Whole-process intelligent management and control method, device and equipment for power grid project
By building a unified database and using linear regression models, integrating data from ERP, PMS, and ECP systems, the problem of system data separation is solved, the overall reflection of project management and risk warning is achieved, cross-professional communication costs are reduced, and project management is improved efficiency.
Patent Information
- Application Number
- CN202411410440.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-10
- Publication Date
- 2024-12-20
AI Technical Summary
In the prior art, the data separation between ERP, PMS, ECP and other systems cannot fully reflect the overall picture of project management, resulting in incomplete information, high cross-professional communication costs, insufficient risk warning capabilities, which increases the complexity of project management and Workload.
By building a unified database, integrating data from ERP, PMS, and ECP systems, extracting characteristic data, calculating project expenses, generating a detailed list of project expense decomposition, establishing an accounting progress control system, and using a linear regression model to obtain project budget expenses and conducting risk warnings.
It realizes seamless access and information sharing of data in each system, reduces cross-professional communication costs, improves project management efficiency, enhances risk warning capabilities, and ensures that the project is carried out smoothly within the budget.
Smart Images
Figure CN119168591A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of power grid project management, and more specifically, to a method, device and equipment for intelligently controlling the entire process of a power grid project. Background Art
[0002] At present, in the process of power grid production cost project management, the data between ERP, PMS, ECP and other systems are fragmented, and each system cannot fully reflect the overall picture of engineering project management. Through offline manual statistical analysis of the status information of various stages such as the early stage, plan and final settlement of cost projects, there are problems such as low efficiency in collecting project progress information and high cross-disciplinary communication costs. Therefore, it is necessary to improve project management efficiency and ensure the quality and effect of project construction through intelligent management and control technology.
[0003] Deficiencies of existing technologies: In existing technologies, data between systems such as ERP, PMS, and ECP are fragmented, and each system cannot fully reflect the overall picture of project management, resulting in incomplete information and difficulty in achieving effective project management; the cost of cross-professional communication is high, and there is a lack of data interaction and integration between different systems. The cost of cross-professional communication is high, which increases the complexity and workload of project management; the risk warning capability is insufficient, and the existing technology lacks a unified risk warning mechanism, which makes it impossible to timely identify and warn of possible risks during project implementation, resulting in blind spots in project management.
[0004] In view of the above problems, the present invention proposes a solution. Summary of the invention
[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method for intelligent management and control of the entire process of a power grid project, which solves the problem of intelligent management and control of the entire process of a power grid project by fusing and analyzing system data.
[0006] To achieve the above object, the present invention provides the following technical solutions: A whole-process intelligent control method for power grid projects obtains data from an ERP system, a PMS system, and an ECP system, and builds a unified database to integrate and synchronize the data in the ERP system, the PMS system, and the ECP system; extracts feature data from the database, analyzes and decomposes the extracted feature data, calculates various costs, and generates a project cost breakdown list; establishes an account entry progress control system based on the generated project cost breakdown list to obtain the actual cost of the project; collects historical data, calculates and generates a trained linear regression model based on the historical data, and brings the feature data into the trained linear regression model to obtain the project budget cost; compares and analyzes the absolute error between the project budget cost and the project actual cost with a preset safety threshold, and issues a risk warning for the project based on the comparison and analysis results.
[0007] In a preferred embodiment, the specific steps for integrating and synchronizing the data are as follows: constructing a unified database to receive and store data from ERP, PMS, and ECP systems, developing data import and export interfaces, and seamlessly connecting the data of each system to the database; writing a data parsing algorithm to convert the imported data into a unified format to obtain synchronized system data.
[0008] In a preferred embodiment, the characteristic data includes the cost allocation of various resources in the project; the additional costs incurred due to changes; the working time records of project team members; and the resource consumption during the project implementation.
[0009] In a preferred embodiment, the actual project cost includes material costs, labor costs and other direct costs.
[0010] In a preferred embodiment, the specific method for obtaining the project budget costs is as follows: collect historical data, create a linear regression model, extract quantifiable characteristic data and the actual historical costs of the project from the collected historical data; substitute the characteristic data and the actual historical costs of the project into the linear regression model to obtain the linear regression coefficients; substitute the linear regression coefficients into the created linear regression model to obtain a trained linear regression model; substitute the characteristic data into the trained linear regression model to obtain the project budget costs.
