AI-driven enterprise customization service plan intelligent generation system

Abstract

The application relates to the technical field of intelligent generation and compliance control of enterprise service schemes, in particular to an AI-driven enterprise customized service scheme intelligent generation system, which receives enterprise demand texts and analyzes the same into structured demand information; generates a service scheme text containing multiple hierarchical texts according to the structured demand information; extracts commitment statements and index items from the service scheme text and assigns unique identifiers to the commitment statements and the index items, calculates commitment strength values and caliber consistency deviation indexes; determines risk heat according to the commitment strength values and the caliber consistency deviation indexes and self-adaptively updates multilevel threshold values, outputs a control instruction set to constrain commitment and index generation; finally generates a publishable scheme and outputs regulation and control records and version information, realizes demand analysis to hierarchical scheme automatic generation and unique identification of commitment / index, quantifies commitment strength and caliber deviation to evaluate risks, outputs control instructions according to risk heat self-adaptive threshold values and leaves trace versions, and improves compliance, traceability and acceptance consistency.

Description

Technical Field

[0001] This invention relates to the field of intelligent generation and compliance management of enterprise service solutions, and more specifically, to an AI-driven intelligent generation system for customized enterprise service solutions. Background Technology

[0002] In the pre-sales and delivery phases of customized enterprise services, service solutions typically take into account requirements interview minutes, bidding documents, historical cases, and industry standards as inputs to form textual deliverables, which are commonly divided into multiple levels, such as objective description, implementation path, and acceptance criteria. In current practice, one approach to solution development relies on human experience and template reuse, improving writing efficiency and format consistency through standard directories, clause libraries, deliverable lists, and acceptance checklists. Another approach introduces rule engines or knowledge bases to automatically assemble paragraphs, check required fields, or generate checklists under given constraints. In recent years, the use of natural language generation technology has also emerged to quickly generate drafts, expand expressions, or polish and rewrite them, shortening the output cycle from requirements to solutions. These methods have shown positive effects in improving productivity and structural standardization.

[0003] However, the key risks of enterprise service solutions often stem not only from "whether they are written completely," but also from the consistency and verifiability of cross-layer content. Firstly, commitment statements written by different people or in different versions are prone to inconsistencies in the strength of obligatory modalities, unclear time constraints, or omissions of exceptions, making it difficult to quantify and compare the feasibility of commitments and to ensure timely constraints during the generation phase. Secondly, the description of indicators at the target, implementation, and acceptance layers often deviates due to differences in calculation methods, numerator and denominator definitions, data source caliber, time windows, baseline selection, elimination rules, and missing data handling. Even if each paragraph seems reasonable individually, the overall result may still lead to acceptance disputes and rework. While existing technologies possess tools and methods for indicator management, data lineage, master data governance, and acceptance standards, these capabilities are often scattered across different stages: indicator definitions may exist in indicator dictionaries, data sources may be traced back to data lineage platforms, and text generation and review are completed by document tools or manual verification. There is a lack of a linkage mechanism that structurally expresses textual commitments and indicator calibers, aligns measurements across layers, and feeds back to the generation strategy.

[0004] Furthermore, in actual business operations, risks are not static: different companies, different project stages, different compliance requirements, and different historical performance will lead to dynamic adjustments in risk tolerance and audit intensity. Existing practices often rely on manual review meetings or fixed threshold rules for screening, which can play a certain screening role. However, with multiple indicators, multiple commitments, and frequent version iterations, manual review costs increase and consistency is difficult to guarantee. Fixed thresholds may also be too strict or too lenient for certain scenarios, making it difficult to form a traceable closed-loop control of risk intensity and actions such as generation strategy switching, phrase constraints, and freezing of definitions. At the same time, the external release of solutions often requires the retention of the basis and version information of the formation process to facilitate subsequent review, auditing, and dispute resolution. While traditional document circulation can record versions, it is difficult to structurally record key decisions such as commitment intensity, definition deviation, threshold changes, and generation selection. Based on the above background, there is a demand in the industry for an integrated technical solution that can automatically generate layered solutions based on demand texts, and quantitatively evaluate, dynamically adjust, and traceably output commitments and indicator definitions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide an AI-driven intelligent generation system for customized enterprise service solutions.

[0006] To achieve the above objectives, the present invention provides the following technical solution: An AI-driven intelligent generation system for customized enterprise service solutions includes: The requirement parsing module is used to receive enterprise requirement texts and generate structured requirement information; The layered generation module is used to generate service plan text based on structured requirement information. The service plan text includes multiple layers of text. The risk assessment module is configured to: extract commitment statements and indicator items from the service plan text, and generate a unique identifier for each commitment statement and indicator item; construct a commitment strength feature vector for the commitment statements and generate a commitment strength value; construct an indicator caliber specification diagram for each indicator item and generate cross-layer field mapping relationships; and generate a caliber deviation feature set and a caliber consistency deviation index based on the indicator caliber specification diagram and cross-layer field mapping relationships. The dynamic control module is used to determine the risk intensity based on the commitment strength value and the consistency deviation index, and to update the first preset threshold, the second preset threshold, and the third preset threshold based on the risk intensity. It is also used to output control instruction set for the process of generating commitment statements and indicator items by the hierarchical generation module based on the first preset threshold, the second preset threshold, and the third preset threshold. The compliance output module is used to generate publishable solutions based on the service solution text after being regulated by the control instruction set, and to output regulation records and version information.

[0007] Furthermore, the requirements analysis module performs sentence segmentation, topic identification, element entity extraction, synonym unification, and constraint disambiguation on the enterprise requirements text, and generates structured requirements information according to preset field templates. The structured requirements information includes at least service objectives, service scope, constraints, budget and schedule, compliance requirements, and candidate indicators; the service plan text includes at least target layer text, implementation layer text, and acceptance layer text.

[0008] Furthermore, the risk assessment module performs segmentation, sentence-level segmentation, hierarchical text location, referential resolution, lexical and syntactic analysis, semantic role labeling, and entity recognition on the service plan text. It identifies commitment statements by combining the set of commitment verbs, the set of obligatory modal words, and the set of limiting trigger words. It identifies indicator items by combining the set of indicator keywords, numerical unit combination patterns, and indicator structure templates. The risk assessment module stores unique identifiers associated with the locations of the target layer text, implementation layer text, and acceptance layer text.

