An AI semantic enhancement unstructured manufacturing document data structuring method

Abstract

The application relates to the technical field of electric digital data processing, and discloses an AI semantic enhancement unstructured manufacturing document data structuring method, which comprises the following steps: obtaining character recognition flow of a target document to extract semantic anchor points and topological coordinates thereof, and vectorizing a field to be structured to generate a to-be-processed vector; querying an engineering logic mapping table to determine an association strength, and calculating a logic gravitational field strength value of the to-be-processed vector relative to the semantic anchor points according to the association strength; and associating the to-be-processed vector to a target semantic anchor point according to the field strength value, and when it is identified that the to-be-processed vector is interfered by a homogeneous semantic anchor point in the association stage, starting a logic polarization program, extracting a bias feature to generate a polarization vector, and modulating the original gravitational field strength. The application solves the attribution ambiguity of similar semantic entities in a dense distribution scene by introducing a logic gravitational constraint, eliminates the structural conflict caused by the decoupling of spatial distribution and engineering logic, and ensures the topological consistency of the data conversion process.

Description

Technical Field

[0001] This invention relates to a method for structuring unstructured manufacturing document data with AI semantic enhancement, belonging to the field of electronic digital data processing technology. Background Technology

[0002] The current digital governance process in the manufacturing industry utilizes optical character recognition combined with natural language processing to extract unstructured document data. This approach, which processes text streams in a regular format, has broad applicability and constitutes the main method for current data preprocessing. Manufacturing documents contain physically directional spatial information and engineering logic features. In multi-station parallel process scenarios, documents contain a large number of engineering parameters with consistent semantic tags and dense arrangement. Conventional data recognition schemes follow the linear extraction logic of text streams, ignoring the coupling relationship between the topological distribution of document pages and the manufacturing logic skeleton. This makes it difficult to determine the physical ownership of homogeneous data entities. The linear recognition method incurs a logical reconstruction cost, causing the data structuring process to face ownership ambiguities and mapping conflicts, leading to the need for manual intervention to correct errors later.

[0003] To address these challenges, the industry has attempted to alleviate the contradictions by increasing the corpus size or improving the model's computational dimensions. However, these linear improvements increase computational load and fail to establish a non-linear relationship between spatial topological coordinates and logical levels at the underlying level. When faced with complex layouts or noise interference, the system's lack of physical background knowledge leads to output results deviating from objective laws, failing to resolve structural conflicts caused by the decoupling of spatial layout and engineering logic. While current industrial data acquisition solutions have made progress in optimizing document scanning hardware parameters and improving the accuracy of basic algorithms, improvements in physical resolution alone are insufficient to address the logical mapping challenges caused by complex topological relationships. Software processing mechanisms and control logic still have shortcomings. For example, the publication number CN119829723A... A Chinese invention patent discloses an AI-driven method for structured storage and retrieval of document data. It constructs a structured question-and-answer database using large-scale language models and vectorized retrieval technology, and uses historical dialogue context to assist in intent recognition. However, it is limited to keyword matching and semantic vector indexing methods in the general natural language processing field. It does not address the deep coupling between the physical location of the manufacturing document space and the engineering logic skeleton. When faced with layouts such as multi-station parallel process tables and high-density engineering parameters, it lacks perception and dynamic adjustment of page visual boundaries, topological distances, and physical dimension constraints. Under conditions such as the determination of the ownership of homogeneous engineering entities and the dislocation of key anchor points across pages, it faces ownership ambiguity and mapping conflicts, making it difficult to ensure that the structured output conforms to the physical reality of the manufacturing site.

[0004] Therefore, how to achieve precise alignment between spatial topological distribution and semantic logical distribution in unstructured manufacturing documents, and establish a data organization mechanism with physical logical calibration capabilities, has become the technical problem to be solved by this invention. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: A method for structuring unstructured manufacturing document data with AI semantic enhancement, comprising the following steps:

[0006] Step 101: Obtain the character recognition stream of the target document, extract keywords with physical attributes by matching with industry dictionary and define them as semantic anchors, and obtain the spatial topological coordinates of each semantic anchor in the coordinate system of the target document page;

[0007] Step 102: Perform feature vectorization processing on the fields to be structured in the target document to obtain a vector to be processed containing text semantic features and positional distribution features;

[0008] Step 103: Determine the logical association strength between the semantic anchor point and the vector to be processed, and calculate the logical gravitational field strength value of the vector to be processed relative to each semantic anchor point based on the Euclidean distance between spatial topological coordinates and the logical association strength.

[0009] Step 104: Associate the vector to be processed with the target semantic anchor point according to the order of logical gravitational field strength values ​​from largest to smallest; during the association process, if the difference between the logical gravitational field strength values ​​of the vector to be processed and multiple homogeneous semantic anchor points is less than the preset polarization threshold, then start the logical polarization procedure.

