State data visualization method and system applied to smart meter

Abstract

Embodiments of the present application disclose a state data visualization method and system applied to a smart meter, wherein the embodiments of the present application receive a state data information stream containing a meter operation state record sequence with a collection time mark and corresponding meter identification information transmitted by a smart meter through a power communication network, perform geospatial correlation processing thereon, establish a mapping relationship with a meter installation position in a GIS system, and obtain a spatialized state data set; construct a smart meter state knowledge graph containing a meter entity node, a state attribute edge and a geospatial correlation edge based on the set, wherein the state attribute edge represents an operation state influence relationship between the meter entity nodes; finally, fuse and render the knowledge graph with an electronic map of the GIS system to generate a multi-level visual display interface containing a meter distribution density heat layer, a state abnormality propagation path layer and a regional correlation relationship layer, thereby realizing intuitive presentation of smart meter state data.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, specifically to a method and system for visualizing the status data of smart meters. Background Technology

[0002] In power systems, monitoring and managing smart meter status data is crucial. Existing methods for processing smart meter status data primarily focus on collecting and simply analyzing operating parameters. They typically only receive the status data transmitted by smart meters, perform basic statistics, and store it. However, this approach lacks in-depth data mining and effective integration, failing to comprehensively reflect the interrelationships between smart meters and their connection to geographic space. While some systems record the meter's installation location, this is merely a simple location marker, without effectively linking it to the meter's operating status, making it difficult to conduct a comprehensive analysis of the meter's operation from a geographic perspective. Furthermore, most existing technologies use tables or simple charts, which cannot intuitively display the complex information related to the meters. Summary of the Invention

[0003] This invention provides a method and system for visualizing the status data of smart meters.

[0004] In a first aspect, embodiments of the present invention provide a method for visualizing the status data of smart meters, applied to a status data visualization system. The method includes: receiving a status data information stream transmitted by a smart meter through a power communication network, the status data information stream containing a sequence of meter operating status records with a collection time stamp and corresponding meter identification information; performing geospatial association processing on the status data information stream to establish a mapping relationship between the meter operating status record sequence and the meter installation location in a GIS system, obtaining a spatialized status data set containing geographic coordinate attributes; constructing a smart meter status knowledge graph based on the spatialized status data set, the smart meter status knowledge graph containing meter entity nodes, status attribute edges, and geographic region association edges, the status attribute edges representing the operational status influence relationship between meter entity nodes; and performing fusion rendering processing on the smart meter status knowledge graph and the electronic map of the GIS system to generate a multi-level visualization display interface containing a meter distribution density thermal layer, a status anomaly propagation path layer, and a regional association relationship layer.

[0005] Secondly, embodiments of the present invention provide a state data visualization system, comprising:

[0006] processor;

[0007] Storage device, on which computer programs are stored,

[0008] When the computer program is executed by the processor, the processor implements any of the aforementioned methods for visualizing status data of smart meters.

[0009] This invention provides a readable storage medium storing a program or instructions, which, when executed by a processor, implement the steps of the method for visualizing the status data of a smart meter.

[0010] The embodiments of the present invention enable a comprehensive, intuitive and in-depth visualization of smart meter status data, significantly improving the efficiency and accuracy of power system management and operation and maintenance. In detail, by receiving the status data information stream transmitted by smart meters through the power communication network, the real-time and completeness of the acquired data are ensured. Geospatial correlation processing is performed on the status data information stream, combining the meter's operating status with geographic information to obtain a spatialized status data set. This breaks the limitation of traditional data processing that only focuses on operating parameters while ignoring geographical location factors, enabling analysis of meter operating status from a geospatial dimension and uncovering potential regional characteristics and patterns. Based on the spatialized status data set, a smart meter status knowledge graph is constructed. Through the setting of meter entity nodes, status attribute edges, and geographic region association edges, the operational status influence relationships between meters and their connections with geographic regions are accurately displayed. The smart meter status knowledge graph is then integrated and rendered with the electronic map of the GIS system to generate a multi-level visualization interface. Abstract and complex data are presented in intuitive graphical and thermal layer forms, allowing users to quickly and accurately grasp information such as meter distribution, operating status, and anomaly propagation paths. This greatly improves the speed and accuracy of fault diagnosis and decision-making, thereby achieving power system optimization and efficient operation. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating a method for visualizing the status data of a smart meter, as provided in an embodiment of the present invention.

[0012] Figure 2 This is a schematic diagram of the basic structure of a state data visualization system provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] See Figure 1 As shown, this figure is a flowchart of a status data visualization method for smart meters provided by an embodiment of the present invention. This method can be applied to a status data visualization system. Figure 1 As shown, the method includes steps 110-140.

[0015] Step 110: Receive the status data information stream transmitted by the smart meter through the power communication network. The status data information stream includes a sequence of meter operating status records with acquisition time stamps and corresponding meter identification information.

[0016] In the actual operation of power systems, smart meters play a crucial role in monitoring and recording their own operational status in real time. These smart meters are distributed across different geographical locations, continuously collecting operational status data such as voltage, current, and power. To ensure data traceability and analytical value, each set of collected data is marked with a precise collection time stamp, forming a complete sequence of meter operational status records. Simultaneously, each smart meter is assigned unique meter identification information, which is the key basis for distinguishing different meters.

[0017] The status data collected by smart meters is transmitted through a power communication network. This network, used for power system data transmission, is characterized by high stability and reliability, ensuring accurate transmission of status data to the designated receiving end. Upon receiving this data, the receiving end integrates it into a status data stream. This stream contains a sequence of meter operating status records with timestamps and corresponding meter identification information. For example, multiple smart meters in a given area collect their own operating status data, including voltage and current values ​​at different times. This data, along with their respective meter identification information, is then transmitted to the receiving end via the power communication network. The receiving end then aggregates this data to form the status data stream.

[0018] Step 120: Perform geospatial association processing on the status data information stream to establish a mapping relationship between the meter operation status record sequence and the meter installation location in the GIS system, thereby obtaining a spatialized status data set containing geographic coordinate attributes.

[0019] After obtaining the status data stream, in order to more intuitively and comprehensively understand the relationship between the smart meter's operating status and its geographical location, geospatial correlation processing is required. A GIS (Geographic Information System) stores a large amount of geospatial data, including smart meter installation location information. By establishing a mapping relationship between the meter's operating status record sequence and the meter's installation location in the GIS system, abstract status data can be combined with specific geographical locations.

[0020] Specifically, the purpose of geospatial correlation processing is to add geographic coordinate attributes to status data, making it a spatialized set of status data. This allows for a direct view of the operational status of smart meters in different geographical locations during data analysis and visualization. For example, it can accurately locate on a map which areas have smart meters operating well and which areas may experience anomalies. This approach enables more efficient management and maintenance of the power system.

[0021] As one implementation, the state data information stream undergoes geospatial correlation processing to establish a mapping relationship between the meter operation status record sequence and the meter installation location in the GIS system, resulting in a spatialized state data set containing geographic coordinate attributes, including:

[0022] Step 121: Parse the meter identification information in the status data information stream, and extract the transformer area code and installation location code of the meter through the preset identification coding rules.

[0023] The meter identification information in the status data stream is encoded according to certain rules. The preset identification encoding rules are designed to facilitate the parsing and processing of the meter identification information. In practical applications, the meter identification information can be a complex string containing multiple levels of information, including the transformer substation code and the installation location code to which the meter belongs.

[0024] The process of parsing meter identification information requires adherence to pre-defined identification coding rules, which define the meaning and location of each part of the meter identification information. For example, meter identification information may consist of a string of characters and numbers of a predetermined length, where the first few characters represent the transformer substation code, and the last few represent the installation location code. By analyzing and comparing the meter identification information digit by digit, the transformer substation code and installation location code of the meter can be accurately extracted. The purpose of extracting these codes is to enable more precise lookup of the meter's installation location in the GIS system.

[0025] Step 122: Call the spatial query interface of the GIS system to retrieve the corresponding geographic coordinate data based on the substation code and installation location code. The geographic coordinate data is represented by latitude and longitude coordinates.

[0026] After extracting the transformer substation code and installation location code of the electricity meter, these codes can be used to query the corresponding geographic coordinate data in the GIS system. The GIS system provides a spatial query interface, which allows users to use the transformer substation code and installation location code as query criteria to retrieve data from the GIS system's database.

[0027] The GIS system's database stores a large amount of geospatial data, including detailed information on each transformer substation and its installation location. When a transformer substation code and installation location code are entered, the system matches this information against the database to find the corresponding geographic coordinates. Geographic coordinates are typically represented using latitude and longitude coordinates, a globally universal method of geographic positioning that accurately determines a location on Earth. For example, a query can retrieve the latitude and longitude coordinates of a specific electricity meter, which can then be accurately marked on a map to indicate the meter's installation location.

[0028] Step 123: Add the geographic coordinate data as an additional attribute field to each status record in the meter operation status record sequence to generate a spatiotemporal status record unit containing spatiotemporal dimension information.

[0029] After obtaining the geographic coordinate data of the electricity meter, this data needs to be associated with the electricity meter operation status record sequence. Specifically, geographic coordinate data is added as an additional attribute field to each status record in the electricity meter operation status record sequence.

[0030] Each status record originally contained the collection time stamp and the meter's operating status information, such as voltage and current. Now, with the addition of geographic coordinate data, the status record gains geospatial dimension information, thus transforming each status record into a spatiotemporal status record unit containing spatiotemporal information. For example, a status record that originally recorded the meter's voltage value at a certain moment can now, with the addition of geographic coordinate data, determine the specific location of the meter where that voltage value was collected. This approach allows for a more comprehensive understanding of the smart meter's operational status.

[0031] Step 124: Perform data optimization processing on the spatiotemporal state recording unit to remove abnormal state records that have missing geographical coordinates or whose coordinates exceed the preset area range.

