A big data resource allocation method and system for smart grid

Abstract

The present invention discloses a method and system for allocating big data resources for smart grids, which relates to the technical field of resource allocation. The method and system obtain power data and network node information in the smart grid; divide the power data into a number of power data blocks according to the power data and the network node information at each time node; the number of the power data blocks is not greater than the number of edge nodes in the smart grid; determine the network resource allocation priority corresponding to each power data block; and allocate a corresponding network node to each power data block according to the network resource allocation priority, so as to process the power data block through the network node. By adopting the embodiment of the present invention, the overall time series corresponding to the power data can be processed in blocks, and different data blocks can be allocated to corresponding network nodes for processing, thereby improving the rationality of Internet big data resource allocation and the processing efficiency of big data resources.

Description

Technical Field

[0001] The present invention relates to the technical field of resource allocation, and in particular to a big data resource allocation method and system for a smart grid. Background Art

[0002] Electricity data has penetrated into every aspect of life. With the vigorous development of the Internet, the emergence of intelligent terminal devices has brought revolutionary changes to the power network system. The entire power network is monitored through the smart grid. When using the smart grid, the optimization of network resources is crucial to the construction of the smart grid.

[0003] Traditional network resource optimization generally increases the scheduling and on-demand allocation of network resources to increase the working efficiency of network resources. However, the network transmission requirements of the power system are complex, and traditional network resource optimization methods are not well applicable to the power system. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to allocate network resources for a smart grid, and provides a big data resource allocation method and system for a smart grid, which can take into account the demand for corresponding network resources of the power system and meet the timeliness and stability requirements of the power system.

[0005] In order to solve the above technical problems, an embodiment of the present invention provides a big data resource allocation method for a smart grid, comprising:

[0006] Obtain power data and network node information in the smart grid;

[0007] According to the power data and the network node information at each time node, the power data is divided into a plurality of power data blocks; the number of the power data blocks is not greater than the number of edge nodes in the smart grid;

[0008] Determining a network resource allocation priority corresponding to each of the power data blocks;

[0009] According to the network resource allocation priority, a corresponding network node is allocated to each of the power data blocks, so that the power data blocks are processed by the network node.

[0010] As an improvement of the above solution, the obtaining of power data and network node information in the smart grid includes:

[0011] Obtain the power consumption current, power voltage, power consumption and electricity fee of each power user in the smart grid as power data;

[0012] The power consumption location information of each power user in the smart grid, the node load information and node location information of the corresponding power consumption node are obtained as network node information.

[0013] As an improvement of the above solution, the power data is divided into several power data blocks according to the power data and the network node information at each time node, including:

[0014] Determine an initial segmentation time node of the power data according to the sub-data of the power data in each dimension at each time node;

[0015] Segmenting the power data using the initial segmentation time nodes to obtain initial power data blocks;

[0016] Calculate the connectivity threshold between the network nodes having connectivity relationship at each time node according to the network node information and the initial power data block;

[0017] According to the patency value threshold, the initial segmentation time nodes are screened to obtain final segmentation time nodes;

[0018] According to the final segmentation time node, the power data is divided into a plurality of power data blocks.

[0019] As an improvement of the above solution, determining the initial segmentation time node of the power data according to the sub-data of the power data in each dimension at each time node includes:

[0020] pass Calculate the The power data at the time node is The first partition result of the sub-data of dimensions; wherein, For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; is a linear normalization function;

[0021] According to the first division result, an initial segmentation time node is obtained.

[0022] As an improvement of the above solution, obtaining the initial segmentation time node according to the first division result includes:

[0023] pass Calculate the The second division result of time nodes; among them, For the A first division result of the sub-data of the largest dimension of the power data at a time node; is the number of dimensions of the power data;

[0024] pass The second division result is adjusted to obtain an initial segmentation time node.

[0025] As an improvement of the above solution, the calculation of the connectivity threshold between the network nodes having a connectivity relationship at each time node according to the network node information and the initial power data block includes:

[0026] According to the network node information, the data flow value between the network nodes having a connectivity relationship at each time node is calculated;

[0027] A patency value threshold is determined according to the data patency value.

[0028] As an improvement of the above solution, the step of calculating the data connectivity value between the network nodes having a connectivity relationship at each time node according to the network node information includes:

[0029] According to the network node information, the network nodes that have a connected relationship at each time node, the time length for power data to be transmitted to each connected node, and the occupancy rate of each connected node are obtained;

[0030] Multiplying the time length for transmitting the power data to each connected node by the occupancy rate of each connected node to obtain an intermediate product value of each connected node;

[0031] Selecting a minimum intermediate product value from the intermediate product values;

[0032] An exponential operation is performed on the opposite number of the minimum intermediate product value to obtain the data flow value between the network nodes that have a connectivity relationship at each time node.

