Intelligent analysis and sharing system and method for financial industry of hydropower enterprise

Abstract

The invention provides a system and a method for intelligently analyzing and sharing business and financial of a hydropower enterprise, and relates to the technical field of hydropower enterprise data processing, comprising the following steps: collecting original index data from each business system and financial system of a target hydropower enterprise; carrying out preprocessing operation on the collected original index data to generate corresponding business and financial index data; performing trend analysis, anomaly detection and cost-benefit analysis on the business and financial index data based on a machine learning algorithm to generate a corresponding intelligent analysis result; according to an intelligent analysis result, a corresponding personalized business financial management strategy is formulated in combination with the actual demand of the target hydropower enterprise; and based on a user interaction sharing interface, displaying an intelligent analysis result and a personalized business and financial management strategy to a user side of a target water and power enterprise, thereby realizing comprehensive analysis, sharing and management of business and financial data of the water and power enterprise through an intelligent system, improving security during data sharing, and improving user experience. And the requirements of rapid decision making and efficient operation of hydropower enterprises are met.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower enterprise data processing, and in particular to a hydropower enterprise business and financial intelligent analysis and sharing system and method thereof. Background Art

[0002] Business and finance integration refers to the unified management of the business and financial activities of an enterprise in an information-based and systematic manner, aiming to achieve information sharing through information technology, and then integrate and manage business processes and financial processes in a unified manner to achieve the accuracy, timeliness, authenticity and completeness of business and financial information sharing and transmission. This integration not only strengthens information communication and sharing within the enterprise, but also significantly improves the work efficiency of the business and financial departments.

[0003] In actual operation, due to the decentralized deployment of information systems in various departments, different application software brands, databases, module architectures, and functional scopes, information islands and data barriers are common, and the supervision mechanism for business and financial sharing is difficult to improve. This makes the connectivity mechanism between business departments and financial departments incomplete, the degree of data concatenation is insufficient, and the value of data collaboration cannot be fully realized. At present, some hydropower companies have limited mastery and application of intelligent technologies when implementing business and financial integration. The promotion and application scope of artificial intelligence technology is limited, and the innovative construction of a financial sharing intelligent system has not been realized in accordance with the needs of accounting automation. This often leads to the obstruction of the comprehensive innovation and optimization of financial data and forms, and the inability to effectively improve the operational effectiveness of basic work such as financial sharing.

[0004] Therefore, it is necessary to provide a hydropower enterprise financial intelligent analysis and sharing system and method to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a hydropower enterprise business and financial intelligent analysis sharing system and method for solving the problems of information islands and data barriers, insufficient intelligence level, poor sharing security, low analysis efficiency and difficulty in meeting the rapid decision-making needs of enterprises in traditional hydropower enterprise business and financial intelligent analysis sharing systems. The present invention provides a hydropower enterprise financial intelligent analysis and sharing system, the system comprising: Data collection module, used to collect original indicator data from various business systems and financial systems of target hydropower enterprises; A data preprocessing module, used to perform preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data; An intelligent analysis module, used to perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm, and generate corresponding intelligent analysis results; A strategy formulation module, used to formulate corresponding personalized business and financial management strategies based on the intelligent analysis results and the actual needs of the target hydropower enterprise; The data sharing module is used to display the intelligent analysis results and personalized business and financial management strategies to the user end of the target hydropower enterprise based on a user interactive sharing interface.

[0006] Preferably, according to the technical requirements of the business systems and financial systems of the target hydropower enterprise, corresponding API interfaces or database connections are configured between the business systems and financial systems and data acquisition modules of the target hydropower enterprise.

[0007] Preferably, the data preprocessing module is used to perform preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data, specifically including: A data cleaning unit, used to identify and remove invalid, duplicate or erroneous records in the original indicator data, and generate corresponding first indicator data; A data conversion unit, used to unify the data format of the first indicator data, including converting the date format into a standard format, and converting the text description into a numerical code, to generate corresponding second indicator data; The data verification unit is used to verify the consistency and completeness of the second indicator data, fill in the missing data values, and generate the corresponding business and financial indicator data.

