A low-orbit satellite communication integrated space-time domain power consumption data intelligent management and control method, system, device and medium

Abstract

The application relates to the technical field of power data management and satellite communication, and discloses a kind of spatiotemporal electric energy data intelligent management and control method and system fusing low-orbit satellite communication, method includes: obtaining power grid operation data and environmental data;Based on double model fusion, abnormal detection is carried out, standardized data and abnormal data events are obtained;The comprehensive health score of equipment is calculated, and the equipment state account containing state classification is generated;In response to the state classification reaching the preset abnormal level or receiving abnormal data events, generate graded early warning information;Adopt adaptive tile loading mechanism and spatiotemporal cube index model for visualization;In response to the geographic location information associated with abnormal data events received, adjust the perspective positioning of the abnormal area;Based on user interaction, output power grid operation auxiliary decision information.The application can improve the efficiency of global data collection management, combined with health assessment and spatiotemporal index optimization to shorten the fault response and visualization delay, meet the safe operation requirements.

Description

Technical Field

[0001] This invention relates to the fields of power data management and satellite communication technology, and in particular to a method, system, device and medium for intelligent management and control of spatiotemporal power consumption data that integrates low-orbit satellite communication. Background Technology

[0002] The operation of power systems involves operational data generated by various devices such as sensors, metering terminals, and monitoring systems, as well as multi-source spatiotemporal information such as geospatial data and meteorological data. The acquisition and utilization of this data are the foundation for power grid optimization scheduling and fault early warning. Low-Earth orbit (LEO) satellite communication has a wide coverage area, significantly lower transmission latency than geostationary satellites, and is not limited by geographical environment, thus providing an effective supplement to the collection of electricity consumption data in remote areas.

[0003] However, existing technologies still face many limitations in practical applications. Terrestrial communication networks have significant coverage blind spots in remote areas such as mountainous regions and islands, making it difficult to transmit electricity data in real time and creating data silos. Electricity data comes from diverse sources with inconsistent formats and varying quality. Traditional data cleaning methods consume a significant portion of the data processing time, and the problem of missing abnormal data is also prominent. Regarding communication protocols, the transport layer protocols used in low-Earth orbit (LEO) satellite communication differ from power system-specific protocols in syntax, semantics, and timing, requiring manual intervention for conversion. Furthermore, LEO satellite communication equipment and power terminal equipment lack a unified status monitoring and control mechanism, resulting in significant delays in fault detection and early warning response. Traditional fixed-tile map loading methods exhibit lag in scenarios with large data volumes, and the response time for spatiotemporal data queries is insufficient to meet the speed requirements of real-time decision-making. Therefore, how to deeply integrate LEO satellite communication with power data management is an urgent problem to be solved. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a method and system for intelligent management and control of spatiotemporal power consumption data that integrates low-orbit satellite communication to solve the problems of poor integration between low-orbit satellite communication and power data management, high rate of anomaly detection failure, untimely equipment status monitoring and fault response, and slow response to visualization rendering and spatiotemporal query.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, the present invention provides a method for intelligent management and control of spatiotemporal power consumption data that integrates low-orbit satellite communication, including: acquiring power grid operation data and environmental data; The acquired data is preprocessed, and anomaly detection is performed based on dual-model fusion to obtain standardized data and anomalous data events; Based on the standardized data, a comprehensive health score of the equipment is calculated, and an equipment status ledger containing status classification is generated. In response to the status level in the ledger reaching a preset abnormal level or the receipt of abnormal data events, a graded early warning message is generated and pushed to the corresponding responsible end. An adaptive tile loading mechanism and a spatiotemporal cube indexing model are used to visualize standardized data and equipment status hierarchical data; in response to the reception of geographic location information associated with abnormal data events, the visualization perspective is automatically adjusted to locate abnormal areas; Based on user interaction, it outputs auxiliary decision-making information for power grid operation.