[0033] As an improvement of the above solution, determining the patency value threshold according to the data patency value includes:

[0034] Taking the initial segmentation time node whose data flow value is greater than a first preset threshold as the target initial node;

[0035] Performing a sum operation on the data flow values ​​of the target initial node to obtain a flow sum value;

[0036] The patency sum value is linearly normalized to obtain a patency value threshold.

[0037] As an improvement of the above solution, the initial segmentation time nodes are screened according to the patency value threshold to obtain the final segmentation time nodes, including:

[0038] Calculating the connection duration of the initial power data block;

[0039] sorting the initial power data blocks according to the connection duration;

[0040] According to the order of the initial power data blocks, the initial segment time nodes corresponding to the network nodes whose data smoothness values ​​are greater than the smoothness value threshold are used as the final segment time nodes.

[0041] As an improvement of the above solution, the determining the network resource allocation priority corresponding to each power data block includes:

[0042] Calculate the emergency state value of each power data block according to the starting position time node, the ending position time node and the data time length corresponding to each power data block;

[0043] According to the emergency status value, a priority is assigned to the network resources corresponding to each of the power data blocks.

[0044] As an improvement of the above solution, the emergency state value of each power data block is calculated according to the starting position time node, the end position time node and the data time length corresponding to each power data block, including:

[0045] Obtaining a starting position time node, an end position time node and a data time length corresponding to each of the power data blocks;

[0046] Sum the starting position time node and the end position time node to obtain a first intermediate value;

[0047] Taking the first intermediate value and the data time length as a quotient to obtain a second intermediate value;

[0048] The second intermediate value is normalized to obtain an emergency state value.

[0049] As an improvement of the above solution, the step of allocating a priority to the network resources corresponding to each power data block according to the emergency state value includes:

[0050] sorting the power data blocks according to the emergency state value;

[0051] According to the order of the power data blocks, a network resource allocation priority is set for the power data blocks.

[0052] As an improvement of the above solution, the allocating a corresponding network node to each of the power data blocks according to the network resource allocation priority, so as to process the power data block through the network node, includes:

[0053] Get the data throughput value of each network node;

[0054] Sorting the network nodes according to the data flow value;

[0055] Allocating the sorted network nodes to the power data blocks according to the network resource allocation priority;

[0056] The corresponding power data block is processed by the network node.

[0057] The embodiment of the present invention further provides a big data resource allocation system for a smart grid, comprising:

[0058] Data acquisition module, used to obtain power data and network node information in the smart grid;

[0059] A data block division module, used for dividing the power data into a plurality of power data blocks according to the power data and the network node information at each time node; the number of the power data blocks is not greater than the number of edge nodes in the smart grid;

[0060] A priority determination module, used to determine the network resource allocation priority corresponding to each of the power data blocks;

[0061] The resource allocation module is used to allocate a corresponding network node to each of the power data blocks according to the network resource allocation priority, so as to process the power data blocks through the network node.

[0062] Compared with the prior art, the present invention discloses a big data resource allocation method and system for smart grids, which obtains power data and network node information in the smart grid; divides the power data into several power data blocks according to the power data and the network node information at each time node; the number of the power data blocks is not greater than the number of edge nodes in the smart grid; determines the network resource allocation priority corresponding to each power data block; and allocates a corresponding network node to each power data block according to the network resource allocation priority, so as to process the power data block through the network node. By adopting the embodiments of the present invention, the overall time series corresponding to the power data can be processed in blocks, and different data blocks can be allocated to corresponding network nodes for processing, which improves the rationality of Internet big data resource allocation and also improves the processing efficiency of big data resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the steps of a method for allocating big data resources for a smart grid provided by an embodiment of the present invention;

[0064] Figure 2 It is a structural diagram of a big data resource allocation system for a smart grid provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] In the description of the specification and claims, it is to be understood that the terms first, second, etc. in the specification and claims are only used for the purpose of describing the same technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated, nor necessarily describing the order or time sequence. The terms are interchangeable where appropriate. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0067] Traditional network resource optimization generally increases the scheduling and on-demand allocation of network resources to increase the working efficiency of network resources.

