Management method and system based on special construction project planning implementation scheme

Abstract

The application provides a management method and system based on a special construction project planning implementation scheme, relates to the technical field of data management, and obtains project basic data through a preset database, performs risk identification and weight analysis, obtains a weight data set, performs scheme comparison and analysis, and obtains a scheme comparison sequence; performs forward verification and reverse verification according to the scheme comparison sequence, adjusts the scheme according to the forward verification result and the reverse verification result, determines a main selected scheme and an alternative scheme; and performs fit degree examination, analysis and adjustment on the main selected scheme and the alternative scheme, and obtains scheme management data; and the application greatly improves the intelligence and accuracy of project implementation management.

Description

Technical Field

[0001] This invention proposes a management method and system based on the implementation plan of special construction projects, which relates to the field of data management technology, specifically to the field of management technology based on the implementation plan of special construction projects. Background Technology

[0002] In the area of ​​planning and implementation management for special construction projects, the generation of implementation analysis data often relies on manual breakdown of review information. Data collection lacks a systematic approach and is prone to fragmentation. The analytical dimensions are disconnected from the review issues, making it difficult to accurately match core differences. Data formats lack uniform standards, resulting in poor readability and reusability. The lack of clear attribution of subdivided datasets leads to chaotic data hierarchy and frequent analytical biases. These problems make it difficult for implementation analysis data to effectively support review and adjustment decisions, increasing the cost of scheme optimization and implementation risks, and failing to meet the high requirements of accuracy and reliability for planning implementation in special construction projects. Summary of the Invention

[0003] This invention provides a management method and system based on the implementation plan of special construction projects to solve the above-mentioned problems: The present invention proposes a management method and system based on a special construction project planning and implementation scheme, wherein the method includes: S1. Obtain basic project data through a pre-set database, perform risk identification and weight analysis to obtain a weight dataset, conduct scheme comparison analysis, and obtain a scheme comparison sequence; S2. Perform forward and reverse verification based on the scheme comparison sequence, adjust the scheme based on the results of forward and reverse verification, and determine the primary and alternative schemes. S3. Conduct a fit review and analysis of the primary and alternative solutions and make adjustments to obtain solution management data.

[0004] Further, S1 includes: Obtain a preset database, and collect basic project information based on the preset database to obtain basic project data. Risk points are identified based on the basic data collected from the project, resulting in risk point identification data. A basic dataset is then generated by combining the basic data collected from the project with the risk point identification data. Perform data category weight analysis on the basic dataset to obtain dataset weight analysis data; Weights are set on the base dataset based on the dataset weight analysis data to obtain a weighted dataset. Multiple scheme comparison data are obtained by comparing multiple schemes based on the weighted dataset; Obtain the scheme comparison sequence based on the multi-scheme comparison data.

[0005] Furthermore, the step of identifying risk points based on the project's basic data collection to obtain risk point identification data, and generating a basic dataset based on the project's basic data collection combined with the risk point identification data, includes: Obtain risk characteristic information of the implementation of the preset plan, identify risk points in the basic data collected for the project based on the risk characteristic information of the implementation of the preset plan, and obtain risk point identification data. The risk point identification coefficient is obtained by comparing the risk point identification data with the risk point identification threshold. The basic dataset is obtained by classifying the risk level of the basic data collected for the project based on the risk point identification coefficient.

[0006] Furthermore, the step of performing data category weight analysis on the basic dataset to obtain dataset weight analysis data includes: The basic dataset is divided into categories to obtain dataset category data. Obtain the preset weight information of the category data to obtain the preset weight data of the category; Obtain the proportion of the risk point identification coefficient of the dataset category classification data in the comprehensive risk point identification coefficient of the basic dataset, and obtain the category risk weight data; Obtain the average of the preset category weight data and the category risk weight data to obtain the category classification weight data; The weight data for classifying the various categories in the basic dataset is the dataset weight analysis data.

[0007] Further, S2 includes: Based on the scheme comparison sequence, obtain the scheme phase task information, perform positive control requirements matching on the scheme phase tasks, and obtain positive verification information; Perform reverse constraint matching on the tasks in the solution phase to obtain reverse verification information; Based on the positive verification information and the negative verification information, the scheme verification analysis is performed to obtain scheme verification analysis data; The scheme comparison sequence is adjusted based on the scheme verification analysis data to obtain the scheme adjustment sequence; The primary and alternative schemes are determined based on the scheme adjustment sequence.

[0008] Furthermore, the step of performing scheme verification analysis based on the combination of forward verification information and reverse verification information to obtain scheme verification analysis data includes: The pass rate of positive verification is obtained from the positive verification information of the task information in the solution stage. The reverse verification pass rate of the task information in the scheme stage is obtained based on the reverse verification information. The average of the forward and reverse validation pass rates is used to obtain the task validation pass rate. The task verification pass rate is compared with the preset verification pass threshold to obtain verification pass comparison information; The verification results obtained through comparison are the solution verification analysis data.

