Intelligent analysis method for electric power engineering cost data based on intelligent agent

Abstract

The invention discloses an intelligent analysis method for electric power engineering cost data based on an intelligent agent, and the method comprises the following steps: S1, providing the intelligent agent, carrying out the nonlinear analysis through constructing a node distribution matrix and a dynamic weight matrix, and optimizing a prediction model; s2, analyzing cost data input by a natural language, extracting, predicting or repairing tasks, and extracting project nodes and data types; s3, repairing data missing through an adaptive recovery algorithm; s4, dynamically adjusting node weights by using a multi-order reasoning algorithm; s5, analyzing a multi-layer dependency relationship, and generating an optimization cost decision; s6, generating a cost analysis report in combination with the repair data; and S7, adjusting the weight and the recovery strategy through real-time feedback, and optimizing data analysis. According to the method, the problems of low cost data processing efficiency, difficulty in data missing repair and complexity in multi-layer dependency relationship processing in a traditional method are effectively solved, and the overall analysis precision and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an intelligent analysis method for power engineering cost data based on an intelligent agent. Background Art

[0002] In the cost management of power engineering projects, traditional management and analysis methods usually rely on linear analysis, simple dependency processing and manual repair of missing data. This method has many shortcomings when facing modern complex power engineering projects. Complex power engineering projects usually involve many nodes, stages and different types of resource management. The interactions and dependencies between different nodes are not only multi-level, but also may have nonlinear characteristics. In addition, engineering projects often face incomplete or missing data during the data collection process. Especially when faced with nonlinear missing data, traditional simple filling methods cannot effectively deal with it, resulting in a significant decrease in the accuracy of engineering cost management and prediction.

[0003] The first major problem in traditional technology is the lack of effective tools to handle nonlinear interactions in complex engineering projects. In many power engineering projects, the interactions between nodes may present nonlinear relationships, and this complex interaction will greatly affect the prediction results of engineering costs. However, traditional cost management methods are mostly based on linear models, which cannot capture these complex nonlinear behaviors, resulting in large deviations in cost predictions and reduced prediction accuracy.

[0004] Limited ability to repair missing data. In actual power engineering projects, project cost data is often missing due to incomplete data collection and incomplete information records. Existing technologies mainly deal with missing data by linear interpolation or simple average value filling. Although this method is suitable for simple data structures, it is incapable of dealing with nonlinear data missing and cannot ensure the accuracy and completeness of cost analysis.

[0005] Traditional technologies are not capable of handling multi-layer dependencies. Complex power engineering projects usually involve the interdependence of multiple nodes, and decisions between nodes often affect each other. Especially in engineering cost management, different project nodes may be in different hierarchical structures, and decisions at one level will affect multiple lower-level nodes. Traditional cost analysis systems can usually only handle simple single-layer dependencies, and have limited processing capabilities for multi-layer complex dependencies. They cannot fully analyze the interactions between nodes, resulting in inaccurate prediction results.

[0006] The existing cost management system lacks an intelligent feedback mechanism and cannot effectively handle real-time changes in the project. During the execution of the project, the cost data will change dynamically with the passage of time and changes in the construction progress. The existing technology usually relies on manual intervention to adjust the system, lacks an automated feedback and adjustment mechanism, and affects the efficiency of cost management. Therefore, how to provide an intelligent analysis method for power engineering cost data based on an intelligent agent is an urgent problem that technicians in this field need to solve. Summary of the invention

[0007] One purpose of the present invention is to propose an intelligent analysis method for power engineering cost data based on intelligent agents, aiming to solve the problems of nonlinear interaction of complex nodes, data missing repair and multi-layer dependency processing in power engineering projects.

[0008] According to an embodiment of the present invention, an intelligent analysis method for power engineering cost data based on an intelligent agent comprises the following steps:

[0009] S1. Provide an intelligent agent of a prediction algorithm, which constructs a distribution matrix of each node in the project, uses a dynamic weight matrix to perform nonlinear analysis, and dynamically optimizes the prediction model according to the interaction between nodes;

[0010] S2. Business personnel input the extraction, prediction or repair tasks of power engineering cost data through natural language, and the intelligent agent parses the task information, extracts data types, project nodes and analysis dimensions;

[0011] S3. In the case of missing data, the agent uses an adaptive recovery algorithm based on heterogeneous data to identify multi-dimensional missing patterns in incomplete data sets and achieves high-precision data repair by building an adaptive recovery model;

[0012] S4, the intelligent agent uses the node multi-order variable weight deep reasoning algorithm to perform multi-order reasoning analysis on the nonlinear interactions in the engineering node distribution matrix and dynamically adjust the weights between nodes;

[0013] S5. For multi-layer dependencies, the agent analyzes the dependencies between layers through the layer-priority link feedback algorithm and generates an optimized cost decision-making plan;

[0014] S6. Based on the repair data and reasoning results, the agent combines the dynamic weight adjustment mechanism of the engineering node distribution matrix to generate a cost analysis report and output the prediction results;

[0015] S7. During the task execution, the intelligent agent adjusts the weight matrix and recovery strategy through real-time feedback to optimize the final data analysis and processing tasks.

