Cost consultation data arrangement and analysis system based on cloud platform

Abstract

The invention relates to the technical field of data processing and analysis, and discloses a cost consultation data arrangement and analysis system based on a cloud platform, and the system comprises a data collection module which is used for obtaining and standardizing original cost data; the data clustering module is used for clustering data according to specific dimensions and extracting key feature vectors; the strategy generation module is used for constructing candidate analysis strategies; the strategy optimization module is used for screening an optimization strategy; and the execution engine module processes the data according to the optimization strategy to generate an analysis result. In addition, a data verification module is arranged to verify results, a dynamic adjustment module is arranged to monitor data changes, and a knowledge graph module is arranged to construct an entity relation graph. The system can solve the problems of data source heterogeneity, single analysis strategy and the like in cost data processing, realizes efficient data processing and accurate analysis, ensures that the result is accurate and reliable, and improves the data processing and analysis level of the cost consultation industry.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and analysis, and in particular to a cost consulting data sorting and analysis system based on a cloud platform. Background Art

[0002] In today's cost consulting industry, data processing and analysis face many challenges, and traditional cost data processing methods can no longer meet the needs of industry development.

[0003] First, the heterogeneity of data sources has become a major problem. With the development of information technology, cost data comes from a wide range of sources, including files in different formats (such as Excel, PDF, etc.), various databases (relational databases, non-relational databases), and numerous business system interfaces. These data sources vary greatly in data format, storage structure, and data standards, making it difficult to directly integrate and analyze the original cost data obtained from multiple sources. For example, cost data exported from architectural design software may be stored in a specific binary format, while cost data in the financial system is stored according to the table structure of a relational database, which makes data collection and preprocessing complicated and cumbersome, consuming a lot of manpower and time costs.

[0004] Secondly, the explosive growth of data volume has brought tremendous pressure to data processing. With the continuous expansion of the scale and number of construction projects, the cost data has grown exponentially. The traditional single-machine processing method is seriously insufficient in computing power and storage capacity when facing massive data. For example, for large-scale urban infrastructure construction projects, the cost data involved may contain tens of thousands of detailed records. Single-machine processing is not only extremely slow, but may even fail to complete the data processing task due to insufficient memory, and cannot meet the requirements of real-time and accuracy.

[0005] Furthermore, the singleness and static nature of data analysis strategies make it difficult to adapt to complex and changing business scenarios. In actual cost consulting work, different types of projects (such as residential, commercial, industrial, etc.) have different characteristics, and the distribution and regularity of data are also different. Existing analysis strategies are often based on fixed rules and models, and lack adaptability to dynamic changes in data. For example, when analyzing the cost of residential projects, if the same analysis model as commercial projects is used, the unique cost components in residential projects (such as the impact of apartment structure on cost) may be ignored, resulting in inaccurate analysis results and failure to provide effective support for decision-making.

[0006] In addition, the accuracy and reliability of the data are difficult to guarantee. In the process of processing cost data, data quality problems frequently occur due to the complexity of data sources and the possibility of manual intervention. Problems such as data entry errors, data missing, and data inconsistency will seriously affect the credibility of the analysis results. Moreover, the existing data verification mechanism is not perfect enough to comprehensively and deeply detect logical errors and abnormal situations in the data. For example, when calculating the cost of building materials, if the unit price of the material entered manually is wrong, and the system fails to detect and correct it in time, it will lead to deviations in the entire cost analysis results, bringing risks to project decisions. Summary of the invention

[0007] The purpose of the present invention is to provide a cost consulting data collation and analysis system based on a cloud platform to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solution: a cost consulting data collation and analysis system based on a cloud platform, the system comprising: A data acquisition module is used to obtain original cost data from multiple heterogeneous data sources in real time, and perform format standardization processing on the original cost data to generate a structured data set; A data clustering module, used to dynamically cluster the structured data set according to a preset classification dimension, generate multiple data subsets, and extract key feature vectors of each data subset; A strategy generation module is used to construct multiple candidate analysis strategies based on the key feature vectors of each data subset. Each candidate analysis strategy includes data cleaning rules, association models, and optimization parameter combinations. The strategy optimization module is used to calculate the estimated computational complexity, estimated storage occupancy rate and estimated response time of each candidate analysis strategy according to the preset optimization goal, and select the preferred analysis strategy that meets the constraint conditions; An execution engine module, used to perform data cleaning, association modeling and parameter optimization operations on the corresponding data subset according to the preferred analysis strategy to generate a standardized cost analysis result; Among them, the strategy generation module pre-stores mapping relationships between multiple types of standard data scenarios and candidate analysis strategies, and the optimization goals include minimum computational complexity, minimum storage occupancy and shortest response time.

