Method and system for determining grid-connected transmission time of power grid data, equipment and medium

Abstract

The invention discloses a method and system for determining grid-connected transmission time of power grid data, equipment and a medium, and the method comprises the steps: firstly constructing a time sequence model which reflects a mapping relation between a time node and communication state data; the communication state data of the power grid at the current set grid-connected transmission time point under the condition that the new user is not connected into the power grid can be predicted based on the time sequence model, meanwhile, a linear regression model reflecting the mapping relation between the newly-added transmission data volume and the communication state data is constructed, and when the new user needs to be connected into the power grid, the communication state data is obtained. After the data volume of data to be transmitted of a user is obtained, communication state data of a power grid after a new user is merged into the power grid can be predicted based on the linear regression model, and difference analysis is performed on the two pieces of predicted communication state data, so that whether a currently set grid-connected transmission time point meets requirements or not can be determined. The rationality of the new user grid-connected transmission time point can be accurately evaluated, and the communication efficiency, reliability and stability of a power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of grid data grid-connected transmission, and in particular to a method and system for determining grid data grid-connected transmission time, an electronic device, and a computer-readable storage medium. Background Art

[0002] The safe and stable operation of the power grid is directly linked to the security of grid company assets and the safety of people's lives and property. As a key support platform for ensuring the safe and stable operation of the power grid, the power communication network carries a variety of services, including grid production, control, and management. The safe operation of the power grid is increasingly dependent on the communication network, and the security and reliability requirements of the power communication network are also increasing. As the scale and complexity of the power grid expand, its monitoring, control, and management tasks have become significantly more dependent on reliable communication information technology. Due to its rapid development, the power communication network has become increasingly large and complex, carrying a rapidly increasing volume of services, and the security risks and management difficulties have increased accordingly. Therefore, it is extremely necessary to carry out risk prevention, effective prediction, and pre-control measures for the communication network.

[0003] Because grid communication capacity requirements vary across different regions and at different times, some times experience peak traffic, placing grid communications under high load. If users connect to the grid and transmit data, this increases grid communication traffic. Large or frequent data transmissions can lead to network congestion, increased communication latency, and even packet loss, impacting the real-time and reliability of grid communications, potentially adversely affecting grid operation and monitoring. Therefore, if users fail to accurately assess the current grid communication capacity at a given time and instead transmit data directly, network congestion and failures can occur, impacting the stability of grid communications across the entire region. Summary of the Invention

[0004] The present invention provides a method and system for determining the grid data grid connection transmission time, an electronic device, and a computer-readable storage medium, which can accurately evaluate the rationality of the grid connection transmission time point of a new user, thereby improving the communication efficiency, reliability, and stability of the grid.

[0005] According to one aspect of the present invention, a method for determining grid data transmission time is provided, comprising the following steps:

[0006] Collect the communication status data of the power grid at different time nodes and construct a time series model that reflects the mapping relationship between time nodes and communication status data;

[0007] Collect the communication status data of the power grid after the grid-connected transmission of different newly added transmission data volumes, and construct a linear regression model reflecting the mapping relationship between the newly added transmission data volumes and the communication status data;

[0008] The grid-connected transmission time point is set and input into the time series model to obtain the communication status data of the power grid at the set grid-connected transmission time point, which is used as the benchmark communication status data;

[0009] Obtain the amount of data to be transmitted by the user and input it into the linear regression model to obtain the communication status data of the power grid after the user data is connected to the grid and transmitted, and use it as the predicted communication status data;

[0010] Perform a difference analysis on the predicted communication status data and the reference communication status data, and determine whether the currently set grid-connected transmission time point meets the requirements based on the difference analysis results.

[0011] Furthermore, if the difference between the predicted communication status data and the reference communication status data is less than a preset threshold, it is determined that the currently set grid-connected transmission time point meets the requirements, and the user will be connected to the power grid when the currently set grid-connected transmission time point is reached in the future; if the difference between the predicted communication status data and the reference communication status data is not less than the preset threshold, it is determined that the currently set grid-connected transmission time point does not meet the requirements, and a new grid-connected transmission time point needs to be reset until the difference between the predicted communication status data and the reference communication status data is less than the preset threshold.

