Data processing method and device, equipment, storage medium and computer program product

Abstract

This application relates to a data processing method, apparatus, device, storage medium, and computer program product. The method includes: acquiring characteristic parameters of each power device in a power system; determining a graph data model based on the characteristic parameters, the graph data model including device nodes representing each power device and electrical nodes storing electrical data of each power device; obtaining state parameters of each power device based on the characteristic parameters; dividing the graph data model into multiple sub-graph data models based on the state parameters; identifying target device nodes with abnormal electrical data in each sub-graph data model; identifying other device nodes connected to the target electrical node along with the target device node; obtaining target electrical data of the target device node based on the electrical data of the other device nodes; and obtaining target characteristic parameters of the power system based on the target electrical data. This method can improve the efficiency and accuracy of generating target electrical data in ultra-large-scale power systems.

Description

Technical Field

[0001] This application relates to the field of power system technology, and in particular to a data processing method, apparatus, device, storage medium, and computer program product. Background Technology

[0002] With the connection of massive distributed power equipment in the power system, the bidirectional flow of active and reactive current between the main distribution network, i.e., the high-voltage and low-voltage grids, has increased. In order to ensure the stable operation of the power system, it is necessary to stitch together the data models of the high-voltage and low-voltage grids to obtain the whole network model, so as to predict and optimize the state variables such as voltage amplitude of the power system based on the whole network model.

[0003] In existing technologies, the characteristic parameters and electrical data such as active and reactive power of various power equipment in a power system are organized in the form of CIM (Common Information Model) files. After reading the CIM file, a relational database is typically used to store the characteristic parameters, electrical data, and topological connections of each power equipment. For example, when using data from a relational database to stitch together high- and low-voltage power grid data models, if the electrical data of some power equipment is missing or incorrect, an online parameter estimation algorithm combined with a traditional power flow calculation model will be used to estimate the missing or incorrect electrical data.

[0004] However, traditional methods are inefficient and inaccurate in generating electrical data estimates for ultra-large-scale power systems with a large number of nodes, abundant electrical data, and multiple regions. Summary of the Invention

[0005] Therefore, it is necessary to provide a data processing method, apparatus, device, storage medium, and computer program product that can improve the generation efficiency and accuracy of electrical data estimation values ​​in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a data processing method. The method includes:

[0007] The characteristic parameters of each power device in the power system are obtained, and the graph data model is determined based on each characteristic parameter. The graph data model includes device nodes for representing each power device and electrical nodes for storing electrical data of each power device.

[0008] The state parameters of each power device are obtained based on the characteristic parameters, and the graph data model is divided into multiple sub-graph data models based on the state parameters.

[0009] In each subgraph data model, the target device node and target electrical node with abnormal electrical data are identified, and other device nodes that are connected to the target electrical node are identified. The target electrical data of the target device node is obtained based on the electrical data of the other device nodes, so as to obtain the target characteristic parameters of the power system based on the target electrical data.

[0010] In one embodiment, the electrical data includes a quality identifier to characterize whether the electrical data is abnormal; the target electrical data of the target device node is obtained based on the electrical data of other device nodes, including:

[0011] The electrical data of other device nodes is determined based on their quality indicators. If the electrical data of other device nodes is normal, the target electrical data is determined based on their electrical data. If the electrical data of other device nodes is abnormal, an equivalent model is obtained based on their electrical data, and the target electrical data is determined based on the equivalent model.

[0012] In one embodiment, the feature parameters include a device type identifier; obtaining an equivalent model based on the electrical data of other device nodes, and determining the target electrical data based on the equivalent model, includes:

[0013] Obtain the target device type identifier for each target device node, determine the target equivalent model based on the target device type identifier, and determine the target electrical data for the target device node based on the target equivalent model.

[0014] In one embodiment, determining the target equivalent model based on the target device type identifier includes:

[0015] The target device node is determined to be an end device based on the target device type identifier. If all target device nodes are end devices, the target equivalent model is determined to be used to determine the target electrical data based on the electrical data of other device nodes with normal electrical data.

[0016] If the target device node has non-terminal devices, the target equivalent model is determined to determine the target electrical data based on the electrical data corresponding to the superior electrical node located immediately adjacent to the target electrical node. The superior electrical node is located upstream of the target electrical node in the current direction.