[0009] Furthermore, the risk assessment module extracts the commitment subject, commitment action, deliverable, quantification target, time constraint, condition exception, external dependency, verification method, and context constraint for each commitment statement. Based on the preset feature dimension set, it constructs a commitment strength feature vector and inputs the commitment strength feature vector into the commitment strength scoring model to obtain the commitment risk probability. The confidence level calibration of the commitment risk probability is then performed to generate the commitment strength value.

[0010] Furthermore, the risk assessment module generates multi-perspective caliber representations from target-level text, implementation-level text, and acceptance-level text based on the indicator caliber specification diagram and cross-layer field mapping relationships. It then performs graph structure constraint matching, comparison alignment scoring, and optimal transmission distance calculation on the multi-perspective caliber representations to obtain an alignment distance set. This alignment distance set is then projected using features to generate a caliber deviation feature set. Feature normalization is performed on the caliber deviation feature set to obtain a normalized feature vector. A summation operation is then performed on the normalized feature vector to obtain a caliber deviation score. The caliber deviation score is then processed using a probability mapping function to generate a caliber deviation risk probability. Finally, confidence calibration is performed on the caliber deviation risk probability to generate a caliber consistency deviation index.

[0011] Furthermore, the risk assessment module's process of constructing an indicator caliber specification diagram for each indicator item includes: extracting the indicator definition, calculation method, variable and caliber constraints, data source, time window, and baseline for each indicator item to form a caliber element set; creating indicator definition nodes, calculation method nodes, variable and caliber constraint nodes, data source lineage nodes, time window nodes, and baseline nodes for the caliber element set in the indicator caliber specification diagram, and writing the attribute fields corresponding to the caliber element set for each node; determining the dependencies between nodes based on graph construction rules and generating dependency edges between nodes, thereby forming the indicator caliber specification diagram.

[0012] Furthermore, the inter-node dependency edges include at least the dependency edges from the indicator definition node to the calculation method node, the dependency edges from the calculation method node to the variable and caliber constraint node, the dependency edges from the variable and caliber constraint node to the data source lineage node, and the dependency edges from the time window node and the baseline node to the calculation method node, respectively.

[0013] Furthermore, the dynamic control module determines the risk intensity by: performing a normalized mapping on the commitment strength value corresponding to each commitment statement to obtain the normalized commitment strength; performing a normalized mapping on the caliber consistency deviation index corresponding to each indicator item to obtain the normalized caliber deviation; determining the maximum value among all normalized commitment strengths as the commitment intensity, determining the maximum value among all normalized caliber deviations as the caliber intensity; and determining the maximum value between the commitment intensity and the caliber intensity as the risk intensity.

[0014] Furthermore, the dynamic control module outputs a set of control instructions based on the first preset threshold, the second preset threshold, and the third preset threshold. The set of control instructions includes at least the commitment statement generation mode control instruction, the commitment statement phrase selection constraint instruction, and the indicator item caliber freezing control instruction.

[0015] Furthermore, the regulation records and version information output by the compliance output module include commitment strength value, consistency deviation index, first preset threshold, second preset threshold, third preset threshold, risk heat, generation mode identifier, phrase entry identifier, caliber reference identifier, and frozen caliber record identifier. Among them, the generation mode identifier is used to identify the natural language generation mode or the compliance phrase set rendering mode, and the phrase entry identifier is the identifier corresponding to the selected phrase in the compliance phrase set.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention receives enterprise requirement text and generates structured requirement information, enabling the subsequently generated service solution text to have a unified semantic skeleton and information boundaries across multiple levels, including the target layer, implementation layer, and acceptance layer. Simultaneously, it assigns unique identifiers to each commitment statement and indicator item in the service solution text, supporting precise location and difference comparison of the same commitment and indicator during version iteration, multi-person collaboration, and reuse / rewriting. This significantly reduces the costs of manual organization, alignment, and repeated revisions without sacrificing customized expression. By constructing a commitment strength feature vector from commitment statements and generating commitment strength values, commitments are transformed from subjective judgments into measurable objects. This can be used to identify potential risks such as highly obligatory statements and rigid deadlines lacking preconditions. Furthermore, for each indicator item, an indicator specification diagram and cross-layer field mapping relationship are constructed, generating a set of deviation features and a deviation index of consistency. This enables the discovery of implicit deviations caused by differences in time windows, data source lineage, calculation methods, and exclusion rules in cross-layer texts, achieving early exposure and explainable attribution of acceptance disputes and rework risks. The risk intensity is determined based on the deviation index between the commitment strength value and the caliber consistency. The first, second, and third preset thresholds are then adaptively updated based on the risk intensity, so that the constraint strength can be dynamically matched with the project stage, compliance requirements, and historical risk levels, avoiding fixed thresholds being too strict or too lenient in different scenarios. Based on the threshold output control instruction set, constraints and corrections are implemented on the generation strategy of commitment statements and indicator items during the layered generation process. At the same time as generating a publishable solution, control records and version information are output, so that each mode switch, phrase selection, and caliber freeze can be reproduced, audited, and rolled back, providing a structured evidence chain to support compliance review, dispute resolution, and subsequent review. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall structure of the AI-driven intelligent generation system for customized enterprise service solutions of the present invention. Figure 2 This is a flowchart illustrating the AI-driven intelligent generation method for customized enterprise service solutions of the present invention. Figure 3 This is a schematic diagram illustrating the structure of the risk assessment module in this invention and its quantitative assessment of commitment statements and indicator items; Figure 4 This is a closed-loop diagram illustrating the dynamic control module adaptively updates the threshold based on risk intensity and outputs a set of control instructions, and the compliance output module outputs publishable solutions and control records / version information in this invention. Detailed Implementation