[0009] Preferably, the data clustering module includes: Dimension definition unit, used to define classification dimensions according to cost item attributes, time series characteristics and cost component factors; The clustering algorithm unit is used to use a hybrid clustering algorithm with adaptive weight allocation to calculate the clustering weight of each data point according to the following formula: ; in, is the clustering weight of the jth data point, Indicates the similarity between the data point and the item attribute, Represents the continuity score of the data point in the time series, and is the dynamic adjustment coefficient; n is the total number of valid data points in the current cluster, represents the sum of weighted comprehensive scores of all data points; The subset generation unit is used to iteratively divide the data points according to the clustering weights to generate the data subsets.

[0010] Preferably, the strategy generation module includes: A rule base unit stores a set of abnormal data detection rules obtained through training based on historical cost data; A model library unit stores linear regression models, Bayesian network models and neural network models; A strategy building unit, used to match candidate rules and models from the rule base unit and the model base unit according to the key feature vector of the data subset, and generate a parameter optimization space , construct the candidate analysis strategy.

[0011] Preferably, the formula for calculating the estimated computational complexity in the strategy optimization module is: ; in, Indicates the estimated computational complexity, Indicates The execution time of each cleaning rule is Indicates The memory usage of the associated model, and is the weight coefficient.

[0012] Preferably, the system further comprises: The data verification module is used to perform multi-level verification on the standardized cost analysis results, specifically including: The consistency check unit removes the result data exceeding the threshold value based on the preset cost index threshold value range; The logic verification unit uses a directed acyclic graph (DAG) to model the dependency relationship between various cost parameters and detect logical conflicts in the result data; The traceability unit records the generation path and data lineage of each analysis result.

[0013] Preferably, the dependency modeling method in the logic verification unit includes: Step A1: Extract cost parameter set ; Step A2: Analyze the conditional probability distribution between parameters based on historical data , construct edge sets ,in is the probability threshold; Step A3, according to and The directed acyclic graph is generated.

[0014] Preferably, the system further comprises: The dynamic adjustment module is used to monitor data distribution changes in real time during data cleaning and calculate the data distribution offset according to the following formula: : ; in, Indicates the frequency of occurrence of the i-th data feature in the current data stream, represents the benchmark frequency of the ith data feature in the historical data set, m is the total number of preset key features, is the preset threshold, if The strategy generation module is then triggered to reconstruct the candidate analysis strategy.

[0015] Preferably, the execution engine module includes: Distributed computing unit, used to split the data subset into multiple data blocks and distribute them to computing nodes of the cloud platform for parallel processing; The fault-tolerant unit adopts a checkpoint mechanism based on redundant calculations to save intermediate states at preset time intervals and resume calculations from the most recent checkpoint when a node fails.

[0016] Preferably, the system further comprises: The knowledge graph module is used to construct an entity relationship graph in the cost field, including: Entity extraction unit, extracting cost-related entities and attributes from unstructured text; The relational reasoning unit uses a graph neural network model to learn implicit association rules between entities; The graph update unit dynamically expands entity nodes and relationship edges based on new data.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The data acquisition module can obtain raw cost data from multiple heterogeneous data sources in real time, standardize the format, and generate a structured data set. This function effectively solves the data integration problem caused by heterogeneous data sources in traditional methods, and greatly improves the efficiency and accuracy of data acquisition. For example, whether it is data from design software, financial systems or other business platforms, it can be quickly and accurately collected and converted into a unified format, laying a solid foundation for subsequent analysis work, reducing the tedious work of manually processing data format differences, and reducing the probability of errors.