[0016] Optionally, the S1 includes the following steps:

[0017] S11. Construction of the project node distribution matrix M ij , the matrix M ij represents the mutual dependence between node i and node j;

[0018] S12, in the node distribution matrix M ij Based on this, initialize the dynamic weight matrix W ij (t 0 ), where W ij (t 0 Represents the relationship between node i and node j at the initial time t 0 The initial weights of interaction are based on historical resource allocation and task urgency;

[0019] S13. Combined with time evolution, the agent dynamically adjusts the weight relationship between nodes through a nonlinear reasoning model; the interaction between nodes is processed through a hyperbolic sine function, and the weight is adjusted based on the interaction difference;

[0020] S14. During the project progress, the agent further corrects the weights through the feedback correction mechanism; the weights are adjusted through historical interactive feedback and time-dependent characteristics;

[0021] S15. In cost prediction, the agent predicts the project cost through a multivariate regression model, combined with the node distribution matrix M ij With the weight matrix W ij (t), and introduce the time progress factor for correction;

[0022] S16. The intelligent agent dynamically adjusts the key parameters η and ζ in the weight matrix through a real-time feedback mechanism, and corrects the key parameters in the prediction model in real time based on the interaction between nodes in the project progress;

[0023] S17. In the final optimization stage of project cost forecasting, the intelligent agent combines the cost data of historical projects with the real-time feedback signals of the current project, iteratively adjusts key parameters, generates optimized project cost forecast results, and outputs the final forecast report.

[0024] Optionally, S2 includes the following steps:

[0025] S21. The business personnel input task requirements through natural language, where the task requirements include extraction, prediction or repair of power engineering cost data, and the task information covers project nodes, data types and analysis dimensions;

[0026] S22, the agent parses the natural language input through the adaptive semantic parsing model and extracts the core elements of the task, including the project node set N = {n 1 ,n 2 ,...,nk}、Data type set D={d 1 ,d 2 ,...,d m And the analysis dimension set A = {a 1 ,a 2 ,...,a l}, where n k represents the kth item node, d m Indicates the mth data type, a l Indicates the lth analysis dimension;

[0027] S23, for the data extraction task, the agent combines the node distribution matrix M ij and the dynamic weight matrix W ij (t), extracting nonlinear data associated with the nodes and data types;

[0028] S24. For the prediction task, the agent combines historical data and current node weights and uses a prediction algorithm based on a multi-order time regression model to calculate the key parameters of the future moment:

[0029]

[0030] Among them, P(t+1 ) is the predicted value at time t+1, λ is the time regression adjustment coefficient, T ( t ) is the current progress time of the project, represents the predicted difference between historical data and current data, ω is the prediction adjustment parameter, Indicates the difference in task completion rate between nodes;

[0031] S25. For the data repair task, the agent uses a dynamic breakpoint reconstruction algorithm to repair the data based on the extracted data set. The repair process is described by the following formula:

[0032]

[0033] Among them, R ij ( t ) is the node data repaired at time t, α is the repair adjustment coefficient, β is the repair feedback adjustment coefficient, and the integral term represents the value from the initial time t 0 Cumulative feedback change to time t, Indicates the difference value of missing data between nodes, It is a repair function used to dynamically adjust the reconstruction process of missing data;

[0034] S26. Based on natural language analysis, the agent structures the extracted, predicted or repaired data and displays it through visualization tools. The displayed data includes node data changes, predicted trends and data integrity analysis after repair.

[0035] S27. During the task execution, the intelligent agent dynamically adjusts the key parameters λ, α and β through a real-time feedback mechanism, and optimizes the data extraction, prediction and repair processes according to data fluctuations and predicted repair effects.

[0036] Optionally, S3 includes the following steps:

[0037] S31, the intelligent agent performs preliminary processing on the incomplete data set in the power engineering project, and analyzes the node set N = {n 1 ,n 2 ,...,n k}Identify multi-dimensional missing patterns and generate missing pattern tensors The calculation formula is:

[0038]

[0039] Among them, Θ i ( t ) represents the local missing strength of node i, Φ j ( t ) is the global missing impact factor of node j, is the missing difference between node i and node j, μ 1 , μ 2 is the missing strength adjustment coefficient, η 1 is the nonlinear adjustment coefficient, Ω ij ( t ) is the global dynamic missing factor; ij ( t ) represents the interaction bias of the missing mode, ν 1 , η 2 are the corresponding adjustment parameters;

[0040] S32, based on missing pattern tensor The agent generates a recovery weight matrix through a heterogeneous data adaptive recovery algorithm The core formula is:

[0041]

[0042] Among them, Γ i ( t ) represents the local recovery potential of node i, Ψj ( t ) is the global recovery capability of node j, κ is the recovery adjustment coefficient, is the inter-node recovery error, β 1 is the nonlinear adjustment index, χ ij ( t ) is the dynamic weight correction factor between nodes, ν 2 , ν 3 is the parameter of recovery potential and capacity, τ ij is the recovery feedback coefficient, controlling the repair dynamics, β 2 To restore the adjustment index;

[0043] S33, the agent repairs the node data and restores the weight matrix Tensor with missing pattern The repair formula is:

[0044]

[0045] in, is the data after repair at time t, α is the repair increment coefficient, which controls the impact of the repair, γ is the missing mode enhancement factor, and θ 1 ,θ 2 is the nonlinear adjustment parameter for repair, ζ is the smoothing coefficient, which controls the smoothing effect of repair, and Ω ij ( t ) is the global missing pattern factor, capturing complex dynamic missing information, λ is the dynamic adjustment coefficient, Ξ ij ( t ) for interactive biases in missing patterns during repair;

[0046] S34. The repaired data is adaptively evaluated by the agent to evaluate the accuracy and completeness of the repair and calculate the repair feedback term F(t), which is described by the following formula:

[0047]

[0048] Among them, F ( t ) is the repair feedback value at time t, δ 1 , δ 2 , δ 3 is the error adjustment parameter, ∈ is the smoothing feedback coefficient, and controls the influence of the missing mode in the feedback process, λ 2 is the feedback adjustment coefficient;

[0049] S35, the intelligent agent based on the repair feedback value F(t ), dynamically adjust the repair weight matrix and the missing pattern tensor

[0050] S36. During the repair process, the agent updates the key repair parameters α, γ, and λ in real time. 3 , optimize the repair effect according to the feedback mechanism, and make adaptive corrections based on historical data;

[0051] S37. The intelligent agent outputs the repaired data set and generates a repair report, which includes data comparison before and after repair, repair error analysis, and repair parameter adjustment history.