[0007] As a preferred embodiment of the intelligent management and control method for spatiotemporal power consumption data integrating low-orbit satellite communication described in this invention, the method includes: anomaly detection based on dual-model fusion to obtain standardized data and anomalous data, including: The first model is used to obtain the temporal dependencies of power grid operation data within a preset time window. The second model is used to identify outliers in the high-dimensional feature space of multidimensional operating parameters; The outputs of the first model and the second model are weighted and fused to generate a comprehensive anomaly score; Based on the different threshold ranges in which the comprehensive anomaly score falls, the corresponding level of abnormal data events is determined and output.

[0008] As a preferred embodiment of the intelligent management and control method for spatiotemporal power consumption data integrating low-orbit satellite communication described in this invention, wherein: the comprehensive health score of the calculation device based on the standardized data includes: Convert equipment operating parameters and communication link status parameters into standardized scoring values; A weight matrix is ​​constructed based on the analytic hierarchy process (AHP), and the standardized parameters are weighted to obtain the overall health score of the device. Based on the threshold range of the equipment's overall health score, the equipment status is divided into several levels and dynamically updated to the ledger database.

[0009] As a preferred embodiment of the intelligent management and control method for spatiotemporal power consumption data integrating low-orbit satellite communication described in this invention, wherein: in response to the status classification in the ledger reaching a preset abnormal level or receiving an abnormal data event, a graded early warning information is generated and pushed to the corresponding responsible end, including: Establish mapping rules between equipment status levels, data anomaly scores, and early warning levels; When the device status level reaches the preset abnormal level, or when the score of the abnormal data event meets the preset triggering conditions, the abnormal device identifier, abnormal type and geographical location information are extracted, and the warning content is assembled according to the preset template. According to the push strategy corresponding to the warning level, the warning content will be distributed to the responsible terminal through at least one communication channel; If no action feedback is received within the preset time limit, the warning level will be automatically adjusted.

[0010] As a preferred embodiment of the intelligent management and control method for spatiotemporal power consumption data integrating low-orbit satellite communication described in this invention, the adaptive tile loading mechanism includes: Based on the current visualization distance and data density, adjust the loading type of map tiles: load vector tiles for near-view distances and switch to raster tiles for far-view distances. As a preferred embodiment of the intelligent management and control method for spatiotemporal power consumption data integrating low-orbit satellite communication described in this invention, the spatiotemporal cube index model includes: constructing a spatiotemporal cube index for power consumption data according to geographic grid coordinates and time dimension, so as to support data comparison and spatial drill-down analysis across time slices.

[0011] As a preferred embodiment of the intelligent management and control method for spatiotemporal power consumption data integrating low-orbit satellite communication described in this invention, it further includes: implementing encryption processing during data transmission and storage; The system employs the national standard SM4 block cipher algorithm to encrypt data before it enters the storage medium and before it is transmitted via satellite communication links; it implements role-based access control for user operation requests; and it logs and audits operation activities.

[0012] Secondly, this invention provides an intelligent management and control system for spatiotemporal power consumption data that integrates low-orbit satellite communication, comprising: The acquisition module is used to acquire power grid operation data and environmental data; The intelligent data governance module is used to preprocess the acquired data, perform anomaly detection based on dual-model fusion, and obtain standardized data and anomalous data events. The equipment management module is used to calculate the comprehensive health score of the equipment based on the standardized data and generate an equipment status ledger that includes status classification. The early warning release module is used to generate graded early warning information and push it to the corresponding responsible end when the status level in the ledger reaches a preset abnormal level or when abnormal data events are received. The visualization module is used to visualize standardized data and equipment status classification data using an adaptive tile loading mechanism and a spatiotemporal cube indexing model; in response to the receipt of geographic location information associated with abnormal data events, it automatically adjusts the visualization perspective to locate abnormal areas; The decision output module is used to output auxiliary decision-making information for power grid operation based on user interaction.

