Industrial-grade industry and financial fusion method and system based on project management, and storage medium

Abstract

The invention relates to the technical field of computer data processing and project management, and discloses an industrial-grade business and financial fusion method and system based on project management and a storage medium, and the method comprises the following steps: S1, obtaining multi-source heterogeneous data in a project, and mapping the data into business and financial entities; s2, abstracting the state of the business and financial entity into a system state vector, and establishing a state transition matrix for defining a time evolution rule of the system state vector; s3, performing prediction or risk analysis on future financial indexes of the project based on evolution of the system state vector driven by the state transition matrix; and S4, according to the prediction or risk analysis result, generating a decision scheme for intervening the project execution, the real-time business operation of the project and the financial state change are deeply fused, and through establishing the dynamic state space evolution model, the prospective and quantitative prediction of future financial indexes of the project is realized.

Description

Technical Field

[0001] This application relates to the fields of computer data processing and project management technology, specifically to an industrial-grade business-finance integration method, system, and storage medium based on project management. Background Technology

[0002] In the current field of project management, such as industrial manufacturing and engineering construction, achieving deep integration of business and financial information flows—that is, "business-finance integration"—has become a core requirement for enterprises to improve their level of refined management and enhance project profitability. The execution process of a project involves complex and ever-changing business activities, such as production scheduling, material flow, equipment usage, and supply chain coordination. These business activities are closely linked to the project's financial indicators, such as costs, revenues, and cash flow, and dynamically affect the project's final economic benefits.

[0003] However, existing technologies still have significant limitations in achieving efficient and dynamic industrial-grade business-finance integration. Currently, enterprises generally rely on multiple heterogeneous information systems to support project operations. For example, they use Enterprise Resource Planning (ERP) systems to manage the general ledger and purchase orders, while using Manufacturing Execution Systems (MES) to track production progress and resource consumption on-site. This fragmented system architecture naturally leads to "information silos" between business and financial data. Business data obtained by the finance department often has significant time delays; cost accounting and financial analysis can usually only be conducted after the accounting cycle (such as the end of the month or quarter). This results in management seeing only a lagging summary of past events in financial reports, making it difficult to provide timely guidance and intervention for ongoing projects, leaving project management in a passive "post-event management" state for a long time.

[0004] Furthermore, existing models for the relationship between business and finance are relatively rudimentary and static. The collection and allocation of project costs largely rely on pre-defined standard cost models or simplified accounting allocation rules. This approach struggles to accurately capture the true cost transmission paths and impact intensity caused by dynamic factors such as fluctuations in equipment efficiency, changes in process routes, and differences in personnel skills during actual operations. When faced with external disturbances such as frequent fluctuations in raw material prices or sudden supply chain disruptions, this static, linear model cannot effectively extrapolate the chain reaction on the entire project cost structure, and its analytical results often deviate significantly from reality.

[0005] Furthermore, existing technologies are insufficient for predicting the future state of projects and assessing risks. Most project management tools offer predictive capabilities limited to simple trend extrapolation based on historical data, failing to quantify future uncertainties or provide probability ranges for key financial indicators. This leaves decision-makers lacking reliable quantitative data when formulating response strategies. Simultaneously, risk management activities often focus on monitoring surface-level financial indicators or key performance indicators (KPIs), lacking effective identification and early warning capabilities for deep-seated structural risks—those arising from complex internal interactions and feedback mechanisms that are difficult to detect but could trigger systemic collapse. Once such risks materialize, they often cause irreversible and significant losses to the project. Therefore, a new technological solution is urgently needed that can penetrate the surface of data, understand the project's internal dynamic evolution mechanisms, and achieve forward-looking prediction and closed-loop control. Summary of the Invention

[0006] This application aims to address the technical problem in the prior art that the lack of accurate modeling methods for the dynamic causal relationship between business and financial status makes it difficult to make real-time quantitative predictions of the future status of industrial-grade projects and provide in-depth structural risk warnings.

[0007] The industrial-grade business-finance integration method based on project management provided in this application includes the following steps: S1. Obtain multi-source heterogeneous data from the project and map the data to business and financial entities; S2. Abstract the state of the business and financial entities into a system state vector, and establish a state transition matrix to define the rules for the evolution of the system state vector over time. S3. Based on the evolution of the system state vector driven by the state transition matrix, predict or analyze the future financial indicators of the project. S4. Based on the results of the prediction or risk analysis, generate a decision-making plan for intervening in the execution of the project.

[0008] Preferably, establishing the state transition matrix includes: The business and financial entities are constructed as nodes in a dynamically weighted directed graph; Determine the edges and weight functions between nodes based on the relationships between the entities; The state transition matrix is ​​generated based on the topology and weight function of the graph.

[0009] Preferably, the forecasting of the project's future financial indicators includes: Define the uncertainty parameters in the state transition matrix or external input as random variables; The probability distribution of the financial indicators is obtained by performing Monte Carlo simulations on the evolution of the system state vector.

