Whole-process consultation project management monitoring system and method

Abstract

The invention discloses a whole-process consultation project management monitoring system and method, and relates to the technical field of project management, and the monitoring system comprises a data obtaining module which is used for obtaining the original data of the whole-process stage of a current project, preprocessing the original data, and constructing a data set; and the business docking module is used for specifically disassembling the engineering project into a three-dimensional gene sequence of a process characteristic factor, a resource consumption fingerprint and an environmental response parameter, finding a matched step node for each piece of data in the data set through a gene sequence comparison algorithm, and enabling the data in the data set to correspond to specific steps in the engineering project. According to the invention, progress, quality and safety data are analyzed through machine learning and risk levels are calculated, and the early warning pushing unit actively pushes information according to the levels, so that risk beforehand management and control are realized; and the decision support unit reversely retrieves a successful case by means of big data, extracts intervention measures to generate a report and optimization suggestions, so that the decision is more feasible, and the team cooperation efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering management, in particular to a whole-process consulting project management monitoring system and method. BACKGROUND

[0002] With the development of specialization and complexity of the engineering construction industry, the whole-process engineering consulting mode gradually becomes the core trend of engineering project management, which covers the whole life cycle of project planning, design, construction and operation, and needs to collaboratively manage multi-dimensional information such as progress, quality, safety, resources and environment. Among them, the engineering project process is complex, and there are many participants. The traditional management method relies on manual disassembly of the process and matching of data, which is easy to cause deviation of the corresponding process characteristics and data due to differences in human experience, and it is difficult to quantify the correlation between resource consumption, environmental impact and process nodes, affecting the management accuracy and efficiency. The risk analysis of the existing system mainly depends on preset thresholds or static rules, lacks real-time learning and adaptive analysis capabilities for dynamic project data, and is difficult to accurately identify potential risks such as progress delay, quality defects and safety hazards, so the timeliness of early warning is insufficient, which is easy to lead to risk expansion. To this end, we propose a whole-process consulting project management monitoring system and method. SUMMARY

[0003] To solve the above technical problems, the whole-process consulting project management monitoring system and method are provided, which solves the above problems.

[0004] To achieve the above purpose, the technical scheme adopted by the present application is as follows: a whole-process consulting project management monitoring system, the monitoring system comprising: A data acquisition module is used to acquire original data of the current engineering whole-process stage, pre-process the original data, generate a characteristic frequency spectrum graph through Fourier transform, and store and construct a data set according to the space-time attribute label; A business docking module is used to specifically disassemble the engineering project into a three-dimensional gene sequence of process characteristic factors, resource consumption fingerprints and environmental response parameters, find the matching step node of each data in the data set through a gene sequence comparison algorithm, and correspond the data in the data set to the specific steps in the engineering project. The process characteristic factor disassembly includes: extracting the engineering core process parameters, compressing them into codes by using a hash algorithm, constructing a dynamic response threshold matrix based on environmental interaction, and obtaining a three-dimensional gene sequence through the interaction of the three; An intelligent judgment module includes a risk identification unit, an early warning pushing unit and a decision support unit, wherein the risk identification unit analyzes the project progress, quality and safety data based on a machine learning algorithm, and calculates the risk level; The early warning pushing unit pushes the early warning information based on the current risk level; The decision support unit is based on a big data network, in which successful cases contrary to the current early warning features are retrieved in reverse, key intervention measures thereof are extracted as basic suggestions, a stage analysis report is generated, and optimization suggestions are given; The output interaction module outputs the optimization suggestions through a display terminal for interaction, supports task allocation, progress reporting, and problem feedback.

[0005] Preferably, the original data is acquired through a distributed sensing network to establish a body perception matrix to capture engineering parameters, pre-processing is performed by Fourier transform on the collected data to generate a feature spectrum, and the pre-processed data is marked and stored according to spatial dimensions, time dimensions, and attribute dimensions.

