A centralized control center-oriented water and electricity equipment anomaly and energy efficiency intelligent report integration method

Abstract

The application discloses a kind of water and electricity equipment exception and energy efficiency intelligent report integration method for centralized control center, belong to industrial automation, intelligent monitoring field.Support the intelligent fusion and labeling of multiple source heterogeneous data, and realize efficient data integration by adaptive field alignment and expert assisted labeling.Introduce causal link reasoning algorithm, combined with equipment topology and multidimensional association information, form progressive abnormal diagnosis and causal assessment.Construct hierarchical weight self-evolution energy efficiency evaluation model, dynamically aggregate equipment health, resource utilization, load and environmental factors, and use neural network to realize adaptive weight adjustment.Report generation module is based on semantic fragment and rule engine, automatically assemble visual content, realize personalized and aesthetic optimization human-computer interaction.

Description

Technical Field

[0001] This invention belongs to the field of industrial automation and intelligent monitoring, and more specifically relates to an integrated method for intelligent reporting of water and electricity equipment anomalies and energy efficiency for centralized control centers. Background Technology

[0002] In the field of hydropower stations and related energy management, data acquisition and report processing are crucial for ensuring safe equipment operation and optimal resource allocation. Currently, mainstream data access methods primarily include direct database connections and integration with business component data sources. Direct database connections support various mainstream databases, including MySQL, Oracle, and SQL Server. Users can directly access database resources and process data by inputting the corresponding connection parameters to meet diverse data analysis needs. Meanwhile, with the continuous improvement of enterprise information system architecture, Spring Bean data sources have achieved integration with business components under the Spring framework, seamlessly calling data services provided by Beans, thereby improving system compatibility and scalability. Furthermore, some platforms have pre-built data sources, such as system parameters and basic code, allowing users to easily call them when designing simple reports.

[0003] In terms of report design and presentation, existing report designers typically offer a rich set of features. Users can fine-tune report fonts, including various font types and sizes, and support text effects such as bold, italics, and underlines to meet the layout needs of different scenarios. Report appearance settings are flexible, supporting a wide range of color choices, border types, and line thickness adjustments, facilitating the creation of aesthetically pleasing and standardized report structures. Features such as cell merging and splitting, horizontal and vertical alignment, diagonal headers, multi-level header design, and the insertion of images and charts enhance the expressiveness of reports and the visualization of data. The platform also supports various expression and function operations, including mathematical, string, and date functions, enabling users to perform complex data calculations, transformations, and conditional judgments, meeting the diverse needs of business data analysis.

[0004] For the operation and management of hydropower stations, water information reports monitor key indicators such as power generation, equipment status, and fault conditions through daily, weekly, and monthly reports, ensuring the safe and stable operation of the system. Statistical reports can assist in analyzing water resource utilization efficiency and power output and consumption, helping to optimize resource allocation and improve overall operational efficiency. However, existing technologies still need improvement in multi-source data fusion, intelligent tagging, anomaly causal diagnosis, and energy efficiency evaluation. They have not yet achieved comprehensive intelligent integration and dynamic generation of personalized reports for multiple scenarios, which is precisely the direction of improvement and innovation aimed at in this invention. Summary of the Invention

[0005] This invention aims to solve the technical problems of existing hydropower equipment management systems, such as low integration, insufficient intelligence, weak customization capabilities, and difficulty in efficiently supporting the diverse business needs of centralized control centers in terms of intelligent fusion of multi-source heterogeneous data, automated tagging, complex anomaly causal reasoning, multi-factor adaptive evaluation of energy efficiency, and intelligent dynamic generation of reports. It aims to achieve efficient integration and anomaly diagnosis of hydropower equipment data, accurate evaluation of energy efficiency levels, and automated and personalized generation of reports in multiple scenarios.

