A laboratory original record cloud collaborative batch intelligent maintenance method and system

Abstract

The application discloses a kind of laboratory original record cloud end collaborative batch intelligent maintenance method and system, it is related to experimental data processing technical field.The present application relates to the field of internet and cloud computing data processing technology, the method receives cloud batch Word format original record document, parses bottom layer extensible markup language to extract spatial layout attribute and text key-value pair feature, constructs multidimensional node structure tree;Response maintenance instruction locks the node to be maintained, backtracks hierarchical depth and table header text semantic anchor point extraction topological constraint mapping rule;According to the rule, locate homologous data node across document, execute bottom layer text node assignment cover and reconstruct label rendering closed attribute;Extract update data stream import experimental procedure dependent graph to execute context logical coherence deduction, issue abnormal early warning label, and the document that deduces passes is packaged as compliance review snapshot to execute cloud archiving.The system is used to realize the above method.

Description

Technical Field

[0001] This invention relates to the field of experimental data processing technology, specifically to a cloud-based collaborative batch intelligent maintenance method and system for laboratory raw records. Background Technology

[0002] Laboratory original records are the fundamental data carriers for recording daily scientific research and testing processes. With the digital transformation of testing and inspection services, various research institutions and laboratories generate massive amounts of experimental records every day. These records are usually distributed in laboratory management systems in formats such as Word, containing a large amount of paragraph text and complex table data. In the daily management of original records, changes in parameters or modifications to test content for the same batch of experiments often require extensive maintenance and updating of massive amounts of original records.

[0003] Currently, the management of original laboratory records relies entirely on manual processing and line-by-line maintenance of Word format records. When dealing with massive amounts of records, the overall workflow is extremely cumbersome and inefficient. Furthermore, after each record is maintained, it must be reviewed and proofread manually. Even with existing automated document processing methods, it typically involves simple global text replacement. When processing Word records containing complex tables, this low-level character traversal operation easily severs the start and end tags of the underlying Extensible Markup Language (XML), causing physical damage to the document's rendering structure and rendering it unopenable. Simultaneously, multiple data points in experimental records have strong upstream and downstream process relationships. Existing simple replacement operations only cover isolated nodes, ignoring the coherence of the document's contextual logic. This easily leads to hidden process state conflicts and data chain breaks in the replaced document, resulting in a surge in manual review costs and failing to truly replace the traditional manual single-repair and manual proofreading model. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a cloud-based collaborative batch intelligent maintenance method and system for laboratory original records to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a cloud-based collaborative batch intelligent maintenance method for laboratory raw records, comprising the following steps: S1, receiving batch Word format raw record documents imported from the cloud, parsing the underlying Extensible Markup Language (EXPLAIN) of the documents, extracting the spatial layout attributes of paragraph tags and table cells, as well as text key-value pair features, and constructing a multi-dimensional node structure tree that integrates document layout logic; S2, responding to batch maintenance instructions, locking the target data node to be maintained in the multi-dimensional node structure tree, tracing back the hierarchical depth of the target data node and the associated semantic anchor points of the table header text, and extracting topological constraint mapping rules with relative positional features. Then; S3, perform cross-document structure traversal on batch Word format original record documents according to topological constraint mapping rules, locate the same source data nodes inside each document using relative position features, perform underlying text node assignment overwrite on the same source data nodes, and reconstruct the label rendering closure attribute of the same source data nodes after overwrite; S4, extract the overwritten same source data nodes and text key-value pair features to form an update data stream, import the update data stream into the preset experimental process dependency graph to perform context logic coherence deduction, issue abnormal warning labels to the document positions where process state conflicts occur, and encapsulate the documents that pass the deduction into compliance review snapshots for cloud archiving.

[0006] Prioritizing the reception of batches of Word format raw record documents imported from the cloud, the specific process of parsing the underlying Extensible Markup Language (Extreme Markup Language) of the documents is as follows: intercept the batch of Word format raw record document data streams imported from the cloud, perform data stream unpacking operations, separate the document body markup file containing the core text and the associated style definition markup file; load the document body markup file into the in-memory document object model tree, and extract the namespace declaration dictionary in the in-memory document object model tree; traverse the in-memory document object model tree based on the namespace declaration dictionary, identify and extract the first-level markup nodes representing paragraph structure and the second-level markup nodes representing table structure, strip away redundant spell check tags unrelated to document layout logic, and output the cleaned standard Extensible Markup Language node sequence.

[0007] Prioritizing the extraction of spatial layout attributes and text key-value pair features from paragraph tags and table cells, the specific process for constructing a multi-dimensional node structure tree that integrates document layout logic is as follows: Traverse the Standard Extended Markup Language (SELL) node sequence. When the first-level marker node is read, extract paragraph layout features including alignment and indentation levels. When the second-level marker node is read, extract two-dimensional grid coordinates including row and column numbers. Identify separator identifiers in the paragraph layout features or adjacent cell boundaries in the two-dimensional grid coordinates, and split the text content inside the marker node into attribute key name features and attribute key value features, combining them to generate text key-value pair features. Based on the physical reading order of the SELL node sequence, inject the paragraph layout features, two-dimensional grid coordinates, and text key-value pair features as node attributes into the corresponding marker nodes. Connect each marker node through hierarchical parent-child pointers to generate a multi-dimensional node structure tree.

[0008] Prioritizing the response to batch maintenance commands, the specific process of locking the target data node to be maintained in the multidimensional node structure tree, and backtracking the hierarchical depth of the target data node and the associated semantic anchor point of the table header text is as follows: Parse the target entity identifier carried by the batch maintenance command, perform a preorder traversal in the multidimensional node structure tree, match nodes containing the target entity identifier and mark them as target data nodes to be maintained; trace back from bottom to top along the parent-child association pointers of the multidimensional node structure tree, record the number of association pointers traversed by the target data node to reach the root node, and generate the hierarchical depth; within the range of the second-level marked nodes where the target data node is located, perform a reverse scan in the direction of the top-level row sequence number, extract the attribute key name features in the text key-value pair features associated with the top-level row, and determine the attribute key name features as the semantic anchor point of the table header text.

[0009] Firstly, the specific process for extracting topological constraint mapping rules with relative positional features is as follows: Obtain the target data node's own column sequence number in the two-dimensional grid coordinates, align and verify the alignment of the target data node's own column sequence number with the column sequence number of the semantic anchor point in the header text, and generate a relative offset vector of cell position within the same vertical column range; encapsulate the semantic anchor point in the header text, the hierarchy depth, and the relative offset vector of cell position to construct a structured addressing path; bind and configure the structured addressing path with the target replacement value in the batch maintenance instruction to generate topological constraint mapping rules.