[0012] Furthermore, the time series model is:

[0013] Y(t)=Φ(B)*Y(t-1)+Θ(B)*ε(t-1)+ε(t)

[0014] Where Y(t) represents the communication status data of the power grid at time node t, Φ(B) represents the autoregressive coefficient polynomial, which is determined by the autoregressive order p, B represents the delay operator, which is used to shift the time series data backward by one time unit, Y(t-1) is the communication status data sequence of the power grid at the past time node, containing p past values, Θ(B) represents the moving average coefficient polynomial, which is determined by the moving average order q, ε(t-1) represents the error term sequence of the past time node, containing q past values, and ε(t) is the error term at time node t.

[0015] Furthermore, after the set grid-connected transmission time point is input into the time series model and the communication status data of the power grid at this time is predicted, if the communication status data of the power grid at this time exceeds the preset threshold, a new grid-connected transmission time point needs to be reset.

[0016] Furthermore, it also includes the following:

[0017] When the number of times the grid-connected transmission time point is reset reaches the preset value, the user's data to be transmitted is divided into multiple data packets based on their importance, and the grid-connected transmission time period of each data packet is determined to achieve hierarchical data transmission.

[0018] Furthermore, the following contents are also included:

[0019] After the user is connected to the power grid, the actual communication status data of the power grid is collected, and the difference analysis is performed between the actual communication status data and the benchmark communication status data. If the difference between the actual communication status data and the benchmark communication status data is not less than the preset threshold, an early warning is issued.

[0020] Furthermore, the communication status data of each time node is calculated based on the following formula:

[0021] x=w1x1+w2x2+w3x3

[0022] Where x represents the communication status data, x1, x2, and x3 represent the normalized communication volume, communication delay, and packet loss rate, respectively, w1, w2, and w3 are weight coefficients, and w1+w2+w3=1.

[0023] In addition, the present invention also provides a system for determining grid data transmission time, comprising:

[0024] A time series model building module is used to collect communication status data of the power grid at different time nodes and build a time series model that reflects the mapping relationship between time nodes and communication status data;

[0025] A linear regression model building module is used to collect communication status data of the power grid after different amounts of newly transmitted data are connected to the grid, and to build a linear regression model that reflects the mapping relationship between the newly transmitted data amount and the communication status data;

[0026] A first communication state data prediction module is used to set a grid-connected transmission time point and input it into a time series model to obtain communication state data of the power grid at the set grid-connected transmission time point and use it as reference communication state data;

[0027] The second communication status data prediction module is used to obtain the amount of user data to be transmitted and input it into the linear regression model to obtain the communication status data of the power grid after the user data is connected to the grid and transmitted, and use it as the predicted communication status data;

[0028] The grid-connected transmission time point determination module is used to perform difference analysis on the predicted communication status data and the reference communication status data, and determine whether the currently set grid-connected transmission time point meets the requirements based on the difference analysis results.

[0029] In addition, the present invention also provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the above method by calling the computer program stored in the memory.

[0030] In addition, the present invention also provides a computer-readable storage medium for storing a computer program for determining the grid data grid-connected transmission time, wherein the computer program executes the steps of the above-mentioned method when running on a computer.

[0031] The present invention has the following beneficial effects:

[0032] The present invention provides a method for determining the grid data grid-connected transmission time. The method first collects historical communication status data of the user's grid at different time points and constructs a time series model reflecting the mapping relationship between the time points and the communication status data. After setting the grid-connected transmission time point, the method predicts the grid's communication status data at the set grid-connected transmission time point, assuming no new user has been added, based on the time series model. This data is used as a benchmark for determining whether the new user is added to the grid. Simultaneously, the method collects historical communication status data of the grid after different amounts of newly added transmission data have been added to the grid, and constructs a linear regression model reflecting the mapping relationship between the newly added transmission data amounts and the communication status data. When a new user needs to be added to the grid, the method obtains the amount of data to be transmitted by the user and predicts the grid's communication status data after the new user is added based on the linear regression model. This data is used as the predicted communication status data. Finally, based on the difference analysis results between the predicted communication status data and the benchmark communication status data, it is determined whether the currently set grid-connected transmission time point meets the requirements. This allows for accurate assessment of the rationality of the new user's grid-connected transmission time point, thereby improving the communication efficiency, reliability, and stability of the grid.

