Electric energy project management and intelligent decision-making system based on large model

Abstract

The invention relates to the technical field of electric power management systems, and provides an electric power energy project management and intelligent decision-making system based on a large model, which comprises an electric power project data collection terminal, an electric power project data processing terminal, a decision-making analysis terminal, a user interface terminal and an intelligent decision-making execution terminal, the electric power project data collection terminal is used for collecting electric power project data of an electric power project; the electric power project data processing terminal is used for cleaning, integrating and preprocessing the electric power project data; the decision analysis terminal is used for carrying out risk assessment, resource optimization and predictive analysis on the data and outputting decision suggestion information; the user interface terminal is used for providing a human-computer interaction interface for an administrator; and the intelligent decision execution terminal is used for executing work according to the confirmed decision content and adjusting resource allocation, working parameters and working states of the electric power project according to the decision content. The present invention has an effect of improving the management quality of power project management.

Description

Technical Field

[0001] The present invention relates to the technical field of power management systems, and particularly to a power energy project management and intelligent decision-making system based on a large model. Background Art

[0002] A large model refers to a model with a large number of parameters and a complex structure, capable of processing diverse data sets. A power energy project management and intelligent decision-making system refers to a system specifically designed for the power industry to assist in managing and making decisions on various power projects, such as power plant construction, transmission line layout, maintenance management, etc. This system improves the efficiency and effectiveness of project management by integrating functions of data analysis, resource optimization, and risk management.

[0003] Many power project management systems have now been developed. After a large amount of retrieval and reference by us, it is found that the existing power project management systems include those disclosed in CN105303332A, CN110245862A, CN112085481A, US20120078680A1. These power project management systems generally include: a project data collection terminal, a project data analysis terminal, and a project management decision-making terminal; the project data collection terminal is used to collect cost data, resource data, and equipment data of power projects; the project data analysis terminal is used to analyze all project data; the project management decision-making terminal is used to generate and execute project management decisions based on the analysis results. Due to the relatively single management process of the above power project management systems, lacking a multi-dimensional analysis process, the accuracy of management decisions decreases, resulting in the defect of reduced management quality when the system manages power projects. Summary of the Invention

[0004] The object of the present invention is to propose a power energy project management and intelligent decision-making system based on a large model in view of the deficiencies of the above power project management systems.

[0005] The present invention adopts the following technical solutions:

[0006] A power energy project management and intelligent decision-making system based on a large model, including a power project data collection terminal, a power project data processing terminal, a decision analysis terminal, a user interface terminal, and an intelligent decision execution terminal; the power project data collection terminal is used to collect real-time and historical power project data of power projects, and the power project data includes project progress data, financial data, weather data, and equipment performance data; the power project data processing terminal is used to clean, integrate, and preprocess the collected power project data; the decision analysis terminal is used to perform risk assessment, resource optimization, and predictive analysis based on the preprocessed data, and output decision recommendation information; the user interface terminal is used to provide a human-computer interaction interface for administrators, for administrators to input decisions, adjust decisions, or confirm the decisions in the decision recommendation information, so that administrators can view analysis results, monitor project status, and make decisions; the intelligent decision execution terminal is used to perform execution work according to the confirmed decision content, and adjust the resource allocation, working parameters, and working status of the power project according to the decision content.

[0007] Optionally, the power project data collection terminal includes a project progress data collection module, a financial data collection module, a weather data collection module, and an equipment performance data collection module; the project progress data collection module is used to collect real-time and historical project progress data of power projects; the project progress data includes the task completion percentage, task deadline, actual progress, and planned progress; the financial data collection module is used to collect real-time and historical financial data of power projects; the financial data includes project budget, actual expenditure, and resource cost; the weather data collection module is used to collect real-time and historical weather data of power projects; the weather data includes temperature, rainfall, wind speed, extreme weather events, and the impact of weather on the project; the equipment performance data collection module is used to collect real-time and historical equipment performance data of power projects; the equipment performance data includes equipment operation status, equipment maintenance records, and equipment replacement frequency.