[0010] Prioritizing the cross-document structure traversal of batch Word format raw record documents based on topological constraint mapping rules, the specific process of locating the same source data nodes within each document using relative position features is as follows: Extract the structured addressing path from the topological constraint mapping rules, and construct a parallel node structure tree in the unprocessed documents of the batch Word format raw record documents; scan and lock the mapping reference coordinate node in the parallel node structure tree based on the hierarchical depth and semantic anchor point of the header text in the structured addressing path; apply the relative offset vector of the cell position in the structured addressing path to the mapping reference coordinate node, pointing to the target grid coordinate in the parallel node structure tree, extract the underlying extensible markup language elements occupying the target grid coordinate, and establish the underlying extensible markup language elements as the same source data nodes.

[0011] The specific process of performing underlying text node assignment overriding on source data nodes and reconstructing the label rendering closure attribute of source data nodes after overriding is as follows: Separate the outer wrapping tag sequence and the inner character carrier sequence in the source data nodes, and remove the original text traces remaining in the inner character carrier sequence; extract the target replacement value in the topological constraint mapping rule, inject the target replacement value into the inner character carrier sequence, and generate the updated character carrier sequence; detect the start tag segment and the end tag segment in the outer wrapping tag sequence, embed the updated character carrier sequence between the start tag segment and the end tag segment, verify the pairing integrity code of the start tag segment and the end tag segment, generate a legal extensible markup language code block with label rendering closure attribute, and complete the underlying text node assignment overriding and label rendering closure attribute reconstruction.

[0012] Firstly, the updated data stream is formed by extracting the covered source data nodes and text key-value pair features. The specific process of importing the updated data stream into the pre-set experimental process dependency graph to perform contextual logical coherence deduction is as follows: The business state attributes carried by the covered source data nodes and the attribute key name features in the text key-value pair features are concatenated to construct an updated data stream containing process stage identifiers and parameter entity identifiers; the updated data stream is mapped to the input port of the experimental process dependency graph, and downstream pre-dependency verification nodes are activated along the directed edges of the experimental process dependency graph; the business flow state constraint set built into the downstream pre-dependency verification node is extracted, and the process stage identifier in the updated data stream is compared with the admission state sequence contained in the business flow state constraint set, outputting the contextual logical coherence deduction result representing the state matching degree.

[0013] Prioritizing the issuance of anomaly warning tags to document locations where process status conflicts occur, the specific process of encapsulating the deduced documents into compliance review snapshots and performing cloud archiving is as follows: Capture erroneous routing nodes where the contextual logic coherence deduction results present exclusion vectors; forcibly insert anomaly warning tags containing conflict type codes into the original tagging level of the document corresponding to the erroneous routing node; extract legitimate documents where the contextual logic coherence deduction results present fusion vectors; lock the underlying extensible markup language structure of the legitimate documents; inject irreversible operation timestamp streams and user digital signature chains; compress the legitimate documents injected with operation timestamp streams and user digital signature chains into read-only encrypted data packets, generate compliance review snapshots, and call the cloud distributed storage interface to synchronously persist the compliance review snapshots to disk.

[0014] A cloud-based collaborative batch intelligent maintenance system for laboratory raw records, used to execute the aforementioned cloud-based collaborative batch intelligent maintenance method for laboratory raw records, includes: a document parsing module, used to receive batch Word format raw record documents imported from the cloud, parse the underlying Extensible Markup Language (XML) of the documents, extract the spatial layout attributes of paragraph tags and table cells, as well as text key-value pair features, and construct a multi-dimensional node structure tree integrating document layout logic; and a rule extraction module, used to respond to batch maintenance instructions, lock the target data node to be maintained in the multi-dimensional node structure tree, trace back the hierarchical depth of the target data node and the associated semantic anchor points of the table header text, and extract topological constraints with relative positional features. The system includes a mapping rule and a node reconstruction module. The node reconstruction module performs cross-document structure traversal on batch Word format original record documents according to topological constraint mapping rules. It uses relative position features to locate the same source data nodes within each document, performs underlying text node assignment overwriting on the same source data nodes, and reconstructs the label rendering closure attributes of the same source data nodes after overwriting. The logic deduction module extracts the overwritten same source data nodes and text key-value pair features to form an update data stream. It imports the update data stream into a preset experimental process dependency graph to perform context logic coherence deduction, issues abnormal warning tags to the document locations where process state conflicts occur, and encapsulates the deduced documents into compliance review snapshots for cloud archiving.

[0015] The technical effects and advantages of this invention are as follows: (1) In the technical solution described in this invention, a batch of original Word format record documents imported from the cloud are received, the underlying extensible markup language of the documents is parsed, the spatial layout attributes of paragraph tags and table cells and the text key-value pair features are extracted, and a multi-dimensional node structure tree is constructed; in response to the batch maintenance command, the target data node to be maintained is locked, and the hierarchical depth of the target data node and the semantic anchor points of the associated table header text are traced back to extract the topological constraint mapping rules. Thus, relying on the intelligent parsing capability, the unstructured document layout is converted into machine-readable structured spatial topological features, overcoming the defect of conventional text retrieval in complex tables where the location is disordered, effectively replacing the cumbersome process of traditional manual single-document lookup and location, and improving the locking accuracy of the same source target data.

[0016] (2) In the technical solution described in this invention, the source data nodes within each document are located by traversing the document structure according to the topological constraint mapping rules. The source data nodes are assigned values ​​to the underlying text nodes and the label rendering closure attributes are reconstructed. The updated data stream is extracted and imported into the preset experimental process dependency graph to perform context logic coherence deduction. Anomaly warning labels are issued and the documents that pass the deduction are packaged into review snapshots for cloud archiving. As a result, high-intensity batch automated maintenance is achieved. The label closure state is automatically repaired when the underlying data is overwritten to avoid document damage. At the same time, the intelligent verification capability of the graph deduction replaces the lengthy steps of manual review and verification, accurately identifies hidden context state conflicts, and greatly improves the overall work efficiency and data consistency of massive record maintenance.

[0017] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] Figure 1 This is a flowchart of a cloud-based collaborative batch intelligent maintenance method for laboratory original records according to the present invention; Figure 2 This is a schematic diagram of the probability density distribution of the character sequence splitting boundary in an embodiment of the present invention; Figure 3 This is a 3D scatter diagram illustrating the multidimensional influencing factors of logic state adaptability in an embodiment of the present invention. Figure 4 This is a flowchart of a cloud-based collaborative batch intelligent maintenance system for laboratory original records according to the present invention. Detailed Implementation

[0019] This application provides a cloud-based collaborative batch intelligent maintenance method and system for laboratory raw records. It solves the problems of cumbersome and inefficient workflow caused by relying on manual single-file processing and line-by-line maintenance of Word format raw records, as well as the problems of existing simple replacement methods easily damaging the underlying document structure and causing contextual logic conflicts.