[0068] When optimizing the allocation of network resources for smart grids, it is necessary to consider the demand for network resources of the power system to ensure that the power system can be used stably during network transmission or network resource scheduling. At the same time, some of the acquired power data has timeliness requirements. For example, fault conditions need to be handled quickly, so fast network data transmission is required. Existing network resource optimization methods generally allocate on-demand by increasing network resources. However, due to the variable demand of the power system and the high stability requirements, traditional resource optimization methods are difficult to apply to complex power systems.

[0069] Based on the above considerations, an embodiment of the present invention provides a big data resource allocation method for a smart grid. Figure 1 In this embodiment, the big data resource allocation method for smart grid is specifically performed through steps S1 to S4:

[0070] S1. Obtain power data and network node information in the smart grid.

[0071] It should be noted that in the smart grid, when the power company manages the power system, it needs to transmit the acquired information to the power system database through network equipment. At the same time, after the user purchases electricity from the power company, the power company transmits the acquired power data back to the smart meter. Based on this, the data of each network device, that is, the power user, and the information of each network node can be obtained in the smart grid.

[0072] S2. Divide the power data into a number of power data blocks according to the power data and the network node information at each time node; the number of the power data blocks is not greater than the number of edge nodes in the smart grid.

[0073] In some embodiments, when performing data segmentation, when the acquired data itself appears abnormal, segmentation can be performed based on the degree of abnormality of the acquired data or the emergency situation. However, it should be noted that the upper limit of the segmentation at different locations is different, and the maximum number of segments cannot exceed the number of connected edge devices when segmenting.

[0074] When utilizing network resources, it is necessary to ensure the stability of the acquired network while also meeting the timeliness of the power data. Traditional network resource utilization methods generally ensure the stability of the acquired data through redundant transmission, but redundant transmission of data will result in poor timeliness of the data. In an embodiment of the present invention, by processing the acquired power data in blocks, a task is divided into multiple subtasks, and only the subtasks need to be redundantly transmitted, and the same task can be processed by multiple edge devices, which increases the efficiency of task processing and ensures the stability of the network system.

[0075] S3. Determine the network resource allocation priority corresponding to each of the power data blocks.

[0076] In the embodiment of the present invention, the network resource allocation priority can represent the urgency of processing the corresponding power data block. The higher the network resource allocation priority, the higher the timeliness requirement of the data block, and the earlier it needs to be processed.

[0077] S4. Allocate a corresponding network node to each of the power data blocks according to the network resource allocation priority, so that the power data blocks can be processed by the network node.

[0078] It should be noted that when performing data processing, the acquired complete data will be transmitted through different network node paths. The data on each path is complete data, but the network node on each path only processes a part of the divided power data blocks when performing data processing, and the other power data blocks are processed through other network nodes.

[0079] In the above scheme, the overall time series corresponding to the power data can be processed in blocks. When dividing the blocks, considering that the connectivity of edge devices corresponding to power data in different locations is also different, different data blocks are allocated to corresponding network nodes for processing, which improves the rationality of Internet big data resource allocation and also improves the processing efficiency of big data resources.

[0080] As a preferred implementation, step S1, obtaining power data and network node information in the smart grid, includes:

[0081] Obtain the power consumption current, power voltage, power consumption and electricity fee of each power user in the smart grid as power data;

[0082] The power consumption location information of each power user in the smart grid, the node load information and node location information of the corresponding power consumption node are obtained as network node information.

[0083] In an embodiment of the present invention, the time interval of the power data and the network node information is a preset time interval, that is, the power data and the network node information are time series data. The power data has four sub-data dimensions, namely, power current, power voltage, power consumption and electricity fee; the network node has three sub-data dimensions, namely, power location information, node load information of the corresponding power node and node location information.

[0084] It should be noted that the sub-data dimensions of the power data and the sub-data dimensions of the network nodes are only used as examples. In actual applications, the sub-data dimensions of the power data may also include power consumption, etc. The specific sub-data dimensions do not affect the beneficial effects of the present invention.

[0085] As a preferred implementation, step S2, according to the power data and the network node information at each time node, divide the power data into a plurality of power data blocks, and execute through steps S21-S25:

[0086] S21. Determine an initial segmentation time node of the power data according to the sub-data of the power data in each dimension at each time node.

[0087] When allocating network resources for power data, the acquired power data is analyzed to find that different dimensions or types of data have different requirements for the timeliness of the power data. Therefore, in an embodiment of the present invention, when blocking the power data, the overall time series can be blocked according to the changes in a single dimension. When blocking, the connectivity of edge devices corresponding to the power data acquired at different locations is also different, so the number of blocks also needs to consider the edge device to which each power data is connected.

