Automatic technical specification revision method based on dynamic parameter adjustment

Abstract

The invention provides an automatic technical specification revision method based on dynamic parameter adjustment. The automatic technical specification revision method mainly comprises the steps of unified and centralized configuration of technical parameters, template generation, and automatic revision and rapid approval of an intelligent optimization algorithm. According to the method, historical revision data, market dynamics and industry standards are integrated, and technical parameters are automatically analyzed and dynamically adjusted by using a nonlinear regression model and an intelligent optimization algorithm. The system can respond to market price fluctuation, supply chain change and industry standard update in real time, generate an optimal revision scheme, and automatically check revision parameters to ensure the accuracy and timeliness of revision. Meanwhile, a centralized management and version control mechanism ensures that all business departments use latest parameters, and the cooperation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of technical specification book revision, and in particular to an automatic revision method for technical specification books based on dynamic parameter adjustment. Background Art

[0002] In the production, procurement and engineering management activities of modern enterprises, the technical specification is a vital document. It is usually used to describe in detail the technical requirements for products or services in the project, covering technical parameters, standard specifications, quality requirements and work processes. This document ensures that both the supply and demand sides reach an agreement on the technical requirements and reduces the risk of technical problems during project execution. However, with the expansion of the scale of enterprises and changes in the market environment, technical specifications need to be updated frequently to adapt to new technical standards, market demands and business development. Especially in complex business scenarios such as material procurement, supply chain management, and project implementation, the revision and management of technical specifications is particularly important. However, the traditional manual revision method has many problems and limitations.

[0003] First of all, the revision of technical specifications usually involves multiple business departments, including technology, procurement, engineering, and quality. Since the revision work requires a high degree of collaboration, each department needs to modify the technical parameters according to its own needs, so the manual revision process is often very cumbersome. In the traditional way, the revision work relies on manual operations, which takes too long. In addition, the manual processing of a large number of technical parameters in the technical specifications often leads to inconsistent data, repeated information, and inconsistent formats. These problems are further amplified in the information flow between multiple departments, increasing the complexity of the revision work. In addition, errors and omissions are also prone to occur during the manual revision process, especially in complex projects or large-scale material procurement scenarios. Any small error will affect subsequent procurement, construction and other links, and may even lead to project delays or increased costs.

[0004] Another problem is the difficulty of version management under the traditional method. With the multiple revisions of technical specifications, the management and traceability of old versions have become extremely complicated. Enterprises usually need to save all historical versions of technical specifications for future inquiries or audits. However, manual management of historical versions is prone to confusion or information loss, which not only affects management efficiency, but also may bring potential compliance risks. The revision of technical specifications is not only driven by internal business needs, but also needs to adapt to changes in the external market environment. For example, market price fluctuations, supply chain changes, and updates to industry standards will have an impact on the content of technical specifications. In the process of material procurement, changes in raw material prices directly affect project costs, so the relevant technical parameters in the technical specifications must be adjusted in time according to market conditions to ensure the economy of procurement. Similarly, changes in the supply chain, such as supplier supply capacity, logistics conditions, and delivery cycles, will also affect the delivery time and supply chain requirements in the technical specifications. This dynamic change in the external environment puts higher requirements on the update of technical specifications.

[0005] In addition, the update of industry standards and regulations is also one of the key factors in the revision of technical specifications. With the advancement of technology and the continuous updating of international and national standards, the content of technical specifications needs to meet the latest compliance requirements. For example, changes in environmental protection regulations or quality certification standards may directly affect the technical requirements in the technical specifications, and enterprises must update technical parameters in a timely manner to ensure compliance with relevant regulations. Approval issues in the traditional technical specification revision process are also one of the reasons for the low efficiency of revision. Usually, the revision of technical specifications requires review and approval from multiple departments to ensure that the needs of each department are fully considered. This approval process often becomes a bottleneck in the revision, because cross-departmental coordination, the arrangement of the approver's daily work, and the complexity of the approval process will prolong the entire revision cycle.

[0006] The approval process is usually lengthy, and the complexity of technical parameters requires the approver to spend a lot of time reviewing each item. This not only increases the time cost of approval, but may also lead to information asymmetry due to the opaque approval progress. Delays in approval may even further slow down the entire revision process. In addition, the approval process is highly dependent on manual labor, and the approver needs to manually handle a large number of technical parameters, which increases the risk of omissions and delays. Even the approved revision plan may have potential problems. In the face of these challenges, the application of automation technology in the revision of technical specifications has become a growing trend. Automation technology can not only improve revision efficiency, but also reduce human errors and enhance the flexibility and accuracy of technical specification management. Through automated revision tools based on big data and intelligent algorithms, enterprises can automatically analyze technical parameters and automatically generate optimal revision suggestions based on historical data, market trends, industry standards and other information. This greatly reduces the burden of manual operations while ensuring the accuracy and timeliness of the revision content.