[0020] The overall approach of the scheme in this application embodiment is as follows: Receive batches of original Word format record documents; parse the underlying Extensible Markup Language (EXPLAIN) of the documents and extract spatial layout attributes and text key-value pair features to construct a multi-dimensional node structure tree; lock the target data nodes to be maintained, trace back the hierarchical depth and related header text semantic anchors to extract topological constraint mapping rules; based on the topological constraint mapping rules, locate the same source data nodes across documents, perform underlying text node assignment overlay, and reconstruct the label rendering closure attribute after overlay; finally, import the updated data stream into a pre-set experimental process dependency graph to perform contextual logic coherence deduction, issue abnormal warning tags to the document locations where process state conflicts occur, and encapsulate the deduced documents as review snapshots for cloud archiving.

[0021] Example 1; please refer to Figure 1 This invention provides a technical solution: a cloud-based collaborative batch intelligent maintenance method for laboratory raw records, comprising the following steps: S1, receiving batch Word format raw record documents imported from the cloud, parsing the underlying Extensible Markup Language of the documents, extracting the spatial layout attributes of paragraph tags and table cells as well as text key-value pair features, and constructing a multi-dimensional node structure tree that integrates document layout logic; S2, responding to batch maintenance instructions, locking the target data node to be maintained in the multi-dimensional node structure tree, tracing back the hierarchical depth of the target data node and the associated semantic anchor points of the table header text, and extracting topological constraint mapping rules with relative position features; S3. Based on the topological constraint mapping rules, perform cross-document structure traversal on batch Word format original record documents, locate the same source data nodes within each document using relative position features, assign values ​​to the underlying text nodes of the same source data nodes to cover them, and reconstruct the label rendering closure attributes of the same source data nodes after covering them; S4. Extract the covered same source data nodes and text key-value pair features to form an update data stream, import the update data stream into a pre-set experimental process dependency graph to perform context logic coherence deduction, issue abnormal warning labels to the document locations where process state conflicts occur, and encapsulate the documents that pass the deduction into compliance review snapshots for cloud archiving.

[0022] In this implementation scheme, step S1 mainly performs the low-level decomposition and object-oriented modeling of unstructured documents. The underlying Extensible Markup Language (XML) refers to the XML code structure of a Word document at the file level, containing various markup symbols that constitute text, paragraphs, and tables. The system intercepts and unpacks these document data streams, reads the XML markup nodes representing paragraph and table levels, and records their physical arrangement and nesting relationships within the document layout. Text key-value pair features refer to the common data representation format in laboratory records where attribute names and values ​​are bound together, such as a direct combination of test item names and corresponding measurement result values. The system uses the extracted spatial layout attributes and text key-value pairs as node attributes, and connects all unpacked markup nodes using parent-child association pointers to generate a multi-dimensional node structure tree that reflects the original physical layout and data hierarchy of the document.

[0023] Step S2 is primarily used to establish the relative physical location addressing path of the target maintenance data within the document. The semantic anchor point of the header text refers to the title text in a complex table structure that has a direct correspondence with the target value in the same column or row, providing a semantic-level positioning benchmark. When the system receives an instruction to modify specific data, it will perform a preorder traversal of the multidimensional node structure tree to find the specific node corresponding to that data. Subsequently, the system traces upwards along the pointers in the structure tree, recording the number of marked node levels the node has traversed from the root node, and then scans backwards to obtain the header text to which it belongs. The system encapsulates and combines the recorded level depth parameters with the header text information to generate topological constraint mapping rules. Topological constraint mapping rules are essentially a relative coordinate addressing sequence unaffected by overall document page number offsets or word count changes, used to guide the precise location of data with the same attributes in similar documents with different layouts.

[0024] Step S3 is responsible for precise data replacement and underlying structure repair across documents. Same-source data nodes refer to data units in experimental record documents from different batches or samples that bear the same business attributes and have consistent relative topological coordinates. The system traverses unprocessed documents carrying topological constraint mapping rules, finding the underlying elements that need to be uniformly replaced according to the same hierarchical depth and header anchor points. When performing underlying text node assignment overwrite, the system removes the original character sequence and injects new data carried by the instruction. Tag rendering closure attributes refer to the matching state of start and end tags that must appear in strict pairs in Extensible Markup Language syntax. After injecting new data, the system regenerates a complete outer wrapper tag sequence to fill in any truncated syntax nodes, ensuring that the replacement operation does not damage the underlying tag structure of the Word document and that the maintained document can be correctly parsed and formatted by a conventional text rendering engine.

[0025] Step S4 executes the business logic linkage verification and anti-tampering solidification of the modified underlying data. The experimental procedure dependency graph is a pre-constructed directed network model that internally maps the data flow sequence and parameter constraints between various pre-operations and post-calculation steps in the laboratory. The system concatenates and encapsulates the modified source data nodes and their adjacent key-value pairs to form an updated data stream, which is then input into the corresponding state node of the graph. Contextual logic coherence deduction refers to the system tracing the directed edges of the graph to verify whether the update and overlay of the current data causes a contradiction in the business flow constraints of downstream strongly dependent experimental parameters. If the comparison finds that the modification operation causes a break in the procedure state or parameter mismatch, the system will insert an anomaly warning tag containing the conflict type at the corresponding document location. For documents with completely valid deduction, the system extracts its underlying code structure and injects an operation timestamp stream and user digital signature chain for read-only encryption, generates a compliance review snapshot, and calls the distributed storage interface to synchronously write it to disk, forming an irreversible electronic certificate for subsequent business traceability and external audit.

[0026] Specifically, the process of receiving batches of Word format raw record documents imported from the cloud and parsing the underlying Extensible Markup Language (Extreme Markup Language) of the documents is as follows: intercept the batch of Word format raw record document data streams from the cloud, perform data stream unpacking operations, and separate the document body markup file containing the core text and the associated style definition markup file; load the document body markup file into the in-memory document object model tree, and extract the namespace declaration dictionary in the in-memory document object model tree; traverse the in-memory document object model tree based on the namespace declaration dictionary, identify and extract the first-level markup nodes representing paragraph structure and the second-level markup nodes representing table structure, strip away redundant spell check tags unrelated to document layout logic, and output the cleaned standard Extreme Markup Language node sequence.