[0017] In one embodiment, determining the target electrical data based on an equivalent model includes:

[0018] Determine whether the number of target device nodes is equal to a preset threshold. If the number of target device nodes is equal to the preset threshold, the calculation result of the target equivalent model is used as the target electrical data. If the number of target device nodes is not equal to the preset threshold, the target electrical data is obtained by allocating the target electrical data according to the proportion of the device capacity of each target device node based on the calculation result of the target equivalent model.

[0019] In one embodiment, the state parameters include the location information and voltage level information of each power device; the graph data model is divided into multiple sub-graph data models based on the state parameters, including:

[0020] Based on the location information and / or voltage level information of each power equipment, the graph data model is divided into multiple sub-graph data models.

[0021] Secondly, this application also provides a data processing apparatus. The apparatus includes:

[0022] The graph data model generation module is used to obtain the characteristic parameters of each power device in the power system and determine the graph data model based on each characteristic parameter. The graph data model includes equipment nodes for representing each power device and electrical nodes for storing the electrical data of each power device.

[0023] The graph data model partitioning module is used to obtain the state parameters of each power device based on the feature parameters, and to partition the graph data model into multiple sub-graph data models based on the state parameters.

[0024] The electrical data processing module is used to identify the target device node and target electrical node in each subgraph data model where the electrical data is abnormal, and to identify other device nodes that are connected to the target electrical node. Based on the electrical data of the other device nodes, the target electrical data of the target device node is obtained, and the target characteristic parameters of the power system are obtained based on the target electrical data.

[0025] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:

[0026] The characteristic parameters of each power device in the power system are obtained, and the graph data model is determined based on each characteristic parameter. The graph data model includes device nodes for representing each power device and electrical nodes for storing electrical data of each power device.

[0027] The state parameters of each power device are obtained based on the characteristic parameters, and the graph data model is divided into multiple sub-graph data models based on the state parameters.

[0028] In each subgraph data model, the target device node and target electrical node with abnormal electrical data are identified, and other device nodes that are connected to the target electrical node are identified. The target electrical data of the target device node is obtained based on the electrical data of the other device nodes, so as to obtain the target characteristic parameters of the power system based on the target electrical data.

[0029] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:

[0030] The characteristic parameters of each power device in the power system are obtained, and the graph data model is determined based on each characteristic parameter. The graph data model includes device nodes for representing each power device and electrical nodes for storing electrical data of each power device.

[0031] The state parameters of each power device are obtained based on the characteristic parameters, and the graph data model is divided into multiple sub-graph data models based on the state parameters.

[0032] In each subgraph data model, the target device node and target electrical node with abnormal electrical data are identified, and other device nodes that are connected to the target electrical node are identified. The target electrical data of the target device node is obtained based on the electrical data of the other device nodes, so as to obtain the target characteristic parameters of the power system based on the target electrical data.

[0033] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:

[0034] The characteristic parameters of each power device in the power system are obtained, and the graph data model is determined based on each characteristic parameter. The graph data model includes device nodes for representing each power device and electrical nodes for storing electrical data of each power device.

[0035] The state parameters of each power device are obtained based on the characteristic parameters, and the graph data model is divided into multiple sub-graph data models based on the state parameters.

[0036] In each subgraph data model, the target device node and target electrical node with abnormal electrical data are identified, and other device nodes that are connected to the target electrical node are identified. The target electrical data of the target device node is obtained based on the electrical data of the other device nodes, so as to obtain the target characteristic parameters of the power system based on the target electrical data.

[0037] The aforementioned data processing methods, apparatus, equipment, storage media, and computer program products acquire characteristic parameters of each power device in a power system, and determine a graph data model based on these characteristic parameters. The graph data model includes device nodes representing each power device and electrical nodes storing the electrical data of each power device. State parameters of each power device are obtained based on the characteristic parameters, and the graph data model is divided into multiple sub-graph data models based on these state parameters. Within each sub-graph data model, target device nodes with abnormal electrical data and target electrical nodes are identified, along with other device nodes connected to the target device nodes. Target electrical data of the target device nodes is obtained based on the electrical data of the other device nodes, and the target characteristic parameters of the power system are obtained from the target electrical data. This application employs the above method, first determining the graph data model based on the characteristic parameters of each power device, then dividing the graph data model into multiple sub-graph data models based on the state parameters of each power device, and then synchronously calculating the target electrical data of the target device nodes with abnormal electrical data in each sub-graph data model based on the electrical data of other device nodes. This is beneficial for improving the efficiency and accuracy of generating target electrical data in ultra-large-scale power systems. Attached Figure Description

[0038] Figure 1 This is a diagram illustrating the application environment of a data processing method in one embodiment.