[0088] S22. Segment the power data using the initial segmentation time node to obtain initial power data blocks.

[0089] S23. Calculate the connectivity threshold between the network nodes that are connected at each time node based on the network node information and the initial power data block.

[0090] S24. Screening the initial segmentation time nodes according to the patency value threshold to obtain final segmentation time nodes.

[0091] S25. Divide the power data into a plurality of power data blocks according to the final segmentation time node.

[0092] Further, preferably, step S21 includes:

[0093] pass Calculate the The power data at the time node is The first partition result of the sub-data of dimensions; wherein, For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; is a linear normalization function;

[0094] According to the first division result, an initial segmentation time node is obtained.

[0095] It should be noted that, through the above operation, the time division corresponding to the sub-data of each dimension at all time nodes can be quantified and presented. In some embodiments, the division results of the sub-data of different dimensions at all time nodes can be marked on the same time axis.

[0096] It should also be noted that, in the embodiment of the present invention, since the power data at the same time node has sub-data of multiple dimensions, the same time node corresponds to multiple first division results, for example, Time nodes exist A plurality of first division results.

[0097] Furthermore, preferably, obtaining the initial segmentation time node according to the first division result includes:

[0098] pass Calculate the The second division result of time nodes; among them, For the A first division result of the sub-data of the largest dimension of the power data at a time node; is the number of dimensions of the power data;

[0099] pass The second division result is adjusted to obtain an initial segmentation time node.

[0100] It should be noted that the integrity of the changes in the partitioned data corresponding to each node adjusts the partitioning nodes of the acquired data. When partitioning the acquired data, the integrity of each segment of the partitioned data needs to be considered. For example, when the voltage of the power data is abnormal, the corresponding state will change, but when the acquired state changes, the voltage data has already become abnormal. The corresponding two partitioning situations will divide a complete event into two data. In the subsequent analysis, incomplete data will affect the robustness of data feature extraction or information extraction. Therefore, when partitioning the acquired time data, the partitioning of each time data and the time in the neighborhood should be considered. If there is an obvious node partitioning situation in the neighborhood, it means that the corresponding node may cause incomplete data when partitioning the data.

[0101] In this embodiment, the division of each time node with other time nodes in the neighborhood is considered during the division. If there is obvious data incompleteness in the neighborhood, the initial segmentation time nodes of the power data can be adjusted based on the integrity of the initial division results corresponding to each initial segmentation time node.

[0102] It should also be noted that in order to ensure that the calculation results are meaningful, when performing fractional operations in the embodiments of the present invention, if the denominator is 0, it is necessary to add a parameter adjustment factor greater than 0 to the denominator to prevent the denominator from being 0. The value of the parameter adjustment factor is set by the implementer according to actual conditions, and the present invention does not impose any special restrictions.

[0103] The embodiment of the present invention adjusts the division situation of the current time node through the division situation of the previous time node. It can be understood that the larger the division situation of the previous time node is, the greater the probability that the generation of the i-th problem is affected by the previous problem, that is, the two are an event, so the initial result of the division of the current time node can be adjusted smaller.

[0104] Further, preferably, step S23, based on the network node information and the initial power data block, calculates the connectivity value threshold between the network nodes having a connectivity relationship at each time node, which is specifically performed through steps S231-S232:

[0105] S231. Calculate the data connectivity value between the network nodes that are connected at each time node according to the network node information.

[0106] It should be noted that, taking the example of a connection relationship between network node A and network node B, the connection relationship includes direct connection and indirect connection. When the connection relationship is direct connection, the network node A and the network node B directly transmit data; when the connection relationship is indirect connection, the network node A and the network node B are connected through at least one intermediate network node.

[0107] It should also be noted that the network node can be an electricity user or a node of the Internet of Things in a smart grid, and both can transmit data through the smart grid.

[0108] S232: Determine a patency value threshold value according to the data patency value.

[0109] Furthermore, as a preferred implementation, step S231 includes:

[0110] According to the network node information, the network nodes that have a connected relationship at each time node, the time length for power data to be transmitted to each connected node, and the occupancy rate of each connected node are obtained;

[0111] Multiplying the time length for transmitting the power data to each connected node by the occupancy rate of each connected node to obtain an intermediate product value of each connected node;

[0112] Selecting a minimum intermediate product value from the intermediate product values;

[0113] An exponential operation is performed on the opposite number of the minimum intermediate product value to obtain the data flow value between the network nodes that have a connectivity relationship at each time node.