[0010] Preferably, the risk analysis includes: Calculate the spectral radius of the state transition matrix; and trigger a structural risk warning when the spectral radius meets a preset risk condition, wherein the spectral radius ρ(A(t)) is determined by the following formula: Where A(t) is the state transition matrix at time t, and λ i (t) is the i-th eigenvalue of the state transition matrix A(t), max i The expression represents taking the maximum value of the modulo of all eigenvalues, and |·| represents the modulo operation on a complex number.

[0011] Preferably, the preset risk condition is that the spectral radius is greater than or equal to 1.

[0012] Preferably, the decision generation scheme includes: Construct and solve the following robust optimization objective function to obtain the optimal decision scheme d. opt : Where d is a candidate decision scheme, D is the set of all feasible decision schemes, and C(d) is a random variable representing a certain financial indicator of the project after adopting decision scheme d. Let C(d) be the expected value of the random variable C(d), Std[C(d)] be the standard deviation of the random variable C(d), and k be the preset risk aversion coefficient. This means finding the decision scheme d in set D that minimizes the objective function value.

[0013] Preferably, the method further includes: According to the optimal decision scheme d opt The system automatically updates the relationship between the business and financial entities upon which the state transition matrix is ​​based, thereby achieving closed-loop adaptive modeling.

[0014] Preferably, the evolution of the system state vector is represented by the following state evolution equation: S(t+1)=A(t)S(t)+B(t)U(t); Where t is a discrete time point, S(t) is the system state vector at time t, S(t+1) is the system state vector at time t+1, A(t) is the state transition matrix at time t, B(t) is the input matrix at time t, used to define how external inputs affect the system state, and U(t) is the external input vector at time t, representing external events or disturbances.

[0015] Secondly, the industrial-grade business-finance integration system based on project management provided in this application includes: The model building module is used to acquire project data, map it to business and financial entities, abstract the state of the entities into system state vectors, and establish a state transition matrix to define the evolution rules of the system state vectors. The analysis and prediction module is used to predict or analyze the future financial indicators of the project based on the evolution of the system state vector driven by the state transition matrix. The decision generation module is used to generate a decision plan for intervening in the execution of the project based on the results of the analysis and prediction module.

[0016] This application also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0017] In summary, this application includes at least one of the following beneficial technical effects: 1. This application deeply integrates the real-time business operations and financial status changes of a project. By establishing a dynamic state-space evolution model, it achieves forward-looking and quantitative prediction of future financial indicators of the project. This method directly maps high-frequency business events such as actual work order progress, material consumption, and equipment operation into dynamic impacts on future costs, profits, and other financial statuses through a state transition matrix. This overcomes the limitations of traditional financial accounting and project management, which are often separated and can only perform lagging analysis, and provides managers with the ability to anticipate the future and proactively intervene. 2. By abstracting the project's internal logic into a state transition matrix and calculating its spectral radius, this application can diagnose, from the perspective of system dynamics, whether there is a vicious positive feedback loop that leads to an exponential increase in costs or delays. This achieves a fundamental early warning of the project's structural instability risk, which is far earlier than the significant deterioration of financial results, thus providing a valuable window of time for corrective measures. 3. This application provides a closed-loop adaptive intelligent decision-making and model optimization mechanism. When risks or deviations are predicted, this method not only generates robust optimal decision schemes that take uncertainty into account, but more importantly, it can automatically update the relationships between business and financial entities and reconstruct the entire state transition matrix based on the adopted decision schemes. This closed-loop adaptive capability enables the model to continuously learn and evolve, ensuring that it always accurately reflects the latest reality after project intervention, thereby transforming project management from an open-loop model relying on personal experience into an intelligent closed-loop management system that can self-correct and continuously optimize. Attached Figure Description

[0018] Figure 1This is a flowchart of the method in this application; Figure 2 This is a system architecture diagram for this application. Detailed Implementation

[0019] The following is in conjunction with the appendix Figure 1 This application will be described in further detail below.

[0020] Example: An industrial-grade business-finance integration method based on project management, comprising the following steps: S1. Obtain multi-source heterogeneous data from the project and map the data to business and financial entities; This invention provides an industrial-grade business-finance integration method, system, and readable storage medium based on project management. It aims to achieve accurate projection of the entire project lifecycle, quantitative risk assessment, and intelligent decision support by constructing a business-finance twin model that maps to the physical project in real time and has dynamic evolution capabilities.

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be described in detail below with reference to specific embodiments.

[0022] In this embodiment, the industrial-grade business-finance integration method based on project management first performs data acquisition and entity mapping. This step is the data cornerstone of the entire technical solution, and its core purpose is to overcome the data silo problem that commonly exists between internal and external information systems of enterprises, laying a high-quality and standardized data foundation for the subsequent construction of a unified, dynamic, and computable business-finance integration model.

[0023] Specifically, this step involves two sub-processes: acquiring multi-source heterogeneous data and mapping the data into standardized business and financial entities.

[0024] Regarding the acquisition of multi-source heterogeneous data: In the technical solution of this invention, data acquisition is a continuous and dynamic process aimed at capturing real-time changes in the project's status. The system connects to various existing information systems of the enterprise by deploying a series of data adapters or interfaces.

