An engineering management monitoring system

Abstract

The application relates to the technical field of engineering management monitoring, and discloses an engineering management monitoring system. The system comprises: a collection module, which collects task states, resource parameters and buffer zone indexes and stores the indexes in a block chain; an analysis module, which calculates an engineering health value; a comparison module, which divides an operation area to generate a trigger signal; an identification module, which analyzes abnormal parameters to identify intervention points; a verification module, which verifies instructions through the block chain; and a control module, which executes adjustment and records results. The application integrates key chain buffer zone management and block chain technology, constructs an automatic monitoring system based on engineering health measurement, and realizes closed-loop control of real-time evaluation of engineering states, automatic identification of abnormalities and accurate allocation of resources.

Description

Technical Field

[0001] This invention relates to the field of engineering management and monitoring technology, and in particular to an engineering management and monitoring system. Background Technology

[0002] In the engineering industry, project management faces challenges such as high complexity, difficult resource allocation, and significant progress control difficulties. Traditional project management methods primarily rely on tools like Critical Path Method (CPM), Work Breakdown Structure (WBS), and Gantt charts for planning and monitoring. Critical Chain Project Management (CCPM), as an improved approach, manages project uncertainty by identifying critical task chains under resource constraints and establishing buffer zones. Meanwhile, the application of digital technologies such as Building Information Modeling (BIM) and Enterprise Resource Planning (ERP) systems in project management has significantly enhanced data collection and processing capabilities. In recent years, blockchain technology, with its distributed ledger, immutability, and smart contracts, has begun to attract attention in the field of project management, offering new possibilities for multi-party collaboration and trusted information transmission.

[0003] However, existing technologies have significant shortcomings. Traditional engineering management methods lack real-time monitoring and dynamic adjustment capabilities, failing to respond promptly to changes on-site. While CCPM introduces the concept of buffer zones, buffer zone monitoring and management often remain superficial, lacking systematic and automated implementation tools. Existing digital systems are mostly information silos, making data sharing and verification difficult among different stakeholders. Furthermore, the application of blockchain technology in engineering management is still in its early stages, lacking deep integration with specific engineering management processes. More critically, existing solutions lack a closed-loop control mechanism that integrates real-time data acquisition, intelligent analysis, decision support, and execution control, leading to frequent project delays, resource conflicts, and cost overruns. Summary of the Invention

[0004] This invention provides an engineering management and monitoring system that integrates critical chain buffer management and blockchain technology to build an automated monitoring system based on engineering health metrics, thereby achieving closed-loop control of real-time assessment of engineering status, automatic anomaly identification, and precise resource allocation.

[0005] This invention provides an engineering management and monitoring system, the engineering management and monitoring system comprising:

[0006] The data acquisition module is used to collect the task execution status, resource utilization parameters and buffer consumption indicators of the construction project in real time and store them in encrypted form via blockchain to obtain the project operation status data.

[0007] The analysis module is used to perform critical chain parameter calculation and schedule deviation analysis based on the project operation status data to obtain project health metrics.

[0008] The comparison module is used to compare the engineering health measurement value with the preset control threshold, divide the system status into normal operation zone, deviation warning zone and abnormal control zone, and obtain control trigger signal;

[0009] The identification module is used to perform correlation analysis between the abnormal parameters indicated by the control trigger signal and the resource control variables, identify the control intervention point and adjustment target, and obtain the resource control instruction set;

[0010] The verification module is used to verify the resource control instruction set through a blockchain smart contract and distribute it to the field control node to obtain resource adjustment execution instructions;

[0011] The control module is used to perform closed-loop regulation and control of construction production elements according to the resource adjustment execution instructions, and record the adjustment results on the blockchain to obtain the engineering control parameter table.

[0012] The technical solution provided by this invention uses a data acquisition module to collect real-time task execution status, resource utilization parameters, and buffer consumption indicators of a construction project, and stores them using blockchain encryption. This ensures the authenticity and immutability of the project data, effectively solving the problem of low data reliability in traditional project management. The analysis module calculates critical chain parameters and analyzes schedule deviations based on the project's operational status data. It uses algorithms incorporating critical chain theory to quantitatively assess the project's health status, providing more objective and accurate project health metrics and overcoming the limitations of traditional experience-based judgments. The comparison module compares the project health metrics with preset control thresholds, employing an adaptive partitioning algorithm to scientifically divide the system status into normal operation, deviation warning, and abnormal control zones. This achieves precise classification and early warning of the project status, significantly improving anomaly identification. The accuracy and timeliness of resource allocation are ensured. The identification module performs correlation analysis between abnormal parameters indicated by control trigger signals and resource control variables. Through artificial intelligence algorithms such as resource conflict network models and principal component analysis, it accurately identifies control intervention points and adjustment targets, providing a data-driven scientific basis for resource allocation decisions and avoiding the blind approach of traditional "treating the symptoms rather than the root cause." The verification module executes and verifies the resource control instruction set through blockchain smart contracts and distributes it to on-site control nodes, ensuring the legality and consistency of control instructions and solving trust and collaboration issues in multi-party projects. The control module performs closed-loop adjustment and control of construction production elements according to resource adjustment execution instructions and records the adjustment results on the blockchain, realizing closed-loop control of the entire process from monitoring, analysis, decision-making to execution, significantly improving the accuracy and timeliness of resource allocation. Of particular note is the graph theory algorithm and deep learning model applied in the resource conflict identification and optimization allocation field of this system. These algorithms fully consider the adaptability of the algorithms to different types of engineering projects, continuously optimizing model parameters through a self-learning mechanism. This allows the algorithm to automatically adjust according to the characteristics of the project, adapting to construction projects of different scales and types, achieving a technological leap from passive response to proactive prediction. Simultaneously, the integrated blockchain technology and smart contracts play a unique role in specific application areas of engineering management. They not only ensure data security but also construct a decentralized multi-party collaborative decision-making mechanism, effectively overcoming the information silos and ambiguous responsibilities in traditional engineering management. This provides an innovative solution for the digital transformation of the AEC industry, significantly improving the scientific rigor, transparency, and efficiency of engineering management. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic diagram of one embodiment of the engineering management and monitoring system in this invention. Detailed Implementation

[0015] This invention provides an engineering management and monitoring system. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0016] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 One embodiment of the engineering management and monitoring system in this invention includes:

[0017] The data acquisition module is used to collect the task execution status, resource utilization parameters and buffer consumption indicators of the construction project in real time and store them in encrypted form via blockchain to obtain the project operation status data.

[0018] The analysis module is used to perform critical chain parameter calculation and schedule deviation analysis based on the project operation status data to obtain project health metrics.

[0019] The comparison module is used to compare the engineering health measurement value with the preset control threshold, divide the system status into normal operation zone, deviation warning zone and abnormal control zone, and obtain control trigger signal;

[0020] The identification module is used to perform correlation analysis between the abnormal parameters indicated by the control trigger signal and the resource control variables, identify the control intervention point and adjustment target, and obtain the resource control instruction set;

[0021] The verification module is used to verify the resource control instruction set through a blockchain smart contract and distribute it to the field control node to obtain resource adjustment execution instructions;

[0022] The control module is used to perform closed-loop regulation and control of construction production elements according to the resource adjustment execution instructions, and record the adjustment results on the blockchain to obtain the engineering control parameter table.

[0023] It is understood that the executing entity of this invention can be an engineering management and monitoring system, a terminal, or a server; no specific limitation is made here. This embodiment of the invention will be described using a server as an example.