[0052] Optionally, S4 includes the following steps:

[0053] S41, project node distribution matrix M based on construction time t ij ( t ) , initialize the interaction strength based on the project nodes;

[0054] S42, the agent analyzes the complex interactions between nodes through a hierarchical multi-order variable weight reasoning model. The reasoning model combines multi-layer interactions and dynamic changes in weights. The formula is:

[0055]

[0056] Among them, W ij ( t+1 ) represents the weight adjustment value of node i and node j at time t+1, α is the adjustment step size, which controls the weight change, and μ 1 is the nonlinear adjustment parameter for the interaction between nodes, represents the nonlinear interaction strength between node i and node k, ζ is the nonlinear difference adjustment parameter, θ 2 is the dynamic nonlinear index in the inference;

[0057] S43. During the weight adjustment process, the agent makes inference corrections based on the dynamic interaction balance model to capture the dynamic balance relationship between weights;

[0058] S44, the agent further corrects the weight adjustment in the reasoning process through a multi-level dynamic feedback mechanism;

[0059] S45. In view of the complexity of weight adjustment during reasoning, the agent performs global optimization correction based on real-time reasoning and feedback results;

[0060] S46 In each time step t of reasoning, the agent adjusts key parameters according to the real-time feedback signal;

[0061] S47, the agent generates the optimized weight matrix W ij ( t+2 ) , output the inference results and provide an inference report, including the weight change analysis between nodes, inference feedback and key parameter adjustment in the global optimization process.

[0062] Optionally, S5 includes the following steps:

[0063] S51. Build a multi-layer dependency matrix matrix It is used to represent the priority dependency link between node i and node j at level l. The initial value of the matrix is ​​defined according to the association priority and distance between nodes.

[0064] S52. For multi-layer dependencies, the agent prioritizes the link relationships between high-weight nodes through the hierarchical priority reasoning model and calculates their impact on low-level nodes:

[0065]

[0066] in, is the change in the priority dependency link between node i and node j at level l at time t+1, and are the priority link strengths of node i and node k, and node k and node j respectively, κ is the priority reasoning adjustment coefficient, which controls the link change rate, is the priority link error between nodes, η 1 is the nonlinear adjustment parameter, represents the priority weight difference between node i and node j, τ and θ 1 Adjust parameters for priority smoothing;

[0067] S53. Based on the priority reasoning model, the agent corrects the reasoning results through the link priority feedback mechanism, focusing on the feedback impact of high-priority nodes. The formula is as follows:

[0068]

[0069] in, is the feedback correction value at level l at time t+1, γ is the feedback adjustment coefficient, λ is the correction strength parameter, and controls the dynamic change of link feedback. is the priority link inference error, η 2 is the nonlinear adjustment parameter in the feedback, tanh is the hyperbolic tangent function, and the smooth feedback result, f(Ω ij (t is a dynamic link correction function, which handles the dynamic balance of links between different layers;

[0070] S54, the agent optimizes and adjusts the priority links of all levels through the global link optimization model, and the formula is:

[0071]

[0072] Among them, L ij ( t+1 ) is the global link optimization result of node i and node j at time t+1, ξ is the global adjustment coefficient, controlling the optimization step size, ω is the global feedback adjustment parameter, is the global error in the priority inference result, θ 3 is the nonlinear adjustment parameter for global optimization, Ω ij ( t ) is the global feedback function in the multi-layer link relationship;

[0073] S55. In the process of priority reasoning and feedback correction, the agent applies the priority adaptive decision optimization algorithm based on the real-time link data to globally optimize the reasoning results and generate the final cost decision plan. The optimization formula is:

[0074]

[0075] Among them, C(t+1) represents the optimal value of the cost decision at time t+1, α is the optimization adjustment coefficient, which controls the optimization step length, and W ij ( t ) is the weight matrix between nodes, ζ is the decision balance adjustment parameter, ΔC ij ( t ) is the optimization error between links at each level, θ 4 Tuning parameters for multi-layer optimization;

[0076] S56, during the reasoning and feedback process, the agent dynamically adjusts the parameters ξ, γ, α, ζ and λ according to the historical link data;

[0077] S57. The intelligent agent outputs an optimized cost decision plan and generates a reasoning report, which includes reasoning analysis of multi-layer priority dependencies, link feedback correction results and the global optimization process.

[0078] The beneficial effects of the present invention are:

[0079] The present invention proposes an intelligent analysis method for power engineering cost data based on intelligent agents. By constructing an engineering node distribution matrix and a dynamic weight matrix, the nonlinear interaction between nodes is analyzed, and the node weights are dynamically adjusted, which significantly improves the accuracy of cost data prediction, especially in the case of multi-node interaction in complex projects.

[0080] The present invention uses an adaptive recovery algorithm to automatically identify data missing patterns and perform high-precision repairs for the common cost data missing problems in power engineering projects, overcoming the problem of error accumulation caused by the simple interpolation method used in the prior art, ensuring the integrity and reliability of the data and improving the accuracy of the overall cost analysis.

[0081] The present invention analyzes and optimizes multi-layer dependencies through a hierarchical priority link feedback algorithm, and can make precise adjustments in node decisions at different levels to generate the optimal cost decision-making solution. This method solves the problem that traditional cost analysis systems cannot handle multi-layer dependencies, effectively reduces prediction errors caused by complex dependencies, and improves the scientific nature of decision-making and the overall management efficiency of the project.

