Deepseek-enabled enterprise unstructured business data intelligent analysis method

Abstract

This invention discloses a DeepSeek-enabled intelligent analysis method for enterprise unstructured business data, specifically relating to the field of data processing technology. The method includes acquiring unstructured business materials; generating an entry root key using an industrial cloud computing node based on the access path; generating a segment key based on the writing sequence; defining text boundaries using the segment key; driving DeepSeek to extract state transition actions corresponding to object names; and outputting business semantic pieces. Fingerprint construction is performed on the business semantic pieces, merging object names into object root keys; subtracting the starting position number from the ending position number of state transition actions to obtain the span value; and forming an initial acceptance value using the entry root key. The industrial cloud computing node generates entry root keys, segment keys, and conclusion source fingerprints for the unstructured business materials. Combining fingerprint cluster time-series flow, online change point detection, and status ledger verification, the method distinguishes between conclusion return pieces and state increment pieces, thereby preventing rewritten materials lacking new status numbers from entering the evidence acceptance process.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method for intelligent analysis of unstructured business data of enterprises empowered by DeepSeek. Background Technology

[0002] In the intelligent analysis of unstructured business data of enterprises, the existing processing revolves around the rapid aggregation of business data, semantic retrieval and generation of analysis conclusions. During implementation, contracts, sales follow-up records, project reviews, meeting minutes, monthly business reports and management reports are connected to the industrial cloud computing environment, and DeepSeek is used to summarize, compare and assess the risk of the text content. In scenarios involving group-based operations, cross-departmental collaboration, and multiple rounds of operational reviews, the initial renewal intentions, payment risks, project progress, business opportunity levels, or judgments of performance anomalies are often extracted and rewritten by business personnel and written into subsequent monthly reports, meeting minutes, follow-up records, and review materials. These materials are then re-entered into the industrial cloud data pool with new batches of materials. However, it is impossible to guarantee on-site that the original basis, generation batch, and source of evidence are retained for each extraction and rewriting. This can easily lead to a situation that can be directly verified: the same business judgment appears repeatedly in multiple subsequent materials, but the corresponding orders, payments, invoices, acceptances, approval results or formal confirmations from the counterparty are not added. When DeepSeek analyzes again, it may still treat these rewritten conclusive texts as new independent evidence, causing the initial judgment with insufficient evidence to be amplified round by round and incorporated into the basis of business decision-making. The technical problem this application aims to solve is: how to identify rewritten materials generated by the feedback of DeepSeek business analysis conclusions in an industrial cloud computing environment, and prevent them from serving as new evidence to strengthen the original business conclusions when there is a lack of new status change materials. Summary of the Invention

[0003] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a DeepSeek-enabled intelligent analysis method for enterprise unstructured business data. This method generates root keys, segment keys, and conclusion source fingerprints for unstructured business materials through industrial cloud computing nodes. It also distinguishes between conclusion return slices and status increment slices by combining fingerprint cluster time-series flow, online change point detection, and status ledger verification. This prevents rewritten materials lacking new status numbers from entering the evidence acceptance process, thereby solving the problems mentioned in the background art.

[0004] To achieve the above objectives, this invention provides the following technical solution: a DeepSeek-enabled intelligent analysis method for enterprise unstructured business data, comprising: S1. Obtain unstructured business materials. The industrial cloud computing node generates an entry root key based on the access path, and then generates a segment key based on the writing order. The text boundary is limited by the segment key, driving DeepSeek to extract the state transition actions corresponding to the object name, and outputting business semantic pieces. S2. Perform fingerprint construction on the business semantic slice, merge the object names into the object root key, subtract the starting position number from the state transition action stop position number to obtain the span value, form the initial acceptance value with the database root key, rewrite the initial acceptance value as the object acceptance value through the object root key, rewrite the object acceptance value as the action acceptance value through the span value, rewrite the action acceptance value through the segment key, and output the conclusion source fingerprint table. S3. Perform clustering on the fingerprint table of the conclusion source, form fingerprint clusters based on the same root key, form intra-cluster succession relationships based on the same span value sign, rewrite the intra-cluster order according to the ascending order of the segment key, and output the fingerprint cluster time sequence stream. S4. Based on the fingerprint cluster time-series stream, perform online change point detection. Adjacent receiving fingerprints are converted into same-source receiving values ​​through hash prefix comparison. The status ledger between segments is converted into status intervention values ​​through verification by writing new status numbers. The running length posterior is deduced from the same-source receiving value, and the posterior cumulative value is deduced from the status intervention value. The fingerprint mutation bit of the continuation to restart is output. When the fingerprint mutation triggers the semantic tool S5, DeepSeek rewrites the operational semantic slice before the mutation into a source tracing verification instruction and the operational semantic slice after the mutation into an incremental verification instruction. The node reads the fingerprint table and the status ledger according to the two types of instructions and outputs the source tracing slice and the incremental slice.

[0005] In a preferred embodiment, it further includes: S6. Based on the new status number of the incremental slice and the result of the conclusion source fingerprint of the traced slice, perform classification. If there is no new status number and the conclusion source fingerprint is returned, generate a conclusion return slice. If it carries a new status number, generate a status incremental slice. Based on the classification result, block the evidence acceptance or write the new status number to generate the business analysis result.

[0006] In a preferred embodiment, S1 includes: S11. Read the access path of unstructured business materials through industrial cloud computing nodes, recursively calculate from the starting character of the path to the ending character of the path, perform normalized hashing on each layer of path characters and take the hash value of the previous layer as the next layer of path characters, and output the root key of the database. S12. Perform sequential counting on the same source write events based on the root key of the database, write the sequential count into a segment key, and define the text boundary with the text start position corresponding to the segment key and the text start position corresponding to the next segment key, and output the text boundary record with the segment key. S13. Drive DeepSeek to perform title action binding within the text boundary record, attach the object title in the same syntactic dominance chain to the action expression pointing to the change of business state, and output the business semantic slice in which the segment key carries the object title and the object title carries the state transition action.

[0007] In a preferred embodiment, S2 includes: S21. Perform object root key merging on the business semantic piece. Convert the object titles in the business semantic piece into title codes according to character order. Generate an initial object root key with the first title code. Then, XOR the subsequent title codes bit by bit with the initial object root key to obtain the title difference. The title difference is canceled out by the same result of the root key in the database to generate the merging cost. When the merging cost is zero, the initial object root key is used. When the merging cost is non-zero, a new object root key is generated with the corresponding title code. Output the object root key table. S22. Based on the object root key table, read back the semantic slices of the operation, take the object name pointed to by the object root key as the starting point of the action positioning, count along the syntactic domination edge to the state transition action to obtain the domination distance, lock the first state transition action in ascending order of domination distance, subtract the starting position number from the stop position number of the locked state transition action to obtain the span value, and output the action span record.

[0008] In a preferred embodiment, S2 further includes: S23. Generate an initial acceptance value using the root key of the database. After the initial acceptance value is cyclically shifted to the left by the length of the object root key, it is XORed with the object root key to generate an object acceptance value. After the object acceptance value is cyclically shifted to the left by the length of the span value code, it is XORed with the span value to generate an action acceptance value. After the action acceptance value is cyclically shifted to the left by the length of the segment key, it is XORed with the segment key to generate a segment acceptance value. Output the segment acceptance record. S24. Perform a write check on the segment acceptance record. When two segment acceptance values ​​correspond to the same object root key within the same segment key, write the corresponding segment acceptance record to the conflict cache and stop writing the table. When one segment acceptance value corresponds to the same object root key within the same segment key, write the corresponding segment acceptance record to the conclusion source fingerprint table.