[0006] Preferably, the disassembling step is: Core process parameters in the engineering step are extracted, the process connection logic is sorted out, and the starting time limit after the completion of the pre-process acceptance is determined; quality acceptance nodes are determined to obtain a 128-dimensional feature vector; Dynamic data in the engineering step is captured and compressed into a 64-bit unique code through a hash algorithm; Based on the interactive characteristics of the environment, a dynamic response threshold matrix is constructed, and the three are combined to form a three-dimensional gene sequence.

[0007] Preferably, the corresponding matching step is: An improved gene sequence alignment algorithm is selected to globally align the data feature sequence with the three-dimensional gene sequence of each step, and a dynamic programming algorithm is used to calculate the matching score to quantify the degree of fit of the data and the step in the process, resource, and environment dimensions; For structure safety, resource, and environmental data, the matching weight of the corresponding dimension is increased, a unique matching code containing the step ID, matching confidence, and associated dimension is generated, and the binding and correspondence of the data and the step are completed.

[0008] Preferably, the specific calculation step in the corresponding matching step is: The data feature sequence and the three-dimensional gene sequence of each dimension parameter are uniformly mapped to the [0, 1] interval, the 128-dimensional vector of the process feature factor is normalized according to the process standard deviation rate, the 64-bit code is converted to a similarity value through the Hamming distance, and the environmental parameters are assigned according to the threshold matching degree for quantization; A score matrix is constructed with the length of the data feature sequence as the row and the length of the step gene sequence as the column, the matrix element H(i, j) represents the matching score of the i-th feature of the data and the j-th gene of the step, and is initialized as H(0, 0)=0, and the edge elements are filled according to the single dimension missing penalty coefficient; Iterative calculation is performed, and the calculation formula is: H(i, j) = max [H(i-1, j-1) + S(i, j), H(i-1, j) - d, H(i, j-1) - d] wherein H(i, j) represents the optimal matching score to the current position when the ith element of the data feature sequence is aligned with the jth element of the step three-dimensional gene sequence, H(i-1, j-1) represents the optimal score when the i-1th element of the data feature sequence is aligned with the j-1th element of the step three-dimensional gene sequence; S(i, j) represents the matching score of the ith element of the data feature sequence with the jth element of the step three-dimensional gene sequence in the current dimension, and the weight value of the corresponding dimension is taken when the matching is good; H(i-1, j) represents the optimal score when the i-1th element of the data feature sequence is aligned with the jth element of the step three-dimensional gene sequence; and H(i, j-1) represents the optimal score when the ith element of the data feature sequence is aligned with the j-1th element of the step three-dimensional gene sequence; and d is a gap penalty value. After traversing the matrix to obtain the optimal path score, the final score is obtained by weighted sum according to the three-dimensional weight, and the final score is in the range of [0, 1]. If ≥ 0.7, it is determined as an effective match, and the matching degree details of each dimension are output simultaneously.

[0009] Preferably, the risk level calculation is performed by screening the core data of the project progress, quality and safety, eliminating abnormal values and filling in missing values, mapping the progress delay days into labels of 0-no delay, 1-mild, 2-moderate and 3-severe, extracting the number of repeated occurrences of unqualified items from the quality data to derive features, generating a hidden danger rectification and timely rate index from the safety data, and scaling the feature values to the interval [0, 1] through Min-Max normalization; The random forest algorithm constructed is selected as the basic model, and the preprocessed real-time data is input into the model to output the risk probability distribution of each dimension; and the comprehensive risk level is calculated by using a weighted sum formula, and the calculation formula is: Comprehensive risk value = progress risk probability x w1 + quality risk probability x w2 + safety risk probability x w3 wherein w1, w2 and w3 are weight values.

[0010] Preferably, after the risk level is determined, the early warning pushing unit starts the early warning pushing mechanism, and performs a simple prompt for a low risk level; and simultaneously pushes through multiple channels for a medium-high risk level.

[0011] Preferably, after receiving the core features of the current early warning event, the decision support unit starts a reverse search engine, filters out successful cases that are inversely associated with the current early warning features through a feature matching algorithm, structurally analyzes the core elements in the cases, refines the key intervention measures for reuse, and forms a standardized basic suggestion library. ​

[0012] Preferably, a multi-dimensional stage analysis report is generated based on the basic suggestion library and the real-time data of the current warning, which gives the core tasks and priorities of each stage and presents optimization suggestions for different stages.

