Multi-business data integration method, system, medium and equipment

Abstract

The invention provides a multivariate service data integration method and system, a medium and equipment, and relates to the technical field of data integration. The method comprises the following steps: acquiring multivariate business data of a target water conservancy project, wherein the multivariate business data comprises a plurality of original business data of different dimensions; performing data preprocessing and normalization processing on the original business data in sequence to obtain standard business data; classifying according to the data features of the standard business data to obtain a classification result; and establishing a water conservancy data association network based on the classification result, and performing data association analysis based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, an operation state and performance evaluation. By implementing the technical scheme provided by the invention, the overall association analysis of the multi-dimensional business data can be realized, so that the accuracy of state evaluation of the water conservancy project is enhanced.

Description

Technical Field

[0001] The present application relates to the technical field of data integration, and in particular to a method, system, medium and device for integrating multiple business data. Background Art

[0002] As water conservancy projects become larger and more complex, their operation and management face increasing challenges. Water conservancy projects involve multiple subsystems such as hydraulic structures, electromechanical equipment, and control systems, and there are complex interactions between the subsystems. In order to ensure the safe operation of the project, it is necessary to collect and analyze various monitoring data in real time.

[0003] At present, the business data collection and analysis of water conservancy projects mainly adopts a decentralized management model. Each subsystem independently collects and processes data to form a relatively independent business data set. These data sets include information in multiple dimensions such as hydrological monitoring data, equipment operation data, and safety monitoring data. In actual applications, operation and maintenance personnel need to view the data of different systems separately and make decisions based on experience. Due to the lack of overall correlation analysis of multi-dimensional business data, it is difficult to achieve refined data management, resulting in inaccurate status assessment of water conservancy projects. Summary of the invention

[0004] The present application provides a multi-dimensional business data integration method, system, medium and equipment, which can realize the overall correlation analysis of multi-dimensional business data, thereby enhancing the accuracy of the status assessment of water conservancy projects.

[0005] In a first aspect, the present application provides a method for integrating multiple business data, the method comprising: Acquire multiple business data of a target water conservancy project, wherein the multiple business data includes original business data of multiple different dimensions; Performing data preprocessing and normalization processing on each of the original business data in turn to obtain each standard business data; Classify the standard business data according to the data characteristics of each of the standard business data to obtain a classification result; A water conservancy data association network is established based on the classification results, and data association analysis is performed based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation.

[0006] By adopting the above technical solution, by obtaining multivariate business data including multiple original business data of different dimensions, and preprocessing and normalizing these original business data to obtain standard business data, unified management of data of each subsystem of water conservancy project is realized. Then, by classifying the standard business data according to data characteristics, and establishing a water conservancy data association network based on the classification results, the scattered business data are effectively associated. Finally, data association analysis is performed based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation, so that operation and maintenance personnel can intuitively grasp the interaction relationship between various systems of the project, breaking through the limitations of the traditional decentralized management model, and realizing the overall association analysis of multi-dimensional business data, thereby significantly improving the accuracy of water conservancy project status assessment.

[0007] In a second aspect of the present application, a multi-business data integration system is provided, the system comprising: A data acquisition module is used to acquire multiple business data of a target water conservancy project, wherein the multiple business data includes original business data of multiple different dimensions; A data processing module, used to perform data preprocessing and normalization processing on each of the original business data in turn to obtain each standard business data; A data classification module, used to classify the standard business data according to the data characteristics of each of the standard business data to obtain a classification result; The data integration module is used to establish a water conservancy data association network based on the classification results, and to perform data association analysis based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation.

[0008] In a third aspect of the present application, a computer storage medium is provided, wherein the computer storage medium stores a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the above method steps.

[0009] In a fourth aspect of the present application, an electronic device is provided, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the above-mentioned method steps.

[0010] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: This application achieves unified management of data of each subsystem of a water conservancy project by acquiring multiple business data including original business data of multiple different dimensions, and preprocessing and normalizing these original business data to obtain standard business data. Then, by classifying the standard business data according to data characteristics and establishing a water conservancy data association network based on the classification results, the scattered business data are effectively associated. Finally, data association analysis is performed based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation, so that operation and maintenance personnel can intuitively grasp the interaction relationship between the various systems of the project, breaking through the limitations of the traditional decentralized management model, and realizing the overall association analysis of multi-dimensional business data, thereby significantly improving the accuracy of water conservancy project status assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flowchart of a multi-business data integration method provided in an embodiment of the present application; Figure 2 It is a module diagram of a multi-business data integration system provided by an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0012] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0013] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0014] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0015] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0016] The following will provide a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0017] Please refer to Figure 1 , a flowchart of a multi-business data integration method is proposed, the method can be implemented by a computer program, can be implemented by a single-chip microcomputer, and can also be run on a multi-business data integration system, the computer program can be integrated in a computer device, and can also be run as an independent tool application. Specifically, the method includes steps 10 to 40, and the above steps are as follows: Step 10: Obtain the multi-business data of the target water conservancy project, where the multi-business data includes original business data of multiple different dimensions.

[0018] In the embodiment of the present application, the target water conservancy project refers to a large-scale water conservancy facility with multiple functional subsystems. For example, it may be a reservoir dam, a hydroelectric power station, a sluice pump station, and other water conservancy engineering projects with multiple subsystems such as hydraulic structures, electromechanical equipment, and control systems. These engineering projects usually have complex hydraulic characteristics and system interactions.