[0011] In a preferred embodiment, the specific method for obtaining the project budget costs is as follows: collect historical data, create a linear regression model, extract quantifiable characteristic data and the actual historical costs of the project from the collected historical data; substitute the characteristic data and the actual historical costs of the project into the linear regression model to obtain the linear regression coefficients; substitute the linear regression coefficients into the created linear regression model to obtain a trained linear regression model; substitute the characteristic data into the trained linear regression model to obtain the project budget costs.
[0012] A device for a whole-process intelligent control method of a power grid project comprises a data integration module, a feature data extraction module, an actual cost acquisition module, a budget cost acquisition module, and a comparison and analysis module, wherein the modules are connected as follows: a data integration module: acquiring data from an ERP system, a PMS system, and an ECP system, and constructing a unified database to integrate and synchronize the data in the ERP system, the PMS system, and the ECP system; a feature data extraction module: extracting feature data from a database, analyzing and decomposing the extracted feature data, calculating various costs, and generating a project cost breakdown list; an actual cost acquisition module: establishing an account entry progress control system based on the generated project cost breakdown list to obtain the actual cost of the project; a budget cost acquisition module: creating a project budget linear regression model, bringing feature data into the linear regression model, and obtaining the project budget cost; a comparison and analysis module: comparing and analyzing the absolute error between the project budget cost and the project actual cost with a preset safety threshold, and issuing a risk warning for the project based on the comparison and analysis result.
[0013] An electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the full-process intelligent management and control method for power grid projects provided in an embodiment of the present invention.
[0014] The technical effects and advantages of the whole-process intelligent control method of a power grid project of the present invention are as follows: 1. The present invention integrates the data of ERP, PMS, ECP and other systems by building a unified database, realizes information convergence and seamless connection between various systems, comprehensively reflects the overall picture of project management, and eliminates the problem of data fragmentation.
[0015] 2. The present invention integrates the data of various professional systems to form a unified information platform, promotes data sharing and information exchange among various professions, and reduces the cost and complexity of cross-professional communication. The visual progress control module presents data in a visual way through charts, establishes a panoramic dashboard of cost items, realizes multi-professional information data sharing, and improves the efficiency of cross-professional communication and collaboration. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 This is a structural schematic diagram of a whole-process intelligent management and control method for a power grid project according to the present invention.
[0017] Figure 2 This is a structural schematic diagram of a device for a whole-process intelligent control method of a power grid project according to the present invention.
[0018] Figure 3This is a structural schematic diagram of an electronic device for a method for intelligently controlling the entire process of a power grid project according to the present invention. DETAILED DESCRIPTION
[0019] 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.
[0020] Embodiment 1, Figure 1 The present invention provides a whole-process intelligent control method for a power grid project.
[0021] S10, obtaining data from the ERP system, PMS system, and ECP system, and building a unified database to integrate and synchronize the data from the ERP system, PMS system, and ECP system; By building a unified database, the database can receive and store data from ERP, PMS, ECP and other systems, and realize the import, extraction and analysis of data from each system; Develop data import and export interfaces to ensure that data from each system can be seamlessly connected to the database; Write data parsing algorithms to convert imported data into a unified format for subsequent processing and analysis; The ERP system (Enterprise Resource Planning) integrates various departments of an enterprise, such as finance, sales, production, inventory, human resources, etc., into a unified platform to improve data sharing and management efficiency; Help enterprises improve operational efficiency and reduce human errors by standardizing and automating business processes; Provide real-time business data and reports to help management make quick and accurate decisions; Optimize the allocation and use of enterprise resources, including financial, material and human resources; Ensure that enterprises comply with relevant laws and regulations and facilitate auditing and monitoring; The PMS system (project management system) helps the team develop a detailed project plan, set tasks, milestones and timelines; Effectively allocate human resources and materials to ensure projects are completed on time; Monitor project progress in real time, identify potential delays and risks, and adjust plans in a timely manner; Provide collaboration tools among team members to support file sharing, discussion and feedback; Evaluate the success of projects, including budget control, time management, and quality monitoring; The ECP system (Enterprise Collaboration Platform) provides a centralized workspace to promote communication and cooperation between teams and improve work efficiency; Help enterprises manage and share knowledge, and support the construction of document management, knowledge base and training resources; Ensure team members are clear about their responsibilities by assigning tasks and setting deadlines; Supports integration of various tools and applications, such as calendar, email, instant messaging, etc., to improve work efficiency; Especially suitable for modern enterprises, supporting remote work and cross-regional team collaboration; S20, extracting characteristic data from the database, analyzing and disassembling the extracted characteristic data, calculating various expenses, and generating a detailed breakdown of project