A model distillation solidification and parameterization deployment method for industry data analysis equipment

Abstract

This application discloses a method for model distillation solidification and parameterized deployment for industry data analysis equipment. The method includes: a cloud-based teacher agent receiving industry analysis task requests and forming a teacher trajectory library; performing a reliable gating score on each trajectory sample in the teacher trajectory library to generate a gating distillation training set; training a local student model using a multi-objective loss function for gating distillation; outputting a student model weight parameter set and capability package after training; loading the student model weight parameter set and capability package on the industry data analysis equipment side; initiating an industry analysis task request on the industry data analysis equipment side; and automatically completing multi-source data collection, arrangement, and structured report generation under constraints, outputting a due diligence report and corresponding trajectory and log records. This application can reduce overall usage costs and improve the reusability and implementation efficiency of cross-customer deployment.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence and data analysis system engineering technology, and in particular to a method for model distillation solidification and parameterized deployment of industry data analysis equipment. Background Technology

[0002] To enhance automation, existing solutions typically employ cloud-based large-parameter models combined with workflow orchestration platforms to achieve a chained processing of "multi-interface querying—result cleaning—evidence aggregation—report writing." The workflow orchestration platform provides node-based model invocation, tool invocation, and retrieval enhancement capabilities, supporting the visual construction of industry analytics agent applications and retrieval enhancement generation (RAG) pipelines. For example, some open-source or commercial platforms have publicly described how to orchestrate LLM applications, RAG processes, and agent capabilities through workflow nodes.

[0003] Existing technologies for "automatic generation of industry analysis reports" have yielded various implementation paths: for example, intelligent report generation solutions for financial assets and corporate information automate the analysis process by integrating large models, online retrieval tools, and data table tools; solutions for automated project survey analysis include steps such as information extraction, credible / uncredible item labeling, and report generation. These existing solutions provide a foundation for the automation of industry analysis reports, but most still rely on online retrieval and collaboration with external toolchains.

[0004] However, in the promotion, delivery, and engineering operation and maintenance of the aforementioned industry analytical intelligent agent solutions, the following technical problems and defects are generally present: (1) High cost due to reliance on cloud models and data centers: Existing industry data analysis agents mostly use cloud-based large models and cloud-based workflow / computing services for inference and orchestration, which require continuous use of data center computing, storage and network bandwidth resources, and are usually billed based on the number of calls, concurrent quotas or subscriptions; when the task involves multiple data interface calls, repeated retrieval and multiple rounds of generation, the consumption of cloud resources increases significantly, which increases the overall usage cost and expense fluctuations, making it difficult for customers to achieve cost-controllable long-term operation.

[0005] (2) Risk of sensitive data leaving the domain: Industry analysis often requires the integration of local enterprise data (financial, contract, business information, etc.). When the system adopts cloud inference or cloud workflow orchestration, there are compliance and leakage risks of enterprise data being processed and stored in the cloud.

[0006] (3) The workflow and prompts are complex to configure and have poor portability: Existing solutions generally require repeated configuration of workflow node links, parameter mapping rules, prompts and paragraph templates for different customers, different data source permissions and different report templates. The delivery cycle is long, the maintenance cost is high, and it is easy to fail due to changes in interface fields, changes in node versions or process adjustments.

[0007] In the area of ​​integrated hardware and software devices, existing technologies and product forms typically pre-integrate computing hardware such as GPUs / CPUs with the inference runtime environment, and pre-configure several optional models or model images, enabling users to load the target model and start the inference service in their local intranet environment. Based on this, users can then perform secondary development or optimization around specific business needs, such as configuring inference parameters (batch size, concurrency, context length, etc.), connecting to business interfaces, and adjusting prompts or application logic to achieve question answering or report generation for specific scenarios. This type of solution focuses on "localized model operation and inference service deployment," but in the cross-project / cross-customer delivery of industry analysis agents, it still often requires significant investment in workflow orchestration and writing template configuration, resulting in high reuse costs. Simultaneously, there is a lack of a systematic technical path to solidify the tool-calling orchestration capabilities of cloud-based teacher agents and the structured writing capabilities of industry analysis reports into locally runnable small model capability packages.

[0008] In academic and engineering practice, there have been studies on distilling the task-solving behavior of large-scale intelligent agents into smaller models (including retrieval and code tools), as well as studies on migrating retrieval enhancement capabilities from large models to smaller models to reduce resource consumption. However, these studies mostly focus on general inference tasks or the migration of general retrieval enhancement capabilities, and usually do not address engineering problems such as the orchestration of multi-source interfaces that are heavily dependent on industry data analysis scenarios, the solidification of industry-specific parameter knowledge, and the parameterized deployment of adapters that are closely related to the delivery of integrated machines.

[0009] Therefore, existing technologies lack a core technical solution for industry data analysis equipment that combines "gated distillation solidification + parameterized deployment": on the one hand, it can reliably solidify the capabilities of cloud-based teacher intelligence agents in multi-interface call orchestration and report structure generation into a locally runnable small model; on the other hand, it can quickly adapt to different customers' data source permissions, field mappings, and report templates through parameterization, achieving rapid delivery and deployment with low configuration costs and high portability while ensuring that sensitive data does not leave the domain. Summary of the Invention

[0010] To address the shortcomings of existing industry data analysis intelligent agent solutions, such as high costs due to reliance on cloud models and data centers, the risk of sensitive data leaving the domain when integrating local enterprise data, and the complexity and poor portability of workflow and prompt word configuration, this application provides a model distillation solidification and parameterized deployment method for industry data analysis devices. This method solidifies the multi-source data interface call orchestration capability and the structured generation capability of analysis reports of the industry data analysis intelligent agent into a locally deployable small model, and completes rapid deployment and operation in industry data analysis devices through adapter parameterization.

[0011] This application discloses a method for model distillation solidification and parameterized deployment of industry data analysis equipment, which includes: Step 1: The industry analysis methodology, data collection field definitions, and report writing specifications are encapsulated into a version-managed Industry Analysis Parameter Package (IAP). The cloud-based teacher agent receives industry analysis task requests and completes multi-source data collection and report generation under the constraints of the IAP's chapter framework, field definitions, and report skeleton. The system collects and records the execution process of the cloud-based teacher agent in a structured manner, forming a Teacher Trajectory Library (TTR). Step 2: Based on multidimensional evidence, a credible gating score G is applied to each trajectory sample in the Teacher Trajectory Database (TTR). Samples are then graded and screened according to the credible gating score G and a preset gating threshold to generate a gating distillation training set D_train containing a high-credibility sample set D_high and a suspicious sample set D_mid. The credible gating score G is then mapped to the weights of the training samples. The multidimensional evidence includes at least tool call compliance, parameter consistency, report structure compliance, and operational stability. The tool call compliance is used to characterize whether the tool call sequence meets the capability catalog constraints. The system assesses whether unauthorized tools, unauthorized data domains, or invalid action types are present. The parameter consistency is used to characterize the consistency between tool call parameters and the field specification dictionary, including units, enumerated values, range constraints, default value completion, and specification identifier matching. The report structure compliance is used to characterize whether the report's structured writing trajectory meets the report skeleton and paragraph slot constraints, including chapter tree completeness, paragraph tag validity, slot coverage, and missing key paragraphs. The operational stability is used to characterize stability indicators during trajectory execution, including anomaly rate, retry count, time consumption statistics, and status code distribution. Step 3: Script the trajectory samples in the gated distillation training set D_train into a tool orchestration script TOS and a report structure script RSS. Based on the weights of the training samples, use a multi-objective loss function that includes tool script distillation loss L_TOS, report script distillation loss L_RSS, consistency alignment loss between tool results and report slots L_ALIGN, and penalty term L_NEG for violations to train the local student model using gated distillation. After training, output the student model weight parameter set W_S and capability package SB. The capability package SB includes student model weights, adapter parameters, tool capability index, report skeleton and slot label set, and vocabulary and caliber mapping metadata related to the IAP version. Step 4: Load the student model weight parameter set W_S and the capability package SB on the industry data analysis device side, and complete the inference service online and rapid adaptation with minimal deployment configuration. The system automatically loads the required tool adapters and report structure rules according to the capability package SB, and completes the self-check of connectivity and permission consistency. When authentication failure, interface unreachability or permission mismatch is detected, the system outputs a configuration gap list and executes a security degradation strategy. After loading is completed, an industry analysis task request is initiated on the industry data analysis device side. Under the constraints of SB, the student model automatically completes multi-source data collection and arrangement and structured report generation, and outputs an archiveable due diligence report and corresponding trajectory and log records.