[0019] In the embodiment of the present application, multi-business data refers to various monitoring data and business data related to the operation of the target water conservancy project, including but not limited to: structural monitoring data of hydraulic structures (such as displacement, stress, seepage, etc.), hydrological monitoring data (such as water level, flow, water quality, etc.), operation data of electromechanical equipment (such as speed, vibration, temperature, etc.), control system status data (such as opening, pressure, current, etc.) and other business data related to project operation management. These data come from different subsystems and have different data characteristics and collection frequencies.

[0020] Specifically, since a large amount of different types of data will be generated during the operation of each subsystem of a water conservancy project, and these data are scattered and stored in different business systems, in order to achieve a comprehensive assessment of the project status, it is necessary to first obtain these multi-business data. First, based on the historical operation data of the target water conservancy project, the data change rules of the key parts of the project under different operating conditions are analyzed, and data change characteristics such as data fluctuation range and change rate are extracted to obtain data characteristic indicators that can characterize the operation characteristics of the project. Then, the optimal collection frequency of different types of data is determined based on these data characteristic indicators. After determining the collection frequency, distributed data collection nodes are deployed at the key monitoring points of the project. These collection nodes interact with data through the edge computing network and collect original business data of different dimensions in real time, including structural monitoring data of hydraulic structures, hydrological monitoring data, and electromechanical equipment operation data. Finally, the various types of collected original business data are uniformly converted into a preset standard data format to form a standardized multi-business data set.

[0021] Based on the above embodiment, as an optional embodiment, the step of obtaining the multi-service data of the target water conservancy project may further include the following steps: Step 101: Based on the historical operation data of the target water conservancy project, extract the data change characteristics of the key parts of the project to obtain data characteristic indicators.

[0022] Specifically, since the key parts of the water conservancy project show different data characteristics under different operating conditions, in order to accurately grasp these characteristics and determine a reasonable data collection strategy, it is necessary to conduct an in-depth analysis of the historical operation data. First, the historical operation data of the target water conservancy project in the past five years are retrieved from the project operation management system. These data include dam deformation monitoring data, gate opening and closing process data, pump operation data, etc. For each key part, the data sequence under its typical operating conditions is selected, such as water level change data during flood season and non-flood season, equipment parameter change data during the start-up and shutdown process of the unit, etc. Then, the sliding time window method is used to segment these data sequences and calculate the statistical characteristics of the data in each time window, including mean, standard deviation, change rate, etc. By comparing the statistical characteristics under different working conditions, the change laws of various types of data are determined, such as the periodicity, mutation characteristics, correlation, etc. of the data. Based on these change laws, a feature vector reflecting the dynamic characteristics of the data is constructed. The feature vector contains information such as the fluctuation range, change rate, and trend of the data, so as to obtain data feature indicators that can quantitatively describe the change characteristics of the data of each key part. In this way, not only the changing patterns of various types of data are clarified, but also a scientific basis is provided for the subsequent determination of the frequency of data collection, which helps to improve the pertinence and efficiency of data collection.

[0023] Step 102: Determine the data collection frequency based on the data characteristic indicators, and use distributed data collection nodes to obtain original business data of different dimensions based on the data collection frequency. The distributed data collection nodes interact with each other through the edge computing network.

[0024] Specifically, according to the fluctuation range and change rate information in the data characteristic indicators, the Nyquist sampling theorem is used to calculate the minimum sampling frequency, and the frequency is adjusted in combination with the actual project conditions. For example, a sampling frequency of 100Hz is used for fast-changing parameters such as equipment vibration, and a sampling frequency of 1Hz is used for slowly changing parameters such as water level and temperature. After determining the sampling frequency, distributed data acquisition nodes are deployed at key parts of the project. These nodes include strain acquisition nodes set at different elevations of the dam, vibration acquisition nodes installed on the bearing seat of the unit, and water level acquisition nodes arranged upstream and downstream. Each acquisition node is equipped with an edge computing unit, which can perform local data preprocessing and caching. Adjacent acquisition nodes communicate through the edge computing network, use a time synchronization protocol to ensure the timing consistency of the data, and realize data complementarity and redundant backup between nodes through a data sharing mechanism. When a node detects abnormal data, it can quickly notify the relevant nodes through the edge network to increase the sampling frequency, thereby realizing adaptive adjustment of data acquisition.

[0025] Step 103: Convert each original business data into a preset standard data format to obtain the multivariate business data of the target water conservancy project.

[0026] Specifically, the collected raw business data contains different data formats, such as binary data streams, text data, analog data, etc., which need to be uniformly converted into a preset standard data format. During the conversion process, the key fields such as the timestamp, measurement point information, and numerical values ​​of the data are first extracted, and then formatted according to the predefined data template to generate a standardized data set with a unified metadata structure. In this way, the real-time and reliability of data collection are guaranteed, and the unified management of data formats is realized.

[0027] Step 20: Perform data preprocessing and normalization processing on each original business data in turn to obtain each standard business data.

