Construction enterprise financial income and expenditure analysis system based on cloud platform

Abstract

The invention relates to the technical field of financial analysis, and discloses a construction enterprise financial income and expenditure analysis system based on a cloud platform, which is provided with a cross-platform data integration module, a real-time data stream processing module, an intelligent data processing module, a cloud resource dynamic scheduling module and a display module, data in the cross-platform data integration module is analyzed through the intelligent data processing module, so that project data of the construction enterprise is associated with financial data, and the accuracy of a financial income and expenditure condition analysis result of the construction enterprise is improved; besides, computing resources are dynamically distributed and adjusted through a cloud resource dynamic scheduling module according to the real-time requirement of the intelligent data processing module for executing analysis operation, and distribution and use of the cloud resources are optimized, so that the computing efficiency and the resource utilization efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of financial analysis, and in particular to a construction enterprise financial revenue and expenditure analysis system based on a cloud platform. Background Art

[0002] The financial revenue and expenditure of construction companies are quite different from those of other companies. Their main income comes from specific engineering projects, which are project-driven income structures. Each project has an independent contract and payment schedule. Income is usually paid in stages according to the project progress, including advance payments, interim payments, and completion settlement payments. It is phased and cyclical, which is different from the structure of other companies that are mainly based on product or service sales revenue. Therefore, the financial revenue and expenditure of construction companies have the characteristics of complex cost structure associated with projects, long financial cycles, high financial risks, and complex income recognition.

[0003] At present, the analysis of the financial income and expenditure of construction companies often focuses on the analysis of funds, without linking them to specific projects, resulting in low accuracy in cost forecasting and risk assessment. In addition, the financial data structure of construction companies is complex, the cycle is long, and there are many data sources. It is difficult to cope with the financial analysis of construction companies using traditional computing resources and calculation methods, and there is a problem of low analysis efficiency. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a construction enterprise financial revenue and expenditure analysis system based on a cloud platform to solve the current problems of low accuracy and low analysis efficiency of construction enterprise financial revenue and expenditure analysis results.

[0005] The present invention discloses a construction enterprise financial revenue and expenditure analysis system based on a cloud platform, the system comprises a cross-platform data integration module, a real-time data stream processing module, an intelligent data processing module, a cloud resource dynamic scheduling module and a display module; wherein,

[0006] The cross-platform data integration module is used to integrate the data of the construction company's ERP system, financial management system and project management system;

[0007] The real-time data stream processing module is used to process data from the construction project site and update it to the cross-platform data integration module in real time;

[0008] The intelligent data processing module is used to perform analytical operations on the data in the cross-platform data integration module through deep learning and big data analysis algorithms; wherein the analytical operations include cost forecasting, capital liquidity analysis and risk assessment;

[0009] The cloud resource dynamic scheduling module is used to dynamically allocate and adjust computing resources according to the real-time demand of the intelligent data processing module to perform analysis operations; wherein the computing resources include computing resources of the cloud platform;

[0010] The display module includes a multi-dimensional, interactive financial data visualization screen, which supports multi-project, multi-dimensional data display and real-time interaction.

[0011] Furthermore, the cross-platform data integration module includes a data acquisition interface, a data conversion unit, and a data synchronization unit; wherein,

[0012] The data collection interface is used to collect data from the construction company's ERP system, financial management system, and project management system;

[0013] The data conversion unit is used to convert the collected data into a unified format;

[0014] The data synchronization unit is used to synchronize and update data with the real-time data stream processing module in real time.

[0015] Furthermore, the intelligent data processing module includes a cost prediction unit, a capital liquidity analysis unit and a risk assessment unit; wherein,

[0016] The cost forecasting unit is used to perform cost forecasting analysis operations based on historical cost data and project progress;

[0017] The capital liquidity analysis unit is used to analyze the capital liquidity of the enterprise;

[0018] The risk assessment unit is used to assess financial risks and perform early warning functions based on risk results.

[0019] Furthermore, the intelligent data processing module also includes a natural language processing unit, which is used to automatically analyze and process unstructured data in construction contracts and financial reports, and assist the cost forecasting unit, capital liquidity analysis unit and risk assessment unit in performing corresponding analysis operations.

