A method for analyzing the financial risks of mergers and acquisitions of wind power plants

Abstract

The present invention relates to the technical field of financial management, and discloses a method for analyzing the financial risks of mergers and acquisitions of wind power stations; the method comprises obtaining the operating characteristics of the wind power station, combining the operating characteristics into operating events, determining whether basic conditions for mergers and acquisitions are met, predicting the financial status value of the wind power station, determining whether to issue a financial risk warning prompt, identifying risk parameters from comprehensive financial data, and formulating a financial risk level; compared with the prior art, the present invention can prevent wind power stations with poor basic operating status from participating in subsequent analysis processes, thereby reducing the workload of merger and acquisition financial risk analysis, and at the same time can obtain the financial risk situation of the wind power station from multiple angles and in a real manner, thereby providing reliable and accurate data support for the financial risks of mergers and acquisitions of wind power stations, and improving the accuracy and efficiency of merger and acquisition financial risk analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of financial management, and more specifically, to a method for analyzing the financial risks of mergers and acquisitions of wind power stations. Background Art

[0002] A wind power station is a facility that converts wind energy into electrical energy. A wind power station converts the kinetic energy of wind into electrical energy through a series of wind turbines, so that the electrical energy can be connected to the power grid and provided to the user end for use. When acquiring a wind power station, it is necessary to comprehensively consider the operation and financial situation of the wind power station, so as to provide a basis for the subsequent financial risk analysis of the wind power station merger and acquisition, and accurately analyze the financial risks that the merger and acquisition may face.

[0003] The patent application with reference publication number CN111798297A discloses a method and device for early warning analysis of financial risks, which does not require manual analysis and intervention, can automatically use big data to analyze the financial risks of a single enterprise, improve the accuracy and comprehensibility of financial risk early warning, and can automatically conduct multi-dimensional cross-analysis on the financial risks of the enterprise, and use the linkage logic between financial indicators to discover risk anomalies in the financial development process of the enterprise, and build a multi-dimensional risk early warning indicator system, build rules for automated financial risk cross-analysis, and automatically warn of financial risks;

[0004] In the existing M&A financial risk analysis, all operating data and financial data before the M&A are uniformly collected and analyzed, and the M&A financial risk is comprehensively evaluated based on multiple different types of results analyzed. Although the evaluation and analysis effect of the M&A financial risk can be achieved, a large amount of different types of original data need to be collected and analyzed one by one, which makes the data not hierarchical during the financial risk analysis. This not only leads to a significant increase in the workload of financial risk analysis and the possibility of inaccurate analysis results, but also fails to identify obvious financial risks in advance, thereby reducing the analysis efficiency and accuracy of M&A financial risks.

[0005] In view of this, the present invention proposes a method for analyzing the financial risks of mergers and acquisitions of wind power stations to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for analyzing the financial risk of mergers and acquisitions of wind power stations, comprising:

[0007] S1: Obtain basic operation data of the wind power station, extract operation characteristics from the basic operation data, the operation characteristics include effective operation time and unit power generation, and combine the operation characteristics into operation events;

[0008] S2: Based on the status identification criteria, the operation event is identified, the event status of the operation event is identified, and the event status includes valid events and invalid events, and whether the basic conditions for merger and acquisition are met;

[0009] S3: If the basic conditions for merger and acquisition are met, the financial risk period of the wind power station is set, and the comprehensive financial data of the wind power station during the financial risk period is obtained. The comprehensive financial data includes asset-liability balance, effective power generation rate, transaction increase rate and investment return rate;

[0010] S4: Input the comprehensive financial data into the pre-trained financial risk prediction model to predict the financial status value of the wind power station and determine whether to issue a financial risk warning prompt;

[0011] S5: If a financial risk warning is issued, risk parameters are identified from the comprehensive financial data and a financial risk level is formulated based on the number of risk parameters.

[0012] Furthermore, the method for extracting the effective operating time and unit power generation includes:

[0013] All the original data of the wind power station are queried through the database, and the state types of the original data are identified one by one, and the original data with the state type of the valid state is recorded as the basic operation data, and i basic operation data are obtained;

[0014] Query the time when i basic operation data are stored and recorded one by one through the timestamp to obtain i recording times;

[0015] Arrange the i recording moments in chronological order, and record the duration from the first recording moment to the last recording moment as the effective running duration;

[0016] The text part and the numerical part of the i basic operation data are identified one by one by natural language processing technology, and the basic operation data whose text part is the power generation is recorded as the target data, and the numerical part of the target data is recorded as the sub-power generation, and the Power generation per unit;

[0017] Will The power generation of each unit is accumulated one by one and compared with the effective operation time to obtain the unit power generation;

[0018] The expression of unit power generation is:

[0019] ;

[0020] In the formula, is the unit power generation, For the The power generation capacity of each The effective running time.

[0021] Furthermore, the combination method of running events includes:

[0022] Filter out basic events with two event units from the database, and record the two event units as the first event unit and the second event unit respectively;

[0023] Importing the effective operation time and the unit power generation into the first event unit and the second event unit respectively, generating an operation time unit and a power generation unit;

[0024] The operation time unit is assigned a numerical number 1, and the power generation unit is assigned a numerical number 2, and an operation event is generated.