[0009] Further, the step of adjusting the scheme comparison sequence based on the scheme verification analysis data to obtain the scheme adjustment sequence includes: Sequence verification analysis data is obtained by comparing sequences according to the scheme; The sequence verification analysis data is sorted for verification to obtain the scheme verification sequence; The scheme comparison sequence is updated based on the scheme verification sequence to obtain the scheme adjustment sequence.

[0010] Further, S3 includes: The primary and alternative solutions are reviewed for their suitability, and information on the suitability review is obtained. Based on the information from the plan fit review, conduct project planning and implementation data analysis to obtain implementation analysis data; Based on the implementation analysis data, a review and adjustment analysis is conducted to obtain scheme review and adjustment information; Based on the information from the plan review and adjustment, plan review and adjustment are carried out to obtain plan management information.

[0011] Furthermore, the process of analyzing project planning and implementation data based on the scheme fit review information to obtain implementation analysis data includes: The matching degree review information of the proposed solutions is broken down into matching degree types, and the matching degree difference information of each type is extracted; Establish a mapping relationship between fit difference information and analysis dimensions; Collect basic supporting data and difference compensation data, verify and integrate the data to form multi-dimensional subsets; Feasibility analysis was performed on each dimension of the subdivided subsets to obtain feasibility analysis data for each dimension. Based on the feasibility analysis data from each dimension, standardized structured data is generated.

[0012] Furthermore, the system includes: The data analysis module is used to obtain basic project data through a preset database, perform risk identification and weight analysis, obtain a weight dataset, conduct scheme comparison analysis, and obtain a scheme comparison sequence. The verification and adjustment module is used to perform forward and reverse verification based on the scheme comparison sequence, adjust the scheme based on the results of forward and reverse verification, and determine the primary and alternative schemes. The data management module is used to review, analyze, and adjust the suitability of the primary and alternative solutions to obtain solution management data.

[0013] The beneficial effects of this invention are as follows: This method solves the core technical problems of fragmented processes, strong subjectivity in decision-making, and weak implementation coordination in the planning and management of traditional special construction projects. It realizes the transformation of planning implementation schemes from qualitative judgment to quantitative analysis, improving the scientific rigor and accuracy of scheme selection and optimization; through a closed-loop design of the entire process, it connects the links of data collection, scheme generation, verification and adjustment, and implementation management, avoiding the problem of repeated scheme modifications caused by disconnections in each stage, and improving the efficiency of project planning implementation; at the same time, through multi-dimensional constraints and verification mechanisms, it reduces the risk of implementation failure due to insufficient compliance and lack of risk prediction, improving the compliance and risk controllability of project planning. Attached Figure Description

[0014] Figure 1 This is a schematic diagram illustrating the management methods for implementing plans based on special construction projects. Detailed Implementation

[0015] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0016] In one embodiment of the present invention, a management method and system based on a special construction project planning and implementation scheme are proposed, wherein the method includes: S1. Obtain basic project data through a pre-set database, perform risk identification and weight analysis to obtain a weight dataset, conduct scheme comparison analysis, and obtain a scheme comparison sequence; S2. Perform forward and reverse verification based on the scheme comparison sequence, adjust the scheme based on the results of forward and reverse verification, and determine the primary and alternative schemes. S3. Conduct a fit review and analysis of the primary and alternative solutions, and adjust accordingly to obtain solution management data, such as... Figure 1 As shown.

[0017] Example as follows: Based on a pre-set database, basic data such as the nature of project land use and ecological red line related information are collected; risk points such as vegetation destruction and illegal land use are identified by combining pre-set risk characteristics, and risk levels are divided to generate a basic dataset; data types are divided according to dimensions such as natural geographical conditions and the impact of ecologically sensitive points, and the weights of each dimension are determined by integrating pre-set weights and risk weights; based on the weight dataset, three alternative routes A, B, and C are compared to form a scheme comparison sequence (A > B > C).

[0018] Extract task information for each phase of the plan and match it with positive control requirements (ecological construction specifications) and negative constraints (negative list of construction in protected areas) to obtain dual-dimensional verification information; calculate the average pass rate of positive and negative verification and compare it with the preset threshold to complete the verification analysis; adjust the sequence according to the verification results and determine that plan A is the primary option and plan B is the alternative.

[0019] Review and management information generation. Conduct fit reviews of options A and B, verifying the fit of rigid constraints, deviations from planning objectives, and matching of implementation conditions; break down review information to extract differences, establish mapping relationships with analysis dimensions, collect supplementary data to form dimensional subsets, and complete feasibility analysis; after review and adjustment analysis, implement hierarchical optimization, and generate scheme management information including optimized parameters and risk control responsibilities.