[0010] Step 105: Extract the logical bias features within the preset neighborhood of the vector to be processed, generate a polarization vector based on the logical bias features and the endogenous engineering correlation of each homogeneous semantic anchor point, and use the polarization vector to perform nonlinear weight offset modulation on the original gravitational field strength value of each homogeneous semantic anchor point to determine the unique affiliation of the vector to be processed.

[0011] Preferably, step 103 further includes: identifying visual dividing lines and background abrupt changes in the target document's page and establishing them as logical impedance boundaries; detecting whether the logical connection between the vector to be processed and the semantic anchor point crosses the logical impedance boundary; if the logical connection crosses the logical impedance boundary, increasing the equivalent logical distance between the vector to be processed and the target semantic anchor point according to the number of logical impedance boundaries crossed, so as to perform attenuation correction of the logical gravitational field strength value.

[0012] Preferably, the steps further include: extracting and caching semantic anchor points of processed pages in the target document and defining them as virtual state features; projecting the virtual state features onto the initial logical position of the current page to be processed, and assigning persistent weights according to the engineering attributes of the virtual state features; calculating the compensated gravitational field strength value of the virtual state features for the vector to be processed in the current page to be processed based on the persistent weights and page number offsets; and fusing the compensated gravitational field strength value with the original gravitational field strength value in the current page to be processed, and performing cross-page logical structured judgment.

[0013] Preferably, in step 105, the step further includes: if the difference in gravitational field strength between the vector to be processed and multiple homogeneous semantic anchors is less than a preset threshold, then the vector to be processed is virtually associated with each homogeneous semantic anchor to construct a corresponding candidate logical state space; the engineering logic consistency entropy value corresponding to each candidate logical state space is obtained; and the homogeneous semantic anchor corresponding to the candidate logical state space with the smallest engineering logic consistency entropy value is determined as the final destination of the vector to be processed.

[0014] Preferably, the steps further include: extracting the recognition confidence residuals corresponding to each field to be structured in the character recognition stream; constructing a convergence damping operator for each vector to be processed based on the recognition confidence residuals; and using the convergence damping operator to modulate the convergence speed of each vector to be processed in the logical gravitational field; wherein, the larger the recognition confidence residual, the stronger the hindering effect of the convergence damping operator on the association of the vector to be processed with the semantic anchor point.

[0015] Preferably, the industry dictionary and engineering logic mapping table are stored as hexadecimal matrices, with pre-set correlation coefficients representing the physical magnitude changes of different manufacturing parameters in the process sequence; in step 103, the processor calls the pre-stored hexadecimal matrix in the memory and determines the logical correlation strength through iterative calculation.

[0016] Preferably, in the logic polarization procedure, the logic for performing nonlinear weighted offset modulation on the native gravitational field strength using the polarization vector satisfies the following formula: ,in, This represents the modulated target gravitational field strength. This represents the original gravitational field strength of the vector to be processed relative to a specific homogeneous semantic anchor point. The preset polarization response coefficients, For the generated polarization vector, The feature vector is used to characterize the degree of engineering logical correlation between the vector to be processed and homogeneous semantic anchors.

[0017] Preferably, after step 104, the steps further include: capturing residual information of the process chain generated during the calculation of the gravitational field of the vector to be processed; when an orphan field without a corresponding semantic anchor is identified in the target document, performing topological matching in the current logical space using the residual information of the process chain; inverting the missing semantic anchor category to which the orphan field belongs based on the matching result, and creating a virtual placeholder anchor to perform logical association compensation for the orphan field.

[0018] Preferably, the steps further include: performing a logical conflict scan on the preliminary structured tree generated in step 104 using preset physical dimension constraints; when a physical dimension conflict is identified, extracting the context features in the neighborhood of the conflict point and performing a secondary path redirection to re-determine the hierarchical affiliation of the vector to be processed.

[0019] Preferably, during the secondary path redirection process, the path that makes the local logical structure tend to be ordered is selected as the final merging path by calculating the local logical structure entropy value of the conflict point under different redirection paths, and an updated memory mapping index is generated.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. In AI-enhanced unstructured manufacturing document data, a gravitational field model based on the semantic anchor point of physical pointing keywords and the field to be processed is established. By using the Euclidean distance of spatial topological coordinates combined with the preset engineering logic coupling coefficient, weight mapping is performed so that discrete fields are automatically merged into the target anchor point with the maximum potential energy under the constraint of gravitational potential energy. This solves the belonging conflict caused by the spatial distribution interference of similar semantic tag data entities in densely arranged scenarios, and realizes the logical transformation of unstructured text flow into hierarchical nested structured tree.