[0039] Figure 2 This is a flowchart of a data processing method in one embodiment;

[0040] Figure 3 This is a flowchart illustrating how target electrical data is determined based on electrical data from other device nodes in one embodiment.

[0041] Figure 4 This is a flowchart illustrating the determination of a target equivalent model based on a target device type identifier in one embodiment;

[0042] Figure 5 This is a flowchart illustrating how to determine target electrical data based on the number of target device nodes in one embodiment;

[0043] Figure 6 This is a structural block diagram of a data processing device in one embodiment;

[0044] Figure 7 This is an internal structural diagram of a computer device in one embodiment;

[0045] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0047] The data processing method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located in the cloud or on other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0048] In one embodiment, such as Figure 2 As shown, this method is applied to Figure 1 Taking a terminal as an example, it can be understood that this method can also be applied to a server, and to a system that includes both a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0049] Step 202: Obtain the characteristic parameters of each power device in the power system, and determine the graph data model based on each characteristic parameter. The graph data model includes device nodes for representing each power device and electrical nodes for storing the electrical data of each power device.

[0050] The power system is a series of interconnected electrical devices, including power plants, substations, transmission lines, distribution networks, and related control and protection devices. The main function of the power system is to transfer electricity generated by power plants to end users to meet people's electricity needs. Simultaneously, the power system must ensure a safe, reliable, and efficient supply of electricity, as well as balance and manage the power load. The characteristic parameters of power equipment are parameters used to describe the characteristics of the equipment, including the equipment name, equipment number, equipment type identifier, and equipment numbers of associated equipment with connection relationships. A graph data model is a way to represent and store data. In the graph data model of this application, the device nodes correspond to specific power equipment, and electrical nodes are virtual nodes used to store electrical data of each device node belonging to the same transmission line. Electrical data includes active power, reactive power, and current magnitude, and can be stored in tabular form. The graph data model also includes edges used to represent the connection relationships between device nodes or between device nodes and electrical nodes.

[0051] For example, after obtaining the characteristic parameters of each power device in the power system, device nodes and edges are constructed according to the device number of each power device and the device number of the associated device. The device nodes of the end devices are determined according to the device type identifier. Then, multiple transmission lines are determined according to the device nodes corresponding to the end devices. The number of electrical nodes is determined according to the number of transmission lines, so that each electrical node corresponds to one transmission line. Connection edges are set between the electrical node and multiple device nodes on its respective transmission line.

[0052] Step 204: Obtain the state parameters of each power device based on the feature parameters, and divide the graph data model into multiple sub-graph data models based on the state parameters.

[0053] Specifically, state parameters are used to characterize the current state information of power equipment. Typically, the state parameters of power equipment belonging to the same substation are consistent. Therefore, power equipment in the power system can be grouped or classified according to the state parameters. This allows the graph data model to be divided into multiple sub-graph data models based on the state parameters, so that the corresponding target electrical data can be calculated separately and in parallel in each sub-graph data model, thereby improving the generation efficiency of the target electrical data.

[0054] Step 206: In each subgraph data model, identify the target device node and target electrical node where the electrical data is abnormal, and identify other device nodes that are connected to the target electrical node together with the target device node. Obtain the target electrical data of the target device node based on the electrical data of the other device nodes, so as to obtain the target characteristic parameters of the power system based on the target electrical data.

[0055] Among these, anomalies in electrical data occur when equipment used for collecting or calculating electrical data malfunctions during the power system's data acquisition process, leading to errors or even missing values ​​in the calculated electrical data, thus resulting in anomalies. Target characteristic parameters of a power system are used to characterize the stability of its operating state. These parameters include state variables such as voltage amplitude and phase angle. Once the electrical data of all power equipment in the power system is confirmed to be correct, state estimation methods are used to predict the operating state of the power system. These methods typically use electrical data and the power system's model equations to balance the state variables of various nodes within and outside the power system, including information such as voltage amplitude and phase angle, to infer the operating state of each component (such as power transformers and lines) and load state. This allows for the monitoring and analysis of the power system's operating state, enabling timely detection and handling of grid faults or anomalies, thereby ensuring the safe and stable operation of the power system.