[0020] The working principle and technical effects of the above-mentioned technical solution are as follows: This method completes the standardized collection of basic project data through a pre-set database, combines risk identification and weight analysis to achieve quantitative comparison of multiple solutions, and outputs a preliminary solution comparison sequence; then, by introducing a two-dimensional verification mechanism of positive control matching and reverse constraint matching, the solution comparison sequence is dynamically adjusted to select the primary and alternative solutions; through multi-dimensional fit review of the core solutions, combined with implementation data analysis and hierarchical adjustment, solution management information that can be directly implemented is formed. The entire process is based on the pre-set database and uses quantitative analysis as the core means to achieve full-link control from solution generation and screening to optimization and implementation, ensuring the correlation and traceability of inputs and outputs at each stage.

[0021] This method addresses the core technical challenges of fragmented processes, highly subjective decision-making, and weak implementation coordination in traditional special construction project planning and management. It shifts the focus of planning implementation from qualitative judgment to quantitative analysis, improving the scientific rigor and accuracy of scheme selection and optimization. Through a closed-loop design, it streamlines the data collection, scheme generation, verification and adjustment, and implementation management processes, avoiding repeated scheme modifications caused by disconnects in each stage and improving the efficiency of project planning implementation. Simultaneously, through multi-dimensional constraints and verification mechanisms, it reduces the risk of implementation failure due to insufficient compliance or lack of risk assessment, enhancing the compliance and risk controllability of project planning.

[0022] In one embodiment of the present invention, S1 includes: A preset database is obtained, and basic project information is collected based on the preset database to obtain basic project data. The preset database includes common indicators for special construction projects, such as land use, ecological red line range, safety protection standards, construction technical parameters, and historical data of similar projects. Risk points are identified based on the basic data collected from the project, resulting in risk point identification data. A basic dataset is then generated by combining the basic data collected from the project with the risk point identification data. Perform data category weight analysis on the basic dataset to obtain dataset weight analysis data; Weights are set on the base dataset based on the dataset weight analysis data to obtain a weighted dataset. Multiple scheme comparison data are obtained by comparing multiple schemes based on the weighted dataset; Obtain the scheme comparison sequence based on the multi-scheme comparison data.

[0023] Collect core elements such as the project's construction background, basis, nature, functional zoning, land use scale, and compilation and review body; Automatically identify illegal land use risk points and generate standardized basic analysis datasets; The analysis of category weights is conducted based on dimensions such as socio-economic carrying capacity, natural geographical conditions (topography, geology, hydrology, etc.), infrastructure compatibility, and the degree of impact of sensitive points.

[0024] The working principle and technical effects of the above technical solution are as follows: A pre-screening system is constructed, encompassing basic data collection, risk integration, weight quantification, and scheme ranking. A pre-set database serves as the data collection benchmark. This database integrates common core indicators for special construction projects, ensuring the comprehensiveness and relevance of basic information collection. Simultaneously, it supplements the collection with specific core elements such as project construction background and land use scale, achieving full coverage of both common and specific data. Based on pre-set planning and risk characteristic information, risk points are identified in the basic data collection. Through risk point identification coefficients and level classifications, risk information is integrated with basic data to form a standardized basic dataset (including subdivided dimensions such as illegal land use risk). Furthermore, the basic dataset is categorized from core dimensions such as socio-economic carrying capacity and natural geographical conditions. Combined with pre-set weights and risk coefficient proportions, a comprehensive weight is calculated to achieve differentiated data quantification. Based on the weighted dataset, a multi-scheme horizontal comparison is conducted, outputting a priority-ranked scheme comparison sequence.

[0025] This method addresses the technical problems of traditional basic data collection being blind, risk identification being fragmented, and the lack of quantitative basis for scheme comparison. By guiding data collection through a pre-set database, it improves the completeness and relevance of basic data, avoiding the omission of key information; it deeply integrates risk identification with basic data, achieving a linkage between data and risk, and improving the foresight of risk prediction; weighted analysis, combined with multi-dimensional actual influencing factors, avoids the one-sidedness of single-dimensional ranking, improving the scientific rigor of scheme comparison; and it automatically identifies core risk points such as illegal land use and generates standardized datasets, reducing the subjective errors of manual analysis and improving the accuracy and efficiency of risk identification.

[0026] In one embodiment of the present invention, the step of identifying risk points based on basic project data to obtain risk point identification data, and generating a basic dataset based on the basic project data and the risk point identification data, includes: Obtain risk characteristic information of the implementation of the preset plan, identify risk points in the basic data collected for the project based on the risk characteristic information of the implementation of the preset plan, and obtain risk point identification data. The risk point identification coefficient is obtained by comparing the risk point identification data with the risk point identification threshold. The basic dataset is obtained by classifying the risk level of the basic data collected for the project based on the risk point identification coefficient.