[0006] To achieve the above objectives, the present invention employs the following technical solution: the method comprising: Intelligent fusion and tagging of multi-source heterogeneous data; design of multi-source dynamic tag sensing; automatic acquisition and streaming tagging of multi-channel data from different types of hydropower equipment and operating environments. An adaptive field alignment algorithm is used to dynamically identify key fields in new data sources based on the co-occurrence characteristics of historical samples, and automatically generate labels and data clusters. The design of the causal link reasoning algorithm for anomaly detection incorporates multi-dimensional correlation information such as equipment operating topology, historical maintenance events, and energy consumption fluctuations. Through the event reasoning chain, it automatically generates a progressive link from the original signal to the core anomaly and evaluates the causal strength between each anomaly. A hierarchical weighted self-evolving energy efficiency evaluation model is constructed, integrating multiple factors such as equipment health coefficient, water resource utilization rate, real-time power generation load, and external climate influence. By employing a weighted nested hierarchical neural network, the weight allocation of each evaluation factor is dynamically adjusted based on feedback from actual operational data, and the model output can generate customized energy efficiency levels for different devices and different stages. Intelligent report generation and human-computer interaction adaptation are achieved by introducing a semantic fragment module—meaning that each visual element in the report corresponds to a semantic command. Through its semantic rule engine, the platform can automatically identify data types, business scenarios, and user customization needs, and intelligently assemble report structures and content to achieve high personalization and automatic adjustment.

[0007] In one approach, the intelligent fusion and labeling of multi-source heterogeneous data includes: automatically analyzing fields using co-occurrence features of historical samples to dynamically identify key fields and field types in new data sources; and establishing field-label mapping relationships through field vectorization representation and co-occurrence feature comparison. An adaptive optimal matching algorithm is introduced, which treats the new field set and the historical normalized field set as nodes, and performs weighted graph matching based on co-occurrence probability or semantic distance as weights to automatically generate labels and data clusters. For fields that cannot be automatically aligned, the system triggers an expert intelligent auxiliary annotation mechanism to recommend tags and matching scores, allowing personnel to quickly confirm or make minor adjustments.

[0008] In one scheme, the causal link reasoning algorithm for design anomaly detection includes: using an event reasoning chain mechanism, taking each original anomaly signal as the starting point of the chain, and reasoning progressively along the dimensions of device level, functional module relationship, time sequence dependency, maintenance history and energy efficiency fluctuation; An abnormal event network is constructed, and abnormal nodes are associated with relational edges. Causal inference edges are formed by combining physical connections of devices, functional dependencies, historical co-occurrence, and environmental interference. For each type of abnormal signal, the set of physically connected devices is automatically searched in the device topology relation table, and the associated historical maintenance data, energy consumption time series, and environmental interference records are queried to evaluate the causal strength of the abnormal signal on downstream devices.

[0009] In one scheme, the construction of a hierarchical weighted self-evolving energy efficiency evaluation model includes: encapsulating multiple evaluation factors such as equipment health coefficient, water resource utilization rate, power generation load, environmental climate, historical operation fluctuations and maintenance cycle into independent sub-networks, with each sub-network responsible for inputting the features of its respective factor, and generating a normalized output through factor scoring; All factor scores are aggregated hierarchically and then fed into a weighted nested hierarchical aggregation network. In this structure, a self-evolving weight update mechanism is used to automatically optimize the weight distribution of each factor based on the residual between the actual energy efficiency performance of the equipment and the model prediction.

[0010] In one solution, the intelligent report dynamic generation and human-computer interaction adaptation includes: managing all visual elements of the report in the form of semantic fragments, which are driven by intelligent instructions based on data analysis reasoning, scenario matching, and user intent adaptation; the system generates report generation instructions by jointly mapping data types, business scenarios, and user operation preferences through a multi-layer semantic rule system, automatically assembling and splitting each report visual element and dynamically matching the content. By employing a multi-factor priority matching scoring method in the semantic rule engine, each candidate semantic fragment is given priority based on its similarity to the analyzed content, business scenario, user intent, and historical user interaction feedback. Priority queue scheduling is used to ensure content adaptability and flexible arrangement. After outputting the report structure, the overall layout, color scheme, font, and other aesthetic and functional interactions are automatically optimized through built-in visual collaboration rules and neural network models, ultimately achieving intelligent dynamic generation of reports for multiple scenarios and personalized customization.

[0011] In one approach, for missing values ​​or redundant fields, the mechanism automatically detects abnormal distributions and categorizes them as "unknown" or "discarded." All integrated data is reorganized into "tagged data clusters," with each data stream having a fine-grained semantic tag attached. These tags are recorded in the system's metadata dictionary to support rapid preprocessing and integration of new data sources.

[0012] In one approach, the event reasoning chain is generated using a recursive deep search algorithm. A causal weight decay mechanism is introduced during the progressive process to filter high-intensity causal nodes and generate an event sequence chain. Each chain node is labeled with causal strength, timestamp, device type, and event category. The system anomaly impact range, impact depth, and core causal nodes are output, enabling multi-level progressive localization and causal assessment for anomaly diagnosis.