[0007] To sum up, the traditional method of revising technical specifications faces problems such as low efficiency, high error rate, and complicated approval process. Summary of the invention

[0008] In order to solve the above problems in the prior art, the present invention proposes an automated revision method for revising a technical specification book, the automated revision method comprising the following steps:

[0009] Step 1: Centrally configure technical parameters;

[0010] Step 2: Generate a technical specification book by referencing the technical specification template or procurement standard template according to the application scenario;

[0011] Step 3: Automatically revise technical parameters according to material procurement standards and actual needs. Based on big data, intelligent optimization algorithms automatically analyze historical revision data and standardized data to generate optimal parameter revision suggestions. Further dynamically adjust technical parameters according to changes in the external environment. When market price fluctuations, supply chain changes, or industry standard updates are detected, relevant parameters in the technical specifications are adjusted in real time according to the updated data.

[0012] Step 4: Quick approval, simplify the approval process, and ensure that the technical parameters are quickly approved after revision.

[0013] The step 1 of centrally configuring the technical parameters further includes:

[0014] Through the unified association configuration module of technical parameters, technical parameters from different business departments are centrally managed to form a unified technical parameter database;

[0015] The technical parameter database includes material procurement standards, industry standards and various technical parameter data, and supports manual input, external data import and automatic collection to obtain technical parameters;

[0016] Standardizing the input technical parameters, including formatting, categorizing, grading and deduplication operations, so that the technical parameters can be centrally configured and managed in a unified format;

[0017] Each technical parameter is managed by a unique identifier (ID), and when a technical parameter changes, it automatically triggers the synchronization update of related businesses, so that the technical parameters used by each department are the latest versions.

[0018] The step 2 specifically includes: the scenario application module automatically selects a suitable technical specification template or procurement standard template from the template library according to the user's actual business needs; the template library includes standardized templates for multiple business scenarios, and the templates are constructed based on industry standards, internal enterprise standards and historical business data; the scenario selection module recommends the most suitable business scenario according to the business needs input by the user, and the user can preview the template content; the user adjusts the variable part of the template according to specific needs; when a key parameter is modified, other related parameters are automatically adjusted.

[0019] The step 3 further comprises the following steps:

[0020] Data collection and analysis: extract data from the system's historical revision records and analyze them in combination with current market dynamics, industry standards, and changes in the external environment. The historical data includes revision time, parameter values ​​before and after revision, revision reasons, and influencing factors;

[0021] Data integration: Obtain real-time market data from external supplier databases or market monitoring platforms through interfaces, and synchronize industry standards from automatically updated standard libraries;

[0022] Intelligent optimization and multivariate analysis: Analyze data in multiple dimensions based on nonlinear regression models and intelligent optimization algorithms, generate technical parameter revision suggestions, and capture the complex interactions between parameters. The model includes interaction terms and quadratic terms to further analyze the relationship between variables.

[0023] Dynamic parameter adjustment: When the system detects market price fluctuations, supply chain disruptions or industry standard updates, it dynamically adjusts the relevant parameters in the technical specifications. The adjustment process is based on a multi-variable adjustment formula to calculate the impact of market price fluctuations, supply chain conditions and industry standard updates on technical parameters;

[0024] Revision scheme generation: Generates a revision scheme including revised parameter values, revision reasons, revision basis and related influencing factors, and provides a user interface for manual adjustment or direct adoption of automatically generated revision suggestions.

[0025] The intelligent optimization and multivariate analysis steps include the following nonlinear regression model:

[0026]

[0027] Among them, P(t) is the technical parameter value at time point t, X i (t), X j (t) are the i-th and j-th influencing factors, n is the total number of influencing factors, β i is the linear regression coefficient, γ ij is the interaction term coefficient, δ1 is the quadratic effect coefficient, and β0 is the constant term.

[0028] The dynamic parameter adjustment step calculates the revision amplitude based on the following formula:

[0029] ΔP(t)=α1·M(t)+α2·S(t)+α3·R(t)

[0030] in:

[0031] ΔP(t) is the revision amplitude of the technical parameters at time point t;

[0032] M(t) is the degree of influence of market price fluctuations at time point t;

[0033] S(t) is the change in supply chain status at time t;

[0034] R(t) is the updating degree of industry standards at time point t;

[0035] α1, α2, and α3 are the weight coefficients corresponding to market price fluctuations, supply chain changes, and industry standard updates.

