Power distribution network project full-link optimization management system and method

Abstract

The invention relates to the technical field of power grid artificial intelligence control, in particular to a power distribution network project full-link optimization management system and method. The system comprises a project management process optimization module, an investment management system optimization module, an intelligent management application module, a training and popularization module and a comprehensive monitoring and evaluation module. The method comprises the steps of process modeling and optimization, investment analysis and prediction, intelligent management and decision making, training and technical support, comprehensive monitoring and evaluation and the like. According to the invention, for power distribution network project management, the management efficiency can be improved, the investment decision scientificity can be enhanced, the intelligent management level can be improved, knowledge sharing and skill improvement can be promoted, and comprehensive monitoring and continuous optimization can be realized.

Description

Technical Field

[0001] The present invention relates to the field of power grid artificial intelligence control technology, and in particular to a full-link optimization management system and method for a distribution network project. Background Art

[0002] With the rapid development of the power industry and the continuous expansion of the power grid, the complexity and management difficulty of distribution network projects have also increased significantly. The traditional distribution network project management model can no longer meet the needs of modern power grids in terms of efficiency, accuracy and intelligence. In particular, in the face of massive data, complex equipment and diversified needs, how to achieve full-link optimization management of distribution network projects has become an urgent problem to be solved.

[0003] In the prior art, for example, "A data-driven full-process control method for distribution network engineering" with application number 201910261816.2 provides a distribution network power supply capacity assessment and risk management method based on real-time data. Although this method can improve the power supply capacity and risk management level of the distribution network to a certain extent, it has not yet fully covered the full-link optimization management of the distribution network project, especially in terms of project management process, investment strategy, smart management application, and comprehensive monitoring and evaluation. There is still room for further improvement. Summary of the invention

[0004] The purpose of the present invention is to provide a distribution network project full-link optimization management system and method which improves management efficiency, enhances the scientific nature of investment decisions, and improves the level of intelligent management, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: a project management process optimization module, which is used to sort out and set the management process of all aspects of the distribution network project, clarify the responsibilities, content, and processes of each department and each node, and form a normalized workflow;

[0006] The investment management system optimization module collects the workflow execution information of the project management process optimization module to carry out precise investment strategy research and execution, build an investment management comprehensive effectiveness evaluation system, and complete investment scale structure calculation, key node management and control, project quantification, and investment effectiveness analysis through data analysis and prediction;

[0007] The smart management application module monitors the management process of all aspects of the project in real time based on the investment performance results and workflow execution information, and provides adjustment information for the project management process optimization module and the investment management system optimization module. By integrating the big model, it conducts real-time monitoring, data analysis and intelligent decision-making of the distribution network project;

[0008] The training and promotion module is used to provide online learning resources and technical support, and to share knowledge on the intelligent management application of distribution network projects in the investment management system optimization module and the project management process optimization module;

[0009] The comprehensive monitoring and evaluation module collects information from the project management process optimization module, investment management system optimization module, smart management application module and training and promotion module, including project execution status, investment project effectiveness, and distribution network project evaluation indicators. It is used to monitor the operation of the distribution network projects managed by the system in real time, and optimize system performance and functions through regular evaluation and feedback mechanisms.

[0010] Furthermore, the project management process optimization module includes:

[0011] The process modeling unit executes the "build model" instruction to generate a project management flowchart; builds the distribution network project management process according to the project target information, and defines the process nodes and flow logic through a graphical interface or programming interface;

[0012] The distribution network project management process described configures the process nodes of project initiation, execution, monitoring and completion according to project requirements, the sequence and execution relationship between process nodes, project target information and task information of different nodes;

[0013] The role authority management unit executes the "assign authority" instruction to set access and operation authority for different users at each node in the distribution network project management process, including role definition, authority allocation and authority verification;

[0014] The automated task scheduling unit executes the "task scheduling" instruction, automatically assigns tasks to users according to preset rules, configures the distribution network project management process, and monitors the execution progress; automatically assigns and schedules tasks to corresponding executors according to the task definition and priority in the project management process, and monitors the task execution status;

[0015] The automatic assignment of tasks to users includes:

[0016] (1.1) Calculate the time CGG(v,w) taken by the main task v to be transmitted to the node w:

[0017] CGG(v,w)=C up (v)+C down (w)+C ud (v,w);

[0018] C up (v) is the time required to upload data from the node where the main task v is located, C down (w) is the time required for node w to download the task, C ud(v,w) is the time it takes for data to be transmitted from node v to node w through the network;

[0019]

[0020] size is the data size of the task, Sid is the task number, and Oj is the rate at which the node uploads or downloads data;

[0021]

[0022] α is the value coefficient;

[0023] (1.2) Calculate the task computation time CPG(Sid,w) of the task at node j:

[0024]

[0025] Yth(Sid) is the task calculation length, Zip(w) is the node computing capacity;

[0026] Calculate the expected completion time CSG(Sid,w) of task v assigned to node j:

[0027] CSG(Sid,w)=CFG(Sid,w)+CPG(Sid,w);

[0028] CFG(Sid,w) is the time when task v starts executing on node j;

[0029] (1.3) Check whether all nodes have been traversed. If not, change the node to j+1 and re-execute step (1.1); if yes, execute the next step;

[0030] (1.4) Assign the task to the server node with the fastest expected completion time;

[0031] (1.5) Check whether all tasks have been assigned. If not, the task is changed to v+1 and step (1.1) is executed again; if yes, the task assignment is completed.

[0032] Furthermore, the investment management system optimization module includes:

[0033] The big data analysis unit executes the "data processing" instruction, cleans and organizes the project data in the automated task scheduling unit; collects, processes and analyzes task execution status data, and extracts data information;

[0034] The intelligent prediction model unit executes the "model prediction" instruction to generate prediction results; based on the data information provided by the big data analysis unit, it builds and runs the intelligent prediction model to predict the effectiveness of the investment project;

[0035] The comprehensive evaluation system construction unit executes the "evaluation construction" instruction to evaluate the investment project according to the preset standards; constructs the investment effectiveness evaluation system according to the investment objectives and investment project effectiveness, and generates an evaluation report;

[0036] The predicted investment project results include:

[0037] (2.1) Calculate the investment P that can be included in the transmission and distribution price items issued by the distribution network in the planned year in , including investment in infrastructure, technical renovation and overhaul, marketing, and informatization;

[0038] (2.2) Forecast of annual incremental investment internal rate of return on electricity purchase cost

[0039]

[0040] is the average unit price of electricity purchased by the enterprise, is the estimated annual additional purchased electricity, α ba is the base year network loss rate;

