Big data-based consulting project whole-cycle tracking management method and system

Abstract

The application provides a consulting project whole-cycle tracking management method and system based on big data, relates to the technical field of cycle management, collects and optimizes data integration of demand research of the consulting project, carries out project approval and initialization on the data integration optimization information, decomposes and standardizes tasks according to the tracking management starting data, carries out whole-cycle data monitoring on the decomposed data, carries out resource scheduling and cooperation according to the whole-cycle monitoring data, carries out index monitoring according to the management implementation data, obtains index monitoring data, carries out deviation problem analysis according to the index monitoring data, obtains deviation problem analysis data, carries out adjustment and optimization according to the deviation problem analysis, and obtains adjustment and optimization data; and the adjustment and optimization data is used for data summarization, acceptance and archiving, so that the application realizes fine and data-based management of the whole cycle of the consulting project.

Description

Technical Field

[0001] This invention proposes a method and system for full-cycle tracking and management of consulting projects based on big data, which relates to cycle management, specifically to the field of full-cycle tracking and management technology for consulting projects based on big data. Background Technology

[0002] Traditional consulting project management relies heavily on manual experience and judgment, leading to problems such as disorganized data collection, low processing efficiency, and data silos formed by gaps between different stages. This results in inaccurate project requirements analysis and non-standard task decomposition. Furthermore, the lack of real-time monitoring throughout the project implementation process leads to delayed deviation identification, rigid resource allocation, and difficulty in responding quickly to project changes, easily causing resource waste and schedule delays. In addition, deviation analysis lacks scientific basis, adjustment measures are insufficiently targeted, and project experience cannot be effectively accumulated and reused, further increasing management costs and reducing project management quality and efficiency. Summary of the Invention

[0003] This invention provides a method and system for full-cycle tracking and management of consulting projects based on big data, in order to solve the above-mentioned problems: The present invention proposes a method and system for full-cycle tracking and management of consulting projects based on big data. The method includes: S1. Conduct needs assessment and data integration and optimization for consulting projects, obtain data integration and optimization information, approve and initialize the data integration and optimization information, and obtain tracking and management start data; S2. Based on the tracking and management start-up data, decompose and standardize the tasks to obtain decomposed processing data. Perform full-cycle data monitoring on the decomposed processing data to obtain full-cycle monitoring data. Based on the full-cycle monitoring data, perform resource scheduling and collaboration to obtain management implementation data. S3. Monitor indicators based on management implementation data, obtain indicator monitoring data, analyze deviations based on indicator monitoring data, obtain deviation analysis data, and make adjustments and optimizations based on deviation analysis to obtain adjustment and optimization data. S4. Summarize, accept, and archive the data based on the adjusted and optimized data.

[0004] Furthermore, the system includes: The integration and optimization module is used to conduct needs surveys and data integration and optimization for consulting projects, obtain data integration and optimization information, approve and initialize projects based on the data integration and optimization information, and obtain tracking and management start data. The decomposition and monitoring module is used to decompose and standardize tasks based on the tracking and management initiation data to obtain decomposition and processing data, perform full-cycle data monitoring on the decomposition and processing data to obtain full-cycle monitoring data, and perform resource scheduling and coordination based on the full-cycle monitoring data to obtain management implementation data. The deviation adjustment module is used to monitor indicators based on management implementation data, obtain indicator monitoring data, analyze deviation problems based on indicator monitoring data, obtain deviation problem analysis data, and make adjustments and optimizations based on deviation problem analysis to obtain adjustment and optimization data. The integrated archiving module is used for data aggregation, acceptance, and archiving based on adjusted and optimized data.

[0005] The beneficial effects of this invention are as follows: This method solves the technical problems of data fragmentation, process disconnect, lagging control, and unreusable experience in the traditional full-cycle management of consulting projects; it realizes systematic and data-driven tracking management of the entire consulting project lifecycle, breaks down data silos at each stage, and ensures that the entire process from project initiation to completion is traceable and controllable; it improves the precision and efficiency of project management, reduces errors caused by human intervention, and achieves reasonable allocation and efficient utilization of resources; it reduces the risk of deviation, resource waste, and management costs during project implementation, while realizing the structured accumulation of project management experience, providing reusable management basis for similar consulting projects, and improving the overall management level and quality stability of consulting projects. Attached Figure Description

[0006] Figure 1 This is a schematic diagram of a big data-based consulting project full-cycle tracking and management method. Detailed Implementation

[0007] 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.

[0008] Example 1

[0009] In one embodiment of the present invention, the present invention proposes a big data-based full-cycle tracking management method and system for consulting projects, the method comprising: S1. Conduct needs assessment and data integration and optimization for consulting projects, obtain data integration and optimization information, approve and initialize the data integration and optimization information, and obtain tracking and management start data; S2. Based on the tracking and management start-up data, decompose and standardize the tasks to obtain decomposed processing data. Perform full-cycle data monitoring on the decomposed processing data to obtain full-cycle monitoring data. Based on the full-cycle monitoring data, perform resource scheduling and collaboration to obtain management implementation data. S3. Monitor indicators based on management implementation data, obtain indicator monitoring data, analyze deviations based on indicator monitoring data, obtain deviation analysis data, and make adjustments and optimizations based on deviation analysis to obtain adjustment and optimization data. S4. Based on the adjusted and optimized data, summarize, accept, and archive the data, such as... Figure 1 As shown.

[0010] The working principle and technical effects of the above technical solution are as follows: This method completes demand surveys and data integration and optimization, and builds a basic data system for project tracking by combining project approval and initialization; it completes task decomposition and standardized processing based on the start-up data, realizes full-process tracking of sub-tasks by relying on full-cycle data monitoring, and ensures the controllability of the project implementation process by combining resource scheduling and collaboration; it conducts indicator monitoring and deviation analysis based on implementation data, corrects implementation deviations and optimizes management processes through targeted adjustments and optimizations; and it achieves data traceability and experience reusability by completing full-cycle data aggregation, acceptance and archiving, forming a closed-loop management system for the entire process of data collection, processing, monitoring, optimization and archiving, ensuring smooth connection of work at each stage and efficient data linkage, and realizing refined and data-driven tracking of the entire consulting project lifecycle.

[0011] This method solves the technical problems of data fragmentation, process disconnect, lagging control, and unreusable experience in the traditional full-cycle management of consulting projects. It enables systematic and data-driven tracking management of the entire consulting project lifecycle, breaks down data silos at each stage, and ensures that the entire process from project initiation to completion is traceable and controllable. It improves the precision and efficiency of project management, reduces errors caused by human intervention, and achieves rational allocation and efficient utilization of resources. It reduces the risks of deviation, resource waste, and management costs during project implementation, while also enabling the structured accumulation of project management experience, providing reusable management basis for similar consulting projects, and improving the overall management level and quality stability of consulting projects.

[0012] In one embodiment of the present invention, S1 includes: Obtain information about the special research team, and based on that information, obtain information about the research cycle and key time points. Based on the survey cycle information and key event node information, core survey information is identified to obtain core identification data; The core identification data is entered into the big data management system, and external data is correlated and identified based on the big data management system to obtain the data collected from the demand survey. The data collected from the demand survey is segmented and categorized to obtain segmented and categorized data. Perform data cleaning on the segmented and classified data to obtain segmented and cleaned data. Based on the segmented, classified, and cleaned data, we conduct mapping analysis and consulting solution optimization for the same project to obtain data integration and optimization information. The data integration and optimization information is used for multi-node project approval to obtain multi-node project approval data. Establish a project-specific database based on multi-node project approval data, and output tracking and management start data based on the project-specific database.