[0025] Preferably, these connection methods can be flexibly selected based on the characteristics of the target system. For example, for modern systems that support APIs (Application Programming Interfaces), such as some cloud-based ERP or MES systems, this method can retrieve or subscribe to data updates in near real-time by calling their provided RESTful APIs or SOAP APIs. For traditional systems based on relational databases, this method can directly query relevant database tables to obtain data by configuring JDBC or ODBC connectors. To achieve event-driven real-time data updates, this method can also configure topics to listen to message queues, thereby capturing the corresponding data as soon as a business event (such as work order completion or material outbound) occurs.

[0026] The acquired data sources cover all key aspects of project operation and management, including but not limited to: At the operational execution level: Detailed progress of work orders, actual material consumption, equipment operating status parameters (such as uptime and energy consumption), and personnel work hour records are obtained from the Manufacturing Execution System (MES) and Equipment Management System (EMS). This high-frequency operational data is core to capturing the physical progress and resource consumption of projects.

[0027] At the enterprise management level: Financial charts, cost center structures, project budget data, purchase order information, and supplier master data are obtained from the Enterprise Resource Planning (ERP) system. Material inventory levels, logistics in-transit information, and estimated delivery times are obtained from the Supply Chain Management (SCM) system. This management data endows the project with financial and supply chain dimensions.

[0028] External environment level: By accessing third-party data service platforms, we can obtain external dynamic variables that affect project costs and schedules, such as market transaction prices of commodities, exchange rate changes of major currencies, and macroeconomic prosperity indices.

[0029] Regarding mapping data to business and financial entities: After acquiring the raw data, this invention performs a crucial mapping process that transforms the raw data, which has diverse structures and meanings, into standardized business and financial entities that can be understood and processed by the subsequent graph model.

[0030] The mapping process begins by cleaning and transforming the raw data. Cleaning includes handling missing values, correcting outliers, and standardizing data formats and units (e.g., unifying energy consumption reported by different devices to kilowatt-hours). Transformation involves converting the raw data into a more business-meaning format, such as converting device start / stop timestamps into continuously running "effective working hours".

[0031] After cleaning and transformation are complete, the core mapping process begins. Here, "business and financial entities" are structured data objects that contain not only their own attributes but also their relationships with other entities. They are designed as the basic nodes of the dynamically weighted directed graph constructed in step S2.

[0032] For example, a production record retrieved from the MES system stating that "Work order W001 consumed 10 units of material batch B-XYZ on equipment M05 between 10:00 AM and 11:00 AM on June 12, 2025" will not be simply stored as a flat log. In this invention, this record will be parsed and used to create or update the following interrelated business and financial entities: A "work order" type entity, identified as "W001", will have its status attributes (such as "completed hours" and "material costs consumed") updated.

[0033] An entity of type "Device": identified as "M05", will have its status attributes (such as "cumulative uptime") updated.

[0034] An entity of type "Material Batch" is identified as "B-XYZ", and its status attributes (such as "Current Inventory") will be reduced accordingly.

[0035] At the same time, the event will also trigger the generation of new "cost" type entities, such as "direct labor costs", "equipment depreciation costs", and "direct material costs". These cost entities will be associated with work orders, equipment, materials and other entities through preset accounting rules.

[0036] Through the above methods, step S1 successfully transforms the scattered raw data streams from different systems into a standardized, interconnected set of business and financial entities. This set provides all the necessary node elements and relationship information for the subsequent step S2 to construct a dynamic business and financial state diagram that fully and accurately reflects the complex logic within the project, serving as a crucial bridge for the transformation from data to model. This mapping process ensures that data from different sources and at different granularities can be integrated and computed within a unified framework, providing logically consistent and data-complete input for the subsequent establishment and evolution of the entire state space model.

[0037] S2. Abstract the state of business and financial entities into system state vectors, and establish a state transition matrix to define the rules for the evolution of system state vectors over time. In this embodiment, immediately following the aforementioned data acquisition and entity mapping step S1, the technical solution of the present invention will construct a dynamic business and financial state diagram. This step is the core modeling link of the methodology of the present invention, and its purpose is to organize the relatively discrete business and financial entities produced in step S1 into a network model that can completely, intuitively and mathematically express the complex, dynamic causal and transmission relationships between the elements within the project.

[0038] The dynamic business and financial state diagram is not merely a visual representation, but also a rigorous mathematical structure. It provides direct, structured input and a logical foundation for establishing the state-space evolution model in the subsequent step S3. In essence, this diagram formally encodes the project's business logic, cost drivers, and value transfer paths.

[0039] Specifically, this step is implemented through three sub-processes: defining a set of nodes, defining a set of directed edges, and defining a dynamic weight function.

[0040] Regarding the construction of the node set (V): In the technical solution of this invention, the node set V of the graph consists of all the standardized business and financial entities generated in step S1. Each entity is instantiated as an independent node in the graph, representing a basic unit in the project that can be tracked and measured.