[0012] Furthermore, the Teacher Trajectory Library (TTR) includes tool call arrangement trajectory and report structured writing trajectory; the tool call arrangement trajectory is used to record the data domain selection, interface call, parameter filling, return summary and exception handling processes performed by the cloud-based teacher agent to complete the industry analysis task; the report structured writing trajectory is used to record the chapter tree, paragraph tags, slot filling results and their corresponding text fragments or structured paragraph objects output by the cloud-based teacher agent. The Industry Analysis Parameter Package (IAP) can be versioned and records version numbers and change summaries to maintain traceable consistency across different batches of tasks and deliveries to different customers. The Industry Analysis Parameter Package (IAP) includes: The industry analysis framework and chapter structure are used to define the macro / meso / micro organizational methods of the industry analysis report and the chapter positions and key outputs of commonly used analysis models; The data domain and capability directory is used to define the data domains for business and judicial affairs, public opinion information, intellectual property, investment and financing, and macro and industry indicators, and to associate the data acquisition capabilities that can be accessed by each data domain. The field and caliber dictionary is used to define field names and aliases, units of measurement, caliber identifiers, enumeration / value ranges, default values, update frequencies, and source tags. The report skeleton and paragraph slots are used to define the directory tree structure of the industry analysis report, paragraph labels, and variable slots that need to be filled in each paragraph.

[0013] Furthermore, in step 1, a single trajectory in the Teacher Trajectory Database (TTR) includes: Task specifications, including task type, target object, time range, output format and constraints; The IAP version identifier referenced; The tool call sequence includes the tool identifier, action type, parameter fields and references, actual parameters, return summary, and status code / error information; The report output includes a chapter tree, paragraph labels, slot filling results, and corresponding text fragments or structured paragraph objects. Runtime metadata, including model version, tool version, permission identifier, time consumption statistics, and log index.

[0014] Furthermore, in step 2, the trusted gating score G is calculated using a weighted normalization fusion method, and its calculation formula is as follows:

[0015] in, , , , All are weighting coefficients. For bias terms, To compress the score to the [0,1] interval, a nonlinear function, To ensure compliance of tool usage, For parameter caliber consistency, To ensure compliance with report structure, For operational stability; Highly reliable sample set Directly used for subsequent gated distillation training; suspicious sample set After entering deweighted training or triggering automatic repair, then enter gated distillation training. Automatic repair includes parameter correction, structure completion, and abnormal fragment pruning; low-confidence sample set. Excluded or used only for negative sample constraints, where, and All are gate thresholds. These are trajectory samples from the Teacher Trajectory Database (TTR).

[0016] Furthermore, in step 3, the tool script distillation loss L_TOS and the report script distillation loss L_RSS adopt a knowledge distillation form based on KL divergence, and their expressions are as follows:

[0017] in, The knowledge distillation form of KL divergence, where T is the temperature parameter. For the teacher model, the logits are located at the corresponding script token position. For the logits of the student model, Let be the divergence function. It is a normalized exponential function; The tool script distillation loss L_TOS or report script distillation loss L_RSS is calculated jointly by the knowledge distillation loss L_KD and the cross-entropy loss L_CE, and its expression is:

[0018] in, Let L_TOS be the distillation loss of the tool script or L_RSS be the distillation loss of the report script, and β be the balancing weight.

[0019] Furthermore, in step 3, the scripted representation specifically refers to: Convert tool calls and orchestration trajectories into tool orchestration scripts (TOS), where each action is expressed in the form of <tool identifier, action type, parameter mode reference, parameter value, return key>. The structured writing process of the report is converted into a report structure script RSS, in which each paragraph is expressed in the form of <section identifier, tag, slot key, slot value reference, text fragment>. Furthermore, each trajectory sample in the Teacher Trajectory Library (TTR) is appended with a TaskSpec and an IAP version reference (IAP_ver) to form the training input. With training objectives The standard form, These are trajectory samples from the Teacher Trajectory Database (TTR).

[0020] Furthermore, the total loss of the multi-objective loss function L in step 3 for training sample i is expressed as:

[0021] in, Let be the weight of the i-th training sample. The distillation loss is the value of the tool script corresponding to the i-th training sample. The distillation loss is the reporting script corresponding to the i-th training sample. Let $\frac{i}{i}$ be the consistency alignment loss between the tool result and the report slot corresponding to the $i$-th training sample. The penalty term loss for the i-th training sample is the penalty term for low-confidence or violation actions in the low-confidence sample set D_low or the suspicious sample set D_mid. , , , All are balance coefficients; Generate alignment keys for each tool call's returned summary and corresponding report slot reference, and encode them as follows: and Alignment loss L_ALIGN between tool results and report slots can be obtained by cosine similarity, or by using the InfoNCE contrast loss function.

[0022] in, For the first time returned by the tool call The vector corresponding to each summary In order to be with the first Each report slot references the corresponding vector. It is a cosine function; The penalty term loss L_NEG is implemented in the following way: Using segments marked as violations or failures from the low-confidence sample set D_low or the suspicious sample set D_mid, negative constraint samples are constructed, and the prediction probability of the violation action or combination of violation parameters is minimized during training. The expression for this is:

[0023] in, Set of illegal actions The actions in the middle, This represents the implicit state of the model.

[0024] Further, step 4 includes: The system performs automatic assembly and self-checks based on the minimum deployment configuration: automatically starts and stops the corresponding data domain tool adapters and binds authentication; automatically performs connectivity tests and permission consistency checks; automatically loads the chapter structure and paragraph slot rules of the report template to generate structured report objects; if authentication is missing, interface is unreachable, or permissions are inconsistent, the system generates a configuration gap list and triggers a security degradation strategy.

[0025] Furthermore, step 4 also includes: the system generates a deployment configuration snapshot, which records the student model version, student model weight parameter set W_S version, selected scenario and template version, authentication and data source configuration summary and timestamp used in this deployment, for subsequent reproduction, migration and regression verification.

[0026] Furthermore, the minimum deployment configuration in step 4 includes at least: authentication materials for external data interfaces, connection parameters for internal network data sources, and selection options for industry scenarios or report templates; the system automatically loads the corresponding field definition version and report structure rules according to the selected scenario, and automatically determines the data domains and tool sets to be enabled.

[0027] Due to the adoption of the above technical solution, this application has the following advantages: 1. Compared with existing solutions that rely on cloud-based large models and workflow orchestration platforms to generate industry analysis reports, this application takes "teacher trajectory collection under IAP constraints of industry analysis parameter package - trusted gating scoring - gating distillation solidification - all-in-one machine parameterized deployment" as the main line. It transfers the capabilities of cloud-based teacher intelligence agents in multi-source data interface calling orchestration and report structured writing to student models that can run locally. It also achieves engineering implementation through minimum configuration and automatic assembly mechanism on the deployment side. This provides an improved path to address the problems of high cost, risk of sensitive data leaving the domain, and complex delivery configuration and poor portability in existing technologies.