[0022] 2. Construct a boundary impedance modulation mechanism, extracting the visual segmentation line of the page and the background abrupt change as the impedance boundary. When the gravitational field path crosses the impedance boundary, a nonlinear attenuation impedance factor is generated to correct the equivalent logical distance, so that semantic gravity is preferentially transmitted along the low resistance region. This eliminates the risk of false capture caused by the physical proximity and logical partition isolation in complex form layouts, and ensures the topological consistency of data recognition under visual container interference. Establish a state tunnel mechanism, encapsulating the high-confidence semantic anchor points of processed pages into virtual state particles and projecting them to the initial position of subsequent pages. Combine the page number offset and persistent weight to calculate the compensation gravitational field strength, and establish a cross-page dimension semantic association transmission path. This solves the problem of orphan data fragmentation caused by the absence of key anchor points in the current window in long document processing, and maintains the global logical integrity of large-scale data governance within a limited processing window.

[0023] 3. Execute a dynamic self-calibration process based on logical topological consistency potential energy. For logical fields to be arbitrated, simulate and generate multiple candidate attribution paths. Use the engineering evolution matrix to perform entropy value determination, select the path that makes the local logical structure tend towards an ordered state for final merging, and synchronously update the gravitational field strength distribution. Use the inherent closed-loop constraints of the data as the judgment criterion to achieve adaptive correction of highly overlapping semantic features, improving the robustness of the data processing system under extreme conditions. Use physical dimensional constraints to perform logical conflict scanning on the initially generated structured tree, and perform secondary path redirection based on neighborhood context for conflict points. Dynamic arbitration of attribution relationships is achieved through the correlation between logical perturbation and system consistency entropy value, avoiding the semantic collapse risk of traditional static mapping models when facing non-standard documents, and ensuring that the output structured data stream conforms to the logical evolution law. Attached Figure Description

[0024] Figure 1This is a structured processing flowchart of the logical gravitational field and polarization mechanism of the present invention;

[0025] Figure 2 This is a performance comparison curve of the attribution determination accuracy under different signal-to-noise ratio environments of the present invention;

[0026] Figure 3 This is a schematic diagram of the system hardware architecture and data flow of the integrated logic arbitration core of this invention. Detailed Implementation

[0027] This detailed description aims to illustrate the present invention so that those skilled in the art can understand and implement it. The following description is for illustrative purposes only and does not constitute a limitation on the scope of protection of the present invention.

[0028] This invention provides an AI-enhanced method for structuring unstructured manufacturing document data. Operating in an electronic digital data processing environment, it transforms discrete manufacturing document data into a structured tree with hierarchical nesting relationships by constructing a gravitational field model based on semantic anchors. The method includes the following stages: acquiring the character recognition stream of the target document and establishing semantic anchors and their spatial topological coordinates; performing feature vectorization on the fields to be structured to generate vectors to be processed; calculating the logical gravitational field strength value based on the association strength determined by the engineering logic mapping table; performing logical merging based on the field strength value, and initiating a logical polarization program to lock unique attribution when field strength interference is detected; and the processor receiving the character recognition stream of the target document and comparing it with pre-stored data in memory. The industry dictionary performs keyword matching, extracts keywords with physical attributes and defines them as semantic anchors. Semantic anchors are selected from equipment numbers, key part names, or process specification titles. The processor synchronously obtains the spatial topological coordinates of each semantic anchor in the target document page coordinate system, that is, obtains the center point coordinates (x, y) of the bounding rectangle of the character to which the semantic anchor belongs. For the fields to be structured in the document, the system performs feature vectorization processing to obtain a vector to be processed containing text semantic features and positional distribution features. The text semantic features represent the physical quantity category to which the field belongs, while the positional distribution features are established based on the topological position of the field in the page. In this way, the standardized representation of discrete parameter entities in the feature space is achieved.

[0029] Because the engineering parameters in the manufacturing documentation are logically constrained by physical entities, the system constructs a logical gravitational field calculation model. The execution steps are as follows: the processor calls a pre-stored hexadecimal engineering logic mapping table in memory, and a correlation coefficient is set to characterize the physical magnitude changes of different manufacturing parameters in the process timeline. The processor determines the logical correlation strength between semantic anchors and the vector to be processed based on the correlation coefficient. Based on the Euclidean distance between each semantic anchor and the vector to be processed and the logical correlation strength, the processor calculates the logical gravitational field strength value of the vector to be processed relative to each semantic anchor. The processor associates the vector to be processed with the logical level of the target semantic anchor with the highest gravitational potential energy according to the logical gravitational field strength values ​​in descending order. To ensure the determinism of the parameter determination process, this logical correlation strength... The calculations meet the preset calibration procedures, namely, querying the cosine similarity between two feature vectors based on the preset industry association matrix and multiplying it by the corresponding engineering weight coefficient; the processor retrieves the association coefficient from the hexadecimal engineering logic mapping table in memory; the Euclidean distance is calculated based on the spatial coordinates of the vector to be processed and the semantic anchor point; field strength mapping is performed using the gravity calculation operator; the parameter gradient is determined by the physical property directional experiment; if the vector to be processed is located in the overlapping area of ​​multiple anchor points, the system constructs a logical gravitational potential energy map, merges discrete fields into the level of the target anchor point with the maximum gravitational field strength, maps the association coefficient to the hexadecimal encoding position, and updates it in real time by combining the cosine similarity with the preset engineering weight to ensure that the calculation accuracy meets the monotonic evolution law, and completes the logical transformation of unstructured text flow into a hierarchical nested structured tree.