[0013] This involves inputting core identification data into a big data management system, and then using this system to perform external data correlation and identification to obtain data collected from the demand survey, including: The core identification data is standardized according to the preset data format, and invalid data fields are removed before being entered into the big data management system. Through the association identification module of the big data management system, key search terms are extracted from the core identification data, and external data such as industry benchmark data, similar consulting project case data, and policy and regulatory data stored in the key search term matching system are used. The matched external data is filtered for relevance, and external data that is highly relevant to the current consulting project needs is retained. The standardized core identification data is then combined with the filtered external data to obtain the needs survey data.

[0014] This involves segmenting and classifying the data collected from the demand survey to obtain segmented and classified data, including: Determine the classification dimensions of the data collected in the demand survey, including structured data / unstructured data, internal data / external data, and basic data / core data; The data collected from the demand survey was segmented according to the classification dimensions, separating structured data from unstructured data, internal survey data from external related data, and basic background data from core demand data. The segmented data are uniformly labeled, including the data source, collection time point, and related nodes, forming segmented and classified data with clear classification and standardized labeling.

[0015] The working principle and technical effects of the above technical solution are as follows: A basic data system for project tracking is constructed through standardized research, data processing, and project initiation. Information on the specialized research team is obtained, the research cycle and key time nodes are clarified, and the scope and pace of the research work are defined. Based on the research cycle and key nodes, core research information is accurately identified, and core identification data related to project needs and objectives is selected, avoiding interference from invalid data. After standardized processing, the core identification data is entered into a big data management system. External related data is matched through the system's association identification module to supplement project research dimensions and enrich the data collected from the needs research. The collected multi-source data is segmented and classified according to preset dimensions, clarifying data types and attributions for easier subsequent processing. Invalid and duplicate data are removed through data cleaning to improve data quality. Based on the cleaned and classified data, combined with similar project mapping analysis and consulting solution optimization, data integration and optimization information tailored to project needs is formed. Multi-node project initiation approval ensures the rationality and feasibility of the plan and data. After approval, a project-specific database is established, data initialization is completed, and tracking management start data is output.

[0016] This method addresses the technical problems of traditional consulting project initiation phases, such as non-standardized research, disorganized data collection, low data quality, lack of data support for solutions, and a crude project approval process. It achieves standardization of needs assessment, comprehensiveness of data collection, and refinement of data processing, ensuring the accuracy, completeness, and standardization of data during the initiation phase. It improves the rationality and feasibility of consulting solutions, avoiding the problem of solutions being out of touch with actual needs. It reduces the difficulty and cost of data processing, minimizing project initiation delays or deviations in direction caused by data issues. Through the establishment of a dedicated project database, it achieves centralized management of initiation data, improving management efficiency and standardization during the project initiation phase.

[0017] In one embodiment of the present invention, the step of performing project-based mapping analysis and consulting solution optimization based on segmented, classified, and cleaned data to obtain data integration and optimization information includes: Core feature parameters are extracted from the segmented, classified, and cleaned data to obtain feature parameter extraction data. Based on the feature parameters, a feature parameter coordinate system is established by extracting data. Based on the feature parameter coordinate system, the core feature parameters are mapped and matched with historical data of similar consulting projects stored in the big data management system to obtain the mapping and matching results. By comparing the mapping and matching results with the preset goals of the consulting project, information on the differences in project requirements can be obtained. The initial consulting plan was adjusted based on the comparison of differences in project requirements to obtain data integration and optimization information.

[0018] Specifically, a feature parameter coordinate system is established based on extracted data, and the core feature parameters are mapped and matched with historical data of similar consulting projects stored in the big data management system to obtain the mapping and matching results, including: Using the extracted core feature parameters as coordinate axis variables, the value range and weight of each feature parameter are set to establish a two-dimensional or three-dimensional feature parameter coordinate system; Substitute the core feature parameters into the coordinate system to determine the specific coordinate position of each feature parameter in the coordinate system; The system retrieves the feature parameter coordinate data from historical data of similar consulting projects in the big data management system and calculates the similarity between the current project's core feature parameter coordinates and the historical project's feature parameter coordinates. Historical project data with high matching degree is filtered out based on similarity threshold, and the similarity data, historical project feature parameters and association experience are integrated to obtain the mapping matching result.

[0019] This includes adjusting the initial consulting plan based on comparisons of project requirements to obtain data integration and optimization information, and making comparisons, including: Break down the information comparing project requirements to identify the types, degrees, and core reasons for the differences. Based on the optimization experience of similar projects in the mapping and matching results, corresponding adjustment measures are formulated for various differences, with a focus on adjusting the links that deviate significantly from the project's preset goals. The adjusted consulting plan was validated with data to confirm that the adjusted data parameters were compatible with the project requirements, preset goals, and experience from similar projects. By integrating the adjusted consultation plan, difference comparison information, adjustment measures, and verification data, we can obtain data integration and optimization information.

[0020] The working principle and technical effects of the above technical solution are as follows: The core of this method is to optimize consulting solutions by combining big data mapping analysis technology with experience from similar projects, ensuring that the solutions are adapted to project needs. Core feature parameters are extracted from high-quality data after segmentation, classification, and cleaning to capture the project's core needs, objectives, and key constraints, forming feature parameter extraction data. Using the extracted core feature parameters as coordinate axis variables, weights and value ranges are set to establish a feature parameter coordinate system, transforming the current project's core feature parameters into quantifiable and comparable coordinate data. The feature parameter coordinates of historical data from similar consulting projects in the big data management system are called, and through similarity calculation, historical project data with high matching degrees are selected. Historical project experience and feature parameters are integrated to form mapping matching results. The mapping matching results are compared with the current project's preset goals to accurately identify the types, degrees, and core reasons for differences between the two, forming project requirement difference comparison information. Combining optimization experience from similar projects, targeted solution adjustment measures are formulated for the differences, focusing on optimizing aspects with large deviations from the preset goals. The adjusted solutions are verified with data to ensure that the solutions are adapted to project needs, preset goals, and experience from similar projects. Finally, relevant information is integrated to form data integration and optimization information.

[0021] This method addresses the technical problems of traditional consulting solution optimization, such as reliance on manual experience, inability to leverage the advantages of similar projects, and poor solution adaptability. It achieves intelligent and data-driven optimization of consulting solutions, fully drawing on historical project experience through similar project mapping analysis to avoid repeated trial and error. It improves the fit between consulting solutions and project needs and preset goals, ensuring the scientific validity and feasibility of the solutions. It reduces the manual and time costs of solution optimization, minimizing the risk of subsequent project adjustments due to unreasonable solutions. Simultaneously, it enables the efficient reuse of experience from similar projects, improving the efficiency and quality of consulting solution optimization.

[0022] In one embodiment of the present invention, S2 includes: Based on the tracking and management initiation data, the overall task of the consulting project is divided and decomposed to obtain multiple sub-tasks; Perform subtask feature recognition on multiple subtasks to obtain subtask feature recognition data; A standardized task list is generated based on the sub-task feature identification data, and decomposed processing data is obtained based on the standardized task list. Based on the decomposed and processed data, data monitoring is performed on sub-tasks at each stage of the entire lifecycle to obtain continuous monitoring data; Anomaly identification is performed on continuous monitoring data to obtain monitoring anomaly identification data; Based on the anomaly identification data, node information is recorded in the continuous monitoring data to obtain full-cycle monitoring data; Based on the full-cycle monitoring data, sub-task resource schedule deviation analysis is performed to obtain sub-task resource schedule deviation analysis data. Based on the sub-task progress deviation analysis data and resource allocation targets, dynamic resource scheduling is performed to obtain management implementation data.