[0008] Preferably, the intelligent analysis module is used to perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm to generate corresponding intelligent analysis results, specifically including: Based on the long short-term memory network, the trend analysis of the business and financial indicator data is performed, and the information flow in the business and financial indicator data is controlled by introducing the forget gate, input gate, output gate and cell state, and the time series in the business and financial indicator data is memorized for a long time. The calculation formula of the forget gate is: In the formula, Indicates the current time The output of the forget gate is used to determine the last moment The cell state The business and financial indicator data information retained in the Indicates the current time The weight matrix of the forget gate; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of Represents the bias term of the forget gate; represents the activation function; The calculation formula of the input gate is: In the formula, Indicates the current time The output of the input gate; Indicates the current time Candidate cell states; and The weight matrices representing the input gate and candidate cell states respectively; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of and Represent the bias terms of the input gate and candidate cell state respectively; represents the activation function; represents the hyperbolic tangent function; The calculation formula for updating the cell state is: In the formula, Indicates the current time The cell state; Indicates the current time The output of the forget gate; Indicates the last moment The cell state; Indicates the current time The output of the input gate; Indicates the current time Candidate cell states; The calculation formula of the output gate is: In the formula, Indicates the current time The output of the output gate; represents the activation function; Represents the weight matrix of the output gate; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of Represents the bias term of the output gate; Indicates the current time The hidden state of represents the hyperbolic tangent function; Indicates the current time The cell state; According to the calculation results of the forget gate, input gate, cell state update and output gate, the information flow in the business and financial indicator data is controlled, and the time series in the business and financial indicator data is subjected to long-term memory and trend analysis to generate corresponding trend analysis results.

[0009] Preferably, the business and financial indicator data is subjected to anomaly detection based on a support vector machine, and the support vector machine is used as a binary classifier to distinguish normal data from abnormal data in the business and financial indicator data. The corresponding linear support vector machine decision function is calculated as follows: In the formula, Represents the decision function of the linear support vector machine, which is used to judge the input business and financial indicator data Category of; represents the number of support vectors; Indicates The coefficients of the support vectors; Indicates The labels of the support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; represents the intercept term of the support vector; ∑ represents the summation symbol; Based on the linear support vector machine decision function, the support vector machine optimization problem for abnormal detection of business and financial indicator data is determined, and the corresponding calculation formula is: In the formula, represents the number of support vectors; Indicates The coefficients of the support vectors; Indicates The coefficients of the support vectors; Indicates The labels of the support vectors; Indicates The labels of the support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; represents the regularization parameter; Indicates the minimum value operation; represents a constraint condition; ∑ represents a summation symbol; Determine the optimal support vector coefficient based on the support vector machine optimization problem , at this time, the classification interval of the business and financial indicator data is maximized; Normal data and abnormal data in the business and financial indicator data are distinguished according to the classification interval, anomaly detection of the business and financial indicator data is completed, and corresponding anomaly detection results are generated.

[0010] Preferably, when performing cost-benefit analysis on the business and financial indicator data, the initial cost of the target hydropower enterprise is set to The project period is , No. The cost inflow for the period is , the corresponding target hydropower enterprise internal benefit is The internal rate of return is ; The target hydropower enterprise internal benefits The calculation formula is: In the formula, represents the initial cost; Indicates the project duration; Indicates Cost inflows during the period; represents the internal benefits of the target hydropower enterprise; represents the internal rate of return of the target hydropower enterprise; ∑ represents the summation symbol; When the target hydropower enterprise internal benefits When the internal rate of return of the target hydropower enterprise is obtained , the corresponding solution equation is as follows: In the formula, represents the initial cost; Indicates the project duration; Indicates Cost inflows during the period; represents the internal rate of return of the target hydropower enterprise; ∑ represents the summation symbol; Solving the above equations yields the target hydropower enterprise internal rate of return: , complete the cost-benefit analysis of the business and financial indicator data, and generate corresponding cost-benefit analysis results; The trend analysis results, anomaly detection results and cost-benefit analysis results are statistically summarized to obtain intelligent analysis results of the business and financial indicator data.

[0011] Preferably, the personalized business and financial management strategy includes electricity price management strategy, cost control measures and equipment operation and maintenance optimization decision; After obtaining the personalized business and financial management strategy, it is necessary to conduct a risk assessment on the personalized business and financial management strategy and make corresponding adjustments based on the risk assessment results.