[0036] The revision scheme generation step includes automatically checking whether the revised technical parameters meet the preset range and logical relationship, marking the parameters that do not meet the standards, and prompting the user to make corrections.

[0037] Beneficial effects:

[0038] Through intelligent optimization algorithms and big data analysis, the system can automatically generate the best technical parameter revision suggestions, and dynamically adjust technical parameters in combination with market dynamics and industry standards, reduce manual intervention, and ensure the efficiency and accuracy of revisions. The introduction of nonlinear regression models and interaction terms and quadratic terms can more accurately analyze the complex relationships between technical parameters, adapt to complex business scenarios where multiple variables change simultaneously, and improve the prediction accuracy of revision plans. The system has the ability to respond to market price fluctuations, supply chain changes, and industry standard updates in real time, and dynamically adjusts the formula to calculate the revision range to ensure that the content of the technical specification is synchronized with the latest market and business needs. Through a unified technical parameter database, all business departments can access the latest version of technical parameters to avoid misoperation caused by inconsistent parameters, ensure that each department uses the latest revision results, and improve collaboration efficiency. The revised technical parameters are automatically verified to ensure that they meet the preset range and logical relationship, and support version control and traceability management to ensure the standardization and traceability of the revision process. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present application, but do not constitute an improper limitation of the present invention. In the drawings:

[0040] Figure 1The flow of the automated revision method for revising a technical specification book of the present invention is shown. DETAILED DESCRIPTION

[0041] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, wherein the illustrative embodiments and descriptions are only used to explain the present invention but are not intended to limit the present invention.

[0042] Embodiment 1 Overall automated revision method

[0043] like Figure 1 As shown, this embodiment provides an automated revision method for revising technical specifications, including centralized configuration of technical parameters, automatic generation of technical specifications, intelligent revision of technical parameters, and automated processing of approval. This method can effectively improve the efficiency and accuracy of revising technical specifications through intelligent algorithms and automation technology, and adapt to the needs of dynamically changing external environments.

[0044] Step 1: Unified configuration of technical parameters

[0045] First, the system centrally manages technical parameters from different business departments through the unified association configuration module of technical parameters to form a unified technical parameter database. The database includes material procurement standards, industry standards and various technical parameter data. The system supports manual input, external data import and automatic collection to obtain technical parameters. In this step, the system standardizes the input technical parameters to ensure that the technical parameters are centrally configured and managed in a unified format. Standardization includes formatting, classification, grading and deduplication of technical parameters. For example, the system will automatically unify the material specifications of different units into the same metric, and grade each parameter according to its importance. In addition, for repeated parameters from different departments, the system can deduplicate and merge through automatic algorithms to avoid data redundancy.

[0046] The system further provides real-time synchronization of technical parameters to ensure that after the technical parameters are updated, all relevant business departments can receive the updated data synchronously. Each technical parameter is managed by a unique identifier (ID). When a technical parameter changes, the system automatically triggers the synchronization update of the related business to ensure that each department references the latest version when using the technical parameters.

[0047] Step 2: Generate technical specifications for scenario application

[0048] In the process of generating technical specifications, the scenario application module automatically selects appropriate technical specification templates or procurement standard templates from the template library according to the user's actual business needs (such as material procurement, engineering project construction, etc.). The template library pre-stores standardized templates for multiple business scenarios, and the templates are constructed based on industry standards, internal enterprise standards, and historical business data.

[0049] The scenario selection module recommends the most suitable business scenario based on the business needs entered by the user, and provides a variety of optional templates. Users can preview the template content. After the template is called, the user can also adjust and customize the variable part of the template according to specific needs, such as adding or modifying technical parameters, adjusting material specifications, etc. The system integrates an automatic prompt function. When the user modifies a parameter, the system will automatically give a prompt based on historical data and industry standards to help the user optimize the adjustment process.

[0050] In addition, the system also supports a parameter linkage mechanism. When a key parameter is modified, the system will automatically adjust other related parameters. For example, when material specifications change, the system can automatically adjust the corresponding technical requirements or procurement standards to maintain the consistency of the entire technical specification.