[0027] In this implementation, the system first uses a decompression algorithm to restore the archive file with the .docx extension into a binary file directory, and then performs low-level byte stream reading on the document.xml file within the directory. Upon loading into memory, the system invokes the DOM parsing engine to scan the XML byte stream and construct a native DOM tree structure consisting of node identifiers, parent node references, and a set of child nodes. It extracts the namespace declaration dictionary, specifically reading the Uniform Resource Identifier (URI) address mapping table bound to the xmlns prefix in the root node of the native DOM tree. When traversing the native DOM tree, for each traversed node x, the system calculates its structural validity determination operator. : ;in, This represents the paragraph tag matching function, which takes a value of 1 when the node name combined with the namespace declaration dictionary matches the first-level tag, and a value of 0 otherwise. This represents the table label matching function, which takes a value of 1 when the node name matches a second-level label, and 0 otherwise. This represents the redundant label matching function, which takes a value of 1 when the node belongs to spell check, syntax error, or revision record identifier, and 0 otherwise. This indicates that the paragraph's basic weight is retained; Indicates the basic weights retained in the table; This represents the redundancy removal penalty coefficient. To ensure that redundant tags unrelated to the layout logic are absolutely removed and to prevent misalignment in subsequent layout parsing, the rules for establishing the redundancy removal penalty coefficient satisfy the following: The criterion for judgment is: when When the node is pushed into the standard extended markup language node sequence allocated in memory; when When the node is in a critical state, the reference pointer between the node and its parent node is severed, and the memory space it occupies is released directly.

[0028] Specifically, the process of extracting the spatial layout attributes of paragraph tags and table cells, as well as text key-value pair features, to construct a multi-dimensional node structure tree that integrates document layout logic is as follows: Traverse the Standard Extended Markup Language (SELL) node sequence. When the first-level tag node is read, extract the paragraph layout features including alignment and indentation levels. When the second-level tag node is read, extract the two-dimensional grid coordinates including row and column numbers. Identify the separator identifiers in the paragraph layout features or the adjacent cell boundaries in the two-dimensional grid coordinates, and split the text content inside the tag node into attribute key name features and attribute key value features, combining them to generate text key-value pair features. Based on the physical reading order of the SELL node sequence, inject the paragraph layout features, two-dimensional grid coordinates, and text key-value pair features as node attributes into the corresponding tag nodes. Connect each tag node through hierarchical parent-child pointers to generate a multi-dimensional node structure tree.

[0029] In this implementation, the system reads the Standard Extended Markup Language (SELL) node sequence in a depth-first traversal order from root to leaf. For first-level marker nodes, the alignment enumeration value and indentation point value are parsed from their internal sub-attribute tags. For second-level marker nodes, the system maintains two integer iterators, incrementing them as the table row and cell markers are traversed to obtain the current horizontal and vertical coordinates of the two-dimensional grid. During key-value pair splitting, the continuous character sequence contained in the marker nodes within the cells is extracted. For each character position n in the sequence, calculate the probability density of the split boundary. ;in, Represents the discrete symbol matching function, if If the symbol is a colon or tab, output a specific prior positive value; otherwise, output 0. Character The corresponding consecutive whitespace placeholder width value; N represents the total length of the character sequence, ; This represents the offset constant of the expected split center position; Indicates the symbolic characteristic activation coefficient; Indicates the activation coefficient of white space spacing; This represents the splitting edge penalty factor. The feature coefficients are established as follows: The value is the absolute confidence constant preset by the dictionary. The value increases linearly with the cumulative number of adjacent whitespace characters. Please refer to [link / reference]. Figure 2 , Figure 2 This diagram illustrates the distribution of probability density variations at character sequence splitting boundaries. The horizontal axis corresponds to the character position index within a continuous character sequence, while the vertical axis corresponds to the splitting boundary probability density value calculated using the aforementioned formula. The black line in the diagram represents the combined influence of typographical physical features such as punctuation and whitespace on the probability density calculation results as the character position index changes. To filter out abnormal disturbances, the system sets a horizontal dynamic splitting threshold baseline in the diagram. By traversing the curve, the system can accurately locate the highest peak of probability density among the peaks exceeding this dynamic splitting threshold baseline. This peak is marked with a solid label in the diagram as a pre-selected splitting point, representing the optimal physical cutoff position between the attribute key name and attribute key value. The system selects the character sequence index position that achieves the highest probability density as the pre-selected splitting point. Set a dynamic splitting threshold. ,when At that time, the character sequence is moved along The system first segments the text, then encapsulates the left-side character fragment as an attribute key name feature and the right-side character fragment as an attribute key value feature. Subsequently, the system instantiates a multidimensional structure node object in memory, serializes the corresponding paragraph formatting features, two-dimensional grid coordinates, and the separated text key-value pair features, and writes them into the object's attribute stack. It then extracts the original Standard Extended Markup Language (SEL) hierarchical nesting information and injects the memory address of the current multidimensional structure node object into the child node pointer array of its direct parent node, thus completing the hierarchical connection of the multidimensional node structure tree.

[0030] Specifically, in response to a batch maintenance command, the process of locking the target data node to be maintained in the multidimensional node structure tree, and tracing back the hierarchical depth of the target data node and the associated semantic anchor point of the table header text is as follows: Parse the target entity identifier carried by the batch maintenance command; perform a preorder traversal in the multidimensional node structure tree to match nodes containing the target entity identifier and mark them as target data nodes to be maintained; trace back from bottom to top along the parent-child association pointers of the multidimensional node structure tree, record the number of association pointers traversed by the target data node to reach the root node, and generate the hierarchical depth; within the range of the second-level marked nodes where the target data node is located, perform a reverse scan in the direction of the top-level row sequence number, extract the attribute key name features from the text key-value pair features associated with the top-level row, and determine the attribute key name features as the semantic anchor point of the table header text.

[0031] In this implementation scheme, the system reads the batch maintenance instruction data packet through a parsing engine and extracts the target entity identifier string representing the modification intention. Then, it initiates a preorder traversal pointer in the multidimensional node structure tree, comparing the text key-value pair feature sequences of each node. When an attribute key value matching the target entity identifier is found, the marker node instance where the current pointer is located is locked as the target data node to be maintained. During the generation of hierarchical depth, the system activates the upward tracing mode of parent-child association pointers to define the target data node. For the initial addressing layer Read the parent node layer by layer The memory address is used to calculate the distance to the root node. Hierarchical depth scalar : Where k represents the tracing level index; K represents the total nesting level of the multidimensional node structure tree; This represents the effective connection distance between adjacent parent and child nodes. It takes a value of 1 if physical memory mapping exists, and 0 otherwise. After determining the depth, the system reads the second-level marker node to which the target data node belongs to obtain the current row sequence number. Then, a descending scan is performed towards the top-level row sequence number. For each candidate row sequence number q on the reverse scan path, the structural semantic confidence of the candidate row as the table header is calculated. ;in, This indicates the bolding feature of the text within candidate line q, with 1 for bold and 0 for non-bold text. The logical state scalar indicates that candidate row q contains horizontally merged cells; it is 1 for merged cells and 0 otherwise. Indicates the confidence weight for bold font; Indicates the confidence weight for cell merging; This represents the line spacing attenuation coefficient. The confidence weights are established as follows: and The rendering cardinality for global heading styles parsed after reading the style definition markup file associated with the document. The value is the reciprocal of the total number of rows in the current table. The system selects the row sequence number that maximizes the structural semantic confidence as the target header row, extracts the corresponding attribute key name features within the same vertical grid coordinates of that row, and binds the memory references containing the attribute key name features as semantic anchors for the header text.