[0041] In this way, the project is deconstructed into a series of separate but internally interconnected nodes. These nodes are diverse and can cover various dimensions of the project, for example: Operational entity nodes include top-level project nodes such as “Project P1”, specific production task nodes such as “Work Order W001”, physical equipment nodes such as “Equipment M05”, raw material batch nodes such as “Material Batch B-20250611”, and “Human Resources Pool” nodes that represent available working hours.

[0042] Financial entity nodes include nodes such as "Cash Account" representing the company's financial status, "Accounts Payable" representing outstanding payments, and "Project Cost" and "Project Revenue" used to aggregate total project costs and revenues.

[0043] Intermediate cost nodes: In order to collect and allocate costs more precisely, various intermediate cost nodes can be set up, such as "direct labor costs", "indirect manufacturing costs", "equipment energy consumption costs", and "material procurement costs".

[0044] Regarding the construction of the directed edge set (E): After defining the node set, the system will establish directed edges between the nodes according to preset business rules and cost accounting logic, forming an edge set E. An edge from node v... i Pointing to node v j The directed edge e ij Its core meaning is that it represents resources, costs, information, or influence from entity v. i To entity v j The flow or transmission of.

[0045] The establishment of edges is based on a deep understanding of the actual business processes of the project. For example: When a work order consumes a batch of materials, the system will automatically create a directed edge from the "material batch" node to the "work order" node, representing the consumption of material resources.

[0046] When a work order is processed on a certain device, a directed edge is created from the "device" node to the "work order" node, representing the service provision of the device resource.

[0047] When a work order is completed, the various costs incurred (such as material costs, labor costs, and energy costs) need to be collected. At this time, directed edges will be created from the "work order" node to the corresponding cost nodes such as "direct material costs" and "direct labor costs".

[0048] In this way, the originally static nodes are connected by directed edges, forming a network topology that reflects the dynamic operation logic of the project.

[0049] Regarding the dynamic weight function (w) i Definition of j(t)): This is a key technical feature in this step, which distinguishes the graph model constructed by this invention from traditional graphs that use static values ​​as weights. In the technical solution of this invention, for each edge e... ij The defined weight is not a fixed scalar, but a multivariate dynamic weight function w. i j(t). This function can dynamically calculate the value of node v at time t based on the real-time changing parameters. i to node v j The specific amount of transfer or the extent of impact.

[0050] Its generalized mathematical form can be expressed as: w ij (t)=f(p i1 (t),p i2 (t),...,p ik (t)); in: w ij(t) is edge e ij The weighting function at discrete time point t.

[0051] f(·) represents a specific computation function, the form of which is determined according to the business meaning of the edge.

[0052] (p i1 (t),p i2 (t),...,p ik (t) is a set of parameters representing k dynamic variables that affect the weight calculation at time t. The values ​​of these variables can be obtained directly from the attributes of the associated entity nodes.

[0053] To illustrate the application of this dynamic weighting function, consider the following example: For a material batch B-XYZ (v i ) to "Work Order W001" (v j The edge of ) has a weight function w i j(t) can be defined as the material cost consumed in this work order. Its calculation function f is a multiplication operation, and the parameters include the quantity p of material consumed in this transaction. i1 (t)(obtained from the BOM consumption record of the work order) and the purchase unit price p of this batch of materials. i2 (t)(obtained from the properties of the material batch entity).

[0054] For a line from "device M05" (v i ) to "Work Order W001" (v j The edge of ) has a weight function w i j(t) can be designed to calculate the equipment energy cost allocated to this work order. Its calculation function f is also a multiplication operation, and the parameters include the equipment runtime p occupied by this work order. i1 (t) (obtained from the work order's time record) and the real-time average power p of the equipment during operation. i2 (t)(Retrieved in real time from the Equipment Management System (EMS)).

[0055] By introducing a dynamic weighting function, the business and financial state diagram constructed in this invention possesses unprecedented flexibility and accuracy. It no longer relies on preset, static standard costs, but can calculate costs and value transmission in real time and accurately based on each actual business transaction and every fluctuation in market prices. This provides a solid guarantee that the subsequent state-space model can truly reflect the dynamic evolution of the project.

[0056] In summary, step S2, by constructing nodes and edges and defining dynamic weight functions for the edges, successfully abstracted and transformed the complex business reality of the project into a mathematically rigorous, logically complete, and dynamically evolving business state diagram G(t) = (V, E). This diagram is not only a visual representation of the project state, but also the direct basis and blueprint for generating the core calculation tool in the subsequent step S3—the state transition matrix.

[0057] S3. Based on the evolution of the system state vector driven by the state transition matrix, predict the future financial indicators or conduct risk analysis for the project. In this embodiment, following the completion of the dynamic business state diagram in step S2, the technical solution of the present invention will proceed to establish a state-space evolution model. This step is a crucial step in the leap from logical modeling to algebraic modeling in the methodology of the present invention. Its core objective is to translate and abstract the graphical model established in step S2, which is more inclined towards intuitive logical expression, into a mathematically rigorous and computationally efficient algebraic model, namely, the state-space evolution model.

