Risk early warning method and system for power marketing based on big data and cloud service

Abstract

The application provides a power marketing risk early warning method and system based on big data and cloud services, comprising a received risk situation spectrum, potential risk events are identified through feature extraction and pattern matching; the potential risk events are comprehensively scored, and are graded according to the obtained score results, to generate the risk level and the priority disposal code of the potential risk events; a preliminary disposal suggestion is generated through a strategy generation network according to the risk level and the priority disposal code; the preliminary disposal suggestion is dynamically adjusted and optimized through a strategy optimization engine by using obtained meteorological environment data and historical disposal effect data, accurate identification, dynamic grading and adaptive disposal of power marketing risks are realized, and the accuracy, timeliness and disposal adaptability of risk early warning are improved.

Description

Technical Field

[0001] This invention relates to the field of power marketing risk analysis technology, and in particular to a risk early warning method and system for power marketing based on big data and cloud services. Background Technology

[0002] In today's booming power industry, electricity marketing, as the core link between power supply companies and users, is becoming increasingly complex. From electricity billing and electricity usage inspections to customer service, every link harbors operational risks—data discrepancies may lead to billing disputes, and equipment malfunctions, if not detected in time, could even cause safety accidents. Traditional management models rely heavily on manual experience and fragmented data verification, much like groping forward in a fog. With the large-scale integration of smart grids and renewable energy, electricity marketing scenarios are becoming more dynamic and fragmented, urgently requiring a "smart sentinel" capable of proactively identifying risks and providing accurate early warnings.

[0003] Traditional power marketing risk early warning methods rely heavily on manual experience and fragmented data verification, which has significant limitations: on the one hand, they cannot effectively integrate multi-source heterogeneous data, resulting in insufficient data utilization, forming "data silos," and making it difficult to comprehensively depict the risk situation; on the other hand, risk identification lacks a dynamic adaptation mechanism and cannot respond to real-time changes in external factors such as weather conditions, leading to a lag in risk response; at the same time, risk classification standards are rigid, and disposal suggestions lack flexibility, making it difficult to match the actual disposal needs of different risk levels, ultimately resulting in a high false alarm rate in risk warnings, a high degree of reliance on manual intervention, and an inability to meet the precise risk prevention and control needs of the entire power marketing process.

[0004] Therefore, how to accurately identify, dynamically classify, and adaptively handle electricity marketing risks, and improve the accuracy, timeliness, and adaptability of risk warnings and responses, is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This invention provides a risk warning method and system for power marketing based on big data and cloud services, which can realize accurate identification, dynamic classification and adaptive handling of power marketing risks, and improve the accuracy, timeliness and adaptability of risk warning.

[0006] On the one hand, this invention provides a risk warning method for electricity marketing based on big data and cloud services, which includes: For the received risk situation map, potential risk events are identified through feature extraction and pattern matching; The potential risk events are comprehensively scored, and classified according to the scoring results to generate the risk level and priority handling code of the potential risk events; The strategy generation network generates preliminary treatment recommendations based on the risk level and the priority treatment code; Using the acquired meteorological and environmental data and historical treatment effect data, the preliminary treatment suggestions are dynamically adjusted and optimized through a strategy optimization engine.

[0007] On the other hand, the present invention also provides a risk warning system for electricity marketing based on big data and cloud services, which includes: The identification module is used to identify potential risk events from the received risk situation map through feature extraction and pattern matching. The grading module is used to comprehensively score the potential risk events, and classify them according to the obtained scoring results, generating the risk level and priority handling code of the potential risk events; The generation module is used to generate preliminary treatment suggestions based on the risk level and the priority treatment code through a policy generation network; The optimization module is used to dynamically adjust and optimize the preliminary treatment suggestions by utilizing the acquired meteorological and environmental data and historical treatment effect data through the strategy optimization engine.

[0008] The present invention provides a risk warning method and system for power marketing based on big data and cloud services. It transforms multi-source heterogeneous data into a risk situation map through a dynamic data aggregation mechanism, identifies potential risk events based on feature extraction and pattern matching, calculates multi-dimensional risk factors by combining meteorological and environmental data and achieves accurate classification through nonlinear aggregation, and finally generates preliminary handling suggestions through a strategy generation network. It also dynamically optimizes the system using real-time environmental data and historical handling effects, thereby achieving accurate identification, dynamic classification and adaptive handling of power marketing risks, and improving the accuracy, timeliness and adaptability of risk warning. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating the risk warning method for power marketing based on big data and cloud services provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the power marketing risk warning system based on big data and cloud services provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0012] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0013] Figure 1 This is a flowchart illustrating the risk warning method for power marketing based on big data and cloud services provided in this embodiment of the invention.