[0012] Preferably, the user interactive sharing interface is provided with security access rights, and the intelligent analysis results and personalized business and financial management strategies are only displayed to the user terminals that have successfully verified, and cannot be viewed by the user terminals that have failed the verification.

[0013] A method for intelligent analysis and sharing of business finance of a hydropower enterprise, the method comprising: Collect original indicator data from the business systems and financial systems of the target hydropower enterprises; Preprocessing the collected original indicator data to generate corresponding business and financial indicator data; Perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on machine learning algorithms to generate corresponding intelligent analysis results; According to the intelligent analysis results and in combination with the actual needs of the target hydropower enterprise, formulate corresponding personalized business and financial management strategies; Based on a user interactive sharing interface, the intelligent analysis results and personalized business and financial management strategies are displayed to the user end of the target hydropower enterprise.

[0014] Compared with the related art, the hydropower enterprise financial intelligent analysis and sharing system and method provided by the present invention have the following beneficial effects: The present invention can collect original indicator data from various business systems and financial systems of the target hydropower enterprise through a data acquisition module, a data preprocessing module, an intelligent analysis module, a strategy formulation module and a data sharing module; perform preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data; perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm to generate corresponding intelligent analysis results; formulate corresponding personalized business and financial management strategies based on the intelligent analysis results and in combination with the actual needs of the target hydropower enterprise; based on a user interactive sharing interface, the intelligent analysis results and personalized business and financial management strategies are displayed to the user end of the target hydropower enterprise, so that seamless connection and real-time sharing of business and financial data can be achieved through the intelligent analysis sharing system, thereby improving the management efficiency of the hydropower enterprise; and a multi-dimensional intelligent analysis report can be provided to provide users with scientific and accurate decision-making basis to meet the needs of enterprises for rapid decision-making; at the same time, the system of the present invention can realize the sharing and coordination of business and financial data of hydropower enterprises, break down information barriers, and improve data utilization; and by strengthening data security and privacy protection measures, the security and compliance of data sharing can be ensured, reducing the operating risks of hydropower enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a system block diagram of a hydropower enterprise financial intelligent analysis and sharing system of the present invention; Figure 2 It is a structural block diagram of a data preprocessing module of a hydropower enterprise financial intelligent analysis and sharing system of the present invention; Figure 3 The present invention is a flow chart of a method for intelligent analysis and sharing of business finances of a hydropower enterprise. DETAILED DESCRIPTION

[0016] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0017] Embodiment 1 like Figure 1 As shown, a hydropower enterprise financial intelligent analysis and sharing system, the system comprises: Data collection module, used to collect original indicator data from various business systems and financial systems of target hydropower enterprises; A data preprocessing module, used to perform preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data; An intelligent analysis module, used to perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm, and generate corresponding intelligent analysis results; A strategy formulation module, used to formulate corresponding personalized business and financial management strategies based on the intelligent analysis results and the actual needs of the target hydropower enterprise; The data sharing module is used to display the intelligent analysis results and personalized business and financial management strategies to the user end of the target hydropower enterprise based on a user interactive sharing interface.

[0018] It is understandable that the data acquisition module can efficiently and accurately collect various raw indicator data from the diversified business systems and complex financial systems of the target hydropower enterprises. These business systems may include power generation management, transmission scheduling, distribution operation and maintenance, water resources management, and marketing, while the financial system usually includes core modules such as general ledger management, cost accounting, asset management, and budget management. The data acquisition module can obtain key data from these systems in real time or on a scheduled basis by integrating data extraction technology to ensure the timeliness and integrity of the data.

[0019] The data preprocessing module can perform refined preprocessing operations on these raw indicator data, including data cleaning, data conversion, data aggregation, etc., so that the raw indicator data can be converted into structured and standardized business and financial indicator data to improve data quality.

[0020] The intelligent analysis module can conduct in-depth analysis of the pre-processed business and financial indicator data based on machine learning algorithms, including trend analysis, anomaly detection and cost-benefit analysis. Specifically, trend analysis can reveal the long-term changes in key indicators; anomaly detection can timely capture abnormal fluctuations or potential risks in the data; and cost-benefit analysis can evaluate the economic benefits of different business activities or projects, thereby greatly improving the efficiency and accuracy of data analysis, generating intelligent analysis results, and meeting the company's needs for rapid decision-making.