[0030] When processing high-density layouts such as complex equipment parameter tables, if the difference between the logical gravitational field strength values ​​of the vector to be processed and multiple homogeneous semantic anchor points is less than a preset polarization threshold, the processor initiates a logical polarization program. The processor extracts logical bias features within a preset neighborhood of the vector to be processed. These logical bias features include associated workstation numbers or cooling circuit identifiers. The processor generates a polarization vector based on the correlation between these logical bias features and each homogeneous semantic anchor point, and uses this polarization vector to perform nonlinear weighted offset modulation on the original gravitational field strength value. The modulation logic satisfies the following formula: ,in, This represents the modulated target gravitational field strength. This represents the original gravitational field strength of the vector to be processed relative to a specific homogeneous semantic anchor point. To preset the polarization response coefficients, For the generated polarization vector, The feature vector is used to characterize the engineering logical correlation between the vector to be processed and a specific homogeneous semantic anchor point. For visual dividing lines and background abrupt changes in the document page, the system establishes logical impedance boundaries to prevent false capture of cross-partition data. The processor detects whether the logical connection between the vector to be processed and the semantic anchor point crosses the logical impedance boundary. If the logical connection crosses the logical impedance boundary, the processor increases the equivalent logical distance between the vector to be processed and the target semantic anchor point according to the number of logical impedance boundaries crossed, thereby performing attenuation correction on the logical gravity field strength value, so that the semantic gravity flows preferentially along low-resistance areas such as the same cell, ensuring the topological consistency of data recognition under visual interference.

[0031] When processing long industrial documents, a state tunneling mechanism is used to maintain global logical integrity. Semantic anchor points of processed pages are extracted and cached, defined as virtual state features. These virtual state features are projected onto the initial logical position of the current page to be processed (i.e., the starting coordinates of the top-left corner of the page). Persistence weights are assigned based on the engineering attributes of the virtual state features. The processor calculates the compensated gravitational field strength value of the virtual state features for the vectors to be processed within the current page based on this persistent weight and the page offset. This value is then fused with the native gravitational field strength value within the current page, and a cross-page logical attribution determination is performed. This procedure addresses the data fragmentation problem caused by the absence of key anchor points in the current window. The processor encapsulates the semantic anchor points of processed pages through a state tunneling protocol, generating virtual state particles carrying engineering attributes and physical dimensions. These particles are projected onto the initial logical position coordinates of the current page, and the compensated gravitational field strength is calculated. The persistence weight and decay factor are calibrated by long-range correlation experiments recorded in the process. It is the page number offset between the current page and the anchor source page. The system performs gravity fusion operation to solve the anchor point missing fault caused by window switching. The compensation field strength score is synchronously written to the structured tree node register to realize the semantic correlation transmission across page dimensions and maintain the global logical integrity of large-scale data governance. In order to deal with the pseudo gravity field generated by smudged or interfering characters, the system uses the OCR recognition confidence residual to construct a convergence damping operator. The processor extracts the recognition confidence residual corresponding to each field to be structured in the character recognition stream, and modulates the convergence speed of each vector to be processed in the logical gravity field accordingly. The larger the recognition confidence residual, the stronger the obstruction effect of the convergence damping operator on the correlation of the vector to be processed to the semantic anchor point, so that the noisy field cannot be effectively correlated within the calculation iteration cycle, thereby improving the processing efficiency of low-quality document scans.

[0032] After generating a preliminary structured tree, the system performs a logical conflict scan using physical dimension constraints. When a physical dimension conflict is detected, the processor extracts contextual features within the neighborhood of the conflict point and performs secondary path redirection. During secondary path redirection, the processor calculates the local logical structure entropy of the conflict point under different redirection paths, selects the path that makes the local logical structure tend to be ordered as the final merging path, and generates an updated memory mapping index. During secondary path redirection, the processor calls the gradient boundary parameters in the hexadecimal engineering evolution matrix to construct multiple virtual logical branches for the vectors to be processed within the neighborhood of the conflict point and obtains the corresponding physical dimension sequences. The system uses the sum of the absolute differences between adjacent sampling points in the sequence as the discreteness criterion and uses a Gaussian kernel function to separate the discrete components. The degree criterion is converted into a path merging probability distribution. The processor selects the path with the lowest entropy value, i.e., the highest logical chain evolution stability, as the final merging path and writes the determination result to the structured tree node register address to trigger the bus control signal to update the memory mapping index. For the problem of missing key anchor points generated during the digitization of old paper documents, the processor captures the residual information of the process chain generated by the structured entity. When an orphan field without a corresponding semantic anchor point is identified, the processor uses the residual information of the process chain to perform topology matching in the current logical space. Based on the matching result, it inverts the missing semantic anchor point category to which the orphan field belongs and creates a virtual placeholder anchor point to perform logical association compensation, thereby realizing information completion under the condition of physical signal loss. During the system operation, the polarization vector generated by the logical polarization program is used. To provide a high-confidence anchoring benchmark for capturing residual information in the process chain, the processor uses the high-confidence anchoring benchmark to delineate the process path search window in the memory address space, narrowing the topological search radius of the semantic anchor category to which the orphan field belongs. This enables the logical polarization program and the semantic residual exploitation mechanism to produce a synergistic effect when processing high-noise and densely arranged unstructured documents. After completing the inversion, the processor creates virtual placeholder anchors to perform logical association compensation.