[0025] Furthermore, the state identification criteria are: when an abnormal unit appears, the state identification operation is stopped;

[0026] Methods for identifying valid events and invalid events include:

[0027] In ascending order, first compare the effective running time in the running time unit numbered 1 with the running time calibration value;

[0028] When the effective running time is less than the running time calibration value, the running time unit is recorded as an abnormal unit, and the running event is identified as an invalid event;

[0029] When the effective operation time is greater than or equal to the operation time calibration value, then the unit power generation in the power generation unit numbered 2 is compared with the power generation calibration value. When the unit power generation is less than the power generation calibration value, the power generation unit is recorded as an abnormal unit, and the operation event is identified as an invalid event.

[0030] When the effective operation time is greater than or equal to the operation time calibration value, and the unit power generation is greater than or equal to the power generation calibration value, the operation event is identified as a valid event;

[0031] The methods for determining whether the basic conditions for mergers and acquisitions are met include:

[0032] When the event status of the running event is a valid event, it is determined that the basic conditions for merger and acquisition are met;

[0033] When the event status of the running event is an invalid event, it is determined that the basic conditions for the merger and acquisition are not met.

[0034] Furthermore, the method for obtaining the effective power generation rate includes:

[0035] During the financial risk period, the power generation of the wind power station at each moment is queried through the database, and the duration from the moment when the power generation is greater than the power standard value for the first time to the moment when the power generation is less than the power standard value for the first time is recorded as the power interval duration;

[0036] Based on the duration of a power interval, mark the financial risk period. power time points, and query them one by one The power generation at each power point is obtained Actual power;

[0037] The maximum power generated by the wind power station can be found from the technical parameter table and recorded as the total power. The actual power of each power is compared with the total power in turn and the average value is calculated to obtain the effective power generation rate;

[0038] The expression of effective power generation rate is:

[0039] ;

[0040] In the formula, is the effective power generation rate, For the The actual power, is the total power.

[0041] Furthermore, the method for obtaining the transaction climbing rate includes:

[0042] Divide financial risk periods into The same length and continuous sales segments are identified, and the starting and ending time of each sales segment are marked. The starting time and The final moment;

[0043] Through the financial management system, the wind power stations are queried one by one The starting time and The power generation sales value at the end time is obtained Starting sales value and End point sales value;

[0044] Will The terminal sales values ​​are respectively After subtracting the starting sales value, and comparing it with the total sales value of the wind power station, we get Segment sales rate;

[0045] The expression for segment sales rate is:

[0046] ;

[0047] In the formula, For the Segment sales rate, For the End point sales value, For the Starting sales value, is the total sales value of the wind power station;

[0048] After subtracting the sales rates of two adjacent segments, we get The rate of increase in size and The individual climbing rates are accumulated and averaged to obtain the transaction climbing rate;

[0049] The expression of transaction climbing rate is:

[0050] ;

[0051] In the formula, is the transaction climbing rate, For the The rate of increase in height.

[0052] Further, methods for obtaining return on investment include:

[0053] Through the equity management system, the investment information of the wind power station is queried, and the investors in the investment information are identified. Investors;

[0054] Through the financial management system, we can query The expected return and actual return of each investor during the financial risk period, and The actual return and After comparing the expected returns, Individual return rate;

[0055] The expression of sub-return rate is:

[0056] ;

[0057] In the formula, For the The rate of return, For the The actual return, For the Expected return amount;

[0058] Will The individual returns are accumulated and averaged to obtain the return on investment;

[0059] The expression for return on investment is:

[0060] ;

[0061] In the formula, For the return on investment, For the Individual return rate.

[0062] Furthermore, the financial status value includes a safety status and a risk status;

[0063] The training methods of the financial risk prediction model include:

[0064] Pre-collecting multiple sets of comprehensive financial data and financial status values ​​corresponding to the comprehensive financial data;

[0065] The comprehensive financial data is converted into a corresponding set of feature vectors, the feature vectors are used as the input of the neural network model, the financial status value is converted into a label corresponding to the comprehensive financial data, the safety status is converted into 0, the risk status is converted into 1, and the financial status value corresponding to each set of comprehensive financial data is used as the output of the neural network model, the financial status value is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The neural network model is trained until the sum of the prediction errors reaches convergence and the training is stopped to obtain a financial risk prediction model;

[0066] The methods for determining whether to issue a financial risk early warning include:

[0067] When the output of the financial risk prediction model is 0, the predicted financial status value is a safe state, and it is determined that no financial risk warning prompt will be issued;

[0068] When the output of the financial risk prediction model is 1, the predicted financial status value is a risky state, and a financial risk warning prompt is issued.

[0069] Furthermore, the risk parameter identification method includes:

[0070] Compare the asset-operating-liability difference with the safety-operating-liability difference. When the asset-operating-liability difference is less than the safety-operating-liability difference, record the asset-operating-liability difference as a risk parameter.

[0071] Compare the effective power generation rate with the safe power generation rate. When the effective power generation rate is less than the safe power generation rate, record the effective power generation rate as a risk parameter.

[0072] Compare the transaction climbing rate with the safety climbing rate. When the transaction climbing rate is less than the safety climbing rate, record the transaction climbing rate as a risk parameter.

[0073] Compare the investment return rate with the safety rate of return. When the investment return rate is less than the safety rate of return, record the investment return rate as the risk parameter.

[0074] Furthermore, the financial risk level includes controllable level and uncontrollable level;

[0075] Methods for setting controllable and uncontrollable levels include:

[0076] Count the number of risk parameters marked as risk parameters and record them as risk value;

[0077] When the risk value is 1 or 2, a controllable level is established;

[0078] When the risk value is 3 or 4, an uncontrollable level is established.