[0018] Reference Figures 1 to 4 An AI-driven intelligent generation system for customized enterprise service solutions, including: The Requirements Analysis module (for receiving enterprise requirement text and generating structured requirement information) transforms the natural language requirements submitted by enterprises from "readable" to "computable, generateable, and traceable" structured expressions, reducing the sensitivity of subsequent generation and evaluation stages to ambiguities in the original text. The requirement analysis module receives enterprise requirement texts from any text medium, such as questionnaires, interview minutes, emails, meeting records, and RFP fragments. The module performs standardized processing on the text, eliminating the need for subsequent modules to repeatedly handle noisy expressions, synonyms, and word order differences. The structured requirement information clarifies the boundaries and constraints of subsequent service solution generation, enabling the layered generation modules to construct content around a unified set of field templates, avoiding semantic drift or omissions of key constraints between "goals, implementation, and acceptance" in the solution. The output of the structured requirement information provides direct input for subsequent layered generation modules and also provides "contextual constraint references" for the risk assessment module. This allows the risk assessment module to make judgments based on the enterprise's explicit compliance requirements, budget constraints, and timelines when identifying commitment statements and indicator items, improving the consistency and interpretability of the assessment.

[0019] In one specific implementation, the generation of structured requirement information from enterprise requirement text can be accomplished through the following steps: After receiving the enterprise's requirement text, the process sequentially performs sentence segmentation and topic identification. Sentence segmentation breaks long requirement texts into semantically complete segments. Topic identification assigns each segment a topic tag corresponding to the service objectives, service scope, constraints, budget period, compliance requirements, and candidate metrics, maintaining a consistent level of expression within the same topic to avoid the same requirement being repeated or omitted in different segments. For example, if the enterprise requirement text includes "Complete customer data governance and meet data compliance review within three months, and deliver a customer growth improvement report," step one would segment it into sentences and identify the budget period as three months, the compliance requirement as passing the data compliance review, and the candidate metrics as customer growth improvement, etc. Under the constraints of topic tags, element entity extraction, synonym unification, and constraint disambiguation are performed on each sentence segment. Element entity extraction extracts elements such as time, quantity, object, action, and deliverable from the sentence segment and assigns them to the corresponding topic fields. Synonym unification unifies synonymous expressions to standard terms in the preset field template to ensure consistent reference in the future. Constraint disambiguation judges ambiguous or conflicting conditions and selects constraint expressions consistent with the context. Finally, structured requirement information is generated according to the preset field template. The structured requirement information includes at least service objectives, service scope, constraints, budget duration, compliance requirements, and indicator candidates. For example, growth improvement reports and growth analysis reports are synonymized into a unified deliverable description, and completion within three months and completion within ninety days at the latest are disambiguated into the same budget duration field value, so that the structured requirement information can be directly used for the stable generation and verification of subsequent solution texts.

[0020] The layered generation module (used to generate service plan text based on structured requirements information, the service plan text comprising multiple layers of text): Based on structured requirements information, it outputs service plan text with a clear hierarchical organizational structure, ensuring that the goal expression, implementation path, and acceptance definition of the plan are aligned within the same framework. The layered generation module maps structured requirements information into a multi-layered text organizational structure, ensuring that each layer has a clear responsibility: for example, the goal layer text expresses service objectives and outcome boundaries, the implementation layer text expresses implementation steps and resource arrangements, and the acceptance layer text expresses verifiable delivery and acceptance methods. The service plan text output by the layered generation module is both externally readable delivery content and the analysis object of the risk assessment module. Through the multi-layered text structure, the risk assessment module can identify whether the expression of the same indicator item is consistent across different layers during cross-layer comparisons, and whether the same commitment statement shows inconsistent strength or escalation in expression across different layers. Under the control instruction set of the dynamic adjustment module, the layered generation module can controllably adjust the generation method of commitment statements and indicator items, thereby ensuring that the generation process is compliant and controllable, rather than being statically generated once and then manually reworked.

[0021] In one specific implementation, generating service plan text based on structured requirements information can be accomplished in the following way: The process involves reading service objectives, service scope, constraints, budget duration, compliance requirements, and candidate metrics from structured requirements information. First, a hierarchical text generation skeleton is constructed, and constraint propagation is executed to ensure consistent referencing and traceable mapping of the same field across the target layer, implementation layer, and acceptance layer texts. Specifically, the target layer text, centered on service objectives, clarifies the boundaries of results, scope of application, key assumptions, and non-committable items, and solidifies constraints and compliance requirements as boundary clauses. The implementation layer text, centered on the service scope, breaks down service objectives into phased tasks, milestones, resource allocation, and risk management paths, and translates the budget duration into weekly or phased delivery schedules. The acceptance layer text, centered on candidate metrics, provides verifiable acceptance criteria, forms of acceptance evidence, and acceptance procedures for each candidate metric. The responsible party and acceptance timeline are defined, and compliance requirements are transformed into a checklist and linked to acceptance evidence. For example, structured requirements information indicates that the service objective is to complete customer data governance and improve customer retention within three months. The service scope includes data access, cleansing, tagging system and dashboard construction. Constraints include not touching sensitive fields, compliance requirements include passing data compliance review, and candidate indicators include improving retention rate and meeting data quality standards. The generated target layer text clearly states that retention improvement and governance closure will be achieved within three months without involving sensitive field processing. The implementation layer text provides a phased access and governance roadmap and arranges review nodes. The acceptance layer text provides the acceptance criteria, evidence, and timelines for retention rate and data quality, thus forming a service plan text that includes target layer text, implementation layer text, and acceptance layer text.