[0027] The working principle and technical effects of the above technical solution are as follows: This method adopts a risk integration logic of feature matching, coefficient quantification, and level classification. It retrieves pre-defined planning risk characteristic information (including judgment criteria and threshold ranges for various risks), matches the project's basic data collection with the risk characteristic information one by one, accurately identifies potential risk points, and forms risk point identification data. By calculating the ratio of the risk point identification data to the pre-defined risk threshold, a quantified risk point identification coefficient is obtained, achieving a quantifiable representation of the risk level. Based on the risk point identification coefficient, multi-level classification standards (such as high, medium, and low risk) are set, and the project's basic data collection is labeled with risk levels. The labeled basic data is integrated with the risk point identification data to form a complete basic dataset containing basic information, risk quantification information, and risk levels.

[0028] This method addresses the technical problems of traditional risk identification, such as qualitative analysis, ambiguous risk levels, and a disconnect between basic data and risk information. By pre-setting risk feature information matching, it improves the accuracy of risk point identification and avoids missed or misjudged risks. By introducing a risk point identification coefficient to quantify the degree of risk, this method solves the problem of difficulty in defining the level of risk and improves the objectivity of risk assessment. By deeply integrating risk information with basic data to form a standardized dataset, it improves the efficiency and accuracy of the analysis process and reduces the deviation in solution optimization caused by missing risk information.

[0029] In one embodiment of the present invention, the step of performing data category weight analysis on the basic dataset to obtain dataset weight analysis data includes: The basic dataset is divided into categories to obtain dataset category data. Obtain the preset weight information of the category data to obtain the preset weight data of the category; Obtain the proportion of the risk point identification coefficient of the dataset category classification data in the comprehensive risk point identification coefficient of the basic dataset, and obtain the category risk weight data; Obtain the average of the preset category weight data and the category risk weight data to obtain the category classification weight data; The weight data for classifying the various categories in the basic dataset is the dataset weight analysis data.

[0030] The working principle and technical effects of the above technical solution are as follows: This method adopts a weight analysis logic of classification, dual-weight fusion, and mean quantification. The basic dataset is categorized according to core impact dimensions (such as socio-economic carrying capacity, natural geographical conditions, etc.) to clarify the classification of each data type. Two types of weight data are acquired simultaneously: categorized pre-set weight data based on industry standards and project requirements, and categorized risk weight data obtained by calculating the proportion of each data type's risk point identification coefficient in the comprehensive risk coefficient. The average of the two types of weight data is taken as the final weight of each data type (categorized weight data), forming complete dataset weight analysis data, achieving a dual weight balance between pre-set experience and actual risk.

[0031] This method addresses the technical problems of traditional weight analysis, such as relying solely on pre-set experience, being disconnected from actual risks, and exhibiting strong subjectivity in weight allocation. By integrating pre-set weights and risk weights, it balances the universality of industry experience with the specificity of actual project risks, improving the scientific rigor and relevance of weight allocation. Data classification based on multiple core dimensions ensures the comprehensiveness of weight analysis and prevents key influencing dimensions from being overlooked. The weight quantification results reduce the bias in comparing multiple solutions and enhance the rationality of solution selection.

[0032] In one embodiment of the present invention, S2 includes: Based on the scheme comparison sequence, obtain the scheme phase task information, perform positive control requirements matching on the scheme phase tasks, and obtain positive verification information; Perform reverse constraint matching on the tasks in the solution phase to obtain reverse verification information; reverse verification includes policy red lines, negative lists, historical failure cases, etc. Based on the positive verification information and the negative verification information, the scheme verification analysis is performed to obtain scheme verification analysis data; The scheme comparison sequence is adjusted based on the scheme verification analysis data to obtain the scheme adjustment sequence; The primary and alternative schemes are determined based on the scheme adjustment sequence.

[0033] Obtain the phase task information of the verified solution based on the verification comparison results; Based on the task verification pass rate, sort the task information in the solution phase to determine the primary and alternative solutions.

[0034] The working principle and technical effects of the above technical solution are as follows: A precise screening system is constructed, comprising dual-dimensional verification, sequence adjustment, and solution selection. Phase task information for each solution is extracted from the solution comparison sequence and matched with positive control requirements (such as planning objectives, technical standards, and schedule requirements) to obtain positive verification information; it is also matched with negative constraints (such as preset red lines, negative lists, and historical failure cases) to obtain negative verification information; solution verification analysis is conducted based on these two types of verification information, and the feasibility of the solution is determined by combining the verification pass rate; the initial solution comparison sequence is then adjusted and updated based on the verification analysis results to form a solution adjustment sequence; based on the solution adjustment sequence and the verification pass rate ranking, the primary and alternative solutions are finally determined, ensuring that the selected solutions possess both theoretical advantages and practical feasibility.