[0028] 2. By constructing a versioned Industry Analysis Parameter Package (IAP) and collecting Teacher Trajectory Database (TTR) under the constraints of the IAP's chapter framework, field definitions, and report skeleton, the training samples maintain consistency and traceability in terms of structure and definition. This allows implicit experiences scattered across "prompt words / paragraph templates / workflow node links" in cross-customer delivery to be transformed into reusable structured trajectory samples, reducing engineering uncertainties in subsequent capability transfer and reuse.

[0029] 3. By applying credible gating scores and tiered screening to teacher trajectory samples, engineering constraints such as compliance of tool calls, consistency of parameter definitions, compliance of report structure, and operational stability are transformed into calculable gating scores, and a credible sample set and sample weights are generated accordingly. This mechanism can reduce the probability of occasional errors, abnormal calls, or non-compliant report fragments being transferred to the student model, thereby improving the stability and controllability of tool arrangement and structured writing behavior on the small model side.

[0030] 4. By performing gated distillation training and capability solidification, the teacher's "tool calling and arrangement ability + report structured writing ability" are converted into a set of student model weight parameters and capability packages, enabling industry analysis tasks to complete multi-source data collection and arrangement and structured report generation in the local environment of the all-in-one machine; thereby reducing the continuous dependence on cloud-based large models and data center resources, and reducing the overall resource consumption and cost fluctuations in multi-round generation and multi-interface linkage scenarios.

[0031] 5. Through parameterized deployment and rapid adaptation of the all-in-one machine, repetitive work on the delivery side is reduced to a small number of minimal deployment configurations (such as interface authentication, intranet data source connection parameters, and scenario / template selection). The system automatically completes tool adapter assembly, self-check verification, and template loading. Since the capability package SB has fixed field definition mapping and report chapter slot structure, the deployment side does not need to repeatedly build workflow node links and prompt word template configurations, thereby shortening the online and migration time, improving the portability and implementation efficiency of cross-customer promotion, and reducing the compliance and leakage risks caused by sensitive data leaving the domain under local operating conditions.

[0032] 6. The deployment configuration snapshot generated in this application records information such as model version, W_S version, scenario and template version, and configuration summary, which facilitates subsequent reproduction, migration and regression verification, and helps maintain deployment consistency and operation and maintenance traceability in multi-customer and multi-batch delivery. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0034] Figure 1 This is a schematic diagram illustrating the industry analysis trajectory collection and knowledge asset injection in an embodiment of this application.

[0035] Figure 2 This is a schematic diagram of the trusted gating scoring and sample screening in an embodiment of this application.

[0036] Figure 3 This is a schematic diagram of gated distillation training and capability solidification in an embodiment of this application.

[0037] Figure 4 This is a schematic diagram illustrating the rapid deployment and parameterization adaptation of the all-in-one machine according to an embodiment of this application.

[0038] Figure 5 This is a schematic flowchart illustrating a method for model distillation solidification and parameterized deployment of an industry data analysis device according to an embodiment of this application. Detailed Implementation

[0039] The present application will be further described in conjunction with the accompanying drawings and embodiments. The described embodiments are only some, not all, of the embodiments of the present application. All other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of the present application.

[0040] See Figure 5 This application provides an embodiment of a model distillation solidification and parameterized deployment method for industry data analysis equipment, comprising: Step 1: The industry analysis methodology, data collection field definitions, and report writing specifications are encapsulated into a version-managed Industry Analysis Parameter Package (IAP). The cloud-based teacher agent receives industry analysis task requests and completes multi-source data collection and report generation under the constraints of the IAP's chapter framework, field definitions, and report skeleton. The system collects and records the execution process of the cloud-based teacher agent in a structured manner, forming a Teacher Trajectory Library (TTR). Step 2: Based on multidimensional evidence, a credible gating score G is applied to each trajectory sample in the Teacher Trajectory Database (TTR). Samples are then graded and screened according to the credible gating score G and a preset gating threshold to generate a gating distillation training set D_train containing a high-credibility sample set D_high and a suspicious sample set D_mid. The credible gating score G is then mapped to the weights of the training samples. The multidimensional evidence includes at least tool call compliance, parameter consistency, report structure compliance, and operational stability. The tool call compliance is used to characterize whether the tool call sequence meets the capability catalog constraints. The system assesses whether unauthorized tools, unauthorized data domains, or invalid action types are present. The parameter consistency is used to characterize the consistency between tool call parameters and the field specification dictionary, including units, enumerated values, range constraints, default value completion, and specification identifier matching. The report structure compliance is used to characterize whether the report's structured writing trajectory meets the report skeleton and paragraph slot constraints, including chapter tree completeness, paragraph tag validity, slot coverage, and missing key paragraphs. The operational stability is used to characterize stability indicators during trajectory execution, including anomaly rate, retry count, time consumption statistics, and status code distribution. Step 3: Script the trajectory samples in the gated distillation training set D_train into a tool orchestration script TOS and a report structure script RSS. Based on the weights of the training samples, use a multi-objective loss function that includes tool script distillation loss L_TOS, report script distillation loss L_RSS, consistency alignment loss between tool results and report slots L_ALIGN, and penalty term L_NEG for violations to train the local student model using gated distillation. After training, output the student model weight parameter set W_S and capability package SB. The capability package SB includes student model weights, adapter parameters, tool capability index, report skeleton and slot label set, and vocabulary and caliber mapping metadata related to the IAP version. Step 4: Load the student model weight parameter set W_S and the capability package SB on the industry data analysis device side, and complete the inference service online and rapid adaptation with minimal deployment configuration. The system automatically loads the required tool adapters and report structure rules according to the capability package SB, and completes the self-check of connectivity and permission consistency. When authentication failure, interface unreachability or permission mismatch is detected, the system outputs a configuration gap list and executes a security degradation strategy. After loading is completed, an industry analysis task request is initiated on the industry data analysis device side. Under the constraints of SB, the student model automatically completes multi-source data collection and arrangement and structured report generation, and outputs an archiveable due diligence report and corresponding trajectory and log records.

[0041] The field definitions include standardized definitions of data fields such as business definitions, units of measurement, value ranges, enumeration lists, and data sources. The report skeleton specifies the report's chapter divisions, paragraph levels, logical relationships between parts, and required core content elements. The capability directory is a data structure recording the names, function descriptions, input / output specifications, and permission requirements of all callable tools. The field definition dictionary is a mapping table defining standardized information such as the business meaning, units of measurement, value ranges, and enumeration lists of data fields. The formula for calculating slot coverage can be: number of filled slots / total number of slots. 100%. The tool capability index is a data structure (such as a list or dictionary) that records the names, function descriptions, input parameter specifications, and output formats of all tools that can be called after the student model is solidified. The report skeleton defines the report's chapter hierarchy and logical structure, while the slot label set clarifies the data fields or generation requirements corresponding to each paragraph to be filled. The caliber mapping metadata is a set of configuration information that defines the rules for how fields in business analysis are converted from source data calibers to target report calibers, including unit conversions, enumerated value mappings, etc.

[0042] Optionally, the cloud-based teacher intelligence agent calls external commercial data interfaces and client-side intranet data sources through a workflow / tool ​​orchestration mechanism, performs parameter normalization and field mapping based on the field and caliber dictionary in IAP, and generates and outputs a structured report based on the report skeleton and paragraph slots.

