A bill of material coding-based project breakdown structure tree automatic generation method

Abstract

This invention relates to the field of big data analytics and discloses an automatic generation method for project decomposition structure trees based on bill of quantities coding. The method includes: instantiating a hierarchical data organization space in memory according to pricing standards; setting an initial mapping threshold by extracting standard node attributes; converting the bill of quantities coding sequence and cost parameters into a bill of quantities feature vector containing economic value components and classification dimension components based on dimensional mapping logic; determining the node path based on the response relationship between the bill of quantities feature vector and the initial mapping threshold, and attaching heterogeneous data to the node with the highest matching degree. This invention effectively eliminates topological discontinuities in heterogeneous cost data flows by constructing a multi-dimensional collaborative feature vector space, improves the alignment accuracy of bill of quantities under complex working conditions, establishes a physical penetration path from the overall summary value to the surface detailed records, and ensures high fault tolerance and logical consistency in the organization of massive cost data.

Description

Technical Field

[0001] This invention relates to the field of big data analytics, and in particular to a method for automatically generating a project decomposition structure tree based on list coding. Background Technology

[0002] Currently, in the full life cycle cost management of large-scale construction projects, a 9-digit standard bill of quantities code is typically used to classify the underlying cost details. The processor, based on the static mapping relationship preset by the pricing standard, attaches discrete bill of quantities items to nodes at all levels of the project decomposition structure tree to achieve structured aggregation of cost data. This hierarchical mapping method based on a fixed coding sequence forms the basis for current cost big data analysis and organization.

[0003] However, with the increasing complexity of engineering projects, heterogeneous data streams exhibit significant non-standard characteristics. During the preliminary estimation, budgeting, and settlement stages, datasets from different sources often contain a large number of enterprise-defined codes or incomplete feature fields. When the system transforms such noisy discrete data into a multi-dimensional topological structure tree, existing rigid mapping mechanisms face fundamental constraints. Not only are the rigid limitations of physical hardware structures difficult to overcome, but software-level methods for handling massive heterogeneous data association and control also have significant shortcomings. For example, Chinese invention patent application CN119919076A discloses an engineering project measurement method, device, electronic equipment, and... The storage medium relies on selecting the smallest unit of measurement from the bill of quantities and directly establishing a strong association between the bill of quantities code and the target level node code of the work breakdown structure template tree to generate an instance tree for subsequent measurement work. It deeply depends on the one-dimensional coding absolute matching control method, which implicitly presupposes that the input data stream has a high degree of standardization and zero character truncation. In actual cost management, the underlying dataset inevitably contains a large number of non-standard characters and noise. Existing methods face the fundamental mismatch between the core presupposition and the actual boundary conditions. When the standard coordinate reference is missing, the conventional software logic induces node matching misjudgment and large-scale isolated data wandering. The underlying mechanism cannot overcome the semantic gap caused by non-standard features.

[0004] Therefore, how to achieve high fault tolerance and path anchoring in the process of converting data to a standard hierarchical model is the technical problem that this invention aims to solve. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: a method for automatically generating an item decomposition structure tree based on inventory coding, comprising the following steps: Step S1: Instantiation of the hierarchical data organization space in memory; The processor instantiates the standard tree model of the project decomposition structure in memory according to the bill of quantities pricing standard, and converts the standard tree model of the project decomposition structure into a hierarchical data organization space; By extracting the attributes of the standard nodes, an initial mapping threshold is set for each non-leaf node of the standard tree model of the project decomposition structure based on the initial mapping threshold representing the ability of the data partition to associate discrete cost data. The initial mapping threshold is a preset value based on historical engineering sample data. Step S2, generating the list feature vector; obtain the heterogeneous list dataset to be processed, and extract the 9-bit standard list code sequence and cost parameter for each detailed record; the processor, according to the preset dimension mapping logic, combines the 9-bit standard list code sequence and cost parameter into a list feature vector containing economic value component and classification dimension component. Step S3, adaptive mapping mounting; import the list feature vector into the hierarchical data organization space; by calculating the structured response between the list feature vector and the initial mapping threshold, determine the node path to which the list feature vector belongs, and mount the heterogeneous data records that do not match the standard code to the node with the highest matching degree in the hierarchical data organization space to generate the project decomposition structure tree.

