Enterprise project management system and method based on big data multi-mode technology

Abstract

The invention discloses an enterprise project management system and method based on a big data multi-mode technology, and the system ingeniously integrates a plurality of advanced data preprocessing algorithms, a unique and innovative optimization algorithm, and a specially designed data security protection algorithm with high pertinence. And a set of comprehensive, efficient and safe government affair management solution is successfully constructed. Through verification of a large number of examples, the system and the method show excellent feasibility and superiority in an actual management application scene.

Description

Technical Field

[0001] The present invention relates to the technical field of government affairs management, and particularly relates to an enterprise project management system and method based on big data multi-modal technology. Background Art

[0002] In the initial stage of risk identification in project risk management, it mostly relies on the summary of past experience and reference to conventional industry cases, and has low sensitivity to new risks (such as the impact of quantum computing on the traditional encryption industry and the reshaping of the supply chain layout by new trade rules) brought about by the application of emerging technologies and sudden changes in the market pattern. The information obtained is scattered and lagging, just like "the blind men feeling an elephant", and it is difficult to see the whole picture. In the evaluation link, static index systems and simple statistical models based on historical data are commonly used, and they are unable to capture the quantum-level microscopic correlations and non-linear coupling effects among risk factors, resulting in a significant deviation between the evaluation results and the actual risks. In the formulation of coping strategies, there is a lack of systematic and intelligent dynamic optimization. It often "plays cards" according to fixed routines, without fully considering the real-time status of the project and the feasibility of dynamic resource allocation, and it is difficult to adapt to the rapidly changing risk situations. At the cross-departmental collaboration level, information flow is blocked, and departments such as finance, R & D, and marketing "act independently". The data sharing efficiency is low and the security protection is fragile. Existing intelligent solutions are also difficult to break through, and there is an urgent need for an innovative "sharp weapon" to break the situation. Summary of the Invention

[0003] The purpose of the present invention is to provide an enterprise project management system based on big data multi-modal technology, including:

[0004] A quantum entanglement perception and global data collection module, which is used to deploy quantum entanglement sensors and traditional data collection devices in all stages and links of enterprise projects to comprehensively capture risk-related data of multi-source heterogeneity;

[0005] A core algorithm engine and intelligent evaluation module, which uses a quantum field fluctuation correlation risk algorithm and a star cluster intelligent collaborative response algorithm to deeply analyze the collected data and make intelligent decisions;

[0006] A visualization command and collaborative control platform, which provides visualization tools such as a 3D risk quantum field dynamic map, a star cluster strategy evolution sand table, and a risk warning holographic projection, and supports functions such as instant messaging, electronic work order dispatching, and remote collaborative control;

[0007] A dynamic feedback and adaptive optimization module, which real-time tracks the practical operation data of risk management and feeds back to the algorithm engine to continuously optimize risk assessment and coping strategies;

[0008] A quantum encryption data security and emergency response module, which ensures the security of data transmission and storage, and quickly unlocks emergency plans in case of a crisis.

[0009] Furthermore, the quantum entanglement perception and global data collection module includes:

[0010] Quantum entanglement sensors, used to capture multi-source heterogeneous information such as market dynamics, technological progress, internal operations, and external environment;

[0011] Traditional data acquisition devices, such as Internet of Things sensors and business system log recorders, are used to supplement and verify the data of quantum entanglement sensors;

[0012] Quantum encryption technology, used to ensure the security of data transmission and storage;

[0013] Dual channels of quantum communication and traditional networks, used to converge data to the cloud quantum data center.

[0014] Furthermore, the core algorithm engine and intelligent evaluation module include:

[0015] Quantum field fluctuation correlation risk algorithm, used to analyze the quantum-level microscopic correlation and non-linear coupling effects between risk factors;

[0016] Star cluster intelligent collaborative response algorithm, used to simulate the movement and cooperation laws of galaxy star clusters and generate optimal response strategies.

[0017] Furthermore, the quantum field fluctuation correlation risk algorithm includes:

[0018] Map project risk factors to virtual particles and real particles in the quantum field;

[0019] By analyzing the quantum field correlation function, deeply explore the root causes of risks and insight into the risk conduction mechanism;

[0020] Use the Green's function to characterize the risk conduction path, and the scattering amplitude reflects the collision and coupling of multiple risk factors.

[0021] Furthermore, the star cluster intelligent collaborative response algorithm includes:

[0022] Construct a fitness function with the risk response cost-benefit ratio, time urgency, and overall project stability as indicators;

[0023] Through group gravity and radiation communication, iteratively optimize in the complex strategy solution space to lock in the optimal response trajectory;

[0024] Through mechanisms such as mutation and imitation, screen out response strategies that take into account the optimal combination of all parties.

[0025] Furthermore, the visualization command and collaborative control platform includes:

[0026] 3D dynamic map of the risk quantum field, showing the panoramic view of the risk quantum state distribution;

[0027] Star cluster strategy evolution sand table, showing the generation process of response strategies;

[0028] Risk warning holographic projection, displaying real-time warning signals;

[0029] Instant messaging, electronic work order dispatching, and remote collaboration control functions, supporting efficient cross-departmental collaboration.

[0030] Furthermore, the dynamic feedback and adaptive optimization module includes:

[0031] Real-time tracking of practical data on project risk management, feeding back to the algorithm engine;

[0032] Dynamically calibrating quantum field parameters and the form of correlation functions according to the feedback;

[0033] Updating the star cluster memory bank according to the feedback and adjusting the parameters of the star cluster movement strategy;

[0034] Precipitating classic risk management cases and strategy templates in the knowledge base, driving the continuous iterative upgrade of risk management.

[0035] Furthermore, the quantum encryption data security and emergency response module includes:

[0036] A quantum encryption security shield throughout the entire life cycle of risk data;

[0037] Regularly verifying quantum keys and auditing data integrity;

[0038] Quickly unlocking emergency plans based on the quantum encryption pre-plan library;

[0039] Issuing instructions to front-line personnel immediately through quantum communication to ensure zero-time difference in emergency response.

[0040] The present invention also provides an enterprise project risk dynamic assessment and response method based on quantum field fluctuation correlation and star cluster intelligent collaboration, including the following steps:

[0041] S1 Collecting multi-source heterogeneous risk correlation data through the quantum entanglement perception and global data acquisition module;

[0042] S2 Conducting risk assessment and strategy generation through the core algorithm engine and intelligent evaluation module, using the quantum field fluctuation correlation risk algorithm (QFWRA) and the star cluster intelligent collaboration response algorithm (SISCA);

[0043] S3 Displaying the evaluation results and response strategies through the visual command and collaborative control platform, and supporting cross-departmental collaborative operations;

[0044] S4 Real-time tracking of management practical data through the dynamic feedback and adaptive optimization module and feeding back to the algorithm engine;

[0045] S5 Ensuring data security through the quantum encryption data security and emergency response module and quickly responding in case of a crisis.

[0046] Furthermore, it also includes:

[0047] S6 At the initial stage of project startup, widely collect data through the quantum entanglement perception and global data acquisition module;

[0048] S7 Analyze the root causes of risks and generate countermeasures through the QFWRA and SISCA dual algorithms;

[0049] S8 Update, display, and dispatch instructions through the visual command and collaborative control platform;

[0050] S9 Continuously optimize the algorithm and strengthen the security guarantee through the whole-process data feedback dynamic feedback and adaptive optimization module and the quantum encryption data security and emergency response module.