[0043] In one embodiment of this application, the Teacher Trajectory Library (TTR) includes a tool call arrangement trajectory and a report structured writing trajectory; the tool call arrangement trajectory is used to record the data domain selection, interface call, parameter filling, return summary, and exception handling processes performed by the cloud-based teacher agent to complete the industry analysis task; the report structured writing trajectory is used to record the chapter tree, paragraph tags, slot filling results, and their corresponding text fragments or structured paragraph objects output by the cloud-based teacher agent. The Industry Analysis Parameter Package (IAP) can be versioned and records version numbers and change summaries to maintain traceable consistency across different batches of tasks and deliveries to different customers. The Industry Analysis Parameter Package (IAP) includes: The industry analysis framework and chapter structure are used to define the macro / meso / micro organizational methods of the industry analysis report and the chapter positions and key outputs of commonly used analysis models; The data domain and capability directory is used to define the data domains for business and judicial affairs, public opinion information, intellectual property, investment and financing, and macro and industry indicators, and to associate the data acquisition capabilities that can be accessed by each data domain. The field and caliber dictionary is used to define field names and aliases, units of measurement, caliber identifiers, enumeration / value ranges, default values, update frequencies, and source tags. The report skeleton and paragraph slots are used to define the directory tree structure of the industry analysis report, paragraph labels, and variable slots that need to be filled in each paragraph.

[0044] In one embodiment of this application, in step 1, a single trajectory in the Teacher Trajectory Database (TTR) includes: Task specifications, including task type, target object, time range, output format and constraints; The IAP version identifier referenced; The tool call sequence includes the tool identifier, action type, parameter fields and references, actual parameters, return summary, and status code / error information; The report output includes a chapter tree, paragraph labels, slot filling results, and corresponding text fragments or structured paragraph objects. Runtime metadata, including model version, tool version, permission identifier, time consumption statistics, and log index.

[0045] In one embodiment of this application, in step 2, the trusted gating score G is calculated using a weighted normalization fusion method, and its calculation formula is as follows:

[0046] in, , , , All are weighting coefficients. For bias terms, To compress the score to the [0,1] interval, a nonlinear function, To ensure compliance of tool usage, For parameter caliber consistency, To ensure compliance with report structure, For operational stability; Highly reliable sample set Directly used for subsequent gated distillation training; suspicious sample set After entering deweighted training or triggering automatic repair, then enter gated distillation training. Automatic repair includes parameter correction, structure completion, and abnormal fragment pruning; low-confidence sample set. Excluded or used only for negative sample constraints, where, and All are gate thresholds. These are trajectory samples from the Teacher Trajectory Database (TTR).

[0047] In one embodiment of this application, in step 3, the tool script distillation loss L_TOS and the report script distillation loss L_RSS adopt a knowledge distillation form based on KL divergence, and their expressions are as follows:

[0048] in, The knowledge distillation form of KL divergence, where T is the temperature parameter. For the teacher model, the logits are located at the corresponding script token position. For the logits of the student model, Let be the divergence function. It is a normalized exponential function; The tool script distillation loss L_TOS or report script distillation loss L_RSS is calculated jointly by the knowledge distillation loss L_KD and the cross-entropy loss L_CE, and its expression is:

[0049] in, Let L_TOS be the distillation loss of the tool script or L_RSS be the distillation loss of the report script, and β be the balancing weight.

[0050] In one embodiment of this application, step 3, specifically the scripted representation, is as follows: Convert tool calls and orchestration trajectories into tool orchestration scripts (TOS), where each action is expressed in the form of <tool identifier, action type, parameter mode reference, parameter value, return key>. The structured writing process of the report is converted into a report structure script RSS, in which each paragraph is expressed in the form of <section identifier, tag, slot key, slot value reference, text fragment>. Furthermore, each trajectory sample in the Teacher Trajectory Library (TTR) is appended with a TaskSpec and an IAP version reference (IAP_ver) to form the training input. With training objectives The standard form, These are trajectory samples from the Teacher Trajectory Database (TTR).

[0051] In one embodiment of this application, the total loss of the multi-objective loss function L in step 3 for training sample i is expressed as:

[0052] in, Let be the weight of the i-th training sample. The distillation loss is the value of the tool script corresponding to the i-th training sample. The distillation loss is the reporting script corresponding to the i-th training sample. Let $\frac{i}{i}$ be the consistency alignment loss between the tool result and the report slot corresponding to the $i$-th training sample. The penalty term loss for the i-th training sample is the penalty term for low-confidence or violation actions in the low-confidence sample set D_low or the suspicious sample set D_mid. , , , All are balance coefficients; Generate alignment keys for each tool call's returned summary and corresponding report slot reference, and encode them as follows: and Alignment loss L_ALIGN between tool results and report slots can be obtained by cosine similarity, or by using the InfoNCE contrast loss function.

[0053] in, For the first time returned by the tool call The vector corresponding to each summary In order to be with the first Each report slot references the corresponding vector. It is a cosine function; The penalty term loss L_NEG is implemented in the following way: Using segments marked as violations or failures from the low-confidence sample set D_low or the suspicious sample set D_mid, negative constraint samples are constructed, and the prediction probability of the violation action or combination of violation parameters is minimized during training. The expression for this is:

[0054] in, Set of illegal actions The actions in the middle, This represents the implicit state of the model.

[0055] In one embodiment of this application, step 4 includes: The system performs automatic assembly and self-checks based on the minimum deployment configuration: automatically starts and stops the corresponding data domain tool adapters and binds authentication; automatically performs connectivity tests and permission consistency checks; automatically loads the chapter structure and paragraph slot rules of the report template to generate structured report objects; if authentication is missing, interface is unreachable, or permissions are inconsistent, the system generates a configuration gap list and triggers a security degradation strategy.

[0056] The paragraph slot rules specify the data types, format templates, and validation logic allowed for each paragraph slot during report generation. Security degradation strategies can include enabling only available data fields with read-only capabilities, enabling only internal network data sources with read-only retrieval, and disabling external interface calls.

[0057] In one embodiment of this application, step 4 further includes: the system generating a deployment configuration snapshot, which records the student model version, student model weight parameter set W_S version, selected scenario and template version, authentication and data source configuration summary and timestamp used in this deployment, for subsequent reproduction, migration and regression verification.

[0058] In one embodiment of this application, the minimum deployment configuration in step 4 includes at least: authentication materials for external data interfaces, connection parameters for internal network data sources, and industry scenario or report template selection options; the system automatically loads the corresponding field definition version and report structure rules according to the selected scenario, and automatically determines the data domains and tool sets to be enabled.

[0059] The field caliber version is a configuration set containing specific version numbers, defining the business meaning, unit of measurement, and value range of the data field. Authentication materials for external data interfaces include, but are not limited to, API keys, access tokens, application IDs (AppIDs), and AppSecrets. Internal network data source connection parameters include the database server's network address (domain name or IP address), port number, database instance name, username, and password (or encrypted credentials). The report template selection options provide templates with different styles (e.g., text only, mixed text and graphics, graphical analysis) or suitable for different scenarios (e.g., PPAP reports, out-of-tolerance reports). The data domain defines the logical classification boundaries of business data, while the toolset contains a list of analysis, processing, or visualization tools that are allowed to be accessed within the permissions of that data domain.

[0060] This application utilizes a gated distillation mechanism to filter and solidify the capabilities of cloud-based teacher intelligence agents in multi-source data interface call arrangement and structured writing of industry analysis reports into locally runnable small models. This enables the models to complete the reasoning and generation of industry analysis tasks within an all-in-one machine, thereby reducing reliance on large cloud models and data center resources and lowering overall usage costs. Simultaneously, it transforms the industry analysis process from "relying on workflow node links and prompt words / paragraph templates for repeated configuration" to "parameterized configuration based on adapters and template parameters," improving reusability and implementation efficiency for cross-customer deployment and reducing compliance and leakage risks associated with enterprise data leaving the domain under local operating conditions.