[0028] Specifically, in order to eliminate the noise interference in the original business data and unify the data scales of different dimensions, the acquired original business data needs to be preprocessed and normalized. First, the original business data is preprocessed, including two steps: data cleaning and data smoothing. In the data cleaning process, the outlier detection method based on the 3σ criterion is used to identify outliers. For the detected outliers, if the deviation from the adjacent data points exceeds the preset threshold, they are marked as invalid data; for the case of missing data, linear interpolation or spline interpolation methods are used to repair the data according to the time continuity characteristics of the data. Then, the adaptive filtering algorithm based on wavelet transform is used to smooth the data. By thresholding the wavelet coefficients of different scales, the influence of random noise is effectively suppressed, while retaining the mutation characteristics of the data. After completing the preprocessing, the corresponding normalization method is used for different types of data. For parameters with a certain range of variation (such as water level, pressure, etc.), the maximum and minimum value normalization method is used to map the data to the [0,1] interval; for parameters that may have large fluctuations (such as vibration, flow, etc.), the Z-score-based normalization method is used to convert the data into a standard normal distribution with a mean of 0 and a standard deviation of 1. This data preprocessing and normalization not only improves the quality and reliability of the data, but also achieves the unification of the scales of data of different dimensions, creating conditions for subsequent data classification and association analysis.

[0029] Based on the above embodiment, as an optional embodiment, the step of performing data preprocessing and normalization processing on each original service data in sequence to obtain each standard service data may also include the following steps: Step 201: extract the time series features of each original business data, and segment the data based on the time series features.

[0030] Specifically, since the original business data generated during the operation of water conservancy projects is uneven in the time dimension, the data in some time periods are too sparse and the data in some time periods are redundant. In order to ensure the accuracy of data analysis, it is necessary to extract time series features and perform segmented balancing on the data. In the specific implementation, the sliding time window method is first used to extract the time series features of each original business data, and the statistical features in each time window are calculated, including the mean, variance, kurtosis, skewness, etc., and the periodicity and trend characteristics of the data are determined in combination with autocorrelation analysis. Based on the extracted time series features, a segmentation algorithm based on change point detection is used to segment the data. The algorithm calculates the distance measurement of the features of adjacent time windows and sets segmentation points at the locations where the features change significantly, thereby dividing the entire time series into multiple data segments with relatively stable characteristics.

[0031] Step 202: Calculate the data density of each segmented data segment, interpolate and supplement the data segment whose data density is not higher than the density threshold, and downsample the data segment whose data density is higher than the density threshold to obtain a first business data set.

[0032] Specifically, for each data segment, calculate its data density, that is, the number of data points per unit time. Set a density threshold based on engineering experience, for example, it can be set to 10 points / minute for water level data. For data segments with data density lower than the density threshold, use an interpolation method that takes into account the physical characteristics of the data to supplement it, such as Fourier interpolation for data with periodic characteristics and cubic spline interpolation for data with monotonic characteristics, to ensure that the interpolation result conforms to the physical meaning of the data. For data segments with data density higher than the density threshold, a downsampling strategy based on data importance is adopted. By calculating the information entropy of the data points, key data points with large amounts of information are retained, and redundant information is eliminated to achieve data dimensionality reduction. Through this segmented equalization processing, the first business data set with reasonable time resolution is formed, which not only ensures the integrity of the data, but also avoids data redundancy and improves the efficiency of subsequent analysis.

[0033] Step 203: Determine different filtering parameters based on the time span of each data segment, and filter each data segment in the first business data set according to the filtering parameters to obtain a second business data set.

[0034] Specifically, since data segments with different time spans may contain noise with different frequency characteristics, an adaptive filtering strategy is needed to improve data quality. First, adaptive filtering parameters are designed based on the time span of each data segment. For data segments with shorter time spans (such as hourly level), a higher cutoff frequency is used to retain effective information with rapid changes; for data segments with longer time spans (such as day level), a lower cutoff frequency is used to filter out interference in long-term trends. After determining the filtering parameters, a multi-scale filtering method based on wavelet transform is used to process each data segment in the first business data set. By selecting appropriate wavelet basis functions (such as db4 wavelet), the data is multi-layered and decomposed. The wavelet coefficients are thresholded at different decomposition levels according to the corresponding filtering parameters, and then the filtered data segments are obtained through wavelet reconstruction, thereby forming the second business data set.

[0035] Step 204: rejoin the data segments in the second business data set, and perform data smoothing processing to obtain preprocessed data.

[0036] Specifically, in order to eliminate the splicing breakpoints between data segments, the data segments in the second business data set are combined by overlapping splicing, and the weighted average method is used to achieve smooth transition in the overlapping area of ​​adjacent data segments. The spliced ​​data is then smoothed overall using the LOESS algorithm based on local regression. By adjusting the window size of the local regression, local fluctuations are suppressed while maintaining the overall trend of the data to obtain preprocessed data.

[0037] Step 205: performing grouping and normalization processing on the pre-processed data according to data characteristic indicators to obtain various standard business data.

[0038] Specifically, the preprocessed data is grouped according to the data characteristic indicators, for example, the data with the same physical quantity or similar change law are divided into one group, and the appropriate normalization method is used for each group of data: the maximum and minimum values ​​are normalized for data with a fixed range, the Z-score is normalized for data with normal distribution characteristics, and the logarithmic transformation is used for data with obvious skewness and then normalized, so as to obtain standard business data with unified dimensions and consistent scales. Through this multi-step data processing, not only the data quality is effectively improved, but also the physical characteristics of the data are maintained, providing a reliable foundation for subsequent data analysis.

[0039] Step 30: Classify the standard business data according to their data features to obtain classification results.