[0006] Preferably, the dimension mapping logic in step S2 includes: extracting hierarchical classification features from the 9-bit standard bill of quantities encoding sequence and constructing a bill of quantities feature vector. The classification dimension components are determined; the cost amount parameter is normalized, and its distribution weight in the global cost flow is calculated as the feature vector of the bill of quantities. The economic value components are calculated; the product of the classification dimension components and the economic value components is generated to produce the list feature vector. .

[0007] Preferably, the initial mapping threshold The following methods were used to determine the cost distribution range and coding deviation frequency of each level of node: A large data sample set of historical similar projects was obtained, and the cost distribution range and coding deviation frequency of each node were extracted. Based on the cost distribution range and coding deviation frequency, the association strength of each non-leaf node with the discrete data was calculated using a preset addressing matching function, and this strength was set as the initial mapping threshold. .

[0008] Preferably, the calculation logic of the address matching function is as follows: ,in, α is the initial mapping threshold; α is the preset industry correction coefficient; Let be the cost distribution entropy of the i-th child node under this node; This represents the average dispersion of the encoding in historical data.

[0009] Preferably, step S3 further includes a data consistency verification step: when the processor is summarizing the cost at the node, it synchronously appends a data source pointer array containing the underlying feature vector identifier to the non-leaf nodes; it utilizes the addressing characteristics of the memory index structure to establish a physical penetration path from the summary value to each list detail record; and it uses an anomaly capture mechanism to identify any initial mapping thresholds that are not hit. The data records are assigned to dynamically generated unclassified nodes, so that the values ​​of all summary nodes in the tree are equal to the sum of the cost parameters of the detailed records of each list under them.

[0010] Preferably, the process of determining the node path in step S3 adopts a bottom-up recursive aggregation logic: when the list feature vector When a leaf node is matched, the total cost status of the parent node is updated; the parent node dynamically adjusts the initial mapping threshold based on the received vector feedback. The value.

[0011] Preferably, the cost parameters extracted in step S2 include unit price, quantity, and total price; the processor analyzes the probability density of the unit price and identifies the cost deviation of the detailed record under the corresponding list code; step S3 further includes: adjusting the initial mapping threshold based on the cost deviation. Perform non-linear weighting to correct the path of the node.

[0012] Preferably, the hierarchical data organization space supports the parsing of enterprise-defined codes: when the 9-digit standard bill of quantities code sequence contains enterprise-defined characters, the processor extracts the common substring features of the sequence and superimposes them with the economic value features formed by the cost amount parameters; by calculating the correlation between the common substring features and the node attributes of the standard tree model of the project decomposition structure, the path of the node to which it belongs is determined.

[0013] Preferably, after generating the project decomposition structure tree, the method further includes: based on the data traceability pointer array, in response to a query request for a summary node, extracting all underlying list detail records under the summary node through the physical penetration path, and outputting a management report containing business and economic dimensions.

[0014] Preferably, the method further includes a dynamic calibration step for spatial status: the processor monitors the newly added cost data stream in real time, and recalibrates the initial mapping threshold of each node after a preset period based on the success rate of data mounting and addressing time. , to make the initial mapping threshold It remains stable within a preset numerical range to maintain the matching accuracy of the hierarchical data organization space.

[0015] The beneficial effects of this invention are: 1. In the automatic generation of the project decomposition structure tree, an adaptive topology addressing mechanism is established to overcome the semantic mapping rigidity between one-dimensional list coding and multi-dimensional hierarchical structure. A weighted multi-path prefix tree index structure is used to replace the traditional static relational mapping table. The 9-bit standard code is split into a byte-level addressing step sequence and assigned to the prefix tree node path. During the addressing process, the processor introduces a local topology gravity threshold and performs probabilistic optimization based on the spatial gravity coefficient of the encoded feature vector and adjacent nodes. When non-standard coding or feature truncation occurs in the data stream, the gravity coefficient drives the system to adaptively attract discrete data to the parent node with the highest logical correlation. This avoids the routing addressing collapse that is inevitably triggered by the traditional hash matching mechanism when facing noisy data, and achieves high fault tolerance and determinism in the process of converting data to a standard hierarchical model.

