A power transmission and transformation project intelligent compilation and review system oriented to whole-process cost

Abstract

This invention relates to the field of intelligent cost auditing and data processing technology, and particularly to an intelligent cost auditing system for power transmission and transformation projects, comprising: a main framework module carrying various functional modules and providing an encrypted environment; a standardized cost module storing hierarchical quota data and providing a probability measure of initial resource density; a splicing and connection module realizing module access and topology construction; a data interface module acquiring environmental state feature vectors and external prices; a control module constructing a cost resistance Riemannian manifold space based on the environmental state feature vectors, using the Iton stochastic differential equation to drive the evolution of initial resource density to generate desired resource measures, and when measures overlap, solving the Monge-Ampère equation to obtain the optimal transmission mapping, using the geodesic compensation term generated based on the mapping to remove redundant engineering quantities, and attaching the removed cost data to the project partitioning tree to generate full-process cost results; and a display module presenting the manifold space topology state and the final cost results.

Description

Technical Field

[0001] This invention relates to the field of intelligent cost auditing and data processing technology, and in particular to an intelligent cost auditing system for power transmission and transformation projects that addresses the entire cost process. Background Technology

[0002] In power transmission and transformation engineering construction, the entire process of cost compilation and review is a crucial link in controlling the scale of project investment and fund allocation. Most existing engineering cost compilation and review software and calculation platforms rely on fixed engineering quota libraries and manually entered bills of quantities, calculating the total price by performing static scalar accumulation under a flat, ideal geographical coordinate system.

[0003] However, traditional cost estimation and review methods mostly use static scalar accumulation, which cannot quantify spatial interference and environmental disturbances, resulting in overlapping pricing and thus distorted estimates. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an intelligent cost estimation and review system for power transmission and transformation projects that addresses the entire process of cost estimation. The system aims to improve upon the traditional cost estimation and review methods that mostly rely on static scalar accumulation, which cannot quantify spatial interference and environmental disturbances, resulting in overlapping pricing and distorted estimates.

[0005] This invention provides the following technical solution: an intelligent compilation and review system for power transmission and transformation engineering cost estimation throughout the entire process, comprising the following modules: The main framework module is used to carry various functional modules and provide an encrypted environment; The standardized cost module is used to store quota data divided by project level and provide a probability measure of initial resource density. The splicing and connection module is used to realize the system access and topology construction of each of the standardized cost modules; The data interface module is used to acquire environmental state feature vectors and exchange data with external price databases; The control module is used to identify and initialize the accessed standardized cost module, construct a cost resistance Riemannian manifold space based on the environmental state feature vector, use the Iton stochastic differential equation to drive the initial resource density to evolve in the Riemannian manifold space to generate the desired resource measure, and when the desired resource measures overlap, obtain the optimal transport mapping by solving the Monge-Ampère equation, use the geodesic compensation term generated based on the optimal transport mapping to remove redundant engineering quantities, and attach the cost data after removing the geodesic compensation term to the project partitioning tree to generate the full-process cost results; The display module is used to present the module topology state under the Riemannian manifold space and the cost results of the entire process.

[0006] By adopting the above technical solution, the optimal transport mapping on the Riemannian manifold space is solved to remove the geodesic compensation term, thereby achieving accurate deduction of redundant engineering quantities. This improves the problem that traditional cost compilation and review mostly use static scalar accumulation, which cannot quantify spatial interference and environmental disturbances, resulting in cross-overlapping pricing and distortion of the preliminary estimate.

[0007] Optionally, the main framework is used to perform: The environmental state feature vector obtained by the data interface module is subjected to input integrity verification. The generated full-process cost estimate results are subject to mandatory encryption. A unique traceability identification code is assigned to the cost results of the entire process.

[0008] Optionally, the standardized cost module is used to perform: The quota data is stored hierarchically according to voltage level and project type; Configure resource consumption coefficients for quota data at each level; A probability measure for generating the initial resource density is based on the resource consumption coefficient.

[0009] Optionally, the splicing and connecting module is used to perform: Monitor the online status of each of the standardized cost estimation modules; Identify the logical connection points between the standardized cost modules; A directed topological graph is constructed at the system's underlying layer among the standardized cost estimation modules.

[0010] Optionally, the data interface module is used to perform: The geographic information and meteorological monitoring data of the target engineering area are retrieved to form the environmental state feature vector; Establish a communication link with external price databases; The price fluctuation matrix of bulk commodities is synchronized through the communication link.