[0035] This method addresses the technical problems of traditional solution selection, which focuses only on positive objectives while neglecting negative constraints and resulting in poor feasibility. By employing both positive and negative dual-dimensional verification, it comprehensively covers the compliance and effectiveness of solutions, improving the overall comprehensiveness of solution selection and reducing the implementation risks caused by violations of negative constraints. Sequence adjustment and solution ranking based on verification pass rates enable quantitative decision-making in solution selection, avoiding biases from subjective judgments. Clearly distinguishing between primary and alternative solutions enhances the flexibility and tolerance of project planning.

[0036] In one embodiment of the present invention, the step of performing scheme verification analysis based on forward verification information and reverse verification information to obtain scheme verification analysis data includes: The pass rate of positive verification is obtained from the positive verification information of the task information in the solution stage. The reverse verification pass rate of the task information in the scheme stage is obtained based on the reverse verification information. The average of the forward and reverse validation pass rates is used to obtain the task validation pass rate. The task verification pass rate is compared with the preset verification pass threshold to obtain verification pass comparison information; The verification results obtained through comparison are the solution verification analysis data.

[0037] The working principle and technical effect of the above technical solution are as follows: This method adopts a verification quantification logic of dual pass rate calculation, mean fusion, and threshold comparison. Based on positive verification information, the proportion of task information in the solution stage that meets the positive control requirements is statistically analyzed to obtain the positive verification pass rate; simultaneously, based on reverse verification information, the proportion of task information in the solution stage that avoids the reverse constraint conditions is statistically analyzed to obtain the reverse verification pass rate; the average of the two pass rates is calculated to obtain the task verification pass rate that comprehensively reflects the feasibility of the solution; the task verification pass rate is compared with the preset verification pass threshold to obtain verification pass comparison information of passed, needing optimization, and failed, which is the solution verification analysis data.

[0038] This method addresses the technical problems of traditional scheme verification, such as qualitative analysis, vague verification standards, and lack of comparability of results. By quantitatively calculating the pass rate across two dimensions, it transforms the positive compliance and negative compliance levels of a scheme into quantifiable indicators, improving the objectivity and comparability of the verification results. By merging the two types of pass rates with average values, it balances the achievability of the scheme's objectives with risk avoidance, avoiding the one-sidedness of single-dimensional verification. By comparing with preset thresholds, it clarifies the feasibility level of the scheme, improves the accuracy of scheme adjustment, and reduces scheme selection errors caused by vague verification standards.

[0039] In one embodiment of the present invention, adjusting the scheme comparison sequence based on scheme verification analysis data to obtain a scheme adjustment sequence includes: Sequence verification analysis data is obtained by comparing sequences according to the scheme; The sequence verification analysis data is sorted for verification to obtain the scheme verification sequence; The scheme comparison sequence is updated based on the scheme verification sequence to obtain the scheme adjustment sequence.

[0040] The working principle and technical effects of the above technical solution are as follows: This method adopts a dynamic adjustment logic of verification data association, verification sorting, and sequence update. The solution comparison sequence is associated and matched with the corresponding solution verification analysis data to ensure that the sorting position of each solution is supported by corresponding verification results. Based on the solution verification analysis data (corely the task verification pass rate), each solution is re-verified and sorted to obtain a solution verification sequence. The initial solution comparison sequence is replaced with the solution verification sequence to complete the sequence update and optimization, forming a solution adjustment sequence. This ensures that the adjusted sequence both takes into account the theoretical advantages of the initial solution and matches the actual feasibility requirements for implementation.

[0041] This method addresses the technical problems of traditional schemes having fixed sequences, mismatch with actual implementation conditions, and lack of basis for adjustment. By associating and matching sequences with verification data, it achieves precise correspondence between ranking and feasibility, improving the targeting of sequence adjustment; re-ranking based on verification results ensures the feasibility priority of the adjusted sequences, avoiding theoretically optimal but infeasible schemes from being ranked at the top; the dynamic update mechanism of the sequences improves the flexibility of scheme selection and reduces the implementation risk caused by the initial sequence being fixed.

[0042] In one embodiment of the present invention, S3 includes: The primary and alternative solutions are reviewed for their suitability, and information on the suitability review is obtained. Based on the information from the plan fit review, conduct project planning and implementation data analysis to obtain implementation analysis data; Based on the implementation analysis data, a review and adjustment analysis is conducted to obtain scheme review and adjustment information; Based on the information from the plan review and adjustment, plan review and adjustment are carried out to obtain plan management information.