[0020] Furthermore, the cost forecasting unit performs a cost forecasting analysis operation based on the historical cost data and the project schedule, including:

[0021] Collect historical cost data and project progress data for construction projects;

[0022] Perform data cleaning and standardization on the collected data;

[0023] Split the data into training and testing sets;

[0024] Training cost prediction models based on Bayesian regression and reinforcement learning algorithms;

[0025] The cost data of construction companies is optimized and predicted through cost forecasting models.

[0026] Furthermore, the process of analyzing the capital liquidity of the enterprise by the capital liquidity analysis unit specifically includes:

[0027] Collect historical capital flow data and project progress data of construction companies;

[0028] Perform data cleaning and standardization on the collected data;

[0029] Build a capital flow network structure;

[0030] Analyze the capital liquidity of enterprises based on the capital flow network structure.

[0031] Furthermore, a capital flow network structure is constructed, and the capital liquidity of the enterprise is analyzed based on the capital flow network structure, including:

[0032] Construct a capital flow network. Assume that the capital flow network is a directed graph, including a node set representing different capital flow nodes and an edge set representing the direction and amount of capital flow. The feature vectors in the node set include account balance, account type and historical capital flow records, and the feature vectors in the edge set include capital flow amount, capital flow time and capital flow type.

[0033] Construct an adjacency matrix to represent the structure of the capital flow network;

[0034] Construct a degree matrix to represent the degree of each node;

[0035] Extract node features and edge features based on the capital flow network, and construct a capital flow feature matrix by combining the constructed adjacency matrix and degree matrix;

[0036] Analyze and predict the liquidity of a company's working capital based on the capital flow network and capital flow characteristic matrix.

[0037] Furthermore, the risk assessment unit assesses financial risks and performs early warning functions based on risk results, specifically including:

[0038] Collect multimodal data of construction enterprises, perform data cleaning and standardization operations, and perform multi-source data fusion; wherein the multimodal data includes historical capital flow data, project progress data, contract data, expenditure data, income data, and market data;

[0039] Extracting features from the multimodal data, the extracted features including time series features, financial indicator features, project progress features, contract features, and market data features;

[0040] Construct a project relationship network, obtain a capital flow network from the capital liquidity analysis unit, analyze the characteristics of the project relationship network and the capital flow network nodes and edges, and calculate the topological characteristics of the network;

[0041] Construct a risk assessment model by combining the long short-term memory network algorithm;

[0042] Conduct risk assessment on new financial data through risk assessment model to obtain risk score;

[0043] Set multi-level risk thresholds, trigger corresponding levels of warnings based on risk scores, and notify staff through one or more methods including email, text messages, and visual large-screen displays.

[0044] Furthermore, the cloud resource dynamic scheduling module includes a task monitoring unit, a resource allocation unit and a resource optimization unit; wherein,

[0045] The task monitoring unit is used to monitor the resource requirements of analytical operations in real time;

[0046] The resource allocation unit is used to dynamically allocate computing resources according to the resource requirements of the analysis operation;

[0047] The resource optimization unit is used to perform optimization operations on resource configuration.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention is provided with a cross-platform data integration module, a real-time data stream processing module, an intelligent data processing module, a cloud resource dynamic scheduling module and a display module. Among them, based on deep learning and big data analysis algorithms, the intelligent data processing module performs analysis operations on the data in the cross-platform data integration module, so as to associate the project data of the construction enterprise with the financial data, thereby improving the accuracy of the analysis results of the financial revenue and expenditure of the construction enterprise; in addition, the cloud resource dynamic scheduling module dynamically allocates and adjusts computing resources according to the real-time needs of the intelligent data processing module to perform analysis operations, and optimizes the allocation and use of cloud resources, thereby improving computing efficiency and resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of the economic application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0051] Figure 1 The present invention discloses a cloud platform-based construction enterprise financial revenue and expenditure analysis system. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0053] Embodiment 1

[0054] The present invention discloses a construction enterprise financial revenue and expenditure analysis system based on a cloud platform. Figure 1 , Figure 1 The present invention discloses a structure diagram of a cloud platform-based construction enterprise financial revenue and expenditure analysis system, wherein the system includes a cross-platform data integration module, a real-time data stream processing module, an intelligent data processing module, a cloud resource dynamic scheduling module, and a display module; wherein:

[0055] The cross-platform data integration module is used to integrate the data of the construction company's ERP system, financial management system and project management system;

[0056] The real-time data stream processing module is used to process data from the construction project site and update it to the cross-platform data integration module in real time.