[0021] The strategy formulation module can customize personalized business and financial management strategies based on the output results of the intelligent analysis module, combined with the strategic goals, operating status and future plans of the target hydropower enterprise, using management science theory and practical experience. These strategies may involve cost control optimization, capital operation strategy adjustment, business structure optimization and other aspects, which can improve the overall operational efficiency and financial performance of the hydropower enterprise.

[0022] Finally, the data sharing module can realize the visualization of intelligent analysis results and personalized business and financial management strategies by building a user interactive sharing interface. This module not only supports multiple display forms such as charts and reports, but also provides flexible security access rights, thereby ensuring that users of the target hydropower enterprise can access relevant information on demand, promoting information flow and decision-making collaboration within the enterprise.

[0023] In the specific implementation process, according to the technical requirements of the business systems and financial systems of the target hydropower enterprise, corresponding API interfaces or database connections are configured between the business systems and financial systems and data acquisition modules of the target hydropower enterprise.

[0024] See also Figure 2 The data preprocessing module 200 is used to perform preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data, specifically including: The data cleaning unit 201 is used to identify and remove invalid, duplicate or erroneous records in the original indicator data to generate corresponding first indicator data; A data conversion unit 202, used to unify the data format of the first indicator data, including converting the date format into a standard format, and converting the text description into a numerical code, to generate corresponding second indicator data; The data verification unit 203 is used to verify the consistency and completeness of the second indicator data, fill in the missing data values, and generate the corresponding business and financial indicator data.

[0025] The data cleaning unit can efficiently identify and remove invalid, duplicate or erroneous records in the original indicator data based on data cleaning algorithms and technologies, and generate purer and more accurate first indicator data. These invalid or erroneous data may be caused by various reasons, such as equipment failure, human operation error, etc.

[0026] Since data from different sources often use different formats and standards, this often brings great inconvenience to subsequent data analysis and processing. The data conversion unit can convert the date format into a unified standard format and convert the text description into an easy-to-process numerical code through a series of standardized conversion rules, thereby generating second indicator data with a unified format and easy to analyze.

[0027] In the process of data conversion, inconsistent or missing data may occur. The data verification unit can timely discover and correct these problems through comparison and verification, fill in the missing data values, and ensure the accuracy and completeness of the final generated business and financial indicator data.

[0028] The intelligent analysis module is used to perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm to generate corresponding intelligent analysis results, specifically including: Based on the long short-term memory network, the trend analysis of the business and financial indicator data is performed, and the information flow in the business and financial indicator data is controlled by introducing the forget gate, input gate, output gate and cell state, and the time series in the business and financial indicator data is memorized for a long time. The calculation formula of the forget gate is: In the formula, Indicates the current time The output of the forget gate is used to determine the last moment The cell state The business and financial indicator data information retained in the Indicates the current time The weight matrix of the forget gate; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of Represents the bias term of the forget gate; represents the activation function; The calculation formula of the input gate is: In the formula, Indicates the current time The output of the input gate; Indicates the current time Candidate cell states; and The weight matrices representing the input gate and candidate cell states respectively; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of and Represent the bias terms of the input gate and candidate cell state respectively; represents the activation function; represents the hyperbolic tangent function; The calculation formula for updating the cell state is: In the formula, Indicates the current time The cell state; Indicates the current time The output of the forget gate; Indicates the last moment The cell state; Indicates the current time The output of the input gate; Indicates the current time Candidate cell states; The calculation formula of the output gate is: In the formula, Indicates the current time The output of the output gate; represents the activation function; Represents the weight matrix of the output gate; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of Represents the bias term of the output gate; Indicates the current time The hidden state of represents the hyperbolic tangent function; Indicates the current time The cell state; According to the calculation results of the forget gate, input gate, cell state update and output gate, the information flow in the business and financial indicator data is controlled, and the time series in the business and financial indicator data is subjected to long-term memory and trend analysis to generate corresponding trend analysis results.