[0024] Specifically, the data acquisition module collects data on the actual operation of each work surface at the construction site through a distributed sensor network, obtaining task execution status data. Simultaneously, it monitors the input and output efficiency ratios of various resources in real time using IoT devices, obtaining resource utilization parameter data. It quantifies and tracks the time deviation between the planned and actual progress of the critical chain, obtaining buffer consumption index data. This data undergoes format standardization to form a unified data packet, which is further converted into blockchain transaction data through digital signature and encryption. After verification and confirmation via a consensus mechanism, it is added to the blockchain system, forming an immutable data record. Finally, it extracts project execution parameters to generate project operation status data. The analysis module receives the project operation status data, extracts the difference between the planned and actual times of each task node on the critical chain, obtaining critical chain progress deviation data. It performs cumulative calculation and standardization on this data to obtain the critical chain completion rate index. Simultaneously, it extracts the original and current values ​​of the input buffers for non-critical tasks, obtaining buffer consumption records. Through weighted average calculation and proportional conversion, it obtains the buffer consumption percentage index. Based on these two key indicators, a two-dimensional state space matrix is ​​constructed to form a project state vector; a preliminary health score is obtained by normalization through fuzzy comprehensive evaluation; a standardized score value is obtained by correction calculation combined with the characteristics of the project type; and the project health metric is finally formed by comparison and analysis with the historical project state database.

[0025] The comparison module performs statistical process control analysis on the project health metrics, generating a health distribution curve; it extracts a set of control threshold parameters matching the current project type from the control parameter library; it performs numerical comparison calculations through a multi-level threshold comparator to obtain deviation data; it constructs system state partition boundaries, forming a three-interval division function; it inputs the health distribution curve into this function for region division, obtaining the normal operation zone, deviation warning zone, and abnormal control zone; it locates the region where the health metric value is located, determining the current control zone of the system; it determines the corresponding control response level based on the control zone, and finally generates a control trigger signal. The identification module extracts the current resource allocation from the project operation status data and compares it with the planned resource demand to obtain a resource load distribution map; it determines the type and severity of abnormal parameters based on the control trigger signal, obtaining an abnormal parameter feature set; it performs cross-correlation calculations on the two to obtain a resource abnormality correlation matrix; it performs principal component analysis to determine key influencing factors; it constructs a resource conflict network model to form a resource conflict topology map; it performs path analysis and node importance calculations to obtain a control intervention point ranking table; it combines project progress network analysis to influence paths and form adjustment target schemes; finally, it converts it into a standardized control instruction format to generate a resource control instruction set.

[0026] The verification module converts the resource control instruction set into a blockchain smart contract data structure, forming a control instruction smart contract; performs digital signature and encryption processing to obtain a control transaction data packet; broadcasts it to blockchain network nodes for transaction verification; performs legality verification and condition judgment to obtain the smart contract execution result; performs permission screening and security checks to generate an approved execution plan; encapsulates it into a blockchain confirmation block and adds it to the distributed ledger to form an immutable control record; generates a control instruction call interface and execution parameter set, and sends it to the field control node through a secure channel, ultimately forming a resource adjustment execution instruction.

[0027] The control module extracts control parameters and execution conditions from resource adjustment execution instructions to obtain a construction production factor control plan; it decomposes and optimizes construction team configuration instructions to form a human resource allocation table; it coordinates equipment usage plans to generate an equipment usage time scheduling table; it rearranges material supply chain plans to form a material supply coordination plan; it integrates various plans into an execution instruction set to obtain a field execution data package; and it records the execution results on the blockchain to finally form an engineering control parameter table.

[0028] Taking a high-rise building project as an example, the data acquisition module collected data from on-site sensors showing that the actual completion time of the foundation work was 5 days later than planned, the steel reinforcement worker resource utilization parameter was 115% (indicating overwork), and the project buffer consumption index was 30%. The analysis module calculated the critical chain completion rate to be 32%, the buffer consumption percentage to be 42%, and obtained a project health metric of 67 points through a two-dimensional state matrix and fuzzy evaluation. The comparison module compared this value with preset thresholds (75 points and 50 points), determining that the project was in the deviation warning zone and generating a yellow warning control trigger signal. The identification module found that the steel reinforcement worker resources were highly correlated with the warning signal (correlation degree 0.85), identifying the steel reinforcement construction stage as the main control intervention point. The verification module generated a smart contract for "increasing steel reinforcement worker resources," which was verified by the blockchain network and then distributed for execution. Based on this, the control module adjusted the human resource configuration, increasing the number of steel reinforcement workers from 15 to 20, and correspondingly adjusted the equipment usage time and material supply plan. The execution result record showed that the project health score improved to 78 points, successfully adjusting the project status to the normal operation zone.

[0029] This invention also integrates VR (Virtual Reality) technology for reservoir engineering management, enabling virtual simulation and immersive experiences. Through the seamless integration of VR technology and monitoring systems, managers can intuitively perceive the project status, identify anomalies, and simulate the effects of control measures in a virtual environment without being physically present, significantly improving remote monitoring capabilities and decision-making efficiency. The immersive VR presentation makes complex engineering data intuitive and visual, transforming abstract health metrics and resource conflict topology diagrams into three-dimensional interactive virtual scenes, significantly lowering the cognitive threshold for management decisions and accelerating the identification and response to anomalies. Furthermore, the graph theory algorithms and deep learning models applied in the field of resource conflict identification and optimized allocation fully consider the adaptability of the algorithms to different types of engineering projects. Through a self-learning mechanism, the model parameters are continuously optimized, enabling the algorithms to automatically adjust according to the characteristics of the project and adapt to construction projects of different scales and types, achieving a technological leap from passive response to proactive prediction. At the same time, the blockchain technology and smart contracts integrated into the system play a unique role in specific application areas of engineering management. They not only ensure data security but also build a decentralized multi-party collaborative decision-making mechanism, effectively breaking through the problems of information silos and ambiguous responsibilities in traditional engineering management. This provides an innovative solution for the digital transformation of the AEC industry and significantly improves the scientific nature, transparency, and efficiency of engineering management.

[0030] In this embodiment of the invention, the acquisition module collects the task execution status, resource utilization parameters, and buffer consumption indicators of the construction project in real time and stores them using blockchain encryption, thus ensuring the authenticity and immutability of the project data and effectively solving the problem of low data credibility in traditional project management. The analysis module performs critical chain parameter calculation and schedule deviation analysis based on the project operation status data, and uses an algorithm that integrates critical chain theory to quantitatively assess the project health status, providing more objective and accurate project health metrics and breaking the limitations of traditional experience-based judgments. The comparison module compares the project health metrics with preset control thresholds and uses an adaptive partitioning algorithm to scientifically divide the system status into normal operation zone, deviation warning zone, and abnormal control zone, achieving accurate classification and early warning of the project status and significantly improving the accuracy of anomaly identification. The accuracy and timeliness of resource allocation are ensured. The identification module performs correlation analysis between abnormal parameters indicated by control trigger signals and resource control variables. Through artificial intelligence algorithms such as resource conflict network models and principal component analysis, it accurately identifies control intervention points and adjustment targets, providing a data-driven scientific basis for resource allocation decisions and avoiding the blind approach of traditional "treating the symptoms rather than the root cause." The verification module executes and verifies the resource control instruction set through blockchain smart contracts and distributes it to on-site control nodes, ensuring the legality and consistency of control instructions and solving trust and collaboration issues in multi-party projects. The control module performs closed-loop adjustment and control of construction production elements according to resource adjustment execution instructions and records the adjustment results on the blockchain, realizing closed-loop control of the entire process from monitoring, analysis, decision-making to execution, significantly improving the accuracy and timeliness of resource allocation. Of particular note is the graph theory algorithm and deep learning model applied in the resource conflict identification and optimization allocation field of this system. These algorithms fully consider the adaptability of the algorithms to different types of engineering projects, continuously optimizing model parameters through a self-learning mechanism. This allows the algorithm to automatically adjust according to the characteristics of the project, adapting to construction projects of different scales and types, achieving a technological leap from passive response to proactive prediction. Simultaneously, the integrated blockchain technology and smart contracts play a unique role in specific application areas of engineering management. They not only ensure data security but also construct a decentralized multi-party collaborative decision-making mechanism, effectively overcoming the information silos and ambiguous responsibilities in traditional engineering management. This provides an innovative solution for the digital transformation of the AEC industry, significantly improving the scientific rigor, transparency, and efficiency of engineering management.