[0061] For ease of understanding, this application provides a more specific embodiment: This application provides an embodiment of a model distillation solidification and parameterized deployment method for industry data analysis equipment, which includes the following steps: Step S1: Industry analysis trajectory collection and knowledge asset injection, such as... Figure 1As shown: To reduce the dependence of industry data analysis agents on cloud models and data center resources, and to decrease the workload of repeatedly configuring workflows and prompts during cross-customer promotion, this application first encapsulates the industry analysis methodology, data collection field definitions, and report writing specifications developed by enterprises in their long-term practices in investment research, business development, and enterprise analysis into an Industry Analysis Parameter Package (IAP). The IAP provides structural constraints and parameter references during the operation of the cloud-based teacher agent, enabling the agent to complete multi-source data collection, arrangement, and report generation under a unified chapter framework, field definitions, and report structure. This provides a consistent source of training samples for subsequent reliable gating scoring and gate distillation. The IAP can be version-managed (e.g., IAP_v1…IAP_vN) and records version numbers and change summaries to maintain traceable consistency across different batches of tasks and deliveries to different customers.

[0062] The IAP includes, but is not limited to, the following four categories: ① Industry analysis framework and chapter structure, used to describe the macro / meso / micro organizational structure of the industry analysis report and the chapter positions and key outputs corresponding to commonly used analysis models; ② Data domains and capability directories, used to define data domains such as industry and commerce, law and order, public opinion information, intellectual property, investment and financing, macro and industry indicators, and associate the data acquisition capabilities (interfaces, search services, or local data tables) that can be called by each data domain; ③ Fields and caliber dictionary, used to define field names and aliases, units of measurement, caliber identifiers, enumeration / value ranges, default values, update frequency, and source tags to constrain the consistency of tool call parameter filling and output caliber; ④ Report skeleton and paragraph slots, used to define the directory tree structure of the industry analysis report, paragraph tags, and variable slots that need to be filled in each paragraph (such as market size, growth rate, policy points, upstream and downstream structure, key enterprises, risk points, etc.) to constrain the structured output form of the generated content.

[0063] During the execution phase, the cloud-based teacher agent receives industry analysis task requests and completes multi-source data collection and report generation under IAP constraints. The cloud-based teacher agent can call external commercial data interfaces and client-side intranet data sources through workflow / tool ​​orchestration mechanisms, and performs parameter normalization and field mapping based on the field and caliber dictionary in IAP. It then generates structured report output based on the report skeleton and paragraph slots. The system collects and records the execution process of the cloud-based teacher agent in a structured manner, forming a Teacher Trajectory Library (TTR). The TTR includes, but is not limited to, two types of information: one is the tool call orchestration trajectory, recording the data domain selection, interface calls, parameter filling, summary return, and exception handling processes performed by the cloud-based teacher agent to complete the task; the other is the report structured writing trajectory, recording the chapter tree, paragraph tags, slot filling results, and their corresponding text fragments or structured paragraph objects output by the cloud-based teacher agent. Through the above collection, the "workflow node links + prompt words / paragraph templates" that originally relied on manual configuration can be transformed into trainable, gated, and solidified structured samples.

[0064] The single trajectory sample in the TTR includes, but is not limited to: task specification information (task type, target object, time range, output format and constraints), the referenced IAP version identifier, tool call sequence (tool identifier, action type, parameter field and caliber reference, actual parameters, return summary, status code / abnormal information), report structure output (chapter tree, paragraph labels, slot filling results and text fragments), and runtime metadata (model version, tool version, permission identifier, time consumption statistics and log index).

[0065] Step S2: Trustworthy gating scoring and sample selection, such as Figure 2 As shown: To avoid directly migrating occasional errors, abnormal tool calls, or report fragments that do not conform to industry standards from the cloud-based teacher AI agent to the local student model, which could lead to uncontrollable output or incorrect arrangement of the small model on the all-in-one machine, this application performs a trustworthy gating score on each trajectory sample in the Teacher Trajectory Library (TTR) after trajectory acquisition, forming a trustworthy sample set that can be used for distillation training. The trustworthy gating score is used to quantitatively evaluate the reliability of trajectory samples in terms of "tool call executability, parameter consistency, structured report compliance, and operational stability," and the score results are used as the basis for sample selection and loss weighting in subsequent gated distillation training.

[0066] The trusted gating score is calculated based on multi-dimensional evidence items. Preferably, the evidence items include: ① Tool call compliance S_tool, used to characterize whether the tool call sequence meets the capability directory constraints, whether unauthorized tools, unauthorized data domains, or invalid action types appear; ② Parameter consistency S_param, used to characterize the consistency between tool call parameters and field specification dictionaries, including units, enumerated values, range constraints, default value completion, and specification identifier matching; ③ Report structure compliance S_struct, used to characterize whether the report structured writing trajectory meets the report skeleton and paragraph slot constraints, including chapter tree completeness, paragraph tag validity, slot coverage, and key paragraph missingness; ④ Operational stability S_run, used to characterize stability indicators such as anomaly rate, number of retries, time consumption statistics, and status code distribution during trajectory execution. The above evidence items can be directly calculated from the tool call arrangement trajectory, report structured writing trajectory, and operational metadata in TTR, thereby avoiding additional reliance on manual annotation.

[0067] In a preferred implementation, the trusted gating score G is calculated using a weighted normalization fusion method, and its calculation formula is as follows:

[0068] Where w1, w2, w3, and w4 are weighting coefficients, b is a bias term, and σ(·) is a nonlinear function (e.g., the Sigmoid function) that compresses the score to the [0,1] interval. Through this fusion, various engineering constraints regarding "usability" can be transformed into a calculable and comparable unified gating score, providing a quantitative basis for subsequent distillation training. Optionally, the weighting coefficients can be calibrated offline based on historical delivery logs or regression sets to match the different emphases of various customers on structural compliance, parameter caliber, or operational stability.

[0069] Based on the reliable gating score G, the system performs hierarchical screening and labeling of trajectory samples. A preferred gating threshold is set. The samples were divided into: ① High-confidence sample set ① Proceed directly to subsequent gated distillation training; ② Suspicious sample set Training should proceed only after the sample has been reduced in weight or after automatic repair (such as parameter correction, structural completion, or abnormal segment pruning) has been triggered; ③ Low-confidence sample set These are either removed or used only for negative sample constraints. Through hierarchical gating, the stability and consistency of training samples can be improved without significantly increasing manual review costs, reducing the risk of migrating incorrect tool call paths or non-compliant report structures to student models.

[0070] Optionally, the trusted gating score is used not only for sample selection but also for generating sample weights and labels for distillation training. Preferably, the system expands each trajectory sample into a triple <x,y,α>, where x is the task request and context input (including TaskSpec, IAP version reference, and necessary search result summaries), y is the structured target output by the teacher (including tool call sequence and report structured output), and α is the training weight or trusted label mapped from the gating score G. This achieves "training data production driven by trusted gating," providing directly usable training samples and weight information for the gated distillation training in step S3.

[0071] Step S3: Gated distillation training and skill consolidation, such as Figure 3 As shown: After completing the trusted gating score and sample classification in step S2, the system constructs a gating distillation training set D_train based on the high-confidence sample set D_high and the suspicious sample set D_mid, and uses the low-confidence sample set D_low as the source of constraints / negative samples to perform joint distillation training of "tool call orchestration ability + report structured writing ability". This enables the capabilities of the cloud-based teacher agent that originally relied on workflow node links and prompt word templates to be solidified into the locally run student model, providing a portable capability kernel for the subsequent parameterized deployment of the all-in-one machine.

[0072] To enhance learnability and controllability, the system represents the teacher trajectory samples collected in step S1 in a scripted manner: the tool call and arrangement trajectory is converted into a Tool Orchestration Script (TOS), where each action is expressed in the form of <ToolID, Action, ParamSchemaRef, ParamValue, ReturnKey>; the report structure writing trajectory is converted into a Report Structure Script (RSS), where each paragraph is expressed in the form of <SectionID, Tag, SlotKey, SlotValueRef, TextSpan>; and a Task Specification (TaskSpec) and IAP version reference (IAP_ver) are attached to each sample to form a standardized form of training input and training objective: x = <TaskSpec, IAP_ver, Context>, y = <TOS, RSS>.