[0033] Example 1: In the processing of multi-page process record documents for large CNC machine tools, two sets of technical parameters, namely spindle one and spindle two, are displayed side by side on the page. The structured field, namely the bearing speed value of 1500, is located in the geometric center region of the two sets of semantic anchor points, namely the spindle one number and the spindle two number. This causes the difference in the Euclidean distance of this value relative to different semantic anchor points to be less than the preset polarization threshold. The processor obtains the character recognition stream of the target document and extracts the center point coordinates of the bounding rectangle of the character to which the semantic anchor point belongs. and To establish a logical coordinate system, the processor performs feature vectorization on the structured field to obtain the vector to be processed. It then checks whether the logical connection between this vector and the semantic anchor point crosses the visual segmentation line of the bearing partition. If the logical connection crosses the visual segmentation line of the bearing partition, the processor establishes this visual segmentation line as a logical impedance boundary and calculates the impedance factor based on the number of times it crosses the boundary. The attenuation correction of the logical gravitational field strength value is achieved by increasing the equivalent logical distance; when the corrected logical gravitational field strength value is still within the equilibrium range, the cooling loop identifier in the neighborhood of the vector to be processed is extracted as a logical bias feature to generate a polarization vector. And combined with feature vectors representing the logical correlation of engineering With the preset polarization response coefficient According to the formula Calculate the modulated target gravitational field strength, where, This represents the modulated target gravitational field strength. This represents the native gravitational field strength of the vector to be processed relative to a specific semantic anchor point. The preset polarization response coefficients, For the generated polarization vector, A feature vector that characterizes the degree of engineering logical correlation between the vector to be processed and a specific semantic anchor point.

[0034] During the document cross-page processing stage, the processor extracts the main axis number as a high-confidence semantic anchor point and defines it as the initial logical position for projecting virtual state features onto subsequent pages, based on the page number offset. The compensation gravitational field strength value is calculated by combining persistent weights, so that the orphan fields (instantaneous operating current) located on subsequent pages are constrained by the compensation gravitational field strength value and merged into the logical level corresponding to the main axis. Finally, the processor calls the hexadecimal engineering evolution matrix pre-stored in memory to perform topology consistency scoring on the generated preliminary structured tree. The calculation is performed, and based on the monotonic evolution of rotational speed and current in the physical dimension sequence, the merging path that minimizes the entropy of the local logic structure is selected, resulting in a topology consistency score. The calculation satisfies the formula ,in, The topology consistency score, The current measurement value in the sequence of physical quantities. This represents the expected theoretical value predicted based on the engineering evolution matrix.

[0035] Example 2: In an experimental environment simulating the digital governance of process record documents in discrete manufacturing, the experimental platform was deployed on a computing node equipped with a processor with a main frequency of 2.8 GHz. It simulated the electrical digital data processing process by calling a hexadecimal industry dictionary matrix stored in memory. The experimental data originated from 1000 multi-station parallel processing process records collected by the physical experimental platform. During the generation of this scanned sequence, Gaussian white noise with a signal-to-noise ratio of 20 dB was superimposed, and 5% nonlinear geometric distortion was introduced to simulate scan skew. In the experimental design, the polarization threshold was set to balance the sensitivity of homogeneous anchor point recognition with noise suppression performance. When the polarization threshold was set to 5% of the average value of the original gravitational field strength, the system's response speed to highly symmetrical layouts was in the optimal range. This experiment... The polarization threshold is set to 5%, and the initial value of the persistence weight is set to 0.85. As described in the previous implementation, the processor extracts the spatial topological coordinates (x, y) of the semantic anchor point and performs feature vectorization processing on the structured field to be structured, and then starts the logical gravity field model to perform data mapping. To verify the effectiveness of the method of the present invention under complex working conditions, a multi-dimensional control system consisting of an experimental group, control group A, control group B, and control group C is set up. Among them, control group A adopts a linear extraction method combining character recognition and named entity recognition, control group B removes the logical polarization procedure and state tunneling mechanism, and control group C sets the polarization threshold to 30%. When processing the principal axis parameter table with high symmetry, control group A is observed to produce a belonging conflict, while control group B is due to the lack of polarization vectors. The modulation resulted in a 54.2% deadlock rate in the gravitational equilibrium region. The experimental group then calculated the modulated target gravitational field strength. Achieving a 98.6% accuracy rate in attribution.