[0057] Specifically, in an embodiment of the present invention, construction project site data changes frequently and is difficult to predict. Therefore, by collecting construction project site data in real time, and effectively processing the data through a real-time data stream processing module and then updating the data in real time to a cross-platform data integration module, the real-time nature of the data and the data processing efficiency can be improved.

[0058] The intelligent data processing module is used to perform analytical operations on the data in the cross-platform data integration module through deep learning and big data analysis algorithms; wherein the analytical operations include cost forecasting, capital liquidity analysis and risk assessment;

[0059] The cloud resource dynamic scheduling module is used to dynamically allocate and adjust computing resources according to the real-time demand of the intelligent data processing module to perform analysis operations; wherein the computing resources include computing resources of the cloud platform;

[0060] The display module includes a multi-dimensional, interactive financial data visualization screen, which supports multi-project, multi-dimensional data display and real-time interaction.

[0061] Specifically, the required financial data, project data and analysis result data are obtained from the cross-platform data integration module and the intelligent data processing module, and pre-processed and formatted for display on the front end, and the data is summarized, classified and counted to generate financial indicators and project data of different dimensions.

[0062] Use front-end frameworks (such as React, Vue.js, or Angular) to design the overall layout of the visualization screen, including the location and size of each chart and interactive control. Use data visualization libraries to draw various charts, including but not limited to bar charts, line charts, pie charts, scatter plots, and heat maps. These charts display financial data and project data of different dimensions, such as budget consumption, income and expenditure, project progress, cash flow, etc. Users can view the corresponding data by selecting different projects and data dimensions, and provide screening and filtering functions to filter data according to different conditions, such as time range, project type, capital flow, etc.

[0063] The interactive operations between the user and the visualization screen include, but are not limited to, setting the switching and overlay display of charts, the user selecting different types of charts to switch or overlaying multiple charts for comparative analysis.

[0064] And execute real-time data push to push the latest data to the front end to ensure that the data on the visualization screen is updated in real time.

[0065] Through the above operations, the financial income and expenditure of the construction company can be displayed more comprehensively and a more concise and clear display interface can be presented, thereby improving the efficiency and depth of users' understanding of the financial income and expenditure of the construction company and facilitating users' decision-making analysis.

[0066] Furthermore, the cross-platform data integration module includes a data acquisition interface, a data conversion unit, and a data synchronization unit; wherein,

[0067] The data collection interface is used to collect data from the construction company's ERP system, financial management system, and project management system;

[0068] The data conversion unit is used to convert the collected data into a unified format;

[0069] The data synchronization unit is used to synchronize and update data with the real-time data stream processing module in real time.

[0070] Furthermore, the intelligent data processing module includes a cost prediction unit, a capital liquidity analysis unit and a risk assessment unit; wherein,

[0071] The cost forecasting unit is used to perform cost forecasting analysis operations based on historical cost data and project progress;

[0072] The capital liquidity analysis unit is used to analyze the capital liquidity of the enterprise;

[0073] The risk assessment unit is used to assess financial risks and perform early warning functions based on risk results.

[0074] Furthermore, the intelligent data processing module also includes a natural language processing unit, which is used to automatically analyze and process unstructured data in construction contracts and financial reports, and assist the cost forecasting unit, capital liquidity analysis unit and risk assessment unit in performing corresponding analysis operations.

[0075] Furthermore, the cost forecasting unit performs a cost forecasting analysis operation based on the historical cost data and the project schedule, including:

[0076] Collect historical cost data and project progress data for construction projects;

[0077] Perform data cleaning and standardization on the collected data;

[0078] Split the data into training and testing sets;

[0079] Training cost prediction models based on Bayesian regression and reinforcement learning algorithms;

[0080] The cost data of construction companies is optimized and predicted through cost forecasting models.