[0029] It should be noted that in the process of trend analysis, the intelligent analysis module can control the information flow in the business and financial indicator data by introducing the forget gate, input gate, output gate and cell state. Specifically, the forget gate can determine the business and financial indicator data information retained in the cell state at the previous moment; the input gate can determine whether the new information at the current moment needs to be written into the cell state; the update of the cell state is based on the output results of the forget gate and input gate, as well as the candidate cell state; the output gate can determine the information in the cell state that is output as the hidden state at the current moment.

[0030] Through the above method, the intelligent analysis module can perform long-term memory and trend analysis on the time series in the business and financial indicator data to generate corresponding trend analysis results.

[0031] Based on the support vector machine, the business and financial indicator data is subjected to anomaly detection, and the support vector machine is used as a binary classifier to distinguish normal data from abnormal data in the business and financial indicator data. The corresponding linear support vector machine decision function is calculated as follows: In the formula, Represents the decision function of the linear support vector machine, which is used to judge the input business and financial indicator data Category of; represents the number of support vectors; Indicates The coefficients of the support vectors; Indicates The labels of the support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; represents the intercept term of the support vector; ∑ represents the summation symbol; Based on the linear support vector machine decision function, the support vector machine optimization problem for abnormal detection of business and financial indicator data is determined, and the corresponding calculation formula is: In the formula, represents the number of support vectors; Indicates The coefficients of the support vectors; Indicates The coefficients of the support vectors; Indicates The labels of the support vectors; Indicates The labels of the support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; represents the regularization parameter; Indicates the minimum value operation; represents a constraint condition; ∑ represents a summation symbol; Determine the optimal support vector coefficient based on the support vector machine optimization problem , at this time, the classification interval of the business and financial indicator data is maximized; Normal data and abnormal data in the business and financial indicator data are distinguished according to the classification interval, anomaly detection of the business and financial indicator data is completed, and corresponding anomaly detection results are generated.

[0032] In practical applications, anomaly detection can be performed on business and financial indicator data based on support vector machines (SVM). In this process, SVM can be used as a binary classifier to distinguish normal data from abnormal data in business and financial indicator data. By introducing the linear support vector machine decision function, the SVM optimization problem in anomaly detection of business and financial indicator data can be determined. By solving the above optimization problem, a set of optimal support vector coefficients can be determined to maximize the classification interval. Among them, the classification interval is an important indicator of SVM performance, which determines the model's generalization ability for unknown data.

[0033] Finally, the normal data and abnormal data in the business and financial indicator data can be distinguished according to the classification interval. Specifically, the input data can be substituted into the decision function, and its category can be judged according to the positive or negative output value. If the output value of the decision function is greater than 0, the input data is classified as normal data; if the output value of the decision function is less than or equal to 0, the input data is classified as abnormal data.

[0034] Through the above methods, the business and financial indicator data can be detected for anomalies and the corresponding anomaly detection results can be generated. These results are of great significance to the financial management and risk control of enterprises, and can help enterprises to timely discover and deal with potential financial risks.

[0035] When conducting cost-benefit analysis on the business and financial indicator data, the initial cost of the target hydropower enterprise is set as The project period is , No. The cost inflow for the period is , the corresponding target hydropower enterprise internal benefit is The internal rate of return is ; The target hydropower enterprise internal benefits The calculation formula is: In the formula, represents the initial cost; Indicates the project duration; Indicates Cost inflows during the period; represents the internal benefits of the target hydropower enterprise; represents the internal rate of return of the target hydropower enterprise; ∑ represents the summation symbol; When the target hydropower enterprise internal benefits When the internal rate of return of the target hydropower enterprise is obtained , the corresponding solution equation is as follows: In the formula, represents the initial cost; Indicates the project duration; Indicates Cost inflows during the period; represents the internal rate of return of the target hydropower enterprise; ∑ represents the summation symbol; Solving the above equations yields the target hydropower enterprise internal rate of return: , complete the cost-benefit analysis of the business and financial indicator data, and generate corresponding cost-benefit analysis results; The trend analysis results, anomaly detection results and cost-benefit analysis results are statistically summarized to obtain intelligent analysis results of the business and financial indicator data.