[0082] The present invention introduces a real-time feedback mechanism, which can automatically adjust the weight matrix, recovery strategy and cost prediction model according to the dynamic changes of the project, making the cost data processing more flexible, adapting to the needs of real-time changes in power engineering projects, and significantly improving the system's response speed and prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0084] Figure 1 This is a flow chart of an intelligent analysis method for power engineering cost data based on an intelligent agent proposed by the present invention;

[0085] Figure 2 A schematic diagram of the structure of the engineering node distribution matrix and dynamic weight matrix constructed by the intelligent agent proposed in the present invention;

[0086] Figure 3 The present invention provides a flowchart of the hierarchical priority link feedback algorithm proposed in the present invention for processing multi-layer dependencies and generating an optimized cost decision-making solution. DETAILED DESCRIPTION

[0087] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0088] refer to Figure 1-3 , an intelligent analysis method for power engineering cost data based on an intelligent agent, comprising the following steps:

[0089] S1. Provide an intelligent agent of a prediction algorithm, which constructs a distribution matrix of each node in the project, uses a dynamic weight matrix to perform nonlinear analysis, and dynamically optimizes the prediction model according to the interaction between nodes;

[0090] S2. Business personnel input the extraction, prediction or repair tasks of power engineering cost data through natural language, and the intelligent agent parses the task information, extracts data types, project nodes and analysis dimensions;

[0091] S3. In the case of missing data, the agent uses an adaptive recovery algorithm based on heterogeneous data to identify multi-dimensional missing patterns in incomplete data sets and achieves high-precision data repair by building an adaptive recovery model;

[0092] S4, the intelligent agent uses the node multi-order variable weight deep reasoning algorithm to perform multi-order reasoning analysis on the nonlinear interactions in the engineering node distribution matrix and dynamically adjust the weights between nodes;

[0093] S5. For multi-layer dependencies, the agent analyzes the dependencies between layers through the layer-priority link feedback algorithm and generates an optimized cost decision-making plan;

[0094] S6. Based on the repair data and reasoning results, the agent combines the dynamic weight adjustment mechanism of the engineering node distribution matrix to generate a cost analysis report and output the prediction results;

[0095] S7. During the task execution, the intelligent agent adjusts the weight matrix and recovery strategy through real-time feedback to optimize the final data analysis and processing tasks.

[0096] In this implementation, S1 includes the following steps:

[0097] S11. Construction of the project node distribution matrix M ij , the matrix M ij represents the mutual dependence between node i and node j;

[0098] S12, in the node distribution matrix M ij Based on this, initialize the dynamic weight matrix W ij ( t 0 ) , where W ij ( t 0 Represents the relationship between node i and node j at the initial time t 0 The initial weights of interaction are based on historical resource allocation and task urgency;

[0099] S13. Combined with time evolution, the agent dynamically adjusts the weight relationship between nodes through a nonlinear reasoning model; the interaction between nodes is processed through a hyperbolic sine function, and the weight is adjusted based on the interaction difference;

[0100] S14. During the project progress, the agent further corrects the weights through the feedback correction mechanism; the weights are adjusted through historical interactive feedback and time-dependent characteristics;

[0101] S15. In cost prediction, the agent predicts the project cost through a multivariate regression model, combined with the node distribution matrix M ij With the weight matrix W ij (t), and introduce the time progress factor for correction;

[0102] S16. The intelligent agent dynamically adjusts the key parameters η and ζ in the weight matrix through a real-time feedback mechanism, and corrects the key parameters in the prediction model in real time based on the interaction between nodes in the project progress;

[0103] S17. In the final optimization stage of project cost forecasting, the intelligent agent combines the cost data of historical projects with the real-time feedback signals of the current project, iteratively adjusts key parameters, generates optimized project cost forecast results, and outputs the final forecast report.

[0104] In this implementation, S2 includes the following steps:

[0105] S21. The business personnel input task requirements through natural language, where the task requirements include extraction, prediction or repair of power engineering cost data, and the task information covers project nodes, data types and analysis dimensions;

[0106] S22, the agent parses the natural language input through the adaptive semantic parsing model and extracts the core elements of the task, including the project node set N = {n 1 ,n 2 ,...,n k}、Data type set D={d 1 ,d 2 ,...,d m And the analysis dimension set A = {a 1 ,a 2 ,...,a l}, where n k represents the kth item node, d m Indicates the mth data type, a l Indicates the lth analysis dimension;

[0107] S23, for the data extraction task, the agent combines the node distribution matrix M ij and the dynamic weight matrix Wij (t), extracting nonlinear data associated with the nodes and data types;

[0108] S24. For the prediction task, the agent combines historical data and current node weights and uses a prediction algorithm based on a multi-order time regression model to calculate the key parameters of the future moment:

[0109]

[0110] Among them, P ( t+1) is the predicted value at time t+1, λ is the time regression adjustment coefficient, T(t) is the current progress time of the project, represents the predicted difference between historical data and current data, ω is the prediction adjustment parameter, Indicates the difference in task completion rate between nodes;

[0111] S25. For the data repair task, the agent uses a dynamic breakpoint reconstruction algorithm to repair the data based on the extracted data set. The repair process is described by the following formula:

[0112]

[0113] Among them, R ij ( t ) is the node data repaired at time t, α is the repair adjustment coefficient, β is the repair feedback adjustment coefficient, and the integral term represents the value from the initial time t 0 Cumulative feedback change to time t, Indicates the difference value of missing data between nodes, It is a repair function used to dynamically adjust the reconstruction process of missing data;

[0114] S26. Based on natural language analysis, the agent structures the extracted, predicted or repaired data and displays it through visualization tools. The displayed data includes node data changes, predicted trends and data integrity analysis after repair.

[0115] S27. During the task execution, the intelligent agent dynamically adjusts the key parameters λ, α and β through a real-time feedback mechanism, and optimizes the data extraction, prediction and repair processes according to data fluctuations and predicted repair effects.