[0013] In one approach, the hierarchical weighted self-evolutionary energy efficiency evaluation model explicitly models the interactions between factors in its aggregation part. It utilizes algorithms such as higher-order aggregation functions, convolution, or gated recurrent units to capture the nonlinear relationships and dynamic coupling between factors, thereby improving the real-time adaptability and interpretability of the evaluation. During continuous operation, the system can customize energy efficiency ratings for different equipment and operating stages, and automatically generate evaluation reasons.

[0014] Beneficial effects of this invention: This invention significantly enhances the data integration and intelligent analysis capabilities of hydropower equipment management systems. It achieves automated fusion and tagging management of multi-source heterogeneous data, greatly improving the efficiency of data acquisition, processing, and consistency mapping, while reducing manual intervention and maintenance costs. In anomaly detection, this invention, through the intelligent construction of causal reasoning chains, can quickly and accurately locate the root causes and ripple effects of equipment anomalies, effectively improving the scientific rigor of fault diagnosis and operation and maintenance decisions. Furthermore, the energy efficiency evaluation model integrates multiple factors such as equipment health status, water resource utilization, load, and environment, and can adaptively adjust evaluation weights to achieve phased and personalized precise energy efficiency assessments, providing strong support for operation and maintenance optimization.

[0015] At the report generation level, this invention supports intelligent and dynamic assembly of report structure and content based on user intent, while also considering report aesthetics and interactive experience, significantly improving report customization efficiency and application flexibility. The system can seamlessly integrate with mainstream databases, Spring Bean components, and built-in data sources, enhancing compatibility and scalability with existing enterprise architectures. Overall, this invention effectively promotes the intelligent and refined management of hydropower equipment operation, helping centralized control centers achieve efficient monitoring and comprehensive analysis of equipment anomalies and energy efficiency status, and driving the safe, economical, and green operation of hydropower stations. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method of the present invention; Figure 2 Flowchart for dynamic generation of intelligent reports and adaptation to human-computer interaction. Detailed Implementation

[0017] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0018] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0019] like Figure 1 As shown, a method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers specifically includes: Step 1: Intelligent fusion and tagging of multi-source heterogeneous data First, a multi-source dynamic tagging sensing mechanism is designed to automatically collect and stream-tag multi-channel data from different types of hydropower equipment (such as generators, transformers, and pumps) and their operating environments (temperature, water level, energy consumption, database logs, etc.). An "adaptive field alignment algorithm" is employed to dynamically identify key fields in new data sources based on the co-occurrence characteristics of historically collected samples, automatically generating tags and data clusters. This effectively solves the fusion challenges of merging fields with different names, missing fields, or redundancies across different databases and IoT protocols.

[0020] Intelligent fusion and tagged acquisition of multi-source heterogeneous data is the most fundamental and critical construction link in a centralized control center for hydropower equipment and its operating environment. Achieving efficient data integration requires addressing not only differences in database types, but also the wide variety of communication protocols and data formats of IoT devices, as well as the heterogeneous distribution of samples from historical data collection processes.

[0021] Therefore, a tag-based data collection mechanism that can dynamically sense and automatically adapt is needed to quickly and accurately integrate any newly connected data source into a unified data infrastructure layer. In this process, algorithms and engineering mechanisms complement each other, and deep optimizations are made from the underlying structure to the semantic layer through adaptive field alignment methods.

[0022] This fusion mechanism is driven by a tag-based approach—whenever new device data flows in, the system automatically analyzes the collected raw fields to determine the field type (such as numeric, enumerated, timestamp, etc.), but the core is far more than simple type identification. The system reads normalized samples from historical data and performs co-occurrence feature comparisons within the field sets of each data source.

[0023] Specifically, assuming the new data source is D, and the new set of fields is denoted as D... The set of historical normalized fields is denoted as For any The system will automatically generate a vectorized representation. (Use one-hot encoding, word embedding, or domain-defined encoding methods), and Compare the co-occurrence probabilities with each field vector v_j in the dataset. The co-occurrence probability can be defined as: in This indicates that they appeared simultaneously in historical samples. and Number of times, For the appearance The total number of times. In this way, the algorithm can identify which new fields are highly associated with existing labels. However, if the meaning of the label is ambiguous, domain rules are still needed. For example, although the "water output" of a water pump and the "output power" of a generator have different physical properties, they can be classified under the unified "equipment output" label in the energy efficiency analysis scenario. This allows the establishment of a field-label mapping relationship. And cluster the fields.