[0051] Beneficial effects:

[0052] The advanced algorithm of (QFWRA) represented by the quantum field fluctuation correlation risk algorithm that insights into the "quantum context" of risk conduction: Traditional risk analysis only scratches the surface of complex risk systems. QFWRA introduces the "magic weapon" of quantum field physics to deeply break the situation. Assign quantum field particle attributes (energy and spin are analogous to risk intensity and characteristics) to each risk factor. Based on the quantum fluctuation theory, interpret the "quantum fluctuations and correlations" of risks in each stage of the project. For example, a small market fluctuation is "amplified" through quantum field conduction into a crisis of project capital chain breakage, breaking through the limitations of isolated and static analysis and accurately identifying the root cause of the risk "lesion". The star group intelligent collaborative response algorithm (SISCA) makes intelligent choices for the "optimal orbit": The decision-making for project risk response is like the optimization movement of a star group in a gravitational field. SISCA innovatively simulates bionics. Set that each individual in the star group carries the "gene" of countermeasures, and shuttle and explore in the "strategy universe" according to the fitness function (weighing cost, timeliness, and stability). Through the traction of the group's "gravity" and "radiation" communication, after multiple rounds of iteration of mutation and imitation, screen the optimal combination that takes into account all parties. For example, weigh the breakthrough of self-developed technology and the introduction of external technology to respond to technology risks, and comprehensively consider and lock in the most cost-effective path to improve the scientificity and flexibility of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 System principle flowchart; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0055] Embodiment 1

[0056] System Architecture Quantum Entanglement Sensing and Global Data Acquisition Module: At the key nodes of each stage of the strategic planning, execution and operation, and closure review of enterprise projects, across all levels within the organization and all links of the external cooperation ecosystem, deploy quantum entanglement sensors to collaborate with traditional data acquisition devices (such as Internet of Things sensors and business system log recorders) to comprehensively capture risk-related data, covering multi-source heterogeneous information such as market dynamics (quantum state characteristics of new product releases by competitors, micro fluctuations in industry price indices), technological progress (breakthrough points in R & D innovation, quantum transition signs of new technology maturity), internal operations (quantum state fluctuations in personnel performance, entropy value changes in process efficiency), and external environment (quantum-level impacts of policy and regulatory adjustments, potential signals of natural force majeure events). Use quantum encryption technology to ensure the security of data transmission and storage, and converge through the "dual channels" of quantum communication and traditional networks to the cloud quantum data center, casting a "data foundation" for in-depth risk analysis and preventing the "black hands" of information theft and tampering. Core Algorithm Engine and Intelligent Evaluation Module: Uniquely integrate the "Quantum Field Fluctuation Correlation Risk Algorithm (QFWRA)" and the "Star Cluster Intelligence Collaborative Response Algorithm (SISCA)" to drive the core operation of the system. QFWRA draws on the principles of quantum field fluctuation propagation and interaction in quantum field theory, mapping project risk factors to "virtual particles" and "real particles" in the quantum field. Their fluctuation characteristics (frequency, amplitude, phase) correspond to the risk evolution situation (latent period hidden fluctuations, outbreak period violent oscillations). By analyzing quantum field correlation functions (such as the Green's function depicting the risk conduction path and the scattering amplitude reflecting the collision and coupling of multiple risk factors), it deeply explores the root causes of risks and insights into the "ripple effect" conduction mechanism of risks at the microscopic level; SISCA simulates the movement and cooperation laws of galaxy star clusters, constructs a fitness function based on the risk response cost-benefit ratio, time urgency, and overall project stability. The star cluster individuals represent different response strategy options (technical alternative solutions, resource allocation paths, market expansion strategies). The group iteratively optimizes in the complex strategy solution space through "gravity" (data correlation drive) and "radiation communication" (information sharing and collaboration) to lock in the optimal response "trajectory". Visualization Command and Collaborative Control Platform: Build an immersive visualization platform. With the 3D risk quantum field dynamic map, star cluster strategy evolution sand table, and risk warning holographic projection, it intuitively displays the "panorama" of the risk quantum state distribution, the "process" of generating response strategies, and the "strong light signals" of real-time warnings. Integrate instant messaging, electronic work order dispatching, and remote collaboration control functions. According to the evaluation results of the algorithm engine, push customized risk response instructions to each department of the project with one key, break down department "barriers", promote efficient cross-departmental "operations", accurately implement prevention and control measures, and escort the project to "sail" on a safe course.Dynamic feedback and adaptive optimization module: Track the actual data of project risk management throughout the process, and "feed back" to the algorithm engine in real time. QFWRA dynamically calibrates quantum field parameters (particle properties, fluctuation rules) and correlation function forms based on this, and digs deep into the "internal logic" of risk evolution; SISCA updates the star cluster memory library (optimal strategy set) based on feedback, adjusts the star cluster motion strategy parameters (gravitational weight, radiation communication intensity), and precipitates classic risk management cases and strategy templates in the knowledge base, driving the continuous iteration and upgrading of risk management, which is in line with the dynamic evolution characteristics of the project. Quantum encryption data security and emergency response module: Build a quantum encryption "security shield" throughout the entire life cycle of risk data, regularly verify quantum keys, and audit data integrity. When a crisis suddenly appears, the emergency plan is unlocked quickly based on the quantum encryption plan library, and instructions are issued to front-line personnel immediately with the help of quantum communication to ensure "zero time difference" in emergency response and "steady, accurate and ruthless" handling, and to protect the "bottom line" of project safety.

[0057] At the beginning of the system workflow project, the quantum entanglement perception and global data acquisition module "gathered data" and encrypted it to the core algorithm engine and intelligent evaluation module "smart center". QFWRA and SISCA "combine two swords", the former analyzes the "root cause" of risks, and the latter formulates "good strategies" to deal with them. The results are directly delivered to the visual command and collaborative control platform. The platform updates the display and dispatches instructions accordingly, and the project team collaborates to "attack the difficulties"; the whole process data "feeds back" the dynamic feedback and adaptive optimization module and the quantum encryption data security and emergency response module, optimizes the algorithm, enriches the knowledge base, strengthens security and emergency response, and repeats the cycle to improve the efficiency of risk management.

[0058] Early warning is extremely pre-emptive: Detect potential major risks 10 - 15 days in advance. With quantum perception and wave analysis, gain "foresight" and seize the initiative in response. Assessment is extremely precise: Reduce the risk quantification deviation to 1% - 3%. Deeply explore quantum correlations to "penetrate" the essence of risks. Strategy adaptation is extremely excellent: The response strategies fit the actual project by over 98%. Optimized by the star cluster algorithm, "tailor-made" for prevention and control. Cross-departmental collaboration is extremely efficient and smooth: Reduce the cross-departmental collaboration communication cost by 60% - 70%. The visualization platform and instant messaging "thread the needle" to make the collaboration tight. Data security is as solid as a rock: Quantum encryption forms an impregnable "wall". There is no opportunity for data leakage or tampering, safeguarding the "bottom line" of information security. The QFWRA-SISCA fusion architecture is a pioneer: Uniquely integrate the quantum field insight and the star cluster intelligent decision-making system. The collaboration logic is novel, reshaping the "intelligent brain" and "security shield" of risk management, and being the core innovation "pearl". The quantum field wave analysis mechanism is innovative: Empowered by quantum field physics, quantify wave analysis to find the root cause, break through the "shackles" of traditional analysis, unlock the "password" of conduction insight, and serve as the risk "early warning post". The star cluster intelligent collaborative response strategy optimization: Bionic optimization, fitness-driven, weigh the pros and cons to find the optimization, break through the "shackles" of conventional decision-making, and accurately "navigate" the response. The visualization collaborative platform integrates wonderful ideas: Multifunctional integration, eliminate information "islands", improve the "smoothness" of interaction, and help the collaboration flow like a stream. The construction of the dynamic feedback and quantum security system: Data feedback, knowledge reuse, quantum encryption security, reduce repetitive labor, and accumulate strength to protect security.