[0033] In addition, the system for determining grid data grid-connected transmission time of the present invention also has the above advantages.

[0034] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0036] Figure 1 1 is a flow chart of a method for determining grid data transmission time in accordance with a preferred embodiment of the present application;

[0037] Figure 2This is another flow chart of the method for determining the grid data transmission time in accordance with a preferred embodiment of the present application;

[0038] Figure 3 This is another flow chart of a method for determining the grid data transmission time in a preferred embodiment of the present application;

[0039] Figure 4 It is a schematic diagram of the module structure of a system for determining grid data grid-connected transmission time according to another embodiment of the present application. DETAILED DESCRIPTION

[0040] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0041] Reference Figure 1 The preferred embodiment of the present application provides a method for determining the grid data transmission time, which includes the following contents:

[0042] Step S1: collecting communication status data of the power grid at different time nodes, and constructing a time series model that reflects the mapping relationship between the time nodes and the communication status data;

[0043] Step S2: collecting communication status data of the power grid after different newly transmitted data volumes are connected to the grid, and constructing a linear regression model reflecting the mapping relationship between the newly transmitted data volumes and the communication status data;

[0044] Step S3: setting a grid-connected transmission time point and inputting it into the time series model to obtain the communication status data of the power grid at the set grid-connected transmission time point, and using it as the benchmark communication status data;

[0045] Step S4: obtaining the amount of user data to be transmitted and inputting it into a linear regression model to obtain communication status data of the power grid after the user data is connected to the grid and transmitted, and using it as predicted communication status data;

[0046] Step S5: performing a difference analysis on the predicted communication status data and the reference communication status data, and determining whether the currently set grid-connected transmission time point meets the requirements based on the difference analysis result.

[0047] It can be understood that the method for determining the grid data grid-connected transmission time of this embodiment first collects the historical communication status data of the user's grid at different time nodes, and constructs a time series model that reflects the mapping relationship between the time node and the communication status data. After setting the grid-connected transmission time point, the communication status data of the grid at the set grid-connected transmission time point can be predicted based on the time series model without the incorporation of the new user, and used as the judgment benchmark for the new user's grid connection. At the same time, the historical communication status data of the grid after the grid-connected transmission of different newly transmitted data volumes is collected, and a linear regression model that reflects the mapping relationship between the newly transmitted data volume and the communication status data is constructed. When a new user needs to be incorporated into the grid, after obtaining the amount of data to be transmitted by the user, the communication status data of the grid after the new user is incorporated can be predicted based on the linear regression model, and used as the predicted communication status data. Finally, based on the difference analysis results between the predicted communication status data and the reference communication status data, it can be determined whether the currently set grid-connected transmission time point meets the requirements. The rationality of the new user's grid-connected transmission time point can be accurately evaluated, thereby improving the communication efficiency, reliability and stability of the grid.

[0048] Moreover, when users are currently connected to the power grid, not only can they avoid high-risk time nodes as much as possible to ensure the stability and performance of power grid communications, but users can also judge the power grid operation status through difference analysis results during actual use, which facilitates timely feedback to the power grid operation platform in the event of anomalies, so that the power grid operation platform can detect power grid anomalies and conduct diagnosis and repairs.