[0058] The establishment of this model makes it possible to use mature tools of linear algebra and control theory to perform dynamic simulation, stability analysis and optimal control of the project system. It is the computational core and engine for all subsequent analysis, prediction and decision-making functions.

[0059] Specifically, this step involves two closely related sub-processes: defining the system state vector and establishing the state evolution equation.

[0060] The definition of the system state vector S(t): In the technical solution of this invention, the state of the entire project first needs to be quantified and vectorized. The system aggregates the key quantifiable attribute values ​​of all n nodes in the dynamically weighted directed graph G(t) in step S2 at a certain discrete time point t to form an n-dimensional column vector, namely the system state vector S(t).

[0061] This vector can be viewed as a complete "state snapshot" of the project at time t. Its mathematical expression is: S(t) = [s1(t), s2(t), ..., s...]. n (t)] T ; Where each component s of the vector i (t) are all related to a specific node v in the graph. i Correspondingly, this represents the core state value of the node at time t. For example, s i (t) can be: For a "cost account" node, its value can be the cumulative amount incurred up to time t for that account.

[0062] For a "Material Batch" node, its value can be the current inventory quantity of the material in that batch.

[0063] For a "work order" node, its value can be the current completion percentage of the work order or the time spent.

[0064] By abstracting the project state into a unified mathematical vector, it facilitates subsequent matrix operations and systematic evolution calculations.

[0065] Regarding the establishment of the state evolution equation: After defining the system state vector, the core of this invention lies in establishing a mathematical rule that can accurately describe how this vector evolves over time. This rule is expressed through a discrete-time linear dynamic system equation, namely the state evolution equation: S(t+1)=A(t)S(t)+B(t)U(t); This equation reveals the intrinsic mechanism by which the system transitions from state S(t) at the current time t to state S(t+1) at the next time t+1. The following will elaborate on each component of this equation: The state transition matrix A(t): This n×n dimensional square matrix is ​​the heart of the evolutionary model of this invention. It is endogenous to the business state diagram constructed in step S2 and fully encodes the business logic and value transmission path within the project. Specifically, each element A in the matrix... ij (t) (located in the i-th row and j-th column) has a clear physical meaning: it quantifies the value of node v at time t. j state s j (t) for node v i The next state s i The contribution or influence coefficient of (t+1). A ij The value of (t) is directly determined by the connection node v. j to node v i The directed edge e ji The dynamic weight function w on ji (t) is derived from the calculation result at time t. If there is no node v in the graph. j to v i If the edge is A, then A ij The value of (t) is zero. For example, if v j Represents "direct labor", v i Represents the cost of work order A, edge e ji The weighting function calculates costs based on hourly rates, then A ij The value of (t) is the hourly rate. In this way, the complex internal dynamic relationships of the entire project, such as cost collection, resource consumption, and process transfer, are rigorously and comprehensively mapped into this state transition matrix.

[0066] External input vector U(t): This m×1 dimensional column vector is used to model events or disturbances that are not caused by the evolution of the system's internal state but by externally imposed factors. It enables the model to respond to changes in the external environment. Preferably, the components u of this vector... k (t) can represent various types of input, for example: Management decision instructions, such as the approval of a budget injection, can be represented by a specific numerical value.

[0067] Changes in the external environment, such as the rise and fall of raw material market prices, can be expressed as a percentage.

[0068] Discrete random events, such as a signal that a key supplier has defaulted on delivery, can be represented by a binary variable (0 or 1).

[0069] Input matrix B(t): This n×m dimensional matrix acts as an "allocator" or "scheduler." Its function is to precisely direct and apply the various external influences in the external input vector U(t) to the specific components of the system state vector S(t) that are affected by them. The elements B of the matrix... ik (t) defines the kth external input u k (t) for the i-th state variable s i The strength of the effect of (t). In many cases, this matrix is ​​a sparse matrix. For example, if u k If (t) represents a cash injection, then in matrix B(t), only the intersection position B of the row and the k-th column corresponding to the "cash account" node is considered. ik The state variable (t) is 1, and all other elements in the k-th column are 0. This ensures that the external input is applied precisely to the correct state variable.

[0070] In summary, step S3, by defining the system state vector and establishing the state evolution equation, successfully transformed a complex, graphical project logic model into a concise, powerful, and computable algebraic model. This model can not only simulate the deterministic evolution within the project based on the state transition matrix A(t), but also accommodate external uncertainties and interventions through the input vector U(t) and input matrix B(t), enabling accurate probabilistic predictions and in-depth risk analysis in the subsequent step S4.

[0071] S4. Based on the results of forecasting or risk analysis, generate decision-making plans for intervening in project execution.

[0072] In this embodiment, after step S3 successfully transforms the dynamic logic of the project into a state-space evolution model, the technical solution of the present invention will execute analysis and prediction steps based on this model. This step is the core link in the present invention to leverage its forward-looking insight capabilities. Its purpose is to use the established mathematical model to quantify the uncertainty of the project's future and to conduct in-depth diagnosis of the potential structural instability risks of the system.

[0073] The output of this step, namely the quantitative prediction results and the qualitative risk warning, serves as the direct basis and triggering condition for generating subsequent guiding decisions.