[0016] 2. By leveraging the multidimensional collaboration of feature state vectors and economic dimension parameters, a high-density business data clustering space is constructed, improving data alignment accuracy under complex operating conditions. The processor encapsulates the list coding sequence and economic dimension features such as unit price distribution into a multidimensional business feature tensor, enabling the location of topological nodes to no longer rely on a single character matching logic. When processing heterogeneous datasets containing enterprise-defined codes, the multidimensional attributes of the feature tensor and the pre-stored topological gravity threshold in the tree model generate a structured response, guiding the system to complete path traversal and dynamic node mounting in an environment lacking absolute coordinates. The combined effect of this multi-mechanism linkage enables the system to identify and correct local distortions in the underlying data stream, ensuring data logic consistency when massive detailed data is aggregated upwards.

[0017] 3. Achieving a deep integration of computational efficiency and mathematical rigor, breaking through the performance bottleneck of big data organization and processing in the field of engineering cost, the solution transforms the business-level list reorganization requirements into pure string matching and tree topology traversal operators, activating a bottom-up recursive aggregation algorithm. When the processor executes node cost summarization, it simultaneously adds a data source pointer array containing the underlying feature tensor identifier to non-leaf nodes. This design utilizes the physical characteristics of the memory index structure to complete the aggregation of tens of millions of entries in milliseconds. At the same time, it establishes an instantaneous physical penetration path from the overall summary value to the surface list details. While ensuring high throughput, the system isolates the missing data to dynamically generated unclassified nodes through an exception capture mechanism, ensuring the mathematical conservation of the total tree cost summary value. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart illustrating the generation process of the project decomposition structure tree for multidimensional features and adaptive matching in this invention. Figure 2 This is a logic diagram for generating list feature vectors that integrate classification components and economic values ​​in this invention. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, an embodiment or embodiment referred to herein refers to a specific feature, structure or characteristic that may be included in at least one implementation of the present invention. An embodiment appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] This invention is described in detail with reference to the schematic diagrams. When describing the embodiments of this invention, for ease of explanation, the cross-sectional views of the device structure will be partially enlarged without adhering to the general scale. Moreover, the schematic diagrams are only examples and should not limit the scope of protection of this invention. In addition, in actual manufacturing, the three-dimensional spatial dimensions of length, width and depth should be included.

[0023] Furthermore, in the description of this invention, it should be noted that the terms such as "upper," "lower," "inner," and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or component referred to has a specific orientation, or is constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention. In addition, the terms "first," "second," or "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0024] Unless otherwise explicitly specified and limited, the terms installation, connection, and linking in this invention should be interpreted broadly. For example, they can refer to fixed connection, detachable connection, or integrated connection; similarly, they can refer to mechanical connection, electrical connection, or direct connection, or indirect connection through an intermediate medium, or internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0025] A method for automatically generating a project decomposition structure tree based on inventory coding includes the following steps: Step S1: Instantiation of the hierarchical data organization space in memory; The processor instantiates the standard tree model of the project decomposition structure in memory according to the bill of quantities pricing standard, and converts the standard tree model of the project decomposition structure into a hierarchical data organization space; By extracting the attributes of the standard nodes, an initial mapping threshold is set for each non-leaf node of the standard tree model of the project decomposition structure based on the initial mapping threshold representing the ability of the data partition to associate discrete cost data. The initial mapping threshold is a preset value based on historical engineering sample data. Step S2, generating the list feature vector; obtain the heterogeneous list dataset to be processed, and extract the 9-bit standard list code sequence and cost parameter for each detailed record; the processor, according to the preset dimension mapping logic, combines the 9-bit standard list code sequence and cost parameter into a list feature vector containing economic value component and classification dimension component. Step S3, adaptive mapping mounting; import the list feature vector into the hierarchical data organization space; by calculating the structured response between the list feature vector and the initial mapping threshold, determine the node path to which the list feature vector belongs, and mount the heterogeneous data records that do not match the standard code to the node with the highest matching degree in the hierarchical data organization space to generate the project decomposition structure tree.

[0026] Preferably, the dimension mapping logic in step S2 includes: extracting hierarchical classification features from the 9-bit standard bill of quantities encoding sequence and constructing a bill of quantities feature vector. The classification dimension components are determined; the cost amount parameter is normalized, and its distribution weight in the global cost flow is calculated as the feature vector of the bill of quantities. The economic value components are calculated; the product of the classification dimension components and the economic value components is generated to produce the list feature vector. .

[0027] Preferably, the initial mapping threshold The following methods were used to determine the cost distribution range and coding deviation frequency of each level of node: A large data sample set of historical similar projects was obtained, and the cost distribution range and coding deviation frequency of each node were extracted. Based on the cost distribution range and coding deviation frequency, the association strength of each non-leaf node with the discrete data was calculated using a preset addressing matching function, and this strength was set as the initial mapping threshold. .