[0022] The risk assessment module (configured to: extract commitment statements and indicators from the service plan text, and generate a unique identifier for each commitment statement and indicator; construct a commitment strength feature vector for the commitment statements and generate commitment strength values; construct an indicator caliber specification diagram for each indicator and generate cross-layer field mapping relationships; generate a caliber deviation feature set and a caliber consistency deviation index based on the indicator caliber specification diagram and cross-layer field mapping relationships): After the service plan text is formed, it performs structured identification, quantitative assessment, and traceable marking of the "external commitment risk" and "indicator caliber consistency risk" in the plan, providing executable risk input for the dynamic control module. The risk assessment module extracts commitment statements and indicators from the service plan text and generates a unique identifier for each commitment statement and indicator. The unique identifier binds the risk assessment results to a specific location in the plan text, ensuring that subsequent control instruction sets can "precisely act on a specific commitment statement or indicator," avoiding generalized control that could lead to large sections of text being mistakenly modified or unlocatable. The risk assessment module constructs a commitment strength feature vector and generates a commitment strength value for the commitment statement. The aim is to transform the commitment statement from simple text into a comparable, threshold-triggered quantitative result. The commitment strength feature vector describes the strength-related information of the commitment statement, while the commitment strength value measures the overall commitment strength of the statement. This value allows the dynamic control module to determine, based on thresholds, whether to switch the generation mode, add a confirmation flag, or restrict the source of the selected phrase. For each indicator item, the risk assessment module constructs an indicator caliber specification diagram and generates cross-layer field mapping relationships. This solidifies the definition, calculation method, variables, and caliber constraints of the indicator item in a structured graph, ensuring that the expression of the same indicator item in the target layer text, implementation layer text, and acceptance layer text can be mapped to the same semantic space for alignment judgment. The cross-layer field mapping relationship establishes the correspondence between "the same indicator item element fields" between different levels of text, ensuring consistency in the objects of alignment comparison. The risk assessment module generates a set of deviation features and a consistency deviation index based on the indicator specification diagram and cross-level field mapping relationships. This quantifies whether there are inconsistencies in the definition of the same indicator item across multiple levels of text, or whether there are changes in calculation methods or time windows. The deviation feature set records the source and dimension of deviation, while the consistency deviation index provides a unified threshold judgment basis for the dynamic control module to trigger the definition freeze control logic when the threshold is exceeded.

[0023] In one specific implementation, extracting commitment statements and indicator items from the service plan text and generating unique identifiers can be accomplished in the following way: The service plan text is segmented and sentence-levelly divided sequentially to obtain a sentence sequence. Then, hierarchical text localization is performed to assign the sentences to the target layer, implementation layer, or acceptance layer text. Next, referential resolution is performed to unify the referents of the plan, the aforementioned indicators, etc., and backfill them to specific entities. Based on this, lexical and syntactic analysis, semantic role labeling, and entity recognition are performed to obtain elements such as predicates, agents, patients, time, quantity, conditions, and deliverables. Commitment statements are identified by combining the set of commitment verbs, the set of obligatory modal words, and the set of limiting trigger words. Indicator items are identified by combining the set of indicator keywords, numerical unit combination patterns, and indicator structure templates. A unique identifier is generated for each commitment statement and each indicator item. The unique identifier must at least consist of a hierarchical identifier, paragraph number, sentence number, and... The system uses element hashes to ensure stable comparison of the same content across different versions. Unique identifiers are associated with the locations of target-level, implementation-level, and acceptance-level texts for accurate subsequent citation and traceability. For example, if the target-level text states that data governance will be delivered within 90 days, pass compliance review, and the retention rate will increase by 5%, if the implementation-level text states that governance progress will be output every two weeks and field anonymization will be ensured, and if the acceptance-level text states that a 5% increase in retention rate is used as an acceptance indicator and the review conclusion is used as compliance evidence, then these are identified as commitment statements and indicator items, respectively, and corresponding unique identifiers are generated. Each unique identifier is bound to its level, paragraph, and sentence position, allowing for direct location of specific statements and indicator descriptions during subsequent generation, control, and auditing.

[0024] In one specific implementation, constructing a commitment strength feature vector and generating commitment strength values ​​for a commitment statement can be accomplished in the following three steps: Step 1: Extract elements from each commitment statement located in the service plan text to obtain nine categories of elements: commitment subject, commitment action, deliverable, quantification target, time constraint, condition exception, external dependency, verification method, and context constraint. Write null value markers for missing elements and conflict markers for conflicting elements so that subsequent calculations can distinguish between missing information and contradictory information. Step two involves constructing a commitment strength feature vector based on a pre-defined set of feature dimensions. This set includes at least the following dimensions: obligatory modality strength, quantification clarity, time rigidity, conditional exception contraction, external dependency controllability, verification method operability, and contextual constraint consistency. Specifically, the obligatory modality strength dimension is encoded through hierarchical mapping of obligatory modal terms such as "must," "guarantee," and "ensure." The quantification clarity dimension is encoded by whether the quantification target provides a numerical range and unit consistency. The time rigidity dimension is encoded through the cutoff or window-type features of the time constraint. The conditional exception contraction dimension is encoded through the number and coverage of conditional exceptions. The external dependency controllability dimension is encoded by whether the external dependency can be directly influenced by the commitment entity. The verification method operability dimension is encoded by whether the verification method can generate archiveable evidence. The contextual constraint consistency dimension is encoded by whether the contextual constraints conflict with compliance requirements. The commitment strength feature vector employs a segmented concatenation structure, concatenating explicit element encoding segments, semantic strength encoding segments, and verifiability encoding segments to simultaneously express the commitment content, commitment strength, and verifiability within the same vector. Step 3: Input the commitment strength feature vector into the commitment strength scoring model to obtain the commitment risk probability. The commitment strength scoring model can be a probability output model trained under supervision with historical plans and performance results. Confidence calibration is performed on the commitment risk probability to generate the commitment strength value. Confidence calibration can be achieved through temperature scaling or binning calibration to match the probability output with the actual performance risk frequency, thereby ensuring the commitment strength value has stability comparable to a threshold. For example, if the commitment statement is: "We guarantee to deliver a data governance acceptance report within sixty days and increase the retention rate by five percent, with extensions if customer data is missing, and verification is based on third-party audit conclusions," then Step 1 extracts the commitment subject as "our own company," the deliverable as the data governance acceptance report, and the quantified target as a five percent increase in the retention rate. The time constraint is within sixty days, the exception condition is missing customer data, the external dependency is provided by customer data, the verification method is the third-party audit conclusion, and the context constraint is compliance requirements restricting the use of fields; Step 2 gives a high-level code in the dimension of mandatory modality strength, a high-score code in the dimension of quantitative clarity, and a high-rigidity code in the dimension of time rigidity. At the same time, due to the existence of exception conditions and external dependencies, corresponding deduction codes are given in the dimensions of exception contraction degree and external dependency controllability. And because the third-party audit conclusion can be archived, a high-score code is given in the dimension of verification method operability; Step 3 outputs the probability of commitment risk and obtains the commitment strength value after confidence calibration, so that the commitment statement can be uniformly and stably quantified and referenced in subsequent risk judgment and control; In one embodiment, the commitment strength scoring model is a binary classification probability output model, and its input is the commitment strength feature vector. The output is the probability of the committed risk. The promised strength value was further obtained through confidence level calibration. .