[0014] like Figure 1 As shown in the figure, the risk warning method for power marketing based on big data and cloud services provided in this embodiment of the invention mainly includes the following steps: 101. For the received risk situation map, identify potential risk events through feature extraction and pattern matching; In this embodiment, the risk situation map can be based on multi-source raw heterogeneous data (such as power grid operation data, electricity customer data, meteorological monitoring data, etc.). After cleaning, standardization, and spatiotemporal alignment, it is combined through a dynamic data aggregation mechanism to form a structured map. This map can comprehensively reflect the correlation and risk correlation of various links in electricity marketing, providing a complete analytical basis for risk identification. When identifying potential risk events, key features related to the risk can be extracted from the map first, and then matched with various preset risk pattern templates (such as billing anomaly patterns, equipment failure correlation patterns, etc.) to filter out events that meet the risk characteristics.

[0015] Specifically, the process of acquiring the risk situation map includes: receiving raw multidimensional heterogeneous data from different sources; cleaning and standardizing the raw multidimensional heterogeneous data to obtain preprocessed multidimensional heterogeneous data; mapping the preprocessed multidimensional heterogeneous data to a unified spatiotemporal coordinate system to generate a spatial mapping data set; and combining the mapping data of the spatial mapping data set into a risk situation map through a dynamic data aggregation mechanism.

[0016] In this embodiment, raw data from multiple different data sources can be received. This raw, multidimensional, heterogeneous data includes, but is not limited to, equipment status data related to power grid operation, electricity load data, electricity billing data, power grid node topology data, and meteorological environment-related data such as wind speed, humidity, precipitation intensity, and extreme weather warnings. These data originate from different acquisition devices and system platforms, exhibiting heterogeneous characteristics such as inconsistent data formats, asynchronous acquisition times, and inconsistent spatial reference coordinates.

[0017] A distributed computing framework based on cloud services can be used to process the received raw multidimensional heterogeneous data in real time. This framework uses parallel sharding technology to divide the continuous data stream into multiple independent data segments according to a preset time window. At the same time, each data segment is subjected to preliminary noise filtering to remove obviously invalid and duplicate data, and data of different formats is uniformly converted into a preset standard data format to ensure data format consistency. Furthermore, an anomaly detection method based on sliding windows is used to continuously monitor the data in each data segment, identify and remove outliers that deviate from the normal data range and data values ​​with logical conflicts, and finally generate a time-series standardized data stream, that is, preprocess the multidimensional heterogeneous data to ensure the accuracy and usability of the data.

[0018] Spatiotemporal alignment processing can be performed on preprocessed multidimensional heterogeneous data: In the time dimension, the original timestamps of each data are extracted, and data with different collection frequencies and different time starting points are uniformly aligned to the preset standard time baseline through the time base correction algorithm to ensure that all data are synchronized in the time dimension; In the spatial dimension, spatial information such as the physical location coordinates of power grid nodes and the geographical coordinates of meteorological monitoring points contained in the data are extracted, and all data are uniformly mapped to the preset spatial grid coordinate system. The spatial grid coordinate system is reasonably divided into grid units according to the coverage of power marketing business, so that data in different spatial locations have a unified spatial reference standard, and finally a spatial mapping data set is generated.

[0019] A dynamic data aggregation mechanism can be used to process spatially mapped datasets: First, based on the real-time change rate of meteorological and environmental data (such as the magnitude of wind speed change, the rate of increase in precipitation intensity, etc.), the dynamic weight of each data point in the spatially mapped dataset is calculated. The more drastic the changes in meteorological factors, the higher the dynamic weight of the corresponding data point, in order to highlight the impact of key environmental factors on risk. Then, based on the dynamic weight of the data points and the business relationships between the data (such as the power supply relationship between power grid equipment and power consumption areas, and the spatial relationship between meteorological monitoring points and power grid nodes), multiple related data points are aggregated into higher-dimensional data blocks. Finally, these data blocks are defined as graph nodes, each containing multi-dimensional attribute information of the corresponding region or equipment. At the same time, the coupling degree between any two nodes is analyzed in terms of spatial proximity, similarity of situation evolution trends, and consistency of weight distribution, etc., to determine the strength value of the connection edge between nodes. All nodes and edges are assembled according to their association relationships to form a multi-level, structured risk situation graph.

[0020] Specifically, the dynamic weight calculation process is as follows: For data points related to meteorological environmental factors (such as wind speed and humidity data points) in the spatial mapping dataset, a continuous time window is selected within its spatiotemporal grid cell. The magnitude of change (i.e., the difference between the starting and ending values ​​of the time window) and the acceleration of change (i.e., the rate of change of the magnitude of change per unit time) of the data point's value within the time window are calculated. Through a preset standardized mapping rule, the original values ​​of the magnitude of change and the acceleration of change are converted into standardized dynamic weight values ​​between 0 and 1. The more drastic the change in meteorological factors, the higher the corresponding dynamic weight value. At the same time, basic weights are assigned to all other types of data points in the spatial mapping dataset (such as power grid equipment status data points) according to their business importance, and then corrected by combining the dynamic weights of associated meteorological data. Finally, a unique dynamic weight is assigned to each data point, generating a dynamic weight mapping table to provide a quantitative basis for subsequent data fusion.