[0056] Specifically, as can be seen from the above, the electrical node stores the electrical data of the equipment nodes belonging to the same transmission line. When the electrical data of one or more equipment nodes connected to the electrical node is abnormal, the target electrical data of the target equipment node is obtained based on the electrical data of other equipment nodes connected to the electrical node, so as to correct the abnormal electrical data and improve the accuracy of predicting the operating status of the power system.

[0057] In the above data processing method, the graph data model is first determined based on the characteristic parameters of each power equipment. Then, the graph data model is divided into multiple sub-graph data models. In each sub-graph data model, the target electrical data of the target equipment node with abnormal electrical data is calculated based on the electrical data of other equipment nodes. This method is beneficial to improving the efficiency and accuracy of generating target electrical data in ultra-large-scale power systems.

[0058] In one embodiment, such as Figure 3 As shown, the electrical data includes quality indicators to characterize whether the electrical data is abnormal; the target electrical data of the target device node is obtained based on the electrical data of other device nodes, including:

[0059] Step 302: Determine whether the electrical data of other device nodes is abnormal based on the quality identifiers of other device nodes. If the electrical data of other device nodes is normal, proceed to step 304; otherwise, proceed to step 306.

[0060] As mentioned above, the electrical data stored in the electrical nodes can be stored in a table format. Each piece of electrical data in the table has a quality identifier to indicate whether it is abnormal, i.e., whether it is missing or erroneous. The quality identifier can be expressed in the form of numbers, letters, special symbols, etc. For example, in this embodiment, the quality identifier is expressed as a number. When the quality identifier is 0, it indicates that the electrical data of the corresponding device node is missing or erroneous; when the quality identifier is 1, it indicates that the electrical data of the corresponding device node is normal.

[0061] For example, when the electrical data of other electrical equipment nodes are all normal, since the electrical data of other equipment nodes are monitored in real time and are accurate, the target electrical data can be calculated directly based on the electrical data of other equipment nodes. However, when the electrical data of other electrical equipment nodes are also abnormal, it indicates that multiple equipment nodes connected to the same electrical node have abnormal electrical data. In this case, in order to ensure the accuracy and reliability of the target electrical data, the calculation method for the target electrical data needs to be changed.

[0062] Step 304: Determine the target electrical data based on the electrical data of other device nodes.

[0063] For example, the formula for determining the target electrical data based on the electrical data of other device nodes is as follows:

[0064] ∑ j X ij =∑X im +∑X ik ;

[0065] Where X represents electrical data, which can be active power P, reactive power Q, or current I; i represents electrical node i, j represents electrical equipment connected to electrical node i, m represents electrical equipment in j that is an end device, and k represents electrical equipment in j that is a non-end device.

[0066] Step 306: Obtain the equivalent model based on the electrical data of other device nodes, and determine the target electrical data based on the equivalent model.

[0067] For example, in this embodiment, the equivalent model refers to approximating or inferring abnormal electrical data by establishing reasonable models and assumptions, and using existing data and other relevant information, in order to continue the analysis and calculation of the power system. When using the equivalent model to calculate abnormal electrical data, the accuracy and applicability of the model are crucial. For different situations of missing electrical data, different equivalent models may be needed to calculate the abnormal electrical data.

[0068] In this embodiment, when there are target equipment nodes and target electrical nodes with abnormal electrical data in the power system, if the electrical data of other electrical equipment nodes associated with the target electrical node is normal, the target electrical data is directly calculated based on the electrical data of the other electrical equipment nodes; if the electrical data of other equipment nodes associated with the target electrical node is also abnormal, the target electrical data is determined based on the equivalent model, thereby ensuring the accuracy and reliability of the target electrical data.

[0069] In one embodiment, the characteristic parameters include a device type identifier; obtaining an equivalent model based on the electrical data of other device nodes, and determining the target electrical data based on the equivalent model, includes:

[0070] Obtain the target device type identifier for each target device node, determine the target equivalent model based on the target device type identifier, and determine the target electrical data for the target device node based on the target equivalent model.