[0025] Commitment strength scoring model: Any of the following can be used: logistic regression, gradient boosting tree, or feedforward neural network. Taking logistic regression as an example, first calculate... Then by The probability of the promised risk is obtained, where For weight vector, This is a bias term. The probability of commitment risk is used to characterize the probability that the corresponding commitment statement will be delayed, fail to deliver, or cause disputes under the given performance conditions.

[0026] Training Samples and Labeling: Model training samples can consist of historical service plan texts and their performance results. Performance results can come from project management systems, delivery and acceptance records, or disputed work order records. Each commitment statement is aligned with the actual result to form a monitoring label: for example, "delivered on time and accepted" is a low-risk label; "delayed delivery, reduced delivery scope, or disputes over acceptance criteria leading to rework or additional confirmation" are high-risk labels. The samples are divided into training and validation sets for parameter learning and calibration.

[0027] Confidence calibration: Temperature scaling or binning calibration can be used. Taking temperature scaling as an example, a temperature parameter is introduced into the validation set. and will As the calibrated probability output; where The determination is made by minimizing the negative log-likelihood or the expected calibration error on the validation set. Taking bin calibration as an example, the uncalibrated probability is divided into several bins according to the numerical range, and the probability output of each bin is replaced by the true high-risk frequency of that bin.

[0028] Commitment strength value: The calibrated probability of commitment risk is denoted as... and constraints This serves as the quantitative basis for subsequent threshold comparisons and control command triggering.

[0029] In one specific implementation, an indicator caliber specification diagram is constructed for each indicator item, and cross-layer field mapping relationships are generated. Based on the indicator caliber specification diagram and cross-layer field mapping relationships, a caliber deviation feature set is generated, and a caliber consistency deviation index is generated. This can be performed according to the following steps: The process involves extracting key performance indicator (KPI) data elements and generating a set of KPI data elements. This involves extracting the KPI definition, calculation method, variables and KPI constraints, data source, time window, and baseline from the target layer text, implementation layer text, and acceptance layer text, respectively, to form a set of KPI data elements. The variables and KPI constraints must at least cover the object scope, exclusion rules, numerator and denominator definitions, missing value handling, outlier handling, and statistical boundary conditions. For example, if the KPI is a 5% increase in retention rate, the target layer text would extract the KPI definition as retention rate, the time window as a calendar month, and the baseline as the previous month's retention rate. The implementation layer text would extract the calculation method as the number of retained users in the following month divided by the number of new users in the current month, excluding test accounts. The acceptance layer text would extract the data source as the lineage relationship between customer relationship management data and log data, constrained to only count real-name users. The process involves constructing an indicator caliber specification diagram and generating inter-node dependency edges. Within the indicator caliber specification diagram, nodes representing the caliber element set are created as follows: indicator definition node, calculation method node, variable and caliber constraint node, data source lineage node, time window node, and baseline node. Attribute fields corresponding to the caliber element set are written for each node. Based on graph construction rules, inter-node dependencies are determined, and inter-node dependency edges are generated. These inter-node dependency edges include at least the dependency edge from the indicator definition node to the calculation method node, the dependency edge from the calculation method node to the variable and caliber constraint node, the dependency edge from the variable and caliber constraint node to the data source lineage node, and the dependency edges from the time window node and baseline node to the calculation method node. This forms an indicator caliber specification diagram that can be used for constraint matching. The attribute fields can include numerical units, caliber version numbers, optional enumerations, allowed intervals, and evidence form requirements, enabling subsequent alignment to compare not only text similarity but also structural consistency. The process involves generating cross-layer field mapping relationships and constructing multi-perspective caliber representations. Based on the indicator caliber specification diagram, multi-perspective caliber representations are generated for the target layer text, implementation layer text, and acceptance layer text. These multi-perspective caliber representations include at least a graph perspective representation to carry nodes and dependent edges, a field perspective representation to carry cross-layer field correspondences, and a constraint perspective representation to carry the logical conditions of variables and caliber constraints. Cross-layer field mapping relationships are generated with the field perspective representation as the core, enabling the field expressions of the same indicator item at different levels to be mapped to a unified set of fields while retaining the level source mark. For example, the natural month retention in the target layer text and the next month retention in the implementation layer text are aligned as time window fields, and the "only count real-name users" in the acceptance layer text and the "exclude test accounts" in the implementation layer text are aligned as different sub-constraints of variable and caliber constraint fields, thereby providing a comparable field space for subsequent distance calculations. The process involves calculating alignment distance sets, generating caliber deviation feature sets, and generating caliber consistency deviation indices. For multi-view caliber representations, graph structure constraint matching is performed to filter out candidate alignments that violate dependency edge directions and the existence of essential nodes. Alignment scoring is then performed to quantify the consistency of different levels of representation in node attributes and field values. Under scoring constraints, optimal transmission distance calculation is performed to obtain alignment distance sets. These alignment distance sets characterize the structural and field differences between target-layer and implementation-layer text, implementation-layer and acceptance-layer text, and target-layer and acceptance-layer text. The alignment distance sets are then projected to generate caliber deviation feature sets, allowing deviations to be decomposed into interpretable dimensions such as time window deviation, data source lineage deviation, variable and caliber constraint deviation, baseline deviation, and unit deviation. Feature normalization is then performed on the caliber deviation feature sets to obtain normalized feature vectors. The quantity is summed to obtain the caliber deviation score. The caliber deviation score is then used to generate the caliber deviation risk probability through a probability mapping function. Confidence calibration is then performed on the caliber deviation risk probability to generate a caliber consistency deviation index, which allows for stable comparison across different indicator items and different text versions. For example, if the target layer text declares a 5% increase in retention rate based on the natural month, the implementation layer text calculates based on the seven-day retention rate and excludes test accounts, and the acceptance layer text calculates based on the next month's retention rate and only counts real-name users and uses log data, then the graph structure constraint matching will result in increased alignment costs at time window nodes and variable and caliber constraint nodes. The optimal transmission distance calculation yields a larger set of alignment distances. After feature projection, the proportion of time window deviation and variable and caliber constraint deviation increases, ultimately raising the caliber consistency deviation index. Thus, the same index can be used to directly reflect the risk intensity and priority of cross-layer caliber inconsistency for this indicator item.