[0041] (2.3) Forecast the average annual internal rate of return on investment for electricity purchase costs

[0042]

[0043] To predict the total annual electricity sales, The newly added fixed assets formed by the current power grid investment, To assess the total value of fixed assets in the year;

[0044] (2.4) Calculate the operation and maintenance costs of the additional fixed assets in the power grid investment assessment year

[0045]

[0046] π is the operation and maintenance fee rate;

[0047] (2.5) Calculate the operation and maintenance costs corresponding to the internal rate of return of the average investment

[0048]

[0049] is the original value of fixed assets owned by the distribution network enterprise in the base year, The annual operation and maintenance fee of the distribution network;

[0050] (2.6) Calculation of taxes including VAT Adjustment of income taxG super and VATsu ;

[0051]

[0052] O f is the electricity gain, O lo For loss reduction benefit, O e For reliability improvement benefits, P o is the electricity purchase cost, P om is the operation and maintenance fee, ρ is the value-added tax rate for electricity charges;

[0053]

[0054] ω is the additional tax rate;

[0055]

[0056] ψ is the residual value rate of fixed assets, τ is the adjusted income tax rate, and a is the depreciation period of newly added fixed assets;

[0057] (2.7) Predicting decommissioning costs

[0058]

[0059] P Invest is the power grid investment in this period, ι is the ratio coefficient of processing cost to investment, ψ is the residual value rate of fixed assets;

[0060] (2.8) Calculate the effectiveness of investment projects:

[0061]

[0062] E ev To evaluate the annual power supply reliability of the power grid, E ba is the power supply reliability rate of the base year, B max The total target benefit of the project.

[0063] Furthermore, the smart management application module includes:

[0064] The real-time monitoring unit executes the "data collection" instruction to obtain real-time data from the IoT device; the operation status data of the distribution network project is collected in real time through the IoT sensor, the automated task scheduling unit and the big data analysis unit; the IoT sensor refers to a device that is connected to the full-link optimization management system of the distribution network project through the network and is responsible for collecting various data of the distribution network project;

[0065] The data analysis and processing unit executes the "data mining" instruction to analyze the data; analyzes the data collected by the real-time monitoring unit, and gives the results of the impact of adjusting the indicators in the Internet of Things sensors, automated task scheduling units and big data analysis units on the effectiveness of the investment project;

[0066] The intelligent decision support unit executes the "decision suggestion" instruction, generates and displays management decisions; based on the results of the data analysis and processing unit, it provides intelligent management decisions to assist decision makers in making better choices;

[0067] The generating and displaying of management decisions refers to adjusting the indicators in predicting the effectiveness of investment projects so as to maximize the calculated investment project effectiveness value.

[0068] Furthermore, the training and promotion module includes:

[0069] The online education platform development unit executes the "platform development" instructions to build and optimize the online learning platform; designs, develops and maintains the online learning platform to implement course management, user management and learning progress tracking;

[0070] The multimedia content production unit executes the "content production" command to produce multimedia content according to training needs; uploads and produces training videos and tutorials;

[0071] The technical support service system unit executes "technical support" instructions, responds to users' technical consultations and problem feedback; provides users with technical support and problem-solving services, including online help, hotline consultation and remote assistance;

[0072] The training videos and tutorials include teaching content on various indicator skills for improving the effectiveness of predicting investment projects and shortening the time for assigning tasks.

[0073] Furthermore, the comprehensive monitoring and evaluation module includes:

[0074] The real-time monitoring unit executes the "status monitoring" instruction to monitor and record the system operation status; it monitors the project status information of the entire system in real time and controls the project status information within the preset threshold range;

[0075] The performance evaluation unit executes the "performance evaluation" instruction, analyzes system performance and generates an evaluation report; based on the preset distribution network project evaluation indicators and algorithms, it evaluates system performance and generates an evaluation report;

[0076] The continuous improvement management unit executes the "optimization suggestion" instruction, proposes to adjust the operation status data, enhance functions, improve performance and optimize processes according to the results of the system performance evaluation, and guides system users to continuously optimize the system configuration;

[0077] The distribution network project evaluation indicators and algorithms described above evaluate system performance, including:

[0078] (3.1) Evaluate the load margin FN of the distribution network:

[0079]

[0080] C0 is the current load size of the distribution network, C cr is the critical load size;

[0081] (3.2) Evaluate the voltage margin D(V) of the distribution network:

[0082]

[0083] V is the actual voltage of the distribution network; V l It is the maximum reasonable value within the threshold specified for distribution network construction; V h The minimum reasonable value within the threshold specified for distribution network construction; V wh It is the maximum warning value within the threshold specified for distribution network construction; V w l Specify minimum warning values ​​within thresholds for distribution network construction;

[0084] (3.3) Evaluate the load transfer capacity τ of distribution network lines l :

[0085]

[0086] I max 2 is the actual maximum current of the load end based on the actual requirements of the project; I lm2 is the actual current value of the load end based on the actual project; I lm1 is the actual current value of the negative power supply unit based on the actual project;

[0087] (3.4) Evaluate the reactive power adequacy capacity DY of the distribution network:

[0088]

[0089] C is the actual load power of the distribution network, is the real-time power factor of the load, D is the target power factor based on the actual load of the project. m is the real-time power of the average load of each node in a single inspection cycle of the distribution network, D m ax is the maximum load of the distribution network during the inspection period;

[0090] (3.5) After standardizing the indicators, the distribution network project evaluation index S is calculated according to the weight value:

[0091] S=η1FN+η2D(V)+η3τ l +η4DY;

[0092] η i is the normalized weight for each evaluation metric; γ i The entropy value of the corresponding indicator for a single node in the distribution network;

[0093] The said making suggestions for function enhancement, performance improvement and process optimization based on the results of the system performance evaluation and promoting continuous optimization of the system refers to judging the distribution network project evaluation index S. When the S value is less than 95% of the threshold value S' set by the system, adjusting the distribution network parameters according to the algorithm to improve the value of the distribution network project evaluation index S; when the S value is greater than or equal to 95% of the threshold value S' set by the system, adjusting the distribution network parameters according to the algorithm to reduce the S value of the distribution network project evaluation index.