[0071] The equipment type identifier is used to characterize the type of power equipment. The equipment type identifier can be an identifier name or an identifier number. In this embodiment, each power equipment can be divided into end equipment and non-end equipment. End equipment includes generators, transformers, motors, load equipment, etc. Each electrical node mentioned above corresponds to a transmission line. The two ends of the transmission line are the end equipment. Non-end equipment includes substations, switchgear, capacitors, busbars, etc. Therefore, the equipment type identifier in this embodiment includes end equipment and non-end equipment.

[0072] For example, due to the inconsistencies in characteristics between end devices and non-end devices, such as nonlinearity and time-varying nature, the amount and accuracy of available data for different types of power equipment also vary in practical applications. Some end devices may have more detailed and accurate electrical data, while the electrical data of non-end devices may be more limited or incomplete. Based on the available electrical data, selecting an appropriate equivalent model can enable analysis and calculation even when the data is incomplete.

[0073] In this embodiment, the corresponding equivalent model is determined by the target equipment type identifier, and the corresponding target electrical data is obtained based on the equivalent model, which helps to ensure the accuracy and reliability of the target electrical data.

[0074] In one embodiment, such as Figure 4 As shown, determining the target equivalent model based on the target equipment type identifier includes:

[0075] Step 402: Determine whether the target device node is an end device based on the target device type identifier. If all target device nodes are end devices, proceed to step 404; otherwise, proceed to step 406.

[0076] For example, as can be seen from the above, the target device type identifier in this embodiment includes end devices and non-end devices. After obtaining the target device type identifier corresponding to the target device node, the target device node is determined to be an end device or a non-end device based on the target device type identifier.

[0077] Step 404: Determine the target equivalent model to determine the target electrical data based on the electrical data of other equipment nodes with normal electrical data.

[0078] For example, due to the electrical connections and mutual influences between various electrical devices in a power system, non-terminal devices such as transmission lines and substations are usually complex systems composed of a group of electrical devices or components. These electrical devices transmit power to each other through electrical connections such as conductors and cables. Therefore, the electrical behavior and characteristics of non-terminal devices are often related to other electrical devices connected to them. So when there are non-terminal devices at the target device node, since other device nodes that are connected to the target electrical node are on the same transmission line as the target device node, the target electrical data can be determined based on the electrical data of other device nodes with normal electrical data.

[0079] Specifically, when all target device nodes are end devices, the corresponding equivalent target model is:

[0080]

[0081] Among them, X ie This is the calculation result of the equivalent model of the objective, { iu} represents the set of all device nodes u that are connected to electrical node i and lack electrical data.

[0082] Step 406: Determine the target equivalent model to determine the target electrical data based on the electrical data corresponding to the superior electrical node located immediately adjacent to the target electrical node. The superior electrical node is located upstream of the target electrical node in the current direction.

[0083] For example, since there are electrical connections and mutual influences between electrical nodes in a power system, and the current and voltage in the power system are distributed and transmitted between electrical nodes according to certain rules and relationships, the target electrical data can be calculated by measuring and analyzing the electrical data of the upper-level electrical nodes, including current and voltage. It is worth noting that when the target electrical data is determined based on the electrical data corresponding to the upper-level electrical node, what is determined is the sum of the electrical data of all equipment nodes connected to the target electrical node.

[0084] Specifically, when there are non-end devices in the target device node, the corresponding target equivalent model is:

[0085]

[0086] Where i0 is the parent electrical node i0 of electrical node i.

[0087] In this embodiment, determining the calculation method for the target electrical data based on whether the target device node is an end device or a non-end device helps to ensure the reliability and accuracy of the target electrical data.

[0088] In one embodiment, such as Figure 5 As shown, the target electrical data determined based on the equivalent model includes:

[0089] Step 502: Determine whether the number of target device nodes is equal to the preset threshold. If the number of target device nodes is equal to the preset threshold, proceed to step 504; otherwise, proceed to step 506.

[0090] For example, when there are multiple target device nodes, the target electrical data calculated according to the equivalent model is a single value. However, when calculating the state variables of the entire power system, it is necessary to collect the electrical data of each power device, i.e., the electrical data corresponding to each device node. Therefore, based on the target electrical data calculated by the equivalent model, the missing electrical data of multiple target device nodes needs to be assigned values ​​so that each device node has corresponding electrical data.

[0091] Step 504: Use the calculation results of the target equivalent model as the target electrical data.

[0092] For example, the preset threshold in this embodiment is 1. When the number of target device nodes is 1, it means that among all the device nodes connected to the corresponding electrical node, only one device node has abnormal electrical data. Therefore, the calculation result of the target equivalent model can be used as the target electrical data to correct the abnormal electrical data of the device node.