[0079] The technical effects and advantages of the financial risk analysis method for wind power station mergers and acquisitions of the present invention are as follows:

[0080] The present invention obtains basic operation data of the wind power station, extracts operation features from the basic operation data, and combines the operation features into operation events. Based on the state recognition criteria, the state of the operation event is recognized, the event state of the operation event is recognized, and it is determined whether the basic conditions for merger and acquisition are met, the financial risk period of the wind power station is set, the comprehensive financial data of the wind power station in the financial risk period is obtained, the comprehensive financial data is input into a financial risk prediction model trained in advance, the financial state value of the wind power station is predicted, and it is determined whether to issue a financial risk early warning prompt, the risk parameters are identified from the comprehensive financial data, and the financial risk level is formulated according to the number of risk parameters; compared with the prior art, by combining the operation features into operation events and performing state recognition on the operation events By identifying the event status, the basic operating status of the wind power station can be analyzed to prevent wind power stations with poor basic operating status from participating in the subsequent analysis process, thereby avoiding a series of useless operations in the subsequent M&A financial risk analysis and reducing the workload of M&A financial risk analysis. At the same time, through the data collection of the wind power station's operating income and expenditure, power generation, duration performance and investment return, the financial risk situation of the wind power station can be obtained from multiple angles and in a real way, and the status value of the M&A financial risk can be predicted based on the prediction model, so as to analyze the possible financial risks of the M&A according to the predicted financial status value, providing reliable and accurate data support for the M&A financial risks of wind power stations and improving the accuracy and efficiency of M&A financial risk analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 A schematic diagram of a flow chart of a method for analyzing the financial risks of mergers and acquisitions of wind power stations provided in the first embodiment of the present invention;

[0082] Figure 2 A schematic diagram of a financial risk analysis system for mergers and acquisitions of wind power stations provided in the second embodiment of the present invention;

[0083] Figure 3A schematic diagram of a structural diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0084] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0085] Example 1: Please refer to Figure 1 As shown, the present embodiment provides a method for analyzing the financial risks of mergers and acquisitions of wind power stations, including:

[0086] S1: Obtain basic operation data of the wind power station, extract operation features from the basic operation data, and combine the operation features into operation events;

[0087] Basic operation data refers to data that can be used to preliminarily represent the basic operation status of a wind power station under daily working conditions, and serves as the data basis for identifying whether the wind power station maintains the most basic working status;

[0088] Since the basic operation data contains many types of information and is complex, in order to accurately and directly represent the real basic operation status of the wind power station, it is necessary to extract the operation features from the basic operation data so that the operation features can be used as a direct representation of the basic operation status of the wind power station and as the basis for the subsequent construction of operation events;

[0089] Operation characteristics include effective operation time and unit power generation; effective operation time refers to the total operation time of the wind power station in effective operation state, and unit power generation refers to the amount of electricity generated by the wind power station per unit time;

[0090] The methods for extracting effective operating time and unit power generation include:

[0091] All the original data of the wind power station are queried through the database, and the state type of the original data is identified one by one, and the original data with a valid state type is recorded as the basic operation data, and i basic operation data are obtained; the state type is used to indicate whether the original data is valid and can be used as the data for subsequent operation feature extraction. The state type includes a valid state and an invalid state. The valid state indicates that the original data can be used as the object of subsequent operation feature extraction, and the invalid state indicates that the original data cannot be used as the object of subsequent operation feature extraction;

[0092] Query the time when i basic operation data are stored and recorded one by one through the timestamp to obtain i recording times;

[0093] Arrange the i recording moments in chronological order, and record the duration from the first recording moment to the last recording moment as the effective running duration;

[0094] The text part and the numerical part of the i basic operation data are identified one by one through natural language processing technology, and the basic operation data with the text part as the power generation is recorded as the target data, and the numerical part of the target data is recorded as the sub-power generation, and the The text part and the number part are used to independently represent the text and numbers in the basic operation data, and serve as the direct object of subsequent operation feature extraction;

[0095] Will The power generation of each unit is accumulated one by one and compared with the effective operation time to obtain the unit power generation;

[0096] The expression of unit power generation is:

[0097] ;

[0098] In the formula, is the unit power generation, For the The power generation capacity of each The effective running time.

[0099] When the operation characteristics are obtained, the operation characteristics at this time can provide a data basis for the basic operation status of the wind power station. In order to comprehensively and accurately evaluate the basic operation status of the wind power station through the operation characteristics, it is necessary to combine the scattered operation characteristics into operation events so that the operation events can comprehensively represent the basic operation status of the wind power station from the two aspects of operation time and power generation.

[0100] The combined methods of running events include:

[0101] A basic event with two event units is selected from the database, and the two event units are recorded as the first event unit and the second event unit respectively; a basic event refers to a blank event outline without substantial content, and serves as the basis for constructing subsequent running events, and an event unit is used to limit the position of importing running features, and serves as the smallest component unit of a basic event;

[0102] Importing the effective operation time and the unit power generation into the first event unit and the second event unit respectively, generating an operation time unit and a power generation unit;

[0103] The operation time unit is assigned a numerical number 1, and the power generation unit is assigned a numerical number 2, and an operation event is generated.

[0104] It should be noted that when combined into operation events, the operation events at this time can not only have evaluation data on the basic operation status of the wind power station, but also can number the data in the operation events separately so that the numbers can be used as the basis for subsequent judgments.