[0094] The present invention also aims to provide a method for optimizing the management of all aspects of a distribution network project, comprising the following steps:

[0095] (1) Process modeling and optimization: sort out and set the management process of all aspects of the distribution network project, clarify the responsibilities, content, and processes of each department and each node, and form a normalized work process; specifically include:

[0096] The process modeling unit executes the "build model" instruction to generate a project management flowchart; builds a model of the distribution network project management process based on the project target information, and defines the process nodes and flow logic through a graphical interface or programming interface; the role and permission management unit executes the "assign permissions" instruction to set access and operation permissions for different users at each node in the project management process, including role definition, permission allocation and permission verification; the automated task scheduling unit executes the "task scheduling" instruction to automatically assign tasks to users according to preset rules, configure the distribution network project management process model, and monitor the execution progress; automatically assigns and schedules tasks to corresponding executors based on the task definition and priority in the project management process, and monitors the task execution status;

[0097] (2) Investment analysis and forecasting: collecting workflow execution information of the project management process optimization module, conducting precise investment strategy research and execution, building a comprehensive investment management effectiveness evaluation system, and completing investment scale structure calculation, key node management and control, project quantification, and investment effectiveness analysis through data analysis and forecasting; specifically including:

[0098] The big data analysis unit executes the "data processing" instruction to clean and organize the project data in the automated task scheduling unit; collects, processes and analyzes the task execution status data and extracts data information; the intelligent prediction model unit executes the "model prediction" instruction to generate prediction results; based on the data information provided by the big data analysis unit, it builds and runs the intelligent prediction model to predict the effectiveness of investment projects; the comprehensive evaluation system construction unit executes the "evaluation construction" instruction to evaluate investment projects according to preset standards; according to the investment goals and investment project effectiveness, it builds an investment effectiveness evaluation system and generates an evaluation report;

[0099] (3) Smart management and decision-making: real-time monitoring of the management process of all aspects of the project based on investment performance results and workflow execution information, and providing adjustment information for the project management process optimization module and the investment management system optimization module. Through the integration of large models, real-time monitoring, data analysis and smart decision-making of distribution network projects are carried out; specifically, the following are included:

[0100] The real-time monitoring unit executes the "data collection" instruction to obtain real-time data from the Internet of Things devices; the operation status data of the distribution network project is collected in real time through the Internet of Things sensors, the automated task scheduling unit and the big data analysis unit; the data analysis and processing unit executes the "data mining" instruction to analyze the data collected by the real-time monitoring unit, and gives the results of the impact of adjusting various indicators in the Internet of Things sensors, the automated task scheduling unit and the big data analysis unit on the effectiveness of the investment project; the intelligent decision support unit executes the "decision suggestion" instruction to generate and display management decisions; based on the results of the data analysis and processing unit, intelligent management decisions are provided to assist decision makers in making better choices; the generation and display of management decisions refers to adjusting the indicators in the predicted investment project effectiveness so that the calculated investment project effectiveness value is the highest;

[0101] (4) Training and technical support: providing online learning resources and technical support, and sharing knowledge on the application of intelligent management of distribution network projects in the investment management system optimization module and the project management process optimization module; specifically including:

[0102] The online education platform development unit executes the "platform development" instruction to build and optimize the online learning platform; designs, develops and maintains the online learning platform to realize course management, user management and learning progress tracking; the multimedia content production unit executes the "content production" instruction to produce multimedia content according to training needs; uploads and produces training videos and tutorials; the technical support service system unit executes the "technical support" instruction to respond to users' technical consultations and problem feedback; provides users with technical support and problem-solving services, including online help, hotline consultation and remote assistance; the training videos and tutorials include teaching content on various indicator skills to improve the effectiveness of predicting investment projects and shortening the time for assigning tasks;

[0103] (5) Comprehensive monitoring and evaluation: The project management process optimization module, investment management system optimization module, smart management application module, and training and promotion module are used to monitor the operation of the distribution network projects managed by the system in real time, including project execution status, investment project results, and distribution network project evaluation indicators. Through regular evaluation and feedback mechanisms, the system performance and functions are optimized; specifically, the following are included:

[0104] The real-time monitoring unit executes the "status monitoring" instruction to monitor and record the system operation status; it monitors the project status information of the entire system in real time; the performance evaluation unit executes the "performance evaluation" instruction to analyze the system performance and generate an evaluation report; based on the preset distribution network project evaluation indicators and algorithms, it evaluates the system performance and generates an evaluation report; the continuous improvement management unit executes the "optimization suggestion" instruction to propose function enhancement, performance improvement and process optimization suggestions based on the results of the system performance evaluation and promote continuous optimization of the system

[0105] Compared with the prior art, the present invention has the following beneficial effects:

[0106] (1) Improve management efficiency:

[0107] Through the project management process optimization module, the responsibilities, content and processes of each department and node are clarified, a normalized work flow is formed, and differentiated management processes are formulated according to the principle of one area, one plan, which effectively reduces redundant links and human errors in management and significantly improves project management efficiency.

[0108] The introduction of the automated task scheduling unit can automatically assign and schedule tasks to corresponding executors and monitor the task execution status in real time, further speeding up task processing and improving work efficiency.

[0109] (2) Enhance the scientific nature of investment decisions:

[0110] The investment management system optimization module has built an investment management comprehensive effectiveness evaluation system through precise investment strategy research and execution. Based on big data analysis and intelligent prediction models, the system can complete investment scale structure calculation, key node management and control, project standard quantification and investment effectiveness analysis, providing a scientific basis for investment decision-making and reducing investment risks.

[0111] Through regular analysis and evaluation of investment effectiveness, we can timely adjust investment strategies, optimize resource allocation and improve investment returns.

[0112] (3) Improve the level of smart management:

[0113] The smart management application module integrates a large model to achieve real-time monitoring, data analysis and intelligent decision-making of distribution network projects. This function can grasp the operating status of distribution network projects in real time, discover and deal with potential problems in a timely manner, and improve the safety and reliability of the project.

[0114] The intelligent decision support unit can provide intelligent management decisions based on the results of the data analysis and processing unit, assist decision makers in making better choices, and improve the intelligence level of management decisions.

[0115] (4) Promoting knowledge sharing and skills improvement:

[0116] The training and promotion module provides users with a wealth of learning resources and technical support through an online learning platform and technical support service system. This not only helps users master the knowledge and skills of intelligent management applications of distribution network projects, but also promotes the sharing and dissemination of knowledge and improves the technical level of the entire industry.

[0117] (5) Comprehensive monitoring and continuous optimization:

[0118] The comprehensive monitoring and evaluation module monitors the operation of the optimization management system of the distribution network project in real time, including process execution efficiency, investment effectiveness and smart application effect. Through regular evaluation and feedback mechanism, problems in the system can be discovered and solved in time, and system performance and functions can be optimized.