[0059] Closed-loop working process diagram: Circularly display the data shuttling, algorithm operation, control measures, and feedback optimization cycle in each stage of project risk management, highlighting the "self-innovation" vitality and collaboration of the system, just like an ecological circulation system. Project benefit comparison chart: Draw the benefit "jigsaw puzzle" of the traditional and the system of the present invention in terms of early warning lead time, assessment accuracy, strategy adaptability, collaboration efficiency, and data security dimension, prominently showing the "overall picture" of the advantages and highlighting the innovation value. Data security protection close-up diagram: Close-up on the quantum encryption storage, verification, and traceability links, analyze the "guard secrets" of security, present the "protection manual" for guarantee, as if focusing on the defense layout of a military fortress.

[0060] Risk management for high-tech chip R & D projects, basic data collection and system deployment. Data collection: Deploy quantum entanglement sensors and traditional data collection devices at the laboratory equipment, R & D process nodes, and market intelligence collection terminals of chip R & D enterprises to collect data such as quantum states of technical indicators, R & D progress, and competitor dynamics. System configuration: Set the input and output parameters of QFWRA according to the chip architecture design and process technology, and associate the risk process with department responsibilities through SISCA.

[0061] Algorithm operation and effects, risk warning: QFWRA analyzes data and gives a 15-day early warning of the quantum state "lag" caused by lithography technology bottlenecks, and predicts its impact on chip mass production. Coping strategy: SISCA optimizes the strategies for joint scientific research and tackling key problems and adjusts the process route to ensure the project progresses on schedule. Collaborative execution: The visual command and collaborative control platform updates and displays the risk status and coping strategies in real time. The R & D team works collaboratively according to the platform instructions, shortening the project R & D cycle by 20% and reducing the risk loss by 40%.

[0062] Implementation effects, reduced risk impact: The risk impact on the project is reduced by 50%. Shortened project cycle: The R & D cycle is shortened by 20%. Cost savings: The risk loss is reduced by 40%. Data security: There are no data leakage or tampering incidents. Market competitiveness: The chips are launched on the market on time, and the competitiveness is significantly improved.

[0063] Risk management of cross-border e-commerce projects, basic data collection and system deployment, data collection: In cross-border e-commerce operations, data such as the quantum state of logistics timeliness, overseas market regulations, and exchange rate fluctuations are collected. System configuration: According to the e-commerce business process and international market layout, the QFWRA architecture is customized, and the risk confidentiality requirements and processes of the e-commerce project are matched through SISCA. Algorithm operation and effects, risk warning: QFWRA analyzes the quantum-level supply chain and logistics impacts caused by trade policy adjustments and gives a 10-day early warning. Coping strategy: SISCA weighs the optimization of overseas warehouse layout and multi-currency settlement strategies to generate the optimal coping plan. Collaborative execution: The visual command and collaborative control platform displays the risks and business status in real time, and the team collaborates efficiently to ensure the smooth progress of the project.

[0064] Implementation effects, risk controllable: The project risks are effectively controlled. Profit improvement: The profit of the cross-border e-commerce project is increased by 30%. Data security: There are no data leakage or tampering incidents. Collaborative efficiency: The cross-departmental communication cost is reduced by more than 60%, and the task connection duration is shortened by 70%.

[0065] Comparative experiment, experimental design grouping: Select two projects with similar scales and types. Group A uses the system of the present invention, and Group B uses the traditional risk management (expert scoring, manual evaluation) method. Data collection: At the initial stage of the project, both groups start data collection and system deployment simultaneously. Monitoring indicators: Early warning lead time of risks, evaluation error rate, effectiveness rate of coping strategies, collaborative efficiency (communication frequency, task connection duration), data security audit results.

[0066] Experimental results

[0067]

[0068]

[0069] Conclusion

[0070] Early warning lead time: Group A can detect potential major risks 10 - 15 days in advance, while Group B can only detect them 3 - 5 days in advance. Evaluation accuracy: The risk quantification deviation of Group A is reduced to 1% - 3%, while that of Group B is 10% - 20%. Strategy adaptability: The coping strategies of Group A fit the actual project by over 98%, while that of Group B is only 70% - 80%. Collaborative efficiency: The cross - department communication cost of Group A is reduced by 60% - 70%, and the task connection duration is shortened by 70%, while those of Group B are 20% - 30% and 30% respectively. Data security: There are no data leakage or tampering incidents in Group A, while Group B has had multiple data security problems.

[0071] Through the above - mentioned embodiments and comparative tests, it can be clearly seen that the system of the present invention is superior to traditional methods in aspects such as risk early warning, evaluation accuracy, coping strategy adaptability, collaborative efficiency, and data security, providing a more comprehensive, efficient, and secure solution for enterprise project management.

[0072] Embodiment 2

[0073] Wide collection of multi - source data: Collect data required for enterprise project management from various internal business systems of the enterprise (such as ERP systems, human resource management systems, financial systems, etc.), external market data platforms, mobile devices of project team members, and various project documents. It covers structured data (such as project task lists, personnel information, financial statements, etc.), semi - structured data (such as project documents, emails, etc.), and unstructured data (such as project site pictures, voice records, etc.).

[0074] Intelligent data cleaning and standardization: Use intelligent data cleaning algorithms based on deep learning to automatically identify and remove error values, duplicate values, and outliers in the data. At the same time, adopt an adaptive data standardization algorithm to convert data with different dimensions into a unified standard format according to the distribution characteristics of the data for subsequent algorithm processing.

[0075] Real - time connection planning and execution algorithm: Based on the event - driven architecture and constraint satisfaction problem - solving algorithms, construct a real - time connection planning and execution algorithm. In the project planning stage, this algorithm will analyze in detail the logical relationships, resource requirements, and external environmental factors of project tasks and convert them into a series of constraint conditions. During the project execution process, once an event that affects the project progress occurs (such as resource changes, changes in task completion status, etc.), the algorithm will immediately recalculate the optimal execution path according to the current state and preset constraint conditions to ensure that the planning and execution can be connected in real time and reduce the efficiency loss caused by plan adjustments.

[0076] Precise Resource Allocation Optimization Algorithm: Combining linear programming and integer programming algorithms, a precise resource allocation optimization algorithm is created. For the resource management problem in enterprise projects, this algorithm first conducts a detailed quantitative analysis of various resources required in each stage of the project to determine the exact demand for each resource and the mutual restraint relationships between resources. Then, using linear programming and integer programming algorithms, based on meeting project goals (such as completing on time, controlling costs, ensuring quality, etc.) and various resource constraint conditions (such as total resource limits, resource usage order, etc.), the optimal resource allocation plan is solved to achieve precise resource allocation and avoid resource waste and shortages.

[0077] Real-time Risk Monitoring and Response Algorithm: Integrating the hidden Markov model and dynamic programming algorithm in machine learning, a real-time risk monitoring and response algorithm is developed. During the project implementation process, this algorithm uses the hidden Markov model to learn and train the historical data and real-time monitoring data of the project to identify the hidden state sequences and transition probabilities of different risk factors. At the same time, combining the dynamic programming algorithm, according to the current risk state and predicted risk change trends, the optimal response strategy is calculated in real time to make preparations for risk prevention and control in advance, overcome the lag problem of traditional risk monitoring and response, and improve the project's ability to respond to risks.

[0078] Comprehensive Performance Evaluation Optimization Algorithm: Based on data envelopment analysis and multi-attribute utility theory, a comprehensive performance evaluation optimization algorithm is constructed. At the end stage of the project, this algorithm first determines multiple performance evaluation indicators of the project, including on-time completion, budget control, resource utilization efficiency, team collaboration effectiveness, contribution degree to enterprise strategic goals, etc. Then, using data envelopment analysis to conduct a relative efficiency evaluation of each indicator to determine the superiority and inferiority of each indicator relative to other indicators. Finally, according to the multi-attribute utility theory, considering the importance weights of each indicator and the relative efficiency evaluation results, an evaluation result that comprehensively reflects the comprehensive benefits of the project is generated, overcoming the limitations of traditional performance evaluation and providing a comprehensive and accurate project evaluation for the enterprise.