[0032] Specifically, the process of extracting topological constraint mapping rules with relative positional features is as follows: Obtain the target data node's own column sequence number in the two-dimensional grid coordinates; align and verify the alignment of the target data node's own column sequence number with the column sequence number of the semantic anchor point in the header text to generate a relative offset vector of cell position within the same vertical column range; encapsulate the semantic anchor point of the header text, the hierarchy depth, and the relative offset vector of cell position to construct a structured addressing path; bind and configure the structured addressing path with the target replacement value in the batch maintenance instruction to generate topological constraint mapping rules.

[0033] In this implementation scheme, the system obtains the column sequence number range of the target data node in the two-dimensional grid coordinates. And extract the reference column sequence number range where the semantic anchor points of the aforementioned header text are located. In a table structure with cross-column merging, the system verifies the alignment of its own column serial number with the serial number of the header reference column and calculates the vertical projection overlap. : ;in, Indicates the sequence number of the right boundary of the overlapping projection interval; Indicates the sequence number of the left boundary of the projected overlapping interval. Sets the alignment pass constant. The value is set to 0.5, which is the lower limit of the standard cell column width ratio in the current table. At this time, it is confirmed that the target data node and the semantic anchor point of the table header text are within the same vertical column range, and then the relative offset vector of the cell position is calculated. : ;in, Represents the relative span coordinates along the row direction; Represents the relative span coordinates along the column direction; This represents the target header row sequence number for achieving the maximum structural semantic confidence. After obtaining the offset vector, the system allocates a contiguous memory buffer and sequentially writes the hash mapping code of the header text semantic anchor point and the hierarchical depth scalar in binary serialization format. and the relative offset vector of cell position The above parameters are encapsulated into a structured addressing path object instance. The system establishes a key-value mapping relationship in the memory dictionary, using the structured addressing path object as the lookup key and the target replacement value byte stream extracted from the batch maintenance instructions as the binding value. The two are combined to generate the final topological constraint mapping rule, which serves as the sole execution basis for subsequent cross-document structure traversal stages for location addressing and assignment of underlying nodes.

[0034] Specifically, the process of performing cross-document structure traversal on a batch of Word format raw record documents based on topological constraint mapping rules and locating the same source data nodes within each document using relative position features is as follows: Extract the structured addressing path from the topological constraint mapping rules and construct a parallel node structure tree in the unprocessed documents of the batch of Word format raw record documents; scan and lock the mapping reference coordinate node in the parallel node structure tree based on the hierarchical depth and semantic anchor point of the header text in the structured addressing path; apply the relative offset vector of the cell position in the structured addressing path to the mapping reference coordinate node, pointing to the target grid coordinate in the parallel node structure tree; extract the underlying extensible markup language elements occupying the target grid coordinates; and establish the underlying extensible markup language elements as the same source data nodes.

[0035] In this implementation, the system instantiates a Document Object Model (DOM) parser for each batch of unprocessed original record document copies in memory, reads its underlying XML tree structure, and constructs a parallel node structure tree in a separate thread. The system extracts the structured addressing path encapsulated within the topological constraint mapping rules, parsing out the baseline level depth constant and the target header string. Within the parallel node structure tree, the system initiates a depth-first traversal pointer, calculating the baseline coordinate mapping fit for each candidate node v encountered within a two-dimensional mesh container. : Where v represents the traversal index of the candidate labeled node in the parallel node structure tree; This represents the equivalent logical value of the tree hierarchy depth structure. It is 1 when the depth of the memory hierarchy where the candidate node v is located is equal to the base hierarchy depth constant, and 0 otherwise. This represents the string edit distance similarity function, used to output the matching ratio between the text fragment inside the candidate node and the target header string; This represents the physical memory address block offset penalty, indicating the physical step size by which the current scan node deviates from the regular header sector of the document object model; Indicates the priority of structural adaptation weights; Indicates the text semantic adaptation weight; This represents the memory offset suppression coefficient. The weights are determined according to the following rules: Set the structural baseline value to 1.0, which is non-degradable. The values ​​are linearly selected based on the reciprocal of the total length of the target table header string. The system dynamically determines the percentage of the total memory block capacity allocated to the current parallel node structure tree. It selects the node with the highest baseline coordinate mapping fit and locks it to its two-dimensional grid coordinate vector. This involves mapping the reference coordinate node. The system then reads the relative offset vector of the cell position in the address path. The system applies this displacement vector to the mapped reference coordinate node to perform a vector space translation, pointing towards the generated target mesh coordinates. The system directly extracts the occupied DOM using the DOM addressing interface. The underlying Extensible Markup Language (XML) element block of the coordinate region is identified as the same source data node to be replaced. .

[0036] Specifically, the process of performing underlying text node assignment overriding on source data nodes and reconstructing the label rendering closure attribute of source data nodes after overriding is as follows: Separate the outer wrapping tag sequence and the inner character carrier sequence in the source data nodes, and remove the original text traces remaining in the inner character carrier sequence; extract the target replacement value in the topological constraint mapping rule, inject the target replacement value into the inner character carrier sequence, and generate the updated character carrier sequence; detect the start tag segment and the end tag segment in the outer wrapping tag sequence, embed the updated character carrier sequence between the start tag segment and the end tag segment, verify the pairing integrity code of the start tag segment and the end tag segment, generate a legal extensible markup language code block with label rendering closure attribute, and complete the underlying text node assignment overriding and label rendering closure attribute reconstruction.