[0011] Optionally, the control module is used to perform the following when constructing the cost-resistance Riemannian manifold space: Elevation, slope, and freeze-thaw parameters are extracted from the environmental state feature vector. The extracted parameters are mapped to cost measurement tensors at coordinate points; The cost metric tensor defines the geodesic distance within the Riemannian manifold space.

[0012] Optionally, the control module is used to perform the following when generating the desired resource metric: The random components in the price fluctuation matrix synchronized by the data interface module are injected as noise terms into the Ito stochastic differential equation; Calculate the initial resource density evolution in the execution time domain; Obtain the expected distribution of the resource boundary after perturbation to generate the expected resource measure.

[0013] Optionally, the control module performs the following when obtaining the optimal transmission mapping: Locate the intersection region of the support sets of two adjacent desired resource measures; Construct a nonlinear partial differential equation for a scalar potential function within the overlapping domain of the support set; Solve for the Riemann gradient of the scalar potential function to derive the optimal transport map.

[0014] Optionally, the control module is used to execute the following when generating the full-process cost estimate: The penalty cost of overlapping micro-elements is calculated based on the transfer path determined by the optimal transport mapping and the geodesic distance. The summation of all the aforementioned penalty costs generates a global deduction compensation scalar as the geodesic compensation item; The cost data after stripping the global deduction compensation scalar is attached to the project partitioning tree.

[0015] Optionally, the display module is used to perform: Render the Riemannian manifold space and module topology view; Highlight the conflict paths corresponding to the geodesic compensation items; The system dynamically displays the changes in numerical values ​​after removing redundant engineering quantities.

[0016] The present invention has the following beneficial effects: 1. In this invention, the optimal transport mapping on the Riemannian manifold space is solved to remove the geodesic compensation term, thereby achieving accurate deduction of redundant engineering quantities. This improves the problem that traditional cost estimation and review mostly adopt static scalar accumulation, which cannot quantify spatial interference and environmental disturbances, resulting in cross-overlapping pricing and thus causing inaccurate estimates.

[0017] 2. In this invention, a cost resistance Riemannian manifold space is constructed by extracting altitude slope and freeze-thaw parameters, thereby quantifying the impact of the physical environment on the efficiency reduction of construction into a tensor. This improves the problem that traditional engineering cost estimates are mostly based on flat space, which does not take into account the resistance of complex micro-topography, resulting in a serious underestimation of budgets in remote areas.

[0018] 3. In this invention, by injecting the price fluctuation matrix as a noise term into Itō's stochastic differential equation, the dynamic random interference of prices and weather on resource consumption is simulated. This improves the problem that traditional budget management, which mostly uses fixed rate calculations, cannot adapt to nonlinear changes in the market and weather, thus causing project overspending.

[0019] 4. In this invention, by highlighting the conflict path corresponding to the geodesic compensation item and displaying the numerical change status, the mathematical trajectory of cost deduction is presented intuitively. This improves the problem that traditional accounting software mostly uses the underlying black box to directly output results, which makes auditing and tracing difficult due to the lack of a visual explanation of the cross-stripping process. Attached Figure Description

[0020] Figure 1 This is an architecture diagram of an intelligent editing and review system for power transmission and transformation engineering oriented towards full-process cost estimation proposed in this invention; Figure 2 This is a flowchart of the control module algorithm for an intelligent compilation and review system for power transmission and transformation engineering oriented towards full-process cost estimation, as proposed in this invention. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: In the first embodiment of the present invention, the present invention provides an intelligent compilation and review system for power transmission and transformation engineering cost estimation throughout the entire process, such as... Figure 1 As shown, it includes the following modules: The main framework module is used to carry various functional modules and provide an encrypted environment; Furthermore, the main framework is used for execution: Perform input integrity verification on the environmental state feature vector obtained by the data interface module; The generated cost estimates for the entire process will be subject to mandatory encryption. A unique traceability identification code is assigned to the cost results throughout the entire process.

[0023] Specifically, the main framework module undertakes the security management and full-cycle verification of the system's underlying data flow, and its execution logic directly connects the system's data input end and final output end.