[0018] The data clustering module defines the classification dimensions according to the cost project attributes, time series characteristics and cost composition factors, and uses a hybrid clustering algorithm with adaptive weight allocation for dynamic clustering to generate multiple data subsets and extract key feature vectors. This clustering method can more accurately reflect the characteristics of different types of cost data, making subsequent analysis more targeted. Taking different types of construction projects as an example, through clustering, the data of residential, commercial and industrial projects can be classified separately, and the unique characteristics of each type of project can be analyzed, which improves the accuracy and practicality of the analysis results.

[0019] The strategy generation module constructs multiple candidate analysis strategies based on the key feature vectors of the data subset, and pre-stores the mapping relationship between standard data scenarios and candidate analysis strategies, providing a rich variety of analysis solutions. The strategy optimization module selects the preferred analysis strategies that meet the constraints according to the preset optimization goals (lowest computational complexity, minimum storage occupancy, and shortest response time). This not only improves the scientificity and rationality of the analysis strategy, but also optimizes the system performance under the resource constraints of the cloud platform. For example, when processing large-scale cost data, it can quickly select the optimal analysis strategy, reduce the waste of computing resources, and improve the operating efficiency of the system.

[0020] The data verification module performs multi-level verification on the standardized cost analysis results, including consistency verification, logic verification and traceability. The consistency verification removes abnormal data based on the preset cost index threshold range. The logic verification uses a directed acyclic graph to model the dependency relationship between various cost parameters to detect logical conflicts. The traceability unit records the generation path and blood relationship of the data. These measures fully guarantee the accuracy and reliability of the analysis results. If an abnormal analysis result is found, the problem can be quickly located through traceability, and the error can be corrected in time, providing reliable data support for cost consulting and reducing decision-making risks.

[0021] The dynamic adjustment module monitors the changes in data distribution in real time during the data cleaning process. When the data distribution offset exceeds the preset threshold, the strategy generation module is triggered to rebuild the candidate analysis strategy. This enables the system to adapt to the dynamic changes in data and always maintain the accuracy of the analysis results. For example, when the price of building materials in the market fluctuates greatly, resulting in changes in the distribution of cost data, the system can adjust the analysis strategy in time to reflect the latest market conditions and provide timely and effective suggestions for project cost control.

[0022] The distributed computing unit of the execution engine module divides the data subset into multiple data blocks and distributes them to the computing nodes of the cloud platform for parallel processing, which improves the data processing speed. The fault-tolerant unit adopts a checkpoint mechanism based on redundant calculations to save the intermediate state at preset time intervals. When a node fails, the calculation is restored from the most recent checkpoint, ensuring the continuity of data processing. This is crucial for processing large-scale and long-term cost data calculation tasks, ensuring the stable operation of the system in a complex computing environment.

[0023] The knowledge graph module constructs an entity relationship graph in the cost field, and presents various cost-related information in the form of a graph through entity extraction, relationship reasoning and graph update. This helps users to more intuitively and deeply understand the internal connections between cost data, tap into potential knowledge and rules, provide a more comprehensive and in-depth analysis perspective for cost consulting, and improve the industry's intelligence level. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a working principle diagram of the cloud platform-based cost consulting data collation and analysis system of the present invention; Figure 2 This is the workflow diagram of the data clustering module; Figure 3 This is the workflow diagram of the data verification module; Figure 4 The following is a working principle diagram of the execution engine module. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] See also Figure 1-4 The present invention provides a technical solution: a cost consulting data collation and analysis system based on a cloud platform, the system comprising: Data acquisition module: It obtains raw cost data from multiple heterogeneous data sources in real time. These data sources may include files in different formats, databases, and various business system interfaces. After obtaining the data, the raw cost data is standardized and converted into a unified format, and finally a structured data set is generated to facilitate subsequent analysis and processing.

[0027] Data clustering module: Dynamically cluster structured data sets according to preset classification dimensions. Through clustering operations, multiple data subsets are generated, and then the key feature vectors of each data subset are extracted to facilitate the subsequent construction of targeted analysis strategies.

[0028] Strategy generation module: Based on the key feature vectors of each data subset, multiple candidate analysis strategies are constructed. Each candidate analysis strategy contains data cleaning rules, association models, and optimization parameter combinations, providing multiple options for subsequent data processing and analysis.