[0105] S2: Based on the state identification criteria, the operation event is identified, the event state of the operation event is identified, and whether the basic conditions for merger and acquisition are met;

[0106] When combined into operation events, it is necessary to analyze and identify the good or bad basic operation status of the wind power station reflected by the operation events, and express the results of the analysis and identification through event status;

[0107] The event status includes valid events and invalid events; a valid event means that the operation characteristics in the operation event can indicate that the basic operation status of the wind power station is in a good state, and the wind power station at this time can enter the subsequent analysis operation; an invalid event means that the operation characteristics in the operation event can indicate that the basic operation status of the wind power station is in a poor state, and the wind power station at this time cannot enter the subsequent analysis operation;

[0108] When identifying the event status of a running event, it is necessary to identify it under certain criteria to ensure that the event status of the running event can be accurately and quickly identified. Therefore, it is necessary to use the status identification criteria for identification;

[0109] The state recognition criterion is: when an abnormal unit appears, the state recognition operation is stopped; this can not only accurately identify abnormal phenomena in operation events, but also avoid the extra burden of continuing to identify after the abnormal unit appears, thus improving the efficiency of state recognition;

[0110] Methods for identifying valid events and invalid events include:

[0111] In ascending order, first compare the effective running time in the running time unit numbered 1 with the running time calibration value;

[0112] When the effective operation time is less than the operation time calibration value, the effective operation time of the wind power station does not reach the minimum value under the normal basic operation state, and the operation time unit is recorded as an abnormal unit, and the operation event is identified as an invalid event;

[0113] When the effective operation time is greater than or equal to the operation time calibration value, the wind power station has a large span of effective operation time, and then the unit power generation in the power generation unit numbered 2 is compared with the power generation calibration value;

[0114] When the unit power generation is less than the power generation calibration value, the power generation of the wind power station per unit time does not reach the minimum value under the normal basic operation state, then the power generation unit is recorded as an abnormal unit, and the operation event is identified as an invalid event;

[0115] When the effective operating time is greater than or equal to the operating time calibration value, and the unit power generation is greater than or equal to the power generation calibration value, the effective operating time and unit power generation of the wind power station have reached the minimum value under the normal basic operating state, and the operating event is identified as a valid event.

[0116] After obtaining the event status of the operation event, it can be judged whether the wind power station meets the basic conditions for merger and acquisition according to the event status. The basic conditions for merger and acquisition are the prerequisites for conducting the financial risk analysis of the wind power station merger and acquisition. Only when the basic conditions for merger and acquisition are met can the subsequent financial risk analysis of merger and acquisition be carried out;

[0117] The methods for determining whether the basic conditions for mergers and acquisitions are met include:

[0118] When the event status of the operation event is a valid event, the basic operation status of the wind power station can meet the basic requirements for subsequent merger and acquisition financial risk analysis, and it is determined that the basic conditions for merger and acquisition are met;

[0119] When the event status of the operation event is an invalid event, the basic operation status of the wind power station cannot meet the basic requirements for subsequent merger and acquisition financial risk analysis, and it is determined that the basic conditions for merger and acquisition are not met.

[0120] S3: If the basic conditions for merger and acquisition are met, the financial risk period of the wind power station is set, and the comprehensive financial data of the wind power station during the financial risk period is obtained. The comprehensive financial data includes asset-liability balance, effective power generation rate, transaction increase rate and investment return rate;

[0121] The financial risk period refers to the maximum span of the data collection time required for the wind power station when conducting the M&A financial risk analysis. It can provide a time range for the data collection of the wind power station, which can not only eliminate the data that is too old, but also ensure that there is enough data to meet the needs of the M&A financial risk analysis;

[0122] When setting the financial risk period, the current time is taken as the end point, and the first raw data recording time of the wind power station is taken as the start point, so that the period from the start point to the end point can be recorded as the financial risk period;

[0123] Comprehensive financial data refers to the data that can affect the financial risk level of the wind power station after the merger and acquisition during the financial risk period. At the same time, comprehensive financial data can also show the various changes of the wind power station during the financial risk period;

[0124] Comprehensive financial data includes asset-liability balance, effective power generation rate, transaction growth rate and return on investment;

[0125] The asset-liability difference refers to the difference between the assets obtained by the wind power station through operation during the financial risk period and the assets invested in production, which can represent the effective profit of the wind power station during the financial risk period. The larger the asset-liability difference is, the more assets the wind power station generates in revenue than the assets invested during the financial risk period, and the lower the financial risk of the wind power station is, and the better the operating status is. The asset-liability difference is obtained by querying the total revenue value and total investment value of the wind power station during the financial risk period, and then subtracting the total revenue value from the total investment value.

[0126] The effective power generation rate refers to the ratio of the actual power generation of a wind power station to the total installed power during the financial risk period, which can be used to indicate the power generation of a wind power station during the financial risk period. The greater the effective power generation rate, the greater the ratio of the actual power generation of a wind power station to the total installed power during the financial risk period. At this time, the financial risk of the wind power station is lower and the operating status is better.

[0127] Methods for obtaining effective power generation rate include:

[0128] During the financial risk period, the power generation of the wind power station at each moment is queried through the database, and the duration from the moment when the power generation is greater than the power standard value for the first time to the moment when the power generation is less than the power standard value for the first time is recorded as the power interval duration; the power standard value refers to the minimum power value at which the power generation of the wind power station can be normally recorded and effectively connected to the grid for conversion, which is used as the power basis for obtaining the power interval duration to ensure that the duration span corresponding to the power interval duration is not too large or too small;

[0129] Based on the duration of a power interval, mark the financial risk period. power time points, and query them one by one The power generation at each power point is obtained Actual power;

[0130] The maximum power generated by the wind power station can be found from the technical parameter table and recorded as the total power. The actual power of each power is compared with the total power in turn and the average value is calculated to obtain the effective power generation rate;

[0131] The expression of effective power generation rate is:

[0132] ;

[0133] In the formula, is the effective power generation rate, For the The actual power, is the total power.