[0116] In this implementation, S3 includes the following steps:

[0117] S31, the intelligent agent performs preliminary processing on the incomplete data set in the power engineering project, and analyzes the node set N = {n 1 ,n 2 ,...,n k}Identify multi-dimensional missing patterns and generate missing pattern tensors The calculation formula is:

[0118]

[0119] Among them, Θ i ( t ) represents the local missing strength of node i, Φ j ( t ) is the global missing impact factor of node j, is the missing difference between node i and node j, μ 1 , μ 2 is the missing strength adjustment coefficient, η 1 is the nonlinear adjustment coefficient, Ω ij ( t ) is the global dynamic missing factor; ij ( t ) represents the interaction bias of the missing mode, ν 1 , η 2 are the corresponding adjustment parameters;

[0120] S32, based on missing pattern tensor The agent generates a recovery weight matrix through a heterogeneous data adaptive recovery algorithm The core formula is:

[0121]

[0122] Among them, Γ i ( t ) represents the local recovery potential of node i, Ψ j ( t ) is the global recovery capability of node j, κ is the recovery adjustment coefficient, is the inter-node recovery error, β 1 is the nonlinear adjustment index, χ ij ( t ) is the dynamic weight correction factor between nodes, ν 2 , ν 3 is the parameter of recovery potential and capacity, τ ij is the recovery feedback coefficient, controlling the repair dynamics, β 2 To restore the adjustment index;

[0123] S33, the agent repairs the node data and restores the weight matrix Tensor with missing pattern The repair formula is:

[0124]

[0125] in, is the data after repair at time t, α is the repair increment coefficient, which controls the impact of the repair, γ is the missing mode enhancement factor, and θ 1 ,θ 2 is the nonlinear adjustment parameter for repair, ζ is the smoothing coefficient, which controls the smoothing effect of repair, and Ω ij ( t ) is the global missing pattern factor, capturing complex dynamic missing information, λ is the dynamic adjustment coefficient, Ξ ij ( t ) for interactive biases in missing patterns during repair;

[0126] S34: The repaired data is adaptively evaluated by the agent to evaluate the accuracy and completeness of the repair and calculate the repair feedback item F ( t ) , described by the following formula:

[0127]

[0128] Among them, F(t) is the repair feedback value at time t, δ 1 ,δ 2 ,δ 3 is the error adjustment parameter, ∈ is the smoothing feedback coefficient, and controls the influence of the missing mode in the feedback process, λ 2 is the feedback adjustment coefficient;

[0129] S35. The agent dynamically adjusts the repair weight matrix according to the repair feedback value F(t). and the missing pattern tensor

[0130] S36. During the repair process, the agent updates the key repair parameters α, γ, and λ in real time. 3 , optimize the repair effect according to the feedback mechanism, and make adaptive corrections based on historical data;

[0131] S37. The intelligent agent outputs the repaired data set and generates a repair report, which includes data comparison before and after repair, repair error analysis, and repair parameter adjustment history.

[0132] In this implementation, S4 includes the following steps:

[0133] S41, project node distribution matrix M based on construction time t ij ( t ), initialize the interaction strength based on the project nodes;

[0134] S42, the agent analyzes the complex interactions between nodes through a hierarchical multi-order variable weight reasoning model. The reasoning model combines multi-layer interactions and dynamic changes in weights. The formula is:

[0135]

[0136] Among them, W ij ( t+1 ) represents the weight adjustment value of node i and node j at time t+1, α is the adjustment step size, which controls the weight change, and μ 1 is the nonlinear adjustment parameter for the interaction between nodes, represents the nonlinear interaction strength between node i and node k, ζ is the nonlinear difference adjustment parameter, θ 2 is the dynamic nonlinear index in the inference;

[0137] S43. During the weight adjustment process, the agent makes inference corrections based on the dynamic interaction balance model to capture the dynamic balance relationship between weights;

[0138] S44, the agent further corrects the weight adjustment in the reasoning process through a multi-level dynamic feedback mechanism;

[0139] S45. In view of the complexity of weight adjustment during reasoning, the agent performs global optimization correction based on real-time reasoning and feedback results;

[0140] S46 In each time step t of reasoning, the agent adjusts key parameters according to the real-time feedback signal;

[0141] S47, the agent generates the optimized weight matrix W ij (t+2), output the inference results and provide an inference report, including the weight change analysis between nodes, inference feedback, and key parameter adjustments in the global optimization process.

[0142] In this implementation, S5 includes the following steps:

[0143] S51. Build a multi-layer dependency matrix matrix It is used to represent the priority dependency link between node i and node j at level l. The initial value of the matrix is ​​defined according to the association priority and distance between nodes.

[0144] S52. For multi-layer dependencies, the agent prioritizes the link relationships between high-weight nodes through the hierarchical priority reasoning model and calculates their impact on low-level nodes:

[0145]

[0146] in, is the change in the priority dependency link between node i and node j at level l at time t+1, and are the priority link strengths of node i and node k, and node k and node j respectively, κ is the priority reasoning adjustment coefficient, which controls the link change rate, is the priority link error between nodes, η 1 is the nonlinear adjustment parameter, represents the priority weight difference between node i and node j, τ and θ 1 Adjust parameters for priority smoothing;

[0147] S53. Based on the priority reasoning model, the agent corrects the reasoning results through the link priority feedback mechanism, focusing on the feedback impact of high-priority nodes. The formula is as follows:

[0148]

[0149] in, is the feedback correction value at level l at time t+1, γ is the feedback adjustment coefficient, λ is the correction strength parameter, and controls the dynamic change of link feedback. is the priority link inference error, η 2 is the nonlinear adjustment parameter in the feedback, tanh is the hyperbolic tangent function, and the smooth feedback result, f(Ω ij (t)) is the dynamic link correction function, which handles the dynamic balance of links between different layers;