[0043] This includes reviewing the suitability of the primary and alternative solutions to obtain suitability review information, including: The rigid constraint dimension fit review retrieves legal constraint indicators (such as ecological red line, permanent basic farmland scope, safety protection distance, etc.) from the preset database, compares the planning parameters of the main / alternate schemes with the constraint indicators with precise thresholds, identifies three types of states: complete fit, boundary fit, and exceeding constraints, determines the risk points of illegality and non-compliance, and obtains the first fit review data; The alignment review of the planning objectives is conducted by comparing the project's core planning objectives (such as functional zoning, land use control, and public service coverage) with the actual content of the plan. The deviation between the actual content of the plan and the objectives is analyzed, the objective achievement rate is quantified, and the causes of deviation (such as terrain limitations and cost constraints) are marked to obtain the second alignment review data. The implementation condition fit review combines the resource conditions required for project implementation (such as funding availability, construction technology adaptability, and infrastructure support capabilities) to assess the matching between the plan and the actual implementation conditions, identify fit gaps such as resource shortages, technical bottlenecks, and supporting shortcomings, and obtain the third fit review data.

[0044] The analysis of the data reveals the core causes of discrepancies between the proposed solutions and actual implementation conditions, distinguishing between three categories of tracing results: planning and design defects, changes in external conditions, and data collection deviations. By combining the core indicators of the weighted dataset (such as the impact weight of ecologically sensitive points and compliance weight), we can quantitatively analyze the degree of impact of differences on the feasibility of project implementation, risk controllability, and benefit achievement rate, and classify them into three levels of impact: high, medium, and low. Based on the impact level, rectification cost, and project duration impact, a priority model is established to rank the differences that need to be adjusted, and three types of adjustment strategies are defined: priority adjustment (high impact, low rectification cost), temporary adjustment (medium impact, dynamically adaptable), and observation and tracking (low impact, no need for rectification). Finally, a scheme review and adjustment information is formed, which includes adjustment items, source tracing results, impact level, priority, and rectification direction.

[0045] Based on the scheme review and adjustment information, a scheme review and adjustment process involving tiered adjustments and dynamic verification is implemented to obtain scheme management information: The adjustment items are classified and processed according to priority. High-priority items are made into hard adjustments through design optimization and expert review. Medium-priority items are given dynamic adaptation plans to reserve adjustment space. Low-priority items are recorded and tracked. After the adjustment is completed, the adjustment results are substituted back into the implementation analysis data for re-verification to verify the feasibility of the adjusted plan. The integrated and adjusted core parameters of the plan, remaining risk points, responsible entities for rectification, and progress milestone requirements are used to form standardized plan management information (including a list of planning parameter revisions that can be directly implemented, a risk prevention and control responsibility matrix, and phased acceptance standards).

[0046] The working principle and technical effects of the above technical solution are as follows: A multi-dimensional fit review, implementation analysis, hierarchical adjustment, and closed-loop optimization system is constructed to ensure successful implementation. A three-dimensional fit review is conducted on the primary and alternative solutions: rigid constraints are compared against legal indicators to determine thresholds; planning objectives are quantified to assess deviations; and implementation conditions are evaluated to assess resource matching, thus integrating comprehensive solution fit review information. Based on the review information, fit differences are extracted, a mapping relationship with the analysis dimensions is established, supplementary data is collected to form dimensional subsets, and feasibility analysis is conducted to obtain implementation analysis data. Then, through a three-stage analysis of difference tracing, impact assessment, and priority ranking, solution review adjustment information is formed. Hierarchical adjustment (processed according to priority) and dynamic verification (adjustment results are back-verified) are implemented, integrating core parameters, risk points, responsible entities, and other information to form standardized solution management information.

[0047] This method addresses the technical problems of traditional solutions, such as a single review dimension, a disconnect between implementation analysis and review, strong arbitrariness in adjustments, and a lack of standardized basis for implementation management. Through multi-dimensional fit review, it comprehensively covers the compliance, goal achievement, and implementation suitability of the solution, improving the comprehensiveness and accuracy of the review; the deep correlation between implementation analysis and review information establishes a clear link between review issues and analysis basis, enhancing the pertinence of issue analysis; the tiered adjustment and dynamic verification mechanism balances the rigidity of adjustments with the flexibility of implementation, reducing cost waste and schedule delays caused by blind rectification; and the standardized solution management information integrates parameters, risks, responsibilities, and standards, achieving seamless connection from analysis results to implementation, improving the operability and management efficiency of project planning and implementation.

[0048] In one embodiment of the present invention, the step of performing project planning and implementation data analysis based on scheme fit review information to obtain implementation analysis data includes: The matching degree review information of the proposed solutions is broken down into matching degree types, and the matching degree difference information of each type is extracted; Establish a mapping relationship between fit difference information and analysis dimensions; Collect basic supporting data and difference compensation data, verify and integrate the data to form multi-dimensional subsets; Feasibility analysis was performed on each dimension of the subdivided subsets to obtain feasibility analysis data for each dimension. Based on feasibility analysis data from various dimensions, standardized structured data is generated. The standardized basic analysis dataset is a sub-type of the basic dataset, focusing on the dimension of illegal land use risk.