[0040] Specifically, a multidimensional feature vector is first constructed to characterize the characteristics of each standard business data. The feature vector contains statistical features (mean, variance, kurtosis, skewness, etc.), time domain features (periodicity, trend, mutation, etc.) and frequency domain features (main frequency, energy distribution, etc.). The principal component analysis method is used to reduce the dimension of the feature vector, and the principal components with a cumulative contribution rate of 85% are retained to reduce the dimension of the feature space and eliminate the correlation between features. Then, the reduced-dimensional features are classified using a clustering method based on the improved K-means algorithm, the optimal number of clusters is determined by calculating the silhouette coefficient, and the initial cluster center selection strategy based on density is used to improve the clustering effect. In the clustering process, an adaptive distance measurement mechanism is introduced to assign dynamic weights to features of different dimensions, so that the clustering results are more consistent with the physical properties of the data. For example, vibration data representing the equipment status, displacement data reflecting engineering safety, and process parameters describing operating conditions are divided into different categories according to their feature similarity. This data classification method not only achieves scientific classification of business data, but also provides a clear data organization structure for subsequent data analysis and application, which helps to improve the efficiency of data management and utilization.

[0041] Based on the above embodiment, as another optional embodiment, the step of classifying the standard service data according to the data features to obtain the classification result may further include the following steps: Step 301: extract hydraulic characteristic parameters of each standard business data and generate a characteristic description matrix.

[0042] Specifically, firstly, the hydraulic characteristic parameters of each standard business data are extracted, which include hydraulic characteristics (such as flow rate, head loss, Reynolds number, etc.), structural characteristics (such as stress distribution, deformation, vibration frequency, etc.) and operation characteristics (such as equipment efficiency, energy consumption index, operation conditions, etc.). For each data sequence, its characteristic value under different working conditions is calculated, and the weight coefficient of the characteristic parameter is determined in combination with expert experience. The extracted characteristic parameters are organized into a characteristic description matrix. The rows of the matrix represent different standard business data, the columns represent different hydraulic characteristic parameters, and the matrix elements are the corresponding characteristic values.

[0043] Step 302: Calculate the similarity between each standard business data based on the feature description matrix, classify the data with a similarity greater than a first threshold into the same category, and obtain a first classification result.

[0044] Specifically, the cosine similarity algorithm is used to calculate the similarity between each row vector in the feature description matrix, and parameter weights are introduced in the calculation process so that the characteristic parameters that have a greater impact on hydraulic performance have a higher weight. The first threshold is set according to the actual needs of the project. For example, the first threshold is set to 0.85. When the similarity between two data sequences is greater than the threshold, they are classified into the same category. Through this classification method based on hydraulic characteristics, data with similar hydraulic performance, structural response or operating characteristics are clustered together to form the first classification result. This classification method not only takes into account the statistical characteristics of the data, but more importantly, it reflects the professional characteristics of water conservancy projects, helps to discover the physical relationship between data, and provides a scientific basis for subsequent engineering analysis and decision-making.

[0045] Step 303: Calculate the hydraulic correlation between different categories of data in the first classification result, merge the data categories with hydraulic correlation greater than the second threshold, and obtain the second classification result.

[0046] Specifically, a hydraulic correlation calculation model is constructed based on the principle of hydraulics, which comprehensively considers factors such as hydraulic connectivity, flow balance, and energy transfer. For different categories in the first classification results, the hydraulic correlation matrix between categories is established by analyzing the spatial relationship of their monitoring locations in the hydraulic system. When calculating the hydraulic correlation, a network analysis method based on graph theory is used, with each monitoring point as a network node and the hydraulic connection relationship as an edge. The hydraulic correlation strength between different categories of data is quantified by calculating indicators such as the shortest path, connectivity, and flow transfer coefficient between nodes. A second threshold based on engineering experience is set, for example, the second threshold is set to 0.75. When the hydraulic correlation between two data categories exceeds this threshold, it means that they have a close functional relationship in the hydraulic system, and these categories need to be merged into a new category. The merging process adopts the idea of ​​hierarchical clustering, and the categories with the highest correlation are merged first, until the correlation between all categories is lower than the second threshold, thereby obtaining the second classification result.

[0047] Step 304: extracting common features and difference features of each type of data in the second classification result, and determining attribute labels of each type of data based on the common features and difference features.

[0048] Specifically, the multidimensional feature analysis method is used to extract the common features of various types of data. By calculating the statistical moments of various types of data, including mean vectors, covariance matrices, etc., the characteristic patterns common to this type of data are identified. At the same time, information theory indicators such as information entropy and mutual information are used to quantify the correlation and information redundancy of data within a class, so as to discover the common laws between data. On the basis of common feature extraction, the feature contrast analysis method is further used to extract difference features. By calculating the Mahalanobis distance of inter-class data, the significant distinguishing features between different types of data are identified. Combined with time-frequency analysis methods such as wavelet transform and empirical mode decomposition, the feature differences of various types of data at different scales are extracted. Based on the extracted common features and difference features, a feature importance evaluation index system is constructed, and the fuzzy comprehensive evaluation method is used to determine the weight of each feature, and the features are physically interpreted in combination with professional knowledge of water conservancy projects. For example, for data categories with obvious periodicity and steady-state characteristics, they can be identified as "steady-state operation data"; for data categories that show mutation characteristics and nonlinear associations, they can be identified as "transitional operating condition data"; for data categories with obvious spatial distribution characteristics, they can be identified as "hydraulic spatial distribution data".

[0049] Step 305: The second classification result and the attribute label of the corresponding category are used as the classification result.