[0036] Table 1: Performance Comparison of Sample Groups in Different Scenarios

[0037]

[0038] During gradient validation, the core issue variable, namely the page number offset, is adjusted. Observed when As the score increases from 1 to 5, the topology consistency score of the experimental group decreases due to the time-step decay factor included in the state tunneling mechanism. It exhibits an exponential downward trend, but... The accuracy rate remained above 92.0%, while the control group B, which did not have persistent weights configured, performed worse. Anchor point misalignment occurs, resulting in an accuracy rate of less than 15%. When the recognition confidence residual increases due to document corruption, the converging damping operator's blocking effect on noisy characters is enhanced, causing the convergence speed to decrease.

[0039] Example 3: This example combines Figures 1 to 3 This document describes a method for structuring unstructured manufacturing document data using AI semantic enhancement, such as... Figure 1 As shown, the process begins with the input target document. The system then enters a parallel processing phase. On one hand, it calls an industry dictionary to obtain the character recognition stream to parse the original text data of the document. Then, it locates semantic anchors and their topological coordinates based on physical attribute directional keywords. On the other hand, it performs feature vectorization processing on the structured fields to generate a vector to be processed containing text semantics and positional distribution features. The system calculates the logical gravitational field strength value by combining the association strength determined by the engineering logic mapping table and the Euclidean distance. Based on this field strength value, it performs a field strength difference judgment on homogeneous anchors. If the field strength difference is found to be less than the polarization threshold, the logical polarization program is started and logical bias features are extracted. Based on this, a polarization vector is generated and nonlinear weight offset modulation is performed on the original gravitational field strength to determine the unique assignment based on the maximum field strength. If the field strength difference is not less than the threshold, the vector to be processed is directly associated with the target semantic anchor. Finally, the structured data is output to end the process.

[0040] like Figure 2 As shown, the horizontal axis represents the signal-to-noise ratio (SNR) in dB, and the vertical axis represents the assignment accuracy in %. The chart includes three curves: the experimental group (solid line), control group A (dashed line), and control group B (long dashed line). Observing the data trend, it can be seen that the experimental group maintained a high accuracy rate and showed a steady upward trend as the SNR increased from 10 dB to 40 dB. Control group A had extremely low accuracy in low SNR environments, but its accuracy increased with increasing SNR. Control group B showed an intermediate growth trend between the two. Figure 3 As shown, the system's overall architecture features a field data acquisition terminal on the left, which integrates an industrial PC or scanning workstation, an industrial document scanner, and a data transmission interface. This terminal is responsible for inputting raw document images and transmitting the character recognition stream to the system on the right. The core area of ​​the architecture is the data center computing node, which integrates a high-performance processor. From top to bottom, the computing node deploys a character recognition and feature extraction engine, a logic gravitational field modeling unit, a logic arbitration module as the core processing unit, a logic polarization program, and an internal control bus. This computing node connects to non-volatile memory, which contains a knowledge base and mapping table that includes a hexadecimal industry dictionary, an engineering logic mapping table, and a memory mapping index. After processing through matrix calls and index updates, the computing node outputs structured tree data in JSON / XML format to the right.

[0041] Example 4: In processing a 150-page heavy equipment maintenance manual, the system's computing nodes face an edge condition where the signal-to-noise ratio (SNR) of the scanned document drops from the normal 35dB to 15dB. At this point, the character recognition confidence residual within the document page increases from 0.02 to 0.28, leading to a risk of uncontrolled collapse of the logic gravitational field. The processor monitors the quality indicators of the character recognition stream through the logic arbitration module and initiates an adaptive calibration procedure for the polarization threshold based on the SNR variable. When the average recognition confidence residual of a local page exceeds 0.15, the processor increases the polarization threshold to 0.25 to improve the recognition of homogeneous semantic anchors. Regarding the tolerance for point conflicts, in practice, the processor receives a 16-bit integer character feature sequence, calls the hexadecimal engineering logic mapping table stored in non-volatile memory to perform addressing operations, calculates the logical gravitational field strength value of the vector to be processed relative to each semantic anchor point, and when processing cross-page associations, extracts the homepage sensor group number as a semantic anchor point and projects it to the starting point of the memory address. To address the situation where the homepage anchor point is missing due to page corruption, the processor captures the residual information of the process chain generated by the structured entity and calls the hexadecimal engineering evolution matrix to perform topology matching, calculating the topology consistency score of the virtual logical branches. At that time, the processor compares the physical dimension sequence through a single loop iterative algorithm. When the field to be structured is modulated by the damping coefficient of the semantic friction operator, its convergence path in the gravitational field shows a nonlinear step convergence trend. Experimental measurements show that when the signal-to-noise ratio drops to 15dB and more than 3 key anchor points are missing, the virtual occupant anchor points reconstructed by the system maintain a topological alignment accuracy of 93.4%, and the data orphanage rate is reduced by 78.5% compared with the sample group without the process chain residual utilization mechanism.