expenses; The ERP system aims to integrate various departments and business processes of an enterprise and provide a unified information platform. The data usually included include: Financial data: including accounting records, budgets, financial statements, accounts receivable, accounts payable, etc.; Human resources data: employee information, recruitment records, salary, attendance, training and performance management, etc.; Inventory management data: inventory levels, purchase orders, sales orders, supplier information, etc.; Production data: production plan, work flow, equipment maintenance records, etc.; Sales and customer relationship management data: customer information, sales records, marketing activities, after-sales service records, etc.; PMS systems are used to manage and monitor the life cycle of a project, covering all stages from planning to execution. The data usually included includes: Project planning data: project goals, scope, task breakdown, milestones, timetable, etc.; Resource management data: allocation and management of project resources such as personnel, funds, equipment and materials; Progress tracking data: task status, completion percentage, reasons for delays, etc. Cost control data: project budget, expenditure, cost estimation, financial analysis, etc.; Risk management data: potential risks, risk assessment, response measures, etc.; Communication and collaboration data: project members’ communication records, meeting minutes, file sharing, etc. An ECP system usually refers to a platform that centrally monitors and controls enterprise operations and may contain the following data: Operational data: key performance indicators (KPIs), production efficiency, operating expenses, etc. Real-time monitoring data: equipment status, production line real-time data, alarm and fault records, etc.; Compliance and audit data: compliance inspection records, audit records, policy compliance, etc.; Data analysis and reporting: data mining results, trend analysis, decision support, etc.; Market and sales data: market analysis, competitor data, customer feedback, etc.; Extract characteristic data related to the whole process of intelligent management and control of power grid projects from the database, including the cost allocation of various resources such as personnel and equipment in the project; Record any additional costs incurred as a result of the change, including the reason for the change and the amount of the change; The working time records of project team members help analyze labor costs; Resource consumption during project implementation, such as man-hours and equipment usage time; Analyze and disassemble the extracted characteristic data, calculate various expenses, and generate a detailed breakdown of project expenses; S30, based on the generated project cost breakdown table, establish an accounting progress control system to obtain the actual project cost; The actual cost of the project includes material costs, labor costs and other direct costs. When the costs are incurred, the actual expenditures shall be recorded in a timely manner and the cost breakdown table shall be updated; The specific calculation formula for the actual cost of the project is as follows:
[0022] In the formula, is the actual cost of the project, It's the material cost. It's labor cost. are other direct costs; By establishing an accounting progress control system and tracking project costs in real time, we can effectively monitor the actual project costs and ensure that the project proceeds smoothly within the budget. This systematic management method helps to identify problems in a timely manner and take measures to ensure the successful completion of the project. S40, collecting historical data, calculating and generating a trained linear regression model based on the historical data, bringing the feature data into the trained linear regression model, and obtaining the project budget cost; First, collect historical data and create a linear regression model. Substitute the quantifiable feature data extracted from the collected historical data and the actual historical costs of the project into the linear regression model to obtain the linear regression coefficient. Substitute the regression coefficient and characteristic data into the linear regression model to obtain the project budget cost; The linear regression model with known regression coefficients is as follows:
[0023] In the formula, is the project budget cost, is the regression coefficient, is the feature data, Is a positive integer, indicating the number of feature data; The linear regression model plays an important role in the intelligent management and control of the entire process of power grid projects. It is a basic and effective statistical learning method that can help the power industry with data analysis and decision support. The following are the specific roles of the linear regression model in power grid projects: Cost control: By monitoring and analyzing various expenses, we can find unnecessary expenses, thereby reducing overall operating expenses, optimizing resource allocation, and ensuring that various resources are used most effectively. By establishing a budget control system, we can ensure that project expenditures are in line with expectations and make timely adjustments; Project investment evaluation: By establishing a linear relationship model between cost and output, the investment cost, operating cost and expected return of the project are quantified. By changing key assumptions (such as electricity price, power generation, cost fluctuations, etc.), the impact of these factors on the economic benefits of the project is analyzed, so as to identify high-risk factors and make corresponding adjustments; Operational expense analysis: Segment the expenses of each operating link of the company, such as power generation, transmission, and distribution, monitor the cost performance of each link in real time, discover abnormal expenditures in a timely manner, formulate detailed operating budgets, and compare them with actual expenditures, analyze the differences, identify the reasons for cost overruns, hold regular expense analysis meetings, urge various departments to conduct in-depth analysis of overrun issues, and propose improvement measures. By establishing a performance appraisal system with expenses as the core, employees are encouraged to pay attention to cost control and improve overall efficiency; S50, compare and analyze the absolute error between the project budget cost and the project actual cost with the preset safety threshold, and issue a risk warning to the project based on the comparison and analysis results.