[0031] In one specific embodiment, the acquisition module is used to acquire the actual working conditions of each work surface at the construction site through a distributed sensor network to obtain task execution status data;

[0032] Based on real-time monitoring of the input and output efficiency ratio of various resources using IoT devices, resource utilization parameter data can be obtained.

[0033] The time deviation between the critical chain plan and the actual progress is quantitatively calculated and tracked to obtain buffer consumption index data.

[0034] The task execution status data, resource utilization parameter data, and buffer consumption index data are standardized to obtain a unified data packet.

[0035] The unified data packet is digitally signed and encrypted to obtain blockchain transaction data.

[0036] The consensus mechanism is used to submit blockchain transaction data to the distributed ledger for verification and confirmation, resulting in confirmed data blocks.

[0037] The confirmed data blocks are added to the blockchain system and synchronized between nodes to obtain immutable data records.

[0038] By extracting project execution parameters from immutable data records through a data interface, project operation status data can be obtained.

[0039] Specifically, the data acquisition module collects actual operational data from various work areas on the construction site through a distributed sensor network. This distributed sensor network refers to a collection of various sensing devices deployed at key nodes of the construction project, including progress monitoring sensors, personnel positioning sensors, and equipment operation status sensors. The raw data collected by these sensors is preprocessed by an edge computing unit to remove noise and perform preliminary filtering, forming task execution status data. This data includes key information such as work area number, task type, start time, completion time, and completion quality, directly reflecting the actual execution of the project tasks. Simultaneously, the acquisition module monitors the input-output efficiency ratio of various resources in real time using IoT devices. These IoT devices include smart safety helmets, smart equipment meters, and material RFID tags, which upload resource usage data in real time via wireless communication networks. Input refers to resource input such as man-hours, equipment operating time, and material usage, while output efficiency refers to the amount of work completed per unit of time. By calculating the ratio of input to output efficiency, resource utilization parameter data is obtained. This data reflects resource utilization efficiency and is an important indicator for assessing the health status of the project.

[0040] The data acquisition module quantifies and tracks the time deviation between the planned and actual progress of the critical chain. The critical chain refers to the longest chain of tasks within a project under resource constraints, and critical chain planning is an important schedule planning method in project management. By comparing the planned and actual completion times of tasks at each node in the critical chain, the time deviation value is calculated, and the changing trends of these deviations are continuously tracked and recorded to obtain buffer consumption index data. Buffer consumption index data directly reflects the health of the project schedule and is a core monitoring indicator in critical chain project management.

[0041] The acquisition module standardizes the three types of data mentioned above, converting them into a unified data structure and format. Standardization includes operations such as unified field naming, data type conversion, unit conversion, and timestamp standardization, ensuring that heterogeneous data from different sources can be processed and analyzed within a unified framework. The resulting unified data package contains standardized data fields and metadata descriptions, facilitating subsequent processing and storage. The acquisition module then performs digital signature and encryption on the unified data package. The digital signature uses an asymmetric encryption algorithm, employing the data source's private key to sign the data package, ensuring the authenticity and non-repudiation of the data source. The encryption process uses a symmetric encryption algorithm to protect the confidentiality of the data content. After these processes, blockchain transaction data is formed, with each transaction containing the encrypted original data, digital signature, and timestamp information.

[0042] The acquisition module then submits the blockchain transaction data to the distributed ledger for verification and confirmation through the blockchain network's consensus mechanism. Consensus mechanisms such as Byzantine Fault Tolerance (PBFT) or Proof-of-Stake (PoS) ensure that all participating nodes agree on the validity of the transaction data. The verification process includes multiple stages such as digital signature verification, data format verification, and business rule verification. Upon successful verification, a confirmed data block is formed. The acquisition module adds the confirmed data block to the blockchain system and achieves data synchronization and updates between nodes through a peer-to-peer network. Each data block contains the hash value of the previous block, forming an immutable chain structure that ensures data cannot be modified once written, thus obtaining an immutable data record. This mechanism is particularly suitable for engineering projects with multiple participants and clearly defined responsibilities, providing a reliable data foundation for subsequent project monitoring and management.

[0043] The data acquisition module extracts project execution parameters from immutable data records through data interfaces. These interfaces include SQL query interfaces, RESTful APIs, and event subscription mechanisms. Based on business needs, these interfaces retrieve relevant data from the blockchain, perform necessary aggregation and transformation, and ultimately obtain project operation status data, providing data support for subsequent analysis and monitoring.

[0044] Taking a high-rise building project as an example, the data acquisition module collected data from sensors installed on the concrete pouring work surface. The actual start time of the pouring was 8:00 AM on September 10th, and the completion time was 5:00 PM on September 12th, while the planned completion time was 5:00 PM on September 11th. This data forms the task execution status data. At the same time, the positioning information of the smart safety helmet recorded that the concrete work team invested a total of 320 hours of work time and completed the pouring of 1600 square meters. The resource utilization parameter was calculated to be 5 square meters / work time. By comparing these data with the critical chain plan, it was calculated that the task was delayed by 24 hours, accounting for 15% of the project buffer, thus forming the buffer consumption index data. After standardization, these three types of data form a unified data packet containing 28 fields. This data packet is then hashed using the SHA-256 algorithm and digitally signed using the RSA algorithm to generate blockchain transaction data. Through the PBFT consensus mechanism, the general contractor, subcontractors, and owner jointly verify and confirm the data, forming a confirmed data block. This block is added to the blockchain and synchronized to all project participants, forming an immutable progress record. Finally, key execution parameters are extracted through the API interface to generate the daily project operation status data, providing input for subsequent analysis module processing.

[0045] In one specific embodiment, the analysis module is used to extract the difference between the planned time and the actual time of each task node on the critical chain from the project operation status data to obtain critical chain progress deviation data.

[0046] The critical chain progress deviation data is cumulatively calculated and standardized to obtain the critical chain completion rate indicator;

[0047] Extract the original and current values ​​of the input buffers for non-critical tasks from the project operation status data to obtain buffer consumption records;

[0048] The buffer consumption records are weighted averaged and proportionally converted to obtain the buffer consumption percentage index.

[0049] A two-dimensional state space matrix is ​​constructed based on the critical chain completion rate metric and the buffer consumption percentage metric to obtain the project state vector;

[0050] The project state vector is normalized using the fuzzy comprehensive evaluation method to obtain a preliminary health score;

[0051] The preliminary health score was corrected and parameters were adjusted based on the characteristics of the project type to obtain a standardized score value.

[0052] By comparing and analyzing the standardized score values ​​with the historical project status database and mapping their locations, the project health metric is obtained.

[0053] Specifically, the analysis module extracts the planned and actual time differences between each task node on the critical chain from the project operation status data. The critical chain refers to the longest task chain in the project considering resource constraints, and it is the main thread of the project schedule. The analysis module uses a data filtering algorithm to filter out node records marked as critical chain tasks from the project operation status data, extracts the planned start time, planned finish time, actual start time, and actual finish time of each task node, and calculates the start deviation and finish deviation of each node by comparing time points, forming critical chain schedule deviation data. This deviation data directly reflects the progress execution on the project's critical path. The analysis module performs cumulative calculation and standardization processing on the critical chain schedule deviation data. Cumulative calculation involves summing the time deviations of each node according to the hierarchical relationship of the Work Breakdown Structure (WBS) to obtain the cumulative deviation at each level. Standardization processing involves dividing the cumulative deviation by the total critical chain duration and converting it into a percentage form for easy horizontal comparison. The resulting critical chain completion rate index includes both the percentage of progress completed and the percentage of deviation, comprehensively reflecting the execution status of the project's critical tasks.