[0023] This includes dynamic resource scheduling based on sub-task progress deviation analysis data and resource allocation targets to obtain management implementation data, including: Extract the deviation level, impact range, and resource gap information from the subtask resource schedule deviation analysis data, and combine them with the preset resource allocation goals to clarify the priority of resource requirements for each subtask; Take stock of the currently available human, material, and financial resources, and calculate the remaining and available resources. Based on the priority of resource demand, resource gaps, and available resources, a dynamic scheduling plan is formulated to allocate idle resources to sub-tasks with larger resource gaps and adjust the resource usage cycle and allocation ratio. Record the resource scheduling process, scheduling plan, resource allocation results, and the expected progress of sub-tasks after scheduling to form management implementation data.

[0024] The working principle and technical effect of the above technical solution are as follows: through task decomposition, full-cycle monitoring and resource scheduling, the project implementation process is ensured to be controllable and efficient. Based on the tracking and management initiation data, the overall consulting project task is divided and decomposed according to implementation stages and work modules, into multiple executable and monitorable sub-tasks. Each sub-task is characterized by feature identification, extracting its core attributes, execution requirements, and resource needs, forming sub-task feature identification data. A standardized task list is generated based on this sub-task feature data, clearly defining the responsible person, completion deadline, and resource requirements for each sub-task, forming decomposed processing data. Using this decomposed processing data as a benchmark, continuous data monitoring is conducted on the execution status of sub-tasks at each stage of the entire lifecycle, collecting relevant data such as progress and resources, forming continuous monitoring data. Anomalies are identified in the continuous monitoring data, capturing abnormal fluctuations, forming monitoring anomaly identification data. Based on the anomaly identification results, node information is recorded in the continuous monitoring data, clarifying the core data of each monitoring node, forming full-cycle monitoring data. Based on the full-cycle monitoring data, resource progress deviations for each sub-task are analyzed, forming sub-task resource progress deviation analysis data. Combined with preset resource allocation targets, a dynamic resource scheduling plan is developed based on the deviation analysis data, optimizing resource allocation, recording the scheduling process and results, forming management implementation data, ensuring reasonable resource allocation and controllable progress during project implementation.

[0025] This method addresses the technical problems of unclear task decomposition, lack of effective monitoring during implementation, rigid resource scheduling, and lagging progress and resource management in traditional consulting projects. It achieves standardized task decomposition and real-time monitoring throughout the entire project lifecycle, ensuring real-time control over sub-task execution. It enhances the flexibility and rationality of resource scheduling, enabling dynamic optimization and avoiding both idle and scarce resources. It reduces the risk of schedule deviations and resource waste during project implementation, minimizing project delays caused by inappropriate resource scheduling. Furthermore, it enables real-time recording and accumulation of implementation process data, improving management efficiency and controllability during the project implementation phase.

[0026] In one embodiment of the present invention, the step of performing sub-task resource schedule deviation analysis based on full-cycle monitoring data to obtain sub-task resource schedule deviation analysis data includes: Task feature extraction data is obtained by extracting task features from each subtask of the full-cycle monitoring data. The task feature extraction data is compared with the standardized task list to obtain task feature comparison data. Establish a deviation analysis model, and based on the deviation analysis model and the task feature comparison data, identify the causes of deviations and obtain characteristic deviation cause identification data. Determine the degree of impact of the deviation level based on the characteristic deviation cause identification data; Based on the deviation level and impact information, record the deviation analysis process, deviation parameters, impact assessment results, and preliminary handling directions to obtain sub-task resource schedule deviation analysis data.

[0027] This includes establishing a deviation analysis model, and using the deviation analysis model in conjunction with task feature comparison data to identify the causes of deviations, thereby obtaining characteristic deviation cause identification data, including: A deviation analysis model is built based on big data algorithms. Core parameters such as progress deviation and resource consumption deviation are input from task feature comparison data. Deviation analysis thresholds and cause identification dimensions are set. By using deep learning to analyze the deviation data through the model, and correlating it with relevant data such as subtask characteristics, resource allocation, and execution progress, the specific causes of the deviation can be identified, including insufficient resources, inadequate task execution, data collection errors, and changes in the external environment. The identified causes of deviations are classified and labeled, and the deviation proportion and influence weight of each type of cause are clarified to form characteristic deviation cause identification data.

[0028] This includes recording the deviation analysis process, deviation parameters, impact assessment results, and preliminary handling directions based on the deviation level and impact information, to obtain sub-task resource schedule deviation analysis data, including: Based on the impact information of the deviation level, the deviations of each sub-task are classified and archived, and the criteria for classifying major deviations, general deviations and minor deviations are clearly defined. The entire process of deviation analysis is documented in detail, including the specific steps of data extraction, comparative analysis, and cause identification; Enter the core parameters of the deviation, including the deviation value, the time point of the deviation, the associated subtasks, and related resource data; Assess the impact of deviations on the progress of sub-tasks, the overall project cycle, and the achievement of objectives. Based on the causes of deviations, formulate preliminary handling directions and integrate all the above information to form sub-task resource schedule deviation analysis data.

[0029] The working principle and technical effects of the above technical solution are as follows: Through big data analytics, sub-task resource schedule deviations are accurately identified, and the causes and impacts of these deviations are clarified. Sub-task features are extracted from the full-cycle monitoring data, extracting core feature data such as actual progress and resource consumption for each sub-task, forming task feature extraction data. This extracted task feature data is compared with the planned data in the standardized task list to capture the differences between actual execution and the plan, forming task feature comparison data. A deviation analysis model is constructed based on big data algorithms. Core parameters such as schedule deviation values ​​and resource consumption deviation values ​​are input, and deviation analysis thresholds and cause identification dimensions are set. Through deep learning analysis of the model, relevant data such as sub-task features, resource configuration, and execution status are correlated to accurately identify the specific causes of deviations. These causes are then categorized, labeled, and weighted, forming feature deviation cause identification data. Based on the deviation cause identification data, the deviation level and impact degree are determined, clarifying the scope of the deviation's impact on subsequent sub-tasks and the project as a whole. According to the deviation level, the deviation analysis process, core parameters, impact assessment results, and preliminary handling directions are recorded in detail, integrating all information to form sub-task resource schedule deviation analysis data.

[0030] This method addresses the technical challenges of inaccurate deviation identification, difficulty in pinpointing the causes of deviations, inability to quantify the impact of deviations, and lack of targeted solutions in the implementation phase of traditional consulting projects. It achieves accurate identification, cause pinpointing, and impact assessment of sub-task resource schedule deviations, improving the scientific rigor and accuracy of deviation analysis. It avoids the subjectivity and errors inherent in manual deviation analysis, reducing the time cost and difficulty of deviation analysis. It clarifies the direction for deviation resolution, enabling timely correction of deviations and minimizing their negative impact on project schedule and resource allocation. Simultaneously, it enhances risk control capabilities during project implementation, ensuring the project progresses as planned.

[0031] In one embodiment of the present invention, S3 includes: Based on the management implementation data, core monitoring indicators are identified, and indicator data monitoring is performed on these core monitoring indicators to obtain indicator data monitoring information. The monitoring information of the indicator data is compared with the preset target data to obtain monitoring comparison information; Problems are identified based on monitoring and comparison information, and deviation problem analysis data is generated. Based on the deviation analysis data, the problem deviation is adjusted to obtain the problem deviation adjustment data; Establish a deviation adjustment mapping relationship based on the deviation adjustment data, and generate adjustment optimization data based on the deviation adjustment mapping relationship.