[0013] Thirdly, the present invention provides a computer device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a method for intelligent management and control of spatiotemporal power consumption data that integrates low-orbit satellite communication.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the aforementioned method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: By deeply integrating low-orbit satellite communication with terrestrial networks, this invention can eliminate communication coverage blind spots in remote areas, realize real-time collection of electricity consumption data across the entire region, and support the horizontal expansion of terminal concurrent access capabilities; combined with an adaptive protocol parsing engine, it can complete bidirectional lossless conversion between satellite transmission protocols and power-specific protocols, thereby solving the data interoperability barriers between heterogeneous networks; by constructing a quantitative health assessment model and a hierarchical early warning closed-loop mechanism, it can shorten the time window for fault detection and maintenance response; by adopting adaptive tile loading and spatiotemporal cube indexing technology, it can reduce the latency of map rendering and spatiotemporal query, ensure the smooth interaction in complex scenarios under satellite links, and integrate an encrypted transmission security defense system throughout the entire process to support stable operation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the overall process of a spatiotemporal domain power consumption data intelligent management and control method integrating low-orbit satellite communication according to an embodiment of the present invention. Detailed Implementation

[0018] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0019] Example 1, referring to Figure 1 As an embodiment of the present invention, based on the above embodiment, a method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication is provided. The method includes: S100: Acquire power grid operation data and environmental data; Specifically, operational data may include low-orbit satellite communication signals, tracking beacon signals and broadband signals, and electricity consumption data from metering terminals in remote areas; as well as power grid operation data such as voltage, current, power, and energy consumption from power sensors, metering terminals and monitoring systems.

[0020] Specifically, environmental data can include multi-source spatiotemporal data such as geospatial data and meteorological data.

[0021] In one optional implementation, the acquired data can be subjected to adaptive protocol parsing using a protocol conversion module: For example, by using the built-in DL / T 645-2007 and Q / CSG1209023-2019 power-specific protocol parsing libraries, the adaptive protocol parsing engine identifies the data source protocol type and converts the low-orbit satellite communication transmission layer protocol data packets based on the TCP / IP protocol stack into the power system-specific protocol format, thereby achieving standardized access to heterogeneous data; In one alternative implementation, based on the above implementation, it is possible to support the dynamic expansion of new protocol types through a visual configuration interface, which can adapt to new devices without modifying the core code. S200: Preprocess the acquired data, perform anomaly detection based on dual-model fusion, and obtain standardized data and anomalous data events; In one alternative implementation, preprocessing includes format standardization conversion of multi-source data to unify data types, units, and encoding formats; In another optional implementation, the preprocessing based on the above implementation also includes deduplication and denoising processing. For example, a deduplication strategy based on the MD5 hash algorithm can be used to calculate data fingerprints to quickly identify and remove duplicate data; combined with wavelet transform denoising algorithm, high-frequency noise interference during data transmission and acquisition is filtered out.

[0022] In this embodiment of the application, step S200 involves anomaly detection based on dual-model fusion to obtain standardized data and anomalous data, including steps A1-A4: A1: Using the first model, obtain the temporal dependencies of power grid operation data within a preset time window; A2: Use the second model to identify outliers in the high-dimensional feature space of multidimensional operating parameters; A3: The outputs of the first model and the second model are weighted and fused to generate a comprehensive anomaly score; A4: Based on the different threshold ranges in which the comprehensive anomaly score is located, determine and output the corresponding level of abnormal data events.