[0013] The monitoring method for full-process consulting project management includes the following monitoring steps: S1. Collect raw data from all stages of the current project; preprocess the collected raw data to construct a project-specific dataset with a unified structure and complete content. S2. Decompose the current project into a three-dimensional gene sequence composed of process feature factors, resource consumption fingerprints, and environmental response parameters; call the gene sequence alignment algorithm to match each data in the dataset with the step nodes in the three-dimensional gene sequence; complete the one-to-one correspondence between all data in the dataset and the specific steps of the project, and realize the accurate association between data and business scenarios. S3. Based on a preset machine learning algorithm, input project data that matches the business scenario; analyze data features through the algorithm to calculate the risk level of the current project; receive the risk level output by the risk identification unit; based on the difference in risk level, activate the corresponding push mechanism, generate early warning information and push it to relevant responsible persons and the system; S4. Connect to the big data network, perform reverse retrieval based on the core characteristics of the current warning, and filter out successful cases with opposite characteristics; extract key intervention measures from the successful cases to form a basic suggestion library; combine current project data and risk dynamics to generate a phase analysis report that includes phase tasks and expected effects; based on the report and resource constraints, supplement targeted optimization suggestions and improve decision-making content.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention addresses data fragmentation by preprocessing raw data to construct a structured dataset, laying the foundation for subsequent analysis. It breaks down projects into three-dimensional gene sequences of processes, resources, and environment, using gene sequence alignment algorithms to precisely correlate data with specific steps, preventing data from becoming disconnected from business operations. Machine learning analyzes progress, quality, and safety data and calculates risk levels, with an early warning push unit proactively pushing information based on these levels, enabling pre-emptive risk control. The decision support unit uses big data to reverse-engineer successful cases, extracting intervention measures to generate reports and optimization suggestions, making decisions more feasible, improving team collaboration efficiency, and driving the transformation of engineering management from experience-driven to data-driven. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the monitoring system framework of the present invention. Detailed Implementation

[0016] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0017] Reference Figure 1 As shown, the full-process consulting project management monitoring system includes: The data acquisition module is used to acquire raw data from all stages of the current project, preprocess the raw data, generate feature spectrum maps through Fourier transform, and store them according to spatiotemporal attributes to construct a dataset. The business integration module breaks down engineering projects into a three-dimensional gene sequence of process feature factors, resource consumption fingerprints, and environmental response parameters. It uses a gene sequence alignment algorithm to find matching step nodes for each data point in the dataset, thus mapping the data in the dataset to the specific steps in the engineering project. The process feature factor decomposition includes: extracting core process parameters of the project, compressing them into codes using a hash algorithm, constructing a dynamic response threshold matrix based on environmental interaction, and obtaining a three-dimensional gene sequence through the interaction of the three. The intelligent judgment module includes a risk identification unit, an early warning push unit, and a decision support unit. The risk identification unit analyzes project progress, quality, and safety data based on machine learning algorithms to calculate the risk level. The early warning push unit pushes early warning information based on the current risk level; The decision support unit, based on the big data network, reverse-searches successful cases with opposite warning characteristics in the big data network, extracts their key intervention measures as basic suggestions, generates a phase analysis report, and provides optimization suggestions; The output interaction module allows optimization suggestions to be displayed and interacted with, supporting task allocation, progress reporting, and problem feedback.

[0018] The data acquisition module of this application integrates and preprocesses the raw data from all stages of the project to build a structured dataset, solving the problem of data fragmentation and providing a high-quality data foundation for subsequent analysis, while reducing the workload of repetitive cleaning. The business integration module breaks down the project into a three-dimensional gene sequence and combines gene sequence alignment algorithms to ensure that each piece of data is accurately matched with the specific steps of the project, avoiding data disconnect from business and facilitating the traceability of data sources and progress. In the intelligent judgment module, the risk identification unit uses machine learning to analyze progress, quality, and safety data in real time and calculate the risk level. The early warning push unit proactively pushes information according to the level to achieve early warning of risks and reduce the probability of accidents and losses. The decision support unit uses big data to retrieve successful cases, extract intervention measures, and generate optimization suggestions to make decisions more feasible. The output interaction module integrates functions such as task allocation and progress reporting through the display terminal, breaking down information silos and improving team collaboration efficiency.