[0021] The process of regional data fusion is as follows: Taking the target entity in the spatial grid (such as a power grid device or a power consumption area) as the center, a neighborhood area with a preset range is defined, and all relevant data points within this neighborhood are selected; according to the dynamic weight values ​​of each data point recorded in the dynamic weight mapping table, a corresponding fusion coefficient is assigned to each data point. The higher the dynamic weight value, the larger the fusion coefficient, which represents the higher the contribution of the data point in regional fusion; through a weighted fusion algorithm, all data points in the neighborhood, together with their fusion coefficients, are integrated and calculated to form an aggregated data block that can represent the overall state of the region, thereby realizing the regional aggregation of distributed data.

[0022] The process of assembling the risk situation map structure is as follows: Each aggregated data block is defined as a node in the risk situation map. Each node contains multi-dimensional attribute information of the fused region (such as the operating status of equipment in the region, average power load, meteorological environmental characteristics, etc.). Further analysis is conducted on the relationship between any two aggregated data blocks. The coupling degree between the two is calculated from multiple dimensions, such as spatial proximity (physical distance between the regions corresponding to the two data blocks), similarity of situation evolution trends (the degree of fit of data change patterns), and consistency of weight distribution (the degree of matching of dynamic weight distribution). The strength value of the connecting edge between nodes is determined according to the coupling degree. The higher the coupling degree, the greater the edge strength value. Finally, according to the edge strength value between nodes, all nodes and edges are assembled according to the actual relationship to form a structured risk situation map with clear hierarchy and clear association.

[0023] In a specific implementation, after receiving a risk situation map, the process of identifying potential risk events through feature extraction and pattern matching may include: a1. Select a core entity node from the risk situation map as the starting point, and traverse along the associated edges with a limited step size to form a local subgraph centered on the core node. Specifically, core entity nodes are key nodes in the risk profile that are related to the core business of electricity marketing, such as important electricity customer nodes, core power supply equipment nodes, and billing system nodes. Limited-step traversal refers to filtering data centered on the core node according to a preset number of association layers (e.g., directly associated nodes, indirectly associated level 1 nodes) to form a local subgraph, focusing on the risk-related areas surrounding the core node and avoiding interference from irrelevant data.

[0024] a2. Extract the distribution entropy of node density and edge weight within the local subgraph and the topological index of meteorological nodes to jointly form a feature vector describing the regional risk characteristics of the local subgraph. Among them, in the composition of the feature vector, the node density reflects the density of risk associations within the local subgraph, the distribution entropy of the edge weights reflects the balance of association strength, and the topological index of meteorological nodes characterizes the closeness of the association between meteorological factors and core business nodes. The three together comprehensively describe the risk characteristics of the local area.

[0025] a3. Based on the severity of the meteorological and environmental data, adjust the weights of each feature in the feature vector using an environmental factor mapping function; It can acquire real-time meteorological environmental data (such as wind speed, precipitation intensity, extreme weather warning levels, etc.) and assess its severity. The severity of meteorological data is determined by a preset level classification standard (such as light wind, strong wind, gale). Based on this severity, the weights of the three types of features in the feature vector are adjusted through a preset environmental factor mapping function: when the meteorological environmental data is of high severity, the weights of meteorological node topology indicators are increased, while the weights of node density and edge weight distribution entropy are decreased to highlight the impact of meteorological factors on risk; when the meteorological environment is relatively stable, the weights of node density and edge weight distribution entropy are appropriately increased to ensure that the core risk features are highlighted in different scenarios. Through this dynamic adjustment mechanism, the feature weights can adapt to changes in the external environment in real time.

[0026] a4. Using the weights of each feature, calculate the matching degree between the feature vector and each risk pattern template; a5. When the matching degree of the feature vector exceeds the preset matching degree threshold, it is identified as a potential risk event.

[0027] When calculating the matching degree, the adjusted feature weights are combined with the preset risk pattern templates (such as equipment failure risk templates and abnormal power consumption risk templates) for similarity comparison. When the similarity exceeds the preset threshold (such as 80%), it is judged as a potential risk event, and the basic attributes such as the occurrence node, related nodes, and initial impact range of the event are recorded.

[0028] 102. The potential risk events are comprehensively scored, and the scores are classified to generate the risk level and priority handling code of the potential risk events; In a specific implementation process, this step can be implemented in the following way: b1. Map the matching degree corresponding to the potential risk event to a standardized risk probability factor; The standardized risk probability factor is a value between 0 and 1 that maps the matching degree of the potential risk events obtained above to a normalized value. The larger the value, the higher the probability of the risk occurring.

[0029] b2. Analyze all downstream nodes directly and indirectly affected by the core node in the risk situation map; When analyzing downstream nodes, the associated links of core nodes in the business topology network are traced, including directly associated business nodes (such as customer nodes in the power supply area corresponding to core equipment) and indirectly associated nodes (such as billing and settlement nodes corresponding to customer nodes), comprehensively covering the business scope that risks may affect.

[0030] b3. By summarizing the preset business importance scores of all downstream nodes, the impact range factor of the potential risk event is calculated; Specifically, the business importance score of downstream nodes is set in advance according to the business type. For example, the importance score of large industrial customer nodes is higher than that of ordinary residential customer nodes, and the score of core nodes of the billing system is higher than that of auxiliary nodes. The resulting impact range factor directly reflects the total business weight of the risk impact.