[0114] For example, by Calculating the data unimpeded value between the network node A and the network node B having a connectivity relationship in the initial power data block; wherein the direction of data transmission is from the network node A to the network node B; is the total number of connected nodes between the network node A and the network node B; represents the transmission time length of the power data, Indicates the occupancy rate of network node B.

[0115] As a preferred implementation, step S232 includes:

[0116] Taking the initial segmentation time node whose data flow value is greater than a first preset threshold as the target initial node;

[0117] Performing a sum operation on the data flow values ​​of the target initial node to obtain a flow sum value;

[0118] The patency sum value is linearly normalized to obtain a patency value threshold.

[0119] For example, when calculating the smooth value threshold of the initial power data block, if it is an indirect connection, the intermediate network nodes of the indirect connection also need to be considered. Calculate the smooth value threshold of the initial power data block; wherein, is the data flow value between the network node A and the network node B that have a connectivity relationship; S is the number of the target initial nodes between the network node A and the network node B; is a linear normalization function.

[0120] In some embodiments, if the number of initial segment time nodes whose data flow rate is greater than a first preset threshold is less than a preset number threshold, the initial segment time nodes are filtered according to the length of the initial power data block to obtain the target initial node.

[0121] As a preferred implementation, step S24, screening the initial segmentation time nodes according to the patency value threshold to obtain the final segmentation time nodes, includes:

[0122] Calculating the connection duration of the initial power data block;

[0123] sorting the initial power data blocks according to the connection duration;

[0124] According to the order of the initial power data blocks, the initial segment time nodes corresponding to the network nodes whose data smoothness values ​​are greater than the smoothness value threshold are used as the final segment time nodes.

[0125] In the above scheme, the patency threshold The block position can be accurately determined, thereby obtaining the final segmentation time node after adjusting the partitioning and sorting results.

[0126] Exemplarily, if there are five initial segment time nodes a, b, c, d, and e, an initial power data block is formed between two adjacent initial segment time nodes. After sorting the above five initial segment time nodes according to the numerical size of the initial power data block, the sorting results are b, a, d, c, and e.

[0127] As a preferred implementation, step S3, determining the network resource allocation priority corresponding to each power data block, is performed through steps S31-S32:

[0128] S31, calculating the emergency state value of each power data block according to the starting position time node, the ending position time node and the data time length corresponding to each power data block;

[0129] S32. Assign a priority to the network resources corresponding to each of the power data blocks according to the emergency status value.

[0130] Further, preferably, step S31 includes:

[0131] Obtaining a starting position time node, an end position time node and a data time length corresponding to each of the power data blocks;

[0132] Sum the starting position time node and the end position time node to obtain a first intermediate value;

[0133] Taking the first intermediate value and the data time length as a quotient to obtain a second intermediate value;

[0134] The second intermediate value is normalized to obtain an emergency state value.

[0135] For example, by Calculate the The emergency state value of the power data block; wherein, For the The starting position time node corresponding to each power data block, For the The end point location time node corresponding to each power data block, is the data time length of the power data block, is a linear normalization function.

[0136] Preferably, step S32 includes:

[0137] sorting the power data blocks according to the emergency state value;

[0138] According to the order of the power data blocks, a network resource allocation priority is set for the power data blocks.

[0139] It can be understood that the emergency state value is positively correlated with the network allocation priority, that is, the larger the emergency state value, the higher the corresponding network allocation priority, so that each data block can be sorted according to the emergency state value to determine the network resource allocation priority corresponding to each data block.

[0140] Exemplarily, five power data blocks A, B, C, D, E, and F are obtained, and the network nodes are sorted according to the data flow values. There are five network nodes a, b, c, d, and e. After sorting, the data blocks and network nodes corresponding to the order are matched into a group, so that the matching relationship between each data block and the network node can be obtained.

[0141] In some embodiments, the number of power data blocks is greater than the number of network nodes, and the network nodes may be repeatedly matched. For example, data block A corresponds to network node a, and the last data block F may also correspond to network node a.

[0142] As a preferred implementation, step S4, allocating a corresponding network node to each of the power data blocks according to the network resource allocation priority, so as to process the power data block through the network node, includes:

[0143] Get the data throughput value of each network node;

[0144] Sorting the network nodes according to the data flow value;

[0145] Allocating the sorted network nodes to the power data blocks according to the network resource allocation priority;

[0146] The corresponding power data block is processed by the network node.