[0019] Raw data acquisition involves constructing a three-dimensional perception matrix using a distributed sensor network to capture engineering parameters. Preprocessing involves performing Fourier transform on the collected data to generate a feature spectrum. Dataset construction involves labeling and storing the preprocessed data according to spatial, temporal, and attribute dimensions.

[0020] Directly performing Fourier transform on the raw data can easily lead to spectral distortion due to signal defects. Therefore, it is necessary to construct a complete workflow framework of preprocessing, transformation, and optimization. The core steps are as follows: Raw data often contains noise, trend terms, and other interference, requiring three preprocessing steps to improve signal quality: Data cleaning and repair employs statistical methods to remove outliers from signals such as vibration and stress, and uses linear interpolation or spline interpolation to fill in missing data caused by sensor offline. Denoising and detrending are first achieved by using a low-pass filter to remove high-frequency electromagnetic interference, and then by using the least squares method to eliminate DC bias and linear trend terms in the signal, thus avoiding baseline drift from affecting the accuracy of the spectral amplitude. The signal is normalized by Z-score through standardization and segmentation, and then the long sequence is cut into fixed-length segments by overlapping segmentation strategy to balance frequency resolution and computational efficiency. To address the spectral leakage problem caused by truncation of signals of finite length, a windowing optimization strategy is introduced; Based on the matching window function of the signal characteristics, the Hanning window is used to suppress side lobes for equipment vibration signals with strong periodicity, and the Blackman window is used for stress signals that require accurate frequency identification, so as to achieve a trade-off between frequency resolution and amplitude accuracy. The time-domain signal is converted into a complex spectrum by a function, and the frequency identification accuracy is improved by more than 3 times by combining multi-window averaging and spectrum correction techniques. The spectrum data is mapped into a two-dimensional heat map, with the horizontal axis being the frequency axis and the vertical axis being the signal amplitude. The energy distribution of different frequency components is presented intuitively through color gradients, highlighting abnormal frequency peaks.

[0021] The disassembly steps are as follows: Extract the core process parameters from the engineering steps, sort out the process connection logic, and clarify the start time limit after the acceptance of the previous process; determine the quality acceptance node and obtain a 128-dimensional feature vector; Dynamic data from the engineering process is captured and compressed into a unique 64-bit code using a hash algorithm. Based on the interactive characteristics of the environment, a dynamic response threshold matrix is ​​constructed, and the three are combined to form a three-dimensional gene sequence.

[0022] The core process parameter extraction covers key input and output indicators of each process. 8-12 core parameters are extracted for each process to ensure that the parameters are quantifiable and traceable. Redundant parameters are eliminated through MES historical data mining and expert review. The 128-dimensional feature vector is constructed by allocating process parameters (60 dimensions) + process connection logic coding (32 dimensions) + start-up time limit threshold (16 dimensions) + acceptance node judgment criteria (20 dimensions). Each feature dimension is standardized using 0-1 to ensure data consistency. The SHA-256 hash algorithm is used to encrypt the captured dynamic data sequence and extract the first 64 bits of binary number as a unique code to avoid data redundancy while ensuring uniqueness. The steps for mutual matching are as follows: An improved gene sequence alignment algorithm was selected to perform a global comparison between the data feature sequence and the three-dimensional gene sequence of each step. The matching score was calculated by dynamic programming algorithm to quantify the degree of consistency between the data and the steps in terms of process, resources, and environment. Global comparison involves quantizing and normalizing heterogeneous data, including 128-dimensional vectors, 64-bit codes, and threshold matrices. Vectors are mapped to feature similarity values ​​[0, 1] based on the process standard deviation rate. Codes are converted to resource similarity using Hamming distance. The matrix is ​​used to calculate environmental adaptability based on threshold matching degree, eliminating dimensional differences. Normalized data is concatenated in the order of process, resource, and environment to form a single one-dimensional sequence, achieving three-dimensional fusion. A weighted global comparison is performed based on an improved algorithm using dynamic programming. A score matrix is ​​constructed and iteratively calculated, incorporating the weights of each dimension. The total score is calculated based on the sum of the weights, and a valid match is determined when the total score is ≥0.7, thus completing the unified comparison of heterogeneous data.