[0050] Specifically, the second classification result is associated with the attribute label of the corresponding category and used as the classification result. This attribute label definition method based on feature analysis gives clear physical meanings and engineering properties to each type of data in the second classification result, forming the final classification result. This classification result not only reflects the inherent characteristics of the data, but also reflects the functional positioning of the data in the water conservancy engineering system.

[0051] Step 40: A water conservancy data association network is established based on the classification results, and data association analysis is performed based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation.

[0052] Specifically, in order to fully grasp the complex associations between various types of data in the water conservancy engineering system and realize the comprehensive analysis and visualization of data, it is necessary to construct a water conservancy data association network and conduct multi-dimensional analysis. In the specific implementation, the water conservancy data association network is first constructed based on the classification results, and different categories of data are used as network nodes. Network connections are established based on the physical association, causal relationship and temporal correlation between data. A weighted directed graph is used to represent the data association network, in which the weight of the edge reflects the strength of the association, and the direction of the edge represents the causal relationship or the direction of influence transmission. In the process of network construction, a time-varying weight mechanism is introduced, and the dynamic association coefficient is calculated through a sliding time window, so that the association network can reflect the time-varying characteristics of the data relationship. Based on the constructed association network, complex network analysis methods are used to perform data association analysis, including calculating node importance, clustering coefficient and path analysis, etc., to identify key data nodes and important association paths. By tracking the abnormal propagation path in the association network and combining it with the set warning threshold, warning information such as equipment abnormality and parameter overrun is generated; by analyzing the state migration characteristics in the association network, the system operation mode is identified and the current operation status is judged; by evaluating the overall topological characteristics and local structural characteristics of the association network and combining it with the performance indicator calculation model, the performance evaluation results of the equipment and system are generated. These analysis results are integrated into the multi-dimensional association view, and a hierarchical visualization scheme is adopted to display the overall situation of the system at the macro level, display the status of key equipment groups at the meso level, and present specific equipment parameters at the micro level, and realize flexible switching between different dimensions through interactive operations. This analysis method based on the association network not only realizes the systematic management of water conservancy project data, but also provides intuitive decision support information, effectively improving the intelligent level of system operation management.

[0053] Based on the above embodiment, as another optional embodiment, the step of establishing a water conservancy data association network based on the classification results and performing data association analysis based on the water conservancy data association network may further include the following steps: Step 4011: Connect the data nodes with the same attribute labels of the classification results, calculate the correlation strength between the nodes based on the common features, and construct an initial data subnet.

[0054] Specifically, we first connect the data nodes with the same attribute labels to build the initial data subnet, and use the correlation strength calculation method based on common features to quantify the degree of correlation between nodes by analyzing the temporal correlation, spectral characteristics and statistical characteristics of the data. When calculating the correlation strength, we introduce an adaptive weight mechanism to dynamically adjust the weight coefficient according to the importance of different features under different working conditions, so that the correlation strength can accurately reflect the actual degree of correlation between data. For each initial data subnet, we use the minimum spanning tree algorithm in graph theory to optimize the network connection structure, retain key connections with high correlation strength, eliminate redundant connections, and form a stable subnet structure.

[0055] Step 4012: Analyze the hydraulic correlation of each node in the initial data subnet, establish cross-network connections between different subnets based on difference characteristics, and generate a hierarchical correlation structure.

[0056] Specifically, the hydraulic correlation of nodes in different initial data subnets is analyzed, and node pairs with significant hydraulic correlation are identified by calculating the upstream and downstream relationships, energy transfer paths, and flow balance relationships of nodes in the hydraulic system. When establishing cross-network connections based on differential features, the analytic hierarchy process is used to determine the weights of different features, focusing on features that can reflect the dynamic characteristics and engineering mechanisms of the system, such as equipment response characteristics, system damping characteristics, and control parameter sensitivity. By establishing cross-network connections, a hierarchical structure containing local subnets and global associations is formed, which not only maintains the close association of similar data, but also reflects the systematic association between different categories of data.

[0057] Step 4013: Calculate the hydraulic transfer characteristics between each connection node in the hierarchical association structure, and determine the directionality of the network connection based on the hydraulic transfer characteristics.

[0058] Specifically, a hydraulic transfer characteristic calculation model is first established. The model is based on the basic principles of fluid mechanics and takes into account factors such as water flow direction, pressure gradient and energy transfer. For each pair of connected nodes in the hierarchical association structure, the time-series characteristics of the monitoring data are analyzed, the time lag relationship and causal relationship between the nodes are calculated, and the Granger causality test method is used to determine the order of data changes. In the analysis of hydraulic transfer characteristics, the hydraulic response function is introduced, and the propagation characteristics of hydraulic disturbances are quantified by solving the transfer function and impulse response between nodes. For nodes with obvious physical connections, such as pump station inlets and outlets, upstream and downstream of canals, etc., the connection direction is determined directly according to the hydraulic flow direction; for nodes without direct physical connections but with statistical correlation, the directionality of information transmission is evaluated by calculating mutual information entropy and conditional entropy. Based on the calculated hydraulic transfer characteristics, a directional attribute is assigned to each connection in the network, and the undirected hierarchical association structure is converted into a directed network.

[0059] Step 4014: Integrate the hierarchical association structure and the directionality of the corresponding network connection into a water conservancy data association network.

[0060] Specifically, a weighted directed graph representation method is used, where the edge weight reflects the hydraulic correlation strength, and the edge direction represents the hydraulic impact transmission direction, forming a complete hydraulic data association network. This directional association network not only reflects the causal relationship and impact transmission path in the system, but also provides a scientific basis for the dynamic characteristics analysis and fault propagation prediction of the system.