[0023] Specifically, the first model can be an LSTM neural network, and the second model is the Isolation Forest algorithm; For example, in models A1-A4, an anomaly detection model is constructed using a dual-model fusion strategy of LSTM neural network algorithm and isolated forest algorithm. The LSTM model is used to learn the long-term temporal dependencies of voltage, current, and power data, and its sliding window size can be set to 96 time points, corresponding to 24 hours of data. The isolated forest algorithm is used to identify outliers in the multi-dimensional feature space, and can construct 100 isolated trees with a sampling ratio of 0.1. The two models are weighted and fused, with LSTM weight of 0.6 and isolated forest weight of 0.4, and an anomaly score is output. Real-time detection of data mutations, numerical limits, and other anomalies is performed, and abnormal data is marked and anomaly event notifications are triggered. The anomaly score can be based on a percentage system, with a score ≥80 indicating a serious anomaly, 60-80 indicating a moderate anomaly, 40-60 indicating a mild anomaly, and <40 indicating normal.

[0024] It should be noted that the anomaly identification model is dynamically updated using machine learning algorithms, which can adapt to changes in data characteristics in different electricity consumption scenarios.

[0025] Furthermore, in an alternative implementation, the processed standardized data can be stored in a distributed database to establish a spatiotemporal index, thereby supporting the secure storage and rapid retrieval of massive amounts of data.

[0026] S300: Calculate the comprehensive health score of the equipment based on the standardized data, and generate an equipment status ledger that includes status classification; In this embodiment of the application, the step S300, which calculates the comprehensive health score of the device based on the standardized data, includes the following steps B1-B3: B1: Convert equipment operating parameters and communication link status parameters into standardized scoring values; Specifically, based on real-time collection of operating parameters of low-orbit satellite communication equipment and power terminal equipment, including voltage, current, temperature, etc., as well as communication link status, such as signal strength, transmission rate, bit error rate, etc., a preliminary equipment status ledger is constructed. Subsequently, data normalization processing is performed, converting operating parameters of different dimensions into standardized values ​​according to preset rules. Voltage and current are expressed as a percentage of rated values, temperature is expressed as an absolute value in degrees Celsius, and communication link status is expressed as a 0-100 rating system.

[0027] For example, a rated voltage of 220V and a measured voltage of 231V represent 105%; the signal strength of the communication link status is linearly mapped from -100dBm to -50dBm to 0-100 points.

[0028] B2: Construct a weight matrix based on the analytic hierarchy process, and perform weighted calculations on the standardized parameters to obtain the overall health score of the device; For example, a weight matrix is ​​constructed based on the analytic hierarchy process (AHP). The weights of the operating parameters can be set to 0.6; voltage 0.25, current 0.2, temperature 0.1, and power 0.05. The weights of the communication links can be set to 0.4; signal strength 0.15, transmission rate 0.1, bit error rate 0.1, and online status 0.05. The overall health score is then calculated using these weights. B3: Based on the threshold range of the equipment's overall health score, the equipment status is divided into several levels and dynamically updated to the ledger database.

[0029] For example, the device status level is determined as follows: a score ≥90 is normal (green), 75-90 is alert (yellow), 60-75 is warning (orange), and <60 is abnormal (red). Key indicators for the classification are recorded simultaneously. The log is dynamically updated in the future, using the device's unique identifier (MAC address or custom code) as the primary key, and storing the original data, health score, status classification, and abnormal events in time series. It supports multi-dimensional retrieval by time, region, and device type.

[0030] S400: In response to the status level in the ledger reaching a preset abnormal level or receiving an abnormal data event, generate a graded early warning information and push it to the corresponding responsible end. In this embodiment of the application, step S400, in response to the status level in the ledger reaching a preset abnormal level or receiving an abnormal data event, generates a graded early warning information and pushes it to the corresponding responsible end, including the following steps C1-C4: C1: Establish mapping rules between equipment status levels, data anomaly scores, and early warning levels; For example, the mapping rule for determining the warning trigger condition is as follows: a Level 1 warning (corresponding to an emergency state) is triggered when the device status is abnormal (score < 60) or the data abnormality score is ≥ 80; a Level 2 warning (corresponding to an important state) is triggered when the device status is warning (score 60-75) or the data abnormality score is 60-80; a Level 3 warning (corresponding to a normal state) is triggered when the device status is attention (score 75-90) or the data abnormality score is 40-60; and the same device automatically upgrades to a Level 1 warning after 3 consecutive Level 2 warnings.