[0031] b4. Based on the severity of the meteorological and environmental data, calculate the urgency coefficient of the potential risk event using an environmental urgency mapping table; Specifically, the environmental urgency mapping table is pre-set with urgency coefficients corresponding to different levels of meteorological severity (e.g., the coefficient for ordinary weather is 1, the coefficient for heavy rain is 1.5, and the coefficient for typhoon is 2.0). The larger the coefficient, the higher the urgency of the response.

[0032] b5. Input the standardized risk probability factor, the scope of influence factor, and the urgency coefficient into a nonlinear aggregation function to output the comprehensive risk score of the potential risk event; Specifically, the standardized risk probability factor, impact range factor, and urgency coefficient can be input into a nonlinear aggregation function. The design of this nonlinear aggregation function ensures that when multiple factors are at high levels simultaneously, the output result (i.e., the comprehensive risk score) will show an accelerated growth trend, thus more sensitively capturing extreme risk situations with high probability, large impact, and high urgency.

[0033] In a specific implementation process, this step can be achieved in the following way: b51. Multiply the standardized risk probability factor and the influence range factor to obtain a synergistic product; Specifically, the calculation of the synergistic product is used to capture the synergistic effect between risk probability and impact range. For example, when high risk probability and large-scale impact are superimposed, the synergistic product will increase significantly.

[0034] b52. Compare the synergistic product with a preset synergistic effect threshold table, and assign dynamic weighting factors to the potential risk events based on the comparison results; wherein, the synergistic effect threshold table includes the amplification intensity corresponding to different synergistic products, and different amplification intensities correspond to different dynamic weighting factors; Specifically, the synergistic effect threshold table pre-divides different synergistic product intervals, each interval corresponding to a different amplification intensity (e.g., low synergistic product corresponds to weak amplification intensity, and high synergistic product corresponds to strong amplification intensity), and matches the corresponding dynamic weighting factor (e.g., weak amplification intensity corresponds to a factor of 1.0-1.2, and strong amplification intensity corresponds to a factor of 1.8-2.0).

[0035] b53. Perform nonlinear synthesis of the dynamic weighting factor and the urgency coefficient to output the composite adjustment coefficient; Specifically, when nonlinearly synthesizing dynamic weighting factors and urgency coefficients, the synthesis weights can be adjusted according to their numerical values. For example, when both dynamic weighting factors and urgency coefficients are high, the synthesized composite adjustment coefficient will have a larger value, thus enhancing the gain effect on the overall score.

[0036] b54. The arithmetic mean of the standardized risk probability factor and the scope of influence factor shall be used as the basic risk value; A simple arithmetic mean of the standardized risk probability factor and the scope of impact factor can be calculated and used as the basic risk value characterizing the basic risk level of the event.

[0037] b55. Using the composite adjustment coefficient as the key parameter for controlling the gain, the gain of the risk baseline value is calculated through the nonlinear aggregation function, and the comprehensive risk score of the potential risk event is output.

[0038] Specifically, the nonlinear aggregation function uses the obtained risk baseline value as the input signal to be processed, and the aforementioned composite adjustment coefficient as the key parameter to control the gain magnitude, to perform gain amplification calculation on the baseline value. The output result is the comprehensive risk score of the risk event. For example, the larger the composite adjustment coefficient, the more significant the gain effect, which reasonably amplifies the comprehensive score of risk events with high synergistic effects and high urgency, and more accurately reflects the severity of the risk.

[0039] Specifically, this step can be achieved as follows: b551. Obtain a family of gain curves consisting of multiple nonlinear curves, where each nonlinear curve corresponds to an intensity range of different composite adjustment coefficients. Among them, the gain curve family is a set of pre-defined nonlinear curves with different shapes. Each curve corresponds to a specific composite adjustment coefficient intensity range (such as low intensity range 0-1.2, medium intensity range 1.2-1.6, and high intensity range above 1.6). The shape of the curve is set according to the gain requirement of the adjustment coefficient within the range. For example, the curve in the high intensity range has a larger gain slope.

[0040] b553. Based on the specific value of the composite adjustment coefficient, select the corresponding nonlinear curve as the gain curve from the intensity range to which the composite adjustment coefficient belongs. After determining the intensity range based on the specific value of the composite adjustment coefficient, the corresponding gain curve can be selected.

[0041] b554. Using the aforementioned risk baseline value as input, calculate based on the functional relationship corresponding to the gain curve, and output the initial gain value; The baseline risk value can be used as an input variable and substituted into the functional relationship defined by the selected gain curve for calculation. This functional relationship describes a non-linear mapping between the input value (baseline risk value) and the output value (initial gain value). For example, under a steep curve, the same baseline risk value will yield a larger initial gain value than under a gentler curve.