[0023] For structural safety, resource, and environmental data, the matching weight of the corresponding dimensions is increased, and a unique matching code containing step ID, matching confidence and related dimensions is generated to complete the binding and correspondence between data and steps.

[0024] The specific calculation steps in the corresponding steps are as follows: The parameters of each dimension of the data feature sequence and the three-dimensional gene sequence of the steps are uniformly mapped to the interval [0, 1]. The 128-dimensional vector of the process feature factor is normalized according to the process standard deviation rate. The 64-bit code is converted into a similarity value through Hamming distance. The environmental parameters are assigned values ​​according to the threshold matching degree and quantified. Construct a score matrix with the length of the data feature sequence as the row and the length of the step gene sequence as the column. The matrix element H(i,j) represents the matching score between the i-th feature of the data and the j-th gene of the step. It is initialized to H(0,0)=0. Marginal elements are filled with a single-dimensional missing penalty coefficient. Perform iterative calculations; the calculation formula is as follows: H (i, j)=max [H (i-1, j-1)+S (i, j), H (i-1, j)-d, H (i, j-1)-d] Where H(i, j) represents the optimal matching score up to the current position when the i-th element of the data feature sequence is compared with the j-th element of the three-dimensional gene sequence in step , and H(i-1, j-1) represents the optimal score when the (i-1)-th element of the data feature sequence is compared with the (j-1)-th element of the three-dimensional gene sequence in step ; S (i, j) represents the matching score of the i-th element of the data feature sequence and the j-th element of the three-dimensional gene sequence in the current dimension. When they match, the weight value of the corresponding dimension is taken. The matching of elements in the 128-dimensional vector is judged based on the process standard deviation rate and combined with the industry's preset allowable deviation threshold. Each element of the 128-dimensional vector corresponds to the core process parameter of the project. The matching standard needs to be set for specific process parameters. When the normalized deviation rate of the parameter is within the preset allowable range of the project, the two elements are judged to match. The preset weight value is determined by the analytic hierarchy process combined with the project priority. The importance of the process parameters corresponding to the 128-dimensional vector is sorted out. The project management and technical experts score the parameters of each dimension, construct the judgment matrix and calculate the weight. At the same time, the fixed weight value of each process parameter dimension is obtained by referring to industry standards and historical project risk data. When they match, S(i, j) takes the preset weight value. H(i-1, j) represents the optimal score when comparing the i-1-th element of the data feature sequence with the j-th element of the three-dimensional gene sequence in the step. H(i, j-1) represents the optimal score when comparing the i-1-th element of the data feature sequence with the j-th element of the three-dimensional gene sequence in the step. The optimal score when comparing an element with the (j-1)th element of the three-dimensional gene sequence in step j; d is the gap penalty value; After traversing the matrix to obtain the optimal path score, the scores are weighted and summed according to the three dimensions to obtain the final score ∈ [0, 1]. A score ≥ 0.7 is considered a valid match, and the details of the matching degree of each dimension are output synchronously.

[0025] This application utilizes an improved gene sequence alignment algorithm and dynamic programming calculation to uniformly quantify multi-dimensional parameters. By combining weight enhancement with data such as structural safety, it reduces dimensional bias interference, making data and steps more accurately linked. Effective matching judgment (≥0.7) further ensures matching reliability. Covering multiple dimensions such as processes, resources, and environment, it can specifically handle different types of data. Through quantification methods such as normalization and similarity conversion, it adapts to different parameter characteristics and can be applied to data and step matching needs in multiple scenarios. It outputs a unique matching code and details of the degree of conformity for each dimension, which not only facilitates rapid identification of matching results but also allows for tracking of the matching status of each dimension, providing a basis for subsequent optimization and adjustment.