[0030] In one embodiment, the specification diagram of the index is denoted as follows: ,in For the set of caliber element nodes, It is a set of dependent edges; the node attribute fields should include at least the field name, field value, unit, time window, data source identifier, and culling / missing handling rules.

[0031] Graph structure constraint matching: When two levels of representations to be compared do not meet the preset essential constraints in the set of node types or the direction of dependent edges (e.g., missing time window nodes or variable and caliber constraint nodes, or the direction of dependent edges is opposite to the preset rules), they are judged to be structurally inconsistent, and a structural penalty term $P_s$ is assigned to the alignment for subsequent distance calculation.

[0032] Comparison of alignment score and cost construction: for any node pair Calculate node attribute similarity It can be obtained by weighting text similarity (e.g., edit distance or cosine similarity) with rule consistency (e.g., whether units are consistent, whether time windows are similar, whether data sources are from the same source). The cost is defined as... And based on this, a cost matrix is ​​formed. When structural constraints are not satisfied, Taking a larger value to ensure that structural inconsistencies will significantly increase the distance.

[0033] A feasible solution for the optimal transmission distance: Construct discrete distributions for the two-level field perspective representations. The supporting points for the distribution are each field node or field entry; weights can be uniformly distributed or assigned according to field importance (e.g., time window, calculation method, data source lineage assigns higher weights). The optimal transmission distance is defined under constraints... Minimize the following objective function: The problem can be solved using an entropy-regularized Sinkhorn iteration to obtain a stable and reproducible alignment distance.

[0034] In one embodiment, the two-level field perspective representations are constructed as discrete distributions. ,and Each component is non-negative and satisfies the following conditions: Cost matrix elements Indicates the first The field node and the first The inconsistency cost between field nodes. The optimal transmission distance is defined as solving the coupling matrix. : under constraints Below, make the objective function ; minimum; among which Here, represents the entropy regularization coefficient. The optimization problem can be solved using Sinkhorn iteration, and... Output as alignment distance.

[0035] Feature projection and interpretable deviation dimensions: The obtained alignment distance set is projected according to dimensions such as "time window deviation, data source lineage deviation, variable and caliber constraint deviation, baseline deviation, and unit deviation". The projection method can use a preset mapping matrix or rule mapping: for example, the distances related to time window nodes are aggregated into time window deviation features, and the distances related to data source lineage nodes are aggregated into data source deviation features, thereby forming a caliber deviation feature set.

[0036] Deviation score, probability mapping and calibration: The deviation score is obtained by summing the interval normalization of each deviation feature. The probability mapping function can be the logistic function. in The validation set can be fitted using historical disputed / reworked samples or default values ​​can be used; then... Confidence calibration is performed to obtain the caliber consistency deviation index. and constraints .

[0037] The dynamic control module (used to determine risk intensity based on commitment strength and consistency deviation index, and to update the first, second, and third preset thresholds based on risk intensity; and to output control instruction sets for the hierarchical generation module's generation of commitment statements and indicator items based on the first, second, and third preset thresholds): transforms the commitment strength and consistency deviation index output by the risk assessment module into a "closed-loop control" of the generation process, making service solution generation not just text generation, but risk-driven dynamic constraint generation. Determining risk intensity based on commitment strength and consistency deviation index forms a unified quantitative measure that comprehensively reflects the current risk status of the solution, driving thresholds and instruction outputs under the same control framework for different types of risks. Updating the first, second, and third preset thresholds based on risk intensity allows the thresholds to adapt to risk intensity, increasing control intensity in high-risk scenarios and reducing unnecessary restrictions in low-risk scenarios, thereby improving generation efficiency and usability. Based on the first, second, and third preset thresholds, the hierarchical generation module outputs a set of control instructions for generating commitment statements and indicator items. This directly transforms risk assessment into executable generation constraints or generation mode control, ensuring that the hierarchical generation module is subject to unified control instructions when generating commitment statements and indicator items. This ensures that commitment expressions and indicator definitions are controlled during the generation stage, rather than discovering risks and having to rework after generation.