[0008] Optionally, the power project data processing terminal includes a data cleaning module, a data integration module, and a data preprocessing module; the data cleaning module is used to remove incorrect, incomplete, and incorrectly formatted data from the power project data; the data integration module is used to integrate the power project data after cleaning, and merge the data from different sources into a single data set for comprehensive analysis; the data preprocessing module is used to perform data format transformation preprocessing and format standardization preprocessing on the power project data after data integration for fast comprehensive analysis.

[0009] Optionally, the decision analysis terminal includes a resource management module, a risk management module, a cost management module, and a decision recommendation information output module; the resource management module is used to perform resource management analysis based on the preprocessed power project data and generate resource management recommendation information; the risk management module is used to perform risk management analysis based on the preprocessed power project data and generate risk management recommendation information; the cost management module is used to perform cost management analysis based on the preprocessed power project data and generate cost management recommendation information; the decision recommendation information output module is used to generate and output decision recommendation information based on the resource management recommendation information, the risk management recommendation information, and the cost management recommendation information.

[0010] Optionally, the user interface terminal includes a human-computer interaction interface module, a decision management module, and an information display module; the human-computer interaction interface module is used to provide an interface for human-computer interaction for the administrator; the decision management module is used to read the decisions input by the administrator, read the content of the decisions adjusted by the administrator, and read the confirmation operations of the administrator on the decisions in the decision recommendation information; the information display module is used to load and display the analysis results, monitor the project status, and the decision recommendation information.

[0011] Optionally, the resource management module includes a task resource quantity calculation sub-module and a resource management recommendation information generation sub-module; the task resource quantity calculation sub-module is used to calculate the task resource quantity of each task according to the task information, the total number of power project tasks, and the power project data; the resource management recommendation information generation sub-module is used to generate resource management recommendation information based on the task resource quantity of each task.

[0012] Optionally, the risk management module includes a power project risk probability calculation sub-module and a risk management recommendation information generation sub-module; the power project risk probability calculation sub-module is used to calculate the power project risk probability of the power project according to the historical risk event information and the power project data; the risk management recommendation information generation sub-module is used to generate risk management recommendation information based on the power project risk probability.

[0013] The beneficial effects achieved by the present invention are:

[0014] 1. Through the settings of the power project data collection terminal, the power project data processing terminal, the decision analysis terminal, the user interface terminal, and the intelligent decision execution terminal, the process of data processing and decision analysis is optimized, and human-computer interaction intervention in decision-making is increased, making the decision execution more intelligent and accurate, thereby being conducive to improving the management quality when the system manages power projects.

[0015] 2. By setting up the project progress data collection module, financial data collection module, weather data collection module, and equipment performance data collection module, the data collection process becomes more diversified and stable, improving the accuracy of power project data collection, which is conducive to enhancing the management quality when the system manages power projects.

[0016] 3. By setting up the data cleaning module, data integration module, and data preprocessing module, the data processing process becomes more temperate and the accuracy of preprocessing is higher, which is conducive to enhancing the management quality when the system manages power projects.

[0017] 4. By setting up the resource management module, risk management module, cost management module, and decision-making advice information output module, the decision-making analysis process is enriched in multiple dimensions, making the analysis process more comprehensive and accurate, which is conducive to improving the accuracy and adaptability of decision-making advice information, and thus conducive to enhancing the management quality when the system manages power projects.

[0018] 5. By setting up the human-computer interaction interface module, decision management module, and information display module, it is conducive for administrators to confirm or intervene in decisions more conveniently, making decision management more accurate and faster, which is conducive to enhancing the management quality when the system manages power projects.

[0019] 6. By setting up the task resource quantity calculation sub-module and the resource management advice information generation sub-module and cooperating with the task resource quantity calculation algorithm, the task resource quantity of each task is calculated based on task information, the total number of power project tasks, and power project data, which is conducive to improving the accuracy of task resource quantity, optimizing resource allocation, and thus conducive to enhancing the management quality when the system manages power projects.