[0024] At the front end of the data inflow system, after the data interface module collects the environmental state feature vector, the main framework module intercepts the data stream for input integrity verification. In real engineering environments, multi-source geographic and meteorological monitoring data are highly susceptible to packet loss or byte reversal during network transmission. If distorted data directly enters the control module, it will cause severe coordinate system distortion when subsequently calculating the Riemann manifold metric tensor for cost resistance. Integrity verification is implemented through a specific mapping operator, with the specific calculation relationship as follows: ; in This represents the system's built-in secure hash mapping operator. This represents an environmental state feature vector that includes altitude, terrain slope, and soil freeze-thaw index. This represents the integrity verification hash digest output after mapping and calculation. The main framework module will receive the hash generated by the receiving end. The data stream is compared with the initial hash value sent from the data source. Only after the comparison is consistent can the data stream be released to the control module, thereby cutting off the interference of erroneous environmental parameters on the cost resistance space modeling.

[0025] At the end of the system's business logic, the control module attaches the cost data (excluding geodesic compensation items) to the project partitioning tree and generates the full-process cost results. Given that the cost details of power transmission and transformation projects involve confidential bidding information, the main framework module forcibly inserts encryption processing before the aforementioned data is written to disk from system memory. The mathematical expression of the encryption process is as follows: ; in This represents the plaintext full-process cost breakdown output by the control module. Asymmetric cryptographic transformation operator representing the built-in dynamic system key. This represents the controlled encrypted output file generated through forced obfuscation. The plaintext cost details are converted into high-entropy scrambled text at this step, preventing unauthorized modification or abnormal reading of the data during subsequent cloud backup or local storage stages.

[0026] To facilitate the retrospective investigation mechanism of subsequent project cost audits and judicial liability determination procedures, the main framework module imprints a unique traceability code on the cost results after the encryption step. This code integrates the time parameters of the generation node and the underlying hardware characteristics, and its generation mechanism is as follows: ; in This represents the absolute timestamp displacement sequence at the moment the entire cost estimate result is generated. The media access control address encoding sequence of the physical server network interface card (NIC) that carries the main framework module for operation. The characteristic hash sequence representing the aforementioned controlled encrypted output file, The bitwise XOR logical operator represents the three sequence items mentioned above. This represents the final output and is permanently bound to the corresponding cost document's traceability identification code. Through the interweaving of time scales, hardware-fixed features, and data content, the system outputs an unalterable identity imprint, ensuring accurate tracing of the cost document's source generation device and specific batch in subsequent settlement disputes.

[0027] The standardized cost module is used to store quota data divided by project level and provide a probability measure of initial resource density. Furthermore, the standardized cost estimation module is used for execution: The quota data is stored hierarchically according to voltage level and project type; Configure resource consumption coefficients for quota data at each level; A probability measure for generating initial resource density based on resource consumption coefficients.

[0028] Specifically, the standardized cost module serves as the initial source of system data computation, executing the transformation logic from static quota scalars to dynamic mathematical measurements. At the input end of the system data flow, this module receives the original cost list and design chart parameters of the project to be compiled and reviewed.

[0029] After receiving the raw data, the system performs dimensionality reduction and hierarchical storage. The specific mechanism involves constructing a multi-dimensional tree-structured storage matrix. The root node of the tree-structured storage matrix corresponds to the voltage level of the target project, branch nodes correspond to project types such as power transmission or substation, and leaf nodes map to specific sub-item quota data such as earthwork excavation or foundation pouring. Relying on the multi-dimensional tree-structured storage matrix, the system extracts quota units with hierarchical topological relationships from the disordered raw list.

[0030] After completing the hierarchical storage, the module traverses each leaf node of the tree-structured storage matrix and configures a resource consumption coefficient for each type of quota data. This consumption coefficient is a built-in prior weight scalar of the system, and its value reflects the material loss standard and mechanical depreciation baseline of a specific process under normal conditions at a specific voltage level.

[0031] After extracting the quota data and resource consumption coefficients, the module performs a measure generation operation at the output. The system transforms the discrete engineering cost scalar into a continuous probability space expression, and constructs a probability measure function for the initial resource density accordingly. The specific measure generation formula is defined as follows: ; in Represents spatial coordinates The initial resource density probability measure is used to characterize the theoretical distribution of cost resources before they are disturbed by meteorological noise. This represents the total number of sub-item quota nodes extracted from the project partitioning tree. The representative system is the first Resource consumption coefficients for each sub-project. Representing the The underlying quota benchmark value corresponding to each sub-item project. Representing the The spatial basis functions of each sub-project on the engineering physical support set characterize the initial spatial occupancy characteristics of the resource under ideal conditions. represents the normalization partition constant, whose value is equal to the spatial integral of the summation term over the entire global support set of the project, constraining the global measure to satisfy the normalization condition of the probability space.