[0036] It is understandable that when conducting a cost-benefit analysis of the financial indicators, the cost-benefit of the target hydropower enterprise can be quantified first. Specifically, the initial cost of the target hydropower enterprise can be set as , which is the total investment before the project starts; and the project period is clearly defined as , that is, the entire cycle of the project from start to finish; determine the The cost inflow for the period is , that is, the capital investment of the project at each stage. The corresponding target hydropower enterprise internal rate of return is R, that is, the net income of the project after deducting the initial cost, and its calculation formula is: ,in, It represents the internal rate of return of the target hydropower enterprise, which is a key indicator of the project investment benefit. It should be noted that when the internal profit of the target hydropower enterprise When it is equal to 0, the internal rate of return of the target hydropower enterprise can be obtained. The solution equation is: By solving this equation, the accurate value of the internal rate of return r of the target hydropower enterprise can be obtained. At this point, the cost-benefit analysis of the business and financial indicator data is completed, and the corresponding cost-benefit analysis results can be generated.

[0037] Finally, the trend analysis results, anomaly detection results, and cost-benefit analysis results can be statistically summarized to obtain comprehensive intelligent analysis results of business and financial indicator data.

[0038] The personalized business and financial management strategy includes electricity price management strategy, cost control measures and equipment operation and maintenance optimization decision; After obtaining the personalized business and financial management strategy, it is necessary to conduct a risk assessment on the personalized business and financial management strategy and make corresponding adjustments based on the risk assessment results.

[0039] Among them, the personalized business and financial management strategy specifically includes electricity price management strategy, cost control measures, and equipment operation and maintenance optimization decision-making. Specifically, the electricity price management strategy can optimize the allocation and use of power resources and improve economic benefits through reasonable electricity price setting; cost control measures focus on the refined management and control of various operating costs to maximize cost-effectiveness; and equipment operation and maintenance optimization decisions focus on the efficient operation and maintenance of equipment to ensure stable equipment performance, extend service life, and reduce failure rates.

[0040] After obtaining personalized business and financial management strategies, in order to ensure their effectiveness and feasibility, these strategies can be systematically risk assessed. Risk assessment will comprehensively consider various factors such as market changes, and comprehensively identify and quantify the risks that the strategies may face. Based on the results of risk assessment, personalized business and financial management strategies can be adjusted and optimized accordingly.

[0041] The user interactive sharing interface is set with security access rights, and the intelligent analysis results and personalized business and financial management strategies are only displayed to the user end who has successfully verified, and the user end who has failed the verification cannot view them.

[0042] It is understandable that the user interactive sharing interface is equipped with a strict security access permission mechanism to fully consider the data security and privacy protection during sharing. This mechanism ensures that only authenticated and authorized users can access and view the intelligent analysis results and personalized business and financial management strategies. Specifically, when a user attempts to access this sensitive information, the system will first authenticate the user, including user name, password, biometrics and other methods. Only when the verification information provided by the user matches the pre-stored information in the system and meets the established security policy, will it be considered verified successfully and allowed to view the relevant content.

[0043] On the contrary, if the user fails to provide correct information during the verification process, or its access request does not meet the requirements of the security policy, it will be regarded as a verification failure. In this case, the system will immediately deny its access request and may trigger corresponding security alerts or logging mechanisms for subsequent security audits or tracking.

[0044] Therefore, this strict security access permission setting not only effectively protects the confidentiality and integrity of the data, but also improves the overall security and credibility of the system.

[0045] Embodiment 2 like Figure 3 As shown, a method for intelligent analysis and sharing of business and finance of a hydropower enterprise, the method comprising: S1, collect original indicator data from various business systems and financial systems of the target hydropower enterprises; S2, performing preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data; S3, performing trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm to generate corresponding intelligent analysis results; S4, formulating a corresponding personalized business and financial management strategy based on the intelligent analysis results and the actual needs of the target hydropower enterprise; S5, based on the user interactive sharing interface, presenting the intelligent analysis results and personalized business and financial management strategies to the user end of the target hydropower enterprise.