[0049] The working principle and technical effects of the above technical solution are as follows: This method adopts the analytical logic of difference extraction, dimension mapping, data supplementation, feasibility analysis, and standardized integration. The information on the suitability review of the solution is broken down into types such as rigid constraints, planning objectives, and implementation conditions, and the suitability difference information (such as the degree of deviation, risk points, and shortcomings) of each category is extracted. A mapping relationship is established between the difference information and the implementation analysis dimensions (such as compliance feasibility and resource guarantee feasibility), clarifying the analysis attribution of each difference. Basic supporting data (historical data of similar projects and actual project data) and difference compensation data (rectification plan data and adaptation data) are then collected, verified, and integrated to form dimensional data subsets. Targeted feasibility analysis is conducted on each subset to obtain feasibility analysis data for each dimension. The data from each dimension are integrated to generate standardized structured data containing difference information, analysis conclusions, and feasibility levels (clarifying the sub-types of illegal land use risks in the basic dataset as the standardized basic analysis dataset).

[0050] This method addresses the technical challenges of traditional implementation analysis, such as fragmented data, disconnect from review issues, lack of data standardization, and weak decision-making support. Through difference extraction and dimension mapping, it achieves a precise correspondence between review issues and analytical dimensions, enhancing the relevance of implementation analysis. The supplementary collection of basic supporting data and difference-compensating data improves the data source for analysis, enhancing the comprehensiveness and reliability of feasibility analysis. The generation of standardized, structured data avoids data chaos, improving readability and reusability. Clearly defining the affiliation of subdivided datasets reduces analytical biases caused by data hierarchy confusion.

[0051] According to one embodiment of the present invention, the system includes: The data analysis module is used to obtain basic project data through a preset database, perform risk identification and weight analysis, obtain a weight dataset, conduct scheme comparison analysis, and obtain a scheme comparison sequence. The verification and adjustment module is used to perform forward and reverse verification based on the scheme comparison sequence, adjust the scheme based on the results of forward and reverse verification, and determine the primary and alternative schemes. The data management module is used to review, analyze, and adjust the suitability of the primary and alternative solutions to obtain solution management data.

[0052] The working principle and technical effects of the above-mentioned technical solution are as follows: This method completes the standardized collection of basic project data through a pre-set database, combines risk identification and weight analysis to achieve quantitative comparison of multiple solutions, and outputs a preliminary solution comparison sequence; then, by introducing a two-dimensional verification mechanism of positive control matching and reverse constraint matching, the solution comparison sequence is dynamically adjusted to select the primary and alternative solutions; through multi-dimensional fit review of the core solutions, combined with implementation data analysis and hierarchical adjustment, solution management information that can be directly implemented is formed. The entire process is based on the pre-set database and uses quantitative analysis as the core means to achieve full-link control from solution generation and screening to optimization and implementation, ensuring the correlation and traceability of inputs and outputs at each stage.

[0053] This method addresses the core technical challenges of fragmented processes, highly subjective decision-making, and weak implementation coordination in traditional special construction project planning and management. It shifts the focus of planning implementation from qualitative judgment to quantitative analysis, improving the scientific rigor and accuracy of scheme selection and optimization. Through a closed-loop design, it streamlines the data collection, scheme generation, verification and adjustment, and implementation management processes, avoiding repeated scheme modifications caused by disconnects in each stage and improving the efficiency of project planning implementation. Simultaneously, through multi-dimensional constraints and verification mechanisms, it reduces the risk of implementation failure due to insufficient compliance or lack of risk assessment, enhancing the compliance and risk controllability of project planning.

[0054] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A management method based on the implementation plan of special construction projects, characterized in that, The method includes: S1. Obtain basic project data through a pre-set database, perform risk identification and weight analysis to obtain a weight dataset, conduct scheme comparison analysis, and obtain a scheme comparison sequence; S2. Perform forward and reverse verification based on the scheme comparison sequence, adjust the scheme based on the results of forward and reverse verification, and determine the primary and alternative schemes. S3. Conduct a fit review and analysis of the primary and alternative solutions and make adjustments to obtain solution management data.

2. The management method based on the implementation plan of a special construction project according to claim 1, characterized in that, S1 includes: Obtain a preset database, and collect basic project information based on the preset database to obtain basic project data. Risk points are identified based on the basic data collected from the project, resulting in risk point identification data. A basic dataset is then generated by combining the basic data collected from the project with the risk point identification data. Perform data category weight analysis on the basic dataset to obtain dataset weight analysis data; Weights are set on the base dataset based on the dataset weight analysis data to obtain a weighted dataset. Multiple scheme comparison data are obtained by comparing multiple schemes based on the weighted dataset; Obtain the scheme comparison sequence based on the multi-scheme comparison data.