[0020] 7. By setting up the power project risk probability calculation sub-module and the risk management advice information generation sub-module and cooperating with the risk probability calculation algorithm, the power project risk probability of the power project is calculated based on historical risk event information and power project data, which is conducive to improving the prediction accuracy of the power project risk probability, and thus conducive to enhancing the management quality when the system manages power projects.

[0021] 8. By setting up the cost-benefit ratio calculation sub-module and the cost management advice information generation sub-module and cooperating with the cost-benefit ratio calculation algorithm, the cost-benefit ratio of the power project is calculated based on power project data, which is conducive to improving the accuracy and calculation efficiency of the cost-benefit ratio, and thus conducive to enhancing the management quality when the system manages power projects.

[0022] To enable a further understanding of the features and technical content of the present invention, please refer to the following detailed description of the present invention and the attached drawings. However, the provided drawings are only for reference and illustration, and are not intended to limit the present invention. Brief Description of the Drawings

[0023] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0024] Figure 2 It is a schematic diagram of the structure of the resource management module in the present invention;

[0025] Figure 3 It is a visual statistical schematic diagram of the task resource management effect in the present invention;

[0026] Figure 4 It is a schematic diagram of the structure of the risk management module in the present invention;

[0027] Figure 5 It is a visual statistical schematic diagram of the risk management effect in the present invention;

[0028] Figure 6 It is a schematic diagram of the method flow of a method for power energy project management and intelligent decision-making based on a large model in the present invention;

[0029] Figure 7 It is a schematic diagram of the structure of the cost management module in another embodiment of the present invention;

[0030] Figure 8 It is a visual statistical schematic diagram of the cost management effect in another embodiment of the present invention. Detailed Embodiments

[0031] The following are specific embodiments to illustrate the implementation manners of the present invention. Those skilled in the art can understand the advantages and effects of the present invention from the content disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and various details in this specification can also be modified and changed based on different viewpoints and applications without departing from the spirit of the present invention. Additionally, the drawings of the present invention are only for simple schematic illustration and are not drawn according to actual dimensions, and this is stated in advance. The following embodiments will further detail the related technical content of the present invention, but the disclosed content is not used to limit the protection scope of the present invention.

[0032] Embodiment 1: This embodiment provides a power energy project management and intelligent decision-making system based on a large model. Combining Figure 1As shown in the figure, a large model-based power energy project management and intelligent decision-making system includes a power project data collection terminal, a power project data processing terminal, a decision analysis terminal, a user interface terminal, and an intelligent decision execution terminal. The power project data collection terminal is used to collect real-time and historical power project data of power projects, and the power project data includes project progress data, financial data, weather data, and equipment performance data. The power project data processing terminal is used to clean, integrate, and preprocess the collected power project data. The decision analysis terminal is used to perform risk assessment, resource optimization, and predictive analysis based on the data processed by the power project data processing terminal, and output decision recommendation information. The user interface terminal is used to provide a human-computer interaction interface for the administrator, for the administrator to input decisions, adjust decisions, or confirm the decisions in the decision recommendation information, so as to facilitate the administrator to view analysis results, monitor project status, and make decisions. The intelligent decision execution terminal is used to perform execution work according to the confirmed decision content, and adjust the resource allocation, working parameters, and working status of the power project according to the decision content.

[0033] Optionally, the power project data collection terminal includes a project progress data collection module, a financial data collection module, a weather data collection module, and an equipment performance data collection module. The project progress data collection module is used to collect real-time and historical project progress data of power projects. The project progress data includes task completion percentage, task deadline, actual progress, and planned progress. The financial data collection module is used to collect real-time and historical financial data of power projects. The financial data includes project budget, actual expenditure, and resource cost. The weather data collection module is used to collect real-time and historical weather data of power projects. The weather data includes temperature, rainfall, wind speed, extreme weather events, and the impact of weather on the project. The equipment performance data collection module is used to collect real-time and historical equipment performance data of power projects. The equipment performance data includes equipment operation status, equipment maintenance records, and equipment replacement frequency.