[0029] Strategy optimization module: Calculate the estimated computational complexity, estimated storage occupancy, and estimated response time of each candidate analysis strategy based on the preset optimization goals. Through these calculations, select the optimal analysis strategy that meets the constraints to improve the system's operating efficiency and performance.

[0030] Execution engine module: performs data cleaning, association modeling and parameter optimization operations on the corresponding data subset according to the optimal analysis strategy, and finally generates standardized cost analysis results to provide strong data support for cost consulting.

[0031] Among them, the strategy generation module pre-stores the mapping relationship between multiple types of standard data scenarios and candidate analysis strategies. The optimization goals include minimizing computational complexity, minimizing storage occupancy, and minimizing response time.

[0032] The present invention will be further described below in conjunction with Examples 1 to 5: Embodiment 1: The data clustering module includes a dimension definition unit, a clustering algorithm unit, and a subset generation unit. The dimension definition unit defines the classification dimension based on the cost project attributes, time series characteristics, and cost composition factors. In actual applications, the cost project attributes include project type (such as residential, commercial, industrial, etc.), project scale (building area, investment amount, etc.); time series characteristics involve the project establishment time, construction period, completion time, etc.; cost composition factors include labor costs, material costs, equipment costs, etc. By comprehensively considering these factors to determine the classification dimension, the clustering results are more in line with actual business needs.

[0033] The clustering algorithm unit uses a hybrid clustering algorithm with adaptive weight allocation. When calculating the clustering weight of each data point, the formula is used. ; in, is the clustering weight of the jth data point, Indicates the similarity between a data point and a project attribute. For example, for a data point of a residential project, if its project type, scale and other attributes are highly similar to other data points in a cluster, then The value of is large; Represents the continuity score of a data point in a time series. If the data point is closely related to the previous and next data points in time and has good continuity, then The value of is higher. and To dynamically adjust the coefficients, according to different business scenarios and data characteristics, the values ​​of these two coefficients can be dynamically adjusted to balance the impact of item attribute similarity and time series continuity in clustering weight calculation. n is the total number of valid data points in the current clustering cluster, Represents the weighted composite score sum of all data points.

[0034] The subset generation unit iteratively divides the data points according to the clustering weights. First, the clusters are initialized and the data points are assigned to each cluster according to the initial clustering weights. Then, the clustering weight of each data point is continuously calculated and the data point is readjusted according to the weight until the clustering result is stable and no longer changes significantly, and finally a data subset is generated.

[0035] The key feature vector is a multidimensional vector extracted from the data subset generated by clustering, which can characterize the core business attributes of the subset. Its dimensions are determined by the preset classification dimensions (such as cost item attributes, time series characteristics, cost component factors) and the dynamic weight calculation results in the clustering algorithm. Specifically, it includes the following elements: Project attribute characteristics: such as project type (residential, commercial, industrial, etc.), project scale (building area, investment amount range), etc.

[0036] Time series characteristics: such as time continuity score (based on the closeness of the distribution of data points on the time axis), construction period length, completion time deviation, etc.

[0037] Cost structure characteristics: such as the proportion of labor costs, material cost fluctuation coefficient, ratio of equipment cost to total cost, etc.

[0038] Clustering weight feature: based on the similarity calculated in the clustering algorithm and temporal continuity score The weighted result , reflecting the comprehensive importance of data points in the clustering process.

[0039] The step of obtaining the key feature vector comprises: Step 1: Dynamically cluster the structured data set through the data clustering module to generate multiple data subsets.

[0040] Step 2: Perform statistical analysis on the data points in each data subset and extract specific indicators of the preset dimensions: Project attribute characteristics: the distribution ratio of project types in the statistical subset (such as residential types accounting for 100%), the mean or range of project scale (such as the mean building area of ​​128,000 square meters).

[0041] Time Series Features: Calculating Temporal Continuity Scores , for example, evaluating continuity by the standard deviation of the construction time intervals between adjacent data points (the smaller the standard deviation, the higher the score).

[0042] Cost composition characteristics: Calculate the average value and proportion of each cost factor. For example, the average labor cost proportion is 35%, and the material cost fluctuation coefficient (standard deviation / mean) is 0.15.