[0119] The continuous improvement management unit can propose and implement optimization measures based on the evaluation results, promote continuous optimization of the system, and ensure that the optimization management system of all aspects of the distribution network project always remains in the best condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0120] Figure 1 This is a schematic diagram of a module for optimizing the management system for all aspects of a distribution network project according to the present invention;

[0121] Figure 2 The present invention is a flow chart of a full-link optimization management system for a distribution network project. DETAILED DESCRIPTION

[0122] 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.

[0123] 1. About the system

[0124] The present invention proposes a full-link optimization management system for distribution network projects, which aims to achieve full-chain management of distribution network projects from project establishment to implementation, monitoring, evaluation and optimization through highly integrated and intelligent means. Figure 1 As shown, the following is the specific implementation process of this system:

[0125] 1. Project management process optimization module

[0126] This module first constructs a graphical model of distribution network project management through the process modeling unit. This model is guided by the project goals and defines in detail every stage of the project from initiation, planning, execution, monitoring to closing, and displays them intuitively through a graphical interface. During the modeling process, the process nodes and flow logic can be flexibly adjusted according to the specific needs of the project to ensure the scientificity and efficiency of the management process.

[0127] The role and permission management unit is responsible for assigning specific access and operation permissions to different users in the project management process. Through the three steps of role definition, permission allocation and permission verification, it ensures that each user operates within the scope of his or her responsibilities, which not only ensures the smooth progress of the project, but also avoids the risk of information leakage and misoperation.

[0128] The automated task scheduling unit automatically assigns and schedules tasks to the corresponding executors based on the task definition and priority in the project management process. At the same time, the unit also monitors the task execution status in real time and issues early warnings for progress delays or abnormal situations, ensuring that the project is carried out efficiently as planned.

[0129] 2. Investment management system optimization module

[0130] As the foundation of investment management, the big data analysis unit is responsible for collecting, cleaning and organizing project data. By processing massive amounts of data, valuable information is extracted to provide data support for subsequent investment decisions.

[0131] The intelligent prediction model unit builds and runs an intelligent prediction model based on the data information provided by the big data analysis unit. The model can comprehensively consider multiple factors and accurately predict the effectiveness of investment projects. The prediction results not only include quantitative indicators such as the investment cost of each project, but also cover the overall effectiveness of the investment project, providing a comprehensive reference for investment decisions.

[0132] The comprehensive evaluation system construction unit conducts a comprehensive evaluation of investment projects according to preset standards. This evaluation system covers multiple dimensions such as investment goals, investment processes, and investment results, and can intuitively display the overall performance of investment projects by generating fixed-paradigm evaluation reports. The evaluation results are not only used for performance evaluation in the later stages of the project, but also provide valuable experience for future investment decisions.

[0133] 3. Smart management application module

[0134] The real-time monitoring unit collects the operating status data of the distribution network project in real time through the IoT sensors. These data include but are not limited to key parameters such as voltage, current, power factor, and equipment temperature, providing basic data support for subsequent data analysis and intelligent decision-making.

[0135] The data analysis and processing unit conducts in-depth analysis on the data collected by the real-time monitoring unit. By using advanced data mining and machine learning algorithms, valuable information and patterns are extracted from massive data, providing a scientific basis for management decisions.

[0136] The intelligent decision support unit provides intelligent management decision suggestions based on the results of the data analysis and processing unit. These suggestions include but are not limited to optimizing investment strategies, adjusting equipment operating status, and warning of potential risks, etc., aiming to help decision makers make better choices and improve the overall benefits of the project.

[0137] 4. Training and promotion module

[0138] The online education platform development unit has built a comprehensive and easy-to-use online learning platform. The platform not only provides a wealth of course resources and learning tools, but also implements functions such as course management, user management, and learning progress tracking, providing users with a convenient learning experience.

[0139] The multimedia content production unit produces a variety of multimedia content based on training needs. These contents include video tutorials, graphic materials, online tests and other forms, aiming to help users fully master the knowledge and skills of intelligent management applications of distribution network projects.

[0140] The technical support service system unit provides users with a full range of technical support services. Whether it is technical consultation, problem feedback or system maintenance, users can get timely and professional answers and help through online help, hotline consultation and remote assistance.

[0141] 5. Comprehensive monitoring and evaluation module

[0142] The real-time monitoring unit monitors the operating status of the entire system in real time. By monitoring and recording key information such as project execution status, investment project results, and distribution network project evaluation indicators, the stable operation and efficient work of the system are ensured.

[0143] The performance evaluation unit evaluates system performance based on preset evaluation indicators and algorithms. The evaluation results not only include the specific values ​​of system performance, but also include detailed evaluation reports and recommended improvement measures, providing strong support for the continuous optimization of the system.

[0144] The continuous improvement management unit proposes and implements optimization measures based on the results of the performance evaluation unit. These measures cover multiple aspects such as function enhancement, performance improvement and process optimization, aiming to promote continuous improvement and upgrading of the system to meet the growing needs of users.

[0145] 2. About Methods

[0146] The method for optimizing and managing all aspects of a distribution network project provided by the present invention ensures that every aspect of a project from initiation to completion is effectively optimized and managed through systematic steps. Figure 2 shown.

[0147] The following is a specific implementation of the method:

[0148] 1. Process modeling and optimization

[0149] (1) Process sorting and modeling: First, the entire process of project management is carefully sorted out, including the project start-up, planning, execution, monitoring and closing stages. Then, the process modeling unit in the project management process optimization module is used to build a distribution network project management flowchart through a graphical interface or programming interface. The flowchart clearly defines the responsibilities, content and processes of each department and node, ensuring that each step has clear guidance.

[0150] (2) Role authority allocation: In the project management process, access and operation permissions are allocated to different users through the role authority management unit. This includes the definition of different roles such as project managers, technical personnel, and financial personnel, as well as the allocation and verification of their permissions at each node in the project management process. This ensures that each user can only operate within the scope of his or her responsibilities, thereby improving the security and efficiency of project management.

[0151] (3) Automated task scheduling: The automated task scheduling unit automatically assigns tasks to corresponding executors based on the task definition and priority in the project management process. The unit also monitors the task execution status in real time and issues early warnings for progress delays or abnormal situations, helping project managers to adjust plans in a timely manner and ensure that the project is carried out efficiently as planned.

[0152] (4) Differentiated management process formulation: Based on the principle of one area, one plan, differentiated management processes are tailored to the geographical, economic, load distribution, equipment conditions and other characteristics of different regions or areas. This ensures that the management process is more targeted and operational, and improves the flexibility and effectiveness of project management.

[0153] 2. Investment analysis and forecasting

[0154] (1) Data collection and processing: The big data analysis unit in the investment management system optimization module is used to comprehensively collect, clean and organize project data. These data include market environment data, project execution status data, equipment status data, etc., providing a basis for subsequent investment analysis and forecasting.