[0032] This includes adjusting the problem deviation based on the deviation analysis data to obtain problem deviation adjustment data, including: Based on the deviation type, deviation level and cause in the deviation problem analysis data, targeted deviation adjustment measures are formulated, and the adjustment objectives, implementing entities, implementation steps and completion deadlines are clearly defined. Based on the project's overall schedule and core objectives, the feasibility of adjustment measures is demonstrated, and potential new deviations and risks that may arise during the adjustment process are identified. The adjustment work is carried out in accordance with the approved adjustment measures, and various data are collected in real time during the adjustment process to track the adjustment progress and effects. Record problems and improvement measures during the adjustment process, integrate adjustment measures, implementation data, adjustment effects and improvement suggestions, and obtain problem deviation adjustment data.

[0033] This includes establishing a deviation adjustment mapping relationship based on the problem deviation adjustment data, and generating adjustment optimization data based on the deviation adjustment mapping relationship, including: Extract the deviation type, adjustment measures, adjustment effects, and related monitoring indicators from the problem deviation adjustment data, establish the correspondence between deviation type and adjustment measures and adjustment effects, and form a preliminary mapping relationship; The initial mapping relationship is associated with the project's core monitoring indicators and preset target data to optimize the adaptability of the mapping relationship and clarify the optimal adjustment path for different types of deviations. Based on the optimized mapping relationship and combined with the project's full lifecycle monitoring data, adjustments and optimizations were made to the data collection specifications, resource scheduling scheme, and indicator monitoring thresholds. Integrate mapping relationships, adjustment and optimization measures, optimization basis and expected results to generate adjustment and optimization data.

[0034] The working principle and technical effects of the above technical solution are as follows: Through indicator monitoring, deviation analysis, and adjustment optimization, the project implementation process is ensured to align with preset goals, continuously improving management quality. Based on management implementation data, core project monitoring indicators are identified, and real-time data monitoring is conducted on each core indicator. Actual operational data of the indicators is collected to form indicator data monitoring information. The indicator monitoring information is compared with preset target data to capture indicator deviations, forming monitoring comparison information. Based on the monitoring comparison information, deviation problems in the project implementation process are identified, generating deviation problem analysis data. For the deviation problem analysis data, combined with the deviation type, level, and cause, targeted deviation adjustment measures are formulated, the feasibility of the measures is demonstrated, and they are implemented. Data on the adjustment process is collected to form problem deviation adjustment data. Based on the problem deviation adjustment data, a mapping relationship is established between deviation type, adjustment measures, and adjustment effects. Combined with the project's core goals and full-cycle monitoring data, monitoring thresholds and management processes are optimized, generating adjustment optimization data to achieve continuous optimization of the project management process and ensure the achievement of project goals.

[0035] This method addresses the technical challenges of unclear monitoring indicators, delayed deviation identification, lack of targeted adjustment measures, and inability to continuously optimize management processes during the traditional consulting project monitoring phase. It achieves real-time monitoring of core project indicators and accurate deviation identification, improving the timeliness and effectiveness of monitoring. Through targeted deviation adjustment and process optimization, it reduces the impact of deviations on project objectives, ensuring that the project implementation process always aligns with the preset goals. It enhances the flexibility and adaptability of project management, enabling dynamic optimization of management processes and monitoring standards based on project implementation progress. Furthermore, it reduces the risk of project goal deviation and improves the refinement and quality stability of project management.

[0036] In one embodiment of the present invention, the step of identifying problems based on monitoring comparison information and generating deviation problem analysis data includes: The monitoring comparison information is segmented to obtain the monitoring comparison segmentation data; Based on the monitoring comparison and segmentation data extraction, abnormal deviation data is extracted to obtain abnormal deviation extraction data; Based on the data extracted from abnormal deviations, the core monitoring indicators and related sub-tasks corresponding to the deviations are identified. The abnormal deviation extraction data is divided into systematic deviations and random deviations to obtain deviation type classification data; Determine the cause of the deviation by classifying the data according to the type of deviation; Based on the core monitoring indicators and related sub-tasks, combined with the information on the causes of deviations, deviation problem analysis data is generated.

[0037] Specifically, the abnormal deviation extraction data is divided into systematic and random deviations to obtain deviation type classification data, including: Define deviation classification criteria, where systematic deviation is defined as a deviation that occurs repeatedly, has a wide impact, and is caused by process defects or model problems, while random deviation is defined as a deviation that occurs once, has a small impact, and is caused by random factors. Perform traceability analysis on the extracted abnormal deviation data to verify the frequency, scope of impact, and scenarios in which the deviations occurred; Based on the classification criteria and traceability analysis results, each abnormal deviation is classified and labeled, clearly indicating whether it is a systematic deviation or a random deviation, and the classification results are integrated to form deviation type classification data.

[0038] Among them, the information on the causes of deviations is determined by classifying the data according to the type of deviation, including: For systematic biases in the data categorized by bias type, analyze their core causes, focusing on identifying problems in data collection standards, model settings, and process design; To address random deviations, analyze the random factors that cause them, including human error, external temporary interference, etc. Record the causes of various deviations in detail, clarify the cause type, influencing factors and conditions of occurrence, and integrate all cause information to form deviation cause information.

[0039] Specifically, based on core monitoring indicators and related sub-tasks, combined with information on the causes of deviations, deviation problem analysis data is generated, including: The core monitoring indicators, related sub-tasks, and the cause information of the deviation are matched and associated to clarify the abnormal points of the indicators, related tasks, and root causes of each deviation. Record the specific manifestations of the deviation, the time of occurrence, the scope of its impact, and the potential losses. The deviation issues are standardized, classified, and archived to form deviation issue analysis data that includes basic deviation information, related information, cause information, and impact assessment.

[0040] The working principle and technical effects of the above technical solution are as follows: This method accurately identifies deviation problems and clarifies deviation attributes, related information, and causes through big data segmentation, extraction, and analysis techniques. Monitoring comparison information is segmented by dimension, such as indicator type and sub-task stage, to obtain segmented monitoring comparison data for accurate analysis. Abnormal deviation data exceeding a preset threshold is extracted from the segmented data to form abnormal deviation extraction data, focusing on core deviation problems. Based on the abnormal deviation extraction data, the core monitoring indicators and related sub-tasks corresponding to the deviations are identified, clarifying the scope of the deviation's impact. Standards for classifying systematic and random deviations are set, and abnormal deviation data is traced and analyzed to verify the frequency, scope, and scenarios of deviation occurrence. Deviations are classified and labeled to form deviation type classification data. For different types of deviations, the core causes are analyzed. Systematic deviations focus on investigating process and model issues, while random deviations focus on analyzing random factors, forming deviation cause information. The core indicators, related sub-tasks, and cause information corresponding to the deviations are correlated and matched, recording the specific manifestations, occurrence time, scope of impact, and expected losses. After classification and archiving, deviation problem analysis data is formed.

[0041] This method addresses the technical problems of low efficiency, unclear deviation classification, inaccurate cause identification, and incomplete related information in traditional deviation identification methods. It achieves precise identification, scientific classification, and root cause identification of deviation problems, improving the efficiency and accuracy of deviation identification. It clarifies the associated indicators and sub-tasks of deviations, facilitating the development of targeted adjustment measures and avoiding blind adjustments. It distinguishes between systematic and accidental deviations, enhancing the targeting and effectiveness of deviation adjustments. It reduces adjustment errors caused by inaccurate deviation identification, minimizes the negative impact of deviations on project implementation, and provides clear directions for improvement in project management process optimization.