[0032] Following the above calculation process, the standardized cost module will generate a probability measure at the output end. The data is then transferred to the control module. This transformation step breaks the limitation of traditional cost comparison software, which can only perform one-dimensional scalar accumulation operations. By converting rigid cost quotas into continuous functions with spatial attributes and probabilistic characteristics, the system directly provides mathematically defined initial boundary conditions for subsequent calculations of Riemannian manifold metric tensors and the evolution of Iton stochastic differential equations. This module fundamentally prevents the curse of dimensionality caused by direct cross-comparison of massive discrete cost quota data under complex topological structures.

[0033] The splicing and connection module is used to realize the system access and topology construction of various standardized cost modules; Furthermore, the splicing and connection module is used to perform: Monitor the online status of each standardized cost estimation module; Identify the logical connection points between the standardized cost modules; A directed topological graph is constructed at the system's underlying layer to connect the various standardized cost estimation modules.

[0034] Specifically, the splicing and connection module executes bridging instructions for data routing and spatial modeling within the system architecture, resolving the topological island problem of isolated cost quota units. At the data input end, this module acquires the data stream of underlying resource nodes instantiated by the standardized cost module.

[0035] In response to the input resource node data stream, the system's underlying layer triggers a heartbeat handshake protocol to monitor the online status of each standardized cost module. The online signals fed back by each module are quantized into a status activation scalar and filled into the underlying temporary register.

[0036] After confirming the bidirectional activation of the module hardware and data interface, the system follows the electrical main wiring diagram of the power transmission and transformation project or the physical boundaries of the construction space to identify the logical connection points between standardized cost modules. The cable trench laying path shared between the main transformer quota module and the high-voltage distribution equipment quota module is identified as a physical logical connection point.

[0037] Based on the extracted state activation scalars and logical connection points, the splicing and connection module constructs a directed topological graph representing the spatial dependency relationships between modules in the system's underlying memory. The specific construction logic of this directed topological graph is implemented by generating a topological adjacency matrix, and the calculated relationships are defined as follows: ; in This represents the topological adjacency matrix element in a directed topological graph, representing the points from the source module node to the target module node. The online status activation scalar represents the source standardized cost module, and its value defines whether the module's resources have been fully loaded into the current memory pool. The online status activation scalar represents the target standardized cost module. This constant represents the coupling strength of the logical connection point between the source module node and the target module node. This constant is forcibly set to zero when there is no physical boundary or electrical logic intersection. It represents a spatially oriented characteristic vector defined by the time sequence of engineering construction or the direction of energy flow.

[0038] At the output end, the splicing and connection module pushes the calculated topological adjacency matrix to the control module in its entirety. This matrix indicates the connectivity of the initial resource density measure of each independent module in the global space. The control module then directly extracts this directed topological graph to delineate the effective domain of the Riemannian manifold metric tensor, and uses this as the geodesic integration path when solving the optimal transport mapping under rigid boundary conditions, thus preventing cost transport mapping errors that violate physical construction principles due to spatial coordinate discontinuities.

[0039] The data interface module is used to acquire environmental state feature vectors and exchange data with external price databases; Furthermore, the data interface module is used to execute: The geographic information and meteorological monitoring data of the target engineering area are used to form an environmental state feature vector; Establish a communication link with external price databases; The price fluctuation matrix of bulk commodities is synchronized through communication links.

[0040] Specifically, the data interface module at the system front end is responsible for collecting and structuring heterogeneous external data. At the data input level, this module first receives the geographic coordinate boundary instructions for the target engineering area. Based on the received coordinate boundaries, the module sends data request requests to the remote 3D geographic information system and weather station. The module extracts the returned elevation slice data and real-time weather monitoring stream, and concatenates the discrete natural environmental parameters to construct an environmental state feature vector. The specific feature vector construction logic is expressed by the following formula: ; in This represents the environmental state feature vector generated and output to the control module. This represents the absolute altitude scalar of the target engineering area extracted from the geographic information system. This represents the maximum approximation slope scalar of the extracted terrain. A scalar representing the characteristic depth of soil freeze-thaw in this region. This represents a meteorological monitoring feature subvector that includes wind speed and rainfall. (Top right corner label) This represents the transpose operation of the matrix. This step transforms the complex natural physical boundaries of the construction site into mathematical tensors that can be directly read by downstream modules, allowing the system to obtain the underlying geometric metric benchmark parameters required for subsequent construction of the cost space of the non-flat Riemannian manifold.