[0073] The gated distillation training employs a multi-objective loss function based on sample weights. For training sample i, its confidence weight α_i is obtained by mapping the fusion score G_i in step S2 (e.g., α_i = clip(G_i, 0, 1) or set according to a threshold segment). The student model performs sequence modeling for TOS and RSS respectively, and introduces teacher soft labels for knowledge distillation. Its total loss can be expressed as:

[0074] Wherein, L_TOS and L_RSS are the distillation losses of the tool script and the report script; L_ALIGN is the consistency alignment loss of the "tool result - report slot"; and L_NEG is the penalty for low-confidence / violation actions (optional).

[0075] As one implementation method, L_TOS(i) and L_RSS(i) can be implemented using KL divergence distillation at temperature T:

[0076] Where z_T is the logits of the teacher model at the corresponding script token position, and z_S is the logits of the student model; it can be used in conjunction with the cross-entropy loss L_CE of supervised learning (e.g., L_TOS = L_KD + β·L_CE) to balance soft label transfer and script accurate fitting.

[0077] To enhance interpretability consistency between tool calls and report writing, the system introduces a cross-channel alignment constraint L_ALIGN(i): the system generates an alignment key (e.g., ReturnKey) for each tool call's returned summary and corresponding report slot reference. SlotValueRef), and encode it into vectors e_ret and e_slot, aligning them using cosine similarity, for example: .

[0078] Alternatively, InfoNCE contrast loss can be used to make the similarity of correctly matched "return slots" higher than that of incorrectly matched slots, thereby reducing the risk of inconsistency between the reported content and the acquired data.

[0079] To prevent the student model from learning the teacher's undesirable behaviors in abnormal paths, the system constructs negative constraint samples using segments labeled as violations / failures in D_low or D_mid, and applies a penalty term L_NEG to the violation actions or combinations of violation parameters during training; for example, for the set of violation actions A_bad, minimizing... Alternatively, restrictive decoding rules can be introduced during the decoding phase to suppress unauthorized tool calls, illegal parameter filling, or outputs that do not conform to IAP standards.

[0080] In training scheduling, the system preferentially adopts higher weights and stricter structural consistency constraints for D_high, and adopts reduced-weight training or "retraining after repair" for D_mid. The repair methods include: correcting units / enumerations according to the IAP field caliber dictionary, deleting unauthorized tool steps, supplementing missing items in the report slots, or marking conflicting fields as pending verification, thereby improving learnability and stability without introducing a large amount of manual review.

[0081] After training is complete, the system outputs the fixed student model parameters Θ_S. It also includes its capability package SB (StudentBundle). The capability package includes: student model weights / adapter parameters, tool capability index (ToolID-ParamSchemaRef mapping), report skeleton and slot tag set (SectionID / Tag / SlotKey), and vocabulary and caliber mapping metadata related to the IAP version.

[0082] Through the above-mentioned gated distillation training and capability solidification, the repetitive "workflow orchestration + prompt word / paragraph template configuration" in cross-customer delivery can be transformed into reusable model capabilities, enabling student models to complete multi-source data acquisition and orchestration and structured report generation in a local all-in-one environment, and providing a foundation for the parameterized deployment and rapid adaptation of step S4.

[0083] Step S4: Rapid deployment and parameterized adaptation of the all-in-one machine, such as... Figure 4 As shown: After obtaining the student model weight parameter set W_S and capability package in step S3, this application performs rapid deployment and adaptation on the industry data analysis equipment side, enabling the model to complete inference service launch, data interface assembly, and report template loading in the local environment, and supporting different permissions and data source differences in different deployment scenarios without sensitive data leaving the domain. The capability package has embedded tool call orchestration rules, field caliber mapping rules, and report chapter slot structure, so that the deployment side does not need to repeatedly configure workflow nodes and prompt word templates, thus providing a foundation for rapid launch.

[0084] The all-in-one machine provides a minimum deployment configuration for going live with minimal input in different deployment scenarios. The minimum deployment configuration includes at least: interface authentication materials (external data interface key / account), internal network data source connection parameters (address / database / account), and report templates or industry scenario selection options; the system automatically loads the corresponding field definition version and report structure rules according to the selected scenario, and automatically determines the data domains and tool sets that need to be enabled.

[0085] The system performs automatic assembly and self-checks based on the minimum deployment configuration: automatically starts and stops the corresponding data domain tool adapters and binds authentication; automatically performs connectivity tests and permission consistency checks; and automatically loads the chapter structure and paragraph slot rules of the report template, enabling the model output to directly generate structured report objects. If authentication gaps, unreachable interfaces, or inconsistent permissions are detected, the system generates a configuration gap list and triggers security degradation policies (e.g., enabling only internal network data sources and read-only retrieval, disabling external interface calls) to ensure availability and compliance.

[0086] The system generates a deployment configuration snapshot, recording the model version, Web Application Server (WAS) version, selected scenario and template versions, authentication / data source configuration summary, and timestamp used in this deployment. This information is used for subsequent reproduction, migration, and regression verification. Through this rapid deployment and adaptation, the repetitive interface assembly, caliber mapping, and template loading on the deployment side can be reduced to a process with a small number of parameter inputs and automated assembly, thereby reducing deployment and maintenance workload and improving localized delivery efficiency.

[0087] Compared with existing solutions that rely on cloud-based large models and workflow orchestration platforms to generate industry analysis reports, this application takes "teacher trajectory collection under IAP constraints of industry analysis parameter packages—credible gating scoring—gating distillation solidification—parameterized deployment of all-in-one machine" as the main line. It transfers the capabilities of cloud-based teacher intelligence agents in multi-source data interface calling orchestration and report structured writing to student models that can run locally. It also achieves engineering implementation through minimum configuration and automatic assembly mechanisms on the deployment side. This provides an improved path to address the problems of high cost, risk of sensitive data leaving the domain, and complex delivery configuration and poor portability in existing technologies.

[0088] By constructing a versioned Industry Analysis Parameter Package (IAP) in step S1, and collecting Teacher Trajectory Database (TTR) under the constraints of the IAP's chapter framework, field definitions, and report skeleton, the training samples maintain consistency and traceability in terms of structure and definition. This allows implicit experiences scattered in "prompt words / paragraph templates / workflow node links" during cross-customer delivery to be transformed into reusable structured trajectory samples, reducing engineering uncertainties in subsequent capability transfer and reuse.

[0089] Step S2 performs credible gating and grading screening on teacher trajectory samples, transforming engineering constraints such as tool call compliance, parameter consistency, report structure compliance, and operational stability into calculable gating scores, and generating a credible sample set and sample weights accordingly. This mechanism can reduce the probability of occasional errors, abnormal calls, or non-compliant report fragments being transferred to the student model, thereby improving the stability and controllability of tool arrangement and structured writing behavior on the small model side.

[0090] By performing gated distillation training and capability solidification in step S3, the teacher's "tool calling and arrangement ability + report structured writing ability" are precipitated into the student model weight parameter set W_S and capability package SB, enabling industry analysis tasks to complete multi-source data collection and arrangement and structured report generation in the local environment of the all-in-one machine. This reduces the continuous dependence on cloud-based large models and data center resources, and reduces the overall resource consumption and cost fluctuations in multi-round generation and multi-interface linkage scenarios.