[0037] In this implementation plan, the system targets data nodes from the same source. Perform string stream splitting based on regular expressions, and according to the angle bracket closure feature of Extensible Markup Language (XML) syntax, decompose data nodes from the same source into starting tag segments with attribute descriptions. Termination tag segment And the internal character carrier sequence enclosed between the two. The system resets and overwrites the memory pointer of the internal character carrier sequence with a null character, thereby completely removing any remaining original detection data and typesetting whitespace traces. Subsequently, the system unpacks the batch maintenance instructions, extracts the hexadecimal byte stream of the target replacement value, performs UTF-8 encoding conversion on it, and injects it into the memory stack area corresponding to the internal character carrier sequence, generating the updated character carrier sequence in a detached state. During the embedding phase, code segment syntax concatenation verification is performed, and the tag rendering closure integrity of the replaced paragraph is calculated. : ;in, This represents a Boolean function for tag symmetry verification, which the system then discards. After the additional attribute string, if the start tag name and the end tag name are exactly the same, the value is 1; otherwise, the value is 0. This indicates the proportion of the printed characters in the sequence distribution, and the output is... The ratio of the number of regular characters to the total number of characters; This parameter represents the frequency of detection of unescaped reserved characters, recording the number of XML reserved symbols that were not correctly escaped in the updated sequence; Indicates the label symmetry multiplier; Indicates the coding normality multiplier; This represents the grammatical violation penalty multiplier. The method for determining the verification parameters is as follows: Forced to set the system syntax blocking baseline value to 10.0. Based on the current operating system's default encoding standard, the matching rate ranges from 0.5 to 1.0. Set the absolute veto constant to 100.0. The system sets a safe merging threshold. When the judgment At that time, the system calls the underlying string concatenation function to concatenate the starting tag segment. Updated character carrier sequence and termination tag segment The continuous permutation and merging process is performed in memory to generate an Extensible Markup Language (EXPLAIN) code block with physical layout parsing validity. The memory address of this code block is then used to overwrite the mounting pointers of the original data nodes in the parallel node structure tree, thus completing the assignment overwriting and closed attribute reconstruction.

[0038] Specifically, the process of extracting the covered source data nodes and text key-value pair features to form an update data stream, and importing the update data stream into a pre-set experimental process dependency graph to perform contextual logical coherence deduction is as follows: The business state attributes carried by the covered source data nodes and the attribute key name features in the text key-value pair features are concatenated to construct an update data stream containing process stage identifiers and parameter entity identifiers; the update data stream is mapped to the input port of the experimental process dependency graph, and downstream pre-dependency verification nodes are activated along the directed edges of the experimental process dependency graph; the business flow state constraint set built into the downstream pre-dependency verification node is extracted, and the process stage identifier in the update data stream is compared with the admission state sequence contained in the business flow state constraint set, outputting the contextual logical coherence deduction result representing the state matching degree.

[0039] In this implementation, the system reads the verification status attribute string recorded in the overwritten source data nodes, concatenates it with the attribute key name in the text key-value pair feature using an underlying memory pointer, and instantiates it into a standard update data stream object. The system loads this update data stream object into the directed acyclic graph data structure of the experimental process dependency graph, locates the input port in the graph using the attribute key name as an index, and then calls the graph traversal algorithm along the directed edges to activate the downstream pre-dependency verification nodes. For each downstream pre-dependency verification node, the system extracts its built-in business flow state constraint set and calculates the logical state fit matrix of the update data stream. : Where J represents the total number of constraint states in the admission state sequence; j represents the discrete position index of the constraint state. This represents the process stage identifier string parsed and extracted from the update data stream; This represents the j-th standard admission state element in the business flow status constraint set; This function determines whether the state semantics are equal. It outputs 1 when the input identifier is completely equal to the character code of the standard state, and outputs 0 otherwise. This represents the process timing compliance verification function, when The bound operation timestamp is strictly later than Output 1 if the completion timestamp is reached, otherwise output 0; This represents the graph topological physical distance between the current node of the updated data stream and the downstream preceding dependent verification node; Indicates strict matching weights for state semantics; Indicates the compliance weight of time sequence flow; This represents the topological distance attenuation coefficient. The method for determining the above weights is as follows: the system reads the attributes of the graph nodes; when a node belongs to a mandatory verification process... and All were assigned the value 0.5. The dynamic value is the reciprocal of the total number of directed edges in the experimental procedure dependency graph. Please refer to [link / reference]. Figure 3 , Figure 3This diagram illustrates the 3D state clustering distribution mapping relationship during the contextual logical coherence deduction process. In the 3D coordinate system, the X-axis (total value of semantic matching and temporal compliance) visually maps the cumulative contribution of state semantic equality judgment and process temporal compliance verification; the Y-axis (topological distance attenuation) characterizes the spatial distance penalty caused by the topological physical distance of the graph; and the Z-axis (logical state fit) characterizes the final deduction evaluation quantity under the interaction of the two. A semi-transparent horizontal cross-section is introduced in the diagram as a coherence adjudication boundary value to divide the legal and abnormal regions in 3D space. Solid nodes distributed above this cross-section indicate that the current operation satisfies strong dependency constraints in both process temporality and semantics, and are judged by the system as fusion vectors (through deduction); while hollow nodes falling below this cross-section are judged as repulsion vectors (state conflicts) due to excessive penalties for spatial distance or temporal contradictions. The system sets the coherence adjudication boundary value. ,when The output context logic coherence deduction result is a fused vector when The output is a repulsion vector.

[0040] Specifically, the process of issuing anomaly warning tags to document locations where process status conflicts occur, and encapsulating the deduced documents into compliance review snapshots for cloud archiving is as follows: Capture erroneous routing nodes where the contextual logic coherence deduction results present exclusion vectors; forcibly insert anomaly warning tags containing conflict type codes into the original tagging level of the document corresponding to the erroneous routing node; extract legitimate documents where the contextual logic coherence deduction results present fusion vectors; lock the underlying extensible markup language structure of the legitimate documents; inject irreversible operation timestamp streams and user digital signature chains; compress the legitimate documents injected with operation timestamp streams and user digital signature chains into read-only encrypted data packets, generate compliance review snapshots, and call the cloud distributed storage interface to synchronously persist the compliance review snapshots to disk.

[0041] In this implementation plan, the system's background daemon thread continuously monitors the above deduction results. When a rejection vector is captured, the system determines that the current update operation violates the sequential constraint logic of the experimental procedures, immediately calls the Document Object Model interface, and forcibly injects a specific exception warning tag node at the termination of the underlying Extensible Markup Language (XML) paragraph mapped by the error routing node. For legitimate documents with a fusion vector, the system serializes its underlying XML structure into a pure byte stream, calls the time microservice interface to obtain the absolute operation timestamp stream, and signs the byte stream using the operator's private key to generate an immutable compliance review snapshot. The specific process follows the following encryption encapsulation formula: ;in, This represents the initial document digital fingerprint generated through XOR calculation; Represents a secure hash mapping function; This represents the underlying Extensible Markup Language (XML) structure byte stream generated after a valid document is serialized; This represents the acquired irreversible timestamp stream; This represents the binary bitwise XOR operator; This indicates the final read-only encrypted data packet generated; This indicates that the Advanced Encryption Standard (AES) replaces network functions; This represents a lossless dictionary compression function based on Markov chains; This indicates the byte stream concatenation operator; This represents a user's digital signature chain generated using the private key; This represents the asymmetric public key issued by the cloud-based distributed storage system. The system will generate... The packaged object is a snapshot for compliance review. It calls the cloud-based distributed storage interface to perform direct memory access and transfer, and finally completes the disk archiving.