[0042] Table 2: Parameter Calibration and Accuracy Records under Different Signal-to-Noise Ratio Gradients

[0043]

[0044] With page number offset Added, the logical arbitration module is based on topology consistency score The gravitational potential energy distribution is corrected, in which, ,in The current measurement value in the sequence of physical quantities. The expected theoretical value is based on the engineering evolution matrix prediction, when the measured value Compared with the expected theoretical value When the deviation fluctuates within a 5% error band, the processor writes the judgment result to the register address corresponding to the structured tree node. When processing complex nested forms, the system identifies the logical impedance boundary generated by the visual segmentation line and introduces an impedance factor. Correcting the equivalent logical distance prevents data from being incorrectly merged between adjacent cells. After the system completes the processing of the entire manual, the processor triggers the bus control signal to update the memory-mapped index, and the generated hierarchical nested structured tree is fixed to the memory partition.

[0045] Example 5: The processor calls the memory address space to write industry dictionary and engineering logic mapping table initialization data. The system extracts 100 standard process card samples to analyze features, calculates the association strength coupling coefficient between each physical entity name and process parameter category, converts it into hexadecimal encoding and stores it in the memory address space. The processor performs clustering on feature vectors with semantic similarity greater than 0.82 in the samples to determine the topological site coordinates in the multidimensional space. Based on the monotonicity of physical parameters in the process time sequence, it assigns gravity weights to the coordinates of each cluster site. The processor calibrates the association coefficients in the mapping table that represent the physical magnitude change law of different manufacturing parameters in the process time sequence. Data testing confirmed that the initial logical gravitational field strength value generated by the vector to be processed had a deviation of less than 4.2% relative to the theoretical expected value. When the system encountered low-resolution scanned document processing conditions, it initiated a pre-calibration procedure. The system retrieved 10 sets of document reference segments containing visual segmentation lines, calculated the recognition confidence residuals at different contrast levels, and determined the dynamic correction step size of the polarization threshold. The processor used a loop iterative algorithm to search for the polarization threshold in the range of 0.05 to 0.30 to improve the tolerance for homogeneous semantic anchor conflicts. Based on the power spectral density of the page background noise, the persistent weight reference point was adjusted to the 0.85 coordinate position to improve the topological consistency score. If the initial calculation deviation is within the preset tolerance range, the processor writes the calibrated parameters into the logic arbitration module configuration register and verifies the impedance factor using test logic connections that cross impedance boundaries. Correction amount, the system's adjustment of page offset under the constraints of this program. The sensitivity to changes was improved by 15.6%, and the final structured tree node register address write action matched the preset memory mapping index.

[0046] In a pre-calibration scenario for the systematic deployment of cross-industry manufacturing documents, the system faces a raw process record data stream containing a mixture of metric and imperial units. The processor initiates a benchmark alignment procedure for physical units to eliminate dimensional deviations in the vectors being processed during gravitational field calculations. Specifically, the processor extracts the numerical bits and corresponding unit descriptor bits from the character recognition stream, and maps the non-standard units to a unified electrical digital processing scale by retrieving a pre-stored hexadecimal dimensional conversion matrix from memory. The normalized computation satisfies the linear transformation relationship. ,in These are the dimensionless eigenvalues ​​after normalization. These are the original measurement values. This is the preset lower limit of the physical quantity range. To set the upper limit of the physical quantity range, the processor writes the transformed dimensionless feature values ​​into the corresponding feature vector dimensions. After completing the dimension alignment, the system initiates a polarization threshold optimization procedure for page layout complexity. The processor retrieves five typical high-density sample tables from the current deployment environment and calculates the local logical structure entropy value, based on the average distribution spacing of homogeneous semantic anchor points in the sample tables. An initial operating point for the polarization threshold is determined. The processor performs a cyclic iterative search in the vicinity of this initial operating point with a step size of 0.01 to obtain the target polarization threshold that makes the deadlock rate less than 0.5%. The target polarization threshold and its corresponding gravity weight coefficient are then fixed to the configuration register partition of the logic arbitration module. Under the constraints of this pre-calibration procedure, when the system processes subsequent manufacturing documents with unknown layouts, it uses the calibrated logical gravity field model to merge different semantic levels. After determining the ownership, the processor triggers the bus control signal and updates the memory mapping index to complete the logical conversion of unstructured documents into hierarchical nested structured trees.

[0047] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0048] Finally, 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.