[0028] Preferably, the calculation logic of the address matching function is as follows: ,in, α is the initial mapping threshold; α is the preset industry correction coefficient; Let be the cost distribution entropy of the i-th child node under this node; This represents the average dispersion of the encoding in historical data.

[0029] Preferably, step S3 further includes a data consistency verification step: when the processor is summarizing the cost at the node, it synchronously appends a data source pointer array containing the underlying feature vector identifier to the non-leaf nodes; it utilizes the addressing characteristics of the memory index structure to establish a physical penetration path from the summary value to each list detail record; and it uses an anomaly capture mechanism to identify any initial mapping thresholds that are not hit. The data records are assigned to dynamically generated unclassified nodes, so that the values ​​of all summary nodes in the tree are equal to the sum of the cost parameters of the detailed records of each list under them.

[0030] Preferably, the process of determining the node path in step S3 adopts a bottom-up recursive aggregation logic: when the list feature vector When a leaf node is matched, the total cost status of the parent node is updated; the parent node dynamically adjusts the initial mapping threshold based on the received vector feedback. The value.

[0031] Preferably, the cost parameters extracted in step S2 include unit price, quantity, and total price; the processor analyzes the probability density of the unit price and identifies the cost deviation of the detailed record under the corresponding list code; step S3 further includes: adjusting the initial mapping threshold based on the cost deviation. Perform non-linear weighting to correct the path of the node.

[0032] Preferably, the hierarchical data organization space supports the parsing of enterprise-defined codes: when the 9-digit standard bill of quantities code sequence contains enterprise-defined characters, the processor extracts the common substring features of the sequence and superimposes them with the economic value features formed by the cost amount parameters; by calculating the correlation between the common substring features and the node attributes of the standard tree model of the project decomposition structure, the path of the node to which it belongs is determined.

[0033] Preferably, after generating the project decomposition structure tree, the method further includes: based on the data traceability pointer array, in response to a query request for a summary node, extracting all underlying list detail records under the summary node through the physical penetration path, and outputting a management report containing business and economic dimensions.

[0034] Preferably, the method further includes a dynamic calibration step for spatial status: the processor monitors the newly added cost data stream in real time, and recalibrates the initial mapping threshold of each node after a preset period based on the success rate of data mounting and addressing time. , to make the initial mapping threshold It remains stable within a preset numerical range to maintain the matching accuracy of the hierarchical data organization space.

[0035] Example 1: In a big data analysis scenario during the preliminary, budget, and settlement stages of an engineering project, the system receives an input stream containing hundreds of thousands of heterogeneous cost data entries. This stream includes discrete detailed records with enterprise-defined codes and feature truncation. The one-dimensional feature coding rule matching, relying on a static relational mapping table, results in routing failures and isolated data points when processing non-standard structured data. This example discloses a data organization process that calculates topological paths based on multi-dimensional feature vectors in a heterogeneous database and completes clustering and mounting. The processor instantiates a standard tree model of the project decomposition structure in memory according to the bill of quantities pricing standard, converts the standard tree model into a hierarchical data organization space, extracts standard node attributes, and sets initial mapping thresholds for each non-leaf node of the standard tree model. Its value is calculated based on historical engineering sample data and is used to characterize the correlation ability of the data partition to discrete cost data. The system obtains the heterogeneous engineering quantity list dataset to be processed.

[0036] For each detailed record, a 9-digit standard bill of quantities code sequence and cost amount parameter are extracted. The processor then extracts hierarchical classification features from the 9-digit standard bill of quantities code sequence according to a preset dimension mapping logic, constructing a bill of quantities feature vector. The processor normalizes the cost amount parameter, calculates its distribution weight in the global cost flow as the economic value component, and then calculates the product of the classification dimension component and the economic value component to generate a list feature vector containing both the economic value component and the classification dimension component. When a 9-bit standard bill of quantities encoding sequence contains enterprise-defined characters that cause a local mapping space conflict, the processor stops the absolute character comparison logic and adjusts the bill of quantities feature vector. Importing a hierarchical data organization space, the system calculates the list feature vector. With the initial mapping threshold The processor responds based on the list of feature vectors. With the initial mapping threshold The structured response determines the list feature vector. The system attaches heterogeneous data records that do not match the standard code to the node with the highest matching degree in the hierarchical data organization space, generating a project decomposition structure tree. The value of each summary node in the structure tree is equal to the sum of the cost amount parameters of each detailed record in the list. The system establishes a data penetration path from the summary value to the detailed record based on this equation, and outputs structured cost data containing multi-dimensional topological relationships.