[0041] At the material price input level, this module establishes a dedicated communication link with the price database of an external bulk commodity trading center. After verifying the access encryption, the module retrieves historical transaction slices and real-time price lists for core bulk commodities such as copper, aluminum, steel, and transformer oil. The module extracts multi-dimensional time-series price sequences, and calculates and synchronizes the price fluctuation matrix of bulk commodities accordingly. The calculation logic for discrete elements within the price fluctuation matrix is ​​defined as follows: ; in The first element in the generated price fluctuation matrix Bulk commodities in the first Fluctuation characteristic elements within a time step. The first one pulled from the external price database Such materials in the current The real-time unit price at any given moment. This represents the material at the previous time point. The unit price. The representative is a weighting adjustment coefficient set based on the proportion of the material in the total life cycle cost of the power transmission and transformation project.

[0042] After completing the matrix calculation, the data interface module pushes the entire price fluctuation matrix containing the aforementioned characteristic elements to the control module at the output end. While conventional cost extraction schemes rely on a static material library to perform absolute value multiplication, this module outputs a fluctuation matrix containing time-series discrete differences. This matrix data is directly mounted as a random disturbance source for Itō's stochastic differential equations in subsequent calculations, thus providing a rigid data flow input foundation for the system to handle the dual nonlinear fluctuations in costs due to weather and market conditions.

[0043] like Figure 2 As shown, the control module is used to identify and initialize the standardized cost module that is connected. It constructs a cost resistance Riemannian manifold space based on the environmental state feature vector. It uses the Iton stochastic differential equation to drive the initial resource density to evolve in the Riemannian manifold space to generate the expected resource measure. When the expected resource measures overlap, it obtains the optimal transport mapping by solving the Monge-Ampère equation. It uses the geodesic compensation term generated based on the optimal transport mapping to remove redundant engineering quantities. It then links the cost data after removing the geodesic compensation term to the project partitioning tree to generate the full-process cost results. Furthermore, the control module is used to execute the following when constructing the cost-resistance Riemannian manifold space: Extract altitude, slope, and freeze-thaw parameters from the environmental state feature vector; The extracted parameters are mapped to cost measurement tensors at the coordinate points; The geodesic distance within a Riemannian manifold space is defined using a cost metric tensor.

[0044] The control module is used to execute the following when generating desired resource metrics: The random components in the price fluctuation matrix synchronized by the data interface module are injected as noise terms into Itō's stochastic differential equation; Calculate the initial resource density evolution in the execution time domain; Obtain the expected distribution of the resource boundary after perturbation to generate the expected resource measure.

[0045] The control module performs the following when obtaining the optimal transport mapping: Locate the intersection region of the support sets of two adjacent desired resource measures; Construct nonlinear partial differential equations for scalar potential functions within the overlapping domain of the support set; Solve for the Riemann gradient of the scalar potential function to derive the optimal transport map.

[0046] The control module is used to execute the following when generating the full-process cost estimate: The penalty cost of overlapping micro-elements is calculated based on the transfer path determined by the optimal transport mapping and the geodesic distance. The total cost of each penalty is aggregated to generate a global deduction compensation scalar as a geodesic compensation item; The cost data after removing the global deduction compensation scale is attached to the project partitioning tree.

[0047] Specifically, the control module is the core computational hub for the system to perform nonlinear spatial cost conflict stripping. Its inputs include the initial resource density measurement from the standardized cost module and the environmental state feature vector and price fluctuation matrix from the data interface module. Its output submits the final cost detail data stream, stripped of redundancy, to the main framework module.

[0048] The control module extracts altitude, slope, and freeze-thaw parameters from the environmental state feature vector. Traditional engineering cost estimation algorithms assume the construction area is an isotropic, flat space. This module transforms the extracted natural parameters into spatial curvature factors, warping the original Euclidean geographic coordinate system into a non-flat cost resistance Riemannian manifold space. The cost metric tensor at any coordinate point within this space is defined as follows: ; in The Kronecker function represents the unit metric of the basic flat space. This represents the altitude parameter being transmitted. This represents the input slope parameter. This represents the input freeze-thaw parameters. The corresponding system preset altitude resistance weight constant. The corresponding slope resistance weighting constant. The corresponding freeze-thaw resistance weighting constant. This represents the Riemannian manifold metric tensor generated after integrating environmental characteristics. The system performs line integration based on this tensor to calculate the geodesic distance between any two points in the manifold space. This distance replaces the conventional straight-line distance, truly quantifying the actual cost span of material handling and labor efficiency reduction under harsh working conditions.