[0038] In one specific implementation, the dynamic control process can be completed and form a closed-loop control of the hierarchical generation process in the following manner: After receiving the deviation index between the commitment strength value and the caliber consistency index, the risk intensity is determined according to a unified risk fusion rule. The risk fusion rule can adopt a combination of weighted summation and nonlinear mapping in existing technologies. For example, the commitment strength value and the caliber consistency deviation index are normalized by interval and then weighted by business weights. The risk intensity is obtained through logistic mapping to enhance the sensitivity to extreme high risks. At the same time, the first, second, and third preset thresholds are updated based on the risk intensity: The first preset threshold is used to characterize the critical point where the commitment strength value triggers strong constraints. It can be determined by selecting points from historical performance risk-labeled samples through the subject operating characteristic curve to meet the preset false positive and false negative rate targets. The second preset threshold is used to characterize the critical point where the caliber consistency deviation index triggers caliber locking. It can be determined by using the quantile method to determine the initial value from historical caliber disputes or rework records and adaptively corrected with the recent deviation level using exponential moving average. The third preset threshold is used to characterize the critical point where the comprehensive risk intensity triggers the generation strategy switch. It can be determined by using the principle of cost sensitivity minimization combined with the risk tolerance of budget period and compliance requirements. The risk intensity is updated stably after confidence calibration to avoid threshold fluctuations. Based on the first, second, and third preset thresholds, a set of control instructions is output and applied to the hierarchical generation process. This set of control instructions includes at least three components: a commitment statement generation mode control instruction, a commitment statement phrase selection constraint instruction, and an indicator item caliber freezing control instruction. When the risk intensity reaches the range corresponding to the third preset threshold, the commitment statement generation mode control instruction switches the generation mode from a strong commitment narrative to a conditional and verifiable narrative, requiring that a time-constrained sentence be output only after the commitment subject, deliverable, and verification method are all present. When the commitment strength value reaches the range corresponding to the first preset threshold, the commitment statement phrase selection constraint instruction restricts the frequency of high-intensity terms in the obligatory modal word set and requires that preconditions and exceptions in the trigger word set appear in pairs to reduce the risk of non-fulfillment. When the caliber consistency deviation index reaches the range corresponding to the second preset threshold, the indicator item caliber freezing control instruction locks the values ​​of the indicator definition, calculation method, time window, data source lineage, and variable and caliber constraint fields, prohibiting the introduction of new units, time windows, or elimination rules in different levels of text. For example, if a service plan guarantees a 5% increase in retention rate within 30 days in the target layer text and requires compliance review, while the implementation layer text rewrites the retention rate to a 7-day retention rate and adds a constraint that only real-name users are counted, and the acceptance layer text uses log data as the sole data source, then the deviation from the consistency standard will increase, triggering a standard freeze control instruction. This will force a rollback and unify the retention time window to a natural month and a consistent data source lineage. At the same time, due to the high commitment strength value, a phrase selection constraint instruction will be triggered, adjusting the guarantee to deliver an acceptable result under the premise that the data meets the integrity requirements and using the review conclusion as the verification method. This will significantly reduce the risk of performance and disputes without changing the service objectives. In one embodiment, the control instruction set is a structured record set, and each control instruction includes at least: a target unique identifier. Instruction type Command parameters Triggering basis (Including risk intensity / threshold / corresponding commitment strength value or consistency deviation index) and instruction validity period or version number. The control instruction set must take effect on the hierarchical generation module in at least the following two ways: (1) Natural language generation mode: Convert control instructions into prompt constraints or decoding constraints and inject them into the generation process, including mandatory slots (commitment subject / deliverable / verification method), mandatory modal word blacklist / whitelist, and mandatory consistent reference of indicator fields (time window / unit / removal rules, etc.); (2) Compliance phrase set rendering mode: Select from the compliance phrase set... The matching phrase template is used to fill the template placeholders with the frozen field values ​​to generate a commitment statement or indicator item description; if the filling fails, it falls back to the natural language generation mode and triggers a second validation.

[0039] In one embodiment, to ensure consistency between risk intensity and threshold comparison, interval normalization is performed on the deviation index of the consistency between the commitment strength value and the threshold. The upper and lower bounds of normalization can be determined by the quantiles of historical samples: for example, the lower bound is taken from the low quantile of the historical distribution, and the upper bound from the high quantile, with values ​​exceeding the bounds truncated to reduce the impact of extreme values. The updates of the first, second, and third preset thresholds can use exponential moving averages: assuming a certain threshold at time... The value is The suggested threshold calculated from the latest sample is Then update to ;in This is a smoothing coefficient used to suppress threshold jitter; the threshold update frequency can be either iterated by version or periodically updated by time window.

[0040] To ensure the deterministic triggering of the control instruction set, a triggering rule table can be established: when the commitment strength value exceeds the first preset threshold, the commitment statement phrase constraint is triggered; when the consistency deviation index exceeds the second preset threshold, the caliber freeze is triggered; when the comprehensive risk heat exceeds the third preset threshold, the generation mode switch is triggered, and the consistency verification of commitment and caliber is required to be re-executed after the mode switch until the risk falls below the threshold or reaches the preset iteration limit.

[0041] The compliance output module (used to generate publishable solutions based on service plan text regulated by the control instruction set, and output regulation records and version information): It organizes the service plan text regulated by the dynamic regulation module into "publishable solutions," and simultaneously outputs regulation records and version information that explain and trace the generation process, thereby meeting the enterprise's needs for compliance auditing, version retrospective, responsibility definition, and approval documentation. Generating publishable solutions based on service plan text regulated by the control instruction set emphasizes that the final output solution text has undergone risk-driven generation constraints and is no longer an uncontrolled original generation result, thus reducing the risks of unfulfilled commitments and inconsistent indicator definitions. The compliance output module outputs regulation records and version information, providing verifiable evidence for subsequent internal audits, customer confirmation, dispute resolution, and version iteration. The regulation records and version information reflect the regulatory actions and corresponding risk backgrounds that occurred during the generation process, ensuring that the solution output not only "provides the result" but also "explains the process," forming a manageable and reusable compliance closed loop in enterprise customized service scenarios.

[0042] In one specific implementation, generating a publishable scheme and outputting control records and version information can be accomplished in the following way: After receiving the service plan text regulated by the control instruction set, pre-release consistency verification and traceable encapsulation are performed. This involves a final alignment check on the commitment statements and indicator items in the target layer text, implementation layer text, and acceptance layer text. It confirms that the natural language generation mode or compliance phrase set rendering mode corresponding to the generation mode identifier matches the current risk intensity, and writes the phrase entry identifier of the selected phrase in the compliance phrase set into the record. Simultaneously, it writes an entry reference identifier for each indicator item and verifies that the entry freeze status corresponding to the frozen entry record identifier has not been broken. A publishable plan is then generated, and the plan text and regulation records are encapsulated and output with the same version information. The regulation records and version information must at least include the commitment strength value, the entry consistency deviation index, and the first preset threshold. The system includes a second preset threshold, a third preset threshold, risk intensity, generation mode identifier, phrase entry identifier, caliber reference identifier, and frozen caliber record identifier. Version number, generation time, difference summary, and verification summary can also be added for review and auditing. For example, when the risk intensity exceeds the third preset threshold, the generation mode identifier switches to a compliant phrase set rendering mode. The commitment statement is changed from ensuring delivery within thirty days to delivering acceptable results under the condition that data integrity is met and the review is passed. The phrase entry identifier corresponding to the selected phrase is recorded. Simultaneously, because the caliber consistency deviation index exceeds the second preset threshold, caliber freezing is triggered. The frozen caliber record identifier and caliber reference identifier are output together, enabling the release plan and its formation process to be accurately reproduced and traced.