[0093] Step 506: Based on the calculation results of the target equivalent model, allocate the target electrical data according to the proportional relationship of the equipment capacity of each target equipment node to obtain the target electrical data.

[0094] For example, when the number of target device nodes is not 1 (i.e., greater than 1), it indicates that there is more than one target device node with missing electrical data. In this case, the calculation result of the target equivalent model is the sum of the target electrical data. The ratio of equipment capacity can be the ratio of the load capacity of each power device corresponding to each target device node. For example, when the number of target device nodes is 2, and the power devices corresponding to the two target device nodes are both transformers with the same equipment capacity, the ratio of equipment capacity is 1:1. Then, the output result of the target equivalent model is allocated according to the 1:1 ratio to obtain the target electrical data for each of the two target device nodes. In addition, as can be seen from the above, when determining the target electrical data based on the electrical data corresponding to the superior electrical node, what is determined is the sum of the electrical data of all device nodes connected to the target electrical node. When calculating each target electrical data, the electrical data corresponding to the superior electrical node should first be subtracted from the electrical data of other device nodes with normal electrical data to obtain the difference, and then allocated according to the ratio of equipment capacity to obtain each target electrical data.

[0095] Furthermore, after obtaining the target electrical data, correction value identifiers can be set for each target electrical data and stored in the electrical nodes. This allows for the identification of correction values ​​as target electrical data when calculating the state variables of the power system based on the electrical data of each device node, indicating a potential deviation in the calculation results. After processing all missing electrical data in the subgraph data models in parallel, state estimation calculations are performed, and the equivalent model construction is saved for subsequent retrieval of relevant information.

[0096] In this embodiment, when there is more than one target device node, the output results of the target equivalent model are allocated according to the proportional relationship of the device capacity corresponding to each target device node, so as to ensure the reliability and accuracy of the target electrical data.

[0097] In one embodiment, the state parameters include the location information and voltage level information of each power device; dividing the graph data model into multiple sub-graph data models according to the state parameters includes: dividing the graph data model into multiple sub-graph data models according to the location information and / or voltage level information of each power device.

[0098] The location information of power equipment refers to the location information of the dispatching department to which the power equipment belongs. In the power system, dispatching departments include power dispatching centers, substations, power plant operation departments, and transmission line inspection and maintenance departments. The location information of the dispatching department is the information of the region where the dispatching department is located. For example, if the location information of a substation and the inspection and maintenance department of a transmission line are both in District 1, East of City B, Province A, then the location information of each subordinate power equipment in the substation and each power equipment under the jurisdiction of the inspection and maintenance department of the transmission line are both in District 1, East of City B, Province A. Voltage level information refers to the voltage range or rated voltage that electrical equipment can handle or operate. Common voltage levels include 110kV, 220kV, and 500kV. For example, if the voltage level information of a substation is 220kV, it means that the substation is designed to handle or operate a 220kV voltage. All electrical equipment in the substation, such as generators, transformers, and switchgear, will be designed to adapt to a 220kV voltage. This also means that the insulation, rated current, and other performance parameters of these devices should meet the requirements of a 220kV voltage level.

[0099] For example, as described above, the power equipment in the power system can be grouped according to the location information of the dispatching department, dividing the graph data model into multiple sub-graph data models corresponding to the location information of different dispatching departments; alternatively, the graph data model can be divided into multiple sub-graph data models according to voltage level information, so that the power equipment in each sub-graph data model has the same voltage level; or the graph data model can be divided according to the voltage level and location information of each power equipment. For example, after grouping the power equipment according to the location information of the dispatching department, it can be further grouped according to the voltage level information of each power equipment; similarly, after grouping the power equipment according to the voltage level information, it can be further grouped according to the location information of the dispatching department to ensure the correlation of the electrical data of each power equipment in each sub-graph data model, which helps to further improve the generation efficiency of the target electrical data.

[0100] In this embodiment, the graph data model is divided into multiple sub-graph data models based on the location information and / or voltage level information of the power equipment, so as to simultaneously calculate the missing electrical data in each sub-graph data model, thereby improving the generation efficiency of the target electrical data.