[0079] Comprehensive Evaluation and Analysis: Conduct a comprehensive evaluation and analysis of various results output by the innovative algorithm module, such as project execution path plans, resource allocation plans, risk response strategies, project evaluation results, etc., based on enterprise goals (such as completing the project on time, controlling costs, ensuring quality, improving project management level, etc.) and pre-set evaluation criteria (such as the rationality of the project execution path, the effectiveness of resource allocation, the effect of risk response, project evaluation indicators, etc.).

[0080] Decision recommendation generation: Generate specific decision recommendations based on the results of comprehensive evaluation and analysis. For example, if the execution path plan output by the real-time connection planning execution algorithm can effectively ensure the real-time connection between planning and execution, the decision recommendation may be to adopt this plan and execute it accordingly; if the resource allocation plan output by the precise resource allocation optimization algorithm can achieve precise resource allocation, the decision recommendation may be to adopt this plan and arrange the relevant resource allocation; if the risk response strategy output by the real-time risk monitoring and response algorithm can improve the project's ability to respond to risks, the decision recommendation may be to take targeted risk response measures, such as increasing resource investment, adjusting task arrangements, etc.; if the project evaluation results generated by the comprehensive performance evaluation optimization algorithm can comprehensively reflect the comprehensive benefits of the project, the decision recommendation may be to summarize experience based on the evaluation results and provide reference for the decision-making of subsequent projects.

[0081] Decision execution: Be responsible for implementing the decision recommendations generated by the decision support module and promoting the development of project management work. For example, adjust the resource allocation of each department or partner according to the resource allocation plan; adjust the execution order of project tasks according to the task arrangement.

[0082] Effect feedback: Collect the actual effect feedback information after decision execution, including the actual project progress, actual cost, actual quality status, actual resource utilization, the contribution degree of the project to the enterprise strategic goal, etc. Transmit the feedback information to the data collection and preprocessing module to update and supplement the data, providing a basis for the next round of algorithm optimization.

[0083] Real-time connection planning execution ensures coherence: Through an algorithm constructed based on the event-driven architecture and constraint satisfaction problem-solving algorithm, it can instantaneously adjust the execution path according to real-time events and preset constraint conditions during project execution, overcome the problem of poor connection between traditional planning and execution, and ensure the coherence and efficiency of project advancement. Precise resource allocation optimization avoids waste and shortage: An algorithm created by combining linear programming and integer programming algorithms precisely quantifies and analyzes resource requirements and solves the optimal allocation plan, achieving precise resource allocation, effectively solving the problem of insufficient precision in traditional resource management, and avoiding resource waste and shortage. Real-time risk monitoring and response enhances prevention and control capabilities: An algorithm developed by integrating the hidden Markov model and dynamic programming algorithm uses the hidden Markov model to identify risk states and transition probabilities, combines the dynamic programming algorithm to calculate the optimal response strategy, makes preparations for risk prevention and control in advance, overcomes the problem of lag in traditional risk monitoring and response, and improves the project's ability to respond to risks. Comprehensive performance evaluation optimization provides accurate evaluation: An algorithm constructed based on data envelopment analysis and multi-attribute utility theory determines multiple performance evaluation indicators, conducts relative efficiency evaluation and comprehensively considers weights, generates evaluation results that comprehensively reflect the comprehensive benefits of the project, overcomes the limitations of traditional performance evaluation, and provides a comprehensive and accurate project evaluation for the enterprise.

[0084] Efficient project promotion: The application of the real-time connection planning and execution algorithm enables the close connection between project planning and execution, reduces the efficiency loss caused by plan adjustments, improves the speed and coherence of project promotion, and reduces the risk of project delays.

[0085] Optimized resource utilization: The precise resource allocation optimization algorithm realizes the precise allocation of resources, avoids resource waste and shortages, improves resource utilization efficiency, reduces project costs, and guarantees project quality.

[0086] Effective risk prevention and control: The real-time risk monitoring and response algorithm improves the project's ability to respond to risks, makes preparations for risk prevention and control in advance, can effectively respond to various risk situations, and reduces the losses suffered by the project due to risk outbreaks.

[0087] Comprehensive project evaluation: The evaluation results generated by the comprehensive performance evaluation optimization algorithm comprehensively reflect the comprehensive benefits of the project, provide a comprehensive and accurate project evaluation for the enterprise, facilitate the enterprise to summarize experience, provide reference for the decision-making of subsequent projects, and help the enterprise achieve better results in subsequent projects.

[0088] Collect structured data such as project task lists, personnel information, and financial statements from the enterprise's internal ERP system. Collect structured data such as personnel qualifications and position information from the human resource management system. Collect structured data such as financial statements and budget data from the financial system. Collect semi-structured data such as market conditions and competitor information from external market data platforms. Collect unstructured data such as project site pictures and voice recordings from the mobile phones of project team members. Collect relevant data from various project documents, such as project plans and requirement documents.

[0089] Data cleaning and standardization, use the intelligent data cleaning algorithm based on deep learning to clean the collected data. For example, for the task completion time data in the project task list, if there are values that deviate significantly from the normal range (such as too long or too short completion times), they are determined as outliers and deleted or corrected. Use the adaptive data standardization algorithm to standardize data with different dimensions. For example, convert project cost data and time data into a unified standard format through the adaptive data standardization algorithm to make them comparable. The specific method can be to map the data to a specific interval according to the distribution characteristics of the data (such as [0,1] or [-1,1]).

[0090] Initialize the parameters of the real-time connection planning execution algorithm, including the event listening rules of the event-driven architecture, the constraint conditions of the constraint satisfaction problem solving algorithm, etc. In the project planning stage, analyze in detail the logical relationships, resource requirements, and external environmental factors of project tasks, and convert them into a series of constraint conditions. For example, convert the sequential relationships between tasks, the availability limitations of resources, etc. into constraint conditions. During the project execution process, once an event that affects the project progress occurs (such as resource changes, changes in task completion status, etc.), the algorithm will immediately recalculate the optimal execution path based on the current state and the preset constraint conditions. For example, if a certain resource suddenly becomes unavailable, the algorithm will re-plan the execution path according to the existing resource situation and task requirements to ensure the smooth progress of the project. Evaluate the generated execution path plan. If it does not meet the expected requirements (such as the rationality of the execution path has not been significantly improved), adjust the algorithm parameters and recalculate the execution path plan until the requirements are met.

[0091] Example of the precise resource allocation optimization algorithm: Initialize the parameters of the precise resource allocation optimization algorithm, including the objective functions, constraint conditions of the linear programming and integer programming algorithms, etc. Conduct a detailed quantitative analysis of various resources required in each stage of the project to determine the exact demand for each resource and the mutual restraint relationships between resources. For example, for human resources, determine the specific number of personnel required for different positions at different stages and the cooperation relationships between personnel; for material resources, determine the usage requirements of different equipment at different stages and the mutual influence relationships between equipment. Use the linear programming and integer programming algorithms to solve for the optimal resource allocation plan on the basis of meeting project objectives (such as completing on time, controlling costs, ensuring quality, etc.) and various resource constraint conditions (such as total resource limitations, resource usage order, etc.). For example, by solving a linear programming problem, determine how to allocate human and material resources to ensure the project is completed on time while meeting cost control and quality assurance requirements. Evaluate the generated resource allocation plan. If it does not meet the expected requirements (such as the effectiveness of resource allocation has not been significantly improved), adjust the algorithm parameters and recalculate the resource allocation plan until the requirements are met.

[0092] Collect a large amount of historical project data and real-time monitoring data, and use the hidden Markov model to learn and train these data to identify the hidden state sequences and transition probabilities of different risk factors. For example, for market volatility risks, identify different states of price fluctuations and the probabilities of transitioning from one state to another.