[0051] Step 3: Automatic revision of technical parameters

[0052] Automatic revision of technical parameters is one of the core steps of the present invention. The system automatically revises technical parameters by integrating intelligent optimization algorithms based on big data. Specifically, the system first extracts data from historical revision records and combines current market dynamics and standardized data to generate optimal parameter revision suggestions.

[0053] The automated revision process of technical parameters includes multivariate analysis of data in multiple dimensions. The system automatically analyzes multidimensional data such as historical revision data, material procurement standards, industry standards, and market change information to generate optimal parameter revision suggestions. For example, the system will capture the complex relationships between technical parameters based on nonlinear regression models, further analyze the interactions between various variables, and provide the optimal revision plan through intelligent optimization algorithms. In particular, the system introduces interaction terms and quadratic terms, which enable it to accurately predict parameter changes in complex business scenarios.

[0054] Furthermore, the system integrates a dynamic parameter adjustment mechanism. When the external environment changes, such as market price fluctuations, supply chain disruptions, or industry standard updates, the system will adjust the relevant parameters in the technical specifications in real time based on the new data. For example, when market price changes are detected, the system automatically pushes new procurement parameter revision suggestions and updates them to the technical specifications to ensure that the revision plan can keep up with market changes and the latest business needs.

[0055] Step 4: Quick Approval

[0056] After the revision of technical parameters is completed, the system starts the fast approval process. The fast approval module can automatically integrate the approval process, merge multiple approval nodes into an integrated process, simplify the approval process, and avoid repeated approval.

[0057] During the approval process, the system automatically selects the appropriate approval path based on the complexity of the technical parameter revision and the scope of business impact. For technical parameter revisions with less impact, the system will select a single department head for direct approval, while for technical parameter revisions involving multiple departments, the system will initiate multi-department collaborative approval. The system supports real-time tracking of approval progress, and users can check the approval status at any time. In addition, when the approval process is interrupted or delayed, the system will automatically send reminders to the relevant approvers to ensure that the approval process can be successfully completed within the specified time.

[0058] In emergency situations, the system also supports an accelerated approval mechanism. Users can set the emergency approval level, and the system will automatically trigger the high-priority approval path to speed up the approval process. For example, for the revision of key technical parameters in major engineering projects, the system can prioritize approval tasks to ensure that technical specifications can be quickly released and applied to actual business.

[0059] Example 2 Automatic revision of technical parameters

[0060] This embodiment provides a detailed technical solution for the automated revision of technical parameters. The system integrates an intelligent optimization algorithm based on big data, which can automatically analyze historical revision data, market dynamics, and standardized data to generate optimal technical parameter revision suggestions, thereby improving the efficiency and accuracy of technical specification revisions.

[0061] Data collection and analysis:

[0062] The first step in the automated revision of technical parameters is to extract data from the system's historical revision records and analyze them in combination with current market dynamics and industry standards. The historical revision data includes all past revision records of technical parameters in the technical specification, such as the time of revision, parameter values ​​before and after revision, reasons for revision and influencing factors, etc. These historical data help the system understand the revision trends and rules of technical parameters and provide data support for subsequent revision decisions.

[0063] The system also combines external market dynamics data (such as market price fluctuations, supply chain changes) and changes in industry standards to ensure that the revision proposals can adapt to the current external environment. Market data is obtained in real time from external supplier databases or market monitoring platforms through interfaces, while industry standards are synchronized regularly through automatically updated standard libraries.

[0064] Intelligent optimization algorithms and multivariate analysis:

[0065] After data collection and sorting are completed, the system will automatically analyze data in multiple dimensions through an intelligent optimization algorithm. The algorithm uses a nonlinear regression model to capture the complex interactions between technical parameters. The specific formula is:

[0066]

[0067] Among them, P(t) is the technical parameter value at time point t, X i (t) is the ith influencing factor (out of n), X i (t) is the jth (out of n) influencing factor, n is the total number of influencing factors, β i is the linear regression coefficient, γ ij is the interaction term coefficient, and δ1 is the quadratic effect coefficient.

[0068] In nonlinear regression models, β0 represents the constant term (Intercept), also known as bias or intercept. In the formula, its role is to provide a benchmark value, that is, when the values ​​of all other influencing factors (i.e. X i When β0 is zero, it represents the basic value or starting value of the technical parameter P(t).

[0069] Through this model, the system can capture the key factors that affect changes in technical parameters, such as the market price of materials, changes in industry standards, supply chain stability, etc. The introduction of interaction terms and quadratic terms enables the system to further analyze the complex relationship between these factors and generate optimal technical parameter revision suggestions. Especially in complex business scenarios where multiple variables change at the same time, the system can more accurately predict the trend of changes in technical parameters and ensure the accuracy of revisions.