[0032] It is understandable that after obtaining the spatiotemporal status recording unit, some abnormal status records may occur due to various problems that may arise during data acquisition and transmission. For example, there may be cases where geographic coordinates are missing, meaning that a certain status record does not have corresponding geographic coordinate data; or the geographic coordinates may exceed the preset area range, which may be due to data errors or abnormal meter installation location.

[0033] To ensure data accuracy and reliability, data optimization processing of the spatiotemporal status recording units is necessary. The main method of data optimization is to remove these abnormal status records. By examining the geographic coordinate data of each spatiotemporal status recording unit, it is determined whether there are any missing or out-of-preset area ranges. If such cases exist, the corresponding status records are removed from the dataset. This avoids abnormal data interfering with subsequent analysis and display, thereby improving data quality.

[0034] Step 125: The optimized spatiotemporal state record units are grouped and aggregated according to the station area code to obtain a spatialized state data set containing geographic coordinate attributes.

[0035] After data optimization, the optimized spatiotemporal status record units need to be grouped and aggregated to facilitate subsequent analysis and management. The grouping and aggregation are based on the distribution area code, as smart meters within the same distribution area may have certain correlations in terms of geographical location and operational characteristics.

[0036] The spatiotemporal status record units are grouped according to their transformer area codes, aggregating status records belonging to the same transformer area together. This allows for the classification of scattered status records by transformer area, forming different groups. Then, the status records within each group are integrated and processed, ultimately resulting in a spatialized status data set containing geographic coordinate attributes. The data in this set is categorized by transformer area and all data carries geographic coordinate attributes, facilitating analysis and visualization based on geospatial location and transformer area. For example, it's possible to analyze the overall operational status of smart meters in different transformer areas or compare differences between different transformer areas.

[0037] Step 130: Construct a smart meter status knowledge graph based on the spatialized status data set. The smart meter status knowledge graph includes meter entity nodes, status attribute edges, and geographical region association edges. The status attribute edges are used to represent the operational status influence relationship between meter entity nodes.

[0038] After obtaining a spatialized state data set containing geographic coordinate attributes, a smart meter state knowledge graph needs to be constructed to gain a deeper understanding of the relationships and operational status of smart meters. A smart meter state knowledge graph is a graph-based data model that can intuitively display the various relationships and attributes between smart meters.

[0039] The knowledge graph contains three main elements: meter entity nodes, state attribute edges, and geographic region association edges. Meter entity nodes represent each smart meter and are the basic unit of the knowledge graph. State attribute edges represent the operational state influence relationships between meter entity nodes; for example, voltage fluctuations in one meter may affect the operational state of adjacent meters, and this influence relationship can be represented by state attribute edges. Geographic region association edges reflect the connection between meters and geographic regions; for example, meters within the same geographic region may share certain common operational characteristics.

[0040] By constructing a knowledge graph of smart meter status, complex smart meter operating status data can be displayed in an intuitive graph structure, facilitating data analysis and mining. For example, potential fault propagation paths can be discovered by analyzing status attribute edges, or the operating status of meters in different regions can be understood through geographical region association edges.

[0041] In a preferred embodiment, the step of constructing a smart meter state knowledge graph based on the spatialized state data set, wherein the smart meter state knowledge graph includes meter entity nodes, state attribute edges, and geographical region association edges, including:

[0042] Step 131: Extract the meter identification information from the spatialized state data set as the unique identifier of the entity node in the knowledge graph, and construct the meter entity node set.

[0043] The spatialized state dataset contains the unique identifier for each smart meter, which distinguishes different meters. When constructing the smart meter state knowledge graph, this identifier is used as the unique identifier for each entity node in the knowledge graph.

[0044] By extracting meter identification information from the spatialized state data set, each meter identification information is mapped to an entity node, thus constructing a set of meter entity nodes. Each node in this set represents a smart meter, and the relationships and attributes between them will be further defined and constructed in subsequent steps. For example, the location and identity of each meter in the knowledge graph can be accurately identified using the meter identification information.

[0045] Step 132: Perform attribute feature extraction processing on the operating state records in the spatialized state data set to obtain a state attribute set containing voltage fluctuation features, current stability features, and power factor features.

[0046] The operational status records in the spatialized state dataset contain a wealth of information. In order to analyze the operational status of smart meters more effectively, it is necessary to extract attribute features from these records.

[0047] The goal of attribute feature extraction is to extract key features reflecting the operating status of the electricity meter from the operating status records. In this embodiment, three main features are extracted: voltage fluctuation features, current stability features, and power factor features. Voltage fluctuation features reflect the changes in the meter's voltage, which can be extracted through time-series analysis of the voltage monitoring data. Current stability features reflect the stability of the current, which can be obtained by performing sliding window statistical processing on the current monitoring data. Power factor features reflect the meter's power utilization efficiency, which can be obtained by calculating and trend fitting the voltage and current monitoring data.

[0048] The extracted voltage fluctuation characteristics, current stability characteristics, and power factor characteristics are combined to form a set of state attributes. Each attribute in this set contains information such as feature name, timestamp, and feature value, which can more comprehensively describe the operating status of the smart meter.

[0049] In one implementation, the step of extracting attribute features from the operating state records in the spatialized state data set to obtain a state attribute set including voltage fluctuation features, current stability features, and power factor features includes:

[0050] Step 1321: Perform time series analysis on the voltage monitoring data in the spatialized state data set, calculate the voltage change frequency and amplitude per unit time, and generate voltage fluctuation characteristics.

[0051] The voltage monitoring data in the spatialized state dataset records voltage values ​​at different times. In order to extract voltage fluctuation characteristics, time series analysis processing is required on these data.

[0052] The main purpose of time series analysis is to calculate the frequency and amplitude of voltage changes per unit time. The frequency of voltage changes reflects the number of voltage fluctuations per unit time, while the amplitude reflects the magnitude of these fluctuations. By analyzing and statistically processing voltage monitoring data point by point, the frequency and amplitude of voltage changes per unit time can be obtained.

[0053] For example, by analyzing voltage monitoring data over a period of time and counting the number of times the voltage value changes, the frequency of voltage change can be obtained. Simultaneously, the difference between adjacent voltage values ​​is calculated, and the largest difference is identified as the voltage change amplitude. Combining these calculation results generates a voltage fluctuation characteristic, which can intuitively reflect the fluctuation of the meter's voltage and provide important information for judging the meter's operating status.

[0054] Step 1322: Perform sliding window statistical processing on the current monitoring data in the spatialized state data set, calculate the current standard deviation and coefficient of variation in different time windows, and generate current stability characteristics.

[0055] To assess the stability of the current, sliding window statistical processing is required on the current monitoring data in the spatialized state dataset. Sliding window statistical processing is a commonly used data analysis method that performs statistical analysis on the data within a fixed-size window that slides across the data sequence.

[0056] In this step, a sliding window is used to process the current monitoring data, calculating the standard deviation and coefficient of variation of the current within different time windows. The standard deviation of the current reflects the dispersion of the current data; a larger standard deviation indicates greater current fluctuation and poorer stability. The coefficient of variation is the ratio of the standard deviation to the mean, which can eliminate the influence of the data mean and more accurately reflect the relative stability of the current.

[0057] By calculating current data within different time windows, a series of current standard deviations and coefficients of variation are obtained. Combining these values ​​generates a current stability characteristic, which helps determine the stability of the current over different time periods, providing a reference for the operation and maintenance of the power system.

[0058] Step 1323: Calculate the power factor value based on the voltage monitoring data and current monitoring data in the spatialized state data set, perform time-dimensional trend fitting processing on the power factor value, and generate power factor features.

[0059] Power factor is an important indicator of power system efficiency, reflecting the efficiency of electrical energy utilization. The power factor value can be calculated based on voltage and current monitoring data from a spatialized state dataset.

[0060] Calculating the power factor requires considering the relationship between voltage and current. After obtaining the power factor values, to better understand their changing trend, it's necessary to perform time-dimensional trend fitting on these values. Trend fitting can be achieved by fitting a curve to the power factor values ​​to identify the pattern of power factor variation over time.

[0061] For example, polynomial fitting or other fitting methods can be used to fit the power factor value to obtain a fitting curve. By analyzing this fitting curve, the future trend of the power factor can be predicted, and the fitting result can be used as a characteristic of the power factor.

[0062] Step 1324: Normalize the voltage fluctuation characteristics, current stability characteristics and power factor characteristics, and combine the normalized voltage fluctuation characteristics, current stability characteristics and power factor characteristics to form a state attribute set. Each attribute feature in the state attribute set includes a feature name, a timestamp and a feature value.

[0063] After obtaining the voltage fluctuation characteristics, current stability characteristics, and power factor characteristics, since the value ranges and dimensions of these characteristics may be different, they need to be normalized to facilitate subsequent analysis and comparison.

[0064] The purpose of normalization is to unify the value range of different features into a set interval, usually [0, 1]. Normalization eliminates dimensional differences between features, making them comparable. There are many normalization methods, such as min-max normalization and Z-score normalization.

[0065] After normalizing the voltage fluctuation characteristics, current stability characteristics, and power factor characteristics, they are combined to form a set of state attributes. Each attribute in this set includes information such as a feature name, a timestamp, and a feature value. The feature name distinguishes different features, the timestamp records the time the feature was collected, and the feature value is the normalized numerical value. In this way, different features can be integrated together, facilitating comprehensive analysis and processing.

[0066] Step 133: Calculate the state influence degree between adjacent meter physical nodes based on the numerical change trend of different attribute features in the state attribute set. The state influence degree is used to quantify the degree of influence of a meter's state change on surrounding meters.

[0067] After obtaining the set of state attributes, in order to understand the mutual influence relationships between adjacent meter entities, it is necessary to calculate the state influence degree between adjacent meter entities. The state influence degree is a quantitative indicator that measures the degree to which a change in the state of a meter affects the operating state of surrounding meters.