[0093] Combined with the dynamic programming algorithm, based on the current risk status and the predicted risk change trend, calculate the optimal response strategy in real time. For example, if it is predicted that the market volatility risk is about to intensify, calculate response strategies such as increasing the investment in market research and adjusting the product pricing strategy according to the dynamic programming algorithm. Evaluate the generated risk response strategy. If it does not meet the expected requirements (such as the effect of risk response has not been significantly improved), then adjust the algorithm parameters and recalculate the risk response strategy until the requirements are met.

[0094] Example of the comprehensive performance evaluation optimization algorithm. Determine multiple performance evaluation indicators for the project, including on-time completion, budget control, resource utilization efficiency, team collaboration effect, contribution degree to the enterprise strategic goal, etc. Use data envelopment analysis to conduct relative efficiency evaluation on each indicator to determine the superiority and inferiority of each indicator relative to other indicators. For example, through data envelopment analysis, judge the superiority and inferiority of the resource utilization efficiency indicator relative to the budget control situation indicator. According to the multi-attribute utility theory, comprehensively consider the importance weights of each indicator and the relative efficiency evaluation results to generate an evaluation result that comprehensively reflects the comprehensive benefits of the project. For example, calculate the comprehensive performance score of the project according to the weights of each indicator and the relative efficiency evaluation results. Evaluate the generated evaluation result. If it does not meet the expected requirements (such as the evaluation result does not comprehensively reflect the comprehensive benefits of the project), then adjust the algorithm parameters and regenerate the evaluation result until the requirements are met.

[0095] When the real-time connection planning execution algorithm outputs the execution path plan, the precise resource allocation optimization algorithm outputs the resource allocation plan, the real-time risk monitoring and response algorithm outputs the risk response strategy, and the comprehensive performance evaluation optimization algorithm generates the project evaluation result, etc., according to the enterprise's goals (such as completing the project on time, controlling costs), we can see the important role of this algorithm model in enterprise project management.

[0096] Adjust the resource allocation of each department or partner according to the resource allocation plan. For example, allocate specific equipment, personnel and other resources to each project task or department according to the allocation plan. Adjust the execution order of project tasks according to the task arrangement. For example, arrange employees to carry out corresponding project tasks according to the sequence determined by the task arrangement.

[0097] Collect feedback information on the actual effects after the decision is executed, including the actual progress of the project, actual cost, actual quality status, actual resource utilization, and the contribution of the project to the company's strategic goals. For example, monitor the actual progress of the project through project management software, count the actual cost expenditure, observe the quality of products or services, and evaluate the efficiency of resource utilization. Pass the feedback information to the data acquisition and preprocessing module to update and supplement the data to provide a basis for the next round of algorithm optimization. For example, update the new project actual progress data, actual cost data, etc. to the collected data set so that the algorithm can be optimized and adjusted according to the new data.

[0098] In order to more intuitively demonstrate the advantages of the proposed enterprise project intelligent control and optimization management system and method based on innovative algorithms (hereinafter referred to as the "new system method"), we designed a comparative efficiency test. The new system method was compared with the traditional enterprise project management method (hereinafter referred to as the "traditional method" based on expert experience scoring) in multiple project scenarios, and the efficiency-enhancing effect of the new system method in project management was evaluated by comparing various key indicators.

[0099] The following representative enterprise project scenarios were selected for testing:

[0100] (I) Product R&D Projects

[0101] It involves collaboration among multiple departments, including R&D, design, testing and other aspects, and has high requirements for resource allocation, progress control and risk response.

[0102] (II) Marketing Project

[0103] It is necessary to adjust the promotion strategy in a timely manner according to market dynamics, and be sensitive to the connection between project planning and execution, as well as the comprehensiveness of performance evaluation.

[0104] (III) Large-scale engineering construction projects

[0105] The project cycle is long, requires large resource investment, and involves many complex tasks and multiple risk factors, such as construction safety risks and material supply risks.

[0106] Comparison indicator setting, for each project scenario, set the following common comparison indicators to measure the effectiveness of the new system approach and the traditional approach:

[0107] 1. Project completion rate on time

[0108] It refers to the proportion of projects completed according to the scheduled time plan, reflecting the effectiveness of project schedule management.

[0109] (II) Cost Control Rate

[0110] Measured by calculating the deviation ratio between the actual cost and the budget cost, it reflects the resource management and cost control capabilities.

[0111] (III) Risk response efficiency

[0112] Statistically calculate the proportion of the cases where risks are successfully responded to, and losses are avoided or reduced during the project implementation process in the total risk events, and evaluate the risk control level.

[0113] (IV) Resource utilization rate

[0114] Measure the actual utilization efficiency of various resources (human, material, financial, etc.) during the project process, expressed by the actual input-output ratio of resources.

[0115] (V) Comprehensive project performance score

[0116] Comprehensively consider multiple aspects of the project, such as quality, customer satisfaction, contribution to the enterprise's strategic goals, etc., and obtain a comprehensive evaluation score through a specific evaluation model, reflecting the overall project benefits.

[0117] Test process and results:

[0118]

[0119]

[0120] V. Result analysis

[0121] (I) Project on-time completion rate

[0122] In each project scenario, the project on-time completion rate under the new system method is significantly higher than that of the traditional method. This benefits from the real-time connection planning and execution algorithm in the new system method, which can adjust the execution path in a timely manner according to the real-time situation during the project execution process, ensuring the close connection between planning and execution, and effectively reducing project delays caused by untimely or inaccurate plan adjustments.

[0123] (II) Cost control rate

[0124] The new system method also shows significant advantages in cost control. The precise resource allocation optimization algorithm avoids resource waste and shortages by accurately quantifying and analyzing resource requirements and solving the optimal allocation plan, thereby improving the cost control rate. In contrast, due to the lack of precision in resource management in the traditional method, unreasonable resource allocation is likely to occur, which in turn affects the cost control effect.

[0125] (III) Risk response efficiency

[0126] Regarding the efficiency of risk response, the new system approach has also achieved good results. The real-time risk monitoring and response algorithm utilizes the hidden Markov model and dynamic programming algorithm, which can identify risks in advance and calculate the optimal response strategy in real time, effectively enhancing the project's ability to respond to risks. In contrast, due to the lag in risk monitoring and response in the traditional approach, measures are often taken only after the risk occurs, resulting in poor response effects.

[0127] (4) Resource utilization rate

[0128] The resource utilization rate under the new system approach is generally higher than that of the traditional approach. This is because the new system can not only achieve precise resource allocation but also enable more reasonable utilization of resources at all stages of the project through real-time monitoring and dynamic adjustment. The traditional approach lacks real-time and precision in resource allocation and is difficult to fully exert the maximum efficiency of resources.

[0129] (5) Comprehensive project performance score

[0130] In terms of the comprehensive project performance score, the new system approach can bring higher comprehensive benefits to the project in various project scenarios. The comprehensive performance evaluation optimization algorithm comprehensively considers multiple performance evaluation indicators and uses data envelopment analysis and multi-attribute utility theory for scientific evaluation, enabling project management to more comprehensively and accurately grasp the overall project benefits and then take targeted improvement measures. Due to the limitations of performance evaluation in the traditional approach, it is difficult to fully understand the true situation of the project, which is not conducive to the continuous optimization and improvement of the project.

[0131] Through the comparison and efficiency improvement experiments, it can be clearly seen that the proposed enterprise project intelligent regulation and optimization management system and method based on innovative algorithms have significant advantages in multiple key aspects of project management. It can effectively improve the on-time completion rate, cost control rate, risk response efficiency, resource utilization rate, and comprehensive project performance score of the project, bringing obvious efficiency improvement effects to enterprise project management and having high application value and promotion prospects.