[0070] Dynamic parameter adjustment mechanism:

[0071] The system further integrates a dynamic parameter adjustment mechanism to cope with changes in the external environment. When the system detects market price fluctuations, supply chain disruptions, or industry standard updates, the automatic revision module will adjust the relevant parameters in the technical specification in real time. The adjustment mechanism is based on the following formula:

[0072] ΔP(t)=α1·M(t)+α2·S(t)+α3·R(t)

[0073] in:

[0074] ΔP(t) is the revision amplitude of the technical parameters at time point t;

[0075] M(t) is the impact of market price fluctuations at time point t (percentage change or price increase or decrease);

[0076] S(t) is the change in the supply chain status at time point t (e.g., supply chain delay, supply interruption, etc., expressed by the corresponding delay time or impact index);

[0077] R(t) is the degree of updating of industry standards at time point t (which can be quantified as the degree of impact of the update on technical parameters, expressed using standard variation coefficients or scoring systems);

[0078] α1, α2, and α3 are weight coefficients corresponding to market price fluctuations, supply chain changes, and industry standard updates, representing their relative importance to technical parameter adjustments.

[0079] Through this formula, the system can dynamically calculate the extent to which technical parameters need to be adjusted to ensure that the parameters in the technical specifications meet the latest market conditions and industry requirements.

[0080] For example, when market prices rise, the system automatically adjusts the procurement cost parameters and pushes the new parameters to the technical specifications. At the same time, the system will record the influencing factors and revision range of this revision for future traceability and further optimization.

[0081] Specific examples are as follows:

[0082] Assume that at time t:

[0083] The market price volatility M(t) increases by 5%;

[0084] Supply chain delay S(t) increases delivery time by 2 days;

[0085] The industry standard update R(t) results in a new requirement that increases the specification parameter requirements by 3%.

[0086] By setting the corresponding weight coefficients, for example, α1 = 0.6, α2 = 0.3, α3 = 0.1, the technical parameter adjustment range ΔP(t) is calculated as:

[0087] ΔP(t)=0.6·5+0.3·2+0.1·3=3+0.6+0.3=3.9

[0088] Therefore, the revision of the technical parameters at time point t is 3.9%. This adjustment value will be automatically applied to the relevant parameters in the technical specifications to adapt to new market and business needs.

[0089] This formula can adjust the various weight coefficients according to the business scenario to better reflect the impact of market prices, supply chains and industry standards on technical parameters.

[0090] Generation and feedback of revision plans:

[0091] The revision plan generated by the technical parameter automatic revision module includes the revised parameter value, revision reason, revision basis and relevant influencing factor analysis. These contents are automatically generated by the system, and users can view the detailed revision suggestions on the interface and make manual adjustments as needed or directly adopt the system's automatic suggestions.

[0092] To ensure the accuracy of the revision plan, the system has a built-in automatic verification function. After the revision proposal is generated, the system will verify each revised technical parameter according to the material procurement standards and industry standards. For example, the system will automatically check whether the revised parameters meet the preset range and logical relationship to ensure that the revised parameters do not exceed the specified upper and lower limits. Any parameters that do not meet the standards will be marked and the user will be reminded to make corrections.

[0093] Automatic verification and real-time synchronization:

[0094] When the technical parameter revision plan passes the verification, the system will automatically synchronize the revised parameters to all related business processes. Since changes in technical parameters may affect multiple business departments, the system uses the unique identifier (ID) of the technical parameter to ensure that all business processes that reference the parameter can obtain the latest revision results. For example, after the technical parameters are updated, the system will automatically synchronize to the material procurement process, project management process, and supply chain management system to ensure that the parameters used by all business departments are consistent and up-to-date.

[0095] In addition, the system supports version control and log recording functions. Each revision of technical parameters will automatically generate a new version, and users can view and compare historical versions at any time. The system will record in detail the time, person who made the revision, reason for the revision, and analysis of influencing factors of each revision to ensure the traceability of the revision process.

[0096] Through intelligent optimization algorithms and dynamic parameter adjustment mechanisms, the technical parameter automatic revision module can automatically analyze and adjust technical parameters to ensure the accuracy and timeliness of technical specification revisions. The system captures key variables in complex business scenarios through multi-dimensional data analysis, and adjusts technical parameters in real time according to dynamic changes in the external environment, so that the revision plan always adapts to the latest market needs and industry standards.