[0031] C2: When the device status level reaches the preset abnormal level, or the score of the abnormal data event meets the preset triggering conditions, extract the abnormal device identifier, abnormal type and geographical location information, and assemble the warning content according to the preset template. Specifically, geographic location information can include the time of occurrence, geographic location, and scope of impact; In an optional implementation, the warning content in implementation C2 may further include suggested handling measures.

[0032] C3: In accordance with the push strategy corresponding to the aforementioned warning level, the warning content will be distributed to the responsible terminal through at least one communication channel; C4: If no action feedback is received within the preset time limit, the warning level will be automatically adjusted.

[0033] For example, in C3-C4, Level 1 warnings can be simultaneously pushed to the provincial dispatch center, municipal maintenance managers, and on-site maintenance personnel via SMS, platform messages, and voice calls; Level 2 warnings can be pushed to municipal maintenance managers and on-site maintenance personnel via SMS combined with platform messages; Level 3 warnings are pushed to on-site maintenance personnel only via platform messages; a countdown can be started after the warning is pushed, with Level 1 warnings requiring confirmation of receipt within 30 minutes and feedback of handling results within 2 hours, Level 2 warnings requiring confirmation within 1 hour and feedback within 4 hours, and Level 3 warnings requiring confirmation within 2 hours and feedback within 8 hours; if no confirmation is received within the time limit, the warning level will be automatically upgraded and pushed to the superior responsible person; the warning generation time, push recipient, confirmation time, handling result, and closure time will be stored in the warning event database, linked to the equipment status ledger to form a complete traceability chain, and the warning records will be archived.

[0034] S500: Employs an adaptive tile loading mechanism and a spatiotemporal cube indexing model to visualize standardized data and equipment status classification data; responds to the reception of geographic location information associated with abnormal data events and automatically adjusts the visualization perspective to locate abnormal areas; In this embodiment of the application, the adaptive tile loading mechanism in step S500 includes step D1: D1: Adjust the map tile loading type according to the current visualization distance and data density. Load vector tiles for near-view distance and switch to raster tiles for far-view distance. Specifically, the adaptive tile hierarchical loading mechanism is mainly based on view distance calculation and data density analysis, dynamically switching the ratio of vector / raster tiles, prioritizing the loading of vector tiles for near-views to ensure boundary accuracy, automatically downgrading to raster tiles for distant views to reduce transmission load, and achieving a smooth transition through progressive rendering when the view distance changes.

[0035] It should be noted that it can support multiple visualization methods such as raster, cluster, heat map, and line chart, and can display power consumption data, equipment status and power grid operation status by time, region and equipment type, and can reduce loading time.

[0036] In this embodiment of the application, the spatiotemporal cube index model in step S500 includes step D2: D2: Construct a spatiotemporal cube index for electricity consumption data according to geographic grid coordinates and time dimension to support data comparison and spatial drill-down analysis across time slices.

[0037] Specifically, electricity consumption data is used to construct a three-dimensional spatiotemporal cube index based on a geographic grid of longitude × latitude × time. Multiple data sources are aggregated within the same cube, including electricity consumption, equipment status, and abnormal events. This supports cross-time slice comparison and spatial drill-down, which can reduce query response time.

[0038] In an optional implementation, based on the above implementation, S500 can also integrate three types of data: power load heat, equipment health level, and communication link quality, and use weighted overlay rendering to color map the load, adjust the transparency of the health level, and convert the link quality into the flashing frequency. This allows for the presentation of the overall operation status of the power grid in a single view, avoiding cognitive load caused by switching between multiple layers. In this embodiment of the application, in S500, in response to the receipt of geographic location information associated with abnormal data events, the visualization perspective is automatically adjusted to locate the abnormal area. Specifically, through anomaly-driven intelligent navigation, the system can receive the coordinates of abnormal events from the equipment's integrated management and control unit, automatically calculate the optimal observation angle (e.g., distance, pitch angle, and azimuth angle), smoothly fly to the abnormal area, and simultaneously overlay historical data for comparison and display, thereby assisting in quickly locating the root cause of the fault.