[0042] In one embodiment of the present invention, the step of adjusting the problem deviation based on the deviation analysis data to obtain problem deviation adjustment data includes: Information on deviation adjustment measures is determined based on the deviation problem analysis data; Based on the project cycle and core objectives, the feasibility of deviation adjustment measures is verified and optimized to obtain adjustment and optimization measures. Implement and collect data on adjustment and optimization measures to obtain execution and data collection data; Based on the collected data, adjustment problems are identified to obtain adjustment problem identification data; Based on the data identified in the problem adjustment, the adjustment and optimization measures are updated to obtain the problem deviation adjustment data.

[0043] This includes verifying and optimizing the feasibility of deviation adjustment measures based on the project cycle and core objectives, resulting in optimized adjustment measures, including: Based on the remaining project period, core objectives, and current progress, assess the difficulty of implementing various deviation adjustment measures, the resources required, and the implementation period, and determine whether the adjustment measures can achieve the adjustment objectives within the specified time. Investigate potential conflicts between adjustment measures and other sub-tasks or resource allocation, and analyze potential new deviations that may arise after the implementation of adjustment measures. Optimize and adjust infeasible adjustment measures, including the implementation steps, resource requirements, and timelines, to ensure that the adjustment measures are highly compatible with the project cycle and core objectives, thus forming optimized adjustment measures.

[0044] The working principle and technical effects of the above technical solution are as follows: Through the standardized formulation, verification, implementation, and optimization of adjustment measures, deviation problems are effectively resolved, ensuring the project implementation returns to normal. Based on deviation problem analysis data, combined with the deviation type, level, and cause, targeted deviation adjustment measures are formulated, clarifying the adjustment objectives, implementing entities, steps, and completion deadlines, forming deviation adjustment measure information. Considering the remaining project cycle, core objectives, and current progress, the difficulty of implementing the adjustment measures, resource requirements, and execution cycle are assessed. Conflicts and potential deviation risks between the measures and other sub-tasks and resource allocations are identified, and infeasible measures are optimized and adjusted, forming optimized adjustment measures. Adjustment work is carried out according to the optimized measures, collecting various data in real time during the adjustment process, tracking the adjustment progress and effects, forming execution data. Based on the execution data, new problems arising during the adjustment process are identified, forming adjustment problem identification data. Based on the identified problems, the adjustment measures are updated and optimized, integrating the adjustment measures, execution data, effects, and improvement suggestions to form problem deviation adjustment data, ensuring that deviations are effectively resolved.

[0045] This method addresses the technical problems of traditional deviation adjustment measures, such as lack of specificity, poor feasibility, lack of tracking during implementation, and inability to guarantee adjustment effects. It enables the scientific and standardized formulation and optimization of deviation adjustment measures, improving their feasibility and specificity. Through real-time tracking and data collection during the adjustment process, it ensures the effective implementation of adjustment measures, improving the efficiency and effectiveness of deviation adjustment. It promptly identifies and resolves new problems during the adjustment process, preventing deviations from escalating or generating new ones. It reduces the impact of deviations on project progress and goal achievement, ensuring that the project implementation process continuously aligns with the preset goals, while also accumulating experience in deviation adjustment.

[0046] In one embodiment of the present invention, the step of establishing a deviation adjustment mapping relationship based on the problem deviation adjustment data and generating adjustment optimization data based on the deviation adjustment mapping relationship includes: Adjustment feature extraction is performed on the problem deviation adjustment data to obtain adjustment feature extraction data; Establish a mapping relationship between deviation types and the effects of regulatory measures based on the data extracted from the regulatory features; The mapping relationship is associated with the project's core monitoring indicators and preset target data to obtain mapping relationship information; Optimize deviation threshold settings and monitoring nodes based on mapping association information to generate a deviation adjustment mapping system; Based on the deviation adjustment mapping system and the project's full-cycle monitoring data, adjustments and optimizations are made to the entire consulting project lifecycle, generating adjustment and optimization data.

[0047] Specifically, based on the deviation adjustment mapping system and combined with the project's full-cycle monitoring data, adjustments and optimizations are made throughout the entire consulting project lifecycle, generating adjustment and optimization data, including: By calling up the project's full-cycle monitoring data, and combining the mapping relationships in the deviation adjustment mapping system with the optimized deviation thresholds and monitoring nodes, a comprehensive review of the execution status of each stage of the project can be conducted. To address potential biases in the full-cycle monitoring data, preventative measures are developed based on the mapping system. The data collection standards, resource scheduling schemes, task execution plans, and indicator monitoring system were systematically adjusted and optimized to ensure that the adjusted and optimized schemes were adapted to the core objectives of the project. Integrate and adjust optimization measures, review results, prevention and control measures, and expected optimization effects to generate adjustment and optimization data.

[0048] The working principle and technical effect of the above technical solution are as follows: by establishing a deviation adjustment mapping relationship, the reuse of deviation adjustment experience and the systematic optimization of management processes are realized, thereby improving the scientific nature of project life cycle management. The process involves extracting adjustment features from the deviation adjustment data, identifying core features such as deviation type, adjustment measures, adjustment effects, and associated monitoring indicators to form adjustment feature extraction data. Based on the extracted feature data, a correspondence is established between deviation types and adjustment measures and effects, clarifying effective adjustment paths for different deviation types and forming a preliminary mapping relationship. This preliminary mapping relationship is then linked to the project's core monitoring indicators and preset target data to optimize its adaptability, eliminate invalid associations, clarify the optimal adjustment path for different deviation types, and generate a deviation adjustment mapping system. The process involves calling up full-cycle monitoring data from the project, combining the mapping relationships in the mapping system with optimized deviation thresholds and monitoring nodes, to conduct a comprehensive review of the project's execution at each stage and identify potential deviation risks. Based on the mapping system, potential deviation prevention and control measures are formulated, and data collection specifications, resource scheduling schemes, task execution plans, and indicator monitoring systems are systematically adjusted and optimized to ensure that the optimized schemes are adapted to the project's core objectives. Finally, the adjustment and optimization measures, review results, prevention and control measures, and expected effects are integrated to generate adjustment and optimization data, enabling continuous iterative optimization of the project management process.

[0049] This method addresses the technical challenges of traditional consulting projects, such as the inability to reuse deviation adjustment experience, the lack of basis for management process optimization, and the inability to prevent potential deviations in advance. It achieves a precise correlation between deviation types, adjustment measures, and effects, establishes a standardized deviation adjustment mapping system, improves the efficiency and standardization of deviation adjustment, and enables the reuse of adjustment experience. Through full-cycle review and potential deviation prevention, it proactively avoids deviation risks and reduces the probability of deviations occurring. It systematically optimizes management processes, enhancing the scientific and refined level of project lifecycle management and ensuring that project management processes continuously adapt to project objectives. Simultaneously, it reduces the labor and risk costs of project management, and improves the project objective achievement rate and the stability of management quality.

[0050] According to one embodiment of the present invention, the system includes: The integration and optimization module is used to conduct needs surveys and data integration and optimization for consulting projects, obtain data integration and optimization information, approve and initialize projects based on the data integration and optimization information, and obtain tracking and management start data. The decomposition and monitoring module is used to decompose and standardize tasks based on the tracking and management initiation data to obtain decomposition and processing data, perform full-cycle data monitoring on the decomposition and processing data to obtain full-cycle monitoring data, and perform resource scheduling and coordination based on the full-cycle monitoring data to obtain management implementation data. The deviation adjustment module is used to monitor indicators based on management implementation data, obtain indicator monitoring data, analyze deviation problems based on indicator monitoring data, obtain deviation problem analysis data, and make adjustments and optimizations based on deviation problem analysis to obtain adjustment and optimization data. The integrated archiving module is used for data aggregation, acceptance, and archiving based on adjusted and optimized data.