[0037] Example 2: In an offline data testing platform with parallel processing capabilities, the system obtains a historical engineering budget database as the test data source, containing 500,000 standard engineering quantity list details. The system actively overlays Gaussian-distributed character truncation noise and hierarchical misalignment markers onto these 500,000 details to construct a heterogeneous cost test dataset simulating real-world substandard engineering conditions. The clustering operation on this heterogeneous cost test dataset faces physical constraints related to feature fidelity and topology computation time. The system sets an industry correction coefficient α to adjust the initial mapping threshold. The absolute value; where the industry correction coefficient α is used to balance the local spatial convergence speed and the global tree structure stability, and the initial mapping threshold. Used to characterize the correlation strength between non-leaf nodes and discrete data; when the system detects the average discreteness of the encoding used to characterize the disorder of the basic service flow. When the value increases, the system reduces the value of the industry correction coefficient α to weaken the initial mapping threshold. The system sets the industry correction coefficient α to a base value of 0.85, based on the characteristics of the building engineering type of the test data source.

[0038] The system initiates batch topology mounting operations, dividing the heterogeneous construction cost test dataset into low-perturbation gradient groups, medium-perturbation gradient groups, and high-perturbation gradient groups with character truncation noise injection ratios of 5%, 20%, and 40%, respectively. A control group using static one-dimensional feature encoding matching logic is simultaneously established, along with an experimental group using multi-dimensional feature vector clustering logic. In the first operation phase, the system extracts the cost amount parameter and the 9-digit standard bill of quantities code sequence from each group's detailed records. For records containing truncation noise, the experimental group processor fuses the economic value component and the residual classification dimension component according to a preset dimension mapping logic to generate the bill of quantities feature vector. Test data shows that, in the 5% low-perturbation gradient group, the experimental group generates a list of feature vectors. Maintaining 99.2% cosine similarity with the noise-free baseline feature, the cosine similarity of this intermediate feature decreases to 87.5% in the 20% of the perturbation gradient groups; in the second computation stage, the processor calculates the list feature vector. Initial mapping threshold of nodes The structured response was processed and node mounting was completed. The final output data showed that the control group achieved a mounting matching rate of 91.3% with a low perturbation gradient of 5%, but this dropped to 14.2% with a medium perturbation gradient of 20% due to the loss of character matching coordinates. In contrast, the experimental group, with a medium perturbation gradient of 20%, relied on the list feature vector... The multidimensional attribute compensation maintained a matching rate of 94.6%. When the noise intensity increased to 40% in the high-perturbation gradient group, the matching rate of the experimental group showed a nonlinear decay and dropped to 61.4%. At this time, the excessively incomplete classification dimension components caused the overlap of gravitational potential traps within the hierarchical data organization space, resulting in a large number of detailed records being incorrectly aggregated to the upper-level summary node. This nonlinear decay trend constitutes the data basis for determining the boundaries of technical parameters, confirming that the system maintains stable clustering accuracy within the operating range where the perturbation ratio is no higher than 35%, and establishing the optimal working window for the dimension mapping logic.

[0039] The comparative data and nonlinear decay boundary of the above cross-gradient perturbation conditions confirm that, based on the inventory feature vectors... With the initial mapping threshold The jointly constructed topological gravity addressing mechanism can suppress routing collapse induced by discrete character truncation; the multidimensional feature fusion and structured response calculation process transforms the alignment transformation of high-dimensional missing data into distance approximation optimization in vector space, and solves the problem of isolated points wandering in the topological reconstruction process of heterogeneous cost data flow within the constraint boundary of maintaining the global conservation of node cost parameters, and establishes a physical penetration path to resist the noise of the underlying data.