[0042] In this embodiment, the step can be implemented as follows: Obtain the baseline response relationship and curvature parameter constituting the functional relationship; wherein the baseline response relationship is used to define the core mapping rule between input and output, and the curvature parameter is used to define the degree of nonlinear bending of the core mapping rule; based on the preset original value range corresponding to the risk baseline value and the ideal input value range defined by the baseline response relationship, linearly scale the risk baseline value to generate a normalized baseline value; using the normalized baseline value as input, calculate the response intensity using the core mapping rule; map the curvature parameter to a baseline modulation coefficient, and obtain the curvature weighting factor based on the mapping relationship between the baseline modulation coefficient and the nominal value; multiply the response intensity by the curvature weighting factor to obtain the initial gain value.

[0043] Specifically, when using gain curves for calculations, a structural analysis can be performed on the selected gain curve to extract its core mathematical relationships. This includes defining a baseline response relationship (e.g., the core of a sigmoid function) that defines the most fundamental relationship pattern between input and output, and a curvature parameter that determines the degree of curvature of that relationship curve.

[0044] Since the risk baseline value has its own original range of values, while the benchmark response relationship is usually defined on a standard ideal input range, the system needs to perform normalization processing first. It analyzes the ranges of the two ranges, and then maps the actual risk baseline value linearly and proportionally from its original range to the ideal range of the benchmark response relationship, thereby generating a normalized baseline value.

[0045] This normalized baseline value can be substituted into the core mapping rule defined by the baseline response relationship for calculation to obtain an initial response strength, which represents the output level corresponding to the input value under standard nonlinearity. Simultaneously, the curvature parameter can be mapped to a modulation coefficient with a base value of 1. When the actual curvature parameter equals this nominal value, the calculated curvature weighting factor is 1; when the curvature parameter is greater than the nominal value, the curvature weighting factor is proportionally greater than 1; and when it is less than the nominal value, it is proportionally less than 1.

[0046] The calculated response intensity can be multiplied and synthesized with a curvature weighting factor. When the curvature weighting factor equals 1, the initial gain value equals the original response intensity, representing the standard nonlinear intensity; when the curvature weighting factor is greater than 1, the initial gain value is proportionally amplified, reflecting a stronger nonlinear enhancement effect; when it is less than 1, it is proportionally reduced. In this way, the curvature parameter flexibly modulates the nonlinear performance of the final output.

[0047] b555. Using the initial gain value as the base and the composite adjustment coefficient as the exponent, perform a power operation to obtain the comprehensive risk score of the potential risk event.

[0048] The composite adjustment coefficient can be used as an exponent to power the initial gain value (i.e., the initial gain value as the base and the composite adjustment coefficient as the exponent). When the composite adjustment coefficient is greater than 1, this operation will have a significant amplifying effect on the initial gain value. The larger the composite adjustment coefficient, the more intense the amplification effect. This ensures that the high-risk situation caused by high synergy and high environmental urgency can be captured and characterized by a sufficiently high comprehensive risk score, thus occupying the highest priority in the hierarchical ranking.

[0049] b6. Compare the comprehensive risk score with the score ranges corresponding to multiple preset risk levels, and classify it into the corresponding risk level; Specifically, after obtaining the comprehensive risk score, it can be compared with a pre-defined score range. For example, 0-30 points is low risk, 31-70 points is medium risk, and 71-100 points is high risk, thereby determining the risk level of the event.

[0050] b7. Based on the risk level, generate priority handling codes for the potential risk events using priority coding rules.

[0051] Specifically, a unique priority handling code can be generated based on the determined risk level, combined with the magnitude of the impact range factor and the urgency coefficient, using a set of priority coding rules. For example, the code "HA-01" might represent "high risk (H), large impact range (A), ranked first in the current high urgency environment (01) to be handled". This code will be used to guide subsequent resource scheduling and handling priority.

[0052] 103. Generate preliminary handling suggestions based on the risk level and the priority handling code through the strategy generation network; In a specific implementation process, this step can be implemented in the following way: c1. Based on the risk level, parse out the corresponding resource configuration requirements; the resource configuration requirements include resource type, resource quantity, and resource attributes; Specifically, resource allocation requirements can be preset according to risk level. For example, when the risk level is high, the resource types include high-voltage repair personnel, emergency power generation equipment, and insulation protection equipment. The resource quantity requirements are no less than 5 repair personnel and no less than 2 emergency power generation equipment. The resource attributes requirements are rainproof, high temperature resistant, and adaptable to high-voltage operation. For medium and low risk, the scale of resource allocation is reduced proportionally.

[0053] c2. Parse the priority handling code and extract the handling target, key action sequence and constraint conditions implied in the priority handling code; Specifically, the handling objectives (restoring power to the core area within 2 hours), key action sequences (equipment fault location → fault isolation → emergency repair implementation → power restoration → acceptance confirmation) and constraints (strictly comply with high-voltage operation safety regulations and avoid affecting the power supply to customers in non-faulty areas) are extracted from the code "high risk - impact level 3 - urgency level 5".

[0054] c3. Determine the core logic flow of the disposal action based on the key action sequence, take the disposal target as the output of the core logic flow, and take the constraints as the boundary rules followed by each link of the core logic flow, thereby constructing a strategy skeleton that includes the starting point, key nodes, decision branches based on the constraints, and the endpoint. Specifically, the core logic flow is based on the sequence of key actions, starting with "receiving risk warnings", with key nodes being each action step. Decision branches include "successful fault location → fault isolation" and "fault location failure → activation of backup detection plan". The endpoint is "acceptance confirmation completed". Constraints serve as boundary rules for each node (e.g., emergency repair implementation nodes must comply with safety regulations).