[0074] Preferably, this step may include two parallel and complementary analysis modes: one is probabilistic financial indicator prediction, and the other is structural risk warning.

[0075] Regarding the prediction of probabilistic financial indicators: This analytical model aims to address the problem that traditional project management's "single-point forecasting" cannot effectively cope with real-world uncertainties. Its core idea is to introduce randomness, transforming the deterministic evolutionary model established in step S3 into a stochastic process model, thereby obtaining the probability distribution of key financial indicators, rather than a single predicted value.

[0076] In its specific implementation, the process includes the following sub-steps: First, the uncertain parameters are identified and modeled. The system analyzes the components of the state evolution equation S(t+1)=A(t)S(t)+B(t)U(t) in step S3, identifying those parameters that are inherently random. For example: In the state transition matrix A(t), there are certain cost transfer coefficients that depend on market conditions (such as raw material prices) or labor consumption rates that depend on personnel efficiency.

[0077] In the external input vector U(t), some variables represent external random events, such as supplier delivery delays or fault-free operating intervals of critical equipment. The system replaces these identified deterministic parameters with random variables that follow a specific probability distribution. For example, a normal distribution can be used to describe fluctuations in delivery time, a log-normal distribution to describe changes in market prices, or an exponential distribution to describe the time intervals between equipment failures.

[0078] Next, a Monte Carlo simulation is performed. The system will execute a simulation loop containing a large number (preferably, thousands or more) of iterations. In each iteration, the system will independently sample all random variables in the model according to their predefined probability distributions, thus obtaining a specific set of parameter samples that are considered deterministic in that iteration. Using this set of samples, the system will construct a state transition matrix A specifically for this iteration.k (t) and input vector U k (t).

[0079] Subsequently, starting from the initial state vector S(0) of the project, the system applies the state evolution equation and recursively calculates (S(t+1)=A k (t)S(t)+B k (t)U k (t)) is used to deduce a complete state evolution trajectory from the start to the end of the project. At the end of the project, T final From the final state vector S k (T final Extract the key financial indicators from this simulation, such as the total project cost C. k Or total profit P k .

[0080] Finally, the probability distribution is generated. After completing all N iterations, the system will obtain a sample set {C1, C2, ..., C...} containing the results of N key financial indicators. N By performing statistical analysis on the sample set, such as constructing histograms or using kernel density estimation, the system can generate the probability density function (PDF) or cumulative distribution function (CDF) of the financial indicator. This allows decision-makers to gain probabilistic insights into the future, such as "there is a 90% probability that the total project cost will fall between 10 million and 12 million," thus providing far richer and more reliable information for risk assessment and decision-making than single-point predictions.

[0081] Regarding structural risk warnings: This analytical model differs from the perspective of probabilistic prediction mentioned above. Instead of focusing on the fluctuation range of specific values, it delves into the internal structure of the system, aiming to discover deep-seated risks that are not yet apparent but indicate that the system may diverge or become out of control in the future.

[0082] In its specific implementation, the process includes the following sub-steps: First, at critical points in the system's evolution, or according to a preset period, the system performs eigenvalue decomposition on the current state transition matrix A(t), solving for all n complex eigenvalues ​​{λ1(t), λ2(t), ..., λ...}. n (t)}.

[0083] Next, the spectral radius ρ(A(t)) of the state transition matrix A(t) is calculated. The spectral radius is defined as the maximum value of the modulus among all eigenvalues, and its calculation formula is as follows: in: ρ(A(t)) is the spectral radius of the state transition matrix at time t.

[0084] A(t) is the state transition matrix at time t, which reflects the internal transmission and gain structure of the project at the current time.

[0085] λ i (t) is the i-th eigenvalue of the state transition matrix A(t).

[0086] |·| represents the modulo operation on a complex number.

[0087] max i This represents the operation of taking the maximum value among the moduli of all i eigenvalues.

[0088] Finally, risk assessment and early warning are triggered based on the spectral radius. In the technical solution of this invention, based on the basic theory of stability of linear discrete systems, the preset risk condition is that the spectral radius is greater than or equal to 1. That is, when the system detects ρ(A(t))≥1, a high-priority structural risk early warning will be triggered immediately. The underlying logic of this early warning is that the spectral radius characterizes the strongest gain amplification capability in the internal state evolution process of the system. Once this value is greater than or equal to 1, it means that there is at least one positive feedback loop in the system (for example, rework leads to schedule delays, which in turn lead to the need to catch up, thereby reducing quality and further increasing rework), and its gain strength is sufficient to cause one or more state variables of the system (such as cost, deviation) to oscillate, diverge, or grow exponentially in the future evolution. This early warning is a "prevention is better than cure" mechanism. It can reveal the instability of the system's internal structure in advance when the surface data such as the project's financial statements have not yet shown obvious abnormalities, giving managers a valuable window of opportunity to make fundamental process interventions or structural adjustments.