[0034] Optionally, the power project data processing terminal includes a data cleaning module, a data integration module, and a data preprocessing module. The data cleaning module is used to remove incorrect, incomplete, and incorrectly formatted data in the power project data. The data integration module is used to integrate the cleaned power project data, and merge data from different sources into a data set for comprehensive analysis. The data preprocessing module is used to perform data format transformation preprocessing and format standardization preprocessing on the integrated power project data for quick comprehensive analysis.

[0035] Optionally, the decision analysis terminal includes a resource management module, a risk management module, a cost management module, and a decision recommendation information output module; the resource management module is used to perform resource management analysis based on the preprocessed power project data and generate resource management recommendation information; the risk management module is used to perform risk management analysis based on the preprocessed power project data and generate risk management recommendation information; the cost management module is used to perform cost management analysis based on the preprocessed power project data and generate cost management recommendation information; the decision recommendation information output module is used to generate and output decision recommendation information based on the resource management recommendation information, the risk management recommendation information, and the cost management recommendation information.

[0036] Optionally, the user interface terminal includes a human-computer interaction interface module, a decision management module, and an information display module; the human-computer interaction interface module is used to provide a human-computer interaction interface for the administrator; the decision management module is used to read the decisions input by the administrator, read the content of the administrator's adjusted decisions, and read the confirmation operations of the administrator on the decisions in the decision recommendation information; the information display module is used to load and display the analysis results, monitor the project status, and the decision recommendation information.

[0037] Optionally, in combination with Figure 2 As shown, the resource management module includes a task resource quantity calculation sub-module and a resource management recommendation information generation sub-module; the task resource quantity calculation sub-module is used to calculate the task resource quantity of each task according to the task information, the total number of power project tasks, and the power project data; the resource management recommendation information generation sub-module is used to generate resource management recommendation information according to the task resource quantity of each task.

[0038] Specifically, when the task resource quantity calculation sub-module works, it reads the task information of all the tasks to be executed in the power project and the total number of power project tasks, and then calculates the task resource quantity of each task in combination with the power project data, satisfying the following formula:

[0039]

[0040] K = k ref + δ·E;

[0041] D i = w1×Q1 + w2×Q2 + w3×Q3 + w4×Q4;

[0042] Among them, R i represents the task resource quantity of the i-th task; D irepresents the demand volume of the i-th task; the demand volume is calculated from the scores of each demand item involved in the task; Q1 represents the numerical value of the human resource demand item of the i-th task, with the unit of number of people, Q2 represents the numerical value of the material demand item of the i-th task, that is, the amount of funds required for articles and appliances, with the unit of RMB yuan, Q3 represents the numerical value of the time demand item of the i-th task, with the unit of days, and Q4 represents the numerical value of the financial demand item of the i-th task, that is, the total amount of funds required for the entire task, with the unit of RMB yuan; the numerical values of the demand items as known quantities are all obtained from the task information; for example: if the human resource demand of the i-th task is 100 people, then Q1 = 100, if the material demand of the i-th task is ￥100,000, then Q2 = 100,000, if the time demand of the i-th task is 5 days, then Q3 = 5, if the financial demand of the i-th task is ￥200,000, then Q4 = 200,000; w1 represents the human resource demand weight, w2 represents the material demand weight, w3 represents the time demand weight, w4 represents the financial demand weight, and w1, w2, w3, and w4 are generally 1.2, 1.2, 1.3, and 1.3 respectively, and the specific values are adjusted by the administrator according to experience; a represents the demand weight adjustment index, and the more demand items the demand volume involves, the larger the demand weight adjustment index, and the value range is 1 to 2, and the specific value is set by the administrator according to experience; n represents the total number of tasks. The task resource volume R corresponding to the task i The larger it is, the more resources recommended to be actually allocated in the resource management advice information.