[0068] The basis for calculating the degree of state influence is the numerical change trend of different attribute characteristics in the state attribute set. For example, the numerical change of the voltage fluctuation characteristic of one electricity meter may affect the operating status of adjacent electricity meters, such as voltage and current. By analyzing the numerical change trend of each characteristic in the state attribute set of adjacent electricity meters, the correlation between them can be found, and the degree of state influence can be calculated.

[0069] Step 1331: Calculate the straight-line distance between any two meter physical nodes based on the geographical coordinate data of the meter physical nodes, and filter out adjacent meter physical node pairs whose straight-line distance is less than the preset distance value.

[0070] Since meters that are geographically close are more likely to interfere with each other, it is first necessary to calculate the straight-line distance between any two meter nodes based on their geographical coordinate data. The geographical coordinate data is represented by latitude and longitude coordinates, and the straight-line distance between any two meters can be obtained using a pre-defined geographical distance calculation method.

[0071] The preset distance value is a pre-defined threshold used to filter out adjacent pairs of electricity meter nodes. When the straight-line distance between two electricity meter nodes is less than the preset distance value, the two meters are considered adjacent and are treated as adjacent electricity meter node pairs for subsequent analysis. For example, if the preset distance value is a certain distance range, then electricity meter pairs within that range will be filtered out for calculating the state impact degree.

[0072] Step 1332: Perform time synchronization processing on the state attribute set of each adjacent meter entity node pair so that the state records of the two meter entity nodes have the same timestamp.

[0073] To accurately calculate the state influence between adjacent meter nodes, it is necessary to synchronize the state attribute sets of each pair of adjacent meter nodes. Because the state data acquisition times of different meters may differ, their state records need to be time-aligned so that the state records of two meter nodes have the same timestamp.

[0074] Time synchronization can be achieved through interpolation or other methods to adjust data collected at different times to the same point in time. This ensures data consistency and accuracy during subsequent correlation analysis. For example, if one meter collects status data at a certain time, while an adjacent meter collects data at a slightly different time, time synchronization can adjust the data from both meters to the same point in time for comparison.

[0075] Step 1333: Calculate the Pearson correlation coefficients of the corresponding features in the synchronized state attribute set to obtain the voltage fluctuation correlation coefficient, current stability correlation coefficient, and power factor correlation coefficient.

[0076] After completing the time synchronization process, for each pair of adjacent meter entity nodes, the Pearson correlation coefficient of the corresponding features needs to be calculated. The Pearson correlation coefficient is a commonly used statistical indicator that measures the degree of linear correlation between two variables.

[0077] Pearson correlation coefficients were calculated for voltage fluctuation characteristics, current stability characteristics, and power factor characteristics in the synchronized state attribute set. These correlation coefficients reflect the correlation between adjacent meters on different characteristics. For example, a higher voltage fluctuation correlation coefficient indicates that the voltage fluctuations of adjacent meters are more similar, and the potential for mutual influence between them is greater.

[0078] Step 1334: Perform weighted summation on the voltage fluctuation correlation coefficient, the current stability correlation coefficient, and the power factor correlation coefficient according to the preset feature weight allocation rules to generate a comprehensive correlation coefficient.

[0079] Since different features may have different importance when measuring the impact of meter status, it is necessary to perform weighted summation of voltage fluctuation correlation coefficient, current stability correlation coefficient and power factor correlation coefficient according to the preset feature weight allocation rules.

[0080] The preset feature weighting rules define the weight value for each feature, reflecting its importance in the overall evaluation. The comprehensive correlation coefficient is obtained by multiplying each correlation coefficient by its corresponding weight value and then summing the results. This comprehensive correlation coefficient takes into account the influence of different features and provides a more complete reflection of the state correlation between adjacent meter nodes.

[0081] Step 1335: Standardize and transform the comprehensive correlation coefficient to obtain the state influence degree. The larger the value of the state influence degree, the stronger the influence relationship between the operating states of adjacent meter physical nodes.

[0082] To improve the comparability and intuitiveness of the composite correlation coefficient, it needs to be standardized. Standardization can unify the range of values ​​for the composite correlation coefficient to a set interval, typically [0, 1].

[0083] After standardization and transformation, the state influence degree is obtained. The larger the state influence degree value, the stronger the influence relationship between the operating states of adjacent meter nodes. For example, a state influence degree close to 1 indicates that the operating states of the two meters have a very large mutual influence; while a state influence degree close to 0 indicates that their mutual influence is small. The state influence degree provides a direct understanding of the relationship between adjacent meters.

[0084] Step 134: Construct state attribute edges between meter entity nodes based on the state influence degree, wherein the weight value of the state attribute edge is positively correlated with the state influence degree.

[0085] After calculating the state influence degree between adjacent meter entity nodes, state attribute edges between the meter entity nodes can be constructed based on this state influence degree. State attribute edges are a type of edge in the smart meter state knowledge graph, used to represent the operational state influence relationship between meter entity nodes.

[0086] The weight of a state attribute edge is positively correlated with its state influence. That is, the greater the state influence, the greater the weight of the state attribute edge. The magnitude of the weight reflects the strength of the influence relationship between the operating states of two meters. For example, when the state influence is large, the corresponding weight of the state attribute edge is also large, indicating a significant mutual influence between the two meters; conversely, when the state influence is small, the weight of the state attribute edge is also small, indicating a weak mutual influence. By constructing state attribute edges, the influence relationship between the operating states of meters can be visually displayed in the knowledge graph.

[0087] Step 135: Calculate the spatial distribution density of meter physical nodes within the same transformer area based on the geographical coordinate data of the meter physical nodes, construct geographical region association edges based on the spatial distribution density, and generate a smart meter status knowledge graph containing multi-level association relationships.

[0088] Electricity meter nodes within the same transformer substation exhibit a certain spatial distribution pattern. By calculating the spatial distribution density of these nodes, we can understand the distribution of electricity meters within that substation. The calculation of spatial distribution density is based on the geographic coordinate data of the electricity meter nodes. Through a defined spatial analysis method, the spatial distribution density of electricity meter nodes within the same transformer substation can be obtained.

[0089] Geographic region association edges are constructed based on spatial distribution density. These edges reflect the connection between electricity meters and geographical regions, and can indicate the common operational characteristics or interrelationships of meters within the same geographical area. For example, electricity meters in areas with high spatial distribution density may exhibit a certain cooperative operational relationship, which can be represented by geographic region association edges.

[0090] By constructing geographic region association edges and combining them with the previously constructed meter entity nodes and status attribute edges, a smart meter status knowledge graph containing multi-level associations can be generated. This knowledge graph not only shows the operational status influence relationship between meters, but also reflects the connection between meters and geographic regions.

[0091] Step 140: The smart meter status knowledge graph is fused and rendered with the electronic map of the GIS system to generate a multi-level visualization interface that includes a thermal layer of meter distribution density, a layer of status anomaly propagation paths, and a layer of regional relationships.

[0092] After constructing the smart meter status knowledge graph, to more intuitively display the distribution and operational status of smart meters, it is necessary to integrate and render the smart meter status knowledge graph with the electronic map of the GIS system. The electronic map of the GIS system provides geospatial background information, while the smart meter status knowledge graph contains various relationships and attributes of smart meters. By integrating the two, the abstract knowledge graph information can be visualized on the map.

[0093] The result of the fusion rendering process is a multi-level visualization interface that includes a thermal layer of meter distribution density, a layer of anomaly propagation paths, and a layer of regional relationships. The thermal layer of meter distribution density visually displays the meter distribution density in different areas, using color depth to represent density. The anomaly propagation path layer shows the propagation path of smart meter anomalies, helping to quickly locate the source of the anomaly and the potentially affected area. The regional relationship layer illustrates the relationships between meters in different geographical areas. Through this multi-level interface, the operation of smart meters can be comprehensively understood from different perspectives.

[0094] In a preferred embodiment, the step of fusing and rendering the smart meter status knowledge graph with the electronic map of the GIS system to generate a multi-level visualization interface including a meter distribution density thermal layer, a status anomaly propagation path layer, and a regional relationship layer includes:

[0095] Step 141: Call the map rendering interface of the GIS system to load the electronic map base map with a preset scale, and set the map projection method to Mercator projection.

[0096] To perform fusion rendering, the electronic map base map from the GIS system must first be loaded. The GIS system provides a map rendering interface, which can be used to load an electronic map base map with a preset scale. The preset scale determines the level of detail and display area of ​​the map; an appropriate scale can be selected based on actual needs.

[0097] Simultaneously, the map projection method is set to Mercator projection. Mercator projection is a commonly used map projection method. It has the characteristic of being isometric, which can maintain the angles and shapes on the map, making it suitable for navigation and geographic information display. By setting Mercator projection, the accuracy and readability of the electronic map base map can be ensured.

[0098] Step 142: Parse the geographic coordinate data of the meter entity nodes in the smart meter status knowledge graph, map the meter entity nodes to the corresponding positions on the electronic map base map, and generate a meter entity distribution layer.

[0099] The meter entity nodes in the smart meter status knowledge graph contain geographic coordinate data. By parsing this geographic coordinate data, the meter entity nodes can be mapped to their corresponding locations on the electronic map base map. The mapping process involves converting the latitude and longitude coordinates of the meter into pixel coordinates on the electronic map, thereby accurately marking the location of each meter on the map.

[0100] After generating the meter entity distribution layer, the distribution of each meter can be seen intuitively on the electronic map. This layer can serve as the basis for subsequent analysis and display. For example, by observing the meter entity distribution layer, one can understand the number and distribution density of meters in different areas.

[0101] Step 143: Calculate the average operating status of the meters in different areas based on the set of status attributes of the meter entity nodes, and generate a meter distribution density heat map layer based on the average operating status using a gradient color mapping method, which is then overlaid on the electronic map base map.