[0089] In summary, step S4 provides decision-makers with a comprehensive understanding of the project's future through two complementary analytical models: probabilistic prediction answers the question of "what might happen and what is the probability," while structural risk warning answers the question of "is the system healthy and is there a risk of collapse?" The outputs of both models together constitute a complete assessment of the project's status and serve as input to drive subsequent intelligent and guiding decision-making.

[0090] According to the optimal decision scheme d opt The system automatically updates the relationship between business and financial entities upon which the state transition matrix is ​​based, thereby achieving closed-loop adaptive modeling.

[0091] In this embodiment, when the technical solution of the present invention generates the optimal decision scheme d for intervening in the project execution in step S5, opt Once the solution is confirmed and adopted by the administrator, the system will execute a critical closed-loop adaptive step.

[0092] This step is the fundamental difference between this invention and open, one-off predictive models. Its core purpose is to ensure that the business-finance integrated model constructed by this method is not a static model, but a "digital twin" that can be synchronized with project management practices in real time and continuously evolve. When management decisions are made and implemented, the project's internal logic and external conditions have already changed; the role of this step is to accurately and automatically reflect these changes back into the mathematical model, thereby ensuring that all subsequent analyses, predictions, and re-decision-making are based on the latest and most realistic system state, forming a complete and intelligent closed loop of "perception-modeling-prediction-optimization-execution".

[0093] Specifically, the reconstruction process of this closed-loop adaptive model includes a series of tightly coupled sub-processes such as receiving and parsing decision instructions, reconstructing the dynamic business state diagram, and finally updating the state transition matrix.

[0094] Regarding the receipt and parsing of decision-making instructions: This process is the trigger point for closed-loop adaptive processing. The system first receives the optimal decision solution d, which is ultimately confirmed by the administrator via the user interface. opt The decision-making scheme is a structured set of instructions. The system's backend parsing module will analyze this set of instructions and break it down into a series of specific atomic operations that modify the model's basic structure.

[0095] For example, a decision that states "Change the raw material supplier of work order W001 from 'Supplier A' to 'Supplier B' and approve an additional 50 hours of overtime for this work order" will be parsed into the following operation atoms: Discontinue the procurement association related to "Supplier A".

[0096] Enable the procurement association associated with "Supplier B" and apply its new quote and delivery cycle.

[0097] Increase the supply of available working hours from the "Human Resources Pool" node to the "Work Order W001" node.

[0098] Regarding the reconstruction of the dynamic business and financial status diagram: This is the core execution step of this process, directly impacting the underlying structure of the dynamic business state diagram G(t) constructed in step S2. The system does not directly modify the values ​​in the state transition matrix A(t), but rather ensures the logical consistency and traceability of model updates by modifying the state diagram upon which it is based—the physical and logical blueprint of the model.

[0099] Based on the operational atoms analyzed in the previous step, the system will make structural or parametric modifications to graph G(t), generating a new graph G that already incorporates the decision-making influence.′ (t). The modification operations mainly include: Modifications to the graph topology: This involves adding, deleting, or modifying nodes or edges in the graph.

[0100] Node addition / deletion: If the decision involves introducing a new resource entity (such as a new subcontractor or a new leased piece of equipment), the system will create a corresponding new node in the graph. Conversely, if the decision is to deactivate a resource, the relevant node will be deactivated (rather than physically deleted to preserve historical traceability).

[0101] Modifying edge connections: This is the most common operation. In the aforementioned example of changing suppliers, the system automatically deletes the directed edge from the "Supplier A" node to the "Material Batch" node and creates a new edge from the "Supplier B" node to that "Material Batch" node. This directly changes the source path of resources in the graph.

[0102] Modification of the dynamic weight function on edges: In many cases, decisions do not change the topology of the graph, but they do change its inherent transmission efficiency or cost coefficients. This can be achieved by modifying the dynamic weight function w on the edges. ij (t) is used to achieve this.

[0103] For example, approving overtime work modifies the weight function of the edge from the "Human Resources Pool" node to the "Work Order" node. This function w ij (t)=f(p i1 In (t),...), the parameters used to calculate labor costs (such as the hourly rate p) ik (t)) may change due to the increase in overtime pay.

[0104] For example, a process optimization decision might reduce the material consumption rate per unit product, and the system will accordingly modify the weight function w from the "material" node to the "work order" node. ij The consumption coefficient parameter in (t).

[0105] Through precise modifications to the graph's topology or weighting functions, the system ensures that the intentions of management decisions are completely and accurately encoded into the new business and financial state graph G. ′ (t) in.

[0106] Regarding the update of the state transition matrix: When the business finance state diagram is reconstructed into G ′ After (t), the system will automatically trigger a recalculation process to generate a new state transition matrix A that reflects the system state after the decision. ′ (t).

[0107] This process reuses the matrix generation logic from step S3: the system traverses the new graph structure G. ′(t), based on its latest node set, edge connections, and the updated dynamic weight function on the edges, recalculate each element A of the matrix. ′ ij (t). This generates a new state transition matrix A. ′ (t), each element of which contains the impact of the decisions already made.