[0147] In an embodiment of the present invention, during the processing of power data blocks, they can be queued according to their corresponding network resource allocation priorities, and a corresponding network node can be allocated to each data block in the queueing order. Specifically, they can be allocated according to the data smoothness value of the network node. According to the principle of allocating data blocks with higher network resource allocation priorities to network nodes with higher data smoothness values, a matching relationship between data blocks and network nodes is established. Therefore, network nodes with excellent smoothness can be allocated to data blocks with high timeliness requirements, so that the most smooth network nodes can process the most urgent data blocks, thereby realizing reasonable allocation of data resources, ensuring the processing timeliness of data resources, and improving data resource processing efficiency.

[0148] In some embodiments, each power data block can also be allocated to its corresponding network node, and the network node stores and processes the corresponding data block. At the same time, the power data obtained by different edge devices can be fed back to the power sensor or user terminal to display abnormal data.

[0149] A big data resource allocation method for a smart grid provided by an embodiment of the present invention takes into account that the connectivity conditions of edge devices corresponding to power data at different locations are different, and the overall time series corresponding to the power data is processed in blocks. Different data blocks are then allocated to corresponding network nodes for processing. This can improve the rationality of Internet big data resource allocation and also improve the processing efficiency of big data resources.

[0150] The embodiment of the present invention provides a big data resource allocation system for smart grid. Figure 2 The big data resource allocation system for smart grid includes a data acquisition module 11, a data block division module 12, a priority determination module 13 and a resource allocation module 14, wherein:

[0151] The data acquisition module 11 is used to acquire power data and network node information in the smart grid;

[0152] A data block division module 12 is used to divide the power data into a plurality of power data blocks according to the power data and the network node information at each time node; the number of the power data blocks is not greater than the number of edge nodes in the smart grid;

[0153] A priority determination module 13, used to determine the network resource allocation priority corresponding to each power data block;

[0154] The resource allocation module 14 is used to allocate a corresponding network node to each of the power data blocks according to the network resource allocation priority, so that the power data blocks can be processed by the network node.

[0155] As a preferred implementation, the data acquisition module 11 includes:

[0156] A power data acquisition unit, used to acquire the power current, power voltage, power consumption and power charges of each power user in the smart grid as power data;

[0157] The network node information acquisition unit is used to acquire the power consumption location information of each power user in the smart grid, the node load information and node location information of the corresponding power consumption node as the network node information.

[0158] As a preferred implementation, the data block division module 12 includes:

[0159] An initial segmentation time node determination unit, configured to determine an initial segmentation time node of the power data according to sub-data of the power data in each dimension at each time node;

[0160] An initial power data block division unit, configured to segment the power data using the initial segmentation time nodes to obtain initial power data blocks;

[0161] A smooth value threshold calculation unit, used to calculate the smooth value threshold between the network nodes having a connectivity relationship at each time node according to the network node information and the initial power data block;

[0162] A final segmentation time node determination unit, configured to screen the initial segmentation time nodes according to the patency value threshold value to obtain a final segmentation time node;

[0163] The power data block division unit is used to divide the power data into a plurality of power data blocks according to the final segmentation time node.

[0164] Further, preferably, the initial segmentation time node determination unit is specifically used to:

[0165] pass Calculate the The power data at the time node is The first partition result of the sub-data of dimensions; wherein, For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; is a linear normalization function;

[0166] According to the first division result, an initial segmentation time node is obtained.

[0167] Furthermore, preferably, obtaining the initial segmentation time node according to the first division result includes:

[0168] pass Calculate the The second division result of time nodes; among them, For the A first division result of the sub-data of the largest dimension of the power data at a time node; is the number of dimensions of the power data;

[0169] pass The second division result is adjusted to obtain an initial segmentation time node.

[0170] Preferably, the patency value threshold calculation unit is specifically used for:

[0171] According to the network node information, the data flow value between the network nodes having a connectivity relationship at each time node is calculated;

[0172] A patency value threshold is determined according to the data patency value.

[0173] Further, preferably, the calculating, according to the network node information, the data flow value between the network nodes having a connectivity relationship at each time node comprises:

[0174] According to the network node information, the network nodes that have a connected relationship at each time node, the time length for power data to be transmitted to each connected node, and the occupancy rate of each connected node are obtained;

[0175] Multiplying the time length for transmitting the power data to each connected node by the occupancy rate of each connected node to obtain an intermediate product value of each connected node;

[0176] Selecting a minimum intermediate product value from the intermediate product values;

[0177] An exponential operation is performed on the opposite number of the minimum intermediate product value to obtain the data flow value between the network nodes that have a connectivity relationship at each time node.