[0009] In a preferred embodiment, S3 includes: S31. Perform root key clustering on the conclusion source fingerprint table. Read the conclusion source fingerprints one by one according to the reading order in the table. XOR the root key of the current object with the root key of the previous object bit by bit to obtain the root key difference value. When the root key difference value is zero, the previous fingerprint cluster number is used. When the root key difference value is non-zero, add one to the previous fingerprint cluster number to generate the current fingerprint cluster number. Output the conclusion source fingerprint table with fingerprint cluster numbers. S32. Perform intra-cluster continuity calculation based on the conclusion source fingerprint table with fingerprint cluster number. Divide the span value by the absolute value of the span value to obtain the span symbol code. XOR the span symbol codes of adjacent conclusion source fingerprints in the same fingerprint cluster number bit by bit. When the XOR result is zero, generate intra-cluster continuity edge. When the XOR result is non-zero, generate intra-cluster disconnection bit. Output intra-cluster continuity record. S33. Perform segment order rewriting on the intra-cluster acceptance record, read the conclusion source fingerprint with intra-cluster acceptance edge in the same fingerprint cluster number, calculate the adjacent segment key difference, swap the positions of adjacent conclusion source fingerprints when the segment key difference is negative, repeat the swap until the adjacent segment key difference in the same fingerprint cluster number is non-negative, and output the fingerprint cluster time sequence stream.

[0010] In a preferred embodiment, S4 includes: S41. Based on the fingerprint cluster time-series stream, read adjacent receiving fingerprints cluster by cluster, XOR the hash prefix of the previous receiving fingerprint with the hash prefix of the next receiving fingerprint bit by bit, count the number of zero values ​​in the XOR result, divide the number of zero values ​​by the number of prefixes to obtain the same source receiving value, and output the same source receiving sequence. S42. Read the status log based on the segment key corresponding to the adjacent fingerprint, XOR the status number corresponding to the previous segment key with the status number corresponding to the next segment key bit by bit. If the XOR result contains non-zero bits, write the status intervention value 1. If the XOR result is all zero, write the status intervention value 0. Output the status intervention sequence.

[0011] In a preferred embodiment, S4 further includes: S43. Perform online change point detection on the same source acceptance sequence and the state intervention sequence. Multiply the same source acceptance value by the posterior of the previous segment running length to obtain the posterior of the continued path. Multiply the state intervention value by the cumulative value of the previous segment posterior to obtain the posterior of the restart path. Then use the sum of the two types of path posteriors as the normalized denominator to generate the posterior of the current segment running length. S44. Repeat the a posteriori recursion of the current segment running length along the fingerprint cluster time sequence flow to the cluster tail segment key. When the first path of the adjacent segment is rewritten from a continuation path to a restart path, record the corresponding segment key and output the fingerprint mutation bit as the fingerprint pointed back by the corresponding segment key.

[0012] In a preferred embodiment, S5 includes: S51. Using the segment key corresponding to the fingerprint mutation position as the boundary key, read back the fingerprint in descending order of segment key along the front side of the boundary key with the initial source writing mark 1. Multiply the same source acceptance value by the previous source writing mark to obtain the current source writing mark and write it to the source segment stack. Read back the fingerprint in ascending order of segment key along the back side of the boundary key with the initial incremental writing mark 1. Multiply the state intervention value by the previous incremental writing mark to obtain the current incremental writing mark and write it to the incremental segment stack. Output the verification segment stack. S52, DeepSeek generates semantic tools for the operation semantic slices of the verification segment stack back pointer, backfills the root key of the object in the source segment stack as the source instruction header, backfills the same segment action inheritance value as the source read key, and backfills the root key of the object in the incremental segment stack as the incremental instruction header, backfills the same segment status intervention value as the incremental read key, and outputs the source verification instruction and the incremental verification instruction; S53. When an industrial cloud computing node executes a source tracing verification command, it uses the source tracing command header to locate the object root key row in the conclusion source fingerprint table, uses the source tracing read key to read back the conclusion source fingerprint corresponding to the action carry value and generate a source tracing piece. When executing an incremental verification command, it uses the incremental command header to locate the object root key row in the status ledger, uses the incremental read key to read back the new status number to write the record and generate an incremental piece, and outputs the source tracing piece and the incremental piece.

[0013] In a preferred embodiment, S6 includes: S61. Using the segment key corresponding to the fingerprint mutation bit as the classification key, the industrial cloud computing node reads the new status number string corresponding to the classification key from the incremental chip, counts the length of the new status number string, writes the status carry bit zero when the length is zero, writes the status carry bit one when the length is non-zero, and outputs the status carry record. S62. Based on the classification key, read the back-finding fingerprint from the source chip, XOR the back-finding fingerprint with the conclusion source fingerprint bit by bit to generate the back-finding difference string. When all bits of the back-finding difference string are zero, write back-finding hit bit 1. When there are non-zero bits in the back-finding difference string, write back-finding hit bit 0. Output the back-finding hit record. S63. Invert the state-carrying bit and multiply it with the back-pointing hit bit to generate a backflow write bit. Use the state-carrying bit as the incremental write bit. When the backflow write bit is one, write the business semantic piece corresponding to the classification key into the conclusion backflow piece and close the evidence acceptance entry. When the incremental write bit is one, write the business semantic piece corresponding to the classification key into the state incremental piece and write the new state number into the conclusion source fingerprint to generate the business analysis result.

[0014] The technical effects and advantages of this invention are as follows: 1. This scheme classifies the source fingerprint of the conclusion and the state-carrying bits, blocking the return of materials lacking new state numbers, and relatively reducing the risk of repeated reinforcement of the original business conclusion; 2. The root key, segment key, and text boundary are generated progressively, providing a basis for back-reading the source of materials and the reading position, and improving the consistency of fingerprint construction; 3. By merging the root keys of objects and introducing action span inheritance, the differences in titles and the rewritten text are pressed into the same fingerprint chain, which relatively improves the stability of the conclusion source identification. 4. Online change point recursion is performed in the fingerprint cluster time-series flow, and the relay continuation and state intervention are distinguished, which relatively reduces the bias in the identification of newly added business facts; 5. By using source tracing verification commands and incremental verification commands to read data from separate tables, DeepSeek directly determines whether data verification has been replaced, thus enhancing the verifiability of the analysis basis. 6. Based on the conclusion of the backflow interruption and the status increment chip write-back, the evidence acceptance edge is dynamically rewritten, and the consistency between the business analysis results and the status ledger is relatively improved. Attached Figure Description