[0046] Through the introduction of the above embodiments, the present invention can collect original indicator data from various business systems and financial systems of target hydropower enterprises through the intelligent analysis and sharing system and method of hydropower enterprise business and finance, through data acquisition module, data preprocessing module, intelligent analysis module, strategy formulation module and data sharing module; preprocess the collected original indicator data to generate corresponding business and financial indicator data; perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on machine learning algorithms to generate corresponding intelligent analysis results; formulate corresponding personalized business and financial management strategies based on the intelligent analysis results and the actual needs of the target hydropower enterprise; and The mutual sharing interface displays the intelligent analysis results and personalized business and financial management strategies to the user end of the target hydropower enterprise, so that the seamless connection and real-time sharing of business and financial data can be achieved through the intelligent analysis sharing system, thereby improving the management efficiency of the hydropower enterprise; and it can provide multi-dimensional intelligent analysis reports to provide users with scientific and accurate decision-making basis to meet the needs of enterprises for rapid decision-making; at the same time, the system of the present invention can realize the sharing and coordination of the business and financial data of hydropower enterprises, break down information barriers, and improve data utilization; and by strengthening data security and privacy protection measures, it can ensure the security and compliance of data sharing, thereby reducing the operating risks of hydropower enterprises.

[0047] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0048] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0049] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A hydropower enterprise financial intelligent analysis and sharing system, characterized in that: The system comprises: Data collection module, used to collect original indicator data from various business systems and financial systems of target hydropower enterprises; A data preprocessing module, used to perform preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data; An intelligent analysis module, used to perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm, and generate corresponding intelligent analysis results; A strategy formulation module, used to formulate corresponding personalized business and financial management strategies based on the intelligent analysis results and the actual needs of the target hydropower enterprise; The data sharing module is used to display the intelligent analysis results and personalized business and financial management strategies to the user end of the target hydropower enterprise based on a user interactive sharing interface.

2. The hydropower enterprise financial intelligent analysis and sharing system according to claim 1 is characterized in that: According to the technical requirements of the business systems and financial systems of the target hydropower enterprise, corresponding API interfaces or database connections are configured between the business systems and financial systems and data acquisition modules of the target hydropower enterprise.

3. The intelligent analysis and sharing system for hydropower enterprise finance according to claim 1 is characterized in that: The data preprocessing module is used to perform preprocessing operations on the collected original indicator data to generate corresponding business and financial indicator data, specifically including: A data cleaning unit, used to identify and remove invalid, duplicate or erroneous records in the original indicator data, and generate corresponding first indicator data; A data conversion unit, used to unify the data format of the first indicator data, including converting the date format into a standard format, and converting the text description into a numerical code, to generate corresponding second indicator data; The data verification unit is used to verify the consistency and completeness of the second indicator data, fill in the missing data values, and generate the corresponding business and financial indicator data.

4. The hydropower enterprise financial intelligent analysis and sharing system according to claim 1 is characterized in that: The intelligent analysis module is used to perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on a machine learning algorithm to generate corresponding intelligent analysis results, specifically including: Based on the long short-term memory network, the trend analysis of the business and financial indicator data is performed, and the information flow in the business and financial indicator data is controlled by introducing the forget gate, input gate, output gate and cell state, and the time series in the business and financial indicator data is memorized for a long time. The calculation formula of the forget gate is: In the formula, Indicates the current time The output of the forget gate is used to determine the last moment The cell state The business and financial indicator data information retained in the Indicates the current time The weight matrix of the forget gate; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of Represents the bias term of the forget gate; represents the activation function; The calculation formula of the input gate is: In the formula, Indicates the current time The output of the input gate; Indicates the current time Candidate cell states; and The weight matrices representing the input gate and candidate cell states respectively; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of and Represent the bias terms of the input gate and candidate cell state respectively; represents the activation function; represents the hyperbolic tangent function; The calculation formula for updating the cell state is: In the formula, Indicates the current time The cell state; Indicates the current time The output of the forget gate; Indicates the last moment The cell state; Indicates the current time The output of the input gate; Indicates the current time Candidate cell states; The calculation formula of the output gate is: In the formula, Indicates the current time The output of the output gate; represents the activation function; Represents the weight matrix of the output gate; Indicates the last moment The hidden state of Indicates the current time Input business and financial indicator data; Indicates the last moment The hidden state of and the current moment Input business and financial indicator data The vector composed of represents the bias term of the output gate; Indicates the current time The hidden state of represents the hyperbolic tangent function; Indicates the current time The cell state; According to the calculation results of the forget gate, input gate, cell state update and output gate, the information flow in the business and financial indicator data is controlled, and the time series in the business and financial indicator data is subjected to long-term memory and trend analysis to generate corresponding trend analysis results.