[0081] Furthermore, the optimization and prediction of the cost data of the construction enterprise by the cost prediction model includes:

[0082] Bayesian Regression Layer:

[0083] y~N(Xβ,σ 2 I)

[0084] Among them, y is the target variable, that is, the predicted cost, X is the input feature matrix, which contains the historical cost data and progress data of the construction project, β is the coefficient vector of the Bayesian regression model, and σ 2 is the noise error of the Bayesian regression model, and N represents positive distribution;

[0085] C0 * (t+n)=E[C|X]

[0086] Among them, C0 * (t+n) is the initial cost prediction value, that is, the cost of the future time period t+n predicted by Bayesian regression. E[C|X] is the expected value of the cost C given the input feature X, that is, the initial prediction value.

[0087] Reinforcement Learning Layer:

[0088] s t =(C0 * (t+n),P(t))

[0089] R t =-|Cactua l(t+n)-C0 * (t+n)|-λ·Cost(a t )

[0090] a t = arg max a Q(s t ,a)

[0091] Q(s t ,a t )=Q(s t ,a t )+α[R t +γmax a′ Q(s t+1 ,a′)-Q(s t ,a t )]

[0092] C1 * (t+n)=E[C|X,a t ]

[0093] Among them, s t is a state variable, indicating the current cost and progress status, P(t) is the project progress data at the current time point t; a t is the action variable, indicating that in state s t The actions taken to adjust costs and resource allocation under t is the reward function, which represents the reward value at time point t and is used to measure the accuracy of cost prediction and resource utilization. actual (t+n) is the actual cost value, λ is the weight factor used to control the balance between prediction error and adjustment cost, Cost(a t ) indicates execution of action a t The cost, Q(s t ,a t ) is the Q function, representing the state s t Take action a t The expected cumulative reward value obtained, γ is the discount factor, which represents the present value weight of future rewards, and its value range is 0≤γ≤1. α is the learning rate, which is used to control the step size of Q function update, s t+1 is the state at the next time point t+1, max a Q(s t , a) is the maximum Q value obtained by taking action a at time point t, max a′ Q(s t+1 ,a′) is the maximum Q value obtained by taking the optimal action a′ at the next time point t+1, C1 * (t+n) is the cost prediction value adjusted by multi-level Bayesian reinforcement learning, E[C|X,at ] is given the input feature X and action a t The expected value of cost C under the circumstances, that is, the adjusted cost forecast value.

[0094] Furthermore, the process of analyzing the capital liquidity of the enterprise by the capital liquidity analysis unit specifically includes:

[0095] Collect historical capital flow data and project progress data of construction companies;

[0096] Perform data cleaning and standardization on the collected data;

[0097] Build a capital flow network structure;

[0098] Analyze the capital liquidity of enterprises based on the capital flow network structure.

[0099] Furthermore, a capital flow network structure is constructed, and the capital liquidity of the enterprise is analyzed based on the capital flow network structure, including:

[0100] Construct a capital flow network. Assume that the capital flow network is a directed graph, including a node set representing different capital flow nodes and an edge set representing the direction and amount of capital flow. The feature vectors in the node set include account balance, account type and historical capital flow records, and the feature vectors in the edge set include capital flow amount, capital flow time and capital flow type.

[0101] Construct an adjacency matrix to represent the structure of the capital flow network;

[0102] Construct a degree matrix to represent the degree of each node;

[0103] Extract node features and edge features based on the capital flow network, and construct a capital flow feature matrix by combining the constructed adjacency matrix and degree matrix;

[0104] Analyze and predict the liquidity of a company's working capital based on the capital flow network and capital flow characteristic matrix.