[0054] The percentage of buffer consumption can be calculated using the following formula:

[0055]

[0056] Where BCP represents the buffer consumption percentage metric, N is the total number of non-critical tasks, and ω k OB is the weight coefficient of the k-th non-critical task. k It is the original value of the input buffer for the k-th non-critical task, CB k This is the current value of the input buffer for the k-th non-critical task. Weight coefficient ω k The size of the task is related to its importance, resource consumption, and proximity to the critical chain. The higher the importance, the greater the resource consumption, and the closer the task is to the critical chain, the greater its weight.

[0057] The analysis module extracts input buffer information for non-critical tasks from the project's operational status data. Input buffers are time buffers set between non-critical task chains and critical chain connection points to protect the critical chain from delays caused by non-critical tasks. By comparing the initial settings and current remaining values ​​of the input buffers, the analysis module generates buffer consumption records, documenting the buffer consumption for each non-critical task chain. The analysis module then performs a weighted average calculation and proportional conversion on these buffer consumption records. The weighted average calculation considers the importance, scale, and resource consumption of different non-critical tasks, assigning different weighting coefficients to calculate a weighted average buffer consumption rate. The proportional conversion transforms the buffer consumption rate into a standardized percentage form, yielding a buffer consumption percentage indicator. This indicator reflects the potential impact of non-critical tasks on the project schedule.

[0058] Based on the critical chain completion rate and buffer consumption percentage metrics, the analysis module constructs a two-dimensional state space matrix. This is a two-dimensional space with the critical chain completion rate on the horizontal axis and the buffer consumption percentage on the vertical axis, integrating the two key metrics to form a comprehensive description of the project status. In this space, the current project status is represented by a point with specific coordinates, i.e., the project status vector, which intuitively reflects the health status of the project.

[0059] The analysis module then normalizes the project state vector using a fuzzy comprehensive evaluation method. Fuzzy comprehensive evaluation is a multi-index evaluation method based on fuzzy mathematics, suitable for handling evaluation problems with high uncertainty and fuzziness. This method first establishes an evaluation index system and a set of comments, then determines the membership function, constructs a fuzzy relation matrix, and finally obtains the evaluation result through fuzzy synthesis operations. The preliminary health score obtained after processing is a value between 0 and 100, initially reflecting the project's health status.

[0060] The standardized score can be calculated using the following formula:

[0061]

[0062] Among them, S std S represents the standardized score. init This is a preliminary health score, where P is the number of engineering type characteristics, and CF... p A is the correction factor for the p-th project type characteristic. p This is the attribute value of the p-th project type feature. Correction factor CF p Based on statistical analysis of historical project data, different types of engineering projects have different sets of correction factors. Attribute value A p It is a value between -1 and 1, representing the strength of the current project's performance on this feature.

[0063] Subsequently, the analysis module performs correction calculations and parameter adjustments on the preliminary health score based on project type characteristics. Project type characteristics include factors such as project scale, complexity, degree of technological innovation, and resource constraints, all of which significantly impact the evaluation criteria for project health status. Correction calculations apply project type-specific correction factors to weight and adjust the preliminary health score, making it more aligned with the characteristics of specific project types. Parameter adjustment dynamically modifies the weight parameters in the scoring model based on project characteristics, improving the score's relevance and accuracy. The resulting standardized score is a corrected health score that takes into account project type characteristics. The analysis module then compares and maps the standardized score with a historical project status database. This database stores a large amount of health status data and final results from completed projects at different stages, serving as a crucial reference for assessing the current project status. Comparative analysis identifies cases in the historical database with similar characteristics and scores to the current project, analyzing their development trends and outcomes; location mapping positions the current score within the historical distribution, calculating its percentile and risk level. Through these analyses, the final engineering health metric is obtained, which includes not only absolute scores but also relative location and risk prediction information, comprehensively reflecting the health status of the project.

[0064] Taking a commercial building project as an example, the analysis module extracts the planned and actual time data for three key nodes on the critical chain: foundation construction, main structure, and exterior wall installation, from the project operation status data. The planned duration for foundation construction is 30 days, with an actual duration of 33 days, a delay of 3 days; the planned duration for the main structure is 60 days, with an actual duration of 40 days, exceeding the deadline by 5 days; exterior wall installation has not yet started. Calculations show that the total duration of the project's critical chain is 180 days, with a current cumulative delay of 8 days, resulting in a critical chain completion rate of (30+40) / 180=38.9%, and a schedule deviation of 8 / 180=4.4%. Simultaneously, input buffer data for non-critical tasks such as water and electricity installation is extracted. The original buffer was 15 days, with 10 days remaining, consuming 33.3%; the original buffer for interior decoration was 20 days, with 18 days remaining, consuming 10%. Considering the weights of 0.6 for water and electricity installation and 0.4 for interior decoration, the calculated buffer consumption percentage is 0.6×33.3%+0.4×10%=24%. Based on a critical chain completion rate of 38.9% and a buffer consumption percentage of 24%, the project state vector is located in a two-dimensional state space. A preliminary health score of 75 is obtained through fuzzy comprehensive evaluation. Considering the project is a large commercial building, with a complexity feature correction factor of 0.08 and an attribute value of 0.5, a scale feature correction factor of 0.05 and an attribute value of 0.7, the standardized score is calculated to be 75 × (1 + 0.08 × 0.5) × (1 + 0.05 × 0.7) = 78.8. Comparing this score with 200 similar projects in the historical database, it is found to be at the 65th percentile. The final project health metric is determined to be 78.8, with a risk level of "low risk," indicating that the project is in a healthy state, but the progress of the main structure needs close monitoring.

[0065] In one specific embodiment, the comparison module is used to perform statistical process control analysis on the engineering health metrics to obtain a health distribution curve;

[0066] Extract the set of control threshold parameters that match the current project type from the control parameter library to obtain the preset control threshold;

[0067] A multi-level threshold comparator is used to compare the engineering health measurement value with the preset control threshold to obtain the deviation data.

[0068] Based on the deviation degree data, the system state partition boundary is constructed to obtain the three-interval partitioning function;

[0069] The health distribution curve is input into a three-interval division function to divide the region, resulting in the normal operation zone, the deviation warning zone, and the abnormal control zone.

[0070] The location analysis of the engineering health measurement values ​​in the three intervals is performed to obtain the current control area of ​​the system;

[0071] The corresponding control response level is determined based on the current control area of ​​the system, resulting in a control response level table;

[0072] An activation signal is generated by controlling the mapping relationship between the response level table and the current health data, thus obtaining a control trigger signal.

[0073] Specifically, the comparison module first performs statistical process control analysis on the project health metrics. Statistical process control (SPC) is a technique for monitoring processes using statistical methods. In the project management and monitoring system, the comparison module performs time-series analysis on historically continuously collected project health metrics, calculating their mean, standard deviation, coefficient of variation, and other statistical characteristics, and plots control charts. The control charts include a center line (mean), upper control limit (UCL), and lower control limit (LCL), used to identify trends and abnormal fluctuations in the project's health status. By analyzing the distribution characteristics of the health metrics, a health distribution curve is generated, which visually displays the probability distribution characteristics and fluctuation patterns of the project's health status. The comparison module then extracts a set of control threshold parameters matching the current project type from the control parameter library. The control parameter library is a database containing control parameters for different project types, storing standard control thresholds and adjustment factors for various project types. By querying the current project's type identifier (e.g., residential building, commercial building, infrastructure, etc.), the comparison module extracts a matching set of control threshold parameters from the control parameter library, including upper and lower limits for normal, warning, and abnormal intervals. These threshold parameters take into account the characteristics and risk tolerance of different project types, and serve as the basis for dividing control intervals, ultimately forming preset control thresholds.