[0150] S54, the agent optimizes and adjusts the priority links of all levels through the global link optimization model, and the formula is:

[0151]

[0152] Among them, L ij (t+1) is the global link optimization result of node i and node j at time t+1, ξ is the global adjustment coefficient, controlling the optimization step size, ω is the global feedback adjustment parameter, is the global error in the priority inference result, θ 3 is the nonlinear adjustment parameter for global optimization, Ω ij (t) is the global feedback function in the multi-layer link relationship;

[0153] S55. In the process of priority reasoning and feedback correction, the agent applies the priority adaptive decision optimization algorithm based on the real-time link data to globally optimize the reasoning results and generate the final cost decision plan. The optimization formula is:

[0154]

[0155] Among them, C(t+1) represents the optimal value of the cost decision at time t+1, α is the optimization adjustment coefficient, which controls the optimization step length, and W ij (t) is the weight matrix between nodes, ζ is the decision balance adjustment parameter, ΔC ij (t) is the optimization error between links at each level, θ 4 Tuning parameters for multi-layer optimization;

[0156] S56, during the reasoning and feedback process, the agent dynamically adjusts the parameters ξ, γ, α, ζ and λ according to the historical link data;

[0157] S57. The intelligent agent outputs an optimized cost decision plan and generates a reasoning report, which includes reasoning analysis of multi-layer priority dependencies, link feedback correction results and the global optimization process.

[0158] Embodiment 1:

[0159] In a large-scale power engineering project, the accuracy and real-time performance of the project cost have become key issues in project management. The project includes multiple construction phases, the scheduling and management of numerous materials and equipment, and involves collaborative work of multiple departments. Due to the large scale of the project, complex nodes, and long construction period, there are complex nonlinear interactions between the various construction nodes, and data missing and multi-layer dependency management problems often occur during project implementation. Traditional cost analysis methods are difficult to respond to dynamically changing project requirements in real time, resulting in budget overruns, unreasonable resource allocation, and other problems. In order to improve project management efficiency and the accuracy of cost forecasting, the project team decided to use the intelligent analysis method for power engineering cost data based on intelligent agents proposed in this invention for cost management.

[0160] In this scenario, the project team first deployed an intelligent agent system to build the project's engineering node distribution matrix and dynamic weight matrix by real-time monitoring and collecting data from the entire project. Since the project construction involves hundreds of different engineering nodes, the construction progress, resource consumption, material usage and other data of each node are automatically collected and analyzed through the intelligent agent system. The intelligent agent uses a nonlinear cost prediction algorithm to update the interactions between nodes in real time and dynamically adjust the weight matrix to ensure the accuracy of the prediction results.

[0161] For example, in the second phase of construction, the installation and commissioning of multiple key equipment are involved. However, due to delayed equipment delivery and sudden problems on site, the progress data of some nodes is missing. In traditional methods, the project team needs to manually analyze historical data and perform simple interpolation repairs. This processing method often leads to error accumulation and cannot accurately reflect the actual project progress. Through the adaptive recovery algorithm in the present invention, the intelligent agent automatically identifies the data missing pattern and constructs an adaptive recovery model for data repair. By analyzing the status and historical data of other nodes, the intelligent agent not only makes up for the data missing, but also further optimizes the project cost forecast results. During the data recovery process in this stage, the system repaired more than 20% of the node data missing, and the cost forecast error after data repair dropped from the original 8.5% to 1.3%.

[0162] In the fourth phase of the project, the project management team faced an important issue, namely how to deal with the complex dependencies between various levels. The multi-layer dependencies of this project are very complex, and changes in certain upstream decision nodes will have a chain reaction on multiple downstream nodes, affecting the resource allocation and schedule of the entire project. Through the hierarchical priority link feedback algorithm proposed in the present invention, the intelligent system analyzes the dependencies between nodes at different levels, prioritizes the link feedback of high-priority nodes, and automatically generates an optimized cost decision plan. The application of this algorithm significantly reduces construction delays caused by decision-making errors and chain reactions. After actual verification, at this stage, the accuracy of cost forecasting has increased by 15%, and the decision feedback time of multi-layer dependent nodes has been shortened from the original 3 days to less than 1 hour.

[0163] At the same time, during the entire project implementation process, the intelligent system dynamically adjusts the model parameters through a real-time feedback mechanism, further improving the system's response speed and prediction accuracy. For example, in the sixth phase of the project, due to external supply chain issues, material prices fluctuated greatly, and traditional budget adjustment methods were difficult to respond to such market changes in a timely manner. The intelligent system in the present invention collects material price fluctuation data in real time, and updates the cost prediction model by dynamically adjusting the weight matrix. The error between the adjusted forecast data and the actual procurement data was reduced from 4.2% to 1.1%, enabling the project to accurately control the budget in the event of material cost fluctuations.

[0164] Through data comparison and analysis, without the use of the intelligent system, the overall cost prediction error of the project was more than 6%, while after using the intelligent system of the present invention, the cost prediction error was controlled within 1.5%. The repair rate of data loss during construction increased by 30%, and the prediction error after data recovery dropped to 1.3%. The processing time of multi-layer dependencies in the project was shortened from an average of 3 days to 2 hours, and the overall budget control ability of the project was improved by 20%.

[0165] Table 1 Data table

[0166]

[0167] Through the above examples, the project team successfully solved the problems of missing data, nonlinear interactions and multi-layer dependencies in complex power engineering projects, greatly improving the accuracy of cost forecasting and project management efficiency.