[0155] (2) Intelligent prediction model construction: Based on the data information provided by the big data analysis unit, the intelligent prediction model unit constructs and runs the intelligent prediction model. The model can comprehensively consider multiple factors and predict the effectiveness of investment projects by predicting indicators such as investment scale structure, key node management and control, project establishment standard quantification and investment effectiveness, providing a scientific basis for investment decisions.

[0156] (3) Investment effectiveness evaluation: The comprehensive evaluation system construction unit evaluates investment projects according to preset standards. By constructing an investment effectiveness evaluation system, an evaluation report is generated to intuitively display the overall performance of the investment project. The evaluation results are not only used for performance evaluation in the later stage of the project, but also provide valuable experience for future investment decisions.

[0157] 3. Smart management and decision-making

[0158] (1) Real-time monitoring and data analysis: The real-time monitoring unit in the smart management application module collects the operating status data of the distribution network project in real time through IoT sensors. The data analysis and processing unit conducts in-depth analysis on the collected data to extract valuable information and patterns.

[0159] (2) Intelligent decision support: Based on the results of the data analysis and processing unit, the intelligent decision support unit generates and displays management decisions. These decisions include optimizing investment strategies, adjusting equipment operating status, and warning of potential risks, etc., aiming to help decision makers make better choices and improve the overall benefits of the project.

[0160] 4. Training and promotion

[0161] (1) Online education platform construction: The online education platform development unit in the training and promotion module builds and optimizes the online learning platform. The platform provides rich course resources and learning tools, and implements functions such as course management, user management, and learning progress tracking.

[0162] (2) Multimedia content production: The multimedia content production unit produces a variety of multimedia content according to training needs, including training videos, tutorials, etc. These contents cover teaching content on various indicators and skills such as improving the effectiveness of forecasting investment projects and shortening the time for allocating tasks.

[0163] (3) Technical support services: The technical support service system unit provides users with a full range of technical support services. Whether it is technical consultation, problem feedback or system maintenance, users can obtain timely and professional answers and help through online help, hotline consultation and remote assistance.

[0164] 5. Comprehensive monitoring and evaluation

[0165] (1) Real-time monitoring: The real-time monitoring unit in the comprehensive monitoring and evaluation module monitors the operating status of the entire system in real time, including monitoring and recording of key indicators such as server load, network traffic, and task execution status, to ensure stable operation and efficient work of the system.

[0166] (2) Performance evaluation: The performance evaluation unit evaluates system performance based on preset evaluation indicators and algorithms. The evaluation results include not only the specific values ​​of system performance but also a detailed evaluation report and recommended improvement measures.

[0167] (3) Continuous Improvement: The continuous improvement management unit proposes and implements optimization measures based on the results of the performance evaluation unit. These measures cover multiple aspects such as functional enhancement, performance improvement, and process optimization, aiming to promote continuous improvement and enhancement of the system to meet the growing needs of users.

[0168] Preferably, the automatic assignment of tasks to users includes:

[0169] (1.1) Calculate the time CGG(v,w) taken by the main task v to be transmitted to the node w:

[0170] CGG(v,w)=C up (v)+C down (w)+C ud (v,w);

[0171] C up (v) is the time required to upload data from the node where the main task v is located, C down (w) is the time required for node w to download the task, C ud (v,w) is the time it takes for data to be transmitted from node v to node w through the network;

[0172]

[0173] size is the data size of the task, Sid is the task number, and Oj is the rate at which the node uploads or downloads data;

[0174]

[0175] α is the value coefficient;

[0176] (1.2) Calculate the task computation time CPG(S id,w) of the task at node j:

[0177]

[0178] Yth(Sid) is the task calculation length, Zip(w) is the node computing capacity;

[0179] Calculate the expected completion time CSG(Sid,w) of task v assigned to node j:

[0180] CSG(Sid,w)=CFG(Sid,w)+CPG(Sid,w);

[0181] CFG(Sid,w) is the time when task v starts executing on node j;

[0182] (1.3) Check whether all nodes have been traversed. If not, change the node to j+1 and re-execute step (1.1); if yes, execute the next step;

[0183] (1.4) Assign the task to the server node with the fastest expected completion time;

[0184] (1.5) Check whether all tasks have been assigned. If not, the task is changed to v+1 and step (1.1) is executed again; if yes, the task assignment is completed.

[0185] The creativity of this method is reflected in three aspects:

[0186] (1) Improve the efficiency of task allocation:

[0187] The present invention can automatically assign tasks to users according to preset rules and monitor the progress of task execution in real time through an automated task scheduling unit. This mechanism greatly reduces the time and labor cost of manually assigning tasks and improves the efficiency of task assignment.

[0188] (2) Optimize resource utilization:

[0189] In the process of automatically allocating tasks, the system considers multiple factors such as data size, node computing power, data transmission time, etc., and calculates the expected completion time of each node, and allocates the task to the node with the fastest expected completion time. This method optimizes resource utilization, makes full use of the computing power of each node, and avoids idle or overloaded resources.

[0190] (3) Improve project execution efficiency:

[0191] Automated task scheduling also ensures that tasks can be executed in order according to priority and urgency, avoiding conflicts and delays between tasks. This helps improve the execution efficiency of the entire distribution network project and ensure that the project is completed on time and with quality.

[0192] Preferably, the predicted investment project effectiveness includes:

[0193] (2.1) Calculate the investment P that can be included in the transmission and distribution price items issued by the distribution network in the planned year in , including investment in infrastructure, technical renovation and overhaul, marketing, and informatization;

[0194] (2.2) Forecast of annual incremental investment internal rate of return on electricity purchase cost

[0195]

[0196] is the average unit price of electricity purchased by the enterprise, is the estimated annual additional purchased electricity, α ba is the base year network loss rate;

[0197] (2.3) Forecast the average annual internal rate of return on investment for electricity purchase costs

[0198]

[0199] To predict the total annual electricity sales, The newly added fixed assets formed by the current power grid investment, To assess the total value of fixed assets in the year;

[0200] (2.4) Calculate the operation and maintenance costs of the additional fixed assets in the power grid investment assessment year

[0201]

[0202] π is the operation and maintenance fee rate;

[0203] (2.5) Calculate the operation and maintenance costs corresponding to the internal rate of return of the average investment

[0204]

[0205] is the original value of fixed assets owned by the distribution network enterprise in the base year, The annual operation and maintenance fee of the distribution network;

[0206] (2.6) Calculation of taxes including VAT Adjustment of income taxG superand VAT su ;

[0207]

[0208] O f is the electricity gain, O lo For loss reduction benefit, O e For reliability improvement benefits, P o is the electricity purchase cost, P om is the operation and maintenance fee, ρ is the value-added tax rate for electricity charges;

[0209]

[0210] ω is the additional tax rate;

[0211]

[0212] ψ is the residual value rate of fixed assets, τ is the adjusted income tax rate, and a is the depreciation period of newly added fixed assets;

[0213] (2.7) Predicting decommissioning costs

[0214]

[0215] P Invest is the power grid investment in this period, ι is the ratio coefficient of processing cost to investment, ψ is the residual value rate of fixed assets;

[0216] (2.8) Calculate the effectiveness of investment projects:

[0217]

[0218] E ev To evaluate the annual power supply reliability of the power grid, E ba is the power supply reliability rate of the base year, B max The total target benefit of the project.