[0134] The transaction climbing rate refers to the rate of increase in the transaction sales of the wind power station's power generation during the financial risk period, which can be used to indicate the activity of the wind power station in the market. The greater the transaction climbing rate, the greater the rate of increase in the transaction sales of the wind power station's power generation during the financial risk period, and the lower the financial risk of the wind power station, and the better the operating status;

[0135] The methods for obtaining the transaction climbing rate include:

[0136] Divide financial risk periods into The same length and continuous sales segments are identified, and the starting and ending time of each sales segment are marked. The starting time and End time; the same length and continuous sales segments can ensure the continuity of each sales segment on the timeline, and also ensure that the duration of each sales segment is equal, thus improving the consistency of the subsequent sales value collection time span;

[0137] Through the financial management system, the wind power stations are queried one by one The starting time and The power generation sales value at the end time is obtained Starting sales value and End point sales value;

[0138] Will The terminal sales values ​​are respectively After subtracting the starting sales value, and comparing it with the total sales value of the wind power station, we get The total sales value of a wind power station refers to the maximum power generation sales value pre-set during the financial risk period, which can be used as the final sales target during the financial risk period;

[0139] The expression for segment sales rate is:

[0140] ;

[0141] In the formula, For the Segment sales rate, For the End point sales value, For the Starting sales value, is the total sales value of the wind power station;

[0142] After subtracting the sales rates of two adjacent segments, we get The rate of increase in size and The individual climbing rates are accumulated and averaged to obtain the transaction climbing rate;

[0143] The expression of transaction climbing rate is:

[0144] ;

[0145] In the formula, is the transaction climbing rate, For the The rate of increase in height.

[0146] The return on investment refers to the rate of achievement of the return on investment given to investors by a wind power station during the investment return cycle within the financial risk period, which can be used to indicate the return on investment of a wind power station. The greater the return on investment, the greater the rate of achievement of the return on investment given to investors during the investment return cycle within the financial risk period, and the lower the financial risk of the wind power station, and the better the operating status;

[0147] Methods for obtaining return on investment include:

[0148] Through the equity management system, the investment information of the wind power station is queried, and the investors in the investment information are identified. Investors;

[0149] Through the financial management system, we can query The expected return and actual return of each investor during the financial risk period, and The actual return and After comparing the expected returns, Individual return rate;

[0150] The expression of sub-return rate is:

[0151] ;

[0152] In the formula, For the The rate of return, For the The actual return, For the Expected return amount;

[0153] Will The individual returns are accumulated and averaged to obtain the return on investment;

[0154] The expression for return on investment is:

[0155] ;

[0156] In the formula, For the return on investment, For the Individual rate of return.

[0157] S4: Input the comprehensive financial data into the pre-trained financial risk prediction model to predict the financial status value of the wind power station and determine whether to issue a financial risk warning prompt;

[0158] After obtaining the comprehensive financial data of the wind power station, the comprehensive power generation data can be input into the pre-trained financial risk prediction model, so that the financial risk prediction model can accurately and quickly predict the financial status value of the wind power station in the financial risk period according to the input comprehensive financial data, and express the financial risk situation of the wind power station according to the different financial status values, thereby providing a judgment basis for the historical financial risk situation of the wind power station, so as to better evaluate the merger and acquisition risks of the wind power station;

[0159] The financial status value is used to indicate the state of the wind power station in which financial risks and operational crises occur during the financial risk period. The financial status value includes the safety status and the risk status. The safety status indicates that the probability of the wind power station in which financial risks and operational crises occur during the financial risk period is low, and the risk status indicates that the probability of the wind power station in which financial risks and operational crises occur during the financial risk period is high. The financial status value is obtained by collecting a large number of historical asset-liability differences, effective power generation rates, transaction climbing rates and investment return rates in the safety status and risk status.

[0160] The financial risk prediction model is a model that can predict the financial status value after training the neural network model, and can thus provide a prediction effect for the financial risk analysis of the merger and acquisition of wind power stations;

[0161] The training methods of the financial risk prediction model include:

[0162] Pre-collecting multiple sets of comprehensive financial data and financial status values ​​corresponding to the comprehensive financial data;

[0163] The comprehensive financial data is converted into a corresponding set of feature vectors, and the feature vectors are used as the input of the neural network model. The financial status values ​​are converted into labels corresponding to the comprehensive financial data. For example, the safety status is converted into 0, and the risk status is converted into 1. The financial status value corresponding to each set of comprehensive financial data is used as the output of the neural network model. The financial status value is used as the prediction target, and minimizing the sum of prediction errors of all training data is used as the training target. The neural network model is trained until the sum of prediction errors converges and the training is stopped to obtain a financial risk prediction model.

[0164] Exemplarily, the financial risk prediction model is any one of a CNN neural network model or AlexNet;

[0165] The calculation formula for the prediction error is:

[0166] ;

[0167] In the formula, is the prediction error, is the group number of the eigenvector; For the The predicted state value corresponding to the group feature vector, For the The actual state value corresponding to the group training data.

