An information technology-based multi-dimensional monitoring system and method for project progress

Abstract

This invention discloses a multi-dimensional monitoring system and method for engineering project progress based on information technology, applicable to the monitoring field. The system includes: an initial construction plan construction module, a data acquisition and environmental monitoring module, a data analysis and plan optimization module, a construction progress prediction module, and a risk management module. By collecting multi-dimensional data from the construction site in real time and applying an intuitionistic fuzzy inference algorithm, this invention can dynamically adjust the construction plan to cope with constantly changing site conditions and external environmental factors. Utilizing the intuitionistic fuzzy inference algorithm to analyze the needs and bottlenecks of the construction project, combined with construction logic and physical constraints, allows for more precise resource allocation. A fuzzy logic controller handles fuzzy and uncertain situations, ensuring the project complies with all relevant building and safety standards. Through continuous monitoring and timely adjustments, project quality and safety are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of monitoring, and more particularly to a multi-dimensional monitoring system and method for engineering project progress based on information technology. Background Technology

[0002] Engineering projects involve complex design, planning, construction, and management activities, with the primary objective of achieving specific building or infrastructure goals and meeting requirements for functionality, safety, efficiency, and economy. Project monitoring, on the other hand, aims to ensure the smooth progress and successful completion of the project.

[0003] Project monitoring is a key component of project management, involving the continuous tracking and control of project schedule, cost, quality, scope, risks, and resources throughout the project implementation process. The purpose of project monitoring is to ensure that the project proceeds smoothly according to its predetermined goals and plans, while addressing problems and risks that arise during implementation to guarantee successful project completion.

[0004] Traditional construction planning and risk management methods are often static and inflexible, making it difficult to respond promptly to rapid changes in site conditions and uncertainties in the external environment. This leads to delays in construction schedules and inefficient resource allocation, increasing project costs and wasting time. In traditional methods, risk assessment is usually based on experience and intuition, lacking precise data support. The uncertainty and subjectivity of this approach may lead to misjudgments of risks, making it impossible to effectively predict and manage potential risk events. In traditional construction management, responses to emergencies (such as weather changes and equipment failures) are often delayed due to a lack of rapid data processing and real-time risk assessment capabilities. This delayed emergency response may lead to safety risks and further project delays.

[0005] No effective solutions have yet been proposed to address the problems in the relevant technologies. Summary of the Invention

[0006] To overcome the above problems, this invention aims to propose a multi-dimensional monitoring system and method for engineering project progress based on information technology. The purpose is to solve the problem that in traditional construction management, the response to emergencies (such as weather changes, equipment failures, etc.) is often delayed due to the lack of rapid data processing and real-time risk assessment capabilities. This delayed emergency response may lead to safety risks and further project delays.

[0007] Therefore, the specific technical solution adopted by the present invention is as follows:

[0008] According to one aspect of the present invention, an information technology-based multi-dimensional monitoring system for engineering project progress is provided. The multi-dimensional monitoring system for engineering project progress includes: an initial construction plan construction module, a data acquisition and environmental monitoring module, a data analysis and plan optimization module, a construction progress prediction module, and a risk management module.

[0009] The initial construction plan building module is used to develop an initial construction plan based on the design and planning of the engineering project;

[0010] The data acquisition and environmental monitoring module is used to collect multi-dimensional data on the construction progress at the construction site in real time and to monitor environmental factors simultaneously.

[0011] The data analysis and planning optimization module is used to analyze multidimensional data of construction progress using intuitionistic fuzzy reasoning algorithms, generate a construction project ranking table by combining construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time.

[0012] The construction progress prediction module is used to predict the construction progress based on the optimized construction plan and real-time environmental factors, while monitoring the construction efficiency of the project.

[0013] The risk management module is used to assess construction risks during the project construction process and dynamically adjust and optimize the construction plan based on the predicted construction progress.

[0014] The data analysis and planning optimization module utilizes intuitionistic fuzzy reasoning algorithms to analyze multidimensional data on construction progress, combines construction logic and physical constraints to generate a construction project ranking table, and optimizes the initial construction plan based on the ranking table and expected completion time.

[0015] Collect multidimensional data on construction progress, identify the actual needs of the construction project, and clarify logical relationships and physical constraints based on the actual needs;

[0016] The key input variables of the intuitionistic fuzzy reasoning algorithm are set based on logical relationships and physical constraints. The key input variables include the maximum fitness change rate and the average fitness change rate.

[0017] An intuitionistic fuzzy reasoning model is established, and the crossover rate and mutation rate of the intuitionistic fuzzy reasoning algorithm are adjusted based on the maximum fitness change rate and the average fitness change rate.

[0018] Based on the parameters of the adjusted intuitionistic fuzzy reasoning model, adaptive crossover and mutation operations are performed to iteratively update the population until the algorithm converges to the optimal construction project sorting scheme.