[0024] First, calculate the absolute error between the project budget and the actual project cost, which is recorded as the budget error. The specific calculation formula of the budget error is as follows:
[0025] In the formula, is a budget error, is the project budget cost, is the actual cost of the project; Compare and analyze the budget error with the preset safety threshold. When the budget error is greater than the preset safety threshold, the project risk increases and immediate measures need to be taken to issue risk warnings and conduct timely maintenance; When the budget error is less than the preset safety threshold, the project is within the controllable range and the risk is low. Continue to monitor and no risk warning is required. Continue to monitor; Risk early warning for projects can have a positive impact on project management in the following aspects: Improve transparency: Regularly comparing budgets with actual costs can improve the transparency of project management and allow all stakeholders to understand the financial status of the project in a timely manner; Early identification of risks: Through comparative analysis, potential risks can be identified in advance to avoid problems from breaking out in the later stages of the project; Optimize decision-making: Data-driven analysis can provide a basis for project decision-making, making project management more scientific; Enhance execution: When problems are discovered, the plan can be adjusted in a timely manner to improve the execution of the project team and ensure that the project is completed on schedule; The comparative analysis between project budget costs and actual costs is an indispensable part of project management. It can effectively improve the management level of the project and reduce risks. Through timely risk warnings and appropriate response measures, the project team can better control the project progress and costs and ensure the successful delivery of the project.
[0026] Embodiment 2, Figure 2 The present invention provides a device for a whole-process intelligent control method of a power grid project.
[0027] It includes data integration module, feature data extraction module, actual cost acquisition module, budget cost acquisition module, and comparative analysis module. There are connections between modules: Data integration module: obtain data from the ERP system, PMS system, and ECP system, and build a unified database to integrate and synchronize data from the ERP system, PMS system, and ECP system; Feature data extraction module: extract feature data from the database, analyze and disassemble the extracted feature data, calculate various costs, and generate a detailed breakdown of project costs; Obtaining actual cost module: Based on the generated project cost breakdown table, establish an accounting progress control system to obtain the actual cost of the project; Budget cost acquisition module: collect historical data, calculate and generate a trained linear regression model based on the historical data, and bring the feature data into the trained linear regression model to obtain the project budget cost; comparative analysis module: compare and analyze the absolute error between the project budget cost and the actual project cost with the preset safety threshold, and issue risk warnings for the project based on the comparative analysis results.
[0028] like Figure 3 As shown, it is a structural schematic diagram of an electronic device of a whole-process intelligent control method of a power grid project according to the present invention.
[0029] The electronic device may include a processor, a memory, a communication bus and a communication interface, and may also include a computer program stored in the memory and executable on the processor, such as a method for intelligent management and control of the entire process of a power grid project.
[0030] Among them, the processor is the control core of the electronic device, which uses various interfaces and lines to connect the various components of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory, and calling data stored in the memory.
[0031] The memory includes at least one type of readable storage medium, and in some embodiments, the memory may be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may be used to store not only application software installed in the electronic device but also various data.
[0032] The communication bus is configured to realize connection and communication between the memory and at least one processor.
[0033] The communication interface is used for communication between the above electronic device and other devices, including a network interface and a user interface.
[0034] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.
[0035] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.
[0036] Specifically, the specific implementation system of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.
[0037] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.
[0038] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.
[0039] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0040] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
[0041] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.
[0042] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.