[0043] The normalization upper and lower bounds, business weights, temperature scaling parameters, number of bins, exponential moving average smoothing coefficient, threshold update window length, and probability mapping function parameters involved in the above calculations can be determined as follows: (i) Based on historical performance risk samples and dispute / rework samples, the false positive rate and false negative rate are selected by fitting the validation set or cross-validation to ensure that the false positive rate and false negative rate meet the preset targets. (ii) When there are insufficient historical samples, the preset default parameters are used and adaptive updates are made based on the newly added samples in subsequent version iterations; (iii) Constrain the range of values ​​of each parameter to ensure that the output value range is stable and the threshold comparability is satisfied, so that the method of the present invention can be directly implemented by those skilled in the art.

[0044] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An AI-driven intelligent generation system for customized enterprise service solutions, characterized in that: include: The requirement parsing module is used to receive enterprise requirement texts and generate structured requirement information; The layered generation module is used to generate service plan text based on structured requirement information. The service plan text includes multiple layers of text. The risk assessment module is configured to: extract commitment statements and indicator items from the service plan text, and generate a unique identifier for each commitment statement and indicator item; construct a commitment strength feature vector for the commitment statements and generate a commitment strength value; construct an indicator caliber specification diagram for each indicator item and generate cross-layer field mapping relationships; and generate a caliber deviation feature set and a caliber consistency deviation index based on the indicator caliber specification diagram and cross-layer field mapping relationships. The dynamic control module is used to determine the risk intensity based on the commitment strength value and the consistency deviation index, and to update the first preset threshold, the second preset threshold, and the third preset threshold based on the risk intensity. It is also used to output control instruction set for the process of generating commitment statements and indicator items by the hierarchical generation module based on the first preset threshold, the second preset threshold, and the third preset threshold. The compliance output module is used to generate publishable solutions based on the service solution text after being regulated by the control instruction set, and to output regulation records and version information.

2. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The requirements analysis module performs sentence segmentation, topic identification, element entity extraction, synonym unification, and constraint disambiguation on the enterprise's requirements text, and generates structured requirements information according to preset field templates. The structured requirements information includes at least the service objectives, service scope, constraints, budget and schedule, compliance requirements, and candidate indicators; the service plan text includes at least the target layer text, implementation layer text, and acceptance layer text.

3. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The risk assessment module performs segmentation, sentence-level segmentation, hierarchical text location, referential resolution, lexical and syntactic analysis, semantic role labeling, and entity recognition on the service plan text. It identifies commitment statements by combining a set of commitment verbs, a set of obligatory modal words, and a set of limiting trigger words. It identifies indicator items by combining a set of indicator keywords, numerical unit combination patterns, and indicator structure templates. The risk assessment module stores unique identifiers associated with the locations of the target layer text, implementation layer text, and acceptance layer text.

4. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The risk assessment module extracts the commitment subject, commitment action, deliverable, quantifiable target, time constraint, condition exception, external dependency, verification method, and context constraint for each commitment statement. It constructs a commitment strength feature vector based on a preset feature dimension set, and inputs the commitment strength feature vector into the commitment strength scoring model to obtain the commitment risk probability. It then performs confidence calibration on the commitment risk probability to generate a commitment strength value.

5. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The risk assessment module generates multi-perspective caliber representations from target layer text, implementation layer text, and acceptance layer text based on the indicator caliber specification diagram and cross-layer field mapping relationship. It performs graph structure constraint matching, comparison alignment scoring, and optimal transmission distance calculation on the multi-perspective caliber representations to obtain an alignment distance set, and then generates a caliber deviation feature set by feature projection of the alignment distance set. The feature normalization of the feature set of caliber deviation is performed to obtain the normalized feature vector. The summation operation of the normalized feature vector is performed to obtain the caliber deviation score. The caliber deviation score is used to generate the caliber deviation risk probability through the probability mapping function. The confidence calibration of the caliber deviation risk probability is performed to generate the caliber consistency deviation index.

6. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The risk assessment module constructs an indicator caliber specification diagram for each indicator item, including: extracting the indicator definition, calculation method, variable and caliber constraints, data source, time window, and baseline for each indicator item to form a caliber element set; creating indicator definition nodes, calculation method nodes, variable and caliber constraint nodes, data source lineage nodes, time window nodes, and baseline nodes for each caliber element set in the indicator caliber specification diagram, and writing the attribute fields corresponding to the caliber element set for each node; determining the dependencies between nodes based on graph construction rules and generating dependency edges between nodes, thereby forming the indicator caliber specification diagram.

7. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 6, characterized in that, The inter-node dependency edges include at least the dependency edges from the indicator definition node to the calculation method node, the dependency edges from the calculation method node to the variable and caliber constraint node, the dependency edges from the variable and caliber constraint node to the data source lineage node, and the dependency edges from the time window node and the baseline node to the calculation method node.

8. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The dynamic control module determines risk intensity by: performing normalization mapping on the commitment strength value corresponding to each commitment statement to obtain normalized commitment strength; performing normalization mapping on the caliber consistency deviation index corresponding to each indicator item to obtain normalized caliber deviation; determining the maximum value among all normalized commitment strengths as commitment intensity, determining the maximum value among all normalized caliber deviations as caliber intensity; and determining the maximum value between commitment intensity and caliber intensity as risk intensity.

9. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The dynamic control module outputs a set of control instructions based on the first preset threshold, the second preset threshold, and the third preset threshold. The set of control instructions includes at least the commitment statement generation mode control instruction, the commitment statement phrase selection constraint instruction, and the indicator item caliber freeze control instruction.

10. The AI-driven intelligent generation system for customized enterprise service solutions according to claim 1, characterized in that, The regulation records and version information output by the compliance output module include commitment strength value, consistency deviation index, first preset threshold, second preset threshold, third preset threshold, risk heat, generation mode identifier, phrase entry identifier, caliber reference identifier, and frozen caliber record identifier. Among them, the generation mode identifier is used to identify the natural language generation mode or the compliance phrase set rendering mode, and the phrase entry identifier is the identifier corresponding to the selected phrase in the compliance phrase set.

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