[0074] The comparison module uses a multi-level threshold comparator to perform numerical comparisons between engineering health metrics and preset control thresholds. The multi-level threshold comparator is a processing unit that compares engineering health metrics with multiple preset thresholds one by one to determine their corresponding interval. The comparator first compares the health metric with the highest-level threshold; if the condition is not met, it compares with the next highest-level threshold, and so on, until a matching interval is found. The comparison results include not only the interval determination result but also the deviation amount and deviation ratio between the health metric and each threshold. These data are combined to form deviation degree data, which quantitatively describes the distance relationship between the engineering health status and each control threshold. Based on the deviation degree data, the comparison module constructs system state partition boundaries. The partition boundaries define the dividing lines between different state intervals. By analyzing the distribution characteristics of the deviation degree data and combining it with the preset control thresholds, the comparison module calculates the optimal partition point location. These partition points form dividing lines in the health space, dividing the space into multiple regions. To handle boundary ambiguity, the comparison module uses a step function to approximate a smooth interval transformation, forming a three-interval partitioning function. This function is mathematically represented as a piecewise function, which can map any health measurement value to a corresponding interval category.

[0075] The comparison module inputs the health distribution curve into a three-interval partitioning function for region division. This step combines the health distribution obtained from statistical analysis with the interval partitioning function to calculate the probability distribution and expected coverage of each interval. By dividing the health value range according to the partitioning function, three regions are obtained: the normal operation zone, the deviation warning zone, and the anomaly control zone. The normal operation zone indicates that the project health status is good and the system is operating normally; the deviation warning zone indicates that the project health status has slight anomalies and requires attention; the anomaly control zone indicates that the project health status has seriously deviated from expectations and requires immediate intervention and control. Subsequently, the comparison module performs a location analysis on the position of the project health measurement value within the three intervals. The location analysis not only determines the interval category to which the health measurement value belongs, but also calculates its relative position and trend direction within the interval. By analyzing the distance ratio between the health measurement value and the interval boundary, as well as the health change trend at multiple consecutive time points, the comparison module determines whether the project status is in the middle of the interval, near the upper boundary, or near the lower boundary, and whether it is improving or deteriorating. These analysis results collectively determine the current control area of ​​the system, providing an accurate status judgment for subsequent control decisions.

[0076] Based on the system's current control zone, the comparison module determines the corresponding control response level. The control response level defines the strength of the response strategy the system should adopt for different state zones, typically categorized into several levels such as normal operation, monitoring, general intervention, and emergency intervention. The comparison module converts the current control zone into the corresponding response level according to a predefined mapping relationship, while dynamically adjusting the specific parameter settings of the response level, taking into account health status trends and historical response effects. These response levels and their execution parameters form a control response level table, providing guidance for subsequent control operations.

[0077] The comparison module generates activation signals by mapping the control response level table to the current health data. The activation signal is an instruction that triggers system control actions, containing information such as control type, priority, and target parameters. Based on the triggering conditions and control parameters defined in the control response level table, and combined with the specific values ​​and characteristics of the current health data, the comparison module generates corresponding activation instructions. These instructions are formatted and prioritized to form standardized control trigger signals, which are then transmitted to the subsequent identification module for processing.

[0078] Taking a large infrastructure project as an example, the comparison module performs statistical process control analysis on the project health metrics over 30 consecutive days, calculating a mean of 72.5 and a standard deviation of 8.3, and plotting a health distribution curve exhibiting a left-skewed distribution. The control threshold parameter set for infrastructure projects is retrieved from the control parameter library, including a normal range threshold of 75 points, a warning range threshold of 60 points, and an abnormal range threshold of 45 points. A multi-level threshold comparator compares the current health metric of 68.7 with each threshold, calculating a deviation of -6.3 points from the lower limit of the normal range (a deviation rate of 8.4%) and a distance of +8.7 points from the lower limit of the warning range, forming deviation degree data. Based on this data, a system state partition boundary is constructed, resulting in a three-interval division function that divides the health metric range into a normal operation zone (above 75 points), a deviation warning zone (60-75 points), and an abnormal control zone (below 60 points). Location analysis shows that the current health metric of 68.7 points is located in the upper-middle part of the deviation warning zone and has shown a downward trend for three consecutive days, confirming that the system is currently in the deviation warning zone. Based on the control response level of "monitoring focus" for this area, a control response level table is generated, containing two instructions: "increase monitoring frequency" and "prepare resource allocation plan". Finally, based on the mapping relationship between this level table and the current health data, a control trigger signal with priority level 2 is generated and transmitted to the identification module.

[0079] In one specific embodiment, the identification module is used to extract the current resource allocation from the project operation status data and compare it with the planned resource demand to obtain a resource load distribution map;

[0080] The type and severity of abnormal parameters are determined based on the control trigger signal, and an abnormal parameter feature set is obtained.

[0081] Cross-correlation calculations are performed on the abnormal parameter feature set and the resource load distribution map to obtain the resource anomaly correlation matrix;

[0082] Principal component analysis was performed on the resource anomaly correlation matrix to obtain a list of key influencing factors.

[0083] A resource conflict network model is constructed based on a list of key influencing factors, and a resource conflict topology is obtained.

[0084] Path analysis and node importance calculation are performed on the resource conflict topology to obtain a ranking table of control intervention points;

[0085] By combining the project schedule network, an impact path analysis is performed on the intervention points in the control intervention point ranking table to obtain the adjustment target scheme;

[0086] The adjustment target scheme is converted into a standardized control instruction format and execution parameters are added to obtain the resource control instruction set.

[0087] Specifically, the identification module extracts the current resource allocation from the project operation status data and compares it with the planned resource requirements. Resource allocation includes the actual allocated quantity and usage period of various resources (manpower, equipment, materials), while planned resource requirements are the resource requirements and timeframes for each task in the project plan. The identification module extracts these two types of data through data filtering and aggregation operations, and compares and calculates them according to resource type and time dimension to form a resource load distribution map. The resource load distribution map is a multi-dimensional data visualization representation; the horizontal axis is the time axis, the vertical axis is the resource type, and the color depth or height indicates the resource load level (the ratio of actual allocated quantity to planned requirement). This map intuitively shows the load status of various resources at different time periods, facilitating the rapid identification of resource over-allocation or idle areas. The identification module determines the type and severity of abnormal parameters based on the control trigger signals from the preceding comparison module. The control trigger signals contain information such as the trigger cause, abnormal area, and abnormality level. The identification module analyzes this information to determine the specific parameter type causing the abnormality (such as schedule delay, cost overrun, quality deviation, etc.) and the severity of the abnormality (minor, moderate, severe). By extracting and classifying the features of abnormal parameters, an abnormal parameter feature set is formed. This feature set contains multi-dimensional feature information such as the identifier, value, trend of change, and fluctuation range of the abnormal parameters.

[0088] The identification module performs cross-correlation calculations on the abnormal parameter feature set and the resource load distribution map to obtain the resource anomaly correlation matrix. The mathematical expression for the cross-correlation calculation is:

[0089]

[0090] Among them, RAM rc This represents the correlation between resource type r and anomaly parameter c, where T is the time window length, and α is the correlation between the two. t It is a time-weighted factor (usually more recent data has a higher weight), WD r (t) is the resource load deviation value of resource type r at time t, APS c (t) is the score of the degree of anomalousness of the anomalous parameter c at time t. Through this calculation, the identification module establishes a correlation matrix between resource load and anomalous parameters, where each element represents the correlation strength between a specific resource and a specific anomalous parameter.

[0091] The resource anomaly correlation matrix is ​​processed by the identification module using Principal Component Analysis (PCA). PCA is a dimensionality reduction technique that reduces data dimensionality while retaining key information by identifying the main directions of change in the data. The identification module first standardizes the correlation matrix, then calculates the covariance matrix, solves for eigenvalues ​​and eigenvectors, sorts the eigenvalues ​​by magnitude, selects the top few principal components whose cumulative contribution rate exceeds a threshold, and calculates the projection and factor loadings of each resource onto the principal components. Through this series of calculations, a list of key influencing factors is obtained, which includes the resource types that significantly affect the anomaly parameters and their influence weights.