[0091] Through the parameterized deployment and rapid adaptation of the all-in-one machine in step S4, the repetitive work on the delivery side is reduced to a small number of minimal deployment configurations (such as interface authentication, intranet data source connection parameters, and scenario / template selection). The system automatically completes the tool adapter assembly, self-check verification, and template loading. Since the capability package SB has fixed field caliber mapping and report chapter slot structure, the deployment side does not need to repeatedly build workflow node links and prompt word template configurations, thereby shortening the online and migration time, improving the portability and implementation efficiency of cross-customer promotion, and reducing the compliance and leakage risks caused by sensitive data leaving the domain under local operating conditions.

[0092] Optionally, the deployment configuration snapshot generated in step S4 records information such as model version, W_S version, scenario and template version, and configuration summary, which facilitates subsequent reproduction, migration, and regression verification, and helps maintain deployment consistency and operational traceability in multi-customer, multi-batch delivery.

[0093] This embodiment, based on the method of this application, selects an industry analysis of an investment institution / fund company conducting investment research and due diligence on a potential investee company E as a typical scenario. This institution needs to complete multi-source data collection and arrangement, evidence aggregation, and structured report generation within a local all-in-one machine intranet environment, and achieve rapid deployment and cross-project reuse without leaving the domain, as detailed below: Step A: Industry Analysis Trajectory Collection and Knowledge Asset Injection On the delivery side, the accumulated industry analysis methodologies, field definitions, and report writing standards are encapsulated into Industry Analysis Parameter Packages (IAPs) and versioned (e.g., IAP_v1…IAP_vN). The IAP includes: an industry analysis framework and chapter structure, data domains and capability directories, field and definition dictionaries, and report skeletons and paragraph slots. Subsequently, the cloud-based teacher agent receives a task request (TaskSpec, e.g., "Analyze the industry of target company E and output a due diligence report, specifying the time frame and output structure") and completes the industry analysis execution under the constraints of the IAP. This includes: 1) selecting data domains and tools and arranging their call order; 2) standardizing parameters, mapping fields, and returning summaries according to the field and definition dictionaries; and 3) generating structured report content according to the report skeleton and slots. The system collects and structures the execution process of the cloud-based teacher agent, forming a Teacher Trajectory Library (TTR). The TTR includes at least the tool call arrangement trajectory, the report structure writing trajectory, and metadata such as task specifications and IAP version references.

[0094] Step B: Trusted Gating Scoring and Sample Selection The system extracts evidence and calculates features for each trajectory sample in the TTR, forming a credible gating score G, and then filters samples according to a threshold: 1) a high-credibility sample set D_high, used directly for distillation training; 2) a suspicious sample set D_mid, used for training with reduced weights or for weak supervision constraints; 3) a low-credibility sample set D_low, discarded or used only for negative sample constraints. Simultaneously, the system generates training weights or credible labels for the samples, used for weighted driving of subsequent gating distillation training, reducing the impact of abnormal tool call paths, inconsistent standards, and non-compliant report structures on student model transfer.

[0095] Step C: Gated distillation training and competency consolidation The system scripts trajectory samples into trainable structured objectives, including a tool orchestration script (TOS) and a report structure script (RSS). Each sample is constructed as a training triple (x, y, α): where x is the task request and context input (including TaskSpec, IAP version reference, and necessary retrieval summary), y is the teacher-side structured objective (TOS and RSS), and α is the sample weight or trusted label obtained from the trusted scoring mapping in step B. The system employs a gated weighted distillation training method to solidify the cloud-based teacher agent's "multi-source tool call orchestration capability + report structured writing capability" into the student model, resulting in the student model weight parameter set W_S and the capability package SB (Student Bundle). SB includes at least: 1) tool capability index and parameter caliber mapping metadata; 2) report chapter / tag / slot set and structured output constraints; 3) caliber dictionary and template metadata associated with the IAP version. Through this solidification process, the repetitive "workflow orchestration + prompt word / paragraph template configuration" in cross-customer delivery can be transformed into reusable local model capabilities.

[0096] Step D: Parametric Deployment and Rapid Adaptation of the All-in-One Machine Import the student model weight parameter set W_S and capability package SB into the industry data analysis device, and complete the inference service launch and rapid adaptation with "minimum deployment configuration". The minimum deployment configuration preferably includes: 1) external data interface authentication materials; 2) internal network data source connection parameters; 3) scenario / report template selection items (such as "investment company due diligence template" or "industry analysis template"). The system automatically loads the required tool adapters and report structure rules according to SB, and completes the self-check of connectivity and permission consistency; when authentication is missing, interface is unreachable or permission is mismatched, the system outputs a configuration gap list and executes a security degradation strategy (such as only enabling available data domains and read-only capabilities) to ensure compliance and availability. After deployment, investment research personnel initiate a task request on the all-in-one machine: "Analyze the prosperity and competitive landscape of the industry in which the target company E is located, and generate a due diligence report by combining the company's public information and internal network financial data (output structure: industry overview - market size and growth rate - policies and regulations - competitive landscape - position in the industry chain - key risks - investment points)". Under SB constraints, the student model automatically completes multi-source data collection, arrangement, and structured report generation, outputting archiveable due diligence reports and corresponding trajectory and log records. The entire process is completed on the local intranet, and sensitive internal data does not leave the domain.

[0097] This embodiment shows that, through steps A to C, the arrangement experience and writing structure capabilities in the industry analysis process are solidified into locally reusable student model capabilities (W_S+SB), and through step D, rapid online deployment and differentiated adaptation are achieved on the all-in-one machine side with a small number of parameter inputs. This reduces the dependence on cloud models and data center resources, reduces the amount of repetitive configuration work in cross-customer delivery, and achieves compliant and cost-controllable engineering implementation in the intranet environment.

[0098] 1. Structured boundary for trajectory acquisition: Trajectory acquisition should simultaneously cover two types of information: 1) Tool call and arrangement trajectories (tool identifier, action type, parameter fields and citations, return summary, status code / abnormal information, etc.); 2) Report structured writing trajectories (chapter tree, paragraph tags, slot filling results, and text fragments / paragraph objects, etc.). It is recommended to use "scripted representation" as the training objective to avoid sparse training signals, difficulty in alignment, and difficulty in reuse resulting from only recording long text outputs.

[0099] 2. Deployment record and reproduction requirements: It is recommended to generate a deployment configuration snapshot, recording the model version, W_S version, IAP / template version, data domain start / stop list and key configuration summary, and associate it with the runtime log index; this record is used for reproduction, migration and regression verification, which facilitates locating the source of differences and reducing operation and maintenance costs when delivering across projects.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of this application. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this application should be covered within the protection scope of the claims of this application.