[0219] The creativity of this method is reflected in three aspects:

[0220] (1) Enhance the scientific nature of investment decisions:

[0221] The investment management system optimization module can accurately predict the effectiveness of investment projects through big data analysis units and intelligent prediction model units. This includes comprehensive consideration of investment scale structure, key node control, project quantification and other aspects, making investment decisions more scientific and reasonable.

[0222] (2) Reduce investment risks:

[0223] By predicting the effectiveness of investment projects, enterprises can have a clearer understanding of the potential benefits and risks of the projects. This helps enterprises to more carefully assess risks during investment decision-making and take appropriate risk control measures, thereby reducing investment risks.

[0224] (3) Improve investment returns:

[0225] Predicting the effectiveness of investment projects can also help companies optimize their investment strategies and improve their return on investment. Companies can adjust the scale and direction of investment based on the forecast results and invest funds in more promising projects to obtain higher returns on investment.

[0226] Preferably, the evaluation of system performance includes:

[0227] (3.1) Evaluate the load margin FN of the distribution network:

[0228]

[0229] C0 is the current load size of the distribution network, C cr is the critical load size;

[0230] (3.2) Evaluate the voltage margin D(V) of the distribution network:

[0231]

[0232] V is the actual voltage of the distribution network; V l It is the maximum reasonable value within the threshold specified for distribution network construction; V h The minimum reasonable value within the threshold specified for distribution network construction; V wh It is the maximum warning value within the threshold specified for distribution network construction; V w l Specify minimum warning values ​​within thresholds for distribution network construction;

[0233] (3.3) Evaluate the load transfer capacity τ of distribution network lines l :

[0234]

[0235] I max 2 is the actual maximum current of the load end based on the actual requirements of the project; I lm2 is the actual current value of the load end based on the actual project; I lm1 is the actual current value of the negative power supply unit based on the actual project;

[0236] (3.4) Evaluate the reactive power adequacy capacity DY of the distribution network:

[0237]

[0238] C is the actual load power of the distribution network, is the real-time power factor of the load, D is the target power factor based on the actual load of the project. m is the real-time power of the average load of each node in a single inspection cycle of the distribution network, D m ax is the maximum load of the distribution network during the inspection period;

[0239] (3.5) After standardizing the indicators, the evaluation index S is calculated according to the weight value:

[0240] S=η1FN+η2D(V)+η3τ l +η4DY;

[0241] η i is the normalized weight for each evaluation metric; γ i The entropy value of the corresponding indicator for a single node in the distribution network.

[0242] The creativity of this method is reflected in three aspects:

[0243] (1) Ensure stable operation of the system:

[0244] The comprehensive monitoring and evaluation module monitors the system's operating status in real time through the real-time monitoring unit, including key indicators such as server load, network traffic, task execution status, etc. This helps to promptly discover and solve potential system problems and ensure stable system operation.

[0245] (2) Optimize system performance:

[0246] The performance evaluation unit evaluates system performance based on preset evaluation indicators and algorithms and generates an evaluation report. This provides the enterprise with a basis for optimizing system performance. The enterprise can enhance system functions, improve performance and optimize processes based on the evaluation results, thereby further improving system performance.

[0247] (3) Improve management efficiency:

[0248] The continuous improvement management unit proposes and implements optimization measures based on the results of the performance evaluation unit to promote continuous optimization of the system. This helps the company form a closed-loop management mechanism, continuously improve management efficiency and reduce management costs.

[0249] Through the implementation of the above steps, the distribution network project full-link optimization management method provided by the present invention can significantly improve project management efficiency, enhance the scientific nature of investment decisions, improve the level of intelligent management, promote knowledge sharing and skill improvement, and achieve comprehensive monitoring and continuous optimization.

[0250] In summary, the distribution network project full-link optimization management system and method proposed in the present invention realizes efficient management of the entire chain of distribution network projects from project establishment to implementation, monitoring, evaluation and optimization through systematic processes and methodologies. The specific implementation methods of the system and method cover multiple aspects such as process modeling and optimization, investment analysis and forecasting, intelligent decision support, and continuous optimization and improvement, providing comprehensive and powerful support for the intelligent management of distribution network projects.

[0251] In addition, since the system and method is a relatively large and complex integrated modular system, the functions and information processing methods that can be directly purchased on the market or implemented through technology introduction will not be elaborated here, and can all be regarded as technical solutions that can be implemented by the existing technology in this field.

[0252] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solutions and inventive concepts of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A full-link optimization management system for distribution network projects, characterized in that: include: The project management process optimization module is used to sort out and set the management process of all aspects of the distribution network project, clarify the responsibilities, content, and processes of each department and each node, and form a normalized work process; The investment management system optimization module collects the workflow execution information of the project management process optimization module to carry out precise investment strategy research and execution, build an investment management comprehensive effectiveness evaluation system, and complete investment scale structure calculation, key node management and control, project quantification, and investment effectiveness analysis through data analysis and prediction; The smart management application module monitors the management process of all aspects of the project in real time based on the investment performance results and workflow execution information, and provides adjustment information for the project management process optimization module and the investment management system optimization module. By integrating the big model, it conducts real-time monitoring, data analysis and intelligent decision-making of the distribution network project; The training and promotion module is used to provide online learning resources and technical support, and to share knowledge on the intelligent management application of distribution network projects in the investment management system optimization module and the project management process optimization module; The comprehensive monitoring and evaluation module collects information from the project management process optimization module, investment management system optimization module, smart management application module and training and promotion module, including project execution status, investment project effectiveness, and distribution network project evaluation indicators. It is used to monitor the operation of the distribution network projects managed by the system in real time, and optimize system performance and functions through regular evaluation and feedback mechanisms.