[0024] To achieve automatic field alignment, this mechanism introduces an adaptive optimal matching algorithm: aiming to minimize the overall label matching loss, it employs a weighted graph matching model. Specifically, it will... and Each node is considered as a set of nodes, and the weight of the edges between each node is set as the co-occurrence probability or semantic distance (such as cosine similarity, Jaccard coefficient, or Euclidean distance based on embedding). The goal is to find a set of matches M that maximizes the total weight, and the loss function can be expressed as follows: in This can be the standardized co-occurrence probability or semantic similarity. A greedy or heuristic search algorithm is used to progressively select the optimal match. When encountering fields that cannot be automatically aligned, a human / expert intelligent annotation stage is triggered—the system provides recommended labels and matching scores, requiring only confirmation or fine-tuning by personnel without the need for entirely manual label creation. For missing values ​​or redundant fields, the mechanism automatically detects anomalous distributions from a single source (e.g., missing rate > 80% or redundancy rate above a threshold), directly classifying them as "unknown" or "discarded."

[0025] After the unified data layer integration is completed, all data is rearranged into "tagged data clusters." Each cluster contains fields integrated from multiple devices, different times, and various business scenarios, and each data stream has fine-grained semantic tags. For example, a water pump data stream, after algorithm processing, will automatically have meta tags such as "Device Type_Water Pump," "Output Category Tag," "Energy Efficiency Analysis Tag," and "Anomaly Detection Tag" added in addition to the original fields. All tags are recorded in the system's metadata dictionary. When new data sources are connected in the future, data preprocessing can be quickly completed by simply matching them against the dictionary.

[0026] Step 2: Design a causal link reasoning algorithm for anomaly detection In the area of ​​equipment anomaly detection, a multi-dimensional relationship-driven causal anomaly inference algorithm is proposed. This algorithm goes beyond threshold alarms or simple time-series detection, incorporating multi-dimensional correlation information such as equipment operating topology, historical maintenance events, and energy consumption fluctuations. Through an event inference chain, it automatically generates a progressive link from the original signal to the core anomaly and evaluates the causal strength between each anomaly. Each anomaly signal triggers this inference chain, helping to pinpoint the nature and scope of the problem, avoiding false alarms and missed alarms, and significantly improving the accuracy and efficiency of anomaly diagnosis.

[0027] The causal link reasoning algorithm for anomaly detection transforms multi-source data from hydropower equipment into a deep, progressive anomaly analysis system. This breaks through the limitations of traditional methods relying on single thresholds or outlier identification, enabling not only accurate capture of each equipment anomaly but also reconstruction of the underlying causal chain. The algorithm's fundamental logic is multi-dimensional relationship-driven, simultaneously incorporating equipment operating topology, historical maintenance events, energy consumption changes, environmental factors, and periodic model anomalies into a unified reasoning framework. The event reasoning chain mechanism uses each original anomaly signal as the starting point of the chain, tracing and expanding the event network along dimensions such as equipment hierarchy, functional module relationships, temporal dependencies, maintenance history, and energy efficiency fluctuations, achieving multi-level progressive anomaly localization and causal assessment.

[0028] The algorithm constructs an anomaly event network. Here, V represents the set of anomalous nodes, and E′ represents the edges between nodes, with each edge representing a potential causal association. During network establishment, the system automatically extracts event features from the collected device anomaly signals and maps them to the entire device topology table; physical connections between devices (such as series and parallel connections), functional dependencies (such as a sub-device affecting a parent device), and historical co-occurrence (such as a fault occurring alongside other faults) all become candidates for causal inference edges. For newly emerging anomalous signals... The system first searches the topology table for the set of directly physically connected devices. For each device Query its historical maintenance data Energy consumption time series And environmental interference records E_i. Then calculate the anomaly. The causal strength of the impact on downstream device i is expressed by the formula: in This represents the correlation coefficient between the abnormal signal and the energy consumption sequence. This represents the ratio of the co-occurrence frequency of anomalies and environmental disturbances. Data-driven scoring based on historical maintenance events. Weights This can be continuously optimized through higher-order statistics or incremental learning methods. In this way, each edge is not just a static physical dependency on the device, but a dynamic, data-driven assessment of causal strength.

