 results, 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 the 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] Obtaining the data transmission volume of the data synchronization network at the current timestamp, introducing a genetic algorithm, and setting a genetic generation based on the genetic algorithm, obtaining the frequency information and file size information of the test data generated within a unit time in the information transmission plan, and sorting the test data in the order of their generation time;

[0033] By sorting, test data in chronological order is obtained, and data synchronization combinations within a unit time are initialized according to the test data 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 the 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, constructing 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 evaluating the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation and analysis result;

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

[0042] 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, the steps of any one of the methods for 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 a transformer to obtain test data, constructs a data synchronization network, constructs a timestamp, obtains test data generated at each timestamp, obtains network fluctuation data of the data synchronization network, and then constructs a data transmission volume prediction model for the data synchronization network based on the network fluctuation data. The data synchronization network is predicted based on the data transmission volume prediction model of the data synchronization network, and the data transmission volume of the data synchronization network at the current timestamp is obtained, thereby setting a data transmission volume threshold. The data synchronization network is evaluated based on the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold, and an evaluation analysis result is obtained. Finally, relevant early warning information or an information transmission plan is obtained based on the analysis result, and the information transmission plan is optimized for test data synchronization using a genetic algorithm. The present invention evaluates the information transmission volume of the data synchronization network based on the network fluctuation data, and then uses a genetic algorithm to find the optimal solution to dynamically adjust the test data transmitted within a unit time, so that the test data can be synchronized according to a preset method, 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 following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, they can also obtain drawings of other embodiments based on these drawings.

[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 The system block diagram of the 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 objects, 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, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[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 scope of protection 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 method for broadband loss testing and data synchronization for transformers, comprising the following steps:

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

[0054] S104: Build a data transmission volume prediction model for the data synchronization network based on the network fluctuation data, predict the data synchronization network based on the data transmission volume prediction model, and obtain 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 based on network fluctuation data, and then uses a genetic algorithm to find the optimal solution to dynamically adjust the test data transmitted within a unit time, so that the test data can be synchronized according to a preset method, 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 the test data generated by each timestamp, and obtaining network fluctuation data of the data synchronization network, specifically:

[0059] Set several operating frequencies, 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 and 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] The data synchronization network is configured to transmit information per unit time based on the total file size per unit time, 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 the data synchronization network is constructed based on a deep neural network, a directed description relationship is constructed, and the first node and the 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 the 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 unit time is a data transmission upper limit, that is, the maximum data transmission volume threshold within unit time, and network fluctuations can affect the data transmission volume of the data synchronization network within 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 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 the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold are evaluated to obtain the evaluation and analysis results, 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 based on 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 indicates 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] According to the frequency information and file size information generated by the test data within a unit time, the data synchronization network for normal data transmission generates an information transmission plan and outputs the information transmission plan.

[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 by the time of generation;

[0084] S204: Obtain test data sorted in chronological order through sorting, initialize data synchronization combinations within a unit time based on the test data sorted in chronological order, and select a random number of test data for data synchronization;

[0085] S206: Obtain 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 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 the 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 at 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 transformers, acquiring test data, building a data synchronization network, constructing timestamps, 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 according to the current timestamp and the data transmission volume threshold, and obtaining the evaluation analysis result;

[0093] The data synchronization module 40 is responsible for obtaining relevant warning information or information transmission plan based on 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 testing on the transformer, obtaining test data, and 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, 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 and 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] The data synchronization network is configured to transmit information per unit time based on the total file size per unit time, 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 the data synchronization network is constructed based on a deep neural network, a directed description relationship is constructed, and the first node and the 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 the 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 unit time is a data transmission upper limit, that is, the maximum data transmission volume threshold within unit time, and network fluctuations can affect the data transmission volume of the data synchronization network within 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 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 the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold are evaluated to obtain the evaluation analysis results, 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 warning information or information transmission plan is obtained based on 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 indicates 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] According to the frequency information and file size information generated by the test data within a unit time, the data synchronization network for normal data transmission generates an information transmission plan and outputs the information transmission plan.

[0118] Furthermore, in this device, the information transmission plan is optimized for test data synchronization through 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 number 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 by the time of generation;

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

[0121] Obtain file size information of each test data in the data synchronization combination, collect 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;

[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 the 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 at 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, it implements any step of the wide-band loss test and data synchronization method for a transformer.

[0125] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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 across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this 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] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware associated with program instructions, and the aforementioned program may be stored in a computer-readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0129] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software functional 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, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or 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 merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection 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, building a data synchronization network, establishing 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 for 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; 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; A data transmission volume prediction model for the 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 for 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 topology diagram; 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 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.

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, constructing 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; Building a data synchronization network, and calculating the total file size information within the unit time based on the frequency information and file size information generated by the test data within the unit time; configuring the data synchronization network for information transmission volume per unit time according to the total file size information per 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. The method for broadband loss testing and data synchronization for transformers 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.

4. 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, and determining whether the data transmission volume of the data synchronization network at the current timestamp is greater than the data transmission volume threshold; 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 transmission volume of the data synchronization network for performing abnormal 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 transmission volume of the data synchronization network for normal data transmission.

5. The method for broadband loss testing and data synchronization for transformers according to claim 1, characterized in that: Obtain relevant 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 the 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.

6. The method for broadband loss testing and data synchronization for transformers 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 by generation time; By sorting, test data in chronological order is obtained, and data synchronization combinations within a unit time are initialized according to the test data 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 the unit time until it is no larger than the data transmission volume of the data synchronization network at the current timestamp.

7. 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 6 are implemented.

8. 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, constructing 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 evaluating the data transmission volume of the data synchronization network at the current timestamp and the data transmission volume threshold to obtain an evaluation and analysis result; The data synchronization module is responsible for obtaining relevant warning information or information transmission plans based on the analysis results, and performing test data synchronization optimization on the information transmission plans through genetic algorithms; A data transmission volume prediction model for the 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 for 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 topology diagram; 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 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.

9. 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 6 are implemented.

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