[0092] Based on a list of key influencing factors, the identification module constructs a resource conflict network model. This model is a graph theory model used to represent conflicts and competition between resources. In this model, nodes represent resources or tasks, edges represent resource conflict relationships, and edge weights represent conflict intensity. The identification module extracts the usage and task dependencies of resources from the project operation status data according to the resource types in the list of key influencing factors, establishing a resource-task bipartite graph. By analyzing resource sharing and task timing relationships, resource conflict points are identified, and conflict intensity is calculated, forming resource conflict relationship edges. These nodes and edges together constitute the resource conflict network, which, after visualization, forms a resource conflict topology graph, intuitively displaying the conflict relationships and intensity distribution among resources.

[0093] The resource conflict network model specifically includes: resource nodes (representing various resources such as manpower, equipment, and materials), task nodes (representing work tasks that require resources), resource allocation edges (connecting resource nodes and task nodes, indicating resource allocation relationships), task dependency edges (connecting the sequential dependencies between tasks), and conflict edges (connecting tasks that overlap in time and share resources, indicating resource conflicts). This model, represented using graph theory, can display both the static resource allocation status and the dynamic resource competition relationships, providing a mathematical foundation for subsequent conflict analysis.

[0094] For the constructed resource conflict topology map, the identification module performs path analysis and node importance calculation. The mathematical expressions for path analysis and node importance calculation are:

[0095]

[0096] Among them, NIP v DC represents the importance index of node v. v It refers to the degree centrality (number of connecting edges) of node v, BC v It is the betweenness centrality of node v (the number of shortest paths through that node), CC v PC is the proximity centrality of node v (the reciprocal of the average distance to other nodes).v γ1 represents the page ranking value of node v (the node weight calculated iteratively), and γ2, γ3, and γ4 are the weight coefficients of each centrality indicator. Through the calculation of these network analysis indicators, the identification module assesses the importance of each node in the network, identifies key resource conflict points, and forms a ranking table of control intervention points. This ranking table is arranged in descending order of node importance and includes the identifier, type, importance index, and recommended intervention measures for each intervention point.

[0097] Next, the identification module combines the project schedule network to perform impact path analysis on the intervention points in the control intervention point ranking table. The project schedule network is a directed graph describing the logical relationships between project tasks. The identification module analyzes the potential impact of resource interventions on project schedule by integrating the resource conflict network with the project schedule network. For each intervention point in the ranking table, the identification module tracks its impact path in the schedule network, calculates the scope and degree of impact, and assesses the effectiveness and risks of the intervention. Through this correlation analysis, the identification module formulates a regulation target plan, which includes specific regulation objectives, direction, magnitude, and expected effects for each intervention point. The identification module converts the regulation target plan into a standardized control instruction format and adds execution parameters. This step transforms the analysis results into executable control commands. Based on a predefined control instruction template, the identification module extracts and maps key information from the regulation target plan to instruction fields, adding necessary execution parameters such as priority, execution time, and verification conditions. After format conversion and parameter supplementation, a standardized resource control instruction set is formed, which will be passed to the subsequent verification module for processing.

[0098] Taking a high-rise office building construction project as an example, the identification module extracted the actual allocation and planned demand data of three types of human resources (steel reinforcement workers, formwork workers, and concrete workers) and two types of equipment resources (tower cranes and concrete pump trucks) from the project operation status data. Through comparison and calculation, a resource load distribution map was generated. The map showed that the load rate of steel reinforcement workers reached 120% in weeks 8-10 of the project, indicating a significant overload. Simultaneously, a yellow warning level control trigger signal was received from the comparison module, indicating abnormal consumption of the project buffer zone and a moderate schedule deviation. By analyzing the signal, the abnormal parameter was identified as "schedule delay," with a severity of "moderate," forming an abnormal parameter feature set containing information such as parameter type, numerical characteristics, and time characteristics. Cross-correlation calculations were performed between this feature set and the resource load distribution map, resulting in a 5×3 resource anomaly correlation matrix. In the matrix, the correlation between steel reinforcement workers and schedule delay was the highest, reaching 0.85. Principal component analysis extracted two principal components with a cumulative contribution rate of 87%, yielding a list of key influencing factors. Among these, steel reinforcement worker resources ranked first, with a weight of 0.72. Based on this list, a resource conflict network model was constructed, generating a resource conflict topology graph containing 15 nodes and 28 edges. The steelworker node in the graph is connected to multiple high-weight conflict edges. Network analysis of this topology graph yielded a steelworker node importance index of 0.92, ranking first, followed by the tower crane node with an index of 0.78, forming a control intervention point ranking table. Combined with the project's engineering schedule network, analysis revealed that steelworker resource conflicts primarily affect the main structure construction phase, involving frame construction tasks on the critical path. Therefore, an adjustment target scheme of "increasing steelworker resource allocation" was generated. Finally, this scheme was converted into a standard format control instruction, including execution parameters such as "Resource Type: Manpower - Steelworker," "Adjustment Direction: Increase," "Adjustment Quantity: 5 people," "Execution Time: Starting from Week 8," and "Priority: High," forming a resource control instruction set.

[0099] In one specific embodiment, the verification module is used to convert the resource control instruction set into a blockchain smart contract data structure to obtain a control instruction smart contract;

[0100] The control instruction smart contract is digitally signed and encrypted to obtain the control transaction data packet;

[0101] The control transaction data packet is broadcast to the blockchain network nodes for transaction verification, and a control instruction verification request is obtained.

[0102] The legality of the control instruction verification request is checked and conditions are judged to obtain the smart contract execution result;

[0103] The execution results of smart contracts are subjected to permission screening and security checks to obtain an approved execution plan;

[0104] The approved execution plan is encapsulated into a blockchain confirmation block and added to the distributed ledger to obtain an immutable control record;

[0105] Based on the immutable control records, a control instruction call interface and execution parameter set are generated to obtain the control instruction transmission packet.

[0106] Control command packets are transmitted through a secure channel to the execution units of each field control node, thereby obtaining resource adjustment execution commands.

[0107] Specifically, the verification module converts the resource control instruction set into a blockchain smart contract data structure. The resource control instruction set is a series of structured control commands generated by the identification module, containing information such as resource type, adjustment direction, and adjustment quantity. The verification module deconstructs these instructions using an instruction parsing engine, extracting key fields and parameters, and then maps and converts them according to a predefined smart contract template. A smart contract is an automated script executed on the blockchain, characterized by conditional triggering and automatic execution. During the conversion process, the verification module converts the conditional part of the resource control instructions into the contract's trigger conditions, the action part into the contract's execution function, and the parameter part into the contract's state variables, ultimately forming a control instruction smart contract that conforms to the format requirements of a specific blockchain platform. The verification module then performs digital signature and encryption processing on the control instruction smart contract. The digital signature ensures data integrity and non-repudiation of origin, while encryption guarantees security during data transmission. The verification module first calculates the hash value of the smart contract data, encrypts the hash value using the system's private key, and generates a digital signature. Then, the original contract data is merged with the digital signature and encrypted using the recipient's public key to form an encrypted data packet. This encrypted data packet also contains metadata such as sending time, sequence number, and priority, collectively forming a complete control transaction data packet. The verification module broadcasts the control transaction data packet to blockchain network nodes for transaction verification. The broadcast uses a peer-to-peer communication protocol, sending the data packet in parallel to multiple blockchain network nodes. These nodes include the node servers of project participants such as construction units, contractors, and supervision units within the blockchain network. The receiving node performs preliminary verification of the transaction data packet according to the blockchain protocol, checking the data format, the validity of the digital signature, etc., and sends the verification result back to the sending node, forming a control command verification request. The verification request contains basic information about the transaction data packet and the preliminary verification result, providing a foundation for subsequent detailed verification.