[0049] Wherein, in the step S1, the historical communication status data of the power grid at different time nodes when no new users are connected to the grid is automatically obtained from the network traffic data of the power grid operation platform through a preset script, and the time nodes and communication status data are stored in a database. Wherein, the operation platform usually records the communication status parameter data of the power grid at each time node, such as total traffic, number of users, transmission rate, delay, packet loss rate, etc., and obtains data from the power grid operation platform by using a Python script. In order to avoid frequent API requests, a scheduled task can also be set to run the script regularly, such as every hour or every day, to store the data in the database, read the data in the database, and use the DataFrame object of pandas to operate and perform statistical analysis on the data, such as calculating the average, maximum, minimum, etc., and then use a drawing tool such as matplotlib to perform visual analysis on the data, such as drawing a time series graph, which is conducive to more intuitively determining the power grid communication status data corresponding to each time node. Then, the power grid communication status data of different time nodes in the database are called out as a training set to train the parameters of the ARIMA model, including the autoregressive coefficient p, the difference order d and the moving average order q, where the autoregressive order p indicates how many past values ​​the model uses to predict the current value, the difference order d indicates the number of times the data is differentiated, and the moving average order q indicates how many past errors the model uses to predict the current value. For example, p = 2 means that the power consumption of the previous two hours is used to predict the power consumption of the current hour; d = 1 means that one difference is performed; q = 1 means that the prediction error of the previous hour is used to predict the prediction value of the current hour; the autocorrelation function and the partial autocorrelation function (PACF) can be used to determine the model parameters. The ARIMA model is trained using the stabilized data to construct a time series model that reflects the mapping relationship between the time node and the communication status data of the power grid. Wherein, the time series model can be expressed as:

[0050] Y(t)=Φ(B)*Y(t-1)+Θ(B)*ε(t-1)+ε(t)

[0051] Where Y(t) represents the communication status data of the power grid at time node t, Φ(B) represents the autoregressive coefficient polynomial, which is determined by the autoregressive order p, B represents the delay operator, which is used to shift the time series data backward by one time unit, Y(t-1) is the communication status data sequence of the power grid at the past time node, containing p past values, Θ(B) represents the moving average coefficient polynomial, which is determined by the moving average order q, ε(t-1) represents the error term sequence of the past time node, containing q past values, and ε(t) is the error term at time node t.

[0052] Optionally, the present invention uses three indicators, namely, communication volume, communication delay, and packet loss rate, to evaluate the communication operation status of the power grid. Specifically, the communication status data of the power grid at each time node is calculated based on the following formula:

[0053] x=w1x1+w2x2+w3x3

[0054] Here, x represents communication status data, x1, x2, and x3 represent the normalized communication volume, communication delay, and packet loss rate, respectively. w1, w2, and w3 are weight coefficients, with w1 + w2 + w3 = 1. It can be understood that communication volume can reflect the communication load of the power grid, communication delay can reflect the transmission efficiency of the power grid, and packet loss rate can reflect the communication stability of the power grid. Therefore, these three indicator parameters can basically reflect the communication status of the power grid in all aspects. Of course, in other embodiments of the present invention, other communication status parameters may also be selected, such as transmission rate, number of users, etc., but this will increase the computational complexity accordingly.

[0055] Furthermore, because the bandwidth of the power grid communication network is limited, when new users join the grid and transmit data, they consume network bandwidth. If the data transmission volume is large or the transmission frequency is high, this can cause congestion in the power grid communication network, which in turn affects communication quality. Network congestion can lead to queuing delays, packet loss, and decreased transmission efficiency. Queuing delays increase the time packets wait in queues in routers or switches, while packet loss means new packets are discarded when the queue is full, resulting in an increased packet loss rate. Decreased transmission efficiency means packets need to be retransmitted, reducing overall transmission efficiency. Therefore, when new users join the grid and transmit data, the communication traffic in the power grid communication network also increases, affecting the communication quality and efficiency of the power grid communication network. Therefore, in order to reflect the mapping relationship between the newly added transmission data volume and the communication status data, in step S2, the historical communication status data of the power grid after the new user is connected to the grid and transmits data is collected through a network monitoring tool (such as Wireshark, Nagios, etc.), that is, different newly added transmission data volumes and corresponding power grid communication status data are collected, and the collected data are normalized, cleaned, converted and stored into a data set. Then, the data set is divided into a training set and a test set. The linear regression model is trained with the training set, and the accuracy of the model is verified with the test set.

[0056] In addition, in step S3, the user can set a grid-connected transmission time point according to his or her own needs, and input the currently set grid-connected transmission time point into the time series model, so as to predict the communication status data of the power grid at that time point, that is, the communication status data of the power grid before this time point can be predicted under the training of historical data, and used as the benchmark communication status data as the standard for subsequent comparison.