[0029] The event reasoning chain is generated using a recursive depth-first search algorithm. From the original signal... Starting from the beginning, the algorithm searches downstream along the causal edges. At each layer, low-relevance nodes are filtered based on their CI scores, and high-intensity nodes are included in the progressive link. During each recursive expansion, the algorithm introduces a decay factor. This ensures the causal chain length is reasonable and avoids excessive expansion. Specifically, the causal weights on any link node j are updated as follows: Progressing continuously until... If the event falls below a threshold or reaches the end of the chain (e.g., no clearly related successor event can be found), this process generates an event sequence chain. Each node is tagged with causal strength, timestamp, device type, and event category. Through this progressive chain, the system not only records all anomaly inference results, but also outputs the scope and depth of the anomaly's impact, as well as the core causal nodes.

[0030] In real-world hydropower scenarios, for example, if a generator experiences a power fluctuation anomaly, the link inference algorithm will simultaneously retrieve information on transformer output anomalies, sudden changes in pump flow, replacement events in recent maintenance records, and water level sensor malfunctions. By integrating the CI values ​​of each causal edge, a multi-dimensional assessment of the impact range can be achieved.

[0031] Link inference results are transformed into interpretable diagnostic reports: for each set of anomalies, an event chain, impact path, causal strength score, and suggested remedial measures are output. False positives and false negatives are suppressed through dynamic adjustment of link weights and semantic constraints on anomaly types; for example, if certain sensor noise is known to easily cause false positives, filtering is increased at the end of the topology due to link attenuation. The system learns to discover new causal patterns and continuously updates the event network using an incremental causal structure optimization algorithm to adapt to new business scenarios, new device types, and environmental changes.

[0032] Step 3: Construct a hierarchical weighted self-evolving energy efficiency evaluation model For energy efficiency analysis, a hierarchical weighted self-evolving energy efficiency evaluation model is constructed. Unlike traditional evaluations using a single indicator, this method integrates multiple factors such as equipment health coefficient, water resource utilization rate, real-time power generation load, and external climate impact. The model structure employs a self-designed "weighted nested hierarchical neural network," where the weight allocation of each evaluation factor is dynamically adjusted based on feedback from actual operational data, achieving self-evolving optimization. The final model output can generate customized energy efficiency levels for different equipment and at different stages, and can explain the evaluation rationale, facilitating subsequent optimization decisions.

[0033] The design and implementation of a multi-factor intelligent energy efficiency evaluation model is a core component in achieving adaptive and refined energy efficiency analysis in hydropower equipment management. Unlike traditional single-index or simple weighted scoring methods, this model constructs a weighted nested hierarchical neural network to achieve progressive perception and intelligent aggregation of multi-factor energy efficiency performance. The evaluation system is comprehensive, encompassing multiple dimensions such as equipment health coefficient, water resource utilization rate, power generation load, environmental climate, historical operational fluctuations, and maintenance cycles. Each layer is based on normalized inputs and dynamically adjusts factor weights according to the actual scenario, providing customized energy efficiency levels and explanations for different equipment and different operational stages, achieving a self-evolving optimization closed loop.

[0034] The model structure employs a nested neural network, not a simple fully connected or shallow structure, but rather encapsulates each energy efficiency factor into an independent sub-network, called... Each FactorNet is responsible for inputting various data features of the attribution factor F_i. For example, the equipment health factor inputs runtime, failure rate, and maintenance frequency; the water resource utilization factor inputs inflow rate, pump efficiency, and return rate; and the power generation load factor inputs actual capacity, load changes, and instantaneous current. The output is a normalized score s_i. Instead of directly summing all factor scores, the scores are aggregated into a weighted network structure with nested hierarchical aggregation. The overall model can be represented as: in It refers to adaptive factor weights, where G is a higher-order aggregation function used to capture complex nonlinear relationships between factors. Weights The algorithm automatically adjusts based on scenario feedback, incorporating a self-evolving weight update mechanism: for each running cycle k, it collects the actual energy efficiency performance of the device. Compared with model predictions The residuals between The residual is then used to optimize the weight distribution. The following formula is used: For learning rate, The gradient of the residual with respect to the weights can be obtained using the backpropagation algorithm of a self-woven network. The weights of each factor are adjusted through self-evolution with feedback, and the system can adapt to the changes in energy efficiency caused by equipment aging, environmental changes, and adjustments in operating strategies during continuous operation.