[0040] Example 3: During the system initialization phase of instantiating hierarchical data organization space and performing topological gravity addressing, the data engine faces engineering obstacles such as inconsistent feature extraction dimensions for heterogeneous detailed records and a lack of quantitative criteria for mounting matching calculations. This example discloses a parameter calibration procedure and algorithm execution path, establishing an initial mapping threshold. Numerical establishment mechanism and list feature vector The structured response operation logic involves the processor acquiring a large dataset of historical detailed records of similar projects. For each non-leaf node within the hierarchical data organization space, it extracts the cost distribution of its child nodes, calculates the probability of this distribution amount relative to the node's total cost, and obtains the cost distribution entropy. ;in, Given the cost distribution entropy of the i-th child node under this node, the system calculates the average variability of the coding based on the overall coding sequence of the historical detailed records of similar projects. The specific calculation process is as follows: The processor traverses all 9-digit standard bill of quantities codes in the historical dataset, mapping each character to a basic value according to its position in the alphabetical order (0-9 digits). For each code record, the absolute difference between the mapped values ​​of adjacent two characters is calculated and summed to obtain the fluctuation value of a single code. The processor then calculates the arithmetic mean of the fluctuation values ​​of all single codes in the historical dataset to obtain a code average dispersion representing the characteristics of the historical data. When the fluctuation value of the code to be processed is higher than 1.2 times this average, it is determined to be a non-standard feature. The processor then calculates the cost distribution entropy of its subordinate child nodes. The sum divided by the average dispersion of the code The output quotient is then multiplied by an industry correction factor α set according to the project type, and the calculation result is set as the initial mapping threshold. This operation transforms the node's ability to converge discrete data into a defined numerical boundary based on historical distribution parameters.

[0041] For a single detailed record in the dataset to be processed, the processor extracts a 9-bit standard bill of quantities code sequence, segments it into four levels of character fragments according to preset rules for professional engineering, appendix classification, sub-projects, and itemized projects, and converts the character fragments into a multi-dimensional one-hot encoding matrix to form the bill of quantities feature vector. The processor extracts the cost amount parameter of the corresponding record for the classification dimension component, calculates the ratio of this cost amount parameter to the total project cost to generate a distribution weight as the economic value component, and calculates the matrix tensor product of the classification dimension component and the economic value component to generate a list feature vector that has both hierarchical structure characteristics and economic distribution attributes. The multidimensional one-hot encoding matrix is ​​flattened into a one-dimensional spatial vector according to a preset hierarchical sequence. The economic value components, which serve as scalar distribution weights, are extracted. The scalar multiplication of each coordinate axis component of the one-dimensional spatial vector is then used to amplify the output, resulting in a one-dimensional list feature vector with uniform dimension and single-dimensional alignment. In the adaptive clustering mounting operation, the system extracts the central feature vector of the target node within the hierarchical data organization space, and the processor calculates the list feature vector. The Euclidean spatial distance between the input data features and the central feature vector of the target node, based on the principle of multidimensional feature space geometric measurement, reflects the degree of physical deviation between the input data features and the baseline features of the topological nodes. This eliminates the geometric distance parameter and the initial mapping threshold generated based on statistical distribution laws. The dimensional barrier between them, before the processor executes the logical comparison instruction, calls the memory-pre-resident linear mapping coefficients λ to the initial mapping threshold. Multiplicative weighting projects the numerical domain into a metric space that matches the Euclidean distance. The linear mapping coefficient λ is a constant with distance dimensions, and its specific value is determined based on the clustering convergence extreme value calibration when inputting the standard test dataset during system initialization. The specific calibration steps are as follows: During the self-test phase of system power-on initialization, the processor retrieves 100 standard sample data pre-stored in read-only memory for trial calculation. The system records the change in the average spatial distance between the list feature vector and the target node center vector after each iteration. When the distance change fluctuation is less than 0.005 for three consecutive iterations, it is determined that the clustering has converged. At this time, the processor extracts the current converged average spatial distance value, uses it as the baseline denominator, and compares it with the initial mapping threshold. The calculated ratio is fixed as the linear mapping coefficient for this running cycle. The system compares this Euclidean spatial distance with the initial mapping threshold corresponding to the target node. When the Euclidean spatial distance is less than or equal to the initial mapping threshold At that time, the system determines the feature vector of the list. The processor generates a structured response with the target node, and then attaches the detailed record containing the structured response to the target node, thus completing the physical path penetration from the dimensionality reduction features to the multidimensional topological space.