[0057] Optionally, the present invention also considers that even in the absence of new users connecting to the grid, the communication state of the power grid at the set grid connection transmission time point may be in an extreme state or congested state. If a new user connects to the grid to transmit data at this time, there is a high probability that the entire power grid will be abnormal, such as a collapse or failure. Therefore, after the present invention inputs the currently set grid connection transmission time point into the time series model and predicts the communication state data of the power grid at this time, if the communication state data of the power grid at this time exceeds a preset safety threshold, it means that the currently set grid connection transmission time point does not meet the grid connection requirements of the new user, and a new grid connection transmission time point needs to be reset. Only when the communication state data of the power grid does not exceed the preset safety threshold is it used as the baseline communication state data for subsequent predicted communication state data prediction and difference analysis. This facilitates the rapid and accurate determination of whether the currently set grid connection transmission time point meets the requirements, accurately avoids the time nodes that cause communication failures, and improves the processing efficiency of the algorithm.

[0058] In step S4, the user inputs information about the data to be transmitted, such as file size, data type, etc., through an interface or an interface. The total data volume of the data to be transmitted is extracted based on the information input by the user, and the data volume of the data to be transmitted is input into a trained linear regression model. The communication status data of the power grid after the new user is connected to the grid and transmits data can be predicted and used as the predicted communication status data.

[0059] In addition, in other embodiments of the present invention, after obtaining the predicted communication status data based on the linear regression model, the predicted communication status data is also compared with the preset safety threshold. If the predicted communication status data is less than the preset safety threshold, it means that the new user will not cause grid abnormality after being connected to the grid, and the new user is allowed to access the grid for data transmission. If the predicted communication status data is not less than the preset safety threshold, it means that the new user will cause grid abnormality after being connected to the grid and transmitting. In this case, the new user is not allowed to upload all the data to be transmitted to the grid at the same time. The new user may be required to upload data in batches, which is conducive to quickly and accurately judging whether the amount of data to be transmitted by the user will affect the security and stability of the grid, and can also improve the processing efficiency of the algorithm.

[0060] In addition, when the predicted standard communication status data and predicted communication status data do not exceed the preset safety threshold, if the new user occupies more communication resources after connecting to the grid and transmitting data, it will cause the communication resources of other users to be significantly reduced, which will lead to a decline in the communication efficiency, reliability and stability of the entire power grid. Conversely, if the new user occupies fewer communication resources after connecting to the grid and transmitting data, it will have less impact on the communication efficiency of other users and the power grid.

[0061] Therefore, in step S5, a difference analysis is performed on the predicted communication state data and the reference communication state data, and then it is determined whether the currently set grid-connected transmission time point meets the requirements based on the difference analysis result. Specifically, if the difference between the predicted communication status data and the benchmark communication status data is less than a preset threshold, it means that the impact of the new user's grid-connected transmission data on other users is small, thereby reflecting that the impact of the new user's grid-connected transmission data on the communication efficiency, reliability and stability of the power grid communication network is small, then it is determined that the currently set grid-connected transmission time point meets the requirements, and the user will be connected to the power grid when the currently set grid-connected transmission time point is reached in the future; if the difference between the predicted communication status data and the benchmark communication status data is not less than the preset threshold, it means that the new user's grid-connected transmission data has a large impact on other users, thereby reflecting that the new user's grid-connected transmission data has a large impact on the communication efficiency, reliability and stability of the power grid communication network, then it is determined that the currently set grid-connected transmission time point does not meet the requirements, and a new grid-connected transmission time point needs to be reset until the difference between the predicted communication status data and the benchmark communication status data is less than the preset threshold, which can better ensure the stability, reliability and communication efficiency of the entire power grid when the new user transmits data on the grid. In addition, when it is necessary to reset the grid-connected transmission time point, the currently set time node can be pushed back by time sliding, for example, to the next time period, the next hour or the next working day, and then the above steps S3 and S5 are re-executed to ensure that the grid communication avoids risky time nodes and improve the operation effect of the grid communication.