[0026] Risk level calculation involves filtering core data on project progress, quality, and safety, removing outliers and filling in missing values, mapping the number of days of schedule delay to labels of 0-no delay, 1-mild, 2-moderate, and 3-severe, extracting the frequency of repeated occurrences of non-conformities from quality data, and generating a hazard rectification timeliness index from safety data. The feature values ​​are scaled to the [0, 1] range through Min-Max normalization. The constructed random forest algorithm is selected as the base model. Preprocessed real-time data is input into the model, and the risk probability distribution of each dimension is output. The weighted summation formula is used to calculate the comprehensive risk level. The calculation formula is as follows: Overall risk value = schedule risk probability × w1 + quality risk probability × w2 + safety risk probability × w3; Among them, w1, w2 and w3 are all weight values.

[0027] The weighting method adopts the analytic hierarchy process (AHP), which constructs a judgment matrix through expert scoring and calculates the weights of each dimension to avoid subjective bias. In addition to the core data, the optimization features of the random forest model are supplemented with derived features to improve prediction accuracy. The number and maximum depth of trees are optimized through cross-validation to avoid overfitting. The importance ranking of each feature is output to provide priority parameters for risk management.

[0028] After identifying the risk level, the early warning push unit activates the early warning push mechanism, providing a simplified alert for low-risk levels and simultaneously pushing alerts through multiple channels for medium- and high-risk levels.

[0029] This application uses only a simple notification for low-risk cases, minimizing manpower and communication costs; for medium- and high-risk cases, multiple channels are used to ensure key personnel are quickly informed, avoiding "over-warning for low-risk cases" or "over-warning for medium- and high-risk cases," thus achieving efficient allocation of warning resources; different levels correspond to differentiated notifications, allowing relevant personnel to directly judge the urgency of the risk based on the form of the warning, and seeing multiple channels of notifications indicates that medium- and high-risk cases need to be addressed first, reducing the extra time spent "identifying risk levels," and providing a window of opportunity for risk rectification, especially reducing the probability of medium- and high-risk cases escalating.

[0030] After receiving the core features of the current warning event, the decision support unit starts the reverse retrieval engine, uses feature matching algorithms to filter out successful cases that are inversely related to the current warning features, performs structured analysis on the core elements in the cases, extracts reusable key intervention measures, and forms a standardized basic suggestion library.

[0031] Based on the basic suggestion library and real-time data of the current warning, a multi-dimensional stage analysis report is generated, which gives the core tasks and priorities of each stage and presents optimization suggestions for different stages.

[0032] This application uses reverse retrieval to quickly match successful cases with reverse associations, eliminating the need for staff to explore solutions from scratch. They can directly obtain references from the key intervention measures extracted, significantly shortening decision-making time. This is especially suitable for rapid response in emergency warning scenarios. The successful cases have been verified in practice, and the extracted intervention measures are practically feasible, avoiding the problem of being unable to be implemented. The structured analysis of core elements makes the content of the basic suggestion library clear and concise, allowing staff to directly reuse or fine-tune it, thus lowering the operational threshold.

[0033] The monitoring method for full-process consulting project management includes the following monitoring steps: S1. Collect raw data from all stages of the current project; preprocess the collected raw data to construct a project-specific dataset with a unified structure and complete content. S2. Decompose the current project into a three-dimensional gene sequence composed of process feature factors, resource consumption fingerprints, and environmental response parameters; call the gene sequence alignment algorithm to match each data in the dataset with the step nodes in the three-dimensional gene sequence; complete the one-to-one correspondence between all data in the dataset and the specific steps of the project, and realize the accurate association between data and business scenarios. S3. Based on a preset machine learning algorithm, input project data that matches the business scenario; analyze data features through the algorithm to calculate the risk level of the current project; receive the risk level output by the risk identification unit; based on the difference in risk level, activate the corresponding push mechanism, generate early warning information and push it to relevant responsible persons and the system; S4. Connect to the big data network, perform reverse retrieval based on the core characteristics of the current warning, and filter out successful cases with opposite characteristics; extract key intervention measures from the successful cases to form a basic suggestion library; combine current project data and risk dynamics to generate a phase analysis report that includes phase tasks and expected effects; based on the report and resource constraints, supplement targeted optimization suggestions and improve decision-making content.