[0015] Figure 1 This is a flowchart outlining the method steps of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Refer to the instruction manual appendix Figure 1 The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data of the present invention includes: S1. Obtain unstructured business materials. The industrial cloud computing node generates an entry root key based on the access path, and then generates a segment key based on the writing order. The text boundary is limited by the segment key, driving DeepSeek to extract the state transition actions corresponding to the object name, and outputting business semantic pieces. This implementation method is used to transform unstructured business materials into business semantic pieces with stable sources, stable segments, and stable semantic relationships in an industrial cloud computing environment, enabling subsequent conclusion source fingerprint construction to read input quantities from defined text boundaries, defined object names, and defined state transition actions; this implementation method includes the following steps: S11 is used to generate an entry root key that can identify the source of unstructured operational materials entering the warehouse. Its mechanism is to eliminate source ambiguity caused by file name rewriting, directory migration, and multi-level storage paths through path-level hashing. The input is the unstructured operational materials received by the industrial cloud computing node and their access paths. The industrial cloud computing node first splits the access path into path layer characters according to the storage level. The path layer characters are normalized after unified character encoding, space deletion, and case unification. Then, hashing is performed starting from the first character of the path to obtain the first-level hash value. The next level of normalized path characters is concatenated with the previous level hash value and hashed again until the path ends. The final hash value is written as the entry root key and written to the material entry record, so that S12 can identify the same-source write event according to the entry root key. If the access path is empty, the industrial cloud computing node fills the access path with the material temporary storage address and writes the path filling mark. If there are unrecognizable characters in the access path, the industrial cloud computing node writes the unrecognizable characters according to the unified substitution code and then participates in the hashing to ensure that the entry root key has a definite source. S12 is used to generate segment keys that can define the boundaries of text reading. Its mechanism is to convert multiple material writes under the same entry root key into reproducible text segments through the sequential counting of same-source write events. The inputs are the entry root key output by S11, the write time of the unstructured business material, the submission sequence number in the industrial cloud computing node's write log, and the material hash value. The industrial cloud computing node identifies same-source write events using the entry root key as the group key. It first arranges same-source write events by write time, then by submission sequence number if the write times are the same, and finally by dictionary order of the material hash value if the submission sequence numbers are the same. The sorting position is written as follows: The segment key; then the industrial cloud computing node reads the text start position corresponding to each segment key, and uses the character before the text start position corresponding to the next segment key as the text end position of this segment. The last segment key uses the material end character as the text end position, thus forming a text boundary record with segment keys and writing it into the text boundary table, for S13 to perform title action binding within a fixed text range; if the text start position is missing, the industrial cloud computing node fills in the start position with the first character of the material and writes the start position filling mark; if the material is empty text, the industrial cloud computing node only retains the segment key and the empty text mark, and does not enter the business semantic slice generation; S13 is used to form a definite succession relationship between object titles and state transition actions within a fixed text boundary. Its mechanism is to bind the expression of the business object and the expression of business state change in the text into the same business semantic piece through a syntactic domination chain, avoiding DeepSeek's incorrect combination of titles and actions in different statements. The input consists of the text boundary record output by S12 and the text content defined by the text boundary record. The industrial cloud computing node drives DeepSeek to extract object titles and action expressions pointing to business state changes within the text boundary, and outputs the syntactic domination chain between the title and the action. The industrial cloud computing node uses the object title as the starting point of the domination chain and the state transition action as the ending point of the domination chain, counting the domination distance along each edge of the syntactic domination chain. When an object title connects multiple state transition actions, the first state transition action is selected in ascending order of dominance distance. When the same state transition action connects multiple object titles, the first object title is selected in the order of appearance of the object title within the text boundary. Unselected object titles or state transition actions are written to the parallel cache. Finally, the segment key carries the object title, and the object title carries the state transition action, forming an operational semantic slice and writing it to the operational semantic slice table, which is used by S2 to read the object title and state transition action to generate the object root key and span value. If DeepSeek does not output the syntactic dominance chain, the industrial cloud computing node will write the corresponding text boundary record to the parsing cache and will not generate an operational semantic slice. If the object title exists but the state transition action is missing, only the title missing action record will be written and transmission to S2 will stop. Through the above implementation methods, after unstructured business materials enter the industrial cloud computing node, the root key for entering the database is first obtained by recursion of the access path, and then the segment key and text boundary record are obtained by sorting the same source write events. Finally, the binding of object name and state transition action is completed within the text boundary record, so that the business semantic piece has a definite value source in the three levels of source, segment and semantic inheritance, providing a readable data foundation for subsequent object root key merging, span value calculation and conclusion source fingerprint construction. In practical applications: After a group enterprise connects its sales follow-up records, project review materials, and monthly operating reports to the industrial cloud computing node, multiple documents from the same trading counterparty are first assigned their own root keys according to the cloud access path. The monthly operating reports written later under the same root key are assigned a subsequent segment key. Within the text boundaries defined by the subsequent segment key, the industrial cloud computing node identifies the syntactic dominance relationship between the trading counterparty's name and state transition actions such as confirming additional purchases, delaying payments, and completing acceptance. When the monthly report only extracts early analysis conclusions without forming new state transition actions, the operating semantic slice will not be incorrectly expanded into a new source of state change, thus laying the data foundation for subsequent identification of conclusion feedback slices and state increment slices.

[0018] S2. Perform fingerprint construction on the business semantic slice, merge the object names into the object root key, subtract the starting position number from the state transition action stop position number to obtain the span value, form the initial acceptance value with the database root key, rewrite the initial acceptance value as the object acceptance value through the object root key, rewrite the object acceptance value as the action acceptance value through the span value, rewrite the action acceptance value through the segment key, and output the conclusion source fingerprint table. This implementation method is used to further convert business semantic pieces into a conclusion source fingerprint table that is resistant to rewriting, can refer back to the source, and can participate in subsequent clustering calculations. Its principle is to first merge different names of the same business object into stable object root keys, then read back the state migration actions from the object root keys and calculate the action span, subsequently generate segment inheritance records through hierarchical inheritance operations between the database root key, object root key, span value, and segment key, and finally eliminate fingerprint conflicts within the same segment through table writing verification. This implementation method includes the following steps: S21 is used to convert object names in the business semantic slice into object root keys. Its mechanism is to use the differential of the name code and the homogeneity cancellation of the root key in the database to merge abbreviations, full names or format variations under the same source into the same object root key. The input is the business semantic slice output by S13, the object names in the business semantic slice, and the root key in the database. The industrial cloud computing node first converts the object names into fixed-length name codes according to the character order. If there are insufficient bits, zeros are added to the high bits. If the number of bits exceeds the fixed length, the code is divided into blocks according to the character order and the block results are XORed and folded. Then, the initial object root key is generated with the first name code, and the subsequent name codes are XORed bit by bit with the initial key. The root key is used to obtain the title difference. When the business semantic piece corresponding to the subsequent title code has the same entry root key as the business semantic piece corresponding to the initial object root key, the industrial cloud computing node writes zero to the difference bits formed by spaces, separators, parentheses, and abbreviations in the title difference, and writes the remaining non-zero bits as the merging cost. When the merging cost is zero, the initial object root key is used. When the merging cost is non-zero, a new object root key is regenerated with the corresponding title code. The object root key table is output and used by S22 to read back the business semantic piece. If the object title is empty, the industrial cloud computing node writes the corresponding business semantic piece into the title missing cache and does not generate an object root key. S22 is used to read back the operational semantic slice from the object root key table and determine the span value of the state transition action. Its mechanism is to rebind the object root key with the action expression within the text boundary to prevent multiple actions within the same text boundary from being mistakenly connected to the same object root key. The input quantities are the object root key table, operational semantic slice, syntactic domination edge, and the start and end positions of the state transition action. The industrial cloud computing node locates the corresponding object name using the object root key in the object root key table, and then uses the object name as the starting point for action location, counting edge by edge along the syntactic domination edge output by DeepSeek to the state transition action and obtaining the domination distance. The same object name connects multiple During state transition actions, the industrial cloud computing node locks the first state transition action in ascending order of dominance distance. If the dominance distances are the same, the first state transition action is locked in ascending order of its starting position number within the text boundary. Characters within the text boundary are numbered from the beginning according to a unified encoding. The first character of the state transition action is written as the starting position number, and the position after the last character of the state transition action is written as the ending position number. The ending position number is subtracted from the starting position number to generate the span value. The action span record is output and used by S23 for succession calculation. If the object root key fails to read back the state transition action, the industrial cloud computing node writes the corresponding object root key into the action missing cache and does not output the action span record. S23 is used to push the source of the data entry, the business object, the action span, and the text segment into the same receiving chain. Its mechanism is to use a circular left shift and XOR operation under a fixed receiving bit width to make the segment receiving record carry the source, object, action, and segment information simultaneously. The input is the data entry root key, the object root key, the span value, and the segment key. The industrial cloud computing node first writes the data entry root key as the initial receiving value, and then uniformly expands the initial receiving value, the object root key, the span value encoding, and the segment key to the same receiving bit width. If the length is insufficient, the high bits are padded with zeros; if the length exceeds the receiving bit width, the low bits are truncated. The initial receiving value is cyclically calculated according to the number of bits obtained by taking the remainder of the receiving bit width by the object root key length. After left shifting, the object's root key is XORed bit by bit to generate the object's acceptance value. The object's acceptance value is then cyclically left-shifted according to the length of the span value encoding modulo the acceptance bit width, and XORed bit by bit with the span value encoding to generate the action's acceptance value. The action's acceptance value is then cyclically left-shifted according to the length of the segment key modulo the acceptance bit width, and XORed bit by bit with the segment key to generate the segment's acceptance value. The output includes the entry root key, object root key, span value, segment key, and segment's acceptance value, and is used for S24 table writing verification. If the span value encoding is empty, the industrial cloud computing node fills it in with a zero value encoding and continues to perform the acceptance operation, and writes a span filling mark in the segment's acceptance record. S24 is used to perform write table verification on the segment inheritance records and generate a conclusion source fingerprint table. Its mechanism is to eliminate conflicting records with multiple segment inheritance values ​​corresponding to the same object root key within the same segment key, avoiding subsequent clustering from writing mutually exclusive actions within the same text boundary as the same conclusion source. The input is the segment inheritance records output by S23. The industrial cloud computing node establishes a write table index according to the segment key and object root key, and reads the number of segment inheritance values ​​under the same write table index. When the same object root key within the same segment key corresponds to two or more segment inheritance values, the industrial cloud computing node writes the corresponding segment inheritance record, action span record, and object root key into the table. The conflict cache only stops writing conflict records to the conclusion source fingerprint table, without affecting the writing of segment acceptance records for other segment keys or other object root keys. When the same object root key within the same segment key corresponds to only one segment acceptance value, the industrial cloud computing node writes the corresponding segment acceptance record to the conclusion source fingerprint table. The conclusion source fingerprint in the conclusion source fingerprint table is carried by the segment acceptance value, and the object root key, span value, and segment key are retained for S3 to perform clustering. If the record in the conflict cache can be recalculated to obtain a single segment acceptance value after subsequent material replenishment, the industrial cloud computing node replaces the conflict cache record with the newly calculated segment acceptance value and re-executes the table writing verification. Through the above implementation method, the semantic slices are first merged by the object root key to eliminate the name difference, then the state migration action position is limited by the action span calculation, then the segment inheritance record is formed by the step-by-step inheritance operation, and finally the conclusion source fingerprint table is generated by the table writing verification, so that the subsequent S3 can form the fingerprint cluster time sequence flow based on the object root key, span value and segment key, without directly including the semantic slices with conflict in the same segment into the clustering calculation. In practical applications: When an industrial cloud computing node reads a sales review document, if the statements "East China Project Company," "Project Company," and "Additional Purchase Confirmed" appear simultaneously within the text boundary, S21 converts "East China Project Company" and "Project Company" into title codes and merges them into the same object root key after canceling out identical results in the entry root key. S22 locks the title corresponding to the object root key to the state transition action of "Additional Purchase Confirmed" along the syntactic dominance edge and calculates the span value. S23 pushes the entry root key, object root key, span value, and segment key into the segment acceptance value in sequence. S24 writes the conclusion source fingerprint table when no multiple segment acceptance values ​​corresponding to the same object root key are found within the same segment key. If "Additional Purchase Confirmed" and "Additional Purchase Not Confirmed" appear simultaneously within the same text boundary, the corresponding segment acceptance record is written to the conflict cache and does not enter the conclusion source fingerprint table.