[0035] The nested hierarchical structure lies in the explicit modeling of interactions between factors. Instead of simple linear weighting, it models the cross-influences between factors through mechanisms such as convolution and gated recurrent units (GRUs) within the G function. For example, power generation load and water resource utilization may exhibit synergistic or competitive relationships within certain threshold ranges. The model can automatically identify signal multinomial mixtures or higher-order interaction terms, making energy efficiency assessments more closely resemble real-world scenarios. The coupling relationships between factors can be illustrated using: in The weights for coupling between factors are updated through self-evolution. Higher-order factor interactions are not limited to second-order; third-order or even more combinations can be introduced to improve the model's explanatory power and accuracy.

[0036] Step 4: Dynamic Generation of Intelligent Reports and Adaptation to Human-Computer Interaction like Figure 2 As shown, based on comprehensive analysis, this method differs from traditional template-based reports by introducing a semantic fragment module—meaning that each visual element in the report (charts, headers, annotations, images, etc.) corresponds to a "semantic instruction," which is driven by prior data analysis and user intent. Through a semantic rule engine, the platform can automatically identify data types, business scenarios, and user customization needs, intelligently assembling the report structure and content to achieve high personalization and automatic adjustment, much like a "what you see is what you get" experience. This enhances report aesthetics and allows for flexible scheduling of functional modules, fully meeting the needs of different scenarios such as operation and maintenance monitoring, energy efficiency analysis, and anomaly tracing.

[0037] S401. All visual elements of the report are semantically broken down and managed as "semantic fragments." Each fragment contains intelligent instructions for data analysis and reasoning, scenario matching, and user intent adaptation. These instructions serve as the driving logic for report generation and are embedded in the engine's dynamic assembly process. Users are no longer constrained by rigid report styles but can trigger the system to automatically integrate data results, assemble content structures, and configure interaction methods through natural language, scenario options, or operating habits, achieving personalized report output that is "what you see is what you get."

[0038] The entire intelligent engine is based on a multi-layered semantic rule system. Each report generation instruction... All of these are derived from data analysis results. Scene label Signature and user intent The joint mapping, the mapping rule can be written as: Where Φ is the semantic rule function, which automatically parses data types (time series, status, abnormal behavior, energy efficiency scores, etc.), matches business scenarios (operation and maintenance monitoring, energy efficiency analysis, anomaly tracing, management summary, etc.), and adjusts the priority and fragment content of instructions in real time based on the user's historical data operations and customized preferences. The pluggable mechanism between semantic fragments stems from the granular decomposition of report elements, specifically each element It can be written as: in For the element assembly function, For driver instructions, To facilitate the synthesis of related data, the system simultaneously scans the semantic fragment library of all possible outputs during report generation. It selects the instruction group with the highest matching degree to the current data analysis and scenario chain, intelligently sorts it, and submits it to the assembly process, automatically splicing the relevant fragments into the overall report content. All charts (bars, line charts, pie charts, etc.), header columns, annotations, warning pop-ups, image support, and even interactive buttons can be semantically fragmented, and can be arbitrarily combined, split, replaced, and expanded.

[0039] S402. The key algorithm lies in the dynamic adjustment mechanism of the semantic rule engine. The platform introduces a multi-factor priority matching scoring method, assigning priority to each candidate semantic segment using the following formula: Sim() is used to analyze the similarity between content and scene tags and user intent. Weighting based on historical user interaction feedback. We tune the model parameters to ensure that automated report output is both faithful to the scenario analysis and responsive to individual needs. The automatic assembly process uses a priority queue for dynamic scheduling, and the order of fragment assembly is affected by the score ranking, achieving flexible scheduling and content adaptation.

[0040] S403. After completion, the system still needs secondary optimization of the report's aesthetics and functional interaction. A convolutional neural network (CNN) is used to evaluate the layout aesthetics score, fine-tuning elements such as panel and chart color schemes, spacing, and font size to ensure the output report meets cognitive fluency and aesthetic requirements. Visual segment aesthetic score. It can be defined as: in To ensure compliance in the layout, For color coordination, For font compatibility, To adjust parameters. In the actual process, candidate report structures undergo multiple rounds of visual score filtering, ultimately retaining outputs that combine aesthetics and functionality.