[0061] Based on the above embodiment, as another optional embodiment, the step of performing data association analysis based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operation status and performance evaluation may also include the following steps: Step 4021: Calculate the hydraulic mutation index of each node in the water conservancy data association network, and track the mutation propagation path based on the transmission direction between nodes, and build an early warning information trigger chain based on the mutation index and the mutation propagation path.

[0062] Specifically, a hydraulic mutation index calculation model is constructed, which comprehensively considers the characteristics of the data such as the jump amplitude, change rate and duration. By performing wavelet transform and singular spectrum analysis on the node data, the mutation points in the data are identified and the mutation intensity index is calculated; at the same time, an adaptive threshold mechanism is introduced to dynamically adjust the mutation judgment standard according to different working conditions and seasonal characteristics. After determining the node mutation characteristics, based on the transmission direction in the association network, the graph traversal algorithm is used to track the propagation path of the mutation. By analyzing the spatiotemporal evolution of the mutation characteristics, the source node and the affected node of the mutation are identified. The mutation index is combined with the propagation path information to construct an early warning information trigger chain, which includes mutation detection, impact assessment and early warning level determination, which can achieve early detection of mutation events and chain reaction prediction.

[0063] Step 4022: extract the operating parameter combinations of the nodes in the water conservancy data association network, analyze the changing characteristics of the parameter combinations, and obtain the operating status range of each part of the project.

[0064] Specifically, the operating parameter combinations of the nodes are extracted from the association network. These parameters include physical quantities such as flow, water level, pressure, and equipment operating parameters such as speed and power. Multidimensional data analysis methods are used to reduce the dimension of parameter space through principal component analysis and extract key feature combinations; cluster analysis methods, such as fuzzy C-means clustering, are used to divide the change characteristics of parameter combinations into different operating modes. For each operating mode, the normal fluctuation range of the parameters is determined through statistical analysis, and different operating status intervals are defined in combination with equipment operating procedures and engineering experience. For example, the change characteristics of parameter combinations are divided into normal operating intervals, transition operating intervals, and warning operating intervals. By establishing a mapping relationship between parameter combinations and operating status, real-time evaluation of the operating status of various parts of the project can be achieved. This analysis method based on data association networks can not only detect system anomalies in a timely manner, but also accurately judge the operating status, providing a reliable basis for operation management decisions.

[0065] Step 4023: Calculate the performance transfer chain based on the hierarchical structure of the water conservancy data association network, and generate a performance evaluation result based on the performance transfer chain.

[0066] Specifically, a performance transfer chain calculation model is constructed based on the hierarchical structure of the water conservancy data association network. The model considers the transfer relationship between equipment performance indicators and system effectiveness indicators. By analyzing indicators such as energy transfer efficiency, equipment operation efficiency and system coordination between nodes in the association network, a performance evaluation index system is established; the fuzzy comprehensive evaluation method is used to weightedly integrate performance indicators at different levels to obtain the overall performance evaluation value of the system. In the performance transfer analysis, a performance attenuation function is introduced to quantify the loss characteristics of performance indicators in the transmission process and identify the performance bottleneck location. Based on the calculated performance transfer chain, a multi-layer evaluation method is used to generate performance evaluation results, including operation efficiency evaluation at the equipment level, collaborative performance evaluation at the system level and economic performance evaluation at the overall level.

[0067] Step 4024: Map the warning information trigger chain, operating status interval and performance evaluation results to the preset engineering structure layout to obtain a multi-dimensional correlation view.

[0068] Specifically, the warning information trigger chain, operating status interval and performance evaluation results are integrated into the preset engineering structure layout, and the layered display technology is used to overlay multi-dimensional information layers on the engineering structure diagram, and different visual coding methods are used to express information of different dimensions. For example, color coding is used to represent the operating status interval, flashing marks are used to display warning information, and numerical labels are used to display performance evaluation results. In the process of visualization, an interactive operation mechanism is introduced to support functions such as switching of information layers, drill-down analysis of data, and playback of time dimensions. By establishing a unified visualization framework, the coordinated display of multi-dimensional information such as warnings, status and performance is realized, forming an intuitive multi-dimensional correlation view. This visualization solution based on engineering structure layout not only makes it easy for managers to quickly grasp the system operation status, but also helps to discover the correlation characteristics between information of different dimensions, providing comprehensive information support for system optimization and decision-making.

[0069] Based on the above embodiment, as another optional embodiment, the multi-business data integration method may further include the following steps: Specifically, in order to realize the active prevention and control of risks in water conservancy engineering systems, it is necessary to conduct in-depth analysis of the warning information in the multi-dimensional correlation view and establish a corresponding emergency response mechanism. In the specific implementation, the propagation characteristics of the warning information are first extracted from the multi-dimensional correlation view, and the risk propagation model is established by analyzing the spatiotemporal distribution law, propagation speed and impact range of the warning events. The network analysis method is used to calculate the possible paths of risk diffusion based on the correlation relationship and propagation path of the warning information, including direct propagation path and indirect propagation path. In the risk path analysis, the risk transmission probability matrix is ​​introduced to quantify the possibility of risk transmission between different nodes, and the dynamic process of risk diffusion is predicted by the Monte Carlo simulation method, and finally a risk prevention and control network including risk source identification, propagation path and impact assessment is formed.