[0102] To more intuitively display the operating status of electricity meters in different areas, it is necessary to calculate the average operating status of electricity meters in different areas based on the set of status attributes of the meter physical nodes. The set of status attributes includes information such as voltage fluctuation characteristics, current stability characteristics, and power factor characteristics. By comprehensively calculating these characteristics, the operating status value of each electricity meter can be obtained.

[0103] Then, the operating status values ​​of the meters within the same area are averaged to obtain the average operating status value for that area. Based on these average operating status values, a thermal layer representing the meter distribution density is generated using a gradient color mapping method. The gradient color mapping method maps different average operating status values ​​to different colors; for example, areas with better operating status are represented by lighter colors, and areas with worse operating status are represented by darker colors.

[0104] Finally, the generated meter distribution density thermal layer is overlaid and displayed on the electronic map base map. Thus, the operating status of meters in different areas can be seen intuitively on the electronic map. By contrasting the shades of color, areas with better and worse operating status can be quickly identified.

[0105] As one implementation, the step of calculating the average operating status of electricity meters in different areas based on the set of state attributes of the electricity meter entity nodes, generating a thermal layer of electricity meter distribution density based on the average operating status using a gradient color mapping method, and overlaying it on the electronic map base map includes:

[0106] Step 1431: Divide the electronic map base map into multiple square grid units using the equidistant grid division method. The side length of each square grid unit is a preset spatial resolution parameter.

[0107] To facilitate the calculation of average meter operating status in different areas, the electronic map base needs to be divided into grids. An equidistant grid division method is used to divide the electronic map base into multiple square grid cells. The side length of each square grid cell is determined by a preset spatial resolution parameter, which can be adjusted according to actual needs, and thus determines the size and number of grid cells.

[0108] By dividing the electronic map base into grids, the base map can be divided into small areas, each area corresponding to a square grid cell. Thus, in subsequent calculations, the operating status of the electricity meter can be statistically analyzed on a grid cell basis.

[0109] Step 1432: Count the number of meter entity nodes contained in each square grid cell, and perform spatial interpolation on the grid cells with zero meter entity nodes to generate a continuously distributed grid data field.

[0110] After completing the grid division, it is necessary to count the number of meter nodes contained in each square grid cell. Some grid cells may contain multiple meters, while others may contain no meters. For grid cells with zero meter nodes, spatial interpolation is required to ensure data continuity.

[0111] Spatial interpolation is a process that estimates the meter operating status values ​​of grid cells with zero meters by using a predefined interpolation method based on the meter operating status data of adjacent grid cells. Through spatial interpolation, a continuously distributed grid data field can be generated, ensuring the continuity of meter operating status data across the entire electronic map.

[0112] Step 1433: Extract the voltage fluctuation characteristics, current stability characteristics, and power factor characteristics from the state attribute set of each meter entity node, and use a weighted average algorithm to calculate the comprehensive operating state average value of each square grid cell.

[0113] Each meter entity node's state attribute set contains information such as voltage fluctuation characteristics, current stability characteristics, and power factor characteristics. To calculate the comprehensive operating state average for each square grid cell, these features need to be extracted and a weighted average algorithm is used for calculation.

[0114] The weighted average algorithm assigns different weight values ​​to different features based on their importance. Then, it multiplies the feature value of each meter by its corresponding weight value, sums these results, and finally divides by the number of meters in the grid cell to obtain the overall average operating status of that grid cell. By using the weighted average algorithm, the influence of different features can be comprehensively considered, allowing for a more accurate assessment of the meter operating status of each grid cell.

[0115] Step 1434: Map the average value of the overall operating status to the corresponding RGB color value according to the preset color mapping scheme, and establish the mapping relationship between the operating status and the color.

[0116] The preset color mapping scheme defines the mapping relationship between the average value of the overall operating status and RGB color values. RGB color values ​​are a commonly used color representation method that can accurately represent various colors. By substituting the average value of the overall operating status into the color mapping scheme, the corresponding RGB color values ​​can be obtained.

[0117] Once the mapping relationship between operating status and color is established, different operating status values ​​can be represented by different colors. For example, areas with better operating status can be represented by green, and areas with poor operating status can be represented by red. Thus, in the subsequent thermal layer display, the operating status of the meters in different areas can be intuitively reflected through color changes.

[0118] Step 1435: Use bilinear interpolation algorithm to smooth the color values ​​of the grid cells to eliminate color abrupt changes at the grid boundaries.

[0119] Due to the mesh generation, the color values ​​of adjacent mesh cells may differ significantly, resulting in abrupt color changes at mesh boundaries. To ensure a more natural color transition in the thermal layer, a bilinear interpolation algorithm is needed to smooth the color values ​​of the mesh cells.

[0120] Bilinear interpolation is a commonly used image interpolation method that calculates the color value at the grid boundary by interpolating the color values ​​of adjacent grid cells. This method allows for smoother color transitions at grid boundaries, eliminates abrupt color changes, and improves the visual effect of the thermal layer.

[0121] Step 1436: Render the processed color data into a semi-transparent heat map texture, and overlay it on the electronic map base map according to the preset transparency parameters to form a heat map layer of meter distribution density.

[0122] After smoothing the color values, the processed color data is rendered as a semi-transparent heatmap texture. This semi-transparent heatmap texture can display the meter's operating status without affecting the display of the electronic map's base map, allowing users to see both map and heatmap information simultaneously.

[0123] The heatmap texture is overlaid on the electronic map base map according to preset transparency parameters. The transparency parameter determines the transparency of the heatmap texture. By adjusting the transparency parameter, the heatmap layer can be better integrated with the electronic map base map, thus forming a heatmap layer of electricity meter distribution density, which can intuitively show the operating status of electricity meters in different areas.

[0124] Step 144: Extract the state attribute edges from the smart meter state knowledge graph, determine the display width of the anomaly propagation path based on the weight value of the state attribute edges, and generate the state anomaly propagation path layer in a time-series animation manner.

[0125] The state attribute edges in the smart meter state knowledge graph record the relationships between the operating states of the meters, which may contain information about the propagation of state anomalies. By extracting the state attribute edges, potential anomaly propagation paths can be obtained.

[0126] The display width of the anomaly propagation path is determined based on the weight value of the state attribute edge. The larger the weight value, the stronger the influence relationship between the two meters, and the greater the possibility of anomaly propagation. Therefore, the display width of the corresponding anomaly propagation path is also wider. Thus, in the visualization, the width of the path can be used to intuitively understand the likelihood of anomaly propagation.

[0127] Anomaly propagation path layers are generated using a time-series animation method. Time-series animation can demonstrate the dynamic process of anomaly propagation. By showing the anomaly propagation path frame by frame at different points in time, the direction and speed of anomaly propagation can be more clearly understood. For example, the animation can show how an anomaly propagates from one meter to an adjacent meter and how the propagation range expands.

[0128] Optionally, the step of extracting state attribute edges from the smart meter's state knowledge graph, determining the display width of the anomaly propagation path based on the weight values ​​of the state attribute edges, and generating a state anomaly propagation path layer according to a time-series animation method includes:

[0129] Step 1441: Select state attribute edges with a state influence greater than a preset influence from the smart meter state knowledge graph as potential anomaly propagation paths.

[0130] Because there are a large number of state attribute edges, not all edges represent obvious anomaly propagation relationships. Therefore, it is necessary to filter state attribute edges from the smart meter state knowledge graph that have a state influence greater than a preset influence threshold. The preset influence threshold is a pre-defined threshold; only state attribute edges with a state influence greater than this threshold are considered potential anomaly propagation paths.

[0131] By filtering, we can exclude state attribute edges with weak influence relationships and retain only edges that may be related to anomaly propagation. This reduces the complexity of subsequent analysis and display and allows for more accurate location of anomaly propagation paths.

[0132] Step 1442: Perform path topology analysis on the potential abnormal propagation paths, identify key path nodes and path branch points in the potential abnormal propagation paths, and construct an abnormal propagation path network.

[0133] After identifying potential anomaly propagation paths, path topology analysis is required. This analysis helps identify critical path nodes and branching points within these paths. Critical path nodes are important nodes in the anomaly propagation process; they can be the source of the anomaly or a key hub in its spread. Branching points are where the anomaly propagation path branches off, reflecting the extent of the anomaly's spread.

[0134] By identifying critical path nodes and branch points, an anomaly propagation path network can be constructed. This network clearly demonstrates the path and structure of anomaly propagation, providing crucial information for subsequent analysis and processing. For example, analyzing the anomaly propagation path network can quickly pinpoint the source of the anomaly and allow for appropriate measures to be taken.

[0135] Step 1443: Calculate the corresponding path display width based on the weight value of the state attribute edge, and establish a linear mapping relationship between the weight value and the display width.

[0136] The weight value of the status attribute edge reflects the strength of the influence relationship between the operating states of two meters; the larger the weight value, the greater the possibility of anomaly propagation. To intuitively represent this relationship in the visualization, the corresponding path display width needs to be calculated based on the weight value of the status attribute edge.

[0137] A linear mapping relationship is established between weight values ​​and display width; that is, the larger the weight value, the wider the corresponding path display width. Through this linear mapping relationship, the weight values ​​of state attribute edges can be converted into visual path widths, allowing users to understand the likelihood of anomaly propagation by observing the path width.

[0138] Step 1444: Extract the time stamp information from the spatialized state data set and perform dynamic evolution analysis on the anomaly propagation path network in chronological order.

[0139] The time stamp information in the spatialized state dataset records the acquisition time of each meter's state data. By extracting this time stamp information, the dynamic evolution analysis of the anomaly propagation path network can be performed according to the chronological order.

[0140] Dynamic evolution analysis can demonstrate how anomaly propagation paths change at different points in time. For example, it can observe how an anomaly spreads from one meter to other meters over time, and how the spread expands or contracts. Through this analysis, a deeper understanding of the mechanisms and patterns of anomaly propagation can be gained.