[0042] Example 4: When the system faces the on-site deployment of a brand-new enterprise-level cost database, the processor initiates a benchmark calibration procedure for the preset dimension mapping logic before starting batch topology mounting operations. It imports a localized prior dataset containing calibration records via the data bus. For each detailed record in this localized prior dataset, the processor extracts its 9-digit standard bill of quantities code sequence and cost amount parameter. Based on the basic extraction rules, it segments character fragments and calculates the distribution density of localized enterprise-defined coding features. The processor calculates the ratio of this distribution density to the preset benchmark density, generating a localized feature drift coefficient. The processor inputs the localized feature drift coefficient into the calculation matrix of the dimension mapping logic to dynamically adjust the classification dimension components and economic value components in the synthesized bill of quantities feature vector. The numerical weights in the process enable the dimension mapping logic to establish a deterministic feature extraction baseline when facing an unknown coding environment.

[0043] After completing the adaptive adjustment of feature weights, the processor uses the calibrated dimension mapping logic to perform pre-parsing on the localized prior dataset, calculates the average clustering radius of the generated list feature vector group in the vector space, and then performs synchronous fine-tuning on the industry correction coefficient α set based on historical engineering sample data based on this average clustering radius. The specific operation logic is as follows: the processor multiplies the industry correction coefficient α by the reciprocal of the localized feature drift coefficient β to obtain the target correction constant applicable to the current database environment. The system replaces the original coefficient with this target correction constant and recalculates the initial mapping threshold of each non-leaf node in the hierarchical data organization space. This on-site pre-deployment calibration process solidifies the discrete feature prior parameters of the specific implementation environment into the computing framework. After eliminating the initial structural differences of different batches of data sources, the data engine enters the normalized operation flow of multi-dimensional topological association reconstruction.

[0044] Example 5: When the system faces a continuously updated heterogeneous cost data stream and node re-clustering causes the cost values ​​of parent and child nodes to become discrete, the system initiates a data consistency verification procedure; for each detailed record in the heterogeneous bill of quantities dataset, the processor generates a bill of quantities feature vector. During the calculation cycle, the storage physical address of the detailed record is converted into a low-level feature vector identifier using a hash mapping algorithm, and the low-level feature vector identifier is injected into the data traceability pointer array. In the hierarchical data organization space aggregation stage, the processor accumulates the cost parameters of all corresponding child nodes under the target non-leaf node to obtain the aggregated cost amount, and extracts the total node cost mapped by the target non-leaf node itself. The processor calculates the difference between the aggregated cost amount and the total node cost to obtain the residual parameter ΔE. The system sets a dynamic tolerance threshold to constrain numerical fluctuations. The dynamic tolerance threshold The value is determined by multiplying the total node cost by the preset system floating-point precision constant γ; the processor compares the absolute value of the residual parameter ΔE with the dynamic tolerance threshold. The magnitude of the value is such that the absolute value of the residual parameter ΔE is greater than the dynamic tolerance threshold. In this state, the system determines that a topological numerical fault has occurred in the target non-leaf node.

[0045] In response to the topological numerical fault state, the processor triggers a reverse penetration calculation mechanism, and the system extracts the data source pointer array bound to the target non-leaf nodes. Traverse the data source pointer array according to the operation logic The internal structure contains all the underlying feature vector identifiers. Based on these identifiers, the system memory is reverse-addressed to the corresponding physical block in the underlying database, thereby retrieving the original cost amount parameters that have not been processed by intermediate layers. The processor then recalculates the algebraic sum of all the retrieved original cost amount parameters and uses the new algebraic sum to overwrite the total cost of the target non-leaf node. The reverse addressing and overwriting process constrains the verification deviation within the hierarchical data organization space to the range of the underlying original cost details. Based on correcting the accumulated errors of intermediate layers and the errors of node mis-attachment, it maintains the mathematical conservation state that the sum of cost amount parameters between global node levels in the project decomposition structure tree is absolutely equal.

[0046] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A method for automatically generating a project decomposition structure tree based on a list encoding, characterized in that, Includes the following steps: Step S1, memory instantiation of hierarchical data organization space; the processor instantiates the standard tree model of project decomposition structure in memory according to the bill of quantities pricing standard, and converts the standard tree model of project decomposition structure into a hierarchical data organization space. By extracting standard node attributes, an initial mapping threshold is set for each non-leaf node of the standard tree model of the project decomposition structure, based on the initial mapping threshold that characterizes the ability of the data partition to correlate discrete cost data. The initial mapping threshold is a preset value based on historical engineering sample data. Step S2: Generate list feature vectors; Obtain the heterogeneous bill of quantities dataset to be processed. For each detailed record, extract the 9-bit standard bill of quantities code sequence and the cost parameter. The processor, based on the preset dimension mapping logic, combines the 9-bit standard bill of quantities code sequence and the cost parameter into a bill of quantities feature vector containing economic value components and classification dimension components. Step S3, adaptive mapping mounting; import the inventory feature vector into the hierarchical data organization space; determine the inventory feature vector by calculating the structured response between the inventory feature vector and the initial mapping threshold. The path of the node to which the project belongs is determined, and heterogeneous data records that do not match the standard code are attached to the node with the highest matching degree in the hierarchical data organization space, generating a project decomposition structure tree.