[0070] Based on the risk prevention and control network, the analytic hierarchy process is used to evaluate the risk level of each part of the project. Factors such as equipment importance, failure impact and maintenance difficulty are considered to establish a risk level assessment index system. By analyzing historical failure cases and expert experience, the criteria and thresholds for different risk levels are determined to achieve dynamic assessment of risk levels. For different risk levels, corresponding emergency response plans are formulated, including emergency response processes, disposal measures and resource allocation strategies. In the process of formulating emergency plans, case reasoning methods are used to generate targeted disposal suggestions based on historical disposal experience and current system status, and the effectiveness of different disposal measures is evaluated through the solution optimization algorithm.

[0071] The generated emergency response plan is integrated into the multi-dimensional associated view, and a layered display method is adopted to add a risk prevention and control information layer on the basis of the original view. Through visual coding, different graphic symbols and colors are used to identify risk levels, dynamic arrows are used to indicate the direction of risk propagation, and pop-up windows are used to display detailed response plan contents. In the interactive design, the rapid call of emergency plans and real-time tracking of the progress of plan execution are supported to achieve visual management of the risk prevention and control process. This multi-dimensional associated view with integrated risk prevention and control functions can not only help managers to discover and evaluate risks in a timely manner, but also guide the orderly development of emergency response work, and achieve active prevention and control and efficient disposal of engineering risks.

[0072] See also Figure 2 , is a module diagram of a multi-business data integration system provided in an embodiment of the present application, the multi-business data integration system may include: a data acquisition module, a data processing module, a data classification module and a data integration module, wherein: A data acquisition module is used to acquire multiple business data of a target water conservancy project, wherein the multiple business data includes original business data of multiple different dimensions; A data processing module, used to perform data preprocessing and normalization processing on each of the original business data in turn to obtain each standard business data; A data classification module, used to classify the standard business data according to the data characteristics of each of the standard business data to obtain a classification result; The data integration module is used to establish a water conservancy data association network based on the classification results, and to perform data association analysis based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation.

[0073] Optionally, the data acquisition module is further used to extract data change characteristics of key parts of the project based on historical operation data of the target water conservancy project to obtain data characteristic indicators; Determine the data collection frequency based on the data characteristic index, and acquire original business data of different dimensions based on the data collection frequency and using distributed data collection nodes, wherein the distributed data collection nodes exchange data through an edge computing network; The original business data are converted into a preset standard data format to obtain the multivariate business data of the target water conservancy project.

[0074] Optionally, the data processing module is further used to extract time series features of each of the original business data, and perform segmented processing on the data based on the time series features; Calculate the data density of each segmented data segment, interpolate and supplement the data segment whose data density is not higher than the density threshold, and downsample the data segment whose data density is higher than the density threshold, to obtain a first business data set; Determining different filtering parameters based on the time span of each of the data segments, and filtering each of the data segments in the first business data set according to the filtering parameters to obtain a second business data set; Rejoining the data segments in the second business data set and performing data smoothing processing to obtain preprocessed data; The pre-processed data are grouped and normalized according to data characteristic indicators to obtain various standard business data.

[0075] Optionally, the data classification module is further used to extract hydraulic characteristic parameters of each of the standard business data and generate a characteristic description matrix; Calculating the similarity between each of the standard business data based on the feature description matrix, classifying the data with a similarity greater than a first threshold into the same category, and obtaining a first classification result; In the first classification result, the hydraulic correlation between different categories of data is calculated, and the data categories with hydraulic correlation greater than the second threshold are merged to obtain a second classification result; Extracting common features and difference features of each type of data in the second classification result, and determining attribute labels of each type of data based on the common features and difference features; The second classification result and the attribute label of the corresponding category are used as the classification result.

[0076] Optionally, the data integration module is further used to connect the data nodes with the same attribute label of the classification result, calculate the correlation strength between the nodes based on the common characteristics, and construct an initial data subnet; Analyze the hydraulic correlation of each node in the initial data subnet, establish cross-network connections between different subnets based on the difference characteristics, and generate a hierarchical correlation structure; Calculating the hydraulic transmission characteristics between each connection node in the hierarchical association structure, and determining the directionality of the network connection based on the hydraulic transmission characteristics; The hierarchical association structure and the directionality of the corresponding network connections are integrated into a water conservancy data association network.

[0077] Optionally, the data integration module is further used to calculate the hydraulic mutation index of each node in the water conservancy data association network, and to track the mutation propagation path based on the transmission direction between nodes, and to build an early warning information trigger chain based on the mutation index and the mutation propagation path; Extracting the operating parameter combinations of the nodes in the water conservancy data association network, analyzing the changing characteristics of the parameter combinations, and obtaining the operating status intervals of various parts of the project; Calculating a performance transfer chain based on the hierarchical structure of the water conservancy data association network, and generating a performance evaluation result based on the performance transfer chain; The warning information trigger chain, operating status interval and performance evaluation result are mapped to a preset engineering structure layout to obtain a multi-dimensional correlation view.

[0078] Optionally, the data integration module extracts the propagation characteristics of the warning information in the multi-dimensional correlation view, and calculates the risk diffusion path to obtain a risk prevention and control network; Analyze the risk level of each part of the project based on the risk prevention and control network and generate an emergency response plan; The emergency response plan is integrated into the multi-dimensional association view to achieve active prevention and control of engineering risks.