[0178] Preferably, determining the patency value threshold according to the data patency value comprises:

[0179] Taking the initial segmentation time node whose data flow value is greater than a first preset threshold as the target initial node;

[0180] Performing a sum operation on the data flow values ​​of the target initial node to obtain a flow sum value;

[0181] The patency sum value is linearly normalized to obtain a patency value threshold.

[0182] As a preferred implementation, the final segmentation time node determination unit is specifically used to:

[0183] Calculating the connection duration of the initial power data block;

[0184] sorting the initial power data blocks according to the connection duration;

[0185] According to the order of the initial power data blocks, the initial segment time nodes corresponding to the network nodes whose data smoothness values ​​are greater than the smoothness value threshold are used as the final segment time nodes.

[0186] As a preferred implementation, the priority determination module 13 includes:

[0187] An emergency state value calculation unit, used to calculate the emergency state value of each power data block according to the starting position time node, the ending position time node and the data time length corresponding to each power data block;

[0188] The priority allocation unit is used to allocate a priority to the network resources corresponding to each of the power data blocks according to the emergency state value.

[0189] Further, preferably, the emergency state value calculation unit is specifically used to:

[0190] Obtaining a starting position time node, an end position time node and a data time length corresponding to each of the power data blocks;

[0191] Sum the starting position time node and the end position time node to obtain a first intermediate value;

[0192] Taking the first intermediate value and the data time length as a quotient to obtain a second intermediate value;

[0193] The second intermediate value is normalized to obtain an emergency state value.

[0194] Preferably, the priority allocation unit is specifically used to:

[0195] sorting the power data blocks according to the emergency state value;

[0196] According to the order of the power data blocks, a network resource allocation priority is set for the power data blocks.

[0197] As a preferred implementation, the resource allocation module 14 includes:

[0198] A data flow value acquisition unit, used to acquire the data flow value of each network node;

[0199] A network node sorting unit, used for sorting the network nodes according to the data flow value;

[0200] A node allocation unit, configured to allocate the sorted network nodes to the power data block according to the network resource allocation priority;

[0201] The power data block processing unit is used to process the corresponding power data block through the network node.

[0202] A big data resource allocation system for a smart grid provided by an embodiment of the present invention takes into account that the connectivity conditions of edge devices corresponding to power data at different locations are different, and the overall time series corresponding to the power data is processed in blocks. Different data blocks are then allocated to corresponding network nodes for processing. This can improve the rationality of Internet big data resource allocation and also improve the processing efficiency of big data resources.

[0203] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0204] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A big data resource allocation method for smart grid, characterized in that: include: Obtain power data and network node information in the smart grid; According to the power data and the network node information at each time node, the power data is divided into a plurality of power data blocks; The number of the power data blocks is not greater than the number of edge nodes in the smart grid; Determining a network resource allocation priority corresponding to each of the power data blocks; Allocating a corresponding network node to each of the power data blocks according to the network resource allocation priority, so as to process the power data blocks through the network node; The power data is divided into a plurality of power data blocks according to the power data and the network node information at each time node, including: Determine an initial segmentation time node of the power data according to the sub-data of the power data in each dimension at each time node; Segmenting the power data using the initial segmentation time nodes to obtain initial power data blocks; Calculate the connectivity threshold between the network nodes having connectivity relationship at each time node according to the network node information and the initial power data block; According to the patency value threshold, the initial segmentation time nodes are screened to obtain final segmentation time nodes; According to the final segmentation time node, the power data is divided into a plurality of power data blocks; The determining, according to the sub-data of the power data in each dimension at each time node, the initial segmentation time node of the power data comprises: pass Calculate the The power data at the time node is The first partition result of the sub-data of dimensions; wherein, For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; For the The sub-data of the dimension is in The time nodes and The absolute value of the difference at each time node; is a linear normalization function; According to the first division result, an initial segmentation time node is obtained; The step of obtaining the initial segmentation time node according to the first division result includes: pass Calculate the The second division result of time nodes; among them, For the A first division result of the sub-data of the largest dimension of the power data at a time node; is the number of dimensions of the power data; pass Adjusting the second division result to obtain an initial segmentation time node; The calculating, according to the network node information and the initial power data block, a connectivity threshold between network nodes having a connectivity relationship at each time node comprises: According to the network node information, the data flow value between the network nodes having a connectivity relationship at each time node is calculated; Determining a patency value threshold value according to the data patency value; The step of calculating the data connectivity value between the network nodes having a connectivity relationship at each time node according to the network node information includes: According to the network node information, the network nodes that have a connected relationship at each time node, the time length for power data to be transmitted to each connected node, and the occupancy rate of each connected node are obtained; Multiplying the time length for transmitting the power data to each connected node by the occupancy rate of each connected node to obtain an intermediate product value of each connected node; Selecting a minimum intermediate product value from the intermediate product values; Performing an exponential operation on the opposite number of the minimum intermediate product value to obtain a data flow value between network nodes that have a connectivity relationship at each time node; Determining a patency value threshold value according to the data patency value includes: Taking the initial segmentation time node whose data flow value is greater than a first preset threshold as the target initial node; Performing a sum operation on the data flow values ​​of the target initial node to obtain a flow sum value; Performing linear normalization processing on the patency sum value to obtain a patency value threshold; The initial segmentation time nodes are screened according to the patency value threshold to obtain the final segmentation time nodes, including: Calculating the connection duration of the initial power data block; sorting the initial power data blocks according to the connection duration; According to the order of the initial power data blocks, the initial segment time nodes corresponding to the network nodes whose data smoothness values ​​are greater than the smoothness value threshold are used as the final segment time nodes.