[0101] This embodiment adopts the above method. First, the graph data model is determined according to the characteristic parameters of each power equipment. Then, according to the state parameters of each power equipment, the graph data model is divided into multiple sub-graph data models. Then, in each sub-graph data model, the target electrical data of the target device node with abnormal electrical data is calculated synchronously according to the electrical data of other device nodes. This is beneficial to improving the efficiency and accuracy of generating target electrical data in ultra-large-scale power systems.

[0102] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0103] Based on the same inventive concept, this application also provides a data processing apparatus for implementing the data processing method described above. The solution provided by this apparatus is similar to the implementation scheme described in the above method; therefore, the specific limitations in one or more data processing apparatus embodiments provided below can be found in the limitations of the data processing method described above, and will not be repeated here.

[0104] In one embodiment, such as Figure 6 As shown, a data processing device is provided, including: a graph data model generation module 602, a graph data model partitioning module 604, and an electrical data processing module 606, wherein:

[0105] The graph data model generation module 602 is used to obtain the characteristic parameters of each power device in the power system and determine the graph data model based on each characteristic parameter. The graph data model includes equipment nodes for representing each power device and electrical nodes for storing the electrical data of each power device.

[0106] The graph data model partitioning module 604 is used to obtain the state parameters of each power device according to the feature parameters, and to partition the graph data model into multiple sub-graph data models according to the state parameters.

[0107] The electrical data processing module 606 is used to identify the target device node and the target electrical node in each subgraph data model where the electrical data is abnormal, and to identify other device nodes that are connected to the target electrical node together with the target device node. Based on the electrical data of the other device nodes, the target electrical data of the target device node is obtained, so as to obtain the target characteristic parameters of the power system based on the target electrical data.

[0108] In one embodiment, the electrical data processing module 606 is further configured to: determine whether the electrical data of other device nodes is abnormal based on the quality identifier of other device nodes; if the electrical data of other device nodes is normal, determine the target electrical data based on the electrical data of other device nodes; if the electrical data of other device nodes is abnormal, obtain an equivalent model based on the electrical data of other device nodes, and determine the target electrical data based on the equivalent model.

[0109] In one embodiment, the electrical data processing module 606 is further configured to: obtain the target device type identifier of each target device node, determine the target equivalent model based on the target device type identifier, and determine the target electrical data of the target device node based on the target equivalent model.

[0110] In one embodiment, the electrical data processing module 606 is further configured to: determine whether the target device node is an end device based on the target device type identifier; if the target device node is an end device, determine the target equivalent model to determine the target electrical data based on the electrical data of other device nodes with normal electrical data.

[0111] If the target device node has non-terminal devices, the target equivalent model is determined to determine the target electrical data based on the electrical data corresponding to the superior electrical node located immediately adjacent to the target electrical node. The superior electrical node is located upstream of the target electrical node in the current direction.

[0112] In one embodiment, the electrical data processing module 606 is further configured to: determine whether the number of target device nodes is equal to a preset threshold; if the number of target device nodes is equal to the preset threshold, then use the calculation result of the target equivalent model as the target electrical data; if the number of target device nodes is not equal to the preset threshold, then allocate the target electrical data according to the proportion of the device capacity of each target device node based on the calculation result of the target equivalent model.

[0113] In one embodiment, the graph data model partitioning module 604 is further configured to: partition the graph data model into multiple sub-graph data models based on the location information and / or voltage level information of each power device.

[0114] Each module in the aforementioned data processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0115] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 7As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores characteristic parameters, electrical data, and other relevant data of various power devices in the power system. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a data processing method.

[0116] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a data processing method. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0117] Those skilled in the art will understand that Figure 7 and Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0118] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0119] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.