[0043] Clustering weight feature: extract the weight value of each data point in the clustering algorithm , and calculate the mean of the weights in the subset (such as 0.85).

[0044] Step 3: Combine the above statistical results into a multi-dimensional vector as the key feature vector of the data subset.

[0045] Embodiment 2: This embodiment describes in detail the workflow of the strategy generation module, which generates diversified candidate analysis strategies for different data subsets based on historical data and multiple models, thereby improving the accuracy and adaptability of the analysis strategies.

[0046] The strategy generation module includes a rule base unit, a model base unit, and a strategy construction unit. The rule base unit stores a set of abnormal data detection rules trained based on historical cost data. These rules are obtained through analysis and learning of a large amount of historical cost data. For example, through statistical analysis of various cost data such as labor costs and material costs in historical data, the reasonable fluctuation range of various costs under a specific project type and scale is determined. When new data exceeds this range, it can be judged as abnormal data. These rules can effectively identify errors, duplications, or unreasonable data in the data, providing a basis for subsequent data cleaning.

[0047] The model library unit stores linear regression models, Bayesian network models and neural network models. The linear regression model is suitable for analyzing the linear relationship between variables in the cost data, such as predicting the construction cost based on factors such as building area and number of floors; the Bayesian network model can handle uncertainty and complex dependencies between variables, and has advantages in analyzing cost risks; the neural network model is good at processing nonlinear and complex data patterns, and can be used to mine deep-level cost data features.

[0048] The strategy building unit matches candidate rules and models from the rule base unit and the model base unit according to the key feature vector of the data subset. For example, for a data subset containing a large amount of time series data and data features showing a certain linear trend, the strategy building unit may select a linear regression model as the association model and select rules related to time series data anomaly detection from the rule base unit. At the same time, the parameter optimization space is generated. Taking the linear regression model as an example, the parameter optimization space may include the range of regression coefficients, regularization parameters, etc. By constructing the parameter optimization space, the parameters of the model can be adjusted and optimized to improve the performance of the model and the accuracy of the analysis results, and finally construct a candidate analysis strategy.

[0049] Embodiment 3: This embodiment mainly illustrates the calculation method of the strategy optimization module and the data verification function of the system, which is to ensure that the selected analysis strategy has better performance and at the same time ensure the accuracy and reliability of the generated standardized cost analysis results.

[0050] In the strategy optimization module, the formula for calculating the estimated computational complexity is ; in, Indicates the estimated computational complexity, Indicates The execution time of each cleaning rule, for example, the time required to execute a rule to remove duplicate data when cleaning a certain data subset; Indicates The memory usage of an associated model, such as the memory size occupied by a linear regression model during operation. and are weight coefficients. The values ​​of these two weight coefficients can be adjusted according to the emphasis of the system on computational time and memory usage. Through this formula, the computational complexity of each candidate analysis strategy can be quantitatively evaluated, providing a basis for screening and optimizing analysis strategies.

[0051] The system also includes a data verification module for multi-level verification of standardized cost analysis results. The consistency verification unit removes the result data that exceeds the threshold based on the preset cost indicator threshold range. For example, in the construction project cost, the cost per square meter usually has a reasonable range, which is determined according to different regions, project types and other factors. If the cost per square meter of a project in the analysis result exceeds the preset threshold range, the consistency verification unit will remove this part of the data to ensure the numerical rationality of the analysis result.

[0052] The logic verification unit uses a directed acyclic graph (DAG) to model the dependencies between various cost parameters and detect logical conflicts in the result data. In the modeling process, the cost parameter set is first extracted. , these parameters may include building area, number of floors, labor costs, material costs, etc. Then, the conditional probability distribution between parameters is analyzed based on historical data , construct edge sets ,in is the probability threshold. For example, if historical data analysis shows that when the building area increases, the probability of the material cost increasing exceeds a certain threshold (such as 0.8), then an edge from the building area parameter node to the material cost parameter node is added to the edge set. and Generate a directed acyclic graph. Through this directed acyclic graph, the dependency relationship between cost parameters can be clearly displayed. When data inconsistent with this dependency relationship appears in the analysis results, it can be determined that there is a logical conflict, so the result data can be corrected or eliminated.