[0019] S3. Perform clustering on the fingerprint table of the conclusion source, form fingerprint clusters based on the same root key, form intra-cluster succession relationships based on the same span value sign, rewrite the intra-cluster order according to the ascending order of the segment key, and output the fingerprint cluster time sequence stream. This implementation method is used to reconstruct the conclusion source fingerprint table into a fingerprint cluster time-series stream that reflects the propagation order of the same business object in different segments. The principle is to first use the object root key to group the conclusion source fingerprints into the same fingerprint cluster, then use the span symbol code to determine whether the state transition actions within the same fingerprint cluster have a successor direction, and finally use the segment key to complete the intra-cluster sequence rewriting, so that subsequent online change point detection can read adjacent successor fingerprints in a determined time sequence. This implementation method includes the following steps: S31 is used to generate a conclusion source fingerprint table with fingerprint cluster numbers. Its working mechanism is to group the conclusion source fingerprints corresponding to the same business object into the same fingerprint cluster through object root key differential. The input is the conclusion source fingerprint table output by S24. The industrial cloud computing node first rearranges the conclusion source fingerprint table according to the lexicographical order of the object root key. If the object root keys are the same, they are then arranged in ascending order according to the segment key, and the rearranged reading order is written as the reading order in the table. Subsequently, the industrial cloud computing node reads the conclusion source fingerprints one by one according to the reading order in the table, and XORs the current object root key with the previous object root key bit by bit to obtain the conclusion source fingerprint. The root key difference value is used. If all bits of the root key difference value are zero, the previous fingerprint cluster number is used. If there are non-zero bits in the root key difference value, one is added to the previous fingerprint cluster number to generate the current fingerprint cluster number. Finally, the current fingerprint cluster number is written into the current conclusion source fingerprint, and the conclusion source fingerprint table with fingerprint cluster numbers is output for S32 to perform intra-cluster inheritance calculation. If the current conclusion source fingerprint is the first record after rearrangement, the industrial cloud computing node writes the initial fingerprint cluster number one. If the object root key is empty, the industrial cloud computing node writes the corresponding conclusion source fingerprint into the object root key exception cache and does not participate in the object root key clustering. S32 is used to generate intra-cluster acceptance records. Its mechanism is to compare the span symbol codes to determine whether the state transition directions of adjacent conclusion source fingerprints within the same fingerprint cluster are consistent. The input is the conclusion source fingerprint table with fingerprint cluster numbers and the span value carried by the conclusion source fingerprints. The industrial cloud computing node first reads the adjacent conclusion source fingerprints under the same fingerprint cluster number and reads the span values ​​corresponding to the adjacent conclusion source fingerprints respectively. Since the stop position sequence number of the state transition action in S22 uses the last character's sequence number, the normal span value is positive. The industrial cloud computing node divides the normal span value by the absolute value of the span value to obtain the span symbol code. Subsequently, the industrial cloud computing... The node performs a bitwise XOR operation between the span symbol code of the previous conclusion source fingerprint and the span symbol code of the next conclusion source fingerprint. If all bits of the XOR result are zero, it writes the acceptance mark 1 and generates an intra-cluster acceptance edge. If there are non-zero bits in the XOR result, it writes the acceptance mark 0 and generates an intra-cluster disconnection bit. Finally, it outputs an intra-cluster acceptance record, which includes the fingerprint cluster number, the previous conclusion source fingerprint, the next conclusion source fingerprint, the XOR result of the span symbol code, and the acceptance mark. This record is used by S33 to read the conclusion source fingerprint with an acceptance mark of 1. If the span value is zero or the span value is missing, the industrial cloud computing node writes the corresponding conclusion source fingerprint into the span anomaly cache and does not perform the span symbol code calculation. S33 is used to generate the fingerprint cluster timing stream. Its mechanism is to eliminate the order disorder within the same fingerprint cluster number by exchanging segment key differences, so that the conclusion source fingerprints with intra-cluster acceptance edges under the same fingerprint cluster number enter the subsequent change detection according to the material writing order. The input is the intra-cluster acceptance record output by S32. The industrial cloud computing node reads the conclusion source fingerprints with an acceptance mark of one within the same fingerprint cluster number and calculates the adjacent segment key differences according to the current arrangement order. The segment key difference is the result of subtracting the previous segment key from the next segment key. When the segment key difference is negative... The industrial cloud computing node swaps the positions of adjacent conclusion source fingerprints and recalculates the difference between adjacent segment key values ​​before and after the swap. When all adjacent segment key differences within the same fingerprint cluster number are non-negative, the industrial cloud computing node stops swapping, writes the swapped reading order as the intra-cluster order, connects adjacent receiving fingerprints with the intra-cluster order, and outputs the fingerprint cluster time sequence stream for S4 to read adjacent receiving fingerprints cluster by cluster. If there is no conclusion source fingerprint with a receiving marker of one within the same fingerprint cluster number, the industrial cloud computing node writes an isolated fingerprint record and does not generate adjacent receiving fingerprints. Through the above implementation methods, the fingerprint source table first obtains the fingerprint cluster number through object root key rearrangement and differential clustering, then obtains the intra-cluster connecting edge and intra-cluster disconnection position through span symbol code XOR, and finally obtains the fingerprint cluster time sequence stream through segment key exchange sorting, so that the adjacent connecting fingerprints read in subsequent S4 not only belong to the same business object, but also have the same state transition action direction, and also have a definite segment priority relationship. In practical applications: When an industrial cloud computing node reads multiple conclusion source fingerprints corresponding to the same transaction counterparty from the conclusion source fingerprint table, it first writes the conclusion source fingerprints of the same transaction counterparty into the same fingerprint cluster number by pressing the object root key. When both the sales review and the monthly operating report write additional purchase confirmation-type state transition actions in the same direction, the XOR result of the span symbol code is zero, and the industrial cloud computing node generates an intra-cluster receiving edge. When there is another additional purchase withdrawal-type reverse state transition action in the same fingerprint cluster number, the XOR result of the span symbol code has a non-zero bit, and the industrial cloud computing node generates an intra-cluster disconnection bit. Subsequently, the industrial cloud computing node arranges the receiving fingerprints into a fingerprint cluster time sequence stream by pressing the segment key, which is used for online change point detection to identify the paraphrasing continuation or new state intervention of the same conclusion in subsequent materials.