[0039] In another optional implementation, based on the above implementation, the S500 can also monitor the network link status through a low-bandwidth adaptive transmission protocol. When the bandwidth is <500kbps, it can automatically enable a data simplification strategy, which may include time-series downsampling, blurring of non-critical areas, and LOD degradation of the 3D model, to ensure the smooth visualization of the satellite communication link.

[0040] S600: Outputs auxiliary decision-making information for power grid operation based on user interaction.

[0041] Specifically, it provides interactive functions such as switching between 2D and 3D views, zooming, roaming, and layer control; combined with power spatiotemporal analysis algorithms, it performs equipment distribution density analysis, power load spatial distribution analysis, and fault impact range analysis, supports multi-dimensional drill-down and display of analysis results, and outputs power grid optimization suggestions.

[0042] In this embodiment of the application, S700 is also included: encryption processing is performed during data transmission and storage; The system employs the national standard SM4 block cipher algorithm to encrypt data before it enters the storage medium and before it is transmitted via satellite communication links; it implements role-based access control for user operation requests; and it logs and audits operation activities.

[0043] Specifically, the network security architecture is a defense-in-depth architecture, deploying firewalls and intrusion detection systems, and closing high-risk ports and unnecessary services; the entire process uses the national cryptographic SM4 algorithm to encrypt and store sensitive data; a role-based access control system is built to perform permission verification for login, query, configuration, and control operations, and supports custom permission configuration for users and user groups; the security audit module records key behaviors such as user operations, data access, and device control throughout the entire process, and the audit logs include event date, time, user, event type, and result information, supporting log querying, export, and traceability.

[0044] Furthermore, in response to a failure in a certain step execution unit, the above processes S100-S700 automatically trigger a fault isolation and backup mechanism to achieve global scheduling and resource coordination, ensuring the continuous and stable operation of the platform.

[0045] Example 2 illustrates a schematic scheme for an intelligent management and control method for spatiotemporal power consumption data integrating low-Earth orbit satellite communication. It should be noted that the technical solution of this system for intelligent management and control of spatiotemporal power consumption data integrating low-Earth orbit satellite communication is based on the same concept as the aforementioned method for intelligent management and control of spatiotemporal power consumption data integrating low-Earth orbit satellite communication. Details not described in detail in this embodiment can be found in the description of the aforementioned method for intelligent management and control of spatiotemporal power consumption data integrating low-Earth orbit satellite communication.

[0046] This embodiment provides an intelligent management and control system for spatiotemporal power consumption data that integrates low-Earth orbit satellite communication, including: The acquisition module is used to acquire power grid operation data and environmental data; Specifically, the acquisition module includes: a satellite data access module, a power equipment data access module, a heterogeneous data access module, and a protocol conversion module; The satellite data access module supports automatic acquisition and reception of low-orbit satellite communication signals and has the ability to track beacon signals and broadband signals. The power equipment data access module is compatible with the interface protocols of power sensors, metering terminals, and monitoring systems to realize real-time acquisition of power grid operation data. The heterogeneous data access module is used to access multi-source environmental data such as geospatial data and meteorological data. The protocol conversion module has built-in power-specific protocol parsing libraries such as DL / T 645-2007 and Q / CSG1209023-2019, and uses an adaptive protocol parsing engine to convert satellite transport layer protocol data based on the TCP / IP protocol stack into dedicated protocols that can be recognized by the power system.