[0034] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A full-process consulting project management and monitoring system, characterized in that, The monitoring system includes: The data acquisition module is used to acquire raw data from all stages of the current project, preprocess the raw data, generate feature spectrum maps through Fourier transform, and store them according to spatiotemporal attributes to construct a dataset. The business integration module breaks down engineering projects into a three-dimensional gene sequence of process feature factors, resource consumption fingerprints, and environmental response parameters. It uses a gene sequence alignment algorithm to find matching step nodes for each data point in the dataset, thus mapping the data in the dataset to the specific steps in the engineering project. The process feature factor decomposition includes: extracting core process parameters of the project, compressing them into codes using a hash algorithm, constructing a dynamic response threshold matrix based on environmental interaction, and obtaining a three-dimensional gene sequence through the interaction of the three. The intelligent judgment module includes a risk identification unit, an early warning push unit, and a decision support unit. The risk identification unit analyzes project progress, quality, and safety data based on machine learning algorithms to calculate the risk level. The early warning push unit pushes early warning information based on the current risk level; The decision support unit, based on the big data network, reverse-searches successful cases with opposite warning characteristics in the big data network, extracts their key intervention measures as basic suggestions, generates a phase analysis report, and provides optimization suggestions; The output interaction module allows optimization suggestions to be displayed and interacted with, supporting task allocation, progress reporting, and problem feedback.

2. The full-process consulting project management and monitoring system according to claim 1, characterized in that: Raw data acquisition involves constructing a three-dimensional perception matrix using a distributed sensor network to capture engineering parameters. Preprocessing involves performing Fourier transform on the acquired data to generate a feature spectrum. The dataset construction involves labeling and storing the preprocessed data according to spatial, temporal, and attribute dimensions.

3. The whole-process consulting project management and monitoring system according to claim 1, characterized in that, The disassembly steps are as follows: Extract the core process parameters from the engineering steps, sort out the process connection logic, and clarify the start time limit after the acceptance of the previous process; determine the quality acceptance node and obtain a 128-dimensional feature vector; Dynamic data from the engineering process is captured and compressed into a unique 64-bit code using a hash algorithm. Based on the interactive characteristics of the environment, a dynamic response threshold matrix is ​​constructed, and the three are combined to form a three-dimensional gene sequence.

4. The whole-process consulting project management and monitoring system according to claim 1, characterized in that, The steps for mutual matching are as follows: An improved gene sequence alignment algorithm was selected to perform a global comparison between the data feature sequence and the three-dimensional gene sequence of each step. The matching score was calculated by dynamic programming algorithm to quantify the degree of consistency between the data and the steps in terms of process, resources, and environment. For structural safety, resource, and environmental data, the matching weight of the corresponding dimensions is increased, and a unique matching code containing step ID, matching confidence and related dimensions is generated to complete the binding and correspondence between data and steps.