Claims
1. A method for intelligent management and control of the entire process of a power grid project, characterized in that: The following steps are involved: Obtain data from ERP system, PMS system, and ECP system, and build a unified database to integrate and synchronize data from ERP system, PMS system, and ECP system; Extract characteristic data from the database, analyze and disassemble the extracted characteristic data, calculate various expenses, and generate a detailed breakdown of project expenses; According to the generated detailed breakdown of project costs, establish an accounting progress control system to obtain the actual project costs; Collect historical data, calculate and generate a trained linear regression model based on the historical data, and bring the feature data into the trained linear regression model to obtain the project budget cost; The absolute error between the project budget cost and the actual project cost is compared and analyzed with the preset safety threshold, and risk warning of the project is issued based on the comparative analysis results.
2. The whole process intelligent management and control method of power grid project according to claim 1 is characterized in that: The specific steps for integrating and synchronizing the data are as follows: Build a unified database to receive and store data from ERP, PMS, and ECP systems, develop data import and export interfaces, and seamlessly connect data from various systems to the database; Write data parsing algorithms to convert imported data into a unified format to obtain synchronized system data.
3. The whole process intelligent management and control method of power grid project according to claim 1 is characterized in that: The characteristic data include the cost allocation of each resource in the project; the additional costs incurred due to changes; the working time records of project team members; and the resource consumption during the project implementation process.
4. The whole process intelligent management and control method of power grid project according to claim 1 is characterized in that: The actual cost of the project includes material costs, labor costs and other direct costs. The specific calculation formula for the actual cost of the project is as follows: In the formula, is the actual cost of the project, It's the material cost. It's labor cost. It is other direct costs.
5. The whole process intelligent management and control method of power grid project according to claim 1 is characterized in that: The specific method for obtaining the project budget cost is as follows: Collect historical data, create a linear regression model, and extract quantitatively analyzable feature data and actual historical project costs from the collected historical data; Substitute the characteristic data and the actual historical cost of the project into the linear regression model to obtain the linear regression coefficient; Substitute the linear regression coefficients into the created linear regression model to obtain the trained linear regression model; Substitute the feature data into the trained linear regression model to obtain the project budget cost.
6. The whole process intelligent management and control method of power grid project according to claim 1 is characterized in that: The specific process of carrying out risk warning for a project based on the comparative analysis results is as follows: calculating the absolute error between the project budget cost and the project actual cost, recording it as the budget error, and comparing and analyzing the budget error with the preset safety threshold; When the budget error is greater than the preset safety threshold, the project risk increases and immediate measures need to be taken to issue risk warnings and conduct timely maintenance; When the budget error is less than the preset safety threshold, the project is within controllable range, the risk is low, and monitoring continues.
7. The whole process intelligent management and control method of power grid project according to claim 5 is characterized in that: The calculation formula of the linear regression model is as follows: In the formula, is the project budget cost, is the regression coefficient, is the feature data, Is a positive integer, indicating the number of feature data.
8. The whole process intelligent management and control method of power grid project according to claim 6 is characterized in that: The calculation formula of the budget error is as follows: In the formula, is a budget error, is the project budget cost, It is the actual cost of the project.
9. A device using the whole-process intelligent control method of a power grid project as described in any one of claims 1 to 8, characterized in that: It includes data integration module, feature data extraction module, actual cost acquisition module, budget cost acquisition module, and comparative analysis module. There are connections between modules: Data integration module: obtain data from the ERP system, PMS system, and ECP system, and build a unified database to integrate and synchronize data from the ERP system, PMS system, and ECP system; Feature data extraction module: extract feature data from the database, analyze and disassemble the extracted feature data, calculate various costs, and generate a detailed breakdown of project costs; Obtaining actual cost module: Based on the generated project cost breakdown table, establish an accounting progress control system to obtain the actual cost of the project; Obtaining budget cost module: collect historical data, calculate and generate a trained linear regression model based on the historical data, bring the feature data into the trained linear regression model, and obtain the project budget cost; Comparative analysis module: compare and analyze the absolute error between the project budget cost and the actual project cost with the preset safety threshold, and issue risk warnings for the project based on the comparative analysis results.
10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, The computer program is executed by the at least one processor, So that the at least one processor can execute the whole-process intelligent management and control method of the power grid project as described in any one of claims 1 to 8.