[0049] After constructing the manifold space, the control module extracts the price fluctuation matrix synchronized by the data interface module. The module extracts the random components from the matrix and injects them as white noise into the Wiener process. The initial resource density initiates its time-domain evolution under this random perturbation. Its dynamic equations are defined as follows: ; in represent The spatial distribution function of resource density at any given time. This represents a deterministic diffusion tensor based on the capacity to schedule construction resources. This represents the divergence and gradient space differential operator. This represents the volatility coefficient generated based on the random component mapping of the price volatility matrix. This represents the random differential perturbation term of standard Brownian motion. The system solves this partial differential equation to obtain the expected distribution state of the resource boundary after undergoing the dual disturbances of market fluctuations and meteorological conditions, outputting the expected resource measure. This step transforms the static quota consumption into a dynamic cost cloud with a risk probability boundary.

[0050] Cost cloud clusters tend to overlap within a limited construction cross-section when subjected to disturbances. The control module locates the overlap region of the support sets of two adjacent desired resource measures. Excessive resource input within the overlap region due to overlapping processes constitutes cost redundancy. The module establishes a nonlinear partial differential equation concerning the scalar potential function within the overlap region to explore the lowest-cost resource dispersal path. The core calculation's Monge-Ampere equation expands as follows: ; in This represents the Hessian differential operator on the Riemannian manifold space. This represents the cost transmission scalar potential function to be solved. This represents the probability density distribution of the expected resource measure of the source. This represents the probability density distribution of the target expected resource measure. The scalar potential function represents the Riemann gradient on the Riemannian manifold space. The determinant on the left-hand side of the equation characterizes the volumetric distortion rate during the spatial transport mapping process. The system calls Newton's method iteratively to find the potential function. Then, its Riemann gradient This was established as the optimal transport mapping. This mapping, from a mathematical perspective, outlines a path with the lowest resistance to move conflicting materials out of the overlap area.

[0051] After determining the optimal transport mapping, the control module extracts the geodesic distance generated in the first step along the aforementioned trajectory. The module performs volume integration on the cost increment caused by resource space conflicts within the overlapping domain, and the calculation result is converted into a penalty cost. The specific integration logic is expressed as follows: ; in This represents the final generated global deduction compensation scalar, also known as the geodesic compensation item. The boundary of the overlapping domain of the support set is defined. This represents the geodesic distance between two points derived from the cost measurement tensor. This represents the coordinates of the source infinitesimal element within the overlapping domain. This represents the coordinates of the target micro-element after following the optimal transport mapping. This represents the minute measurement at the source coordinate point. The control module removes the accumulated amount from the initial total project budget. This system precisely eliminates hidden double-counting loopholes caused by overlapping work areas. The corrected detailed data is then linked to the various levels of the project partitioning tree, outputting a complete cost estimate document free of redundancy.

[0052] The display module is used to present the module topology and the total cost results under the Riemannian manifold space. Furthermore, the display module is used to perform: Render the Riemannian manifold space and module topology view; Highlight the conflict paths corresponding to the geodesic compensation items; The system dynamically displays the changes in numerical values ​​after removing redundant engineering quantities.

[0053] Specifically, the display module, as the final human-computer interaction endpoint of the system, is responsible for transforming the results of high-dimensional tensors and matrix operations from the underlying abstraction into an intuitive and visual form. In terms of data flow, the input end of this module connects to the control module, extracting the cost measurement tensor, optimal transport mapping term, and geodesic compensation scalar generated after algorithm processing. Its output end presents a three-dimensional graphical interface and dynamic data dashboard to cost estimators and reviewers.

[0054] After extracting the cost metric tensor and topological adjacency matrix, the system executes rendering instructions for the Riemannian manifold space and module topology view. Conventional cost estimation software only provides a flat table hierarchy; this system, through a specific visual height mapping operator, transforms invisible cost resistance into visualized 3D terrain undulations. The specific visual rendering height mapping formula is defined as follows: ; in It represents the 3D visual height rendered in the monitor screen coordinate system. This represents the system's preset reference plane visual height. A scalar representing the visual scaling of an adaptive display resolution. This represents the determinant value of the cost measurement tensor input to the control module. Based on this mapping relationship, the system generates a curved manifold surface whose curvature varies with environmental resistance, and overlays node connections composed of topological maps onto this surface, intuitively revealing the spatial distribution of geographical and environmental difficulties in construction cost.