[0108] For control command verification requests, the verification module performs legality checks and conditional judgments. Legality checks include permission checks, rule compliance checks, and business logic checks. Permission checks verify whether the initiator has sufficient permissions to execute the specified resource allocation operation; rule compliance checks verify whether the command conforms to preset business rules and constraints; and business logic checks verify whether the command is reasonable and feasible in the current project state. Conditional judgments assess whether the preconditions for command execution are met, such as resource availability and the reasonableness of the time window. Through these verifications and judgments, the verification module determines whether the smart contract can be executed, the specific execution method and parameters, and generates the smart contract execution result.

[0109] The verification module then performs permission screening and security checks on the smart contract execution results. Permission screening determines the visibility and operational permissions of different participants based on their roles and permission levels. Security checks assess potential risks from a system security perspective, checking for abnormal operation patterns, resource contention conflicts, or system stability issues. If risks are found, the verification module adjusts the execution plan accordingly, such as breaking down large-scale operations or adding security buffers. After permission screening and security checks, an approved execution plan is obtained, containing optimized resource allocation instructions and execution parameters. The verification module encapsulates the approved execution plan into a blockchain confirmation block and adds it to the distributed ledger. The encapsulation process includes serializing the execution plan data, adding block header information (timestamp, previous block hash, Merkle root, etc.), calculating Proof-of-Work (PoW), or obtaining Proof-of-Stake (PoS). Once the block is encapsulated, the verification module submits it to the blockchain network for consensus verification. When more than a preset threshold of nodes reach consensus, the block is officially added to the distributed ledger, forming an immutable control record. This blockchain-based recording mechanism ensures the transparency and traceability of control decisions, effectively preventing subsequent liability disputes.

[0110] Based on immutable control records, the verification module generates control command call interfaces and execution parameter sets. The call interface serves as a bridge between the blockchain layer and the actual execution layer, defining how to convert control records on the blockchain into commands executable by specific devices or personnel. The verification module generates API call interfaces in appropriate formats according to the characteristics of different execution units, and attaches necessary execution parameter sets, such as execution time, execution order, and verification feedback mechanisms. These interfaces and parameter information are packaged into standardized control command transmission packets for easy transmission and parsing between heterogeneous systems. The verification module distributes the control command transmission packets to the execution units of each field control node through a secure channel. The secure channel uses TLS / SSL encryption protocols to ensure the confidentiality and integrity of the command transmission process. Based on the target object and priority of the command, the verification module selects an appropriate transmission path and timing to send the control command transmission packets to the corresponding field control nodes. These nodes can be mobile terminals, intelligent device controllers, or resource management systems on the construction site. After receiving the command, the field control node parses the command content according to its own characteristics, converts it into specific locally executable operation commands, and forms the final resource adjustment execution command for execution by actual personnel or equipment.

[0111] Taking a large residential construction project as an example, the verification module receives a set of resource control instructions from the identification module, including the instruction to "adjust the number of steelworkers," specifying that the number of steelworkers in weeks 10-15 be increased from the originally planned 15 to 20. The verification module converts this instruction into a smart contract format, defining the triggering condition (starting from week 10), the execution content (adding 5 steelworkers), and the verification mechanism (comparing to the actual configuration). The converted smart contract is hashed using a SHA-256 hash value and signed using the project management's RSA private key, then encrypted to form a control transaction data packet. This data packet is broadcast to the five core nodes of the project's blockchain network (construction unit, general contractor, subcontractor, supervision unit, and material supplier) for preliminary verification. After successful verification, a control instruction verification request is generated. The verification module checks and finds that the instruction complies with the project's resource allocation permission rules, and that there are sufficient steelworkers available for allocation in the current resource pool. The condition is deemed met, and the smart contract execution result is generated. After access control screening, it was determined that the general contractor and subcontractors had the right to view the complete execution plan, while other parties could only view summary information. Safety checks revealed that adding steelworkers would lead to dormitory shortages, so a temporary dormitory allocation instruction was added to the execution plan. The approved execution plan was encapsulated into a block, timestamped, and included the hash value of the previous block. It was then confirmed by over 80% of the nodes using the PBFT consensus algorithm and officially added to the project's blockchain ledger. Based on this immutable record, the verification module generated a REST API call interface and a JSON-formatted set of execution parameters, containing a detailed personnel allocation schedule and acceptance criteria. Finally, control instruction packets were transmitted via a TLS 1.3 encrypted channel to the general contractor's project management system and the subcontractors' on-site management terminals, forming clear resource adjustment execution instructions to guide on-site personnel in supplementing the steelworkers.

[0112] In one specific embodiment, the control module is used to extract control parameters and execution conditions from the resource adjustment execution instruction to obtain a construction production factor regulation scheme;

[0113] The construction team allocation instructions in the construction production factor control plan are decomposed and optimized to obtain a human resource allocation table.

[0114] Based on the human resource allocation table, the engineering equipment usage plan is coordinated and conflict is eliminated to obtain the equipment usage time scheduling table.

[0115] Based on the equipment usage time schedule, the material supply chain plan and logistics sequence are rearranged to obtain the material supply coordination plan;

[0116] The human resource allocation table, equipment usage time scheduling table, and material supply coordination plan are integrated into an execution instruction set to obtain a field execution data package;

[0117] The on-site execution results and adjustment process are recorded in a distributed ledger through a blockchain storage interface to obtain an engineering control parameter table.

[0118] Specifically, the control module extracts control parameters and execution conditions from resource adjustment execution instructions. These instructions are certified operation commands issued by the verification module, containing detailed information such as control type, control object, control parameters, and execution conditions. The control module performs syntax and semantic analysis on the received instructions using an instruction parser, extracting key control parameters such as resource type, adjustment direction, adjustment quantity, and time window. Simultaneously, the control module parses execution conditions such as the completion status of prerequisite tasks, resource availability conditions, and weather conditions. Through structured processing of these parameters and conditions, the control module generates a construction production element control plan containing specific control content and implementation constraints. This plan transforms abstract control instructions into concrete on-site operation guidelines, forming the basis for subsequent refined resource allocation. The control module decomposes and optimizes the construction team configuration instructions within the construction production element control plan. A construction team refers to a group of personnel engaged in building construction activities, categorized by specialty as civil engineering teams, rebar teams, formwork teams, concrete teams, etc. The control module first decomposes the team configuration instructions into multiple sub-instructions according to job type, determining the personnel adjustment needs for each job type. Then, based on the actual conditions of the engineering site, such as the distribution of work areas, differences in work content, and skill requirements, the job-level instructions are further decomposed into team-level or individual-level instructions. During the optimization phase, a multi-objective optimization algorithm is used to comprehensively consider factors such as personnel skill matching, work continuity, and efficiency of cross-operations, adjusting and optimizing the decomposed instructions to reduce unnecessary personnel movement and skill waste. The resulting human resource allocation table contains a detailed personnel allocation plan, clearly indicating the work arrangements, work locations, and work content for each team or individual within a specific time period.

[0119] Based on the human resource allocation table, the control module coordinates and resolves conflicts in the engineering equipment usage plan. Engineering equipment includes construction machinery such as tower cranes, excavators, and concrete pump trucks. These are often scarce resources and need to be shared across multiple construction tasks. The control module first derives an equipment demand list from the human resource allocation table, identifying the type, quantity, and time requirements of equipment for each construction activity. Then, it matches these demands with the existing equipment resource library to identify potential equipment usage conflicts. Conflict detection uses time overlap analysis to calculate the overlap of equipment demands across different tasks on the time axis. For detected conflicts, the control module applies a conflict resolution algorithm, resolving conflicts through strategies such as task time fine-tuning, equipment substitution, and equipment rotation, resulting in a conflict-free equipment usage time schedule. This schedule details the usage time, location, and operators for each piece of equipment, ensuring efficient utilization of equipment resources. The control module then reschedules the material supply chain plan and logistics sequence based on the equipment usage time schedule. Building materials such as steel bars, concrete, and blocks need to be delivered to the construction site at appropriate times to ensure the smooth progress of construction activities. The control module analyzes the equipment usage schedule to deduce the material demand time points for each construction stage. Then, combining the material procurement cycle, transportation time, and on-site storage conditions, it calculates the order placement time and expected delivery time for materials. To optimize logistics costs and storage space, the control module adopts a Just-In-Time (JIT) production approach, minimizing material storage time on-site while ensuring that material shortages do not impact construction progress. The resulting material supply coordination plan includes detailed material procurement, transportation, and unloading schedules, working in synergy with human resources and equipment usage plans.