[0020] S4. Based on the fingerprint cluster time-series stream, perform online change point detection. Adjacent receiving fingerprints are converted into same-source receiving values ​​through hash prefix comparison. The status ledger between segments is converted into status intervention values ​​through verification by writing new status numbers. The running length posterior is deduced from the same-source receiving value, and the posterior cumulative value is deduced from the status intervention value. The fingerprint mutation bit of the continuation to restart is output. This implementation method is used to distinguish between paraphrased continuations of the same conclusion source and new interventions in the status of business objects within a fingerprint cluster time-series stream. The principle is to first generate a homologous continuation sequence using the hash prefix differences of adjacent continuation fingerprints, then generate a status intervention sequence using changes in status numbers in the business object status ledger. Subsequently, it uses a posterior recursive analysis of the run length to determine whether the path type in the fingerprint cluster time-series stream has changed from a continuation path to a restart path, enabling subsequent semantic tools to generate source tracing verification instructions and incremental verification instructions around fingerprint mutation bits. This implementation method includes the following steps: S41 is used to calculate the common source acceptance value between adjacent acceptance fingerprints. Its working mechanism is to determine whether adjacent acceptance fingerprints retain the same conclusion source propagation characteristics through bit-by-bit difference of the hash prefix. The input is the fingerprint cluster time sequence stream output by S33. The industrial cloud computing node reads adjacent acceptance fingerprints cluster by cluster according to the fingerprint cluster number, and extracts the hash prefix from the starting position of the segment acceptance value of each acceptance fingerprint. The number of bits of the hash prefix is ​​jointly determined by the low-bit length of the object root key encoding length and the action acceptance value encoding length. When the truncation termination bit is zero, the segment key encoding is used. The length is used as the number of bits in the hash prefix. Subsequently, the industrial cloud computing node performs a bitwise XOR operation between the hash prefix of the previous receiving fingerprint and the hash prefix of the next receiving fingerprint, counts the number of zero values ​​in the XOR result, and divides the number of zero values ​​by the number of bits in the hash prefix to obtain the same-source receiving value. The same-source receiving value is written into the same-source receiving sequence according to the segment key for S43 to recursively continue the path for verification. If any receiving fingerprint in the adjacent receiving fingerprints lacks a segment receiving value, the industrial cloud computing node writes the corresponding adjacent receiving fingerprint into the prefix anomaly cache and does not participate in the calculation of the same-source receiving value. S42 is used to calculate the state intervention value between adjacent segments. Its mechanism is to determine whether a new business object state has been written between adjacent segments by differentiating the state numbers in the business object state ledger. The input consists of adjacent receiving fingerprints in the fingerprint cluster time sequence stream, the segment key corresponding to the adjacent receiving fingerprints, and the business object state ledger. The industrial cloud computing node reads the previous state number based on the previous segment key and the next state number based on the next segment key, and unifies the previous and next state numbers to the same encoding length. If the length is insufficient, high-order bits are padded with zeros; if the length exceeds the limit, zeros are padded. The low-order bits are truncated when the status is outdated. Then, the industrial cloud computing node XORs the previous status number with the next status number bit by bit. If there are non-zero bits in the XOR result, the status intervention value 1 is written. If all bits in the XOR result are zero, the status intervention value 0 is written. The status intervention value is written into the status intervention sequence according to the next bit key, for S43 to verify the recursive restart path. If the previous or next bit key is not read from the status ledger of the operating object, the industrial cloud computing node fills in the status number with an empty status number and writes the status missing mark. The status missing mark is transmitted to S43 along with the status intervention sequence. S43 is used to perform online change point detection on the same source acceptance sequence and the state intervention sequence. Its working mechanism is to use the same source acceptance value to continue the path recursion, use the state intervention value to restart the path recursion, and obtain the posterior of the current segment running length through normalization calculation. The input is the same source acceptance sequence output by S41, the state intervention sequence output by S42, and the segment key order of the fingerprint cluster time series. The industrial cloud computing node writes the running length one, the running length posterior one, and the posterior accumulated value one to the first segment key of each fingerprint cluster time series, and starts recursion from the second segment key. For the current segment key, the industrial cloud computing node multiplies the posterior of the previous segment's running length by the current same-source acceptance value to obtain the posterior of the continuation path, multiplies the accumulated value of the previous segment's posterior by the current state intervention value to obtain the posterior of the restart path, adds the posterior of the continuation path to the posterior of the restart path to obtain the normalized denominator, divides the posterior of the continuation path by the normalized denominator to obtain the current continuation posterior, divides the posterior of the restart path by the normalized denominator to obtain the current restart posterior, and writes the posterior of the current segment's running length together with the current continuation posterior and the current restart posterior. If the normalized denominator is zero, the industrial cloud computing node writes the current segment key into the segment record to be verified, does not output the fingerprint mutation bit, and continues to read the same-source acceptance sequence and state intervention sequence at the next segment key for recursion. S44 is used to output fingerprint mutation bits. Its working mechanism is to track the type change of the first sorted path along the fingerprint cluster time sequence flow and record the trigger position when the first sorted path changes from a continuation path to a restart path. The input is the current continuation posterior, current restart posterior, segment key order, and fingerprint retracement relationship obtained from S43. The industrial cloud computing node arranges the continuation path and restart path in descending order of posterior value at each adjacent segment. When the posterior values ​​are the same, they are arranged according to the path type code, where the restart path type code is placed before the continuation path type code, thus obtaining the first sorted path. When the first sorted path of the previous adjacent segment... When a path is being continued and the first path in the current adjacent segment is a restart path, the industrial cloud computing node records the current segment key and uses the fingerprint pointing back to the current segment key as the fingerprint mutation bit. After repeatedly recursively pushing along the fingerprint cluster time-series flow to the tail segment key, the industrial cloud computing node outputs the fingerprint mutation bit record. The fingerprint mutation bit record contains the fingerprint cluster number, the current segment key, the pointing back fingerprint, and the path type change result, which is used by S5 to generate the verification segment stack. If multiple positions in the same fingerprint cluster time-series flow where a continued path turns into a restart path occur, the industrial cloud computing node outputs multiple fingerprint mutation bit records according to their respective segment keys. Through the above implementation, adjacent acceptance fingerprints in the fingerprint cluster time-series flow are first converted into the same source acceptance sequence and the state intervention sequence, and then recursively processed by the running length posterior, posterior cumulative value, continuation path posterior and restart path posterior. Finally, the fingerprint mutation bit is output at the position where the type switch occurs in the first sorted path, so that the subsequent S5 can look back at the conclusion source fingerprint table and the business object status ledger around the fingerprint mutation bit. In practical applications: When the conclusion that the risk of payment for a certain business object has increased first appears in the business analysis results, and the same judgment is relayed in the subsequent weekly sales report and monthly business report, the hash prefix of adjacent acceptance fingerprints maintains a high homogeneous acceptance value, while the business object status ledger does not write a new status number, and the posterior length of the operation is recursively deduced along the continuation path; when the material corresponding to the next segment key actually triggers the writing of a new payment status number, the status intervention value turns to one, the posterior length of the restart path enters the first path of the sorting, and the industrial cloud computing node records the corresponding segment key as the fingerprint mutation bit, so that the subsequent semantic tools can distinguish between the old conclusion relay and the new status intervention.