[0141] Step 1445: Generate time series animation keyframes based on the dynamic evolution analysis results; where each keyframe corresponds to the anomaly propagation path state at a given time point.

[0142] Based on the dynamic evolution analysis results, keyframes for the time-series animation are generated. Each keyframe corresponds to the anomaly propagation path state at a specific time point, recording information such as the position, width, and color of the anomaly propagation path at that time point.

[0143] By generating keyframes, the dynamic process of anomaly propagation can be displayed in the form of animation. Users can intuitively observe the entire process of anomaly propagation by playing the time-series animation, including changes in the anomaly's starting point, propagation direction, and propagation range.

[0144] Step 1446: Based on the keyframes of the time series animation, add arrow markers to the potential anomaly propagation path to indicate the propagation direction, and use color gradient to indicate the change in propagation intensity to generate the state anomaly propagation path layer.

[0145] After generating keyframes for the time-series animation, to more clearly demonstrate the direction and intensity of anomaly propagation, arrow markers need to be added to potential anomaly propagation paths, and color gradients need to be used to represent changes in propagation intensity. Arrow markers clearly indicate the direction of anomaly propagation, allowing users to immediately understand how an anomaly spreads from one meter to another. Color gradients visually represent changes in propagation intensity; for example, areas with higher propagation intensity can be represented by darker colors, and areas with lower propagation intensity by lighter colors. By adding arrow markers and using color gradients, a state anomaly propagation path layer is generated, which can dynamically display the anomaly propagation process on the electronic map.

[0146] Step 145: Based on the geographical region association edges in the smart meter status knowledge graph, different line types are used to represent different types of regional association relationships to generate a regional association relationship layer.

[0147] The geographical region association edges in the smart meter status knowledge graph reflect the connections between the meter and geographical regions. Different types of geographical region association edges represent different regional relationships. To clearly represent these relationships in the visualization, different line types are needed to represent different types of regional relationships.

[0148] By classifying the edges associated with geographical regions, different line type attributes are assigned to each type of edge, such as line style, line color, and line width. For example, power line edges can be represented by solid lines, transformer substation affiliation edges can be represented by dashed lines, and load characteristic edges can be represented by dotted lines.

[0149] Based on these line type attributes, a regional relationship layer is generated. This regional relationship layer can display different types of regional relationships on an electronic map, helping users understand the interrelationships and common operational characteristics of electricity meters in different geographical areas.

[0150] Preferably, the step of generating a regional association layer based on the geographical region association edges in the smart meter status knowledge graph, using different line types to represent different types of regional association relationships, includes:

[0151] Step 1451: Perform type classification processing on the geographical area association edges in the smart meter status knowledge graph. Based on the association properties, the geographical area association edges are divided into three types: power line association edges, transformer area affiliation association edges, and load characteristic association edges.

[0152] The geographical region association edges in the smart meter status knowledge graph possess different association properties. Based on these properties, these edges can be categorized into three types: power line association edges, transformer substation association edges, and load characteristic association edges. Power line association edges represent the connection between meters via power lines; for example, meters on the same power line may influence each other. Transformer substation association edges reflect the transformer substation to which the meters belong; meters within the same substation may have similar operating characteristics. Load characteristic association edges reflect the association between the load characteristics of the meters; for example, meters with similar load characteristics may have a cooperative operating relationship. By classifying the geographical region association edges, we can gain a clearer understanding of different types of regional association relationships.

[0153] Step 1452: Assign corresponding line type attributes to the associated edges of each type of geographic region. The line type attributes include line style, line color, and line width.

[0154] To differentiate between different types of geographic region association edges in a visualization, corresponding line style attributes need to be assigned to each type of geographic region association edge. Line style attributes include line style, line color, and line width.

[0155] Line styles can be selected from solid lines, dashed lines, dotted lines, and other styles. Line colors can be chosen from various colors, and line widths can be adjusted according to the strength of the association. For example, thicker lines can be used for edges with stronger associations, while thinner lines can be used for edges with weaker associations. By assigning different line style attributes to the association edges of different types of geographical areas, the differences between them can be visually displayed on the electronic map.

[0156] Step 1453: Calculate the spatial path of the geographical region's associated edge based on the geographical coordinate data of the meter's physical node, and smooth the spatial path using the Bezier curve fitting method.

[0157] Geographic region association edges connect different electricity meter entity nodes. Based on the geographic coordinate data of the electricity meter entity nodes, the spatial path of the geographic region association edges can be calculated. The spatial path refers to the specific location and direction of the association edge in geographic space.

[0158] To make the display of geographic region association edges more aesthetically pleasing and natural, a Bézier curve fitting method is used to smooth the spatial paths. Bézier curves are a commonly used curve fitting method that allows adjustment of the curve's shape through control points, resulting in a smoother and more fluid curve. Smoothing the spatial paths avoids harsh, jagged lines on the association edges, improving the visualization effect.

[0159] Step 1454: Perform hierarchical processing on the edges associated with geographical regions, and determine the drawing priority according to the order of association strength from high to low.

[0160] Because there are many edges connecting geographical regions, a hierarchical approach is needed to avoid confusion in visualization. The purpose of this hierarchical approach is to determine the drawing priority based on the strength of the connections, from highest to lowest.

[0161] The strength of the association can be determined based on the weight value of the associated edges or other relevant indicators. Edges with higher association strength have higher drawing priority and will be drawn first; edges with lower association strength have lower drawing priority and will be drawn later. Through hierarchical processing, it can be ensured that important associated edges are clearly displayed and avoid being obscured by other edges.

[0162] Step 1455: Draw different types of geographic region association edges in sequence according to the drawing priority to generate a region association layer containing multiple region association relationships.

[0163] After determining the drawing priority, different types of geographic region association edges are drawn sequentially according to the drawing priority. These edges are then drawn on the electronic map in descending order of priority.

[0164] By drawing sequentially, a regional relationship layer containing multiple regional relationships can be generated. This layer can display different types of geographical regional relationships on an electronic map, helping users understand the interrelationships and common operating characteristics of electricity meters in different geographical areas.

[0165] Step 1456: Add an interactive response mechanism to the region association layer. When the user hovers the mouse over the geographical region association edge, display the detailed attribute information of the association edge.

[0166] To enhance the user experience, an interactive response mechanism has been added to the regional association layer. When the user hovers the mouse over a geographical region association edge, detailed attribute information of that edge, such as association type and association strength, will be displayed.

[0167] Interactive response mechanisms can be implemented by setting up event listeners on the electronic map. When the mouse hovers over a related edge, a corresponding event is triggered, retrieving detailed attribute information of the related edge from the database and displaying it on the interface. In this way, users can gain a deeper understanding of the related edges in a geographical area, providing greater assistance for the analysis and management of the power system.

[0168] Step 146: Overlay and merge the meter entity distribution layer, the meter distribution density thermal layer, the state anomaly propagation path layer, and the regional association layer according to a preset hierarchical order to generate a multi-level visualization display interface. The multi-level visualization display interface supports layer control, zoom and roaming, and attribute query operations.

[0169] After generating the meter entity distribution layer, meter distribution density thermal layer, anomaly propagation path layer, and region association layer, these layers need to be overlaid and merged according to a preset hierarchical order. The preset hierarchical order specifies the display order of each layer. For example, the meter entity distribution layer may be displayed at the bottom, the meter distribution density thermal layer in the middle, and the anomaly propagation path layer and region association layer at the top.

[0170] The purpose of overlay and blending is to combine different layers together to form a unified, multi-level visual display interface. During the overlay and blending process, factors such as the transparency and color blending of each layer need to be considered to ensure consistent display effects across all layers.

[0171] The generated multi-level visualization interface supports layer control, zoom and panning, and attribute query operations. The layer control function allows users to choose to show or hide certain layers according to their needs; for example, only the meter entity distribution layer and the meter distribution density thermal layer can be displayed, while the anomaly propagation path layer and the regional correlation layer can be hidden. The zoom and panning function allows users to zoom in, zoom out, and move the map to view information about different areas in more detail. The attribute query function allows users to click on elements on the map, such as meter entity nodes or associated edges, to obtain their detailed attribute information. Through these operations, users can more flexibly utilize the multi-level visualization interface to meet different analysis and decision-making needs.

[0172] In an exemplary embodiment, the step of overlaying and fusing the meter entity distribution layer, the meter distribution density thermal layer, the state anomaly propagation path layer, and the regional association layer according to a preset hierarchical order to generate a multi-level visualization interface includes:

[0173] Step 1461: Obtain the vector node data of the meter entity distribution layer, the raster color data of the meter distribution density thermal layer, the dynamic path data of the status anomaly propagation path layer, and the associated edge data of the regional association layer to form a layer rendering data set.

[0174] To perform overlay and fusion processing, it is first necessary to obtain the relevant data for each layer. The vector node data of the meter entity distribution layer records the position and attribute information of each meter entity node; the raster color data of the meter distribution density thermal layer contains the color information of the thermal layer; the dynamic path data of the status anomaly propagation path layer records the position, width, and color of the anomaly propagation path; and the associated edge data of the region association layer contains the type, line type attributes, and spatial path information of the associated edges.

[0175] These data are combined to form a layer rendering dataset. This dataset forms the basis for overlay and blending processes, and subsequent operations will be performed based on it.

[0176] Step 1462: Arrange the layer rendering data set in hierarchical order according to the preset layer priority sorting rules, which include spatial coverage parameters and visual saliency parameters, and generate a sorted layer sequence.

[0177] The preset layer priority ranking rules define the display order of each layer. These rules include spatial coverage parameters and visual salience parameters. The spatial coverage parameter reflects the size of the area a layer covers on the map; layers with larger coverage areas may have higher priority. The visual salience parameter considers the visual effect of the layer; for example, layers with bright colors and clear lines may have higher priority.