[0043] T represents the total resource volume of the system. The total resource volume is a score composed of the total human resources, total material resources, total financial resources, total time resources, and total equipment resources of the system. The specific confirmation method can be, but is not limited to: 1. Manually set by the administrator with reference to the total human resources, total material resources, total financial resources, total time resources, and total equipment resources of the system; 2. Normalize the numerical values of the total human resources, total material resources, total financial resources, total time resources, and total equipment resources, and then sum them according to different weights; K represents the resource buffer coefficient; k ref represents the basic buffer coefficient, generally 50; δ represents the risk sensitivity coefficient, and the greater the impact risk of the environment where the power project is located, the greater the risk sensitivity coefficient, and the specific value is set by the administrator according to experience; E represents the project environment variation coefficient, which is obtained by calculating the average value of the resource usage of past projects. The project environment variation coefficient is 1% of the average value of the resource usage of historical projects.

[0044] S iDenote the urgency score of the $i$-th task. The urgency scores, which are known quantities, are all obtained from the task information. The urgency score of each task is pre-evaluated by the administrator. The more the task content and the shorter the task planned time, the higher the urgency score. $\beta$ represents the attenuation factor of the impact of the urgency score on the resource buffer. The larger the total number of tasks, the smaller the attenuation factor, and its value range is between 0.1 and 0.8. The specific value is set by the administrator according to experience.

[0045] To understand and use the above formula more specifically, the following specific implementation examples are shown:

[0046] Suppose in a large-scale power project, the project involves building a new substation, including multiple subtasks such as geological exploration, infrastructure construction, equipment procurement and installation, etc. The total project resource volume is 1000 units, and the demand and task urgency are different. The known parameters are: the total number of tasks $n = 4$, the demand for each task $D=[200, 150, 300, 350]$, the total resource volume $T = 1000$, the resource buffer coefficient $K = 50$, the urgency score of each task $S=[0.2, 0.5, 0.8, 0.1]$, the attenuation factor $\beta = 0.3$, and the demand weight adjustment index $a = 1.5$. Then the calculation steps are as follows:

[0047]

[0048]

[0049] The resource management recommendation information generation sub-module generates resource management recommendation information according to the size order of the task resource volume of each task; in the resource management recommendation information, the larger the task resource volume corresponding to the task, the more resources are actually allocated.

[0050] Combined with Figure 3 shown, Figure 3 is a visualization statistical chart of the above task resource management effect, showing the statistical chart of the effect obtained by using the above formula algorithm in different power projects. The following is the program code for the implementation process of the task resource volume calculation process:

[0051]

[0052]

[0053]

[0054] Optionally, combined with Figure 4As described above, the risk management module includes a power project risk probability calculation sub-module and a risk management recommendation information generation sub-module; the power project risk probability calculation sub-module is used to calculate the power project risk probability of a power project according to historical risk event information and power project data; the risk management recommendation information generation sub-module is used to generate risk management recommendation information according to the power project risk probability.

[0055] Specifically, when the power project risk probability calculation sub-module works, the following formula is satisfied:

[0056]

[0057] Where P(r,t) represents the probability of at least one risk event occurring within the time period t, that is, the power project risk probability; λ i (s) represents the occurrence rate of the i-th type of risk event at time s, that is, the probability of the risk event occurring at any moment. The occurrence rate of each type of risk event at time s is calculated according to the occurrence situation of various risk events at different times in historical data; m represents the number of types of risk events; η ij represents the influence coefficient between the i-th type of risk event and the j-th type of risk event. The higher the similarity between the i-th type of risk event and the j-th type of risk event, the greater the influence coefficient. All influence coefficients are preset by the administrator according to experience to evaluate the interaction between different risk factors; F j (s) represents the cumulative occurrence probability of the j-th type of risk event from time 0 to time s, which is obtained by statistical analysis of historical data; exp represents the power operation of the natural exponential function e, which is used to calculate probability; ∫0 t represents a definite integral, the integral from time 0 to time period t, which is used to calculate the cumulative effect of risk occurrence within the entire time interval; ∏ represents the product symbol;

[0058] represents calculating the sum of the influence values corresponding to all types of risk events, which represents the sum of the adjusted probabilities of all risk events occurring at time s; integrating the above summation result within the time period t, which represents the accumulation of the adjusted risk event occurrence probabilities from time 0 to time t, and then taking the inverse exponent of the integral result using the exp function, which represents the probability that no risk occurs within the time period t. Finally, subtracting this probability that no risk occurs from 1 gives the total probability that at least one type of risk event occurs within the time period t.