[0062] In addition, if Figure 2 As shown, the method for determining the grid data grid-connected transmission time also includes the following contents:

[0063] Step S6: After the user is connected to the power grid, the actual communication status data of the power grid is collected, and a difference analysis is performed between the actual communication status data and the reference communication status data. If the difference between the actual communication status data and the reference communication status data is not less than a preset threshold, an early warning is issued.

[0064] Specifically, the present invention also takes into account that the predicted output of the linear regression model may have errors. Therefore, in order to avoid network congestion at the set grid-connected transmission time point, the present invention also collects the actual communication status data of the power grid in real time after the new user is connected to the grid and transmits data. For example, the `ethtool` command can be used to obtain the communication volume received and sent by the network interface card. Using a network monitoring tool, the connection with the target server is tested using the ping command, and the number of packet losses is counted. This command sends four data packets at an interval of 1 second and counts the number of packet losses. Then, the actual communication status data of the power grid is analyzed for difference with the baseline communication status data. If the difference between the actual communication status data and the baseline communication status data is not less than a preset threshold, an alarm is issued. The alarm forms include sound alarms, light alarms, sound and light alarms, screen alarms, etc. The alarm information can also be displayed on the screen. The alarm information includes but is not limited to the user number, time, alarm type, and alarm level. Among them, the alarm types include communication delay, excessive packet loss rate, insufficient signal strength, etc., and the alarm levels include minor and severe. This helps the power grid operation platform to promptly detect power grid faults and make adjustments or pauses, thereby improving the security of the power grid.

[0065] In addition, if Figure 3 As shown, the method for determining the grid data grid-connected transmission time also includes the following contents:

[0066] Step S7: When the number of times the grid-connected transmission time point is reset reaches a preset value, the user's data to be transmitted is divided into multiple data packets based on importance, and the grid-connected transmission time period of each data packet is determined to achieve hierarchical data transmission.

[0067] Specifically, since repeated resetting of the grid-connected transmission time point causes the system algorithm to continuously repeat calculations, resulting in a heavy load on the system during multiple runs, in order to ensure system stability and ensure that new users can upload their data to the grid in a timely manner to ensure the timeliness of grid data collection, after the grid-connected transmission time point has been reset a preset number of times, the user's data to be transmitted is divided into multiple data packets based on their importance, and the grid-connected transmission time period for each data packet is determined. This implements hierarchical data transmission, which can both reduce the impact on grid communications and ensure the timeliness of grid data collection. The process of dividing the user's data to be transmitted into multiple data packets based on their importance is as follows: core data with high real-time requirements and high sensitivity, such as real-time transaction data and user identity information, is classified as primary data; important data with medium real-time requirements and medium sensitivity, such as business report data and user behavior data, is classified as secondary data; and general data with low real-time requirements and low sensitivity, such as log data and backup data, is classified as tertiary data, thereby dividing the data into three data packets. Different transmission time windows are set for data packets of different levels. For example, primary data can be transmitted at any time, secondary data can be transmitted during off-peak hours, and tertiary data can be transmitted at night or in the early morning, which is conducive to improving the stability of the power grid communication network. Of course, in other embodiments of the present invention, more detailed data classification standards can also be defined according to specific business needs, and different transmission time windows can be set for data of different levels.

[0068] In addition, if Figure 4 As shown, another embodiment of the present invention further provides a system for determining grid data grid-connected transmission time, preferably using the above-mentioned method for determining grid data grid-connected transmission time, the system comprising:

[0069] A time series model building module is used to collect communication status data of the power grid at different time nodes and build a time series model that reflects the mapping relationship between time nodes and communication status data;

[0070] A linear regression model building module is used to collect communication status data of the power grid after different amounts of newly transmitted data are connected to the grid, and to build a linear regression model that reflects the mapping relationship between the newly transmitted data amount and the communication status data;

[0071] A first communication state data prediction module is used to set a grid-connected transmission time point and input it into a time series model to obtain communication state data of the power grid at the set grid-connected transmission time point and use it as reference communication state data;

[0072] The second communication status data prediction module is used to obtain the amount of user data to be transmitted and input it into the linear regression model to obtain the communication status data of the power grid after the user data is connected to the grid and transmitted, and use it as the predicted communication status data;

[0073] The grid-connected transmission time point determination module is used to perform difference analysis on the predicted communication status data and the reference communication status data, and determine whether the currently set grid-connected transmission time point meets the requirements based on the difference analysis results.