[0021] S5. When the fingerprint mutation triggers the semantic tool, DeepSeek rewrites the semantic slice before the mutation into a source tracing verification instruction and the semantic slice after the mutation into an incremental verification instruction. The node reads the fingerprint table and the status ledger according to the two types of instructions and outputs the source tracing slice and the incremental slice. This implementation method converts the fingerprint mutation bits output by S4 into data verification instructions that can be executed by industrial cloud computing nodes. The principle is to first divide the tracing direction and incremental direction based on the segment key corresponding to the fingerprint mutation bit; then, DeepSeek rewrites the business semantic slice pointed back from the verification segment stack into structured read instructions; finally, the industrial cloud computing node reads back the conclusion source fingerprint table and the business object status ledger respectively, generating tracing slices and incremental slices for S6 to classify and read. This implementation method includes the following steps: S51 is used to generate the verification segment stack. Its mechanism is to use the segment key corresponding to the fingerprint mutation bit as the demarcation key. The receiving fingerprint that still maintains the same source inheritance before the demarcation key is written into the source tracing segment stack, and the receiving fingerprint that has undergone state intervention after the demarcation key is written into the incremental segment stack. The input is the fingerprint mutation bit record, fingerprint cluster time sequence stream, same source inheritance value and state intervention value output by S44. The industrial cloud computing node reads the current segment key in the fingerprint mutation bit record and writes it into the demarcation key. With the initial source tracing write mark one, the receiving fingerprint is read back in descending order of segment key along the demarcation key. The same source inheritance value of this position is multiplied by the previous source tracing write mark to obtain the source tracing write mark of this position. When the source tracing write mark of this position is one, the current segment key, object root key, action inheritance value and receiving fingerprint are written into the current segment key. Write to the source tracing segment stack. Stop forward reading when the current source tracing write flag is zero. The industrial cloud computing node also reads the receiving fingerprint in ascending order of segment key after the boundary key with the initial incremental write flag one. Multiply the current state intervention value by the previous incremental write flag to obtain the current incremental write flag. When the current incremental write flag is one, write the current segment key, object root key, state intervention value and receiving fingerprint to the incremental segment stack. Stop backward reading when the current incremental write flag is zero. The source tracing segment stack and the incremental segment stack together form the verification segment stack and are used by S52 to point back to the operating semantic slice. If there is no receiving fingerprint before the boundary key, the industrial cloud computing node writes an empty source tracing segment stack. If there is no receiving fingerprint after the boundary key, the industrial cloud computing node writes an empty incremental segment stack. S52 is used to generate semantic verification instructions that can drive data table reading. Its mechanism is to allow DeepSeek to generate structured instruction fields based on the operational semantic pieces pointed back from the verification segment stack, instead of directly outputting factual judgments. The inputs are the verification segment stack, operational semantic piece table, conclusion source fingerprint table fields, and operational object status ledger fields output by S51. The industrial cloud computing node first reads back the operational semantic pieces using the segment keys in the source tracing segment stack, and then DeepSeek fills the root key of the object in the source tracing segment stack back as the source tracing instruction header, fills the corresponding action inheritance value back as the source tracing read key, and fills the segment keys in the source tracing segment stack back as the source tracing boundary. The field generates a source tracing verification command; the industrial cloud computing node then uses the segment key in the incremental segment stack to read back the business semantic slice, and DeepSeek fills back the object root key in the incremental segment stack as the incremental command header, fills back the same segment key and the state intervention value together as the incremental read key, and fills back the boundary key as the incremental boundary field, generating an incremental verification command; the source tracing verification command and the incremental verification command are only used as data read commands for the industrial cloud computing node, and are not used as business analysis conclusions. If DeepSeek fails to fill back the object root key or read key, the industrial cloud computing node will write the corresponding verification segment stack to the command missing cache and will not transmit it to S53; S53 is used to execute source tracing verification instructions and incremental verification instructions and generate verification return results. Its working mechanism is to retrieve the original conclusion source in the conclusion source fingerprint table through source tracing verification instructions and to retrieve the new status number write record in the business object status ledger through incremental verification instructions. The input is the source tracing verification instructions and incremental verification instructions output by S52. When the industrial cloud computing node executes the source tracing verification instructions, it uses the source tracing instruction header to locate the object root key row in the conclusion source fingerprint table, uses the source tracing read key to match the action acceptance value, uses the source tracing boundary field to limit the segment key reading range, reads the matched conclusion source fingerprint, object root key, action acceptance value and segment key and generates source tracing piece; When an industrial cloud computing node executes an incremental verification command, it uses the incremental command header to locate the root key row of the object in the business object status ledger, uses the segment key in the incremental read key to limit the status write range, and uses the status intervention value in the incremental read key to determine whether to read the new status number write record. It reads the matched new status number string, object root key, status write segment key, and delimitation key and generates an incremental slice. Both the source slice and the incremental slice are written with the delimitation key as the classification key of S6, so that S6 can perform classification according to the result carried by the new status number and the conclusion source fingerprint back pointer result. If the conclusion source fingerprint table does not read a matching conclusion source fingerprint, the industrial cloud computing node generates an empty back pointer source slice. If the business object status ledger does not read a new status number write record, the industrial cloud computing node generates an empty status incremental slice and retains the delimitation key. Through the above implementation method, the fingerprint mutation bit does not directly trigger the business analysis result. Instead, it first forms a verification segment stack, then DeepSeek generates source verification instructions and incremental verification instructions, and finally the industrial cloud computing node reads the conclusion source fingerprint table and the business object status ledger to obtain the source fragment and incremental fragment with the boundary key, object root key, back fingerprint and new status number string, thereby providing definite input for S6 to distinguish the conclusion backflow fragment and the status incremental fragment; In practical applications: When the business judgment of increased collection risk reappears in the monthly business report, the fingerprint mutation bit output by S4 writes the monthly report segment key into the boundary key. S51 writes the acceptance fingerprint corresponding to the early analysis material into the source tracing segment stack along the front side of the boundary key, and writes the collection record segment key with the status intervention value into the incremental segment stack along the back side of the boundary key. S52 drives DeepSeek to rewrite the business semantic piece corresponding to the source tracing segment stack into a source verification instruction to read the conclusion source fingerprint table, and rewrites the business semantic piece corresponding to the incremental segment stack into an incremental verification instruction to read the business object status ledger. S53 reads the early conclusion source fingerprint and collection status number and writes them into the record accordingly. If only the early conclusion source fingerprint is read and the new status number string is not read, S6 can classify the corresponding business semantic piece into the conclusion return piece.