5. The whole-process consulting project management and monitoring system according to claim 4, characterized in that, The specific calculation steps in the corresponding steps are as follows: The parameters of each dimension of the data feature sequence and the three-dimensional gene sequence of the steps are uniformly mapped to the interval [0, 1]. The 128-dimensional vector of the process feature factor is normalized according to the process standard deviation rate. The 64-bit code is converted into a similarity value through Hamming distance. The environmental parameters are assigned values ​​according to the threshold matching degree and quantified. Construct a score matrix with the length of the data feature sequence as the row and the length of the step gene sequence as the column. The matrix element H(i,j) represents the matching score between the i-th feature of the data and the j-th gene of the step. It is initialized to H(0,0)=0. Marginal elements are filled with a single-dimensional missing penalty coefficient. Perform iterative calculations; the calculation formula is as follows: H (i, j)=max [H (i-1, j-1)+S (i, j), H (i-1, j)-d, H (i, j-1)-d] Where H(i, j) represents the optimal matching score at the current position when comparing the i-th element of the data feature sequence with the j-th element of the three-dimensional gene sequence in step , and H(i-1, j-1) represents the optimal score when comparing the i-1-th element of the data feature sequence with the j-1-th element of the three-dimensional gene sequence in step ; S(i, j) represents the matching score of the i-th element of the data feature sequence with the j-th element of the three-dimensional gene sequence in the current dimension, and the weight value of the corresponding dimension is taken when there is a match; H(i-1, j) represents the optimal score when comparing the i-1-th element of the data feature sequence with the j-th element of the three-dimensional gene sequence in step ; H(i, j-1) represents the optimal score when comparing the i-1-th element of the data feature sequence with the j-th element of the three-dimensional gene sequence in step ; The optimal score when comparing each element with the (j-1)th element of the three-dimensional gene sequence in step ; d is the gap penalty value; After traversing the matrix to obtain the optimal path score, the scores are weighted and summed according to the three dimensions to obtain the final score ∈ [0, 1]. A score ≥ 0.7 is considered a valid match, and the details of the matching degree of each dimension are output synchronously.

6. The whole-process consulting project management and monitoring system according to claim 1, characterized in that: Risk level calculation involves filtering core data on project progress, quality, and safety, removing outliers and filling in missing values, mapping the number of days of schedule delay to labels of 0-no delay, 1-mild, 2-moderate, and 3-severe, extracting the frequency of repeated occurrences of non-conformities from quality data, and generating a hazard rectification timeliness index from safety data. The feature values ​​are scaled to the [0, 1] range through Min-Max normalization. The constructed random forest algorithm is selected as the basic model. Preprocessed real-time data is input into the model, and the risk probability distribution of each dimension is output. A weighted summation formula is used to calculate the comprehensive risk level. The calculation formula is as follows: Overall risk value = schedule risk probability × w1 + quality risk probability × w2 + safety risk probability × w3; Among them, w1, w2 and w3 are all weight values.

7. The whole-process consulting project management and monitoring system according to claim 1, characterized in that: After identifying the risk level, the early warning push unit activates the early warning push mechanism and provides a simplified prompt for low-risk levels. For medium- and high-risk levels, information will be disseminated simultaneously through multiple channels.

8. The full-process consulting project management and monitoring system according to claim 1, characterized in that: After receiving the core features of the current warning event, the decision support unit starts the reverse retrieval engine, uses feature matching algorithms to filter out successful cases that are inversely related to the current warning features, performs structured analysis on the core elements in the cases, extracts reusable key intervention measures, and forms a standardized basic suggestion library.

9. The whole-process consulting project management and monitoring system according to claim 8, characterized in that: Based on the basic suggestion library and real-time data of the current warning, a multi-dimensional stage analysis report is generated, which gives the core tasks and priorities of each stage and presents optimization suggestions for different stages.

10. A method for monitoring and managing the entire consulting project process, characterized in that: The monitoring steps are as follows: S1. Collect raw data from all stages of the current project; preprocess the collected raw data to construct a project-specific dataset with a unified structure and complete content. S2. Decompose the current project into a three-dimensional gene sequence composed of process feature factors, resource consumption fingerprints, and environmental response parameters; call the gene sequence alignment algorithm to match each data in the dataset with the step nodes in the three-dimensional gene sequence; complete the one-to-one correspondence between all data in the dataset and the specific steps of the project, and realize the accurate association between data and business scenarios. S3. Based on a preset machine learning algorithm, input project data that matches the business scenario; The risk level of the current engineering project is calculated by analyzing data characteristics through algorithms. Receive the risk level output by the risk identification unit; Based on the differences in risk levels, the corresponding push mechanism is activated to generate early warning information and push it to relevant personnel and the system; S4. Access the big data network and perform reverse retrieval based on the core characteristics of the current warning to filter out successful cases with opposite characteristics; Extract key intervention measures from successful cases to form a basic suggestion library; combine current project data and risk dynamics to generate a phase analysis report that includes phase tasks and expected results; based on the report and resource constraints, supplement targeted optimization suggestions to improve decision-making content.

Images