[0047] The intelligent data governance module is used to preprocess the acquired data, perform anomaly detection based on dual-model fusion, and obtain standardized data and anomalous data events. Specifically, the intelligent data governance module may include: a preprocessing module, a deduplication and noise reduction module, an anomaly detection module, and a data storage module; The equipment management module is used to calculate the comprehensive health score of the equipment based on the standardized data and generate an equipment status ledger that includes status classification. The early warning release module is used to generate graded early warning information and push it to the corresponding responsible end when the status level in the ledger reaches a preset abnormal level or when abnormal data events are received. The visualization module is used to visualize standardized data and equipment status classification data using an adaptive tile loading mechanism and a spatiotemporal cube indexing model; in response to the reception of geographic location information associated with abnormal data events, it automatically adjusts the visualization perspective to locate abnormal areas. It should be noted that the map rendering module of the visualization module supports switching between multiple coordinate systems, adapts to different projection requirements of geospatial data, and has the ability to load offline maps, ensuring visualization display in environments without network access.

[0048] The decision output module is used to output auxiliary decision-making information for power grid operation based on user interaction.

[0049] Furthermore, it may also include: a data storage module that adopts a distributed database architecture, supports independently controllable databases such as DM and Nanda General, and adopts a database sharding and table partitioning strategy, splitting by time and region dimensions to achieve secure storage and fast retrieval of massive spatiotemporal data, meeting the needs of high-concurrency data access.

[0050] Furthermore, it may also include: an extended interface module to support flexible access to intelligent power grid optimization analysis algorithms, with modular expansion capabilities; specifically, it can adopt a standardized interface design (RESTful API, gRPC) to support flexible access to intelligent power grid optimization analysis algorithms, with modular expansion capabilities, and can add analysis functions such as fault prediction and load prediction according to business needs.

[0051] Furthermore, it may also include: a security protection module that supports access to the power monitoring system's network security situation awareness system to achieve real-time monitoring and alarm linkage of network security status.

[0052] Furthermore, it may also include: a core control module, used to coordinate the working sequence of steps S100 to S700, perform data flow control, resource allocation, fault self-diagnosis and fault tolerance processing; and automatically trigger fault isolation and backup mechanisms in response to a fault in a certain step execution unit.

[0053] Specifically, it can use domestically developed and controllable processors (Hygon, Zhaoxin, Phytium, Loongson, Kunpeng series) and operating systems, and supports domestically developed and controllable or open-source operating systems such as Kylin, UnionTech UOS, and SUSE, to coordinate the working sequence of each unit and ensure the stable operation of the platform.

[0054] Furthermore, it may also include a mobile adaptation unit that supports access and operation of mobile devices, enabling functions such as device status query, alarm reception, and simple operation and maintenance, and is compatible with mainstream mobile operating systems such as Android and iOS.

[0055] This embodiment also provides a computer device applicable to a situation of intelligent management and control of spatiotemporal power consumption data integrating low-Earth orbit satellite communication, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the method for intelligent management and control of spatiotemporal power consumption data integrating low-Earth orbit satellite communication as proposed in the above embodiment.

[0056] This embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements a method for intelligent management and control of spatiotemporal power consumption data that integrates low-orbit satellite communication, as proposed in the above embodiment.

[0057] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for intelligent management and control of spatiotemporal power consumption data that integrates low-orbit satellite communication proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0058] From the above description of the implementation methods, those skilled in the art will clearly understand that the present invention can be implemented using software and necessary general-purpose hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0059] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication, characterized in that, include: Acquire power grid operation data and environmental data; The acquired data is preprocessed, and anomaly detection is performed based on dual-model fusion to obtain standardized data and anomalous data events; Based on the standardized data, a comprehensive health score of the equipment is calculated, and an equipment status ledger containing status classification is generated. In response to the status level in the ledger reaching a preset abnormal level or the receipt of abnormal data events, a graded early warning message is generated and pushed to the corresponding responsible end. An adaptive tile loading mechanism and a spatiotemporal cube indexing model are used to visualize standardized data and equipment status classification data; In response to the receipt of geographic location information associated with abnormal data events, the visualization perspective is automatically adjusted to locate the abnormal area; Based on user interaction, it outputs auxiliary decision-making information for power grid operation.