[0055] c4. Fill the resource configuration requirements into the resource nodes corresponding to the key nodes to obtain the resource allocation scheme; Specifically, the resource allocation requirements corresponding to high risks can be filled into key nodes: the fault location node is allocated 2 testing technicians and 1 high-precision testing device; the emergency repair implementation node is allocated 3 high-voltage repair personnel and 2 emergency power generation devices.

[0056] c5. Expand the logical action nodes corresponding to the key nodes into operation steps that include specific execution objects, operation standards, and time limit requirements; Specifically, this step can be achieved as follows: c51. Based on the attribute information of the logical action node, match the execution object with the corresponding qualifications and capabilities from the preset resource pool, and generate the assigned action node. For example, the attribute information of the logical action node "fault location" is "high voltage equipment inspection, accuracy requirement ±0.1kV". The preset resource pool stores the qualification information of each team (such as "Inspection Team 1: has Class A qualification for high voltage equipment inspection and is equipped with an infrared thermometer"). After matching, the assigned action node "Execution object: Inspection Team 1" is generated.

[0057] c52. Based on the action type and associated constraints of the assigned action node, retrieve and extract the corresponding operation standard from the preset operation standard library; the operation standard includes operation methods, safety measures, and quality requirement details; For example, the action type of the assigned action node is "high-voltage equipment fault location", and the constraints are "safe operation and accuracy compliance". The operation method ("visual inspection first → infrared temperature scanning → partial discharge detection → data comparison and analysis"), safety measures ("wearing insulating gloves and insulating boots, setting up safety warning signs"), and quality requirements ("detection data error ≤ 0.1kV, fault location accuracy ≥ 95%) are retrieved from the preset operation standard library.

[0058] c53. Inject the detailed quality requirements into the assigned action node to generate a standardized action node; c54. Determine the execution time based on the complexity, required resources, and historical execution data of the standardized action nodes; For example, the execution time can be determined to be 30 minutes (with reserved redundancy time) by combining the complexity of the standardized action node ("medium complexity"), the required resources ("1 infrared thermometer, 2 technicians") and historical execution data ("average duration of similar actions is 25 minutes").

[0059] c55. Based on the core logic flow, assign start and end times to the standardized action nodes to form the operation steps.

[0060] Specifically, the allocation of start time and end time is based on the core logic flow. The start time of the fault location node is "10 minutes after receiving the risk warning" and the end time is "30 minutes after start". It is seamlessly connected with the start time of the subsequent fault isolation node to form a complete operation procedure.

[0061] This embodiment ensures "person-job fit" by dynamically matching execution objects from the resource pool; guarantees the safety and quality of the operation process by accurately injecting operational details from the standard library; and optimizes the time efficiency of the overall handling process through intelligent scheduling based on multiple factors. This makes the generated handling suggestions no longer vague instructions, but detailed work orders that are actionable, traceable, and assessable.

[0062] c6. Integrate the resource allocation scheme with the operation steps to obtain the preliminary handling suggestions.

[0063] This embodiment utilizes a strategy element decoding component to standardize the parsing of risk event parameters, transforming abstract risk levels and priority handling codes into actionable resource allocation requirements and operational guidelines. The strategy skeleton construction component establishes a decision-making logic framework for handling actions based on the parsing results, ensuring that core operational processes both meet predetermined objectives and constraints. The parameterized instance generation component injects specific execution parameters, transforming the strategy skeleton into an implementation plan with clear resource allocation and operational instructions. This entire mechanism ultimately forms a complete strategy transformation chain from risk identification to execution, achieving structured generation and dynamic adaptation capabilities for handling plans.

[0064] 104. Using the acquired meteorological and environmental data and historical treatment effect data, the preliminary treatment suggestions are dynamically adjusted and optimized through the strategy optimization engine.

[0065] Specifically, the initial recommendations can be adjusted by referring to real-time meteorological data (such as the impact of severe weather such as rainstorms and high temperatures on response efficiency) and the effectiveness data of historical response cases (such as the success rate and time taken for response plans of similar risk events). For example, the repair route can be optimized in rainstorm weather, or the unsuccessful operation steps can be replaced based on historical data.

[0066] This embodiment of the power marketing risk warning method based on big data and cloud services transforms multi-source heterogeneous data into a risk situation map through a dynamic data aggregation mechanism. Then, it identifies potential risk events based on feature extraction and pattern matching, calculates multi-dimensional risk factors by combining meteorological and environmental data, and achieves accurate classification through nonlinear aggregation. Finally, it generates preliminary handling suggestions through a strategy generation network and dynamically optimizes them using real-time environmental data and historical handling effects. This achieves accurate identification, dynamic classification, and adaptive handling of power marketing risks, improving the accuracy, timeliness, and adaptability of risk warnings.