2. The method for automatically generating a project decomposition structure tree based on list coding according to claim 1, characterized in that, The dimension mapping logic in step S2 includes: extracting hierarchical classification features from the 9-bit standard bill of quantities encoding sequence and constructing the bill of quantities feature vector. The classification dimension components are determined; the cost amount parameter is normalized, and its distribution weight in the global cost flow is calculated as the feature vector of the bill of quantities. The economic value components are calculated; the product of the classification dimension components and the economic value components is generated to produce the list feature vector. .

3. The method for automatically generating a project decomposition structure tree based on list coding according to claim 1, characterized in that, Initial mapping threshold The following methods were used to determine the cost distribution range and coding deviation frequency of each level of node: A large data sample set of historical similar projects was obtained, and the cost distribution range and coding deviation frequency of each node were extracted. Based on the cost distribution range and coding deviation frequency, the association strength of each non-leaf node with the discrete data was calculated using a preset addressing matching function, and this strength was set as the initial mapping threshold. .

4. The method for automatically generating a project decomposition structure tree based on list coding according to claim 3, characterized in that, The calculation logic of the address matching function is as follows: ,in, α is the initial mapping threshold; α is the preset industry correction coefficient; Let be the cost distribution entropy of the i-th child node under this node; This represents the average dispersion of the encoding in historical data.

5. The method for automatically generating a project decomposition structure tree based on list coding according to claim 1, characterized in that, Step S3 also includes a data consistency verification step: when the processor is summarizing the cost at the node, it synchronously appends an array of data source pointers containing the underlying feature vector identifiers to the non-leaf nodes; it utilizes the addressing characteristics of the memory index structure to establish a physical path from the summary value to each list detail record; and it uses an anomaly capture mechanism to identify any initial mapping thresholds that are not hit. The data records are assigned to dynamically generated unclassified nodes, so that the values ​​of all summary nodes in the tree are equal to the sum of the cost parameters of the detailed records of each list under them.

6. The method for automatically generating a project decomposition structure tree based on list coding according to claim 1, characterized in that, The process of determining the path of the node in step S3 adopts a bottom-up recursive aggregation logic: when the list feature vector When a leaf node is matched, the total cost status of the parent node is updated; the parent node dynamically adjusts the initial mapping threshold based on the received vector feedback. The value.

7. The method for automatically generating a project decomposition structure tree based on list coding according to claim 1, characterized in that, The cost parameters extracted in step S2 include unit price, quantity, and total price; the processor analyzes the probability density of the unit price and identifies the cost deviation of the detailed record under the corresponding bill of quantities code; step S3 further includes: applying an initial mapping threshold based on the cost deviation. Perform non-linear weighting to correct the path of the node.

8. The method for automatically generating a project decomposition structure tree based on list coding according to claim 1, characterized in that, The hierarchical data organization space supports the parsing of enterprise-defined codes: when the 9-digit standard bill of quantities code sequence contains enterprise-defined characters, the processor extracts the common substring features of the sequence and superimposes them with the economic value features formed by the cost amount parameters; by calculating the correlation between the common substring features and the node attributes of the standard tree model of the project decomposition structure, the path of the node to which it belongs is determined.

9. The method for automatically generating a project decomposition structure tree based on list coding according to claim 5, characterized in that, After generating the project decomposition structure tree, the method also includes: based on the data traceability pointer array, in response to a query request for a summary node, extracting all underlying list details under the summary node through the physical penetration path, and outputting a management report containing business and economic dimensions.

10. The method for automatically generating a project decomposition structure tree based on list coding according to claim 1, characterized in that, The method also includes a dynamic calibration step for spatial status: the processor monitors the newly added cost data stream in real time, and recalibrates the initial mapping threshold of each node after a preset period based on the success rate of data mounting and addressing time. , to make the initial mapping threshold It remains stable within a preset numerical range to maintain the matching accuracy of the hierarchical data organization space.

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