[0178] Based on layer priority sorting rules, the layer rendering data set is arranged hierarchically. By comparing parameters such as spatial coverage and visual saliency of each layer, the layers are arranged in descending order of priority, generating a sorted layer sequence. The sorted layer sequence determines the rendering order of each layer during overlay and blending.

[0179] Step 1463: Apply preset transparency weight parameters and blending mode parameters to the rendering data of each layer in the sorted layer sequence. The transparency weight parameters are used to adjust the visual transparency of each layer, and the blending mode parameters are used to control the superposition calculation method of pixel values ​​of different layers to generate preliminary blended pixel data.

[0180] After obtaining the sorted layer sequence, preset transparency weight parameters and blending mode parameters need to be applied to the rendering data of each layer. The transparency weight parameter adjusts the visual transparency of each layer; by adjusting the transparency weight parameter, some layers can be made more transparent, thus revealing the layer information below. The blending mode parameter controls how the pixel values ​​of different layers are superimposed. For example, different blending modes can be selected, such as Normal, Overlay, and Soft Light, to achieve different superimposed effects.

[0181] After applying transparency weight parameters and blending mode parameters to the rendering data of each layer, pixel values ​​are superimposed to generate preliminary blended pixel data. This preliminary blended pixel data is a collection of pixel values ​​from each layer after superposition, reflecting the initial display effect of each layer after superposition.

[0182] Step 1464: Based on the Mercator projection parameters of the GIS system, perform spatial coordinate alignment processing on the preliminary mixed pixel data. By verifying the geographic coordinate boundary range and pixel resolution consistency of each layer, correct the layer position deviation and generate the coordinate-aligned fused image data.

[0183] Since the layers can be processed in different coordinate systems, in order to ensure that they can be accurately displayed on the map after being overlaid and merged, it is necessary to perform spatial coordinate alignment processing on the initial mixed pixel data based on the Mercator projection parameters of the GIS system.

[0184] Spatial coordinate alignment processing includes verifying the geographic coordinate boundary range and pixel resolution consistency of each layer. The geographic coordinate boundary range refers to the coverage area of ​​the layer in geographic space, and the pixel resolution refers to the pixel size and precision of the layer. By verifying these parameters, layer positional deviations can be identified and corrected, ensuring that the layers are accurately aligned after being overlaid and blended.

[0185] After spatial coordinate alignment, fused image data with aligned coordinates is generated. The fused image data with aligned coordinates is a collection of pixel values ​​of each layer that are accurately aligned in space, and it can be accurately displayed on an electronic map.

[0186] Step 1465: Based on the spatial location data of entity nodes, paths and associated edges in the coordinate-aligned fused image data and layer rendering data set, extract the interactive response trigger area data, establish a mapping relationship between the interactive response trigger area data and the pixel coordinates of the fused image data, and generate interactive response layer data including layer control interface, zoom and roam interface and attribute query interface.

[0187] To achieve interactive functionality in a multi-level visualization interface, it is necessary to extract interactive response trigger area data based on the spatial location data of entity nodes, paths, and associated edges in the coordinate-aligned fused image data and layer rendering data set. Interactive response trigger area data refers to the areas on the map where interactive events can be triggered, such as the areas containing meter entity nodes and associated edges.

[0188] By establishing a mapping relationship between the interactive response trigger area data and the pixel coordinates of the fused image data, when a user performs actions such as clicking or hovering the mouse on the map, the corresponding interactive response trigger area can be found based on the pixel coordinates.

[0189] Generate interactive response layer data that includes layer control interfaces, zoom and roam interfaces, and attribute query interfaces. The layer control interfaces implement the display and hiding of layers, the zoom and roam interfaces implement zooming in, zooming out, and moving the map, and the attribute query interfaces retrieve detailed attribute information of map elements. Through this interactive response layer data, interactive functionality for a multi-level visualization interface can be achieved.

[0190] Step 1466: Integrate the coordinate-aligned fused image data with the interactive response layer data to output the final rendered data of the multi-level visualization interface.

[0191] The coordinate-aligned fused image data is integrated with the interactive response layer data to form a complete dataset. This dataset contains all the information of the multi-level visualization interface, including image data and interactive response information.

[0192] Output the final rendered data for a multi-level visualization interface. This final rendered data can be displayed on an electronic map. Users can control layers, zoom, pan, and query attributes through the interactive response layer's interface, thereby gaining a more comprehensive understanding of the distribution and operating status of smart meters.

[0193] As a non-limiting embodiment, the method further includes: receiving a multi-level visualization display interface and a user-inputted related search request, wherein the related search request contains keywords representing the status features of the meter to be searched; performing keyword extraction processing on the related search request to obtain a search keyword set containing status feature keywords and geographic region keywords; performing semantic matching processing on the search keyword set and meter entity nodes, status attribute edges, and geographic region association edges in the smart meter status knowledge graph to filter out target meter entity nodes and association relationship edges with a matching degree higher than a preset matching value; generating a related search result layer containing highlighted indicators based on the geographic coordinate data of the target meter entity nodes and the attribute information of the association relationship edges, wherein the color attribute of the highlighted indicators is associated with the weight value of the status attribute edges; and overlaying the related search result layer onto the top layer of the multi-level visualization display interface to generate an interactive related search visualization interface, wherein the related search visualization interface responds to the user's selection operation of the highlighted indicators and displays the detailed status attribute set of the corresponding meter entity node and the influence data of the association relationship edges.

[0194] When users need to find information about specific meter status characteristics or geographical areas, they can enter a related search request on the multi-level visual display interface. The related search request includes keywords related to the meter status characteristics to be searched, such as voltage fluctuations and current stability.

[0195] Keyword extraction is performed on related search requests, extracting status feature keywords and geographic region keywords to form a search keyword set. This search keyword set is the foundation for semantic matching.

[0196] Based on the set of search keywords, semantic matching is performed with meter entity nodes, status attribute edges, and geographical region association edges in the smart meter status knowledge graph. Semantic matching compares the keywords with the attribute information of elements in the knowledge graph to identify elements with high matching degrees. Target meter entity nodes and association edges with matching degrees higher than a preset matching value are then selected; these elements are information relevant to the user's search request.

[0197] Based on the geographic coordinates of the target meter entity node and the attribute information of the associated edges, a layer of associated search results containing highlighted indicators is generated. These highlighted indicators are used to emphasize the target element; their color attribute is correlated with the weight value of the status attribute edge. The higher the weight value, the more prominent the highlighted indicator's color.

[0198] The related search results layer is overlaid on top of the multi-level visualization interface to generate an interactive related search visualization interface. When the user selects a highlighted icon, the related search visualization interface will display the detailed status attribute set of the corresponding meter entity node and the influence data of the related relationship edges, allowing the user to gain a deeper understanding of the relevant information.

[0199] As a non-limiting embodiment, the method further includes: acquiring a multi-level visualization display interface and meter status anomaly data to be annotated, wherein the meter status anomaly data includes anomaly meter identifiers and corresponding anomaly status features; performing anomaly feature extraction processing on the meter status anomaly data to obtain anomaly feature vectors containing anomaly type, anomaly occurrence time, and anomaly feature values; based on the anomaly feature vectors associated with a set of status attributes in a smart meter status knowledge graph, extracting historical status attribute edges and geographic region association edges that match the anomaly feature vectors to generate anomaly annotation attribute set; generating a set of annotation elements containing anomaly type icons, anomaly propagation tracing arrows, and anomaly impact range contours based on the anomaly annotation attribute set and the geographic coordinate data of the anomaly meter identifiers; and superimposing the set of annotation elements onto the corresponding layer of the multi-level visualization display interface according to preset annotation hierarchy rules to generate an enhanced visualization interface with anomaly annotation information, wherein the annotation elements in the enhanced visualization interface support mouse hover to view detailed descriptions of the anomaly feature vectors and associated edge attributes.

[0200] When an abnormal meter status is detected, the abnormal data needs to be processed and labeled. This involves obtaining a multi-level visualization interface and the abnormal meter status data to be labeled. The abnormal meter status data includes the abnormal meter identifier and corresponding abnormal status characteristics, such as excessively high voltage or unstable current.

[0201] Anomaly feature extraction is performed on abnormal meter status data to extract information such as anomaly type, anomaly occurrence time, and anomaly characteristic values, forming an anomaly feature vector. The anomaly feature vector contains key information describing the abnormal situation.

[0202] Based on anomaly feature vectors, a set of state attributes in the smart meter's state knowledge graph is associated. By comparing the anomaly feature vectors with the feature information in the state attribute set, historical state attribute edges and geographical region association edges that match the anomaly feature vectors are extracted to generate an anomaly annotation attribute set. This anomaly annotation attribute set contains association edge information related to the anomaly, which helps to understand the propagation path and impact range of the anomaly.

[0203] Based on the set of anomaly annotation attributes and the geographic coordinates of the abnormal meter identifiers, a set of annotation elements is generated, including anomaly type icons, anomaly propagation tracing arrows, and anomaly impact range outlines. The anomaly type icon visually represents the type of anomaly, the anomaly propagation tracing arrows indicate the direction of anomaly propagation, and the anomaly impact range outline displays the scope of the anomaly's impact.

[0204] The set of labeled elements is overlaid onto the corresponding layers of a multi-level visualization interface according to preset labeling hierarchy rules, generating an enhanced visualization interface with anomaly labeling information. The labeled elements in the enhanced visualization interface allow users to view detailed descriptions of anomaly feature vectors and associated edge attributes by hovering the mouse over them, facilitating a deeper understanding of the anomaly.