2. A distribution network project full-link optimization management system as claimed in claim 1, characterized in that: The project management process optimization module includes: The process modeling unit executes the "build model" command to generate a project management flowchart; builds the distribution network project management process according to the project target information, and defines the process nodes and flow logic through a graphical interface or programming interface; The distribution network project management process described configures the process nodes of project initiation, execution, monitoring and completion according to project requirements, the sequence and execution relationship between process nodes, project target information and task information of different nodes; The role authority management unit executes the "assign authority" instruction to set access and operation authority for different users at each node in the distribution network project management process, including role definition, authority allocation and authority verification; The automated task scheduling unit executes the "task scheduling" instruction, automatically assigns tasks to users according to preset rules, configures the distribution network project management process, and monitors the execution progress; automatically assigns and schedules tasks to corresponding executors according to the task definition and priority in the project management process, and monitors the task execution status; The automatic assignment of tasks to users includes: (1.1) Calculate the time CGG(v,w) taken by the main task v to be transmitted to the node w: CGG(v,w)=C up (v)+C down (w)+C ud (v,w); C up (v) is the time required to upload data from the node where the main task v is located, C down (w) is the time required for node w to download the task, C ud (v,w) is the time it takes for data to be transmitted from node v to node w through the network; size is the data size of the task, Sid is the task number, and Oj is the rate at which the node uploads or downloads data; α is the value coefficient; (1.2) Calculate the task computation time CPG(Sid,w) of the task at node j: Yth(Sid) is the task calculation length, Zip(w) is the node computing capacity; Calculate the expected completion time CSG(Sid,w) of task v assigned to node j: CSG(Sid,w)=CFG(Sid,w)+CPG(Sid,w); CFG(Sid,w) is the time when task v starts executing on node j; (1.3) Check whether all nodes have been traversed. If not, change the node to j+1 and re-execute step (1.1); if yes, execute the next step; (1.4) Assign the task to the server node with the fastest expected completion time; (1.5) Check whether all tasks have been assigned. If not, the task is changed to v+1 and step (1.1) is executed again; if yes, the task assignment is completed.

3. A distribution network project full-link optimization management system as claimed in claim 1, characterized in that: The investment management system optimization module includes: The big data analysis unit executes the "data processing" instruction, cleans and organizes the project data in the automated task scheduling unit; collects, processes and analyzes the task execution status data, and extracts data information; The intelligent prediction model unit executes the "model prediction" instruction to generate prediction results; based on the data information provided by the big data analysis unit, it builds and runs the intelligent prediction model to predict the effectiveness of the investment project; The comprehensive evaluation system construction unit executes the "evaluation construction" instruction to evaluate the investment project according to the preset standards; it constructs the investment effectiveness evaluation system according to the investment objectives and investment project results, and generates an evaluation report; The predicted investment project results include: (2.1) Calculate the investment P that can be included in the transmission and distribution price items issued by the distribution network in the planned year in , including investment in infrastructure, technical renovation and overhaul, marketing, and informatization; (2.2) Forecast of annual incremental investment internal rate of return on electricity purchase cost is the average unit price of electricity purchased by the enterprise, is the estimated annual additional purchased electricity, α ba is the base year network loss rate; (2.3) Forecast the average annual internal rate of return on investment for electricity purchase costs To predict the total annual electricity sales, The newly added fixed assets formed by the current power grid investment, To assess the total value of fixed assets in the year; (2.4) Calculate the operation and maintenance costs of the additional fixed assets in the power grid investment assessment year π is the operation and maintenance fee rate; (2.5) Calculate the operation and maintenance costs corresponding to the internal rate of return of the average investment is the original value of fixed assets owned by the distribution network enterprise in the base year, The annual operation and maintenance fee of the distribution network; (2.6) Calculation of taxes including VAT Adjustment of income taxG super and VAT su ; O f is the electricity gain, O lo For loss reduction benefit, O e For reliability improvement benefits, P o is the electricity purchase cost, P om is the operation and maintenance fee, ρ is the value-added tax rate for electricity charges; ω is the additional tax rate; ψ is the residual value rate of fixed assets, τ is the adjusted income tax rate, and a is the depreciation period of newly added fixed assets; (2.7) Predicting decommissioning costs P Invest is the power grid investment in this period, ι is the ratio coefficient of processing cost to investment, ψ is the residual value rate of fixed assets; (2.8) Calculate the effectiveness of investment projects: E ev To evaluate the annual power supply reliability of the power grid, E ba is the power supply reliability rate of the base year, B max The total target benefit of the project.

4. A distribution network project full-link optimization management system as claimed in claim 1, characterized in that: The smart management application module includes: The real-time monitoring unit executes the "data collection" instruction to obtain real-time data from the IoT device; the operation status data of the distribution network project is collected in real time through the IoT sensor, the automated task scheduling unit and the big data analysis unit; the IoT sensor refers to a device that is connected to the full-link optimization management system of the distribution network project through the network and is responsible for collecting various data of the distribution network project; The data analysis and processing unit executes the "data mining" instruction to analyze the data; analyzes the data collected by the real-time monitoring unit, and gives the results of the impact of adjusting the indicators in the Internet of Things sensors, automated task scheduling units and big data analysis units on the effectiveness of the investment project; The intelligent decision support unit executes the "decision suggestion" instruction, generates and displays management decisions; based on the results of the data analysis and processing unit, it provides intelligent management decisions to assist decision makers in making better choices; The generating and displaying of management decisions refers to adjusting the indicators in predicting the effectiveness of investment projects so as to maximize the calculated investment project effectiveness value.

5. A distribution network project full-link optimization management system as claimed in claim 1, characterized in that: The training and promotion modules include: The online education platform development unit executes the "platform development" instructions to build and optimize the online learning platform; designs, develops and maintains the online learning platform to implement course management, user management and learning progress tracking; The multimedia content production unit executes the "content production" command to produce multimedia content according to training needs; uploads and produces training videos and tutorials; The technical support service system unit executes "technical support" instructions, responds to users' technical consultations and problem feedback; provides users with technical support and problem-solving services, including online help, hotline consultation and remote assistance; The training videos and tutorials include teaching content on various indicator skills for improving the effectiveness of predicting investment projects and shortening the time for assigning tasks.