[0055] Based on the completed 3D visualization view, the display module then performs highlighting of conflict paths. The system extracts the optimal transport mapping path output by the control module solving the Monge-Ampère partial differential equation, and applies dynamic luminescent material rendering along this geodesic trajectory. The control logic for luminescence intensity follows the following mathematical relationship: ; in This represents the visual luminescence intensity of the conflict path in the final rendered output to the screen. Represents the default ambient base brightness of the manifold space basemap. The amplification factor represents the luminous intensity. This represents the gradient norm of the scalar potential function in Riemann space. This norm value is directly related to the severity of resource overlap interference. Relying on this visual brightness variable, directly driven by mathematical gradients, reviewers can accurately pinpoint areas of excessive resource overlap and the precise trajectory of the system's stripping algorithm, overcoming the technical barrier of the lack of interpretability in the underlying black-box algorithm.

[0056] At the numerical presentation level, the module synchronously triggers a dynamic display of the numerical changes after removing redundant engineering quantities. The system abandons the static final result overlay mode and introduces a time-series animation function to present the evolving and decreasing process of cost deduction. The visually driven calculation formula for numerical changes is set as follows: ; in Represents parameters over time on the display panel. The current cost display value is updated in real time. This represents the total amount of the initial engineering budget before the system performs conflict calculations. This represents the global deduction compensation scalar calculated by the control module. This represents a time-interpolation animation function whose value range smoothly and monotonically increases from zero to one. Users can visually examine the decrease in total cost due to the removal of the geodesic compensation item through the front-end panel. This mechanism uses a visualized data flow loop to verify the system's calculation trajectory for preventing duplicate and omissions.

[0057] Example 2: In the construction scenario of the 750 kV power transmission and transformation project in the high-altitude mountainous area of ​​Qinghai, the project covers multiple substations and inter-regional transmission lines. The site faces complex micro-topography such as steep slopes and deep freeze-thaw cycles, and is accompanied by extreme snowfall and disorderly fluctuations in the prices of bulk commodities. Due to the cross-operation of multiple sections, the main transformer construction area and the foundation of the line in the same corridor have a large number of overlapping construction surfaces and shared passages in physical space. Under such complex working conditions, traditional cost compilation and review mechanisms reveal physical distortions in their underlying calculation logic: their calculation basis relies on linear accumulation using a flat Euclidean space and static quota scalars, completely severing the nonlinear spatial distortions caused by terrain elevation differences and environmental resistance on cost, and failing to perform time-domain probability tracking of dynamic resource losses caused by random weather disturbances; faced with physical spatial interference from multiple processes, existing mechanisms can only perform crude addition of ledger details or manual deduction based on experience, lacking underlying mathematical tools to analyze resource conflict boundaries under continuous variables, and unable to solve for the lowest-cost transmission trajectory to eliminate overlapping and redundant engineering quantities. This leads to hidden double pricing and distorted investment estimates in the entire process cost results under the dual pressure of complex physical resistance and random environment. To solve these problems, this invention provides an intelligent cost compilation and review system for power transmission and transformation projects, the structure of which is as follows: Figure 1 As shown. The specific implementation process of this system is as follows: The system's underlying structure relies on the main framework module to provide a secure and encrypted carrier, while the data interface module imports external environmental state tensors and bulk commodity market fluctuation data in real time. In the pre-input phase, the standardized cost module and the splicing and connection module collaborate to transform discrete static engineering quotas into initial resource probability measures with underlying topological connectivity. In the core computation flow, the control module abandons the conventional flat spatial scalar accumulation logic, transforming the input environmental parameters into cost measurement tensors to construct a non-flat Riemannian manifold space, and introducing Itō stochastic differential equations to simulate the dynamic evolution interference of real weather and price noise on cost resources. To address the overlapping of expected cost measures caused by multi-process interference, the system solves the Monge-Ampere partial differential equation within the manifold space to extract the optimal transport mapping. The geodesic compensation term generated based on this mapping can accurately remove implicit redundant engineering quantities caused by work surface intersections with the lowest cost path. The net cost data, after eliminating duplicate pricing, is finally linked to the project partitioning tree, and the entire process cost file is rendered and output using the manifold topology interface of the display module. This technical solution transforms static cost accounting into dynamic measurement stripping with environmental resistance, plugging the loophole of overlapping project pricing from the algorithm's underlying layer.