[0168] After the financial status value is predicted, it can be determined whether to issue an early warning for the merger and acquisition of the wind power station based on the predicted financial status value, thereby providing a timely early warning effect for possible merger and acquisition financial risks;

[0169] The methods for determining whether to issue a financial risk early warning include:

[0170] When the output of the financial risk prediction model is 0, the predicted financial status value is a safe state, indicating that the wind power station is in good operation during the financial risk period, and the probability of financial risk after the merger of the wind power station is low, so it is determined that no financial risk warning will be issued;

[0171] When the output of the financial risk prediction model is 1, the predicted financial status value is a risky state, indicating that the operation of the wind power station during the financial risk period is poor, and the probability of financial risk after the merger of the wind power station is high, and a financial risk warning prompt is issued.

[0172] S5: If a financial risk warning is issued, risk parameters are identified from the comprehensive financial data and a financial risk level is formulated based on the number of risk parameters;

[0173] Risk parameters refer to specific data in the comprehensive financial data that will cause the wind power station to have abnormal risk conditions during the financial risk period, and serve as data for the subsequent representation of the specific financial risk conditions of the wind power station;

[0174] Methods for identifying risk parameters include:

[0175] Compare the asset-liability difference with the safe liability difference; the safe liability difference refers to the minimum value of the asset-liability difference when the wind power station does not issue a financial risk warning during the financial risk period, which can provide a basis for distinguishing whether the asset-liability difference is a risk parameter;

[0176] When the asset-liability difference is less than the safety-liability difference, the asset-liability difference of the wind power station in the financial risk period has not reached the minimum value, and the asset-liability difference is recorded as a risk parameter;

[0177] Compare the effective power generation rate with the safe power generation rate; the safe power generation rate refers to the minimum value of the effective power generation rate when the wind power station does not issue a financial risk warning during the financial risk period, which can provide a basis for distinguishing whether the effective power generation rate is a risk parameter;

[0178] When the effective power generation rate is less than the safe power generation rate, the effective power generation rate of the wind power station in the financial risk period has not reached the minimum value, and the effective power generation rate is recorded as a risk parameter;

[0179] Compare the transaction climbing rate with the safety climbing rate; the safety climbing rate refers to the minimum value of the transaction climbing rate when the wind power station does not issue a financial risk warning during the financial risk period, which can provide a basis for distinguishing whether the transaction climbing rate is a risk parameter;

[0180] When the transaction climbing rate is less than the safety climbing rate, the transaction climbing rate of the wind power station in the financial risk period has not reached the minimum value, and the transaction climbing rate is recorded as a risk parameter;

[0181] Compare the investment return rate with the safety return rate; the safety return rate refers to the minimum investment return rate when the wind power station does not issue a financial risk warning during the financial risk period, which can provide a basis for distinguishing whether the investment return rate is a risk parameter;

[0182] When the investment return rate is less than the safety return rate, the investment return rate of the wind power station in the financial risk period has not reached the minimum value, and the investment return rate is recorded as a risk parameter.

[0183] The financial risk level is used to indicate the severity of abnormal financial risk situations in wind power stations that have issued financial risk warnings, thereby distinguishing financial risk situations of different severity levels. The financial risk level includes controllable level and uncontrollable level; the severity of abnormal financial risk situations corresponding to the controllable level is greater than the severity of abnormal financial risk situations corresponding to the uncontrollable level;

[0184] Methods for setting controllable and uncontrollable levels include:

[0185] Count the number of risk parameters marked as risk parameters and record them as risk value;

[0186] When the risk value is 1 or 2, the wind power station has 1 or 2 abnormal data in the financial risk period. The financial risk situation is controllable at this time, and the severity of the abnormal situation is average, so a controllable level is formulated;

[0187] When the risk value is 3 or 4, the wind power station has 3 or 4 abnormal data in the financial risk period. The financial risk situation is uncontrollable at this time. The severity of the abnormal situation is serious, so an uncontrollable level is formulated.

[0188] It should be noted that when a controllable level is formulated, it means that the financial risks after the merger and acquisition of the wind power station are within the controllable range, and the wind power station needs to carefully choose whether to carry out the merger and acquisition. When an uncontrollable level is formulated, it means that the financial risks after the merger and acquisition of the wind power station are within the uncontrollable range, and the wind power station is not recommended to carry out the merger and acquisition.

[0189] In this embodiment, by acquiring basic operation data of the wind power station, operating features are extracted from the basic operation data, and the operation features are combined into operation events, the state of the operation events is identified based on the state identification criteria, the event state of the operation events is identified, and it is determined whether the basic conditions for mergers and acquisitions are met, the financial risk period of the wind power station is set, the comprehensive financial data of the wind power station within the financial risk period is acquired, the comprehensive financial data is input into a pre-trained financial risk prediction model, the financial state value of the wind power station is predicted, and it is determined whether to issue a financial risk warning prompt, the risk parameters are identified from the comprehensive financial data, and the financial risk level is formulated according to the number of risk parameters; compared with the prior art, by combining the operation features into operation events, and performing state identification on the operation events, the event state of the operation events is identified, and it is determined whether the basic conditions for mergers and acquisitions are met, the financial risk period of the wind power station is set, the comprehensive financial data of the wind power station within the financial risk period is acquired, the comprehensive financial data is input into a pre-trained financial risk prediction model, the financial state value of the wind power station is predicted, and it is determined whether to issue a financial risk warning prompt, the risk parameters are identified from the comprehensive financial data, and the financial risk level is formulated according to the number of risk parameters; By identifying the event status of the event, the basic operating status of the wind power station can be analyzed, and the wind power station with poor basic operating status can be prevented from participating in the subsequent analysis process, thereby avoiding a series of useless operations in the subsequent M&A financial risk analysis and reducing the workload of M&A financial risk analysis. At the same time, through the data collection of the wind power station's operating income and expenditure, power generation, duration performance and investment return, the financial risk situation of the wind power station can be obtained from multiple angles and in a real way, and the status value of the M&A financial risk can be predicted based on the prediction model, so as to analyze the possible financial risks of the M&A according to the predicted financial status value, providing reliable and accurate data support for the M&A financial risk of the wind power station, and improving the accuracy and efficiency of the M&A financial risk analysis.