[0051] The working principle and technical effects of the above technical solution are as follows: This system completes demand surveys and data integration and optimization, and builds a basic data system for project tracking by combining project approval and initialization; it completes task decomposition and standardized processing based on the start-up data, realizes full-process tracking of sub-tasks by relying on full-cycle data monitoring, and ensures the controllability of the project implementation process by combining resource scheduling and collaboration; it conducts indicator monitoring and deviation analysis based on implementation data, corrects implementation deviations and optimizes management processes through targeted adjustments and optimizations; and it achieves data traceability and experience reusability by completing full-cycle data aggregation, acceptance and archiving, forming a closed-loop management system for the entire process of data collection, processing, monitoring, optimization and archiving, ensuring smooth connection of work at each stage and efficient data linkage, and realizing refined and data-driven tracking of the entire consulting project lifecycle.

[0052] This system solves the technical problems of data fragmentation, process disconnect, lagging control, and unreusable experience in the traditional full-cycle management of consulting projects. It enables systematic and data-driven tracking and management of the entire consulting project lifecycle, breaking down data silos at each stage and ensuring that the entire process from project initiation to completion is traceable and controllable. It improves the precision and efficiency of project management, reduces errors caused by human intervention, and achieves rational allocation and efficient utilization of resources. It reduces the risks of deviation, resource waste, and management costs during project implementation, while also enabling the structured accumulation of project management experience, providing reusable management basis for similar consulting projects, and improving the overall management level and quality stability of consulting projects.

[0053] Example 2

[0054] One embodiment of the present invention further improves the accuracy of identifying the causes of deviations when multiple subtasks simultaneously experience schedule deviations, resource consumption deviations, and data acquisition anomalies, avoiding directly attributing superficial deviations caused by delays in preceding subtasks, changes in resource usage, or abnormal data acquisition transmission to the current subtask itself. This includes: S1. Conduct needs assessment and data integration and optimization for consulting projects, obtain data integration and optimization information, approve and initialize the data integration and optimization information, and obtain tracking and management start data; S2. Based on the tracking and management start-up data, decompose and standardize the tasks to obtain decomposed processing data. Perform full-cycle data monitoring on the decomposed processing data to obtain full-cycle monitoring data. Based on the full-cycle monitoring data, perform resource scheduling and collaboration to obtain management implementation data. S3. Monitor indicators based on management implementation data, obtain indicator monitoring data, analyze deviations based on indicator monitoring data, obtain deviation analysis data, and make adjustments and optimizations based on deviation analysis to obtain adjustment and optimization data. S4. Summarize, accept, and archive the data based on the adjusted and optimized data.

[0055] S1-4 are the same as in Example 1.

[0056] S5. When performing subtask resource progress deviation analysis based on full-cycle monitoring data, introduce subtask progress deviation, subtask resource consumption deviation, and data acquisition anomaly intensity to generate the subtask's own deviation cause intensity.

[0057] Specifically, after obtaining full-cycle monitoring data, the decomposition monitoring module uses the planned completion nodes, planned resource consumption, and planned collection frequency recorded in the standardized task list as benchmarks to extract the actual completion progress, actual resource consumption, and data collection records of each sub-task at the current monitoring node.

[0058] The difference between the actual completion progress and the planned completion progress is defined as the sub-task progress deviation. The difference between the actual resource consumption and the planned resource consumption is defined as the sub-task resource consumption deviation. Data loss, duplicate collection, delayed upload, abnormal jumps, and inconsistent collection standards are comprehensively defined as the data collection anomaly intensity.

[0059] Among them, the subtask progress deviation is used to characterize the degree of lag or advancement of the current subtask relative to the standardized task list; the subtask resource consumption deviation is used to characterize the degree of excess or deficiency of the current subtask relative to the resource allocation target; and the data acquisition anomaly intensity is used to characterize whether there is a risk of distortion in the deviation judgment due to acquisition anomalies in the current subtask monitoring data itself.

[0060] When generating the intensity of deviation causes for a subtask, the decomposition monitoring module first determines the basic deviation level of the current subtask based on the schedule deviation and resource consumption deviation; then, it adjusts the reliability of the basic deviation level based on the intensity of data acquisition anomalies. When the intensity of data acquisition anomalies is low, the system retains the main influence of schedule deviation and resource consumption deviation on the determination of the cause of deviation for the current subtask; when the intensity of data acquisition anomalies is high, the system reduces the proportion of schedule deviation and resource consumption deviation in the determination of the cause of deviation and increases the influence of the data acquisition anomaly itself in the determination of the cause of deviation.

[0061] Therefore, the intensity of a subtask's deviation is not solely determined by schedule delays or resource overruns, but rather by simultaneously considering the actual execution deviation, resource usage deviation, and the reliability of monitoring data. Through this approach, when a subtask exhibits both schedule and resource consumption deviations, but its data collection anomaly intensity is low, the system is more inclined to identify the subtask as having a genuine execution deviation. Conversely, when a subtask has a large schedule or resource consumption deviation, but its data collection anomaly intensity is also high, the system will not directly identify this deviation as a genuine execution cause. Instead, it will record the data collection anomaly as a possible direct cause and weaken the assessment results regarding the amount of schedule and resource consumption deviation.

[0062] By using the above methods, we can avoid misjudging surface deviations caused by missing data, delayed uploads, duplicate collections, or abnormal jumps as insufficient resources or inadequate task execution, thereby improving the accuracy of identifying the causes of deviations in the current subtask itself.

[0063] S6. After obtaining the intensity of the deviation cause of the subtask itself, the progress deviation of the associated subtask, the resource consumption deviation of the associated subtask, and the task dependency lag matching intensity are introduced to generate the intensity of the subtask deviation propagation effect.

[0064] Specifically, the decomposition monitoring module determines the set of preceding subtasks and the set of related subtasks for the current subtask based on the execution order, delivery dependencies, resource sharing relationships, and stage connection relationships recorded in the standardized task list. If the current subtask has deviations, the system does not directly perform resource scheduling based on the progress deviation and resource consumption deviation of the current subtask. Instead, it further traces the deviations of its preceding and related subtasks in the previous monitoring node or several historical monitoring nodes.

[0065] If a preceding subtask is behind schedule, exceeds its resource usage period, or experiences a delay in data delivery, and there is a relationship between the preceding subtask and the current subtask regarding task delivery, result referencing, personnel reuse, or resource sharing, the system will consider the deviation of the preceding subtask as a potential source of deviation to be transmitted to the current subtask.

[0066] Task dependency lag matching strength characterizes the timing of deviations in preceding subtasks, the timing of deviations in the current subtask, and the degree of matching between the two dependencies. If, after a deviation occurs in the preceding subtask, the current subtask deviates in the same or related direction within the corresponding lag time window, and there is a strong delivery dependency, resource sharing relationship, or stage connection relationship between the two, then the task dependency lag matching strength increases. If there is no temporal lag correspondence between the two, or if they are close in time but there is no explicit dependency, then the task dependency lag matching strength decreases.

[0067] When generating the impact intensity of subtask deviation propagation, the decomposition monitoring module first obtains the progress deviation and resource consumption deviation of each preceding or related subtask within the corresponding lag time window; then, based on the task dependency weight between the preceding or related subtask and the current subtask, it determines the basis of the impact of its deviation on the current subtask; subsequently, combined with the task dependency lag matching strength, it amplifies or weakens the basis of the impact; finally, it summarizes the impact results of multiple preceding or related subtasks on the current subtask to obtain the impact intensity of deviation propagation of the current subtask.