[0097] The above description is only a preferred embodiment of the present invention, so all equivalent changes or modifications made according to the structure, characteristics and principles described in the scope of the patent application of the present invention are included in the scope of the patent application of the present invention.

Claims

1. A method for automatically revising a technical specification based on dynamic parameter adjustment, characterized in that: The automated revision method comprises the following steps: Step 1: Centrally configure technical parameters; Step 2: Generate a technical specification book by referencing the technical specification template or procurement standard template according to the application scenario; Step 3: Automatically revise technical parameters according to material procurement standards and actual needs. Based on big data, intelligent optimization algorithms automatically analyze historical revision data and standardized data to generate optimal parameter revision suggestions. Further dynamically adjust technical parameters according to changes in the external environment. When market price fluctuations, supply chain changes, or industry standard updates are detected, relevant parameters in the technical specifications are adjusted in real time according to the updated data. Step 4: Quick approval, simplify the approval process, and ensure that the technical parameters are quickly approved after revision.

2. The method according to claim 1, characterized in that: The step 1 of centrally configuring the technical parameters further includes: Through the unified association configuration module of technical parameters, technical parameters from different business departments are centrally managed to form a unified technical parameter database; The technical parameter database includes material procurement standards, industry standards and various technical parameter data, and supports manual input, external data import and automatic collection to obtain technical parameters; Standardizing the input technical parameters, including formatting, categorizing, grading and deduplication operations, so that the technical parameters can be centrally configured and managed in a unified format; Each technical parameter is managed through a unique identifier, and when a technical parameter changes, it automatically triggers the synchronization update of related businesses, so that the technical parameters used by each department are the latest versions.

3. The method according to claim 1, characterized in that: The step 2 specifically includes: the scenario application module automatically selects a suitable technical specification template or procurement standard template from the template library according to the user's actual business needs; the template library includes standardized templates for multiple business scenarios, and the templates are constructed based on industry standards, internal enterprise standards and historical business data; the scenario selection module recommends the most suitable business scenario according to the business needs input by the user, and the user can preview the template content; the user adjusts the variable part of the template according to specific needs; when a key parameter is modified, other related parameters are automatically adjusted.

4. The method according to claim 1, characterized in that: The step 3 further comprises the following steps: Data collection and analysis: extract data from the system's historical revision records and analyze them in combination with current market dynamics, industry standards, and changes in the external environment. The historical data includes revision time, parameter values ​​before and after revision, revision reasons, and influencing factors; Data integration: Obtain real-time market data from external supplier databases or market monitoring platforms through interfaces, and synchronize industry standards from automatically updated standard libraries; Intelligent optimization and multivariate analysis: Analyze data in multiple dimensions based on nonlinear regression models and intelligent optimization algorithms, generate technical parameter revision suggestions, and capture the complex interactions between parameters. The model includes interaction terms and quadratic terms to further analyze the relationship between variables. Dynamic parameter adjustment: When the system detects market price fluctuations, supply chain disruptions or industry standard updates, it dynamically adjusts the relevant parameters in the technical specifications. The adjustment process is based on a multi-variable adjustment formula to calculate the impact of market price fluctuations, supply chain conditions and industry standard updates on technical parameters; Revision scheme generation: Generates a revision scheme including revised parameter values, revision reasons, revision basis and related influencing factors, and provides a user interface for manual adjustment or direct adoption of automatically generated revision suggestions.

5. The method according to claim 4, characterized in that: The intelligent optimization and multivariate analysis steps include the following nonlinear regression model: Among them, P(t) is the technical parameter value at time point t, X i (t), X j (t) are the i-th and j-th influencing factors, n is the total number of influencing factors, β i is the linear regression coefficient, γ ij is the interaction term coefficient, δ1 is the quadratic effect coefficient, and β0 is the constant term.

6. The method according to claim 4, characterized in that: The dynamic parameter adjustment step calculates the revision amplitude based on the following formula: ΔP(t)=α1·M(t)+α2·S(t)+α3·R(t) in: ΔP(t) is the revision amplitude of the technical parameters at time point t; M(t) is the impact of market price fluctuations at time point t; S(t) is the change in supply chain status at time point t; R(t) is the updating degree of industry standards at time point t; α1, α2, and α3 are the weight coefficients corresponding to market price fluctuations, supply chain changes, and industry standard updates, respectively.

7. The method according to claim 4, characterized in that: The revision scheme generation step includes automatically checking whether the revised technical parameters meet the preset range and logical relationship, marking the parameters that do not meet the standards, and prompting the user to make corrections.

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