[0190] Example 2: Please refer to Figure 2 As shown, the part not described in detail in this embodiment is described in the first embodiment, and a system for analyzing the financial risk of mergers and acquisitions of wind power stations is provided, which is used to implement a method for analyzing the financial risk of mergers and acquisitions of wind power stations, including an event combination module, an event determination module, a data collection module, a model prediction module and a level formulation module, wherein each module is connected through a wired or wireless network;

[0191] An event combination module is used to obtain basic operation data of the wind power station, extract operation characteristics from the basic operation data, the operation characteristics include effective operation time and unit power generation, and combine the operation characteristics into operation events;

[0192] An event determination module is used to identify the state of the operation event based on the state identification criteria, identify the event state of the operation event, which includes valid events and invalid events, and determine whether the basic conditions for merger and acquisition are met;

[0193] The data collection module is used to set the financial risk period of the wind power station and obtain the comprehensive financial data of the wind power station during the financial risk period. The comprehensive financial data includes asset-liability difference, effective power generation rate, transaction increase rate and investment return rate;

[0194] The model prediction module is used to input the comprehensive financial data into the pre-trained financial risk prediction model, predict the financial status value of the wind power station, and determine whether to issue a financial risk warning prompt;

[0195] The grade setting module is used to identify risk parameters from the comprehensive financial data and to set financial risk grades based on the number of risk parameters.

[0196] Example 3: Please refer to Figure 3 As shown, this embodiment discloses an electronic device, including a processor and a memory;

[0197] Wherein, the memory stores a computer program that can be called by the processor;

[0198] The processor executes the method for analyzing the financial risks of mergers and acquisitions of wind power stations by calling the computer program stored in the memory.

[0199] Since the electronic device introduced in this embodiment is an electronic device used to implement a method for analyzing the financial risk of mergers and acquisitions of a wind power station in the first embodiment of this application, based on the method for analyzing the financial risk of mergers and acquisitions of a wind power station introduced in the embodiment of this application, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as a person skilled in the art implements the electronic device used in the method for analyzing the financial risk of mergers and acquisitions of a wind power station in the embodiment of this application, it belongs to the scope of protection of this application.

[0200] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for analyzing the financial risks of mergers and acquisitions of wind power stations, characterized in that: include: S1: Obtain basic operation data of the wind power station, extract operation characteristics from the basic operation data, the operation characteristics include effective operation time and unit power generation, and combine the operation characteristics into operation events; The methods for extracting effective operating time and unit power generation include: All the original data of the wind power station are queried through the database, and the state types of the original data are identified one by one, and the original data with the state type of the valid state is recorded as the basic operation data, and i basic operation data are obtained; Query the time when i basic operation data are stored and recorded one by one through the timestamp to obtain i recording times; Arrange the i recording moments in chronological order, and record the duration from the first recording moment to the last recording moment as the effective running duration; The text part and the numerical part of the i basic operation data are identified one by one through natural language processing technology, and the basic operation data with the text part as the power generation is recorded as the target data, and the numerical part of the target data is recorded as the sub-power generation, and the Power generation per unit; Will The power generation of each unit is accumulated one by one and compared with the effective operation time to obtain the unit power generation; The expression of unit power generation is: ； In the formula, is the unit power generation, For the The power generation capacity of each For effective operation time; The combined methods of running events include: Filter out basic events with two event units from the database, and record the two event units as the first event unit and the second event unit respectively; Importing the effective operation time and the unit power generation into the first event unit and the second event unit respectively, generating an operation time unit and a power generation unit; Assign the running time unit with the number 1, assign the power generation unit with the number 2, and generate a running event; S2: Based on the status identification criteria, the operation event is identified, the event status of the operation event is identified, and the event status includes valid events and invalid events, and whether the basic conditions for merger and acquisition are met; S3: If the basic conditions for merger and acquisition are met, the financial risk period of the wind power station is set, and the comprehensive financial data of the wind power station during the financial risk period is obtained. The comprehensive financial data includes asset-liability balance, effective power generation rate, transaction increase rate and investment return rate; S4: Input the comprehensive financial data into the pre-trained financial risk prediction model to predict the financial status value of the wind power station and determine whether to issue a financial risk warning prompt; S5: If a financial risk warning is issued, risk parameters are identified from the comprehensive financial data and a financial risk level is formulated based on the number of risk parameters.