[0205] As a non-limiting embodiment, it further includes: receiving a new state data information stream and the current layer rendering data set of the multi-level visualization display interface, wherein the layer rendering data set includes the current rendering parameters of the meter entity distribution layer, the meter distribution density thermal layer, the state anomaly propagation path layer, and the regional association layer; performing derivative parsing processing on the new state data information stream to extract the newly added meter operation status record sequence and the corresponding meter identification information, and generating a derivative state data unit; updating the state attribute edge weight values ​​and the spatial distribution density of the geographic region association edges in the smart meter state knowledge graph based on the derivative state data unit, and generating an updated smart meter state knowledge graph; performing derivative rendering updates on the raster color data of the meter distribution density thermal layer, the dynamic path data of the state anomaly propagation path layer, and the association edge data of the regional association layer according to the updated smart meter state knowledge graph and the current rendering parameters in the layer rendering data set; and overlaying and merging the derived and updated layer data according to a preset hierarchical order to generate a real-time updated multi-level visualization display interface, wherein the real-time updated multi-level visualization display interface retains the user's current map zoom ratio and layer control status.

[0206] Upon receiving the new status data stream and the current layer rendering data set of the multi-level visualization interface, the first step is to perform derivative parsing processing on the new status data stream. The new status data stream contains the latest meter operating status information. To integrate this new information into the existing system, it is necessary to extract the newly added meter operating status record sequences and corresponding meter identification information. This extraction process is based on analyzing the format and content of the new status data stream, separating the required information according to certain rules, thereby generating derived status data units.

[0207] Next, the smart meter state knowledge graph is updated based on the derived state data units. The weight values ​​of state attribute edges in the smart meter state knowledge graph reflect the influence relationships between the operating states of the meters, and the spatial distribution density of geographical region association edges reflects the distribution of meters within a geographical region. When a new derived state data unit is added, these relationships and distributions need to be reassessed. For updating the weight values ​​of state attribute edges, the state influence degree between adjacent meters is recalculated based on the changes in the operating states of the meters in the derived state data unit, combined with the previously used method for calculating state influence, thus updating the weight values ​​of the state attribute edges. For updating the spatial distribution density of geographical region association edges, the spatial distribution density of meter entity nodes within the same transformer area is recalculated based on the geographical coordinate data of the newly added meters, thereby adjusting the relevant attributes of the geographical region association edges, ultimately generating the updated smart meter state knowledge graph.

[0208] After obtaining the updated smart meter status knowledge graph, the meter distribution density thermal layer, status anomaly propagation path layer, and regional association layer should be updated by performing derivative rendering based on the graph and the current rendering parameters in the layer rendering data set.

[0209] The update of the raster color data for the meter distribution density thermal layer begins by recalculating the average operating status of meters in different regions based on the updated smart meter status knowledge graph. This calculation process is similar to the one used when generating the meter distribution density thermal layer, still considering factors such as voltage fluctuation characteristics, current stability characteristics, and power factor characteristics, and employing a weighted average algorithm to obtain the comprehensive operating status average for each region. Then, according to a preset color mapping scheme, the new comprehensive operating status average is mapped to the corresponding RGB color values, updating the raster color data. During the update process, smoothing of the new color data may also be necessary to ensure the continuity and naturalness of the thermal layer colors and avoid abrupt color changes.

[0210] For updating the dynamic path data of the state anomaly propagation path layer, state attribute edges with a state influence greater than a preset influence are re-selected as potential anomaly propagation paths. Next, path topology analysis is performed on these new potential anomaly propagation paths to identify key path nodes and path branch points, constructing a new anomaly propagation path network. Based on the updated weight values ​​of the state attribute edges, the corresponding path display width is recalculated, establishing a new linear mapping relationship between weight values ​​and display width. Simultaneously, time stamp information is extracted from the derived state data units, and dynamic evolution analysis is performed on the new anomaly propagation path network in chronological order to generate new time-series animation keyframes. Finally, based on the new time-series animation keyframes, arrow markers are added to the potential anomaly propagation paths, and color gradients are used to represent changes in propagation intensity, updating the dynamic path data.

[0211] For updating the edge data of the regional association layer, the geographic region edges are reclassified, as the addition of new meters may change the association relationships. Then, corresponding line type attributes, including line style, line color, and line width, are reassigned to each type of geographic region edge. Based on the updated geographic coordinate data of the meter entity nodes, the spatial paths of the geographic region edges are recalculated, and the new spatial paths are smoothed using a Bézier curve fitting method. The geographic region edges are then re-hierarchically processed, and a new drawing priority is determined according to the association strength from high to low. Finally, different types of geographic region edges are drawn sequentially according to the new drawing priority, updating the edge data.

[0212] After completing the derived rendering update of each layer's data, the updated layer data is overlaid and blended according to a preset hierarchical order. This overlay and blending process is similar to the steps taken when generating the multi-level visualization interface. First, the vector node data of the derived and updated meter entity distribution layer, the raster color data of the meter distribution density thermal layer, the dynamic path data of the anomaly propagation path layer, and the associated edge data of the regional relationship layer are acquired to form a new set of layer rendering data. Then, the new set of layer rendering data is arranged hierarchically according to a preset layer priority sorting rule to generate a sorted layer sequence. Preset transparency weight parameters and blending mode parameters are applied to the rendering data of each layer in the sorted layer sequence to generate preliminary blended pixel data. Based on the Mercator projection parameters of the GIS system, the preliminary blended pixel data is spatially aligned to correct layer position deviations, generating coordinate-aligned blended image data. Based on the spatial location data of entity nodes, paths, and associated edges in the coordinate-aligned fused image data and the new layer rendering dataset, interactive response trigger area data is extracted. A mapping relationship is established between the interactive response trigger area data and the pixel coordinates of the fused image data, generating interactive response layer data that includes layer control interfaces, zoom and roaming interfaces, and attribute query interfaces. Finally, the coordinate-aligned fused image data and interactive response layer data are integrated and processed to output the final rendered data of a real-time updated multi-level visualization interface.

[0213] The generated, real-time updated multi-level visualization interface retains the user's current map zoom level and layer control status. This means that user actions during the viewing process, such as zooming in or out, showing or hiding layers, will remain unchanged after the interface update, providing a continuous and convenient user experience. For example, if a user previously zoomed in on a specific area to view detailed electricity meter information, the map will remain at that zoom level after the interface update, allowing the user to continue viewing the relevant content. Similarly, if a user previously hid a layer, the updated interface will maintain that hidden state, without affecting the user's operating habits.

[0214] The embodiments of the present invention enable a comprehensive, intuitive and in-depth visualization of smart meter status data, significantly improving the efficiency and accuracy of power system management and operation and maintenance. In detail, by receiving the status data information stream transmitted by smart meters through the power communication network, the real-time and completeness of the acquired data are ensured. Geospatial correlation processing is performed on the status data information stream, combining the meter's operating status with geographic information to obtain a spatialized status data set. This breaks the limitation of traditional data processing that only focuses on operating parameters while ignoring geographical location factors, enabling analysis of meter operating status from a geospatial dimension and uncovering potential regional characteristics and patterns. Based on the spatialized status data set, a smart meter status knowledge graph is constructed. Through the setting of meter entity nodes, status attribute edges, and geographic region association edges, the operational status influence relationships between meters and their connections with geographic regions are accurately displayed. The smart meter status knowledge graph is then integrated and rendered with the electronic map of the GIS system to generate a multi-level visualization interface. Abstract and complex data are presented in intuitive graphical and thermal layer forms, allowing users to quickly and accurately grasp information such as meter distribution, operating status, and anomaly propagation paths. This greatly improves the speed and accuracy of fault diagnosis and decision-making, thereby achieving power system optimization and efficient operation.

[0215] See Figure 2 As shown in the figure, this is a schematic diagram of the basic structure of a state data visualization system 200 provided in an embodiment of the present invention. The state data visualization system 200 includes:

[0216] Processor 201;

[0217] Storage device 202, on which computer program 2020 is stored;

[0218] When the computer program 2020 is executed by the processor 201, the processor 201 implements any of the aforementioned methods for visualizing the status data of a smart meter.

[0219] Based on the above, a readable storage medium is provided, on which a program or instructions are stored, and when the program or instructions are executed by a processor, the steps of the above method are implemented.

[0220] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems or apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple, and relevant parts can be referred to the method section.

Claims

1. A method for visualizing the status data of smart meters, characterized in that, The method includes: Receive status data information stream transmitted by smart meters through power communication networks, wherein the status data information stream includes a sequence of meter operating status records with acquisition time stamps and corresponding meter identification information; The status data information stream is subjected to geospatial association processing to establish a mapping relationship between the meter operation status record sequence and the meter installation location in the GIS system, thereby obtaining a spatialized status data set containing geographic coordinate attributes. A smart meter status knowledge graph is constructed based on the spatialized status data set. The smart meter status knowledge graph includes meter entity nodes, status attribute edges, and geographical region association edges. The status attribute edges are used to represent the operational status influence relationship between meter entity nodes. The smart meter status knowledge graph is integrated and rendered with the electronic map of the GIS system to generate a multi-level visualization interface that includes a thermal layer of meter distribution density, a layer of status anomaly propagation path, and a layer of regional relationship. The smart meter state knowledge graph is constructed based on the spatialized state data set. This smart meter state knowledge graph includes meter entity nodes, state attribute edges, and geographical region association edges, including: Extract meter identification information from the spatialized state data set as the unique identifier of entity nodes in the knowledge graph, and construct a set of meter entity nodes; The running state records in the spatialized state data set are subjected to attribute feature extraction processing to obtain a state attribute set containing voltage fluctuation features, current stability features and power factor features; Based on the numerical change trends of different attribute features in the state attribute set, the state influence degree between adjacent meter entity nodes is calculated. The state influence degree is used to quantify the degree of influence of a meter's state change on surrounding meters. Based on the state influence degree, state attribute edges are constructed between meter entity nodes, and the weight values ​​of the state attribute edges are positively correlated with the state influence degree. Based on the geographical coordinate data of the meter physical nodes, the spatial distribution density of the meter physical nodes in the same transformer area is calculated. Geographical region association edges are constructed based on the spatial distribution density, and a smart meter status knowledge graph containing multi-level association relationships is generated.