[0074] It can be understood that the system for determining grid data grid-connected transmission time in this embodiment first collects historical communication status data of the user's grid at different time nodes and constructs a time series model that reflects the mapping relationship between time nodes and communication status data. After setting the grid-connected transmission time point, the system can predict the communication status data of the grid at the set grid-connected transmission time point without the incorporation of the new user based on the time series model, and use this as a judgment benchmark for the new user's grid connection. Simultaneously, the system collects historical communication status data of the grid after grid-connected transmission with different amounts of newly transmitted data, and constructs a linear regression model that reflects the mapping relationship between the amount of newly transmitted data and the communication status data. When a new user needs to be incorporated into the grid, after obtaining the amount of data to be transmitted by the user, the system can predict the communication status data of the grid after the new user is incorporated based on the linear regression model, and use this as the predicted communication status data. Finally, based on the difference analysis results between the predicted communication status data and the reference communication status data, it can be determined whether the currently set grid-connected transmission time point meets the requirements. This can accurately evaluate the rationality of the new user's grid-connected transmission time point, thereby improving the communication efficiency, reliability, and stability of the grid.

[0075] In addition, the system for determining the grid data transmission time further includes:

[0076] The operation fault warning module is used to collect the actual communication status data of the power grid after the user is connected to the power grid, and perform difference analysis on the actual communication status data and the benchmark communication status data. If the difference between the actual communication status data and the benchmark communication status data is not less than the preset threshold, an early warning is issued.

[0077] In addition, the system for determining the grid data transmission time further includes:

[0078] The data hierarchical transmission module is used to divide the user's to-be-transmitted data into multiple data packets based on their importance after the number of grid-connected transmission time points is reset to a preset value, and to determine the grid-connected transmission time period for each data packet to achieve data hierarchical transmission.

[0079] It can be understood that the various modules of the system embodiment correspond to the various steps of the above method embodiment, so the specific working principles of each module will not be repeated here, and the corresponding references can be made to the various steps of the above method embodiment.

[0080] In addition, another embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the above method by calling the computer program stored in the memory.

[0081] In addition, another embodiment of the present invention further provides a computer-readable storage medium for storing a computer program for determining the grid data grid-connected transmission time, wherein the computer program executes the steps of the above-described method when running on a computer.

[0082] Common computer-readable storage media include: floppy disks, flexible disks, hard disks, magnetic tape, any other magnetic media, CD-ROMs, any other optical media, punch cards, paper tape, any other physical medium with a pattern of holes, random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), flash-erasable programmable read-only memory (FLASH-EPROM), any other memory chip or cartridge, or any other medium that can be read by a computer. Instructions can further be transmitted or received via a transmission medium. The term transmission medium may include any tangible or intangible medium that can be used to store, encode, or carry instructions for execution by a machine, and includes digital or analog communication signals or other intangible media that facilitate communication of such instructions. Transmission media include coaxial cables, copper wire, and fiber optics, including the wires of a bus used to transmit a computer data signal.

[0083] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal translation scripting language JavaScript, etc.

[0084] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0085] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0087] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0088] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

[0089] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for determining the grid data transmission time, characterized in that: Includes the following: Collect the communication status data of the power grid at different time nodes and construct a time series model that reflects the mapping relationship between time nodes and communication status data; Collect the communication status data of the power grid after the grid-connected transmission of different newly added transmission data volumes, and construct a linear regression model reflecting the mapping relationship between the newly added transmission data volumes and the communication status data; The grid-connected transmission time point is set and input into the time series model to obtain the communication status data of the power grid at the set grid-connected transmission time point, which is used as the benchmark communication status data; Obtain the amount of user data to be transmitted and input it into a linear regression model to obtain the communication status data of the power grid after the user data is connected to the grid and transmitted, which is used as the predicted communication status data; Perform a difference analysis on the predicted communication status data and the benchmark communication status data, and determine whether the currently set grid-connected transmission time point meets the requirements based on the difference analysis results.