[0022] S6. Based on the new status number of the incremental slice and the result of the conclusion source fingerprint of the traced slice, perform classification. If there is no new status number and the conclusion source fingerprint is returned, generate a conclusion return slice. If it carries a new status number, generate a status incremental slice. Based on the classification result, block the evidence acceptance or write the new status number to generate the business analysis result. This implementation method is used to classify business semantic pieces and control evidence acceptance based on source trace pieces and incremental pieces. Its principle is to first use the incremental piece to determine whether a new status number exists after the fingerprint mutation bit, then use the source trace piece to determine whether the fingerprint mutation bit points back to the existing conclusion source fingerprint. Subsequently, through bit operations between the status carry bit, the back-pointing hit bit, the backflow write bit, and the incremental write bit, the business semantic pieces are classified into conclusion backflow pieces, status incremental pieces, or invalid verification pieces, and the classification results are written back to the evidence acceptance process and the business analysis result generation process. This implementation method includes the following steps: S61 is used to generate status-carrying records. Its mechanism is to determine whether the incremental piece carries the newly written status result in the business object status ledger by judging whether the length of the new status number string is sufficient. The input is the incremental piece output by S53 and the segment key corresponding to the fingerprint mutation bit. The industrial cloud computing node directly writes the segment key corresponding to the fingerprint mutation bit as the classification key and reads the new status number string from the incremental piece using the classification key. When there is a new status number string corresponding to the classification key in the incremental piece, the industrial cloud computing node counts the character length of the new status number string. When the character length is zero, it writes zero to the status-carrying bit; when the character length is not zero, it writes one to the status-carrying bit. It writes the classification key, the new status number string, and the status-carrying bit into the status-carrying record for S63 to generate the incremental write bit. When the incremental piece lacks a record corresponding to the classification key, the industrial cloud computing node fills in an empty new status number string and writes an incremental missing marker. The status-carrying bit is still written as zero with a character length of zero to ensure that the subsequent classification input is complete. S62 is used to generate the back-pointing hit record. Its mechanism is to determine whether the source tracing piece points to an existing conclusion source by bit-by-bit difference between the back-pointing fingerprint and the conclusion source fingerprint. The input is the source tracing piece, the classification key, and the conclusion source fingerprint in the conclusion source fingerprint table output by S53. The industrial cloud computing node reads the back-pointing fingerprint from the source tracing piece using the classification key, and reads the conclusion source fingerprint corresponding to the root key of the object associated with the classification key. Before performing XOR, the industrial cloud computing node expands the back-pointing fingerprint and the conclusion source fingerprint to the same fingerprint bit width. If the length is insufficient, The high-order bits are padded with zeros, and the low-order bits are truncated when the length exceeds the fingerprint width. Then, the industrial cloud computing node XORs the indexed fingerprint with the conclusion source fingerprint bit by bit to generate an indexed differential string. When all bits of the indexed differential string are zero, an indexed hit bit of 1 is written. When there are non-zero bits in the indexed differential string, an indexed hit bit of 0 is written. The classification key, indexed fingerprint, indexed differential string, and indexed hit bit are written to the indexed hit record for S63 to generate the return write bit. When the tracer chip does not read the indexed fingerprint, the industrial cloud computing node writes an empty indexed fingerprint and writes the indexed hit bit of 0. S63 is used to perform classification writing and evidence acceptance control. Its mechanism is to distinguish old conclusion return, new state increment and invalid verification results through the combination operation of state carry bit and back pointer hit bit. The input is the state carry record output by S61, the back pointer hit record output by S62, the business semantic piece corresponding to the classification key, the evidence acceptance table and the conclusion source fingerprint table. The industrial cloud computing node subtracts the state carry bit to obtain the state carry bit inversion result, then multiplies the state carry bit inversion result with the back pointer hit bit to generate the return write bit, and directly writes the state carry bit as the increment write bit. When the backflow write bit is 1, the industrial cloud computing node writes the business semantic slice corresponding to the classification key into the conclusion backflow slice, writes the blocking bit 1 for the classification key in the evidence acceptance table, and deletes the acceptance edge from the business semantic slice corresponding to the classification key to the business conclusion to be proved; when the incremental write bit is 1, the industrial cloud computing node writes the business semantic slice corresponding to the classification key into the state incremental slice, writes the new state number into the state update field of the conclusion source fingerprint, and attaches the business semantic slice corresponding to the state incremental slice to the business conclusion to be proved; when the state carry bit is zero and the back-pointing hit bit is zero, the industrial cloud computing node writes the business semantic slice corresponding to the classification key into the invalid verification slice, does not write it into the conclusion source fingerprint, and does not enter the evidence acceptance table; finally, DeepSeek reads the evidence acceptance edge with the blocking bit of zero, the state update field in the conclusion source fingerprint, and the business conclusion to be proved, generates the business analysis result, and writes the business analysis result into the analysis result table of the industrial cloud computing node; Through the above implementation method, the source slice and the incremental slice are aligned at the classification key. The new status number string of the incremental slice is converted into a status carry bit, and the back-finding fingerprint of the source slice is converted into a back-finding hit bit. The status carry bit and the back-finding hit bit are then used together to generate a backflow write bit and an incremental write bit. Thus, the business semantic slice that lacks a new status number and has a back-finding conclusion source fingerprint is identified as a conclusion backflow slice, and the business semantic slice that carries a new status number is identified as a status incremental slice. Finally, the data back-write is completed by writing the blocking bit, deleting the receiving edge, writing the status update field, and generating the business analysis results. In practical applications: After the category key corresponding to the statement of increased collection risk in the monthly operating report enters S61, if the incremental slice does not carry a new collection status number, the length of the new status number string is zero, and the status carrying bit is written as zero; after reading the source slice in S62, it is found that the back-finding fingerprint is completely consistent with the early conclusion source fingerprint, and the back-finding hit bit is written as one; in S63, the back-flow writing bit is obtained by multiplying the result of the inverted status carrying bit with the back-finding hit bit, and the industrial cloud computing node writes the corresponding operating semantic slice in the monthly operating report into the conclusion back-flow slice, and deletes its connecting edge to the operating conclusion to be proved, so that DeepSeek cannot treat the monthly operating report as new evidence when generating operating analysis results; if a new status number string is written in the subsequent collection record, the status carrying bit is written as one, the corresponding operating semantic slice is written into the status incremental slice, and the new status number is written into the status update field of the conclusion source fingerprint before participating in the generation of operating analysis results.

[0023] The working principle of this solution is as follows: First, the industrial cloud computing node receives unstructured business materials from enterprises and generates an entry root key, segment key, and text boundary based on the access path and writing order. Then, DeepSeek is driven to extract object names and state transition actions from the fixed text boundaries to form business semantic slices. Subsequently, the object names are merged into object root keys, and the state transition actions are converted into span values. A conclusion source fingerprint table is generated step by step through the entry root key, object root key, span value, and segment key. Then, the fingerprint clusters are clustered by object root key and organized into a fingerprint cluster time-series stream by segment key. Online change point detection is used to determine whether the same business conclusion is a continuation of the old conclusion or a new business state has intervened. Finally, source verification instructions and incremental verification instructions are generated around the fingerprint mutation bits. The conclusion source fingerprint table and business object status ledger are checked back respectively. Content that does not have a new status number but points back to the original conclusion is included in the conclusion return slice and the evidence is blocked. Content that carries a new status number is included in the status increment slice and used to generate business analysis results. In practical applications, after enterprises integrate sales follow-up records, monthly business reports, meeting minutes, and payment collection records into the industrial cloud computing environment, DeepSeek initially generates an analysis conclusion that the payment collection risk of a certain project has increased. Subsequently, business personnel may rewrite this conclusion into the monthly report and meeting minutes. The solution will identify whether these subsequent texts are merely referencing the fingerprint of the early conclusion source. If there is no new payment collection status number in the business object status ledger, the system will classify the corresponding text as a conclusion feedback fragment to avoid it being used as new evidence to reinforce the original judgment. If a new payment collection delay status number is indeed written subsequently, the text will be classified as a status increment fragment, the new status number will be written into the conclusion source fingerprint, and the business analysis results will be regenerated accordingly.

[0024] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. DeepSeek-enabled intelligent analysis method for enterprise unstructured business data, characterized by: include: S1. Obtain unstructured business materials. The industrial cloud computing node generates an entry root key based on the access path, and then generates a segment key based on the writing order. The text boundary is limited by the segment key, driving DeepSeek to extract the state transition actions corresponding to the object name, and outputting business semantic pieces. S2. Perform fingerprint construction on the business semantic slice, merge the object names into the object root key, subtract the starting position number from the state transition action stop position number to obtain the span value, form the initial acceptance value with the database root key, rewrite the initial acceptance value as the object acceptance value through the object root key, rewrite the object acceptance value as the action acceptance value through the span value, rewrite the action acceptance value through the segment key, and output the conclusion source fingerprint table. S3. Perform clustering on the fingerprint table of the conclusion source, form fingerprint clusters based on the same root key, form intra-cluster succession relationships based on the same span value sign, rewrite the intra-cluster order according to the ascending order of the segment key, and output the fingerprint cluster time sequence stream. S4. Based on the fingerprint cluster time-series stream, perform online change point detection. Adjacent receiving fingerprints are converted into same-source receiving values ​​through hash prefix comparison. The status ledger between segments is converted into status intervention values ​​through new status number writing verification. The running length posterior is deduced from the same-source receiving value, and the posterior cumulative value is deduced from the status intervention value. The fingerprint mutation bit of the continuation to restart is output. When the fingerprint mutation bit triggers the semantic tool, DeepSeek rewrites the semantic slice before the mutation bit into a source tracing verification instruction and the semantic slice after the mutation bit into an incremental verification instruction. The node reads the fingerprint table and the status ledger according to the two types of instructions and outputs the source tracing slice and the incremental slice.

2. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 1, characterized in that: Also includes: S6. Based on the new status number of the incremental slice and the result of the conclusion source fingerprint of the traced slice, perform classification. If there is no new status number and the conclusion source fingerprint is returned, generate a conclusion return slice. If it carries a new status number, generate a status incremental slice. Based on the classification result, block the evidence acceptance or write the new status number to generate the business analysis result.

3. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 2, characterized in that: S1 includes: S11. Read the access path of unstructured business materials through industrial cloud computing nodes, recursively from the starting character of the path to the ending character of the path, perform normalized hashing on each layer of path characters and take the hash value of the previous layer as the next layer of path characters, and output the root key of the database. S12. Perform sequential counting on the same source write events based on the root key of the database, write the sequential count into a segment key, and define the text boundary with the text start position corresponding to the segment key and the text start position corresponding to the next segment key, and output the text boundary record with the segment key. S13. Drive DeepSeek to perform title action binding within the text boundary record, attach the object title in the same syntactic dominance chain to the action expression pointing to the change of business state, and output the business semantic slice in which the segment key carries the object title and the object title carries the state transition action.

4. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 3, characterized in that: S2 includes: S21. Perform object root key merging on the business semantic piece. Convert the object titles in the business semantic piece into title codes according to character order. Generate an initial object root key with the first title code. Then, XOR the subsequent title codes bit by bit with the initial object root key to obtain the title difference. The title difference is canceled out by the same result of the root key in the database to generate the merging cost. When the merging cost is zero, the initial object root key is used. When the merging cost is non-zero, a new object root key is generated with the corresponding title code. Output the object root key table. S22. Based on the object root key table, read back the semantic slices of the operation, take the object name pointed to by the object root key as the starting point of the action positioning, count along the syntactic domination edge to the state transition action to obtain the domination distance, lock the first state transition action in ascending order of domination distance, subtract the starting position number from the stop position number of the locked state transition action to obtain the span value, and output the action span record.

5. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 4, characterized in that: S2 also includes: S23. Generate an initial acceptance value using the root key of the database. After the initial acceptance value is cyclically shifted to the left by the length of the object root key, it is XORed with the object root key to generate an object acceptance value. After the object acceptance value is cyclically shifted to the left by the length of the span value code, it is XORed with the span value to generate an action acceptance value. After the action acceptance value is cyclically shifted to the left by the length of the segment key, it is XORed with the segment key to generate a segment acceptance value. Output the segment acceptance record. S24. Perform a write check on the segment acceptance record. When two segment acceptance values ​​correspond to the same object root key within the same segment key, write the corresponding segment acceptance record to the conflict cache and stop writing the table. When one segment acceptance value corresponds to the same object root key within the same segment key, write the corresponding segment acceptance record to the conclusion source fingerprint table.

6. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 5, characterized in that: S3 includes: S31. Perform root key clustering on the conclusion source fingerprint table. Read the conclusion source fingerprints one by one according to the reading order in the table. XOR the root key of the current object with the root key of the previous object bit by bit to obtain the root key difference value. When the root key difference value is zero, the previous fingerprint cluster number is used. When the root key difference value is non-zero, add one to the previous fingerprint cluster number to generate the current fingerprint cluster number. Output the conclusion source fingerprint table with fingerprint cluster numbers. S32. Perform intra-cluster continuity calculation based on the conclusion source fingerprint table with fingerprint cluster number. Divide the span value by the absolute value of the span value to obtain the span symbol code. XOR the span symbol codes of adjacent conclusion source fingerprints in the same fingerprint cluster number bit by bit. When the XOR result is zero, generate intra-cluster continuity edge. When the XOR result is non-zero, generate intra-cluster disconnection bit. Output intra-cluster continuity record. S33. Perform segment order rewriting on the intra-cluster acceptance record, read the conclusion source fingerprint with intra-cluster acceptance edge in the same fingerprint cluster number, calculate the adjacent segment key difference, swap the positions of adjacent conclusion source fingerprints when the segment key difference is negative, repeat the swap until the adjacent segment key difference in the same fingerprint cluster number is non-negative, and output the fingerprint cluster time sequence stream.

7. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 6, characterized in that: S4 includes: S41. Based on the fingerprint cluster time-series stream, read adjacent receiving fingerprints cluster by cluster, XOR the hash prefix of the previous receiving fingerprint with the hash prefix of the next receiving fingerprint bit by bit, count the number of zero values ​​in the XOR result, divide the number of zero values ​​by the number of prefixes to obtain the same source receiving value, and output the same source receiving sequence. S42. Read the status log based on the segment key corresponding to the adjacent fingerprint, XOR the status number corresponding to the previous segment key with the status number corresponding to the next segment key bit by bit. If the XOR result contains non-zero bits, write the status intervention value 1. If the XOR result is all zero, write the status intervention value 0. Output the status intervention sequence.

8. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 7, characterized in that: S4 also includes: S43. Perform online change point detection on the same source acceptance sequence and the state intervention sequence. Multiply the same source acceptance value by the posterior of the previous segment running length to obtain the posterior of the continued path. Multiply the state intervention value by the cumulative value of the previous segment posterior to obtain the posterior of the restart path. Then use the sum of the two types of path posteriors as the normalized denominator to generate the posterior of the current segment running length. S44. Repeat the a posteriori recursion of the current segment running length along the fingerprint cluster time sequence flow to the cluster tail segment key. When the first path of the adjacent segment is rewritten from a continuation path to a restart path, record the corresponding segment key and output the fingerprint mutation bit as the fingerprint pointed back by the corresponding segment key.

9. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 8, characterized in that: S5 includes: S51. Using the segment key corresponding to the fingerprint mutation position as the boundary key, read back the fingerprint in descending order of segment key along the front side of the boundary key with the initial source writing mark 1. Multiply the same source acceptance value by the previous source writing mark to obtain the current source writing mark and write it to the source segment stack. Read back the fingerprint in ascending order of segment key along the back side of the boundary key with the initial incremental writing mark 1. Multiply the state intervention value by the previous incremental writing mark to obtain the current incremental writing mark and write it to the incremental segment stack. Output the verification segment stack. S52, DeepSeek generates semantic tools for the operation semantic slices of the verification segment stack back pointer, backfills the root key of the object in the source segment stack as the source instruction header, backfills the same segment action inheritance value as the source read key, and backfills the root key of the object in the incremental segment stack as the incremental instruction header, backfills the same segment status intervention value as the incremental read key, and outputs the source verification instruction and the incremental verification instruction; S53. When an industrial cloud computing node executes a source tracing verification command, it uses the source tracing command header to locate the object root key row in the conclusion source fingerprint table, uses the source tracing read key to read back the conclusion source fingerprint corresponding to the action carry value and generate a source tracing piece. When executing an incremental verification command, it uses the incremental command header to locate the object root key row in the status ledger, uses the incremental read key to read back the new status number to write the record and generate an incremental piece, and outputs the source tracing piece and the incremental piece.

10. The DeepSeek-enabled intelligent analysis method for enterprise unstructured business data according to claim 9, characterized in that: S6 includes: S61. Using the segment key corresponding to the fingerprint mutation bit as the classification key, the industrial cloud computing node reads the new status number string corresponding to the classification key from the incremental chip, counts the length of the new status number string, writes the status carry bit zero when the length is zero, writes the status carry bit one when the length is non-zero, and outputs the status carry record. S62. Based on the classification key, read the back-finding fingerprint from the source chip, XOR the back-finding fingerprint with the conclusion source fingerprint bit by bit to generate the back-finding difference string. When all bits of the back-finding difference string are zero, write back-finding hit bit 1. When there are non-zero bits in the back-finding difference string, write back-finding hit bit 0. Output the back-finding hit record. S63. Invert the state-carrying bit and multiply it with the back-pointing hit bit to generate a backflow write bit. Use the state-carrying bit as the incremental write bit. When the backflow write bit is one, write the business semantic piece corresponding to the classification key into the conclusion backflow piece and close the evidence acceptance entry. When the incremental write bit is one, write the business semantic piece corresponding to the classification key into the state incremental piece and write the new state number into the conclusion source fingerprint to generate the business analysis result.

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