3. The management method based on the implementation plan of a special construction project according to claim 2, characterized in that, The process of identifying risk points based on basic project data, obtaining risk point identification data, and generating a basic dataset based on the basic project data and the risk point identification data includes: Obtain risk characteristic information of the implementation of the preset plan, identify risk points in the basic data collected for the project based on the risk characteristic information of the implementation of the preset plan, and obtain risk point identification data. The risk point identification coefficient is obtained by comparing the risk point identification data with the risk point identification threshold. The basic dataset is obtained by classifying the risk level of the basic data collected for the project based on the risk point identification coefficient.

4. The management method based on the implementation plan of a special construction project according to claim 2, characterized in that, The step of performing data category weight analysis on the basic dataset to obtain dataset weight analysis data includes: The basic dataset is divided into categories to obtain dataset category data. Obtain the preset weight information of the category data to obtain the preset weight data of the category; Obtain the proportion of the risk point identification coefficient of the dataset category classification data in the comprehensive risk point identification coefficient of the basic dataset, and obtain the category risk weight data; Obtain the average of the preset category weight data and the category risk weight data to obtain the category classification weight data; The weight data for classifying the various categories in the basic dataset is the dataset weight analysis data.

5. The management method based on the implementation scheme of special construction project planning according to claim 1, characterized in that, S2 includes: Based on the scheme comparison sequence, obtain the scheme phase task information, perform positive control requirements matching on the scheme phase tasks, and obtain positive verification information; Perform reverse constraint matching on the tasks in the solution phase to obtain reverse verification information; Based on the positive verification information and the negative verification information, the scheme verification analysis is performed to obtain scheme verification analysis data; The scheme comparison sequence is adjusted based on the scheme verification analysis data to obtain the scheme adjustment sequence; The primary and alternative schemes are determined based on the scheme adjustment sequence.

6. The management method based on the implementation plan of a special construction project according to claim 5, characterized in that, The step of performing scheme verification analysis based on forward verification information and reverse verification information to obtain scheme verification analysis data includes: The pass rate of positive verification is obtained from the positive verification information of the task information in the solution stage. The reverse verification pass rate of the task information in the scheme stage is obtained based on the reverse verification information. The average of the forward and reverse validation pass rates is used to obtain the task validation pass rate. The task verification pass rate is compared with the preset verification pass threshold to obtain verification pass comparison information; The verification results obtained through comparison are the solution verification analysis data.

7. The management method based on the implementation plan of a special construction project according to claim 5, characterized in that, The step of adjusting the scheme comparison sequence based on scheme verification and analysis data to obtain the scheme adjustment sequence includes: Sequence verification analysis data is obtained by comparing sequences according to the scheme; The sequence verification analysis data is sorted for verification to obtain the scheme verification sequence; The scheme comparison sequence is updated based on the scheme verification sequence to obtain the scheme adjustment sequence.

8. The management method based on the implementation plan of a special construction project according to claim 1, characterized in that, S3 includes: The primary and alternative solutions are reviewed for their suitability, and information on the suitability review is obtained. Based on the information from the plan fit review, conduct project planning and implementation data analysis to obtain implementation analysis data; Based on the implementation analysis data, a review and adjustment analysis is conducted to obtain scheme review and adjustment information; Based on the information from the plan review and adjustment, plan review and adjustment are carried out to obtain plan management information.

9. The management method based on the implementation scheme of special construction project planning according to claim 8, characterized in that, The process of analyzing project planning and implementation data based on the scheme fit review information to obtain implementation analysis data includes: The matching degree review information of the proposed solutions is broken down into matching degree types, and the matching degree difference information of each type is extracted; Establish a mapping relationship between fit difference information and analysis dimensions; Collect basic supporting data and difference compensation data, verify and integrate the data to form multi-dimensional subsets; Feasibility analysis was performed on each dimension of the subdivided subsets to obtain feasibility analysis data for each dimension. Based on the feasibility analysis data from each dimension, standardized structured data is generated.

10. A management system based on the implementation plan of a special construction project, characterized in that, The system includes: The data analysis module is used to obtain basic project data through a preset database, perform risk identification and weight analysis, obtain a weight dataset, conduct scheme comparison analysis, and obtain a scheme comparison sequence. The verification and adjustment module is used to perform forward and reverse verification based on the scheme comparison sequence, adjust the scheme based on the results of forward and reverse verification, and determine the primary and alternative schemes. The data management module is used to review, analyze, and adjust the suitability of the primary and alternative solutions to obtain solution management data.

Images