[0068] By adopting the above method, the system can identify whether the deviation of the current subtask is directly caused by its own execution problem, or by the lag of the preceding subtask, the transmission of resource occupation, the delay in delivery, or the occupation of shared resources when multiple subtasks deviate at the same time.

[0069] For example, if the current subtask is behind schedule, but its own resource consumption deviation is low and the data collection anomaly intensity is low, while the preceding subtask has already shown a significant delay in schedule within the previous delay time window, and there is a dependency relationship between the two in terms of deliverables, then the deviation propagation effect of the current subtask is high. The system will identify the deviation as a deviation caused by the propagation effect of the preceding subtask, rather than simply identifying it as a failure of the current subtask itself.

[0070] For example, if the current subtask and a related subtask share the same executor, the same data collection channel, or the same project resource pool, and the related subtask has already exceeded its resource usage period in the previous monitoring node, and the current subtask subsequently experiences resource shortages or progress delays, the system will increase the propagation influence of the related subtask on the current subtask based on the resource sharing relationship and the lag time matching relationship. This will help identify that the current deviation may originate from resource usage propagation rather than from a resource configuration error in the current subtask itself.

[0071] S7. Generate a set of judgments on the dominant causes of deviations based on the intensity of the deviation causes of the subtasks themselves and the intensity of the influence of the deviation propagation of the subtasks, and generate resource dynamic scheduling optimization data based on the set of judgments on the dominant causes of deviations.

[0072] Specifically, the deviation adjustment module receives the subtask's own deviation cause strength and the subtask deviation propagation impact strength from the decomposition monitoring module, and compares the two under a unified standard. During the comparison, the system uses the current subtask's own deviation cause strength to indicate the likelihood that the deviation is directly caused by anomalies in its own progress execution, resource usage, or data acquisition; and uses the deviation propagation impact strength to indicate the likelihood that the deviation is caused by the propagation effect of preceding or related subtasks.

[0073] The deviation adjustment module generates a dominant cause discrimination value for the current subtask based on the relative magnitude and degree of difference between the two factors. This dominant cause discrimination value characterizes whether the deviation of the current subtask is closer to its own direct cause, a transmitted cause, or a combined cause. The system aggregates the dominant cause discrimination values ​​for each subtask and their corresponding values ​​to form a dominant cause discrimination set for the current monitoring node.

[0074] When the intensity of the deviation's own cause in a subtask is significantly higher than the intensity of its propagation effect, it indicates that the deviation in the current subtask is more likely to be directly caused by its own execution lag, abnormal resource consumption, or abnormal data acquisition. The system marks this subtask as having an inherently dominant deviation. For inherently dominant deviations, the system identifies the current subtask as the direct target of dynamic resource scheduling and prioritizes supplementing manpower, adjusting resource usage cycles, resetting execution nodes, correcting data acquisition specifications, or increasing the subsequent monitoring frequency for this subtask.

[0075] When the propagation impact of a deviation in a subtask is significantly stronger than the causal impact of its own deviation, it indicates that the deviation in the current subtask originates more from the propagation impact of preceding or related subtasks. The system marks this subtask as a propagation-dominant deviation. For propagation-dominant deviations, the system does not prioritize adding resources to the current subtask. Instead, it traces back along the task dependencies to preceding or related subtasks with higher propagation impact, and performs resource compensation, node compression, delivery order adjustment, or resource release on the task nodes that actually caused the deviation propagation.

[0076] When the intensity of both the intrinsic cause and propagation impact of a subtask reaches a level requiring attention, and the difference between the two is not significant enough to clearly distinguish the dominant source, the system marks the subtask as a mixed-cause deviation. For mixed-cause deviations, the system simultaneously generates scheduling measures for the current subtask and collaborative scheduling measures for the preceding subtasks, and incorporates both as part of the management implementation data. The current subtask scheduling measures are used to mitigate the execution deviation already formed at the current node, while the collaborative scheduling measures for the preceding subtasks are used to reduce the possibility of the deviation continuing to propagate to subsequent tasks.

[0077] Through the above processing, the deviation adjustment module generates dynamic resource scheduling optimization data based on the set of dominant causes of deviation. This dynamic resource scheduling optimization data includes whether the current subtask is a direct scheduling target, whether it is necessary to backtrack to the preceding subtask, whether it is necessary to synchronously adjust the resource usage of related subtasks, whether it is necessary to correct the data collection specifications, resource replenishment priority, scheduling execution nodes, and post-scheduling monitoring nodes.

[0078] Subsequently, the deviation adjustment module incorporates the dynamic resource scheduling optimization data into the problem deviation adjustment data and further establishes a deviation adjustment mapping relationship, so that when similar deviations occur in similar consulting projects in the future, the results of the determination of the main causes of the deviation and the basis for scheduling optimization can be reused.

[0079] The working principle and technical effect of the above technical solution are as follows: Based on the original full-cycle data monitoring, deviation analysis model and dynamic resource scheduling, this embodiment further divides the deviation cause identification process into three levels: subtask own deviation cause identification, related subtask deviation propagation identification, and deviation dominant cause judgment.

[0080] First, the system generates the subtask's own deviation cause intensity by measuring subtask progress deviation, subtask resource consumption deviation, and data acquisition anomaly intensity. This enables the system to distinguish between actual execution deviations and surface deviations caused by data acquisition anomalies. For subtasks with high data acquisition anomaly intensity, the system does not mechanically make scheduling decisions based solely on progress or resource consumption deviation. Instead, it first corrects the reliability of the monitored data, thereby reducing the interference of data anomalies in identifying the cause of deviations.

[0081] Secondly, by generating the deviation propagation impact strength by associating the schedule deviation, resource consumption deviation, and task dependency lag matching strength of the associated subtasks, the system can identify the propagation impact of delayed preceding subtasks, overdue resource usage, or delayed deliverables on the current subtask. For deviations in the current task caused by preceding tasks, the system can locate the source of deviation propagation along the task dependency relationship, avoiding simply adding resources to the currently apparent abnormal nodes.

[0082] Finally, a set of judgments for the dominant causes of deviations is generated based on the intensity of the deviation causes of the subtasks themselves and the intensity of the influence of the deviation propagation of the subtasks. This enables dynamic resource scheduling to no longer rely solely on the surface deviation value of the current subtask, but to determine the scheduling objects and scheduling paths based on the dominant causes of the deviations.

[0083] This embodiment addresses the technical problem that traditional deviation analysis often misjudges surface deviations caused by delayed propagation as the direct cause of the current subtask when multiple subtasks simultaneously experience schedule deviations, resource consumption deviations, and data acquisition anomalies. It achieves hierarchical identification of the direct causes, accompanying causes, and propagated effects of subtask deviations, improving the accuracy of deviation cause identification. It avoids blindly adding resources to the current subtask affected by preceding subtasks, reducing the risk of resource misallocation. Simultaneously, it can locate the subtasks that truly require priority scheduling along task dependencies, improving the targeting and effectiveness of dynamic resource scheduling. Furthermore, by incorporating the results of deviation-dominant cause identification into the deviation adjustment mapping relationship, it can also provide reusable deviation handling guidelines for subsequent similar consulting projects, improving the stability and refinement of the full-cycle tracking management of consulting projects.

[0084] 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 big data-based full-cycle tracking and management method for consulting projects, characterized in that: The method includes: S1. Conduct needs assessment and data integration and optimization for consulting projects, obtain data integration and optimization information, approve and initialize the data integration and optimization information, and obtain tracking and management start data; S2. Based on the tracking and management start-up data, decompose and standardize the tasks to obtain decomposed processing data. Perform full-cycle data monitoring on the decomposed processing data to obtain full-cycle monitoring data. Based on the full-cycle monitoring data, perform resource scheduling and collaboration to obtain management implementation data. S3. Monitor indicators based on management implementation data, obtain indicator monitoring data, analyze deviations based on indicator monitoring data, obtain deviation analysis data, and make adjustments and optimizations based on deviation analysis to obtain adjustment and optimization data. S4. Summarize, accept, and archive the data based on the adjusted and optimized data.