[0067] Based on the same general inventive concept, this invention also protects a risk warning system for electricity marketing based on big data and cloud services. The risk warning system for electricity marketing based on big data and cloud services provided by this invention will be described below. The risk warning system for electricity marketing based on big data and cloud services described below can be referred to in correspondence with the risk warning method for electricity marketing based on big data and cloud services described above.

[0068] Figure 2 This is a schematic diagram of the structure of a risk warning system for electricity marketing based on big data and cloud services provided in an embodiment of the present invention, as shown below. Figure 2As shown, the risk warning system for power marketing based on big data and cloud services in this embodiment includes an identification module 21, a classification module 22, a generation module 23, and an optimization module 24.

[0069] The identification module 21 is used to identify potential risk events from the received risk situation map through feature extraction and pattern matching. The grading module 22 is used to comprehensively score the potential risk events and classify them according to the obtained scoring results, thereby generating the risk level and priority handling code of the potential risk events. Generation module 23 is used to generate preliminary treatment suggestions based on the risk level and the priority treatment code through a strategy generation network; The optimization module 24 is used to dynamically adjust and optimize the preliminary treatment suggestions by utilizing the acquired meteorological and environmental data and historical treatment effect data through the strategy optimization engine.

[0070] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device may include: a processor 310, a communication interface 320, a memory 330, and a communication bus 340. The processor 310, communication interface 320, and memory 330 communicate with each other via the communication bus 340. The processor 310 can call logical instructions stored in the memory 330 to execute a risk warning method for electricity marketing based on big data and cloud services.

[0071] Furthermore, the logical instructions in the aforementioned memory 330 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0072] It should be noted that all relevant information that may be involved in the various embodiments of the present invention is processed in strict accordance with the requirements of laws and regulations, following the principles of legality, legitimacy, and necessity, based on the reasonable purpose of the business scenario, and is information that users actively provide or generate during the use of the product / service, as well as information obtained with user authorization.

[0073] The information processed by this invention may vary depending on the specific product / service scenario and should be based on the specific scenario in which the user uses the product / service. This may involve user account information, device information, or other related information. This invention will treat the relevant information and its processing with the utmost diligence.

[0074] This invention places great emphasis on the security of relevant information and has adopted reasonable and feasible security protection measures that comply with industry standards to protect user information and prevent unauthorized access, public disclosure, use, modification, damage or loss of relevant information.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A risk warning method for electricity marketing based on big data and cloud services, characterized in that, include: For the received risk situation map, potential risk events are identified through feature extraction and pattern matching; The potential risk events are comprehensively scored, and classified according to the scoring results to generate the risk level and priority handling code of the potential risk events; The strategy generation network generates preliminary treatment recommendations based on the risk level and the priority treatment code; Using the acquired meteorological and environmental data and historical treatment effect data, the preliminary treatment suggestions are dynamically adjusted and optimized through a strategy optimization engine.

2. The risk warning method for electricity marketing based on big data and cloud services according to claim 1, characterized in that, Potential risk events are identified through feature extraction and pattern matching, including: From the risk situation map, a core entity node is selected as the starting point, and a limited step length is traversed along the associated edges to form a local subgraph centered on the core node. The distribution entropy of node density and edge weights within the local subgraph, along with the topological indices of meteorological nodes, are extracted to form a feature vector describing the regional risk characteristics of the local subgraph. Based on the severity of the meteorological and environmental data, the weights of each feature in the feature vector are adjusted using an environmental factor mapping function; Using the weights of each feature, the matching degree between the feature vector and each risk pattern template is calculated; When the matching degree of the feature vector exceeds a preset matching degree threshold, it is identified as a potential risk event.

3. The risk warning method for electricity marketing based on big data and cloud services according to claim 2, characterized in that, The potential risk events are comprehensively scored, and based on the scoring results, they are classified to generate a risk level and priority handling code for each potential risk event, including: Map the matching degree corresponding to the potential risk events to a standardized risk probability factor; Analyze all downstream nodes directly and indirectly affected by the core node in the risk situation map; The impact range factor of the potential risk event is calculated by aggregating the preset business importance scores of all downstream nodes. Based on the severity of the meteorological and environmental data, the urgency coefficient of the potential risk event is calculated using an environmental urgency mapping table; The standardized risk probability factor, the scope of influence factor, and the urgency coefficient are input into a nonlinear aggregation function to output a comprehensive risk score for the potential risk event. The comprehensive risk score is compared with the score ranges corresponding to multiple preset risk levels, and then assigned to the corresponding risk level. Based on the risk level, priority handling codes for the potential risk events are generated using priority coding rules.

4. The risk warning method for electricity marketing based on big data and cloud services according to claim 3, characterized in that, The standardized risk probability factor, the scope of impact factor, and the urgency coefficient are input into a nonlinear aggregation function to output a comprehensive risk score for the potential risk event, including: Multiply the standardized risk probability factor and the scope of influence factor together to obtain a synergistic product; The synergistic product is compared with a preset synergistic effect threshold table, and a dynamic weighting factor is assigned to the potential risk event based on the comparison result; wherein, the synergistic effect threshold table includes the amplification intensity corresponding to different synergistic products, and different amplification intensities correspond to different dynamic weighting factors; The dynamic weighting factor and the urgency coefficient are nonlinearly synthesized to output a composite adjustment coefficient. The arithmetic mean of the standardized risk probability factor and the scope of influence factor is used as the basic risk value; Using the composite adjustment coefficient as the key parameter for controlling the gain, the gain of the risk baseline value is calculated through the nonlinear aggregation function, and the comprehensive risk score of the potential risk event is output.