2. A method for analyzing the financial risks of mergers and acquisitions of wind power stations according to claim 1, characterized in that: The state recognition criteria are: when an abnormal unit appears, the state recognition operation is stopped; Methods for identifying valid events and invalid events include: In ascending order, first compare the effective running time in the running time unit numbered 1 with the running time calibration value; When the effective running time is less than the running time calibration value, the running time unit is recorded as an abnormal unit, and the running event is identified as an invalid event; When the effective operation time is greater than or equal to the operation time calibration value, then the unit power generation in the power generation unit numbered 2 is compared with the power generation calibration value. When the unit power generation is less than the power generation calibration value, the power generation unit is recorded as an abnormal unit, and the operation event is identified as an invalid event. When the effective operation time is greater than or equal to the operation time calibration value, and the unit power generation is greater than or equal to the power generation calibration value, the operation event is identified as a valid event; The methods for determining whether the basic conditions for mergers and acquisitions are met include: When the event status of the running event is a valid event, it is determined that the basic conditions for merger and acquisition are met; When the event status of the running event is an invalid event, it is determined that the basic conditions for the merger and acquisition are not met.

3. A method for analyzing the financial risks of mergers and acquisitions of wind power stations according to claim 2, characterized in that: Methods for obtaining effective power generation rate include: During the financial risk period, the power generation of the wind power station at each moment is queried through the database, and the duration from the moment when the power generation is greater than the power standard value for the first time to the moment when the power generation is less than the power standard value for the first time is recorded as the power interval duration; Based on the duration of a power interval, mark the financial risk period. power time points, and query them one by one The power generation at each power point is obtained Actual power; The maximum power generated by the wind power station can be found from the technical parameter table and recorded as the total power. The actual power of each power is compared with the total power in turn and the average value is calculated to obtain the effective power generation rate; The expression of effective power generation rate is: ； In the formula, is the effective power generation rate, For the The actual power, is the total power.

4. A method for analyzing the financial risks of mergers and acquisitions of wind power stations according to claim 3, characterized in that: The methods for obtaining the transaction climbing rate include: Divide financial risk periods into The same length and continuous sales segments are identified, and the starting and ending time of each sales segment are marked. The starting time and The final moment; Through the financial management system, the wind power stations are queried one by one The starting time and The electricity sales value at the end time is obtained Starting sales value and End point sales value; Will The terminal sales values ​​are respectively After subtracting the starting sales value, and comparing it with the total sales value of the wind power station, we get Segment sales rate; The expression for segment sales rate is: ； In the formula, For the Segment sales rate, For the End point sales value, For the Starting sales value, is the total sales value of the wind power station; After subtracting the sales rates of two adjacent segments, we get The rate of increase in size and The individual climbing rates are accumulated and averaged to obtain the transaction climbing rate; The expression of transaction climbing rate is: ； In the formula, is the transaction climbing rate, For the The rate of increase in height.

5. A method for analyzing the financial risks of mergers and acquisitions of wind power stations according to claim 4, characterized in that: Methods for obtaining return on investment include: Through the equity management system, the investment information of the wind power station is queried, and the investors in the investment information are identified. Investors; Through the financial management system, we can find out The expected return and actual return of each investor during the financial risk period, and The actual return and After comparing the expected returns, Individual return rate; The expression of sub-return rate is: ； In the formula, For the The rate of return, For the The actual return, For the Expected return amount; Will The individual returns are accumulated and averaged to obtain the return on investment; The expression for return on investment is: ； In the formula, For the return on investment, For the Individual rate of return.

6. A method for analyzing the financial risks of mergers and acquisitions of wind power stations according to claim 5, characterized in that: The financial status value includes safety status and risk status; The training methods of the financial risk prediction model include: Pre-collecting multiple sets of comprehensive financial data and financial status values ​​corresponding to the comprehensive financial data; The comprehensive financial data is converted into a corresponding set of feature vectors, the feature vectors are used as the input of the neural network model, the financial status value is converted into a label corresponding to the comprehensive financial data, the safety status is converted into 0, the risk status is converted into 1, and the financial status value corresponding to each set of comprehensive financial data is used as the output of the neural network model, the financial status value is used as the prediction target, and the sum of the prediction errors of all training data is minimized as the training target. The neural network model is trained until the sum of the prediction errors reaches convergence and the training is stopped to obtain a financial risk prediction model; The methods for determining whether to issue a financial risk early warning include: When the output of the financial risk prediction model is 0, the predicted financial status value is a safe state, and it is determined that no financial risk warning prompt will be issued; When the output of the financial risk prediction model is 1, the predicted financial status value is a risky state, and a financial risk warning prompt is issued.

7. A method for analyzing the financial risks of mergers and acquisitions of wind power stations according to claim 6, characterized in that: Methods for identifying risk parameters include: Compare the asset-operating-liability difference with the safety-operating-liability difference. When the asset-operating-liability difference is less than the safety-operating-liability difference, record the asset-operating-liability difference as a risk parameter. Compare the effective power generation rate with the safe power generation rate. When the effective power generation rate is less than the safe power generation rate, record the effective power generation rate as a risk parameter. Compare the transaction climbing rate with the safety climbing rate. When the transaction climbing rate is less than the safety climbing rate, record the transaction climbing rate as a risk parameter. Compare the investment return rate with the safety rate of return. When the investment return rate is less than the safety rate of return, record the investment return rate as the risk parameter.

8. A method for analyzing the financial risks of mergers and acquisitions of wind power stations according to claim 7, characterized in that: Financial risk levels include controllable and uncontrollable levels; Methods for setting controllable and uncontrollable levels include: Count the number of risk parameters marked as risk parameters and record them as risk value; When the risk value is 1 or 2, a controllable level is established; When the risk value is 3 or 4, an uncontrollable level is established.

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