[0041] Regarding S404 and human-computer interaction adaptation, the platform deeply integrates a user behavior learning model. Every report view, operation, and feedback is recorded by the system, generating a user preference feature vector Uη for subsequent personalized design. The recommendation algorithm uses Principal Component Analysis (PCA) to dynamically adjust existing interaction habits and report structure priorities, allowing users to automatically adjust report content simply by providing natural descriptions or prior scenario selections. If a user inputs "Detect the latest anomaly chain and its impact path," the platform will link the most recent causal inference results, automatically generate a multi-layered interactive report containing anomaly chain diagrams, impact scope analysis, and a list of key alarms, and provide interactive buttons such as source tracing jumps, handling suggestions, and filtering / sorting.

[0042] This invention supports direct database connections to various mainstream databases, such as MySQL, Oracle, and SQL Server. Users only need to correctly enter the database's IP address, port number, database name, username, and password to establish a connection and directly access and process data within the database. The Spring Bean data source is suitable for business logic components managed through the Spring framework. Users can select the corresponding BeanID to obtain the data services provided by that Bean. This method integrates well with existing enterprise Spring architectures, improving system compatibility and scalability. Built-in data sources are pre-configured basic data sets, such as system parameters and basic codes, convenient for users to use directly when creating simple reports.

[0043] In terms of report design, users can utilize the rich features offered by the designer to fine-tune the font settings. For the report's appearance, they can set font color and table fill color, with a wide range of color choices to meet diverse visual needs. Border settings are also highly flexible, offering various line styles such as solid, dashed, and dotted lines, as well as different line thicknesses, making the report borders clearer and more aesthetically pleasing. Furthermore, users can merge or split cells to flexibly adjust the report layout; set horizontal left, center, and right alignment, and vertical top, center, and bottom alignment, making the report content layout more standardized. For complex table headers, diagonal headers can be added to clearly display multi-level information; images and charts can also be inserted, supporting multiple formats such as JPG and PNG, and various chart types including bar charts, line charts, and pie charts, making the reports more intuitive and vivid, facilitating data analysis and understanding.

[0044] Meanwhile, the platform supports a rich set of expressions and functions, such as mathematical functions, string functions, and date functions. Users can use these tools to perform complex data calculations, data transformations, and conditional judgments, meeting the data processing needs of different business scenarios. The water information report can monitor the operational status of hydropower stations through daily, weekly, and monthly reports, including power generation, equipment status, and fault conditions, ensuring the safe and stable operation of the station. Statistical reports on resource usage, such as water resource utilization efficiency and power output vs. consumption comparisons, help optimize resource allocation and improve overall operational efficiency.

[0045] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0046] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for integrating intelligent reports on anomalies and energy efficiency of hydropower equipment in a centralized control center, characterized in that: The method includes: Intelligent fusion and tagging of multi-source heterogeneous data; design of multi-source dynamic tag sensing; automatic acquisition and streaming tagging of multi-channel data from different types of hydropower equipment and operating environments. An adaptive field alignment algorithm is used to dynamically identify key fields in new data sources based on the co-occurrence characteristics of historical samples, and automatically generate labels and data clusters. The design of the causal link reasoning algorithm for anomaly detection incorporates multi-dimensional correlation information such as equipment operating topology, historical maintenance events, and energy consumption fluctuations. Through the event reasoning chain, it automatically generates a progressive link from the original signal to the core anomaly and evaluates the causal strength between each anomaly. A hierarchical weighted self-evolving energy efficiency evaluation model is constructed, integrating multiple factors such as equipment health coefficient, water resource utilization rate, real-time power generation load, and external climate influence. By employing a weighted nested hierarchical neural network, the weight allocation of each evaluation factor is dynamically adjusted based on feedback from actual operational data, and the model output can generate customized energy efficiency levels for different devices and different stages. Intelligent report generation and human-computer interaction adaptation are achieved by introducing a semantic fragment module—meaning that each visual element in the report corresponds to a semantic command. Through its semantic rule engine, the platform can automatically identify data types, business scenarios, and user customization needs, and intelligently assemble report structures and content to achieve high personalization and automatic adjustment.