[0120] The control module then integrates the human resource allocation table, equipment usage time scheduling table, and material supply coordination plan into an execution instruction set. During the integration process, the control module performs time alignment and spatial matching of the three types of plans to ensure that manpower, equipment, and materials can coordinate and cooperate at the same time and place. Simultaneously, the control module adds execution sequence control logic, defining the priority and dependencies of instructions to form a structured execution flow. The integrated execution instruction set is packaged into a standard format data package, containing metadata (such as creation time, version number, and responsible person) and specific instruction data, forming a field execution data package. This data package provides comprehensive execution guidance from macro to micro levels, facilitating understanding and implementation by field management personnel. The control module records the field execution results and adjustment process in a distributed ledger through a blockchain storage interface. During the implementation of the execution instruction set, field personnel or smart devices record the actual execution status, including completion status, deviations, and abnormal events. The control module collects this execution result data and compares it with the original plan, generating an execution deviation report and an adjustment effect evaluation. This data, along with key decisions and status changes during the execution process, is written into the distributed ledger through the blockchain storage interface. The blockchain storage interface employs standardized data formats and communication protocols to ensure that data is securely and immutably recorded in the blockchain network. After recording, the control module integrates key control parameters and status data to form an engineering control parameter table, serving as a snapshot of the project's status and a reference benchmark for subsequent monitoring.

[0121] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An engineering management and monitoring system, characterized in that, The project management and monitoring system includes: a data acquisition module, which is used to collect the task execution status, resource utilization parameters and buffer consumption indicators of the construction project in real time and store them in encrypted form via blockchain to obtain project operation status data; The analysis module is used to perform critical chain parameter calculation and schedule deviation analysis based on the project operation status data to obtain project health metrics. The comparison module is used to compare the engineering health measurement value with the preset control threshold, divide the system status into normal operation zone, deviation warning zone and abnormal control zone, and obtain control trigger signal; The identification module is used to perform correlation analysis between the abnormal parameters indicated by the control trigger signal and the resource control variables, identify the control intervention point and adjustment target, and obtain the resource control instruction set. The verification module is used to verify the resource control instruction set through a blockchain smart contract and distribute it to the field control node to obtain resource adjustment execution instructions; The control module is used to perform closed-loop regulation and control of construction production elements according to the resource regulation execution instructions, and record the regulation results in the blockchain to obtain the engineering control parameter table; The analysis module is used to extract the difference between the planned time and the actual time of each task node on the critical chain from the project operation status data to obtain critical chain progress deviation data; to perform cumulative calculation and standardization on the critical chain progress deviation data to obtain a critical chain completion rate index; to extract the original value and current value of the input buffer of non-critical tasks from the project operation status data to obtain buffer consumption records; to perform weighted average calculation and proportional conversion on the buffer consumption records to obtain a buffer consumption percentage index; to construct a two-dimensional state space matrix based on the critical chain completion rate index and the buffer consumption percentage index to obtain a project state vector; to normalize the project state vector using a fuzzy comprehensive evaluation method to obtain a preliminary health score; to perform correction calculation and parameter adjustment on the preliminary health score in combination with project type characteristics to obtain a standardized score value; and to compare and analyze the standardized score value with the historical project state database and perform position mapping to obtain the project health metric value. The comparison module is used to perform statistical process control analysis on the engineering health metric values ​​to obtain a health distribution curve; extract a set of control threshold parameters matching the current engineering type from the control parameter library to obtain the preset control threshold; perform numerical comparison calculations between the engineering health metric values ​​and the preset control thresholds using a multi-level threshold comparator to obtain deviation data; construct system state partition boundaries based on the deviation data to obtain a three-interval division function; input the health distribution curve into the three-interval division function for region division to obtain the normal operation zone, the deviation warning zone, and the abnormal control zone; perform location analysis on the position of the engineering health metric values ​​in the three intervals to obtain the current control area of ​​the system; determine the corresponding control response level according to the current control area of ​​the system to obtain a control response level table; and generate an activation signal through the mapping relationship between the control response level table and the current health data to obtain the control trigger signal. The identification module is used to extract the current resource allocation from the project operation status data and compare it with the planned resource demand to obtain a resource load distribution map; determine the type and severity of abnormal parameters based on the control trigger signal to obtain an abnormal parameter feature set; perform cross-correlation calculation on the abnormal parameter feature set and the resource load distribution map to obtain a resource anomaly correlation matrix; perform principal component analysis on the resource anomaly correlation matrix to obtain a list of key influencing factors; construct a resource conflict network model based on the list of key influencing factors to obtain a resource conflict topology map; perform path analysis and node importance calculation on the resource conflict topology map to obtain a control intervention point ranking table; perform influence path analysis on the intervention points in the control intervention point ranking table in conjunction with the project progress network to obtain an adjustment target scheme; and convert the adjustment target scheme into a standardized control instruction format and attach execution parameters to obtain the resource control instruction set.

2. The engineering management and monitoring system according to claim 1, characterized in that, The acquisition module is used to collect the actual operation status of each work surface at the construction site through a distributed sensor network to obtain task execution status data; Based on real-time monitoring of the input and output efficiency ratio of various resources using IoT devices, resource utilization parameter data can be obtained. The time deviation between the critical chain plan and the actual progress is quantitatively calculated and tracked to obtain buffer consumption index data. The task execution status data, the resource utilization parameter data, and the buffer consumption index data are standardized to obtain a unified data packet. The unified data packet is digitally signed and encrypted to obtain blockchain transaction data; The blockchain transaction data is submitted to the distributed ledger for verification and confirmation through a consensus mechanism, resulting in a confirmed data block. The confirmed data blocks are added to the blockchain system and synchronized updates between nodes to obtain immutable data records. The engineering execution parameters are extracted from the tamper-proof data records through the data interface to obtain the engineering operation status data.

3. The engineering management and monitoring system according to claim 1, characterized in that, The verification module is used to convert the resource control instruction set into a blockchain smart contract data structure to obtain a control instruction smart contract. The control instruction smart contract is digitally signed and encrypted to obtain a control transaction data packet; The control transaction data packet is broadcast to blockchain network nodes for transaction verification, thereby obtaining a control instruction verification request; The legality of the control instruction verification request is checked and conditions are judged to obtain the smart contract execution result; The execution results of the smart contract are subjected to permission filtering and security checks to obtain an approved execution plan; The approved execution plan is encapsulated into a blockchain confirmation block and added to the distributed ledger to obtain an immutable control record; Based on the immutable control record, a control instruction call interface and an execution parameter set are generated to obtain a control instruction transmission packet; The control command transmission packet is sent to the execution unit of each field control node through a secure channel to obtain the resource adjustment execution command.

4. The engineering management and monitoring system according to claim 1, characterized in that, The control module is used to extract control parameters and execution conditions from the resource adjustment execution command to obtain a construction production factor regulation scheme; The construction team allocation instructions in the construction production factor control scheme are decomposed and optimized to obtain a human resource allocation table. Based on the aforementioned human resource allocation table, the engineering equipment usage plan is coordinated and conflict-resolved to obtain an equipment usage time scheduling table. Based on the equipment usage time scheduling table, the material supply chain plan and logistics sequence are rearranged to obtain the material supply coordination plan; The human resource allocation table, the equipment usage time scheduling table, and the material supply coordination plan are integrated into an execution instruction set to obtain a field execution data package; The on-site execution results and adjustment process are recorded in a distributed ledger through a blockchain storage interface to obtain the engineering control parameter table.

Images