[0053] The traceability unit records the generation path and data lineage of each analysis result. This means that from the collection of raw data, to data processing and analysis, to the final generation of analysis results, every step in the entire process and the data source used are recorded in detail. For example, when a problem is found in a certain analysis result, the traceability unit can be used to trace back to which step of data processing has a deviation, or the raw data itself has an error, which is convenient for troubleshooting and result correction.

[0054] Embodiment 4: This embodiment focuses on the dynamic adjustment mechanism of the system and the fault-tolerant function of the execution engine module, which has the unique function of enabling the system to adapt to dynamic changes in data while ensuring the continuity and accuracy of data processing when encountering node failures during the calculation process.

[0055] The system includes a dynamic adjustment module to monitor data distribution changes in real time during data cleaning. The data distribution offset is calculated according to the following formula: : ; in, Indicates the frequency of occurrence of the i-th data feature in the current data stream, represents the benchmark frequency of the i-th data feature in the historical data set, is the total number of preset key features. For example, in a cost dataset, key features include the price of different materials, labor hours, etc. By counting the frequency of occurrence of these key features in the current data stream and comparing them with the benchmark frequency in the historical dataset, the data distribution offset is calculated. ( is the preset threshold), the strategy generation module is triggered to rebuild the candidate analysis strategy. This enables the system to detect changes in data distribution in a timely manner and adjust the analysis strategy according to the changes to ensure the accuracy of the analysis results.

[0056] The execution engine module includes a fault-tolerant unit, which uses a checkpoint mechanism based on redundant calculations. During system operation, the fault-tolerant unit saves intermediate states at preset time intervals. For example, at regular intervals (such as 10 minutes), the intermediate state information such as the processing progress of the data subset currently being processed and the completed calculation results are saved. When a node failure occurs, the system can resume the calculation from the most recent checkpoint instead of recalculating from scratch. This can greatly reduce the waste of computing time caused by node failures, ensure the continuity and accuracy of data processing, and improve the reliability and stability of the system.

[0057] Embodiment 5: The knowledge graph module includes an entity extraction unit, a relationship reasoning unit, and a graph update unit. The entity extraction unit extracts cost-related entities and attributes from unstructured text. Unstructured text may come from project documents, contract documents, industry reports, etc. For example, entities such as "project name", "construction unit", and "construction unit" are extracted from project documents, as well as attributes such as "project scale" and "cost amount". Through natural language processing technology, such as named entity recognition algorithm, the text is analyzed and processed to accurately identify these entities and attributes.

[0058] The relational reasoning unit uses a graph neural network model to learn implicit association rules between entities. Based on the constructed entity set, the graph neural network model is used to learn and reason about the relationship between entities. For example, by analyzing a large amount of project data and related text, the graph neural network model can find that there may be a cooperative relationship between "construction unit" and "construction unit", and there is an association relationship between "project name" and "cost amount". These implicit association rules are learned by the model and used to construct the entity relationship graph.

[0059] The graph update unit dynamically expands entity nodes and relationship edges based on new data. When new cost data is added to the system, the graph update unit first determines whether the new data contains new entities or relationships. If there is a new entity, such as a new project participant, a new entity node will be added to the graph; if there is a new relationship, such as a business relationship between two previously unrelated entities, the corresponding relationship edge will be added to the graph. In this way, the knowledge graph can be continuously updated and improved as new data is continuously added, always maintaining the latest reflection of cost field knowledge, and providing users with more accurate and comprehensive knowledge support.

[0060] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0061] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cost consulting data collation and analysis system based on a cloud platform, characterized in that: The system comprises: A data acquisition module is used to obtain original cost data from multiple heterogeneous data sources in real time, and perform format standardization processing on the original cost data to generate a structured data set; A data clustering module, used to dynamically cluster the structured data set according to a preset classification dimension, generate multiple data subsets, and extract key feature vectors of each data subset; A strategy generation module is used to construct multiple candidate analysis strategies based on the key feature vectors of each data subset. Each candidate analysis strategy includes data cleaning rules, association models, and optimization parameter combinations. The strategy optimization module is used to calculate the estimated computational complexity, estimated storage occupancy rate and estimated response time of each candidate analysis strategy according to the preset optimization goal, and select the preferred analysis strategy that meets the constraint conditions; An execution engine module, used to perform data cleaning, association modeling and parameter optimization operations on the corresponding data subset according to the preferred analysis strategy to generate a standardized cost analysis result; Among them, the strategy generation module pre-stores mapping relationships between multiple types of standard data scenarios and candidate analysis strategies, and the optimization goals include minimum computational complexity, minimum storage occupancy and shortest response time.