2. The big data-based full-cycle tracking management method for consulting projects according to claim 1, characterized in that, S1 includes: Obtain information about the special research team, and based on that information, obtain information about the research cycle and key time points. Based on the survey cycle information and key event node information, core survey information is identified to obtain core identification data; The core identification data is entered into the big data management system, and external data is correlated and identified based on the big data management system to obtain the data collected from the demand survey. The data collected from the demand survey is segmented and categorized to obtain segmented and categorized data. Perform data cleaning on the segmented and classified data to obtain segmented and cleaned data. Based on the segmented, classified, and cleaned data, we conduct mapping analysis and consulting solution optimization for the same project to obtain data integration and optimization information. The data integration and optimization information is used for multi-node project approval to obtain multi-node project approval data. Establish a project-specific database based on multi-node project approval data, and output tracking and management start data based on the project-specific database.

3. The big data-based full-cycle tracking management method for consulting projects according to claim 2, characterized in that, The process of performing project-based mapping analysis and consulting solution optimization based on segmented, classified, and cleaned data to obtain data integration and optimization information includes: Core feature parameters are extracted from the segmented, classified, and cleaned data to obtain feature parameter extraction data. Based on the feature parameters, a feature parameter coordinate system is established by extracting data. Based on the feature parameter coordinate system, the core feature parameters are mapped and matched with historical data of similar consulting projects stored in the big data management system to obtain the mapping and matching results. By comparing the mapping and matching results with the preset goals of the consulting project, information on the differences in project requirements can be obtained. The initial consulting plan was adjusted based on the comparison of differences in project requirements to obtain data integration and optimization information.

4. The big data-based full-cycle tracking management method for consulting projects according to claim 1, characterized in that, S2 includes: Based on the tracking and management initiation data, the overall task of the consulting project is divided and decomposed to obtain multiple sub-tasks; Perform subtask feature recognition on multiple subtasks to obtain subtask feature recognition data; A standardized task list is generated based on the sub-task feature identification data, and decomposed processing data is obtained based on the standardized task list. Based on the decomposed and processed data, data monitoring is performed on sub-tasks at each stage of the entire lifecycle to obtain continuous monitoring data; Anomaly identification is performed on continuous monitoring data to obtain monitoring anomaly identification data; Based on the anomaly identification data, node information is recorded in the continuous monitoring data to obtain full-cycle monitoring data; Based on the full-cycle monitoring data, sub-task resource schedule deviation analysis is performed to obtain sub-task resource schedule deviation analysis data. Based on the sub-task progress deviation analysis data and resource allocation targets, dynamic resource scheduling is performed to obtain management implementation data.

5. The big data-based full-cycle tracking management method for consulting projects according to claim 4, characterized in that, The step of performing sub-task resource schedule deviation analysis based on full-cycle monitoring data to obtain sub-task resource schedule deviation analysis data includes: Task feature extraction data is obtained by extracting task features from each subtask of the full-cycle monitoring data. The task feature extraction data is compared with the standardized task list to obtain task feature comparison data. Establish a deviation analysis model, and based on the deviation analysis model and the task feature comparison data, identify the causes of deviations and obtain characteristic deviation cause identification data. Determine the degree of impact of the deviation level based on the characteristic deviation cause identification data; Based on the deviation level and impact information, record the deviation analysis process, deviation parameters, impact assessment results, and preliminary handling directions to obtain sub-task resource schedule deviation analysis data.

6. The method for full-cycle tracking and management of consulting projects based on big data as described in claim 1, characterized in that, S3 includes: Based on the management implementation data, core monitoring indicators are identified, and indicator data monitoring is performed on these core monitoring indicators to obtain indicator data monitoring information. The monitoring data of the indicators is compared with the preset target data to obtain monitoring comparison information; Problems are identified based on monitoring and comparison information, and deviation problem analysis data is generated. Based on the deviation analysis data, the problem deviation is adjusted to obtain the problem deviation adjustment data; Establish a deviation adjustment mapping relationship based on the deviation adjustment data, and generate adjustment optimization data based on the deviation adjustment mapping relationship.

7. The big data-based full-cycle tracking management method for consulting projects according to claim 6, characterized in that, The step of identifying problems based on monitoring comparison information and generating deviation problem analysis data includes: The monitoring comparison information is segmented to obtain the monitoring comparison segmentation data; Based on the monitoring comparison and segmentation data extraction, abnormal deviation data is extracted to obtain abnormal deviation extraction data; Based on the data extracted from abnormal deviations, the core monitoring indicators and related sub-tasks corresponding to the deviations are identified. The abnormal deviation extraction data is divided into systematic deviations and random deviations to obtain deviation type classification data; Determine the cause of the deviation by classifying the data according to the type of deviation; Based on the core monitoring indicators and related sub-tasks, combined with the information on the causes of deviations, deviation problem analysis data is generated.

8. The method for full-cycle tracking and management of consulting projects based on big data as described in claim 6, characterized in that, The step of adjusting the problem deviation based on the deviation analysis data to obtain problem deviation adjustment data includes: Information on deviation adjustment measures is determined based on the deviation problem analysis data; Based on the project cycle and core objectives, the feasibility of deviation adjustment measures is verified and optimized to obtain adjustment and optimization measures. Implement and collect data on adjustment and optimization measures to obtain execution and data collection data; Based on the collected data, adjustment problems are identified to obtain adjustment problem identification data; Based on the data identified in the problem adjustment, the adjustment and optimization measures are updated to obtain the problem deviation adjustment data.

9. The big data-based full-cycle tracking management method for consulting projects according to claim 6, characterized in that, The step of establishing a deviation adjustment mapping relationship based on the problem deviation adjustment data, and generating adjustment optimization data based on the deviation adjustment mapping relationship, includes: Adjustment feature extraction is performed on the problem deviation adjustment data to obtain adjustment feature extraction data; Establish a mapping relationship between deviation types and the effects of regulatory measures based on the data extracted from the regulatory features; The mapping relationship is associated with the project's core monitoring indicators and preset target data to obtain mapping relationship information; Optimize deviation threshold settings and monitoring nodes based on mapping association information to generate a deviation adjustment mapping system; Based on the deviation adjustment mapping system and the project's full-cycle monitoring data, adjustments and optimizations are made to the entire consulting project lifecycle, generating adjustment and optimization data.

10. A big data-based consulting project full-cycle tracking management system, characterized in that: The system includes: The integration and optimization module is used to conduct needs surveys and data integration and optimization for consulting projects, obtain data integration and optimization information, approve and initialize projects based on the data integration and optimization information, and obtain tracking and management start data. The decomposition and monitoring module is used to decompose and standardize tasks based on the tracking and management initiation data to obtain decomposition and processing data, perform full-cycle data monitoring on the decomposition and processing data to obtain full-cycle monitoring data, and perform resource scheduling and coordination based on the full-cycle monitoring data to obtain management implementation data. The deviation adjustment module is used to monitor indicators based on management implementation data, obtain indicator monitoring data, analyze deviation problems based on indicator monitoring data, obtain deviation problem analysis data, and make adjustments and optimizations based on deviation problem analysis to obtain adjustment and optimization data. The integrated archiving module is used for data aggregation, acceptance, and archiving based on adjusted and optimized data.

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