[0120] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0124] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A data processing method, characterized in that, The method includes: The characteristic parameters of each power device in the power system are obtained, and a graph data model is determined based on each characteristic parameter. The graph data model includes device nodes for representing each power device and electrical nodes for storing electrical data of each power device. The electrical data includes quality identifiers for representing whether the electrical data is abnormal. The characteristic parameters are parameters used to describe the characteristics of the power device, including the device name, device number, device type identifier, and device numbers of associated devices with connection relationships. The state parameters of each power device are obtained based on the feature parameters. The state parameters include the location information and voltage level information of each power device. Based on the location information and / or voltage level information of each power device, the graph data model is divided into multiple sub-graph data models. In each of the subgraph data models, the target device node and target electrical node whose electrical data is abnormal are identified, and other device nodes that share a common connection with the target device node are also identified. The quality identifiers of these other device nodes are used to determine whether their electrical data is abnormal. If the electrical data of these other device nodes is normal, the target electrical data of the target device node is determined based on their electrical data. If the electrical data of these other device nodes is abnormal, an equivalent model is obtained based on their electrical data, and the target electrical data is determined based on this equivalent model. The target characteristic parameters of the power system are then obtained based on the target electrical data. These target characteristic parameters characterize whether the operating state of the power system is stable, and include voltage amplitude and phase angle. The equivalent model is a model that uses existing data and other relevant information to infer abnormal electrical data. The step of obtaining an equivalent model based on the electrical data of the other device nodes and determining the target electrical data based on the equivalent model includes: Obtain the target device type identifier for each target device node; Based on the target device type identifier, it is determined whether the target device node is an end device. If all target device nodes are end devices, the target equivalent model is determined to be a model used to determine the target electrical data based on the electrical data of the other device nodes with normal electrical data. If the target device node has non-terminal devices, then the target equivalent model is determined to be a model used to determine the target electrical data based on the electrical data corresponding to the upstream electrical node located immediately adjacent to the target electrical node; the upstream electrical node is located upstream of the target electrical node in the current direction. The target electrical data are determined based on the target equivalent model.

2. The method according to claim 1, characterized in that, Determining the target electrical data based on the equivalent model includes: Determine whether the number of target device nodes is equal to a preset threshold. If the number of target device nodes is equal to the preset threshold, the calculation result of the target equivalent model is used as the target electrical data. If the number of target device nodes is not equal to the preset threshold, the target electrical data is obtained by allocating the target electrical data according to the proportion of the device capacity of each target device node based on the calculation result of the target equivalent model.

3. The method according to claim 1, characterized in that, The step of dividing the graph data model into multiple sub-graph data models based on the location information and / or voltage level information of each of the power devices includes: The power equipment is grouped according to its location information, and the graph data model is divided into multiple sub-graph data models; or... The power equipment is grouped according to its voltage level information, and the graph data model is divided into multiple sub-graph data models; or... The power equipment is grouped according to its location information and voltage level information, and the graph data model is divided into multiple sub-graph data models.

4. A data processing apparatus, characterized in that, The device includes: The graph data model generation module is used to obtain the characteristic parameters of each power device in the power system and determine the graph data model based on the characteristic parameters. The graph data model includes device nodes for representing each power device and electrical nodes for storing electrical data of each power device. The electrical data includes quality identifiers for representing whether the electrical data is abnormal. The characteristic parameters are parameters used to describe the characteristics of the power device, including the device name, device number, device type identifier, and device numbers of associated devices with connection relationships. The graph data model partitioning module is used to obtain the state parameters of each power device according to the feature parameters. The state parameters include the location information and voltage level information of each power device. Based on the location information and / or voltage level information of each power device, the graph data model is partitioned into multiple sub-graph data models. An electrical data processing module is used to identify target device nodes and target electrical nodes in each of the subgraph data models where the electrical data is abnormal, and to identify other device nodes that share a connection with the target electrical node. The module determines whether the electrical data of the other device nodes is abnormal based on their quality identifiers. If the electrical data of the other device nodes is normal, the module determines the target electrical data of the target device node based on their electrical data. If the electrical data of the other device nodes is abnormal, the module obtains an equivalent model based on their electrical data and determines the target electrical data based on the equivalent model, thereby obtaining the target characteristic parameters of the power system based on the target electrical data. The target characteristic parameters characterize whether the operating state of the power system is stable, and include voltage amplitude and phase angle. The equivalent model is... A model for inferring abnormal electrical data is used based on existing data and other relevant information. The step of obtaining an equivalent model based on the electrical data of other device nodes and determining the target electrical data based on the equivalent model includes: obtaining the target device type identifier for each target device node; determining whether the target device node is an end device based on the target device type identifier; if all target device nodes are end devices, then determining the target equivalent model as a model for determining the target electrical data based on the electrical data of other device nodes with normal electrical data; if any target device node is a non-end device, then determining the target equivalent model as a model for determining the target electrical data based on the electrical data corresponding to the upstream electrical node immediately adjacent to the target electrical node; the upstream electrical node is located upstream of the target electrical node in the current direction; and determining the target electrical data based on the target equivalent model.

5. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 3.

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