Claims

1. A method for model distillation solidification and parameterized deployment in industry data analysis equipment, characterized in that, include: Step 1: The industry analysis methodology, data collection field definitions, and report writing specifications are encapsulated into a version-managed Industry Analysis Parameter Package (IAP). The cloud-based teacher agent receives industry analysis task requests and completes multi-source data collection and report generation under the constraints of the IAP's chapter framework, field definitions, and report skeleton. The system collects and records the execution process of the cloud-based teacher agent in a structured manner, forming a Teacher Trajectory Library (TTR). Step 2: Based on multidimensional evidence items, a credible gating score G is applied to each trajectory sample in the Teacher Trajectory Database (TTR). Samples are then graded and filtered according to the credible gating score G and a preset gating threshold to generate a gating distillation training set D_train containing a high-credibility sample set D_high and a suspicious sample set D_mid. The credible gating score G is then mapped to the weights of the training samples. The multidimensional evidence items include at least tool call compliance, parameter consistency, report structure compliance, and operational stability. The tool call compliance is used to characterize whether the tool call sequence meets capability directory constraints, whether unauthorized tools, unauthorized data domains, or invalid action types are present. The parameter consistency is used to characterize the consistency between the tool's called parameters and the field definition dictionary, including units, enumerated values, range constraints, default value completion, and definition identifier matching. The report structure compliance is used to characterize whether the structured writing trajectory of the report meets the report skeleton and paragraph slot constraints, including chapter tree completeness, paragraph tag validity, slot coverage, and key paragraph missingness. The operational stability is used to characterize the stability indicators during trajectory execution, including anomaly rate, number of retries, time consumption statistics, and status code distribution. Step 3: Script the trajectory samples in the gated distillation training set D_train into a tool orchestration script TOS and a report structure script RSS; based on the weights of the training samples, use a multi-objective loss function that includes tool script distillation loss L_TOS, report script distillation loss L_RSS, consistency alignment loss between tool results and report slots L_ALIGN, and penalty term L_NEG for violations to train the local student model with gated distillation. After training, the student model weight parameter set W_S and capability package SB are output; the capability package SB includes student model weights, adapter parameters, tool capability index, report skeleton and slot label set, and vocabulary and caliber mapping metadata related to the IAP version; Step 4: Load the student model weight parameter set W_S and the capability package SB on the industry data analysis device side, and complete the inference service online and rapid adaptation with minimal deployment configuration. The system automatically loads the required tool adapters and report structure rules according to the capability package SB, and completes the self-check of connectivity and permission consistency. When authentication failure, interface unreachability or permission mismatch is detected, the system outputs a configuration gap list and executes a security degradation strategy. After loading is completed, an industry analysis task request is initiated on the industry data analysis device side. Under the constraints of SB, the student model automatically completes multi-source data collection and arrangement and structured report generation, and outputs an archiveable due diligence report and corresponding trajectory and log records.

2. The method according to claim 1, characterized in that, The Teacher Trajectory Library (TTR) includes tool call arrangement trajectories and report structured writing trajectories. The tool call arrangement trajectories are used to record the data domain selection, interface calls, parameter filling, summary return, and exception handling processes performed by the cloud-based teacher agent to complete industry analysis tasks. The structured writing trajectory of the report is used to record the chapter tree, paragraph tags, slot filling results and their corresponding text fragments or structured paragraph objects output by the cloud-based teacher intelligent agent. The Industry Analysis Parameter Package (IAP) can be versioned and records version numbers and change summaries to maintain traceable consistency across different batches of tasks and deliveries to different customers. The Industry Analysis Parameter Package (IAP) includes: The industry analysis framework and chapter structure are used to define the macro / meso / micro organizational methods of the industry analysis report and the chapter positions and key outputs of commonly used analysis models; The data domain and capability directory is used to define the data domains for business and judicial affairs, public opinion information, intellectual property, investment and financing, and macro and industry indicators, and to associate the data acquisition capabilities that can be accessed by each data domain. The field and caliber dictionary is used to define field names and aliases, units of measurement, caliber identifiers, enumeration / value ranges, default values, update frequencies, and source tags. The report skeleton and paragraph slots are used to define the directory tree structure of the industry analysis report, paragraph labels, and variable slots that need to be filled in each paragraph.

3. The method according to claim 2, characterized in that, In step 1, a single trajectory in the Teacher Trajectory Database (TTR) includes: Task specifications, including task type, target object, time range, output format and constraints; The IAP version identifier referenced; The tool call sequence includes the tool identifier, action type, parameter fields and references, actual parameters, return summary, and status code / error information; The report output includes a chapter tree, paragraph labels, slot filling results, and corresponding text fragments or structured paragraph objects. Runtime metadata, including model version, tool version, permission identifier, time consumption statistics, and log index.

4. The method according to claim 1, characterized in that, In step 2, the trusted gating score G is calculated using a weighted normalization fusion method, and its calculation formula is as follows: in, , , , All are weighting coefficients. For bias terms, To compress the score to the [0,1] interval, a nonlinear function, To ensure compliance of tool usage, For parameter caliber consistency, To ensure compliance with report structure, For operational stability; Highly reliable sample set Directly used for subsequent gated distillation training; suspicious sample set After entering deweighted training or triggering automatic repair, then enter gated distillation training. Automatic repair includes parameter correction, structure completion, and abnormal fragment pruning; low-confidence sample set. Excluded or used only for negative sample constraints, where, and All are gate thresholds. These are trajectory samples from the Teacher Trajectory Database (TTR).

5. The method according to claim 1, characterized in that, In step 3, the tool script distillation loss L_TOS and the report script distillation loss L_RSS adopt a knowledge distillation form based on KL divergence, and their expressions are as follows: in, The knowledge distillation form of KL divergence, where T is the temperature parameter. For the teacher model, the logits are located at the corresponding script token position. For the logits of the student model, Let be the divergence function. It is a normalized exponential function; The tool script distillation loss L_TOS or report script distillation loss L_RSS is calculated jointly by the knowledge distillation loss L_KD and the cross-entropy loss L_CE, and its expression is: in, Let L_TOS be the distillation loss of the tool script or L_RSS be the distillation loss of the report script, and β be the balancing weight.

6. The method according to claim 1, characterized in that, In step 3, the scripting representation specifically means: Convert tool calls and orchestration trajectories into tool orchestration scripts (TOS), where each action is expressed in the form of <tool identifier, action type, parameter mode reference, parameter value, return key>. The structured writing process of the report is converted into a report structure script RSS, in which each paragraph is expressed in the form of <section identifier, tag, slot key, slot value reference, text fragment>. Furthermore, each trajectory sample in the Teacher Trajectory Library (TTR) is appended with a TaskSpec and an IAP version reference (IAP_ver) to form the training input. With training objectives The standard form, These are trajectory samples from the Teacher Trajectory Database (TTR).

7. The method according to claim 1, characterized in that, The total loss of the multi-objective loss function L in step 3 for training sample i is expressed as: in, Let be the weight of the i-th training sample. The distillation loss is the value of the tool script corresponding to the i-th training sample. The distillation loss is the reporting script corresponding to the i-th training sample. Let $\frac{i}{i}$ be the consistency alignment loss between the tool result and the report slot corresponding to the $i$-th training sample. The penalty term loss for the i-th training sample is the penalty term for low-confidence or violation actions in the low-confidence sample set D_low or the suspicious sample set D_mid. , , , All are balance coefficients; Generate alignment keys for each tool call's returned summary and corresponding report slot reference, and encode them as follows: and Alignment loss L_ALIGN between tool results and report slots can be obtained by cosine similarity, or by using the InfoNCE contrast loss function. in, For the first time returned by the tool call The vector corresponding to each summary In order to be with the first Each report slot references the corresponding vector. It is a cosine function; The penalty term loss L_NEG is implemented in the following way: Using segments marked as violations or failures from the low-confidence sample set D_low or the suspicious sample set D_mid, negative constraint samples are constructed, and the prediction probability of the violation action or combination of violation parameters is minimized during training. The expression for this is: in, Set of illegal actions The actions in the middle, This represents the implicit state of the model.

8. The method according to claim 1, characterized in that, Step 4 includes: The system performs automatic assembly and self-checks based on the minimum deployment configuration: automatically starts and stops the corresponding data domain tool adapters and binds authentication; automatically performs connectivity tests and permission consistency checks; automatically loads the chapter structure and paragraph slot rules of the report template to generate structured report objects; if authentication is missing, interface is unreachable, or permissions are inconsistent, the system generates a configuration gap list and triggers a security degradation strategy.

9. The method according to claim 1, characterized in that, Step 4 further includes: the system generates a deployment configuration snapshot, which records the student model version, student model weight parameter set W_S version, selected scenario and template version, authentication and data source configuration summary and timestamp used in this deployment, for subsequent reproduction, migration and regression verification.

10. The method according to claim 1, characterized in that, The minimum deployment configuration in step 4 includes at least: Authentication materials for external data interfaces, connection parameters for internal network data sources, and options for industry scenarios or report templates; The system automatically loads the corresponding field definition version and report structure rules based on the selected scenario, and automatically determines the data domains and tool sets to be enabled.

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