2. According to the cloud platform-based cost consulting data collation and analysis system of claim 1, it is characterized in that: The data clustering module comprises: Dimension definition unit, used to define classification dimensions according to cost item attributes, time series characteristics and cost component factors; The clustering algorithm unit is used to use a hybrid clustering algorithm with adaptive weight allocation to calculate the clustering weight of each data point according to the following formula: ； in, is the clustering weight of the jth data point, Indicates the similarity between the data point and the item attribute, Represents the continuity score of the data point in the time series, and is the dynamic adjustment coefficient; n is the total number of valid data points in the current cluster, represents the sum of weighted comprehensive scores of all data points; The subset generation unit is used to iteratively divide the data points according to the clustering weights to generate the data subsets.

3. According to the cloud platform-based cost consulting data collation and analysis system of claim 2, it is characterized in that: The strategy generation module includes: A rule base unit stores a set of abnormal data detection rules obtained through training based on historical cost data; A model library unit stores linear regression models, Bayesian network models and neural network models; A strategy building unit, used to match candidate rules and models from the rule base unit and the model base unit according to the key feature vector of the data subset, and generate a parameter optimization space , construct the candidate analysis strategy.

4. According to the cloud platform-based cost consulting data collation and analysis system of claim 3, it is characterized in that: The formula for calculating the estimated computational complexity in the strategy optimization module is: ； in, Indicates the estimated computational complexity, Indicates The execution time of each cleaning rule is Indicates The memory usage of the associated model, and is the weight coefficient.

5. According to the cloud platform-based cost consulting data collation and analysis system of claim 1, it is characterized in that: The system further comprises: The data verification module is used to perform multi-level verification on the standardized cost analysis results, specifically including: The consistency check unit removes the result data exceeding the threshold value based on the preset cost index threshold value range; The logic verification unit uses a directed acyclic graph (DAG) to model the dependency relationship between various cost parameters and detect logical conflicts in the result data; The traceability unit records the generation path and data lineage of each analysis result.

6. According to the cloud platform-based cost consulting data collation and analysis system of claim 5, it is characterized in that: The method for modeling the dependency relationship in the logic verification unit includes: Step A1: Extract cost parameter set ; Step A2: Analyze the conditional probability distribution between parameters based on historical data , construct edge sets ,in is the probability threshold; Step A3, according to and The directed acyclic graph is generated.

7. The cost consulting data sorting and analysis system based on a cloud platform according to claim 1 is characterized in that: The system further comprises: The dynamic adjustment module is used to monitor data distribution changes in real time during data cleaning and calculate the data distribution offset according to the following formula: : ； in, Indicates the frequency of occurrence of the i-th data feature in the current data stream, represents the benchmark frequency of the ith data feature in the historical data set, m is the total number of preset key features, is the preset threshold, if The strategy generation module is then triggered to reconstruct the candidate analysis strategy.

8. The cost consulting data sorting and analysis system based on a cloud platform according to claim 1 is characterized in that: The execution engine module includes: Distributed computing unit, used to split the data subset into multiple data blocks and distribute them to computing nodes of the cloud platform for parallel processing; The fault-tolerant unit adopts a checkpoint mechanism based on redundant calculations to save intermediate states at preset time intervals and resume calculations from the most recent checkpoint when a node fails.

9. The cost consulting data sorting and analysis system based on a cloud platform according to claim 1 is characterized in that: The system further comprises: The knowledge graph module is used to construct an entity relationship graph in the cost field, including: Entity extraction unit, extracting cost-related entities and attributes from unstructured text; The relational reasoning unit uses a graph neural network model to learn implicit association rules between entities; The graph update unit dynamically expands entity nodes and relationship edges based on new data.

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