[0105] Furthermore, the risk assessment unit assesses financial risks and performs early warning functions based on risk results, specifically including:

[0106] Collect multimodal data of construction enterprises, perform data cleaning and standardization processing operations, and perform multi-source data fusion; wherein the multimodal data includes but is not limited to historical capital flow data, project progress data, contract data, expenditure data, income data and market data;

[0107] Extracting features from the multimodal data, the extracted features including time series features, financial indicator features, project progress features, contract features, and market data features;

[0108] Construct a project relationship network, obtain a capital flow network from the capital liquidity analysis unit, analyze the characteristics of the project relationship network and the capital flow network nodes and edges, and calculate the topological characteristics of the network;

[0109] Construct a risk assessment model by combining the long short-term memory network algorithm;

[0110] Conduct risk assessment on new financial data through risk assessment model to obtain risk score;

[0111] Set multi-level risk thresholds, trigger corresponding levels of warnings based on risk scores, and notify staff through one or more methods including email, text messages, and visual large-screen displays.

[0112] Furthermore, the cloud resource dynamic scheduling module includes a task monitoring unit, a resource allocation unit and a resource optimization unit; wherein,

[0113] The task monitoring unit is used to monitor the resource requirements of analytical operations in real time;

[0114] The resource allocation unit is used to dynamically allocate computing resources according to the resource requirements of the analysis operation;

[0115] The resource optimization unit is used to perform optimization operations on resource configuration.

[0116] In an embodiment of the present invention, the optimization operation of resource allocation is mainly carried out by collecting various data generated during the operation of the system in real time through monitoring tools, including the usage of resources such as CPU, memory, disk, network, etc., analyzing the collected historical data and real-time data, identifying the patterns and rules of resource utilization, and finding out the parts with low resource utilization and resource bottlenecks.

[0117] As a preferred implementation, the above process is implemented based on a reinforcement learning model, and specifically includes the following operations:

[0118] The setting status indicates the current resource usage and task load of the system, including CPU usage, memory usage, network bandwidth usage, etc.

[0119] Setting the action space indicates the resource allocation and adjustment strategies that the system can adopt, such as increasing or decreasing the CPU and memory quota of a task, adjusting the priority of the task, etc.

[0120] The reward function is defined based on the system's performance indicators and energy consumption indicators. The goal is to maximize system performance (such as task completion time, system throughput) and minimize resource consumption and energy consumption.

[0121] Use the collected data to train the reinforcement learning model so that the model can learn to choose the optimal resource allocation strategy under different conditions.

[0122] Dynamically adjust the system's resource configuration based on the strategy output by the reinforcement learning model.

[0123] Monitor the effect of resource allocation in real time and adjust the reinforcement learning model according to the actual effect. Through continuous feedback and adjustment, the model will gradually optimize the resource allocation strategy to achieve the best system performance and resource utilization. Resource optimization is an ongoing process. As the system environment and task load change, the resource optimization unit needs to continuously adjust and optimize the resource allocation strategy, regularly analyze the system's resource utilization, and update and adjust the reinforcement learning model to adapt to new workloads and resource requirements.

[0124] In this embodiment, by combining intelligent algorithms, the allocation and use of cloud resources are optimized, thereby improving resource utilization efficiency, reducing resource waste, and ensuring that the system can maintain high performance and stability under high load conditions.

[0125] Finally, it should be noted that the cloud-based construction enterprise financial revenue and expenditure analysis system disclosed in the embodiment of the present invention only discloses a preferred embodiment of the present invention and is only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A construction enterprise financial revenue and expenditure analysis system based on a cloud platform, characterized in that: The system includes a cross-platform data integration module, a real-time data stream processing module, an intelligent data processing module, a cloud resource dynamic scheduling module, a display module and a cloud platform; wherein, The cross-platform data integration module is used to integrate the data of the construction company's ERP system, financial management system and project management system; The real-time data stream processing module is used to process data from the construction project site and update it to the cross-platform data integration module in real time; The intelligent data processing module is used to perform analytical operations on the data in the cross-platform data integration module through deep learning and big data analysis algorithms; wherein the analytical operations include cost forecasting, capital liquidity analysis and risk assessment; The cloud resource dynamic scheduling module is used to dynamically allocate and adjust computing resources according to the real-time demand of the intelligent data processing module to perform analysis operations; wherein the computing resources include computing resources of the cloud platform; The display module includes a multi-dimensional, interactive financial data visualization screen, which supports multi-project, multi-dimensional data display and real-time interaction.

2. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 1 is characterized in that: The cross-platform data integration module includes a data acquisition interface, a data conversion unit, and a data synchronization unit; wherein, The data collection interface is used to collect data from the construction company's ERP system, financial management system, and project management system; The data conversion unit is used to convert the collected data into a unified format; The data synchronization unit is used to synchronize and update data with the real-time data stream processing module in real time.

3. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 1 is characterized in that: The intelligent data processing module includes a cost prediction unit, a capital liquidity analysis unit and a risk assessment unit; wherein, The cost forecasting unit is used to perform cost forecasting analysis operations based on historical cost data and project progress; The capital liquidity analysis unit is used to analyze the capital liquidity of the enterprise; The risk assessment unit is used to assess financial risks and perform early warning functions based on risk results.

4. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 3 is characterized in that: The intelligent data processing module also includes a natural language processing unit, which is used to automatically analyze and process unstructured data in construction contracts and financial reports, and assist the cost prediction unit, capital liquidity analysis unit and risk assessment unit in performing corresponding analysis operations.

5. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 4 is characterized in that: The cost forecasting unit performs cost forecasting analysis based on historical cost data and project progress, including: Collect historical cost data and project progress data for construction projects; Perform data cleaning and standardization on the collected data; Split the data into training and testing sets; Training cost prediction models based on Bayesian regression and reinforcement learning algorithms; The cost data of construction companies is optimized and predicted through cost forecasting models.

6. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 4 is characterized in that: The process of analyzing the capital liquidity of the enterprise by the capital liquidity analysis unit specifically includes: Collect historical capital flow data and project progress data of construction companies; Perform data cleaning and standardization on the collected data; Build a capital flow network structure; Analyze the capital liquidity of enterprises based on the capital flow network structure.

7. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 6 is characterized in that: Construct a capital flow network structure and analyze the capital liquidity of the enterprise based on the capital flow network structure, including: Construct a capital flow network. Assume that the capital flow network is a directed graph, including a node set representing different capital flow nodes and an edge set representing the direction and amount of capital flow. The feature vectors in the node set include account balance, account type and historical capital flow records, and the feature vectors in the edge set include capital flow amount, capital flow time and capital flow type. Construct an adjacency matrix to represent the structure of the capital flow network; Construct a degree matrix to represent the degree of each node; Extract node features and edge features based on the capital flow network, and construct a capital flow feature matrix by combining the constructed adjacency matrix and degree matrix; Analyze and predict the liquidity of a company's working capital based on the capital flow network and capital flow characteristic matrix.

8. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 7 is characterized in that: The risk assessment unit assesses financial risks and performs early warning functions based on risk results, specifically including: Collect multimodal data of construction enterprises, perform data cleaning and standardization operations, and perform multi-source data fusion; wherein the multimodal data includes historical capital flow data, project progress data, contract data, expenditure data, income data, and market data; Extracting features from the multimodal data, the extracted features including time series features, financial indicator features, project progress features, contract features, and market data features; Construct a project relationship network, obtain a capital flow network from the capital liquidity analysis unit, analyze the characteristics of the project relationship network and the capital flow network nodes and edges, and calculate the topological characteristics of the network; Construct a risk assessment model by combining the long short-term memory network algorithm; Conduct risk assessment on new financial data through risk assessment model to obtain risk score; Set multi-level risk thresholds, trigger corresponding levels of warnings based on risk scores, and notify staff through one or more methods including email, text messages, and visual large-screen displays.

9. The cloud platform-based construction enterprise financial revenue and expenditure analysis system according to claim 1 is characterized in that: The cloud resource dynamic scheduling module includes a task monitoring unit, a resource allocation unit and a resource optimization unit; wherein, The task monitoring unit is used to monitor the resource requirements of analytical operations in real time; The resource allocation unit is used to dynamically allocate computing resources according to the resource requirements of the analysis operation; The resource optimization unit is used to perform optimization operations on resource configuration.

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