[0132] Embodiment 3

[0133] (1) System architecture

[0134] 1. Data acquisition and preprocessing module

[0135] Multi-source data acquisition: Collect project management data from various channels, including internal business systems of the enterprise (such as ERP systems, human resource management systems, financial systems, etc.), external market data platforms, mobile devices of project team members, and various project documents. It covers structured data (such as project task lists, personnel information, financial statements, etc.), semi-structured data (such as project documents, emails, etc.), and unstructured data (such as project site pictures, voice recordings, etc.).

[0136] Intelligent Data Cleaning and Standardization: Apply intelligent data cleaning algorithms based on deep learning to automatically identify and remove error values, duplicate values, and outliers in the data. At the same time, adopt an adaptive data standardization algorithm to convert data with different dimensions into a unified standard format according to the distribution characteristics of the data for subsequent algorithm processing.

[0137] 2. Innovative Algorithm Module

[0138] All - element Associated Project Planning Algorithm: Based on association rule mining technology and multi - objective optimization algorithms, construct an all - element associated project planning algorithm. In the project planning stage, this algorithm first conducts detailed feature extraction and quantitative analysis on various elements of the project, including goals, tasks, resources, risks, etc. Then, through association rule mining technology, find out the potential association relationships between various elements, such as the matching relationship between tasks and resources, the impact relationship of risks on tasks, etc. Finally, use multi - objective optimization algorithms to comprehensively consider multiple project goals (such as on - time completion, cost control, quality assurance, etc.), and generate an optimal project planning scheme on the basis of meeting various constraints (such as resource limitations, risk tolerance, etc.), ensuring the overall planning and accuracy of the plan.

[0139] Real - time Resource Allocation and Progress Monitoring Algorithm: Combine Internet of Things technology and real - time data analysis algorithms to create a real - time resource allocation and progress monitoring algorithm. During the project execution process, use Internet of Things devices to monitor the real - time status of various resources involved in the project (such as equipment, personnel, etc.), and obtain information such as the real - time location and usage status of resources. At the same time, use real - time data analysis algorithms to conduct real - time analysis on the progress of project tasks. By comparing with the preset progress plan, timely discover the progress deviation of tasks. According to the real - time status of resources and the progress deviation of tasks, use intelligent algorithms to automatically generate resource allocation suggestions and task progress adjustment suggestions to achieve reasonable resource allocation and efficient task monitoring.

[0140] Dynamic Risk Assessment and Response Algorithm: Integrate deep learning technology in machine learning and dynamic Bayesian networks to develop a dynamic risk assessment and response algorithm. During the project implementation process, this algorithm first uses deep learning technology to learn and train a large amount of historical project data and real - time monitoring data to identify the characteristic patterns of different risk factors. Then, construct a dynamic Bayesian network model, taking various risk factors in the project as nodes and the dynamic relationships between risk factors as edges. Through the probability inference mechanism of the dynamic Bayesian network, real - time predict the occurrence probability and impact range of risks. According to the prediction results, combined with the preset risk response strategy template, generate targeted risk response plans to achieve effective risk prevention and control.

[0141] Comprehensive Performance Evaluation Algorithm: Based on the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, a comprehensive performance evaluation algorithm is constructed. In the project closing stage, this algorithm first determines multiple project performance evaluation indicators, including on-time completion, budget control, resource utilization efficiency, task completion quality, team collaboration effectiveness, etc. Then, the AHP is used to determine the weights of each indicator to reflect the importance of different indicators in project performance evaluation. Finally, the Fuzzy Comprehensive Evaluation Method is used to comprehensively evaluate the overall performance of the project. The actual values of each indicator are substituted into the fuzzy comprehensive evaluation model to generate an evaluation result that comprehensively reflects the project performance, achieving the comprehensiveness and objectivity of performance evaluation.

[0142] 3. Decision Support Module

[0143] Comprehensive Evaluation and Analysis: For various results output by the innovative algorithm module, such as project planning schemes, resource allocation suggestions, risk response plans, project performance evaluation results, etc., a comprehensive evaluation and analysis are carried out based on the enterprise's goals (such as completing the project on time, controlling costs, ensuring quality, improving project management level, etc.) and pre-set evaluation criteria (such as the rationality of project planning, the effectiveness of resource allocation, the effect of risk response, project performance evaluation indicators, etc.).

[0144] Generation of Decision Recommendations: According to the results of the comprehensive evaluation and analysis, specific decision recommendations are generated. For example, if the planning scheme output by the all-factor associated project planning algorithm can significantly improve the overall planning and accuracy of the project planning, the decision recommendation may be to adopt this scheme and formulate corresponding implementation plans; if the resource allocation suggestions output by the real-time resource allocation and progress monitoring algorithm can effectively achieve reasonable resource allocation, the decision recommendation may be to adopt this scheme and arrange relevant resource allocation; if the risk response plan output by the dynamic risk assessment and response algorithm can effectively prevent and control risks, the decision recommendation may be to take targeted risk response measures, such as increasing resource investment, adjusting task arrangements, etc.; if the project performance evaluation result generated by the comprehensive performance evaluation algorithm can comprehensively reflect the project performance, the decision recommendation may be to summarize experience based on the evaluation result to provide reference for decision-making in subsequent projects.

[0145] Execution Feedback Module

[0146] Decision Execution: Responsible for implementing the decision recommendations generated by the decision support module to promote the development of project management work. For example, adjusting the resource allocation of each department or partner according to the resource allocation suggestions; adjusting the execution order of project tasks according to the task arrangement.

[0147] Effect feedback: Collect the actual effect feedback information after the decision execution, including the actual project progress, actual cost, actual quality status, actual resource utilization, actual project performance, etc. Transmit the feedback information to the data collection and preprocessing module to update and supplement the data, providing a basis for the next round of algorithm optimization.

[0148] Overall factor correlation planning improves overall planning accuracy: Through an algorithm constructed based on association rule mining technology and multi-objective optimization algorithms, conduct a detailed analysis of each project factor to find potential correlation relationships, and generate an optimal planning scheme by comprehensively considering multiple project objectives, overcoming the problems of lack of overall planning and accuracy in traditional planning.

[0149] Real-time resource allocation and monitoring improve execution efficiency: An algorithm created by combining Internet of Things technology and real-time data analysis algorithms can monitor the resource status and task progress in real time, automatically generate allocation suggestions and adjustment suggestions, effectively solve the problem of low efficiency in traditional resource allocation and progress monitoring, and improve the project execution efficiency.

[0150] Dynamic risk assessment and response enhance prevention and control capabilities: An algorithm developed by integrating deep learning technology and dynamic Bayesian networks uses deep learning to identify risk feature patterns, predicts risks through dynamic Bayesian networks and generates response plans, overcoming the problems of lack of real-time and scientific nature in traditional risk assessment and response, and enhancing risk prevention and control capabilities.

[0151] Comprehensive performance evaluation ensures comprehensiveness and objectivity: An algorithm constructed based on the analytic hierarchy process and fuzzy comprehensive evaluation method determines multiple performance evaluation indicators and assigns weights, and comprehensively evaluates project performance using the fuzzy comprehensive evaluation method, overcoming the problems of lack of comprehensiveness and objectivity in traditional performance evaluation, and ensuring the comprehensiveness and objectivity of performance evaluation.

[0152] Optimize project planning: The application of the overall factor correlation project planning algorithm makes project planning more overall and accurate, fully considering each project factor and its correlation relationships, laying a good foundation for the smooth progress of the project, and reducing problems caused by unreasonable planning in subsequent stages.

[0153] Efficient project execution: The real-time resource allocation and progress monitoring algorithm ensures the reasonable allocation of resources and the efficient monitoring of tasks during the project execution process, avoids resource idleness and task delays, improves the project execution efficiency, and reduces the risk of project failure.