2. A big data resource allocation method for a smart grid as claimed in claim 1, characterized in that: The obtaining of power data and network node information in the smart grid includes: Obtain the power consumption current, power voltage, power consumption and electricity fee of each power user in the smart grid as power data; The power consumption location information of each power user in the smart grid, the node load information and node location information of the corresponding power consumption node are obtained as network node information.

3. A big data resource allocation method for a smart grid as claimed in claim 1, characterized in that: The determining of the network resource allocation priority corresponding to each of the power data blocks includes: Calculate the emergency state value of each power data block according to the starting position time node, the ending position time node and the data time length corresponding to each power data block; According to the emergency status value, a priority is assigned to the network resources corresponding to each of the power data blocks.

4. A big data resource allocation method for a smart grid as claimed in claim 3, characterized in that: The calculating the emergency state value of each power data block according to the starting position time node, the ending position time node and the data time length corresponding to each power data block includes: Obtaining a starting position time node, an end position time node and a data time length corresponding to each of the power data blocks; Sum the starting position time node and the end position time node to obtain a first intermediate value; Taking the first intermediate value and the data time length as a quotient to obtain a second intermediate value; The second intermediate value is normalized to obtain an emergency state value.

5. A big data resource allocation method for a smart grid as claimed in claim 3, characterized in that: The allocating a priority to the network resources corresponding to each of the power data blocks according to the emergency state value comprises: sorting the power data blocks according to the emergency state value; According to the order of the power data blocks, a network resource allocation priority is set for the power data blocks.

6. A big data resource allocation method for a smart grid according to claim 1, characterized in that: The allocating a corresponding network node to each of the power data blocks according to the network resource allocation priority, so as to process the power data blocks through the network node, comprises: Get the data throughput value of each network node; Sorting the network nodes according to the data flow value; Allocating the sorted network nodes to the power data blocks according to the network resource allocation priority; The corresponding power data block is processed by the network node.

7. A big data resource allocation system for smart grid, characterized in that: The big data resource allocation system for the smart grid executes the big data resource allocation method for the smart grid according to any one of claims 1 to 6, and the big data resource allocation system for the smart grid includes: Data acquisition module, used to obtain power data and network node information in the smart grid; A data block division module, used for dividing the power data into a plurality of power data blocks according to the power data and the network node information at each time node; the number of the power data blocks is not greater than the number of edge nodes in the smart grid; A priority determination module, used to determine the network resource allocation priority corresponding to each of the power data blocks; a resource allocation module, configured to allocate a corresponding network node to each of the power data blocks according to the network resource allocation priority, so as to process the power data blocks through the network node; The data block division module comprises: An initial segmentation time node determination unit, configured to determine an initial segmentation time node of the power data according to sub-data of the power data in each dimension at each time node; An initial power data block division unit, configured to segment the power data using the initial segmentation time nodes to obtain initial power data blocks; A smooth value threshold calculation unit, used to calculate the smooth value threshold between the network nodes having a connectivity relationship at each time node according to the network node information and the initial power data block; A final segmentation time node determination unit, configured to screen the initial segmentation time nodes according to the patency value threshold value to obtain a final segmentation time node; The power data block division unit is used to divide the power data into a plurality of power data blocks according to the final segmentation time node.

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