2. The method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers according to claim 1, characterized in that: The intelligent fusion and tagging of multi-source heterogeneous data includes: automatically analyzing fields using co-occurrence features of historical samples to dynamically identify key fields and field types in new data sources; and establishing field-tag mapping relationships through field vectorization representation and co-occurrence feature comparison. An adaptive optimal matching algorithm is introduced, which treats the new field set and the historical normalized field set as nodes, and performs weighted graph matching based on co-occurrence probability or semantic distance as weights to automatically generate labels and data clusters. For fields that cannot be automatically aligned, the system triggers an expert intelligent auxiliary annotation mechanism to recommend tags and matching scores, allowing personnel to quickly confirm or make minor adjustments.

3. The method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers according to claim 1, characterized in that: The causal link reasoning algorithm for design anomaly detection includes: using an event reasoning chain mechanism, taking each original anomaly signal as the starting point of the chain, and reasoning progressively along the dimensions of device level, functional module relationship, time sequence dependency, maintenance history and energy efficiency fluctuation; An abnormal event network is constructed, and abnormal nodes are associated with relational edges. Causal inference edges are formed by combining physical connections of devices, functional dependencies, historical co-occurrence, and environmental interference. For each type of abnormal signal, the set of physically connected devices is automatically searched in the device topology relation table, and the associated historical maintenance data, energy consumption time series, and environmental interference records are queried to evaluate the causal strength of the abnormal signal on downstream devices.

4. The method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers according to claim 1, characterized in that: The construction of the hierarchical weighted self-evolutionary energy efficiency evaluation model includes: encapsulating multiple evaluation factors such as equipment health coefficient, water resource utilization rate, power generation load, environmental climate, historical operation fluctuations and maintenance cycle into independent sub-networks. Each sub-network is responsible for inputting the features of its respective factor and generating a normalized output through factor scoring. All factor scores are aggregated hierarchically and then fed into a weighted nested hierarchical aggregation network. In this structure, a self-evolving weight update mechanism is used to automatically optimize the weight distribution of each factor based on the residual between the actual energy efficiency performance of the equipment and the model prediction.

5. The method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers according to claim 1, characterized in that: The aforementioned intelligent report dynamic generation and human-computer interaction adaptation includes: managing all visual elements of the report in the form of semantic fragments, which are driven by intelligent instructions based on data analysis reasoning, scenario matching, and user intent adaptation; the system generates report generation instructions by jointly mapping data types, business scenarios, and user operation preferences through a multi-layer semantic rule system, automatically assembling and splitting each report visual element and dynamically matching content; By using the multi-factor priority matching scoring method in the semantic rule engine, each candidate semantic fragment is given priority based on its similarity to the analyzed content, business scenario, user intent, and historical user interaction feedback. Priority queue scheduling is used to ensure content adaptability and flexible arrangement. After the report structure is output, the overall layout, color scheme and font aesthetics and functional interaction are automatically optimized through built-in visual collaboration rules and neural network models, ultimately realizing the dynamic generation of intelligent reports for multiple scenarios and personalized customization.

6. The method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers according to claim 2, characterized in that: For missing values ​​or redundant fields, the mechanism automatically detects abnormal distributions and categorizes them as "unknown" or "discarded". All integrated data is rearranged into "tagged data clusters", with each data stream having a fine-grained semantic tag attached. The tags are recorded in the system's metadata dictionary to support rapid preprocessing and integration of new data sources.

7. The method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers according to claim 3, characterized in that: The event reasoning chain is generated using a recursive deep search algorithm. A causal weight decay mechanism is introduced during the progressive process to filter high-intensity causal nodes and generate an event sequence chain. Each chain node is labeled with causal strength, timestamp, device type, and event category. The system anomaly impact range, impact depth, and core causal nodes are output to achieve multi-level progressive localization and causal assessment for anomaly diagnosis.

8. The method for integrating intelligent reports on water and electricity equipment anomalies and energy efficiency for centralized control centers according to claim 4, characterized in that: The hierarchical weighted self-evolutionary energy efficiency evaluation model explicitly models the interaction between factors in its aggregation part. It uses high-order aggregation functions, convolution or gated recurrent unit algorithm mechanisms to capture the nonlinear relationship and dynamic coupling between factors, thereby improving the real-time adaptability and explanatory power of the evaluation. During continuous operation, the system can customize energy efficiency ratings for different equipment and operating stages, and automatically generate evaluation reasons.

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