5. The risk warning method for electricity marketing based on big data and cloud services according to claim 4, characterized in that, The risk baseline value is calculated using the nonlinear aggregation function to output a comprehensive risk score for the potential risk event, including: Obtain a family of gain curves consisting of multiple nonlinear curves, each nonlinear curve corresponding to an intensity range of different composite adjustment coefficients; Based on the specific value of the composite adjustment coefficient, a corresponding nonlinear curve is selected as the gain curve from the intensity range to which the composite adjustment coefficient belongs. Using the aforementioned risk baseline value as input, the initial gain value is calculated based on the functional relationship corresponding to the gain curve and output. Using the initial gain value as the base and the composite adjustment coefficient as the exponent, a power operation is performed to obtain the comprehensive risk score of the potential risk event.

6. The risk warning method for electricity marketing based on big data and cloud services according to claim 5, characterized in that, Using the aforementioned risk baseline value as input, and calculating based on the functional relationship corresponding to the gain curve, the initial gain value is output, including: Obtain the baseline response relationship and curvature parameters that constitute the functional relationship; wherein the baseline response relationship is used to define the core mapping rule between the input and the output, and the curvature parameters are used to define the degree of nonlinear bending of the core mapping rule; Based on the preset original value range corresponding to the risk baseline value and the ideal input value range defined by the benchmark response relationship, the risk baseline value is linearly scaled to generate a normalized baseline value; Using the normalized base value as input, the response strength is calculated using the core mapping rules; The curvature parameters are mapped to reference modulation coefficients, and the curvature weighting factor is obtained based on the mapping relationship between the reference modulation coefficients and the nominal value. The initial gain value is obtained by multiplying the response intensity by the curvature weighting factor.

7. The risk warning method for electricity marketing based on big data and cloud services according to claim 1, characterized in that, The policy generation network generates preliminary treatment recommendations based on the risk level and the priority treatment code, including: Based on the risk level, the corresponding resource configuration requirements are analyzed; the resource configuration requirements include resource type, resource quantity, and resource attributes. The priority handling code is parsed to extract the handling objectives, key action sequences, and constraints implicit in the priority handling code; The core logic flow of the disposal action is determined based on the key action sequence. The disposal target is taken as the output of the core logic flow, and the constraints are taken as the boundary rules followed by each link of the core logic flow, thereby constructing a strategy skeleton that includes the starting point, key nodes, decision branches based on the constraints, and the endpoint. The resource configuration requirements are filled into the resource nodes corresponding to the key nodes to obtain the resource allocation scheme; Expand the logical action nodes corresponding to the key nodes into operation steps that include specific execution objects, operation standards, and time limit requirements; By integrating the resource allocation scheme and the operational steps, the preliminary handling recommendations are obtained.

8. The risk warning method for electricity marketing based on big data and cloud services according to claim 7, characterized in that, Expand the logical action nodes corresponding to the key nodes into operation steps that include specific execution objects, operation standards, and time limit requirements, including: Based on the attribute information of the logical action node, an execution object with corresponding qualifications and capabilities is matched from a preset resource pool, and an assigned action node is generated. Based on the action type and associated constraints of the assigned action node, the corresponding operation standard is retrieved and extracted from the preset operation standard library; the operation standard includes operation methods, safety measures and quality requirements details; The detailed quality requirements are injected into the assigned action node to generate a standardized action node; The execution time is determined based on the complexity, required resources, and historical execution data of the standardized action nodes; Based on the core logic flow, start and end times are assigned to the standardized action nodes to form the operation steps.

9. The risk warning method for electricity marketing based on big data and cloud services according to claim 1, characterized in that, The process of obtaining the risk situation map includes: Receive raw, multidimensional, heterogeneous data from different sources; The original multidimensional heterogeneous data is cleaned and standardized to obtain preprocessed multidimensional heterogeneous data. The preprocessed multidimensional heterogeneous data is mapped to a unified spatiotemporal coordinate system to generate a spatially mapped data set. Through a dynamic data aggregation mechanism, the mapping data of the spatial mapping data set is combined into a risk situation map.

10. A risk warning system for electricity marketing based on big data and cloud services, characterized in that, include: The identification module is used to identify potential risk events from the received risk situation map through feature extraction and pattern matching. The grading module is used to comprehensively score the potential risk events, and classify them according to the obtained scoring results, generating the risk level and priority handling code of the potential risk events; The generation module is used to generate preliminary treatment suggestions based on the risk level and the priority treatment code through a policy generation network; The optimization module is used to dynamically adjust and optimize the preliminary treatment suggestions by utilizing the acquired meteorological and environmental data and historical treatment effect data through the strategy optimization engine.

Images