[0168] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent analysis method for power engineering cost data based on an intelligent agent, characterized in that: The steps include: S1. Provide an intelligent agent of a prediction algorithm, which constructs a distribution matrix of each node in the project, uses a dynamic weight matrix to perform nonlinear analysis, and dynamically optimizes the prediction model according to the interaction between nodes; S2. Business personnel input the extraction, prediction or repair tasks of power engineering cost data through natural language, and the intelligent agent parses the task information, extracts data types, project nodes and analysis dimensions; S3. In the case of missing data, the agent uses an adaptive recovery algorithm based on heterogeneous data to identify multi-dimensional missing patterns in incomplete data sets and achieves high-precision data repair by building an adaptive recovery model; S4, the intelligent agent uses the node multi-order variable weight deep reasoning algorithm to perform multi-order reasoning analysis on the nonlinear interactions in the engineering node distribution matrix and dynamically adjust the weights between nodes; S5. For multi-layer dependencies, the agent analyzes the dependencies between layers through the layer-priority link feedback algorithm and generates an optimized cost decision-making plan; S6. Based on the repair data and reasoning results, the agent combines the dynamic weight adjustment mechanism of the engineering node distribution matrix to generate a cost analysis report and output the prediction results; S7. During the task execution, the intelligent agent adjusts the weight matrix and recovery strategy through real-time feedback to optimize the final data analysis and processing tasks.

2. According to the agent-based intelligent analysis method for power engineering cost data according to claim 1, it is characterized in that: The S1 comprises the following steps: S11. Construction of the project node distribution matrix M ij , the matrix M ij represents the mutual dependence between node i and node j; S12, in the node distribution matrix M ij Based on this, initialize the dynamic weight matrix W ij (t0), where W ij (t0) represents the interaction weight between node i and node j at the initial time t0. The initial weight is based on historical resource allocation and task urgency; S13. Combined with time evolution, the agent dynamically adjusts the weight relationship between nodes through a nonlinear reasoning model; the interaction between nodes is processed through a hyperbolic sine function, and the weight is adjusted based on the interaction difference; S14. During the project progress, the agent further corrects the weights through the feedback correction mechanism; the weights are adjusted through historical interactive feedback and time-dependent characteristics; S15. In cost prediction, the agent predicts the project cost through a multivariate regression model, combined with the node distribution matrix M ij With the weight matrix W ij (t), and introduce the time progress factor for correction; S16. The intelligent agent dynamically adjusts the key parameters η and ζ in the weight matrix through a real-time feedback mechanism, and corrects the key parameters in the prediction model in real time based on the interaction between nodes in the project progress; S17. In the final optimization stage of project cost forecasting, the intelligent agent combines the cost data of historical projects with the real-time feedback signals of the current project, iteratively adjusts key parameters, generates optimized project cost forecast results, and outputs the final forecast report.

3. According to the agent-based intelligent analysis method for power engineering cost data of claim 1, it is characterized in that: The S2 comprises the following steps: S21. The business personnel input task requirements through natural language, where the task requirements include extraction, prediction or repair of power engineering cost data, and the task information covers project nodes, data types and analysis dimensions; S22, the agent parses the natural language input through the adaptive semantic parsing model and extracts the core elements of the task, including the project node set N = {n1, n2, ..., n k }、Data type set D={d1,d2,...,d m And the analysis dimension set A = {a1, a2, ..., a l }, where n k represents the kth item node, d m Indicates the mth data type, a l Indicates the lth analysis dimension; S23, for the data extraction task, the agent combines the node distribution matrix M ij and the dynamic weight matrix W ij (t), extracting nonlinear data associated with the nodes and data types; S24. For the prediction task, the agent combines historical data and current node weights and uses a prediction algorithm based on a multi-order time regression model to calculate the key parameters of the future moment: Among them, P(t+1) is the predicted value at time t+1, λ is the time regression adjustment coefficient, T(t) is the current progress time of the project, represents the predicted difference between historical data and current data, ω is the prediction adjustment parameter, Represents the difference in task completion rate between nodes; S25. For the data repair task, the agent uses a dynamic breakpoint reconstruction algorithm to repair the data based on the extracted data set. The repair process is described by the following formula: Among them, R ij (t) is the node data repaired at time t, α is the repair adjustment coefficient, β is the repair feedback adjustment coefficient, and the integral term represents the cumulative feedback change from the initial time t0 to time t. Indicates the difference value of missing data between nodes, It is a repair function used to dynamically adjust the reconstruction process of missing data; S26. Based on natural language analysis, the agent structures the extracted, predicted or repaired data and displays it through visualization tools. The displayed data includes node data changes, predicted trends and data integrity analysis after repair. S27. During the task execution, the intelligent agent dynamically adjusts the key parameters λ, α and β through a real-time feedback mechanism, and optimizes the data extraction, prediction and repair processes according to data fluctuations and predicted repair effects.