[0059] To more specifically understand and use the above formula, a specific implementation example is shown as follows:

[0060] Suppose the known parameters are: the number of types of risk events \(m = 3\), namely weather risk, equipment failure risk, and human error risk, the incidence rate \(\lambda=[0.02, 0.01, 0.05]\), and the impact coefficient The time period \(t = 1\) year, then the calculation steps are as follows:

[0061] \(F=[0.02\times1, 0.01\times1, 0.05\times1]=[0.02, 0.01, 0.05]\);

[0062]

[0063] \(P(r,t)=1 - e\) -0.03 \(\approx0.03\);

[0064] If the probability threshold \(P\) set in the risk management recommendation information generation sub-module ref is \(0.02\), then the risk management recommendation information generation sub-module generates risk management recommendation information indicating that the project risk exceeds the standard.

[0065] Combined with Figure 5 as shown, Figure 5 is the visualization statistical chart of the above risk management effect, showing the statistical charts of the risk management effects at different times obtained by using the above formula algorithm in the same power project. The following is the program code for the implementation process of the risk management process calculation:

[0066]

[0067]

[0068]

[0069] In summary, through the settings of the power project data collection terminal, power project data processing terminal, decision analysis terminal, user interface terminal, and intelligent decision execution terminal, the process of data processing and decision analysis is optimized, and human-computer interaction intervention in decision-making is increased, making decision execution more intelligent and accurate; through the settings of the project progress data collection module, financial data collection module, weather data collection module, and equipment performance data collection module, the data collection process is more diversified and the process is more stable, improving the accuracy of power project data collection; through the settings of the data cleaning module, data integration module, and data preprocessing module, the data processing process is more temperate and the accuracy of preprocessing is higher; through the settings of the resource management module, risk management module, cost management module, and decision-making advice information output module, the decision analysis process is enriched in multiple dimensions, making the analysis process more comprehensive and accurate, which is conducive to improving the accuracy and adaptability of decision-making advice information; through the settings of the human-computer interaction interface module, decision management module, and information display module, it is conducive for administrators to confirm or intervene in decisions more conveniently, making decision management more accurate and faster; through the settings of the task resource quantity calculation sub-module and the resource management advice information generation sub-module in cooperation with the task resource quantity calculation algorithm, the task resource quantity of each task is calculated according to the task information, the total number of power project tasks, and the power project data, which is conducive to improving the accuracy of the task resource quantity and optimizing resource allocation; through the settings of the power project risk probability calculation sub-module and the risk management advice information generation sub-module in cooperation with the risk probability calculation algorithm, the power project risk probability of the power project is calculated according to the historical risk event information and the power project data, which is conducive to improving the prediction accuracy of the power project risk probability, thereby conducive to improving the management quality when the system manages the power project.

[0070] A power energy project management and intelligent decision-making method based on a large model, which is applied to the above-mentioned power energy project management and intelligent decision-making system based on a large model, and combines Figure 6 As shown, the power energy project management and intelligent decision-making method includes:

[0071] S1. Collect real-time and historical power project data of the power project;

[0072] S2. Clean, integrate, and preprocess the collected power project data;

[0073] S3. Conduct risk assessment, resource optimization, and predictive analysis based on the preprocessed data, and output decision-making advice information;

[0074] S4. Provide a human-computer interaction interface for administrators to input decisions, adjust decisions, or confirm the decisions in the decision-making advice information;

[0075] S5. Perform execution work according to the confirmed decision content, and adjust the resource allocation, working parameters, and working status of the power project according to the decision content.