5. The hydropower enterprise financial intelligent analysis and sharing system according to claim 4 is characterized in that: Based on the support vector machine, the business and financial indicator data is subjected to anomaly detection, and the support vector machine is used as a binary classifier to distinguish normal data from abnormal data in the business and financial indicator data. The corresponding linear support vector machine decision function is calculated as follows: In the formula, Represents the decision function of the linear support vector machine, which is used to judge the input business and financial indicator data Category of; represents the number of support vectors; Indicates The coefficients of the support vectors; Indicates labels of support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; represents the intercept term of the support vector; ∑ represents the summation symbol; Based on the linear support vector machine decision function, the support vector machine optimization problem for abnormal detection of business and financial indicator data is determined, and the corresponding calculation formula is: In the formula, represents the number of support vectors; Indicates The coefficients of the support vectors; Indicates The coefficients of the support vectors; Indicates labels of support vectors; Indicates labels of support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; Indicates Input business and financial indicator data corresponding to the support vectors; represents the regularization parameter; Indicates the minimum value operation; Indicates constraints; ∑ represents the summation symbol; Determine the optimal support vector coefficient based on the support vector machine optimization problem , at this time, the classification interval of the business and financial indicator data is maximized; Normal data and abnormal data in the business and financial indicator data are distinguished according to the classification interval, anomaly detection of the business and financial indicator data is completed, and corresponding anomaly detection results are generated.

6. The hydropower enterprise financial intelligent analysis and sharing system according to claim 5 is characterized in that: When conducting cost-benefit analysis on the business and financial indicator data, the initial cost of the target hydropower enterprise is set as The project period is , No. The cost inflow for the period is , the corresponding target hydropower enterprise internal benefit is The internal rate of return is ; The target hydropower enterprise internal benefits The calculation formula is: In the formula, represents the initial cost; Indicates the project duration; Indicates Cost inflows during the period; represents the internal benefits of the target hydropower enterprise; represents the internal rate of return of the target hydropower enterprise; ∑ represents the summation symbol; When the target hydropower enterprise internal benefits When the internal rate of return of the target hydropower enterprise is obtained , the corresponding solution equation is as follows: In the formula, represents the initial cost; Indicates the project duration; Indicates Cost inflows during the period; represents the internal rate of return of the target hydropower enterprise; ∑ represents the summation symbol; Solving the above equations yields the target hydropower enterprise internal rate of return: , complete the cost-benefit analysis of the business and financial indicator data, and generate corresponding cost-benefit analysis results; The trend analysis results, anomaly detection results and cost-benefit analysis results are statistically summarized to obtain intelligent analysis results of the business and financial indicator data.

7. The hydropower enterprise financial intelligent analysis and sharing system according to claim 1 is characterized in that: The personalized business and financial management strategy includes electricity price management strategy, cost control measures and equipment operation and maintenance optimization decision; After obtaining the personalized business and financial management strategy, it is necessary to conduct a risk assessment on the personalized business and financial management strategy and make corresponding adjustments based on the risk assessment results.

8. The hydropower enterprise financial intelligent analysis and sharing system according to claim 1 is characterized in that: The user interactive sharing interface is set with security access rights, and the intelligent analysis results and personalized business and financial management strategies are only displayed to the user end who has successfully verified, and the user end who has failed the verification cannot view them.

9. A method for intelligent analysis and sharing of business and finance of a hydropower enterprise, applied to a system for intelligent analysis and sharing of business and finance of a hydropower enterprise as claimed in any one of claims 1 to 8, the method comprising: Collect original indicator data from the business systems and financial systems of the target hydropower enterprises; Preprocessing the collected original indicator data to generate corresponding business and financial indicator data; Perform trend analysis, anomaly detection and cost-benefit analysis on the business and financial indicator data based on machine learning algorithms to generate corresponding intelligent analysis results; According to the intelligent analysis results and in combination with the actual needs of the target hydropower enterprise, formulate corresponding personalized business and financial management strategies; Based on a user interactive sharing interface, the intelligent analysis results and personalized business and financial management strategies are displayed to the user end of the target hydropower enterprise.

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