[0079] It should be noted that: when the system provided in the above embodiment realizes its functions, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0080] An embodiment of the present application also provides a computer storage medium, which can store multiple instructions. The instructions are suitable for being loaded by a processor and executing a multi-business data integration method of the above embodiment. The specific execution process can be found in the specific description of the above embodiment, which will not be repeated here.

[0081] Please refer to Figure 3 The application also discloses an electronic device. Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0082] The communication bus 302 is used to realize the connection and communication between these components.

[0083] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0084] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0085] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0086] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of a multi-business data integration method.

[0087] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program storing a multi-business data integration method in the memory 305, and when executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0088] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0089] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0093] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0094] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not described in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for integrating multiple business data, characterized in that: The method comprises: Acquire multiple business data of a target water conservancy project, wherein the multiple business data includes original business data of multiple different dimensions; Performing data preprocessing and normalization processing on each of the original business data in turn to obtain each standard business data; Classify the standard business data according to the data characteristics of each of the standard business data to obtain a classification result; A water conservancy data association network is established based on the classification results, and data association analysis is performed based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation.

2. The multi-business data integration method according to claim 1, characterized in that: The obtaining of the multi-business data of the target water conservancy project includes: Based on the historical operation data of the target water conservancy project, extract the data change characteristics of key parts of the project to obtain data characteristic indicators; Determine the data collection frequency based on the data characteristic index, and acquire original business data of different dimensions based on the data collection frequency and using distributed data collection nodes, wherein the distributed data collection nodes exchange data through an edge computing network; Each of the original business data is converted into a preset standard data format to obtain the multivariate business data of the target water conservancy project.

3. The multi-business data integration method according to claim 1, characterized in that: The data preprocessing and normalization processing are performed on each of the original business data in sequence to obtain each standard business data, including: Extracting time series features of each of the original business data, and performing segmentation processing on the data based on the time series features; Calculate the data density of each data segment after segmentation processing, interpolate and supplement the data segment whose data density is not higher than the density threshold, and downsample the data segment whose data density is higher than the density threshold to obtain a first business data set; Determining different filtering parameters based on the time span of each of the data segments, and filtering each of the data segments in the first business data set according to the filtering parameters to obtain a second business data set; Rejoining the data segments in the second business data set and performing data smoothing processing to obtain preprocessed data; The pre-processed data are grouped and normalized according to data characteristic indicators to obtain various standard business data.

4. The multi-business data integration method according to claim 1, characterized in that: The classification is performed according to the data features of each of the standard business data to obtain the classification results, including: Extracting hydraulic characteristic parameters of each of the standard business data and generating a characteristic description matrix; Calculating the similarity between each of the standard business data based on the feature description matrix, classifying the standard business data with a similarity greater than a first threshold into the same category, and obtaining a first classification result; In the first classification result, the hydraulic correlation between different categories of data is calculated, and the data categories with hydraulic correlation greater than the second threshold are merged to obtain a second classification result; Extracting common features and difference features of each type of data in the second classification result, and determining attribute labels of each type of data based on the common features and difference features; The second classification result and the attribute label of the corresponding category are used as the classification result.

5. The multi-business data integration method according to claim 4, characterized in that: The step of establishing a water conservancy data association network based on the classification results includes: Connecting the data nodes with the same attribute labels in the classification results, calculating the association strength between the nodes based on the common features, and constructing an initial data subnet; Analyze the hydraulic correlation of each node in the initial data subnet, establish cross-network connections between different subnets based on the difference characteristics, and generate a hierarchical correlation structure; Calculating the hydraulic transmission characteristics between each connection node in the hierarchical association structure, and determining the directionality of the network connection based on the hydraulic transmission characteristics; The hierarchical association structure and the directionality of the corresponding network connections are integrated into a water conservancy data association network.

6. The multi-business data integration method according to claim 1, characterized in that: The data association analysis is performed based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operation status and performance evaluation, including: Calculating the hydraulic mutation index of each node in the water conservancy data association network, tracing the mutation propagation path based on the transmission direction between nodes, and constructing an early warning information trigger chain based on the mutation index and the mutation propagation path; Extracting the operating parameter combinations of the nodes in the water conservancy data association network, analyzing the changing characteristics of the parameter combinations, and obtaining the operating status intervals of various parts of the project; Calculating a performance transfer chain based on the hierarchical structure of the water conservancy data association network, and generating a performance evaluation result based on the performance transfer chain; The warning information trigger chain, operating status interval and performance evaluation result are mapped to a preset engineering structure layout to obtain a multi-dimensional correlation view.

7. The multi-business data integration method according to claim 1, characterized in that: The method further comprises: Extracting the propagation characteristics of the warning information in the multi-dimensional correlation view, and calculating the risk diffusion path to obtain a risk prevention and control network; Analyze the risk level of each part of the project based on the risk prevention and control network and generate an emergency response plan; The emergency response plan is integrated into the multi-dimensional association view to achieve active prevention and control of engineering risks.

8. A multi-business data integration system, characterized in that: The system comprises: A data acquisition module is used to acquire multiple business data of a target water conservancy project, wherein the multiple business data includes original business data of multiple different dimensions; A data processing module, used to perform data preprocessing and normalization processing on each of the original business data in turn to obtain each standard business data; A data classification module, used to classify the standard business data according to the data characteristics of each of the standard business data to obtain a classification result; The data integration module is used to establish a water conservancy data association network based on the classification results, and to perform data association analysis based on the water conservancy data association network to generate a multi-dimensional association view including early warning information, operating status and performance evaluation.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.

Images