2. The method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication as described in claim 1, characterized in that, Anomaly detection is performed based on dual-model fusion, yielding standardized data and anomalous data, including: The first model is used to obtain the temporal dependencies of power grid operation data within a preset time window. The second model is used to identify outliers in the high-dimensional feature space of multidimensional operating parameters; The outputs of the first model and the second model are weighted and fused to generate a comprehensive anomaly score; Based on the different threshold ranges in which the comprehensive anomaly score falls, the corresponding level of abnormal data events is determined and output.

3. The method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication as described in claim 2, characterized in that, The comprehensive health score calculated based on the standardized data includes: Convert equipment operating parameters and communication link status parameters into standardized scoring values; A weight matrix is ​​constructed based on the analytic hierarchy process (AHP), and the standardized parameters are weighted to obtain the overall health score of the device. Based on the threshold range of the equipment's overall health score, the equipment status is divided into several levels and dynamically updated to the ledger database.

4. The method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication as described in claim 3, characterized in that, In response to the status level in the ledger reaching a preset abnormal level or the receipt of abnormal data events, a graded early warning message is generated and pushed to the corresponding responsible end, including: Establish mapping rules between equipment status levels, data anomaly scores, and early warning levels; When the device status level reaches the preset abnormal level, or when the score of the abnormal data event meets the preset triggering conditions, the abnormal device identifier, abnormal type and geographical location information are extracted, and the warning content is assembled according to the preset template. According to the push strategy corresponding to the warning level, the warning content will be distributed to the responsible terminal through at least one communication channel; If no action feedback is received within the preset time limit, the warning level will be automatically adjusted.

5. The method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication as described in claim 4, characterized in that, The adaptive tile loading mechanism includes: Adjust the map tile loading type based on the current visualization distance and data density: load vector tiles for near-view distances and switch to raster tiles for far-view distances.

6. A method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication as described in claim 1 or 5, characterized in that, The spatiotemporal cube index model includes: constructing a spatiotemporal cube index for electricity consumption data according to geographic grid coordinates and time dimension, so as to support data comparison and spatial drill-down analysis across time slices.

7. The method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication as described in claim 6, characterized in that, Also includes: Encryption is implemented during data transmission and storage; The system employs the national standard SM4 block cipher algorithm to encrypt data before it enters the storage medium and before it is transmitted via satellite communication links; it implements role-based access control for user operation requests; and it logs and audits operation activities.

8. A spatiotemporal domain power consumption data intelligent management and control system integrating low-orbit satellite communication, using the method described in any one of claims 1-7, characterized in that, include: The acquisition module is used to acquire power grid operation data and environmental data; The intelligent data governance module is used to preprocess the acquired data, perform anomaly detection based on dual-model fusion, and obtain standardized data and anomalous data events. The equipment management module is used to calculate the comprehensive health score of the equipment based on the standardized data and generate an equipment status ledger that includes status classification. The early warning release module is used to generate graded early warning information and push it to the corresponding responsible end when the status level in the ledger reaches a preset abnormal level or when abnormal data events are received. The visualization module is used to visualize standardized data and equipment status classification data using an adaptive tile loading mechanism and a spatiotemporal cube indexing model. In response to the receipt of geographic location information associated with abnormal data events, the visualization perspective is automatically adjusted to locate the abnormal area. The decision output module is used to output auxiliary decision-making information for power grid operation based on user interaction.

9. A computer device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the method for intelligent management and control of spatiotemporal power consumption data integrating low-orbit satellite communication as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores computer-executable instructions, which, when executed by a processor, implement the steps of the spatiotemporal domain power consumption data intelligent management and control method that integrates low-orbit satellite communication as described in any one of claims 1 to 7.

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