[0076] Embodiment 2: This embodiment includes all the content of Embodiment 1 and provides a power energy project management and intelligent decision-making system based on a large model. Combining Figure 7 As shown, the cost management module includes a cost-benefit ratio calculation sub-module and a cost management advice information generation sub-module; the cost-benefit ratio calculation sub-module is used to calculate the cost-benefit ratio of the power project according to the power project data; the cost management advice information generation sub-module is used to generate cost management advice information according to the cost-benefit ratio.

[0077] Specifically, the timing of cost-benefit ratio calculation has the following stages: project planning stage, detailed design stage, pre-implementation review stage, project adjustment stage, and project completion assessment stage. When the cost-benefit ratio calculation sub-module works, the following formula is satisfied:

[0078]

[0079] Among them, CEB represents the cost-benefit ratio, which is used to evaluate the economy of the project plan; when the cost-benefit ratio is greater than the corresponding threshold, it means that the project plan meets the standard, otherwise it does not meet the standard; B i represents the amount of the i-th revenue source; the revenue sources include but are not limited to: product or service sales revenue, cost savings, indirect revenue; the amount of the revenue source can be the value of the generated revenue or the value predicted by the administrator based on historical data; x i represents the revenue weight of the i-th revenue source. The shorter the revenue spending time, the greater the revenue weight. The specific value of the revenue weight corresponding to each revenue source is set in advance by the administrator according to experience; h represents the total number of revenue sources; C j represents the amount of the j-th cost source; the cost sources include but are not limited to: material cost, labor cost, indirect cost, and operating cost; the amount of the cost source can be the value of the generated cost, the value predicted by the administrator based on historical data, or the value specified in the project budget; y j represents the cost weight of the j-th cost source. The shorter the cost generation time, the greater the cost weight. The cost weight corresponding to each cost source is set in advance by the administrator according to experience; H represents the total number of revenue sources. The units of the amount of the revenue source and the amount of the cost source are both yuan in RMB.

[0080] To more specifically understand and use the above formula, the following specific implementation examples are shown:

[0081] Assume the known parameters are: the total number of benefit sources h = 2, benefit source B = [500, 300], benefit weight x = [1.2, 0.8], the total number of cost sources H = 2, cost source C = [200, 100], cost weight x = [1, 0.5]. Then the calculation steps are as follows:

[0082]

[0083] If the ratio threshold c set in the cost management advice information generation sub-module ref is 1.5, since 1.96 > 1.5, the cost management advice information generation sub-module generates cost management advice information indicating that the project cost management meets the standard.

[0084] Combined with Figure 8 as shown Figure 8 is the visualization statistical chart of the above cost management effect, showing the statistical charts of the cost management effects at different stages obtained by using the above formula algorithm in the same power project. The following is the program code for the implementation process of calculating the cost management process:

[0085]

[0086]

[0087] costs = {

[0088] 'direct_costs':(250000, 1.0),

[0089] 'indirect_costs':(100000, 0.5),

[0090] 'operational_costs':(150000, 1.1)

[0091] }

[0092] #Calculate CEB

[0093] try:

[0094] ceb_result = calculate_ceb(benefits, costs)

[0095] print(f"The Cost-Benefit Ratio(CEB) is: {ceb_result:.2f}")

[0096] except ValueError as e:

[0097] print(str(e))。

[0098] In summary, by setting the cost-benefit ratio calculation sub-module and the cost management recommendation information generation sub-module in cooperation with the cost-benefit ratio calculation algorithm, and calculating the cost-benefit ratio of the power project based on the power project data, it is beneficial to improve the accuracy and calculation efficiency of the cost-benefit ratio, thereby facilitating the improvement of the management quality when the system manages the power project.

[0099] The content disclosed above is only the preferred feasible embodiment of the present invention, and does not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the content of the specification and drawings of the present invention are included in the protection scope of the present invention. In addition, with the development of technology, the elements therein can be updated.