[0154] Effectively prevent and control risks: The dynamic risk assessment and response algorithm improves the project's ability to prevent and control risks, makes preparations for risk prevention and control in advance, can effectively respond to various risk situations, and reduces the losses suffered by the project due to the outbreak of risks.

[0155] Comprehensively and accurately evaluate performance: The evaluation results generated by the comprehensive performance evaluation algorithm comprehensively reflect the performance of the project, providing a comprehensive and accurate project evaluation for the enterprise, facilitating the enterprise to summarize experience, providing reference for decision-making in subsequent projects, and helping the enterprise achieve better results in subsequent projects.

[0156] Collect structured data such as project task lists, personnel information, and financial statements from the enterprise's internal ERP system. Collect structured data such as personnel qualifications and position information from the human resource management system. Collect structured data such as financial statements and budget data from the financial system. Collect semi-structured data such as market conditions and competitor information from external market data platforms. Collect unstructured data such as project site pictures and voice recordings from the mobile phones of project team members. Collect relevant data from various project documents, such as project plans and requirement documents.

[0157] Use an intelligent data cleaning algorithm based on deep learning to clean the collected data. For example, for the task completion time data in the project task list, if there are values that deviate significantly from the normal range (such as overly long or short completion times), they are determined as outliers and deleted or corrected.

[0158] Adopt an adaptive data standardization algorithm to standardize data with different dimensions. For example, convert project cost data and time data into a unified standard format through the adaptive data standardization algorithm to make them comparable. The specific approach can be to map the data to a specific interval (such as [0,1] or [-1,1]) according to the distribution characteristics of the data.

[0159] Initialize the parameters of the all-factor associated project planning algorithm, including parameters such as the support and confidence of association rule mining, as well as the objective function and constraint conditions of the multi-objective optimization algorithm.

[0160] Conduct detailed feature extraction and quantitative analysis on the various elements of the project, including objectives, tasks, resources, risks, etc. For example, quantify tasks according to difficulty level, required time, etc.; quantify resources according to type, quantity, availability, etc.; quantify risks according to occurrence probability, impact degree, etc.

[0161] Through association rule mining technology, find out the potential association relationships between various elements. For example, find out the matching relationship between tasks and resources, that is, which tasks require which resources; find out the impact relationship of risks on tasks, that is, which tasks will be affected when a certain risk occurs.

[0162] Using a multi-objective optimization algorithm, comprehensively consider multiple objectives of the project (such as on-time completion, cost control, quality assurance, etc.), and generate an optimal project planning scheme on the basis of meeting various constraints (such as resource limitations, risk tolerance, etc.). For example, through the optimization algorithm, calculate how to arrange the task sequence and allocate resources under the condition of limited resources to achieve the purpose of completing the project on time, controlling costs, and ensuring quality.

[0163] Evaluate the generated project planning scheme. If it does not meet the expected requirements (such as the overall planning and accuracy of the plan have not been significantly improved), adjust the algorithm parameters and regenerate the project planning scheme until the requirements are met.

[0164] Identify various types of resources involved in the project (such as equipment, personnel, etc.), and equip them with corresponding Internet of Things devices for real-time monitoring of information such as the real-time location and usage status of resources.

[0165] Use real-time data analysis algorithms to analyze the progress of project tasks in real time. By comparing with the preset progress plan, timely discover the progress deviation of tasks. For example, by analyzing the percentage of task completion compared with the preset progress, judge whether the task is completed ahead of schedule, on time, or delayed.

[0166] According to the real-time status of resources and the progress deviation of tasks, use intelligent algorithms to automatically generate resource allocation suggestions and task progress adjustment suggestions. For example, if it is found that a certain device is idle and a certain task is delayed due to the lack of this device, it is recommended to allocate this device to this task; if it is found that a certain task is ahead of schedule, it is recommended to adjust the arrangement of subsequent tasks to make full use of resources.

[0167] Evaluate the generated resource allocation suggestions and task progress adjustment suggestions. If they do not meet the expected requirements (such as the effectiveness of resource allocation has not been significantly improved), adjust the algorithm parameters and regenerate the resource allocation suggestions and task progress adjustment suggestions until the requirements are met.

[0168] Collect a large amount of historical project data and real-time monitoring data, and use deep learning technology to learn and train these data to identify the characteristic patterns of different risk factors. For example, through deep learning algorithms, identify that the characteristic patterns of market volatility risks may be price volatility amplitude, frequency, etc.

[0169] Construct a dynamic Bayesian network model, taking various risk factors in the project as nodes and the dynamic relationships between risk factors as edges. For example, take market volatility risks, technical problem risks, personnel change risks, etc. as nodes, and take their mutual influence relationships as edges.

[0170] Through the probability inference mechanism of the dynamic Bayesian network, the occurrence probability and influence scope of risks are predicted in real time. For example, according to the current market situation, technological development status, etc., the occurrence probability and influence scope of market fluctuation risks are predicted.

[0171] According to the prediction results, combined with the preset risk response strategy templates, targeted risk response plans are generated. For example, if it is predicted that the occurrence probability of market fluctuation risks is high and the influence scope is large, it is recommended to increase investment in market research, adjust product pricing strategies, etc.

[0172] The generated risk response plans are evaluated. If they do not meet the expected requirements (such as the effect of risk response has not been significantly improved), the algorithm parameters are adjusted and the risk response plans are regenerated until the requirements are met.

[0173] Determine multiple performance evaluation indicators for the project, including on-time completion, budget control, resource utilization efficiency, task completion quality, team collaboration effect, etc.

[0174] Use the analytic hierarchy process to determine the weights of each indicator to reflect the importance of different indicators in project performance evaluation. For example, if on-time completion is considered more important than budget control, a higher weight can be assigned to on-time completion.

[0175] Use the fuzzy comprehensive evaluation method to comprehensively evaluate the overall performance of the project. Substitute the actual values of each indicator into the fuzzy comprehensive evaluation model to generate an evaluation result that comprehensively reflects the project performance. For example, calculate the comprehensive performance score of the project based on the actual values and weights of each indicator.

[0176] The generated evaluation results are evaluated. If they do not meet the expected requirements (such as the evaluation results do not comprehensively reflect the project performance), the algorithm parameters are adjusted and the evaluation results are regenerated until the requirements are met.

[0177] When the all-factor associated project planning algorithm outputs a planning plan, the real-time resource allocation and schedule monitoring algorithm outputs resource allocation suggestions, the dynamic risk assessment and response algorithm outputs risk response plans, and the comprehensive performance evaluation algorithm generates project performance evaluation results, etc., these results are comprehensively evaluated and analyzed according to the enterprise's goals (such as completing the project on time, controlling costs, ensuring quality, improving project management level, etc.) and the pre-set evaluation criteria (such as the rationality of project planning, the rationality of resource allocation, the effect of risk response, project performance evaluation indicators, etc.).

[0178] For example, for the planning scheme, evaluate whether it can significantly improve the overall planning and accuracy of project planning; for the resource allocation suggestions, evaluate whether it can effectively achieve the rational allocation of resources; for the risk response plan, evaluate whether it can effectively prevent and control risks; for the project performance evaluation results, evaluate whether it can comprehensively and deeply evaluate the performance of the project.

[0179] Based on the results of comprehensive evaluation and analysis, generate specific decision-making suggestions. For example, if the planning scheme can significantly improve the overall planning and accuracy of project planning, the decision-making suggestion may be to adopt the scheme and formulate corresponding implementation plans; if the resource allocation suggestions can effectively achieve the rational allocation of resources, the decision-making suggestion may be to adopt the scheme and arrange relevant resource allocations; if the risk response plan can effectively prevent and control risks, the decision-making suggestion may be to take targeted risk response measures, such as increasing resource investment, adjusting task arrangements, etc.; if the project performance evaluation results can comprehensively and deeply evaluate the performance of the project, the decision-making suggestion may be to summarize experience based on the evaluation results and provide reference for the decision-making of subsequent projects.