Claims

1. A method for structuring unstructured manufacturing document data with AI semantic enhancement, characterized in that, Includes the following steps: Step 101: Obtain the character recognition stream of the target document, extract keywords with physical attributes by matching with industry dictionary and define them as semantic anchors, and obtain the spatial topological coordinates of each semantic anchor in the coordinate system of the target document page; Step 102: Perform feature vectorization processing on the fields to be structured in the target document to obtain a vector to be processed containing text semantic features and positional distribution features; Step 103: Determine the logical association strength between the semantic anchor and the vector to be processed, and calculate the logical gravitational field strength value of the vector to be processed relative to each semantic anchor based on the Euclidean distance and logical association strength between each semantic anchor and the vector to be processed. Step 104: Associate the vector to be processed with the target semantic anchor point according to the order of logical gravitational field strength values ​​from largest to smallest; During the association process, if the difference between the logical gravitational field strength values ​​of the vector to be processed and multiple homogeneous semantic anchors is less than the preset polarization threshold, the logical polarization procedure is initiated. Step 105: Extract the logical bias features within the preset neighborhood of the vector to be processed, generate a polarization vector based on the logical bias features and the endogenous engineering correlation of each homogeneous semantic anchor point, and use the polarization vector to perform nonlinear weight offset modulation on the original gravitational field strength value of each homogeneous semantic anchor point to determine the unique affiliation of the vector to be processed.

2. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, Step 103 further includes: identifying visual segmentation lines and background abrupt changes in the target document's page and establishing them as logical impedance boundaries; detecting whether the logical connection between the vector to be processed and the semantic anchor point crosses the logical impedance boundary; if the logical connection crosses the logical impedance boundary, increasing the equivalent logical distance between the vector to be processed and the target semantic anchor point according to the number of logical impedance boundaries crossed, in order to perform attenuation correction of the logical gravitational field strength value.

3. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, The steps also include: extracting and caching semantic anchors of processed pages in the target document and defining them as virtual state features; projecting the virtual state features onto the initial logical position of the current page to be processed and assigning persistence weights according to the engineering attributes of the virtual state features; calculating the compensation gravitational field strength value of the virtual state features for the vector to be processed in the current page to be processed based on the persistence weights and page number offsets; and fusing the compensation gravitational field strength value with the original gravitational field strength value in the current page to be processed and performing cross-page logical structured judgment.

4. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, Also includes: In step 105, if the difference in gravitational field strength between the vector to be processed and multiple homogeneous semantic anchors is less than a preset threshold, the vector to be processed is virtually associated with each homogeneous semantic anchor to construct the corresponding candidate logical state space. Obtain the engineering logic consistency entropy value corresponding to each candidate logic state space; determine the homogeneous semantic anchor point corresponding to the candidate logic state space with the smallest engineering logic consistency entropy value as the final destination of the vector to be processed.

5. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, The steps also include: extracting the recognition confidence residuals corresponding to each field to be structured in the character recognition stream; constructing a convergence damping operator for each vector to be processed based on the recognition confidence residuals; and using the convergence damping operator to modulate the convergence speed of each vector to be processed in the logical gravitational field. The larger the recognition confidence residual, the stronger the hindering effect of the convergence damping operator on the association of the vector to be processed with the semantic anchor point.

6. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, The industry dictionary and engineering logic mapping table are stored as hexadecimal matrices, with pre-set correlation coefficients representing the physical magnitude changes of different manufacturing parameters in the process timing. In step 103, the processor calls the pre-stored hexadecimal matrix in the memory and determines the logical correlation strength through iterative calculation.

7. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, In the logic polarization procedure, the logic for performing nonlinear weighted offset modulation on the native gravitational field strength using the polarization vector satisfies the following formula: ,in, This represents the modulated target gravitational field strength. This represents the original gravitational field strength of the vector to be processed relative to a specific homogeneous semantic anchor point. The preset polarization response coefficients, For the generated polarization vector, The feature vector is used to characterize the degree of engineering logical correlation between the vector to be processed and homogeneous semantic anchors.

8. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, Also includes: After step 104, residual information of the process chain generated during the calculation of the gravitational field is captured; When an orphan field without a corresponding semantic anchor is identified in the target document, topological matching is performed in the current logical space using residual information from the process chain; the missing semantic anchor category to which the orphan field belongs is inverted based on the matching result, and virtual placeholder anchors are created to perform logical association compensation for the orphan field.

9. The method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 1, characterized in that, The steps also include: performing a logical conflict scan on the preliminary structured tree generated in step 104 using preset physical dimension constraints; when a physical dimension conflict is identified, extracting the context features in the neighborhood of the conflict point and performing a secondary path redirection to re-determine the hierarchical affiliation of the vector to be processed.

10. A method for structuring unstructured manufacturing document data with AI semantic enhancement according to claim 9, characterized in that, During the secondary path redirection process, the entropy value of the local logical structure of the conflict point under different redirection paths is calculated. The path that makes the local logical structure tend to be ordered is selected as the final merging path, and an updated memory mapping index is generated.

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