Claims

1. A power energy project management and intelligent decision-making system based on large models, characterized in that, It includes a power project data collection terminal, a power project data processing terminal, a decision analysis terminal, a user interface terminal, and an intelligent decision execution terminal; the power project data collection terminal is used to collect real-time and historical power project data of the power project, and the power project data includes project progress data, financial data, weather data, and equipment performance data; the power project data processing terminal is used to clean, integrate, and preprocess the collected power project data. The decision analysis terminal is used to perform risk assessment, resource optimization, and predictive analysis based on the data processed by the power project data processing terminal, and output decision recommendation information. The user interface terminal is used to provide a human-computer interaction interface for the administrator to input decisions, adjust decisions, or confirm the decisions in the decision recommendation information, so that the administrator can view the analysis results, monitor the project status, and make decisions; the intelligent decision execution terminal is used to perform execution work according to the confirmed decision content, and adjust the resource allocation, working parameters, and working status of the power project according to the decision content.

2. The power energy project management and intelligent decision-making system based on a large model according to claim 1, wherein The power project data collection terminal includes a project progress data collection module, a financial data collection module, a weather data collection module, and an equipment performance data collection module; the project progress data collection module is used to collect real-time and historical project progress data of the power project; the project progress data includes the task completion percentage, task deadline, actual progress, and planned progress; the financial data collection module is used to collect real-time and historical financial data of the power project; the financial data includes project budget, actual expenditure, and resource cost; the weather data collection module is used to collect real-time and historical weather data of the power project; the equipment performance data collection module is used to collect real-time and historical equipment performance data of the power project; the equipment performance data includes equipment operation status, equipment maintenance records, and equipment replacement frequency.

3. The power energy project management and intelligent decision-making system based on a large model according to claim 2, characterized in that, The power project data processing terminal includes a data cleaning module, a data integration module, and a data preprocessing module; the data cleaning module is used to remove incorrect, incomplete, and incorrectly formatted data from the power project data. The data integration module is used to integrate the power project data after cleaning, and merge the data from different sources into a data set for comprehensive analysis; the data preprocessing module is used to perform data format transformation preprocessing and format standardization preprocessing on the power project data after data integration for fast comprehensive analysis.

4. The power energy project management and intelligent decision-making system based on a large model according to claim 3, wherein, The decision analysis terminal includes a resource management module, a risk management module, a cost management module, and a decision recommendation information output module; the resource management module is used to perform resource management analysis based on the preprocessed power project data and generate resource management recommendation information. The risk management module is used to perform risk management analysis based on the preprocessed power project data and generate risk management recommendation information. The cost management module is used to perform cost management analysis based on the preprocessed power project data and generate cost management recommendation information. The decision-making recommendation information output module is used to generate and output decision-making recommendation information based on resource management recommendation information, risk management recommendation information, and cost management recommendation information.

5. The power energy project management and intelligent decision-making system based on a large model according to claim 4, wherein The user interface terminal includes a human-computer interaction interface module, a decision-making management module, and an information display module; the human-computer interaction interface module is used to provide an interface for administrators; the decision-making management module is used to read the decisions input by administrators, the content of decision adjustments by administrators, and the confirmation operations of administrators on the decisions in the decision-making recommendation information; the information display module is used to load and display analysis results, monitor project status, and decision-making recommendation information.

6. The power energy project management and intelligent decision-making system based on a large model according to claim 5, wherein, The resource management module includes a task resource quantity calculation sub-module and a resource management recommendation information generation sub-module; the task resource quantity calculation sub-module is used to calculate the task resource quantity of each task according to task information, the total number of power project tasks, and power project data; the resource management recommendation information generation sub-module is used to generate resource management recommendation information based on the task resource quantity of each task.

7. The power energy project management and intelligent decision-making system based on a large model according to claim 6, wherein, The risk management module includes a power project risk probability calculation sub-module and a risk management recommendation information generation sub-module; the power project risk probability calculation sub-module is used to calculate the power project risk probability of a power project according to historical risk event information and power project data; The risk management recommendation information generation sub-module is used to generate risk management recommendation information based on the power project risk probability.

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