[0058] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart editing and review system for power transmission and transformation engineering cost estimation throughout the entire process, characterized in that, Includes the following modules: The main framework module is used to carry various functional modules and provide an encrypted environment; The standardized cost module is used to store quota data divided by project level and provide a probability measure of initial resource density. The splicing and connection module is used to realize the system access and topology construction of each of the standardized cost modules; The data interface module is used to acquire environmental state feature vectors and exchange data with external price databases; The control module is used to identify and initialize the accessed standardized cost module, construct a cost resistance Riemannian manifold space based on the environmental state feature vector, use the Iton stochastic differential equation to drive the initial resource density to evolve in the Riemannian manifold space to generate the desired resource measure, and when the desired resource measures overlap, obtain the optimal transport mapping by solving the Monge-Ampère equation, use the geodesic compensation term generated based on the optimal transport mapping to remove redundant engineering quantities, and attach the cost data after removing the geodesic compensation term to the project partitioning tree to generate the full-process cost results; The display module is used to present the module topology state under the Riemannian manifold space and the cost results of the entire process.

2. The intelligent compilation and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 1, characterized in that, The main framework is used to execute: The environmental state feature vector obtained by the data interface module is subjected to input integrity verification. The generated full-process cost estimate results are subject to mandatory encryption. A unique traceability identification code is assigned to the cost results of the entire process.

3. The intelligent editing and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 1, characterized in that, The standardized cost estimation module is used to perform: The quota data is stored hierarchically according to voltage level and project type; Configure resource consumption coefficients for quota data at each level; A probability measure for generating the initial resource density is based on the resource consumption coefficient.

4. The intelligent editing and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 1, characterized in that, The splicing and connection module is used to perform: Monitor the online status of each of the standardized cost estimation modules; Identify the logical connection points between the standardized cost modules; A directed topological graph is constructed at the system's underlying layer among the standardized cost estimation modules.

5. The intelligent compilation and review system for power transmission and transformation engineering cost estimation based on the whole process, as described in claim 1, is characterized in that... The data interface module is used to perform: The geographic information and meteorological monitoring data of the target engineering area are retrieved to form the environmental state feature vector; Establish a communication link with external price databases; The price fluctuation matrix of bulk commodities is synchronized through the communication link.

6. The intelligent compilation and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 1, characterized in that, The control module is used to perform the following when constructing the cost-resistance Riemannian manifold space: Elevation, slope, and freeze-thaw parameters are extracted from the environmental state feature vector. The extracted parameters are mapped to cost measurement tensors at coordinate points; The cost metric tensor defines the geodesic distance within the Riemannian manifold space.

7. The intelligent editing and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 5, characterized in that, The control module is used to execute the following when generating the desired resource measure: The random components in the price fluctuation matrix synchronized by the data interface module are injected as noise terms into the Ito stochastic differential equation; Calculate the initial resource density evolution in the execution time domain; Obtain the expected distribution of the resource boundary after perturbation to generate the expected resource measure.

8. The intelligent editing and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 1, characterized in that, The control module is used to perform the following when obtaining the optimal transmission mapping: Locate the intersection region of the support sets of two adjacent desired resource measures; Construct a nonlinear partial differential equation for a scalar potential function within the overlapping domain of the support set; Solve for the Riemann gradient of the scalar potential function to derive the optimal transport map.

9. The intelligent editing and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 6, characterized in that, The control module is used to execute the following when generating the full-process cost estimate: The penalty cost of overlapping micro-elements is calculated based on the transfer path determined by the optimal transport mapping and the geodesic distance. The summation of all the aforementioned penalty costs generates a global deduction compensation scalar as the geodesic compensation item; The cost data after stripping the global deduction compensation scalar is attached to the project partitioning tree.

10. The intelligent editing and review system for power transmission and transformation engineering oriented towards full-process cost estimation as described in claim 1, characterized in that, The display module is used to perform: Render the Riemannian manifold space and module topology view; Highlight the conflict paths corresponding to the geodesic compensation items; The system dynamically displays the changes in numerical values ​​after removing redundant engineering quantities.

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