6. A distribution network project full-link optimization management system as claimed in claim 1, characterized in that: The comprehensive monitoring and evaluation module includes: The real-time monitoring unit executes the "status monitoring" instruction to monitor and record the system operation status; it monitors the project status information of the entire system in real time and controls the project status information within the preset threshold range; The performance evaluation unit executes the "performance evaluation" instruction, analyzes system performance and generates an evaluation report; based on the preset distribution network project evaluation indicators and algorithms, it evaluates system performance and generates an evaluation report; The continuous improvement management unit executes the "optimization suggestion" instruction, proposes to adjust the operation status data, enhance functions, improve performance and optimize processes according to the results of the system performance evaluation, and guides system users to continuously optimize the system configuration; The distribution network project evaluation indicators and algorithms described above evaluate system performance, including: (3.1) Evaluate the load margin FN of the distribution network: C0 is the current load size of the distribution network, C cr is the critical load size; (3.2) Evaluate the voltage margin D(V) of the distribution network: V is the actual voltage of the distribution network; V l It is the maximum reasonable value within the threshold specified for distribution network construction; V h The minimum reasonable value within the threshold specified for distribution network construction; V wh Specify the maximum warning value within the threshold for distribution network construction; Specify minimum warning values ​​within thresholds for distribution network construction; (3.3) Evaluate the load transfer capacity τ of the distribution network line l : I is the actual maximum current of the load terminal based on the actual requirements of the project; lm2 is the actual current value of the load end based on the actual project; I lm1 is the actual current value of the negative power supply unit based on the actual project; (3.4) Evaluate the reactive power adequacy capacity DY of the distribution network: C is the actual load power of the distribution network, is the real-time power factor of the load, D is the target power factor based on the actual load of the project. m is the real-time power of the average load of each node in a single inspection cycle of the distribution network, D max is the maximum load of the distribution network during the inspection period; (3.5) After standardizing the indicators, the distribution network project evaluation index S is calculated according to the weight value: S=η1FN+η2D(V)+η3τ l +η4DY; η i is the normalized weight for each evaluation metric; γ i The entropy value of the corresponding indicator for a single node in the distribution network; The said making suggestions for function enhancement, performance improvement and process optimization based on the results of the system performance evaluation and promoting continuous optimization of the system refers to judging the distribution network project evaluation index S. When the S value is less than 95% of the threshold value S' set by the system, adjusting the distribution network parameters according to the algorithm to improve the value of the distribution network project evaluation index S; when the S value is greater than or equal to 95% of the threshold value S' set by the system, adjusting the distribution network parameters according to the algorithm to reduce the S value of the distribution network project evaluation index.

7. A method for optimizing the management of all aspects of a distribution network project, characterized in that: The steps include: (1) Process modeling and optimization: sort out and set the management process of all aspects of the distribution network project, clarify the responsibilities, content, and processes of each department and each node, and form a normalized work process; Specifically include: The process modeling unit executes the "build model" command to generate a project management flowchart; builds a model of the distribution network project management process based on the project target information, and defines the process nodes and flow logic through a graphical interface or programming interface; the role and permission management unit executes the "assign permissions" command to set access and operation permissions for different users at each node in the project management process, including role definition, permission allocation and permission verification; the automated task scheduling unit executes the "task scheduling" command to automatically assign tasks to users according to preset rules, configure the distribution network project management process model, and monitor the execution progress; automatically assigns and schedules tasks to corresponding executors based on the task definition and priority in the project management process, and monitors the task execution status; (2) Investment analysis and forecasting: collecting workflow execution information of the project management process optimization module, conducting precise investment strategy research and execution, building a comprehensive investment management effectiveness evaluation system, and completing investment scale structure calculation, key node management and control, project quantification, and investment effectiveness analysis through data analysis and forecasting; specifically including: The big data analysis unit executes the "data processing" instruction to clean and organize the project data in the automated task scheduling unit; collects, processes and analyzes the task execution status data and extracts data information; the intelligent prediction model unit executes the "model prediction" instruction to generate prediction results; based on the data information provided by the big data analysis unit, it builds and runs the intelligent prediction model to predict the effectiveness of investment projects; the comprehensive evaluation system construction unit executes the "evaluation construction" instruction to evaluate investment projects according to preset standards; according to the investment goals and investment project effectiveness, it builds an investment effectiveness evaluation system and generates an evaluation report; (3) Smart management and decision-making: real-time monitoring of the management process of all aspects of the project based on investment performance results and workflow execution information, and providing adjustment information for the project management process optimization module and the investment management system optimization module. Through the integration of large models, real-time monitoring, data analysis and smart decision-making of distribution network projects are carried out; specifically, the following are included: The real-time monitoring unit executes the "data collection" instruction to obtain real-time data from the Internet of Things devices; the operation status data of the distribution network project is collected in real time through the Internet of Things sensors, the automated task scheduling unit and the big data analysis unit; the data analysis and processing unit executes the "data mining" instruction to analyze the data collected by the real-time monitoring unit, and gives the results of the impact of adjusting various indicators in the Internet of Things sensors, the automated task scheduling unit and the big data analysis unit on the effectiveness of the investment project; the intelligent decision support unit executes the "decision suggestion" instruction to generate and display management decisions; based on the results of the data analysis and processing unit, intelligent management decisions are provided to assist decision makers in making better choices; the generation and display of management decisions refers to adjusting the indicators in the predicted investment project effectiveness so that the calculated investment project effectiveness value is the highest; (4) Training and technical support: providing online learning resources and technical support, and sharing knowledge on the application of intelligent management of distribution network projects in the investment management system optimization module and the project management process optimization module; specifically including: The online education platform development unit executes the "platform development" instruction to build and optimize the online learning platform; designs, develops and maintains the online learning platform to realize course management, user management and learning progress tracking; the multimedia content production unit executes the "content production" instruction to produce multimedia content according to training needs; uploads and produces training videos and tutorials; the technical support service system unit executes the "technical support" instruction to respond to users' technical consultations and problem feedback; provides users with technical support and problem-solving services, including online help, hotline consultation and remote assistance; the training videos and tutorials include teaching content on various indicator skills to improve the effectiveness of predicting investment projects and shortening the time for assigning tasks; (5) Comprehensive monitoring and evaluation: The project management process optimization module, investment management system optimization module, smart management application module, and training and promotion module are used to monitor the operation of the distribution network projects managed by the system in real time, including project execution status, investment project results, and distribution network project evaluation indicators. Through regular evaluation and feedback mechanisms, the system performance and functions are optimized; specifically, the following are included: The real-time monitoring unit executes the "status monitoring" instruction to monitor and record the system operation status; it monitors the project status information of the entire system in real time; the performance evaluation unit executes the "performance evaluation" instruction to analyze the system performance and generate an evaluation report; based on the preset distribution network project evaluation indicators and algorithms, it evaluates the system performance and generates an evaluation report; the continuous improvement management unit executes the "optimization suggestion" instruction to propose function enhancement, performance improvement and process optimization suggestions based on the results of the system performance evaluation and promote continuous optimization of the system.

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