2. The method for determining the grid data transmission time according to claim 1, wherein: If the difference between the predicted communication status data and the benchmark communication status data is less than the preset threshold, it is determined that the currently set grid-connected transmission time point meets the requirements, and the user will be connected to the grid when the currently set grid-connected transmission time point is reached in the future; if the difference between the predicted communication status data and the benchmark communication status data is not less than the preset threshold, it is determined that the currently set grid-connected transmission time point does not meet the requirements, and a new grid-connected transmission time point needs to be reset until the difference between the predicted communication status data and the benchmark communication status data is less than the preset threshold.

3. The method for determining the grid data transmission time according to claim 2, wherein: The time series model is: Y(t)=Φ(B)*Y(t-1)+Θ(B)*ε(t-1)+ε(t) Where Y(t) represents the communication status data of the power grid at time node t, Φ(B) represents the autoregressive coefficient polynomial, which is determined by the autoregressive order p, B represents the delay operator, which is used to shift the time series data backward by one time unit, Y(t-1) is the communication status data sequence of the power grid at the past time node, containing p past values, Θ(B) represents the moving average coefficient polynomial, which is determined by the moving average order q, ε(t-1) represents the error term sequence of the past time node, containing q past values, and ε(t) is the error term at time node t.

4. The method for determining the grid data transmission time according to claim 3, wherein: After the set grid-connected transmission time point is input into the time series model and the communication status data of the power grid at this time is predicted, if the communication status data of the power grid at this time exceeds the preset threshold, a new grid-connected transmission time point needs to be reset.

5. The method for determining the grid data transmission time according to claim 2, wherein: Also included: When the number of times the grid-connected transmission time point is reset reaches the preset value, the user's data to be transmitted is divided into multiple data packets based on their importance, and the grid-connected transmission time period of each data packet is determined to achieve hierarchical data transmission.

6. The method for determining the grid data transmission time according to claim 2, wherein: Also included: After the user is connected to the power grid, the actual communication status data of the power grid is collected, and the difference analysis is performed between the actual communication status data and the benchmark communication status data. If the difference between the actual communication status data and the benchmark communication status data is not less than the preset threshold, an early warning is issued.

7. The method for determining the grid data transmission time according to claim 1, wherein: The communication status data of each time node is calculated based on the following formula: x=w1x1+w2x2+w3x3 Where x represents the communication status data, x1, x2, and x3 represent the normalized communication volume, communication delay, and packet loss rate, respectively, w1, w2, and w3 are weight coefficients, and w1+w2+w3=1.

8. A system for determining grid data transmission time, characterized in that: include: A time series model building module is used to collect communication status data of the power grid at different time nodes and build a time series model that reflects the mapping relationship between time nodes and communication status data; A linear regression model building module is used to collect communication status data of the power grid after different amounts of newly transmitted data are connected to the grid, and to build a linear regression model that reflects the mapping relationship between the newly transmitted data amount and the communication status data; A first communication state data prediction module is used to set a grid-connected transmission time point and input it into a time series model to obtain communication state data of the power grid at the set grid-connected transmission time point and use it as reference communication state data; The second communication status data prediction module is used to obtain the amount of user data to be transmitted and input it into the linear regression model to obtain the communication status data of the power grid after the user data is connected to the grid and transmitted, and use it as the predicted communication status data; The grid-connected transmission time point determination module is used to perform difference analysis on the predicted communication status data and the reference communication status data, and determine whether the currently set grid-connected transmission time point meets the requirements based on the difference analysis results.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to execute the steps of the method according to any one of claims 1 to 7 by calling the computer program stored in the memory.

10. A computer-readable storage medium for storing a computer program for determining grid data transmission time, characterized in that: When the computer program is run on a computer, the steps of the method according to any one of claims 1 to 7 are executed.

Images