[0042] Example 2; please refer to Figure 4 A cloud-based collaborative batch intelligent maintenance system for laboratory raw records, used to execute the cloud-based collaborative batch intelligent maintenance method for laboratory raw records described in the embodiments, includes: a document parsing module, used to receive batch Word format raw record documents imported from the cloud, parse the underlying Extensible Markup Language of the documents, extract the spatial layout attributes of paragraph tags and table cells, as well as text key-value pair features, and construct a multi-dimensional node structure tree that integrates document layout logic; and a rule extraction module, used to respond to batch maintenance instructions, lock the target data node to be maintained in the multi-dimensional node structure tree, trace back the hierarchical depth of the target data node and the associated semantic anchor points of the table header text, and extract the topology with relative positional features. The system includes: a constraint mapping rule module; a node reconstruction module, which performs cross-document structure traversal on batch Word format original record documents according to topological constraint mapping rules, locates the same source data nodes within each document using relative position features, assigns and overwrites the underlying text nodes of the same source data nodes, and reconstructs the label rendering closure attributes of the same source data nodes after overwriting; and a logic deduction module, which extracts the overwritten same source data nodes and text key-value pair features to form an update data stream, imports the update data stream into a pre-set experimental process dependency graph to perform contextual logic coherence deduction, issues abnormal warning tags to the document locations where process state conflicts occur, and encapsulates the deduced documents into compliance review snapshots for cloud archiving.

[0043] In this implementation scheme, the document parsing module serves as the system's data input and underlying structure reconstruction unit. When a batch of original Word format records are transmitted from the cloud, this module uses an unpacking engine to intercept the data stream, separate the main tag files, and load them into memory to construct a document object model tree. This module traverses the memory tree, identifies and extracts structural nodes such as paragraphs and tables, obtains spatial layout attributes including alignment, indentation, and grid coordinates, and generates text key-value pair features according to specific character splitting boundary logic. Finally, the module injects the extracted layout attributes and key-value pair features as internal attributes into the corresponding tag nodes. By establishing hierarchical parent-child relationship pointers, it outputs a structured, multi-dimensional node structure tree in system memory, serving as the underlying data platform for subsequent node positioning and rule generation.

[0044] The rule extraction module is responsible for calculating and generating addressing paths for relative physical locations within a multi-dimensional node structure tree. This module receives and parses batch maintenance commands from the terminal, extracts target entity identifiers, performs a pre-order traversal of the structure tree generated by the document parsing module, compares node content, and locates the target data node to be maintained. This module traces upwards along the pointers in the structure tree, calculating the number of pointers traversed to obtain the level depth, while simultaneously scanning the top row of the table containing the target node to locate the semantic anchor point of the table header text. This module performs vertical projection alignment verification on the target data node's own coordinates and the semantic anchor point coordinates, calculates the relative offset vector, and packages and binds the offset vector, level depth, semantic anchor point, and replacement values ​​from the maintenance commands in memory, outputting topological constraint mapping rules to the system.

[0045] The node reconstruction module is responsible for performing low-level overwriting and syntactic integrity repair of cross-document entity data. This module receives topological constraint mapping rules from the rule extraction module and scans and matches a reference node in the parallel node structure tree of other unprocessed original record documents according to the hierarchical depth and semantic anchor points within the rules. The module applies a position offset vector to the reference node to perform grid coordinate translation, directly locating and extracting the source data node occupying the target grid coordinates. During overwriting, the module separates the external start and end marker sequences of the source nodes through string splitting, clears the internal original data and injects the target replacement value, then calls a concatenation function and a symmetry verification mechanism to reconstruct the start and end marker sequences, outputting a valid Extensible Markup Language (EXPLAIN) code block to overwrite the original memory mount pointer, completing the node assignment overwriting.

[0046] The logical deduction module verifies the legitimacy of underlying modification operations within the overall business chain and performs final anti-tampering disk write-in. This module concatenates the updated source data nodes and related key-value pair features in memory, encapsulating them into an update data stream. This module inputs the data stream into a pre-defined experimental process dependency graph, activates downstream pre-dependency verification nodes along directed edges, and performs coherent deduction by comparing the process identifiers carried in the data stream with the node's built-in admission state sequence. If the module captures a rejection vector as the deduction result, it forcibly inserts an anomaly warning tag into the corresponding document's underlying structure; if the result is a fusion vector, the module extracts the document's underlying markup language byte stream, injects an absolute operation timestamp stream and user digital signature, performs hash operations and compression encapsulation, and finally submits the generated read-only encrypted snapshot to the cloud distributed storage interface for archiving.

[0047] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A cloud-based collaborative batch intelligent maintenance method for laboratory raw records, characterized in that, Includes the following steps: S1. Receive batches of original Word format record documents imported from the cloud, parse the underlying Extensible Markup Language of the documents, extract the spatial layout attributes of paragraph tags and table cells as well as text key-value pair features, and construct a multi-dimensional node structure tree that integrates document layout logic. S2. Respond to the batch maintenance command, lock the target data node to be maintained in the multi-dimensional node structure tree, trace back the hierarchical depth of the target data node and the semantic anchor point of the associated table header text, and extract the topological constraint mapping rules with relative position features. S3. Perform cross-document structure traversal on batch Word format original record documents according to topological constraint mapping rules, locate the same source data nodes inside each document using relative position features, perform underlying text node assignment overwrite on the same source data nodes, and reconstruct the label rendering closure attribute of the same source data nodes after overwriting. S4. Extract the covered source data nodes and text key-value pair features to form an updated data stream. Import the updated data stream into a pre-set experimental process dependency graph to perform contextual logic coherence deduction. Issue anomaly warning tags to the document locations where process state conflicts occur. Encapsulate the deduced documents into compliance review snapshots and perform cloud archiving.

2. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 1, characterized in that: The specific process of receiving batches of raw Word format records imported from the cloud and parsing the underlying Extensible Markup Language (XML) of the documents is as follows: Intercept batch Word format raw record document data streams sent from the cloud, perform data stream unpacking operations, and separate the document body markup file containing the core text and the associated style definition markup file; Load the document body tag file into the in-memory document object model tree, and extract the namespace declaration dictionary from the in-memory document object model tree; Based on the namespace-declared dictionary, the in-memory document object model tree is traversed to identify and extract the first-level tag nodes representing paragraph structure and the second-level tag nodes representing table structure. Redundant spell check tags unrelated to document layout logic are removed, and the cleaned standard extended markup language node sequence is output.

3. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 2, characterized in that: The specific process of extracting the spatial layout attributes of paragraph tags and table cells, as well as text key-value pair features, to construct a multi-dimensional node structure tree that integrates document layout logic is as follows: Traverse the standard extended markup language node sequence. When the first-level markup node is read, extract the paragraph layout features including alignment and indentation levels. When the second-level markup node is read, extract the two-dimensional grid coordinates including row and column numbers. Identify separator markers in paragraph layout features or adjacent cell boundaries in two-dimensional grid coordinates, split the text content inside the marked node into attribute key name features and attribute key value features, and combine them to generate text key value pair features; Based on the physical reading order of the node sequence in the standard extended markup language, paragraph layout features, two-dimensional grid coordinates, and text key-value pair features are injected as node attributes into the corresponding markup nodes. The markup nodes are connected through hierarchical parent-child pointers to generate a multi-dimensional node structure tree.

4. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 3, characterized in that: In response to batch maintenance commands, the specific process of locating the target data node to be maintained in the multidimensional node structure tree, and tracing back the hierarchical depth of the target data node and the associated semantic anchor points of the table header text is as follows: Parse the target entity identifier carried by the batch maintenance command, perform a preorder traversal in the multidimensional node structure tree, match the nodes containing the target entity identifier and mark them as target data nodes to be maintained; Tracing the parent-child relationship pointers along the multidimensional node structure tree from bottom to top, the number of relationship pointers traversed by the target data node to reach the root node is recorded, and the hierarchy depth is generated. Within the range of the second-level marker node where the target data node is located, perform a reverse scan in the direction of the top-level row sequence number, extract the attribute key name features from the text key-value pair features associated with the top-level row, and determine the attribute key name features as the semantic anchor point of the table header text.

5. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 4, characterized in that: The specific process for extracting topological constraint mapping rules with relative positional features is as follows: Obtain the column sequence number of the target data node in the two-dimensional grid coordinates, align and verify the column sequence number with the column sequence number of the semantic anchor point of the table header text, and generate a relative offset vector of the cell position within the same vertical column range. Encapsulate the semantic anchor points of the table header text, the hierarchy depth, and the relative offset vectors of the cell positions to construct a structured addressing path; The structured addressing path is bound to the target replacement value in the batch maintenance instruction to generate topology constraint mapping rules.

6. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 5, characterized in that: The specific process of performing cross-document structure traversal on a batch of Word format original record documents based on topological constraint mapping rules, and locating the common source data nodes within each document using relative position features, is as follows: Extract the structured addressing paths from the topological constraint mapping rules and construct a parallel node structure tree in the unprocessed documents of a batch of Word format original record documents; Based on the hierarchical depth in the structured addressing path and the semantic anchor point of the header text, scan and lock the mapping reference coordinate node in the parallel node structure tree; The relative offset vector of the cell position in the structured addressing path is applied to the mapping reference coordinate node, pointing to the target grid coordinate in the parallel node structure tree. The underlying extensible markup language element occupying the target grid coordinate is extracted, and the underlying extensible markup language element is established as the same source data node.

7. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 6, characterized in that: The specific process of overriding the underlying text node assignment for data nodes from the same source, and then reconstructing the label rendering closure attribute of the data nodes from the same source after overriding is as follows: Separate the outer wrapper marker sequence and the inner character carrier sequence in data nodes from the same source, and remove the original text traces remaining in the inner character carrier sequence; Extract the target replacement value from the topological constraint mapping rule, inject the target replacement value into the internal character carrier sequence, and generate the updated character carrier sequence; The system detects the start and end tag segments in the outer wrapper tag sequence, embeds the updated character carrier sequence between the start and end tag segments, verifies the pairing integrity code of the start and end tag segments, generates a valid extensible markup language code block with tag rendering closure attributes, and completes the assignment overriding of underlying text nodes and reconstruction of tag rendering closure attributes.

8. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 7, characterized in that: The process of extracting the covered source data nodes and text key-value pair features to form an update data stream, and then importing the update data stream into a pre-set experimental procedure dependency graph to perform contextual logical coherence deduction is as follows: The business status attributes and attribute key name features in the text key-value pair features carried by the spliced ​​and covered data nodes from the same source are used to construct an updated data stream containing process stage identifiers and parameter entity identifiers. The updated data stream is mapped to the input port of the experimental process dependency graph, and the downstream preceding dependency verification node is activated along the directed edge direction of the experimental process dependency graph. Extract the business flow state constraint set built into the downstream pre-dependent verification node, compare the process stage identifier in the update data flow with the admission state sequence contained in the business flow state constraint set, and output the context logic coherence deduction result representing the state matching degree.

9. The cloud-based collaborative batch intelligent maintenance method for laboratory original records according to claim 8, characterized in that: The specific process of issuing anomaly warning tags to the document locations where process status conflicts occur, and encapsulating the deduced documents into compliance review snapshots for cloud archiving is as follows: Capture erroneous routing nodes that present exclusion vectors as results of contextual logical coherence deduction, and forcibly insert exception warning tags containing conflict type codes into the original document tag hierarchy corresponding to the erroneous routing node; Extract the contextual logic coherence deduction results to present the fused vector of the legal document, lock the underlying extensible markup language structure of the legal document, and inject the irreversible operation timestamp stream and user digital signature chain; The legitimate document containing the injected operation timestamp stream and user digital signature chain is compressed and converted into a read-only encrypted data packet, generating a compliance review snapshot. The compliance review snapshot is then synchronously written to disk by calling the cloud distributed storage interface.

10. A cloud-based collaborative batch intelligent maintenance system for laboratory original records, used to execute the cloud-based collaborative batch intelligent maintenance method for laboratory original records as described in any one of claims 1-9, characterized in that, include: The document parsing module is used to receive batches of original Word format record documents imported from the cloud, parse the underlying Extensible Markup Language of the document, extract the spatial layout attributes of paragraph tags and table cells as well as text key-value pair features, and construct a multi-dimensional node structure tree that integrates document layout logic. The rule extraction module is used to respond to batch maintenance instructions, lock the target data node to be maintained in the multi-dimensional node structure tree, trace back the hierarchical depth of the target data node and the semantic anchor points of the associated table header text, and extract the topological constraint mapping rules with relative position features. The node reconstruction module is used to perform cross-document structure traversal on batch Word format original record documents according to topological constraint mapping rules, locate the same source data nodes inside each document using relative position features, perform underlying text node assignment overwrite on the same source data nodes, and reconstruct the label rendering closure attributes of the same source data nodes after overwriting. The logic deduction module is used to extract the features of the same source data nodes and text key-value pairs after coverage to form an updated data stream. The updated data stream is imported into a preset experimental process dependency graph to perform contextual logic coherence deduction. Anomaly warning tags are issued to the document locations where process status conflicts occur. The documents that pass the deduction are packaged into compliance review snapshots and archived in the cloud.

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