2. The method for visualizing status data of smart meters as described in claim 1, characterized in that, The process involves geospatial correlation processing of the status data information stream to establish a mapping relationship between the meter operation status record sequence and the meter installation location in the GIS system, resulting in a spatialized status data set containing geographic coordinate attributes, including: The meter identification information in the status data information stream is parsed, and the transformer area code and installation location code of the meter are extracted according to the preset identification coding rules; The spatial query interface of the GIS system is called to retrieve the corresponding geographic coordinate data based on the transformer area code and installation location code. The geographic coordinate data is represented by latitude and longitude coordinates. Add the geographic coordinate data as an additional attribute field to each status record in the electricity meter operation status record sequence to generate a spatiotemporal status record unit containing spatiotemporal dimension information. The spatiotemporal state recording unit is subjected to data optimization processing to remove abnormal state records that have missing geographic coordinates or whose coordinates exceed the preset area range; The optimized spatiotemporal state recording units are grouped and aggregated according to the station area code to obtain a spatialized state data set containing geographic coordinate attributes.

3. The method for visualizing status data of smart meters as described in claim 1, characterized in that, The process of extracting attribute features from the operating state records in the spatialized state data set yields a set of state attributes including voltage fluctuation features, current stability features, and power factor features, including: Time series analysis is performed on the voltage monitoring data in the spatialized state data set to calculate the voltage change frequency and amplitude per unit time and generate voltage fluctuation characteristics. Sliding window statistical processing is performed on the current monitoring data in the spatialized state data set to calculate the current standard deviation and coefficient of variation in different time windows, thereby generating current stability characteristics. The power factor is calculated based on the voltage and current monitoring data in the spatialized state data set. The power factor is then subjected to time-dimensional trend fitting to generate power factor features. The voltage fluctuation characteristics, current stability characteristics, and power factor characteristics are normalized, and the normalized voltage fluctuation characteristics, current stability characteristics, and power factor characteristics are combined to form a state attribute set. Each attribute feature in the state attribute set includes a feature name, a timestamp, and a feature value.

4. The method for visualizing status data of smart meters as described in claim 3, characterized in that, The step of calculating the state influence degree between adjacent meter physical nodes based on the numerical change trends of different attribute characteristics in the state attribute set includes: Calculate the straight-line distance between any two electricity meter physical nodes based on the geographical coordinate data of the electricity meter physical nodes, and filter out adjacent electricity meter physical node pairs whose straight-line distance is less than a preset distance value; Perform time synchronization processing on the state attribute set of each pair of adjacent meter entity nodes so that the state records of the two meter entity nodes have the same timestamp. Calculate the Pearson correlation coefficients of the corresponding features in the synchronized state attribute set to obtain the voltage fluctuation correlation coefficient, current stability correlation coefficient, and power factor correlation coefficient; The voltage fluctuation correlation coefficient, the current stability correlation coefficient, and the power factor correlation coefficient are weighted and summed according to a preset feature weight allocation rule to generate a comprehensive correlation coefficient. The comprehensive correlation coefficient is standardized and transformed to obtain the state influence degree. The larger the value of the state influence degree, the stronger the relationship between the operating states of adjacent meter physical nodes.

5. The method for visualizing status data of smart meters as described in any one of claims 1-2, characterized in that, The process of fusing and rendering the smart meter status knowledge graph with the electronic map of the GIS system to generate a multi-level visualization interface including a thermal layer of meter distribution density, a layer of status anomaly propagation paths, and a layer of regional relationships includes: Call the map rendering interface of the GIS system to load the electronic map base map with a preset scale, and set the map projection method to Mercator projection; The geographic coordinate data of the meter entity nodes in the smart meter status knowledge graph are parsed, and the meter entity nodes are mapped to the corresponding positions on the electronic map base map to generate a meter entity distribution layer. The average operating status of the meters in different regions is calculated based on the set of state attributes of the meter physical nodes. A thermal layer of meter distribution density is generated based on the average operating status using a gradient color mapping method and overlaid on the electronic map base map. Extract the state attribute edges from the smart meter state knowledge graph, determine the display width of the anomaly propagation path based on the weight value of the state attribute edges, and generate the state anomaly propagation path layer in a time-series animation manner; Based on the geographical region association edges in the smart meter status knowledge graph, different line types are used to represent different types of regional association relationships to generate a regional association relationship layer. The meter entity distribution layer, the meter distribution density thermal layer, the state anomaly propagation path layer, and the regional association layer are superimposed and merged according to a preset hierarchical order to generate a multi-level visualization display interface. The multi-level visualization display interface supports layer control, zooming and roaming, and attribute query operations.

6. The method for visualizing status data of smart meters as described in claim 5, characterized in that, The step of calculating the average operating status of electricity meters in different areas based on the set of state attributes of the electricity meter physical nodes, generating a heat map layer of electricity meter distribution density based on the average operating status using a gradient color mapping method, and overlaying it on the electronic map base map includes: The electronic map base map is divided into multiple square grid units using an equidistant grid division method, with the side length of each square grid unit being a preset spatial resolution parameter; The number of meter physical nodes contained in each square grid cell is counted. Spatial interpolation is performed on grid cells with zero meter physical nodes to generate a continuously distributed grid data field. Voltage fluctuation characteristics, current stability characteristics, and power factor characteristics are extracted from the state attribute set of each meter entity node, and a weighted average algorithm is used to calculate the comprehensive operating state average value of each square grid cell. The average value of the overall operating status is mapped to the corresponding RGB color value according to the preset color mapping scheme, and the mapping relationship between the operating status and the color is established. A bilinear interpolation algorithm is used to smooth the color values ​​of the grid cells to eliminate color abrupt changes at the grid boundaries; The processed color data is rendered as a semi-transparent heat map texture, which is then overlaid on the electronic map base according to preset transparency parameters to form a heat map layer of electricity meter distribution density.

7. The method for visualizing status data of smart meters as described in claim 5, characterized in that, The step of extracting state attribute edges from the smart meter's state knowledge graph, determining the display width of the anomaly propagation path based on the weight values ​​of the state attribute edges, and generating a state anomaly propagation path layer according to a time-series animation method includes: State attribute edges with a state influence greater than a preset influence are selected from the smart meter state knowledge graph as potential anomaly propagation paths; Perform path topology analysis on the potential abnormal propagation paths to identify key path nodes and path branch points in the potential abnormal propagation paths, and construct an abnormal propagation path network; Calculate the corresponding path display width based on the weight value of the state attribute edge, and establish a linear mapping relationship between the weight value and the display width; Extract the time stamp information from the spatialized state data set, and perform dynamic evolution analysis on the anomaly propagation path network according to the chronological order; Based on the results of dynamic evolution analysis, keyframes for time-series animation are generated; each keyframe corresponds to the state of an anomaly propagation path at a given time point. Based on the keyframes of the time-series animation, arrow markers are added to the potential anomaly propagation paths to indicate the propagation direction, and color gradients are used to represent changes in propagation intensity, thus generating a state anomaly propagation path layer. The geographical region association edges based on the smart meter status knowledge graph are used to represent different types of regional association relationships using different line types, generating a regional association relationship layer, including: The geographical region association edges in the smart meter status knowledge graph are classified into three types according to their association properties: power line association edges, transformer area affiliation association edges, and load characteristic association edges. Assign corresponding line type attributes to the edges associated with each type of geographic region. The line type attributes include line style, line color, and line width. The spatial path of the associated edge of the geographic region is calculated based on the geographic coordinate data of the meter physical node, and the spatial path is smoothed by the Bézier curve fitting method. The edges associated with geographical regions are processed hierarchically, and the drawing priority is determined according to the association strength from high to low. Based on the drawing priority, different types of geographic region association edges are drawn sequentially to generate a region association layer containing multiple region association relationships; Add an interactive response mechanism to the region association layer so that when the user hovers the mouse over the geographical region association edge, the detailed attribute information of the association edge is displayed.

8. The method for visualizing status data of smart meters as described in claim 5, characterized in that, The process of overlaying and merging the meter entity distribution layer, the meter distribution density thermal layer, the state anomaly propagation path layer, and the regional association layer according to a preset hierarchical order to generate a multi-level visualization interface includes: The vector node data of the meter entity distribution layer, the raster color data of the meter distribution density thermal layer, the dynamic path data of the status anomaly propagation path layer, and the associated edge data of the regional association layer are obtained to form a layer rendering data set. The layer rendering data set is sorted hierarchically according to a preset layer priority sorting rule, which includes spatial coverage parameters and visual saliency parameters, to generate a sorted layer sequence. Preset transparency weight parameters and blending mode parameters are applied to the rendering data of each layer in the sorted layer sequence. The transparency weight parameters are used to adjust the visual transparency of each layer, and the blending mode parameters are used to control the superposition calculation method of pixel values ​​of different layers to generate preliminary blended pixel data. Based on the Mercator projection parameters of the GIS system, the initial mixed pixel data is spatially aligned. By verifying the geographic coordinate boundary range and pixel resolution consistency of each layer, the layer position deviation is corrected, and the coordinate-aligned fused image data is generated. Based on the spatial location data of entity nodes, paths and associated edges in the coordinate-aligned fused image data and layer rendering data set, the interactive response trigger area data is extracted, and a mapping relationship is established between the interactive response trigger area data and the pixel coordinates of the fused image data to generate interactive response layer data including layer control interface, zoom and roam interface and attribute query interface. The fused image data after coordinate alignment is integrated with the interactive response layer data to output the final rendered data of the multi-level visualization interface.

9. A state data visualization system, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of any one of the methods described in claims 1 to 8.

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