[0108] Subsequently, the system will use this updated model for a new round of dynamic evolution calculations, meaning that the subsequent system state will be driven by the following updated equations: S(t+1)=A ′ (t)S(t)+B(t)U(t); In summary, the closed-loop adaptive process of this invention seamlessly and logically integrates the manager's subjective intervention into an objective mathematical model through an automated process of "analyzing decision -> reconstructing the graph -> updating the matrix." This ensures that the model always best reflects the current state of the project (including all past business operations and implemented decisions), thus laying a solid foundation for more accurate predictions and better decisions in the next round, and enabling the entire business-finance integration method to have the ability to continuously learn and self-optimize.

[0109] Combined with appendix Figure 2 This application provides an industrial-grade business-finance integration system based on project management, including: The model building module is used to acquire project data, map it to business and financial entities, abstract the state of the entities into system state vectors, and establish a state transition matrix to define the evolution rules of the system state vectors. The analysis and prediction module is used to predict or analyze the future financial indicators of a project based on the evolution of the system state vector driven by the state transition matrix. The decision generation module is used to generate decision-making plans for intervening in project execution based on the results of the analysis and prediction module.

[0110] This application also provides a storage medium storing a computer program, which is executed by a processor to perform the above-described method.

[0111] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Identical components are represented by the same reference numerals. Therefore, all equivalent changes made to the structure, shape, and principle of this application should be covered within the scope of protection of this application.

Claims

1. An industrial-grade business-finance integration method based on project management, characterized in that: Includes the following steps: S1. Obtain multi-source heterogeneous data from the project and map the data to business and financial entities; S2. Abstract the state of the business and financial entities into a system state vector, and establish a state transition matrix to define the rules for the evolution of the system state vector over time. S3. Based on the evolution of the system state vector driven by the state transition matrix, predict or analyze the future financial indicators of the project. S4. Based on the results of the prediction or risk analysis, generate a decision-making plan for intervening in the execution of the project.

2. The industrial-grade business-finance integration method based on project management according to claim 1, characterized in that, The establishment of the state transition matrix includes: The business and financial entities are constructed as nodes in a dynamically weighted directed graph; Determine the edges and weight functions between nodes based on the relationships between the entities; The state transition matrix is ​​generated based on the topology and weight function of the graph.

3. The industrial-grade business-finance integration method based on project management according to claim 1, characterized in that, The forecasting of the project's future financial indicators includes: Define the uncertainty parameters in the state transition matrix or external input as random variables; The probability distribution of the financial indicators is obtained by performing a Monte Carlo simulation on the evolution of the system state vector.

4. The industrial-grade business-finance integration method based on project management according to claim 1, characterized in that, The risk analysis includes: Calculate the spectral radius of the state transition matrix; and trigger a structural risk warning when the spectral radius meets a preset risk condition, wherein the spectral radius ρ(A(t)) is determined by the following formula: Where A(t) is the state transition matrix at time t, and λ i (t) is the i-th eigenvalue of the state transition matrix A(t), max i The expression represents taking the maximum value of the modulo of all eigenvalues, and |·| represents the modulo operation on a complex number.

5. The industrial-grade business-finance integration method based on project management according to claim 1, characterized in that, The preset risk condition is that the spectral radius is greater than or equal to 1.

6. The industrial-grade business-finance integration method based on project management according to claim 1, characterized in that, The generated decision scheme includes: Construct and solve the following robust optimization objective function to obtain the optimal decision scheme d. opt : Where d is a candidate decision scheme, D is the set of all feasible decision schemes, and C(d) is a random variable representing a certain financial indicator of the project after adopting decision scheme d. Let C(d) be the expected value of the random variable C(d), Std[C(d)] be the standard deviation of the random variable C(d), and k be the preset risk aversion coefficient. This means finding the decision scheme d in set D that minimizes the objective function value.

7. The industrial-grade business-finance integration method based on project management according to claim 1, characterized in that, The method further includes: According to the optimal decision scheme d opt The system automatically updates the relationship between the business and financial entities upon which the state transition matrix is ​​based, thereby achieving closed-loop adaptive modeling.

8. The industrial-grade business-finance integration method based on project management according to claim 1, characterized in that, The evolution of the system state vector is represented by the following state evolution equation: S(t+1)=A(t)S(t)+B(t)U(t); Where t is a discrete time point, S(t) is the system state vector at time t, S(t+1) is the system state vector at time t+1, A(t) is the state transition matrix at time t, B(t) is the input matrix at time t, used to define how external inputs affect the system state, and U(t) is the external input vector at time t, representing external events or disturbances.

9. An industrial-grade business-finance integration system based on project management, wherein the industrial-grade business-finance integration method based on project management according to any one of claims 1-8 is characterized in that, include: The model building module is used to acquire project data, map it to business and financial entities, abstract the state of the entities into system state vectors, and establish a state transition matrix to define the evolution rules of the system state vectors. The analysis and prediction module is used to predict or analyze the future financial indicators of the project based on the evolution of the system state vector driven by the state transition matrix. The decision generation module is used to generate a decision plan for intervening in the execution of the project based on the results of the analysis and prediction module.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1-8.

Images