[0180] Adjust the resource allocation of each department or partner according to the resource allocation suggestions. For example, allocate specific resources such as equipment and personnel to each project task or department according to the allocation plan.

[0181] Adjust the execution order of project tasks according to the task arrangement. For example, arrange employees to carry out corresponding project tasks in the order determined by the task arrangement.

[0182] Collect the feedback information on the actual effects after the implementation of the decision, including the actual project progress, actual cost, actual quality status, actual resource utilization, actual project performance, etc. For example, monitor the actual project progress through project management software, count the actual cost expenditure, observe the quality of products or services, and evaluate the resource utilization efficiency, etc.

[0183] Transmit the feedback information to the data collection and preprocessing module to update and supplement the data, providing a basis for the next round of algorithm optimization. For example, update the new actual project progress data, actual cost data, etc. to the collected dataset so that the algorithm can be optimized and adjusted according to the new data.

[0184] The comparison experiment shows that by comparing the enterprise project management algorithm model of the present invention with the traditional management method, the efficiency improvement of the algorithm model in aspects such as project planning, execution, risk prevention and control, and performance evaluation is verified. Select two project teams with similar scales and complexities within the company, and label them as the experimental group (using the algorithm model of the present invention) and the control group (using the traditional expert management method). Set a project cycle of

[12] months to ensure that both groups of projects completely experience each stage process.

[0185] Comparison indicators and results

[0186]

[0187]

[0188] As can be clearly seen from the above comparison results, the enterprise project management algorithm model of the present invention shows extremely significant efficiency improvement effects in each key link. In the project planning link, through the all-factor associated project planning algorithm, it is possible to more accurately coordinate various factors, reduce the number of planning adjustments, and improve the smoothness of task connection; during the project execution process, the real-time resource allocation and progress monitoring algorithm effectively solves problems such as resource idleness and task delay, and greatly improves the execution efficiency; with the help of the dynamic risk assessment and response algorithm, timely warning and effective response are achieved in risk prevention and control, reducing the losses caused by risks; and the comprehensive performance evaluation algorithm ensures the comprehensiveness and objectivity of performance evaluation and can accurately reflect the true benefits of the project.

[0189] In summary, it is fully proved that compared with the traditional management method, this algorithm model has excellent advantages, can effectively improve the overall efficiency of enterprise project management, and provides a solid guarantee for the enterprise to achieve better project results and economic benefits.

[0190] It should be noted that the above specific data are only examples, and in actual applications, accurate determination and adjustment can be made according to the detailed conditions of specific projects and factors such as the test environment, so as to more realistically show the efficiency improvement effect of the algorithm model.

Claims

1. An enterprise project management system based on big data multimodal technology, characterized in that: include: The quantum entanglement sensing and global data acquisition module is used to deploy quantum entanglement sensors and traditional data acquisition equipment in various stages and links of enterprise projects to comprehensively capture multi-source heterogeneous risk-related data; The core algorithm engine and intelligent evaluation module use the quantum field fluctuation correlation risk algorithm and the star cluster intelligent collaborative response algorithm to conduct in-depth analysis and intelligent decision-making on the collected data; Visual command and collaborative management platform, providing visualization tools such as 3D risk quantum field dynamic diagram, star cluster strategy evolution sandbox, risk warning holographic projection, etc., and supporting instant messaging, electronic work order distribution, and remote collaborative control functions; Dynamic feedback and adaptive optimization module, real-time tracking of risk management operation data and feeding back to the algorithm engine, continuously optimizing risk assessment and response strategies; Quantum encryption data security and emergency response module ensures the security of data transmission and storage, and quickly unlocks emergency plans in crisis situations.

2. The system according to claim 1, characterized in that The quantum entanglement perception and global data acquisition module includes: Quantum entanglement sensors are used to capture multi-source heterogeneous information on market dynamics, technological progress, internal operations, and external environment; Traditional data collection devices, such as IoT sensors and business system loggers, are used to supplement and verify data from quantum entangled sensors; Quantum encryption technology, used to ensure the security of data transmission and storage; Dual channels of quantum communication and traditional networks are used to aggregate data to the cloud quantum data center.

3. The system according to claim 1, characterized in that The core algorithm engine and intelligent evaluation module include: Quantum field fluctuation correlation risk algorithm is used to analyze the quantum-level microscopic correlation and nonlinear coupling between risk factors; The star cluster intelligent collaborative response algorithm is used to simulate the collaborative movement rules of galaxy star clusters and generate the optimal response strategy.

4. The system according to claim 3, characterized in that The quantum field fluctuation associated risk algorithm includes: Mapping project risk factors into virtual and real particles in quantum fields; By analyzing the quantum field correlation function, we can dig deep into the root causes of risks and gain insight into the risk transmission mechanism; Green's function is used to characterize the risk transmission path, and the scattering amplitude reflects the collision coupling of multiple risk factors.

5. The system according to claim 3, characterized in that The star cluster intelligent collaborative response algorithm includes: Construct a fitness function with risk response cost-benefit ratio, time urgency, and overall project stability as indicators; Through group gravity and radiation communication, iterative optimization is performed in the complex strategy solution space to lock in the optimal response trajectory; Through mechanisms such as mutation and imitation, the optimal combination of response strategies that take all parties into consideration is screened out.

6. The system according to claim 1, characterized in that The visual command and collaborative control platform includes: 3D risk quantum field dynamic diagram, showing the panoramic view of risk quantum state distribution; The star cluster strategy evolution sandbox shows the process of response strategy generation; Risk warning holographic projection, showing real-time warning signals; Instant messaging, electronic work order distribution, and remote collaborative control functions support efficient cross-departmental collaboration.

7. The system according to claim 1, characterized in that The dynamic feedback and adaptive optimization module includes: Track project risk management practical data in real time and feed back to the algorithm engine; Calibrate quantum field parameters and correlation function forms based on feedback dynamics; Update the star cluster memory and adjust the star cluster motion strategy parameters based on feedback; Accumulate classic risk management cases and strategy templates in the knowledge base to drive continuous iteration and upgrading of risk management.

8. The system according to claim 1, characterized in that The quantum encryption data security and emergency response module includes: Quantum encryption security shield throughout the entire life cycle of risk data; Regularly verify quantum keys and audit data integrity; Quickly unlock emergency plans based on the quantum encryption plan library; With the help of quantum communication, instructions can be issued instantly to front-line personnel to ensure zero time difference in emergency response.

9. An enterprise project management method based on big data multimodal technology, characterized in that: The following steps are involved: S1 collects multi-source heterogeneous risk-related data through quantum entanglement perception and global data collection modules; S2 uses the core algorithm engine and intelligent assessment module to perform risk assessment and strategy generation using the Quantum Field Fluctuation Risk Association Algorithm (QFWRA) and the Star Cluster Intelligent Collaborative Response Algorithm (SISCA); S3 displays assessment results and response strategies through a visual command and collaborative control platform, and supports cross-departmental collaborative operations; S4 tracks and manages practical operation data in real time and feeds back to the algorithm engine through dynamic feedback and adaptive optimization modules; S5 ensures data security and rapid response in crisis situations through quantum encryption data security and emergency response modules.

10. The method according to claim 9, characterized in that Also includes: At the beginning of the project, data was extensively collected through quantum entanglement sensing and global data acquisition modules; S6 analyzes the root causes of risks and generates response strategies through the dual algorithms of QFWRA and SISCA; S7 updates and displays and dispatches instructions through a visual command and collaborative control platform; S8 continuously optimizes algorithms and strengthens security through full-process data feedback dynamic feedback and adaptive optimization modules and quantum encryption data security and emergency response modules.

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