4. According to the agent-based intelligent analysis method for power engineering cost data of claim 1, it is characterized in that: The S3 comprises the following steps: S31, the intelligent agent performs preliminary processing on the incomplete data set in the power engineering project, and analyzes the node set N = {n1, n2, ..., n k }Identify multi-dimensional missing patterns and generate missing pattern tensors The calculation formula is: Among them, Θ i (t) represents the local missing strength of node i, Φ j (t) is the global missing impact factor of node j, is the missing difference between node i and node j, μ1 and μ2 are missing strength adjustment coefficients, η1 is the nonlinear adjustment coefficient, Ω ij (t) is the global dynamic missing factor; ij (t) represents the interaction deviation of the missing mode, ν1 and η2 are the corresponding adjustment parameters; S32, based on missing pattern tensor The agent generates a recovery weight matrix through a heterogeneous data adaptive recovery algorithm The core formula is: Among them, Γ i (t) represents the local recovery potential of node i, Ψ j (t) is the global recovery capability of node j, κ is the recovery adjustment coefficient, is the inter-node recovery error, β1 is the nonlinear adjustment index, χ ij (t) is the dynamic weight correction factor between nodes, ν2 and ν3 are the parameters of recovery potential and capacity, τ ij is the recovery feedback coefficient, controlling the repair dynamics, and β2 is the recovery regulation index; S33, the agent repairs the node data and restores the weight matrix Tensor with missing pattern The repair formula is: in, is the data after repair at time t, α is the repair increment coefficient, which controls the influence of repair, γ is the missing mode enhancement factor, θ1 and θ2 are the nonlinear adjustment parameters of repair, ζ is the smoothing coefficient, which controls the smoothing effect of repair, and Ω ij (t) is the global missing pattern factor, capturing complex dynamic missing information, λ is the dynamic adjustment coefficient, Ξ ij (t) is the interaction deviation of the missing pattern during the repair process; S34. The repaired data is adaptively evaluated by the agent to evaluate the accuracy and completeness of the repair and calculate the repair feedback term F(t), which is described by the following formula: Where F(t) is the repair feedback value at time t, δ1, δ2, δ3 are the repair error adjustment parameters, ∈ is the smoothing feedback coefficient, which controls the influence of the missing mode in the feedback process, and λ2 is the feedback adjustment coefficient; S35. The agent dynamically adjusts the repair weight matrix according to the repair feedback value F(t). and the missing pattern tensor S36. During the repair process, the agent updates the key repair parameters α, γ, and λ3 in real time, optimizes the repair effect according to the feedback mechanism, and performs adaptive correction based on historical data; S37. The intelligent agent outputs the repaired data set and generates a repair report, which includes data comparison before and after repair, repair error analysis, and repair parameter adjustment history.

5. The method for intelligent analysis of power engineering cost data based on an intelligent agent according to claim 1, characterized in that: The S4 comprises the following steps: S41, project node distribution matrix M based on construction time t ij (t), initialize the interaction strength based on the item nodes; S42, the agent analyzes the complex interactions between nodes through a hierarchical multi-order variable weight reasoning model. The reasoning model combines multi-layer interactions and dynamic changes in weights. The formula is: Among them, W ij (t+1) represents the weight adjustment value of node i and node j at time t+1, α is the adjustment step size, which controls the weight change, and μ1 is the nonlinear adjustment parameter for the interaction between nodes. represents the nonlinear interaction strength between node i and node k, ζ is the nonlinear difference adjustment parameter, and θ2 is the dynamic nonlinear index in inference; S43. During the weight adjustment process, the agent makes inference corrections based on the dynamic interaction balance model to capture the dynamic balance relationship between weights; S44, the agent further corrects the weight adjustment in the reasoning process through a multi-level dynamic feedback mechanism; S45. In view of the complexity of weight adjustment during reasoning, the agent performs global optimization correction based on real-time reasoning and feedback results; S46 In each time step t of reasoning, the agent adjusts key parameters according to the real-time feedback signal; S47, the agent generates the optimized weight matrix W ij (t+2), output the inference results and provide an inference report, including the weight change analysis between nodes, inference feedback, and key parameter adjustments in the global optimization process.

6. The agent-based intelligent analysis method for power engineering cost data according to claim 1, characterized in that: The S5 comprises the following steps: S51. Build a multi-layer dependency matrix matrix It is used to represent the priority dependency link between node i and node j at level l. The initial value of the matrix is ​​defined according to the association priority and distance between nodes. S52. For multi-layer dependencies, the agent prioritizes the link relationships between high-weight nodes through the hierarchical priority reasoning model and calculates their impact on low-level nodes: in, is the change in the priority dependency link between node i and node j at level l at time t+1, and are the priority link strengths of node i and node k, and node k and node j respectively, κ is the priority reasoning adjustment coefficient, which controls the link change rate, is the priority link error between nodes, η1 is the nonlinear adjustment parameter, represents the priority weight difference between node i and node j, τ and θ1 are priority smoothing adjustment parameters; S53. Based on the priority reasoning model, the agent corrects the reasoning results through the link priority feedback mechanism, focusing on the feedback impact of high-priority nodes. The formula is as follows: in, is the feedback correction value at level l at time t+1, γ is the feedback adjustment coefficient, λ is the correction strength parameter, and controls the dynamic change of link feedback. is the priority link inference error, η2 is the nonlinear adjustment parameter in the feedback, tanh is the hyperbolic tangent function, and the smooth feedback result, f(Ω ij (t)) is the dynamic link correction function, which handles the dynamic balance of links between different layers; S54, the agent optimizes and adjusts the priority links of all levels through the global link optimization model, and the formula is: Among them, L ij (t+1) is the global link optimization result of node i and node j at time t+1, ξ is the global adjustment coefficient, controlling the optimization step size, ω is the global feedback adjustment parameter, is the global error in the priority reasoning result, θ3 is the nonlinear adjustment parameter of global optimization, Ω ij (t) is the global feedback function in the multi-layer link relationship; S55. In the process of priority reasoning and feedback correction, the agent applies the priority adaptive decision optimization algorithm based on the real-time link data to globally optimize the reasoning results and generate the final cost decision plan. The optimization formula is: Among them, C(t+1) represents the optimal value of the cost decision at time t+1, α is the optimization adjustment coefficient, which controls the optimization step length, and W ij (t) is the weight matrix between nodes, ζ is the decision balance adjustment parameter, ΔC ij (t) is the optimization error between links at each level, and θ4 is the multi-layer optimization adjustment parameter; S56, during the reasoning and feedback process, the agent dynamically adjusts the parameters ξ, γ, α, ζ and λ according to the historical link data; S57. The intelligent agent outputs an optimized cost decision plan and generates a reasoning report, which includes reasoning analysis of multi-layer priority dependencies, link feedback correction results and the global optimization process.

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