[0019] A construction project sequencing table is generated based on the optimal construction project sequencing scheme, and the initial construction plan is optimized by combining the construction project sequencing table with the expected completion time.

[0020] Optionally, an intuitionistic fuzzy inference model is established, and the crossover rate and mutation rate of the intuitionistic fuzzy inference algorithm are adjusted based on the maximum fitness change rate and the average fitness change rate, including:

[0021] The maximum fitness change rate and the average fitness change rate are used as input variables for building the intuitionistic fuzzy reasoning model;

[0022] Set linguistic values ​​for the input variables and assign appropriate membership functions to each linguistic value to convert the actual data into membership degrees in a fuzzy set;

[0023] Fuzzy inference rules are defined based on the combination of linguistic values ​​of input variables;

[0024] Using fuzzy logic reasoning mechanism, the adjustment values ​​of crossover rate and mutation rate are calculated based on membership degree and fuzzy reasoning rules;

[0025] By using the defuzzification method, the calculation results using the fuzzy logic reasoning mechanism are converted into adjusted values ​​for specific crossover and mutation rates, thus obtaining the adjusted crossover and mutation rates.

[0026] Optionally, using fuzzy logic reasoning mechanisms, the adjusted values ​​for crossover rate and mutation rate are calculated based on membership degree and fuzzy reasoning rules, including:

[0027] Calculate the membership degree of the maximum fitness change rate and the average fitness change rate, and determine the degree of membership of the corresponding language value based on the membership degree;

[0028] Fuzzy inference rules based on the degree of membership of linguistic values;

[0029] By utilizing the influence of various fuzzy inference rules in fuzzy logic operations, a comprehensive fuzzy output is formed;

[0030] Based on the comprehensive fuzzy output, the recommended adjustment values ​​for crossover rate and mutation rate for each fuzzy inference rule are generated using the fuzzy logic inference mechanism.

[0031] All recommended adjustment values ​​are aggregated to form a single fuzzy output value;

[0032] The single fuzzy output value is converted into a specific numerical value through defuzzification, resulting in the final adjusted values ​​for crossover rate and mutation rate.

[0033] Optionally, the expression for calculating the membership degree of the maximum fitness change rate and the average fitness change rate is:

[0034] ;

[0035] In the formula, e This represents the input variables, including the maximum rate of change in fitness and the average rate of change in fitness.

[0036] a Indicates the position where the membership degree starts to increase from 0;

[0037] b This indicates the position where the membership degree reaches its highest value of 1, representing the input value. e It conforms to a predefined language category at a specific point;

[0038] c This indicates the position where the membership degree drops to 0;

[0039] Indicates input variables e The membership degree of a fuzzy set.

[0040] Optionally, based on the comprehensive fuzzy output, the recommended adjustment values ​​for the crossover rate and mutation rate for each fuzzy inference rule are generated using a fuzzy logic inference mechanism, including:

[0041] Based on the established fuzzy logic reasoning mechanism, match the fuzzy reasoning rules;

[0042] By utilizing the influence of fuzzy logic operators on the fuzzy inference rules of the comprehensive matching, a comprehensive fuzzy logic output is formed;

[0043] For each fuzzy rule, calculate and output the membership degree based on the result of the fuzzy logic operation;

[0044] Based on membership degree, specific adjustment suggestions for crossover rate and mutation rate are generated according to the results of fuzzy logic operation using fuzzy logic reasoning mechanism.

[0045] The recommended adjustment values ​​generated by all fuzzy inference rules are aggregated.

[0046] Optionally, based on the parameters of the adjusted intuitionistic fuzzy inference model, adaptive crossover and mutation operations are performed to iteratively update the population until the algorithm converges to the optimal construction project ranking scheme, including:

[0047] Set the initial population size, baseline values ​​for crossover rate and mutation rate, and the maximum number of iterations;

[0048] The fitness of individuals in the initial population is assessed according to the preset evaluation criteria.

[0049] Using an intuitionistic fuzzy reasoning model, the adjusted values ​​for crossover rate and mutation rate are calculated based on the fitness data of the current population;

[0050] Perform crossover and mutation operations based on the adjusted values ​​of the calculated crossover and mutation rates;

[0051] Based on the fitness of individuals in the population, select individuals suitable for forming the next generation of the population;

[0052] After each iteration, check whether the maximum number of iterations has been reached or whether the algorithm has converged to the optimal solution;

[0053] If the preset convergence condition is met, the iteration ends and the current optimal construction project ranking scheme is output; if not, the iteration continues and the fitness of individuals in the population is re-evaluated.

[0054] Optionally, the construction progress prediction module, based on the optimized construction plan and real-time environmental factors, predicts the construction progress while monitoring the construction efficiency of the project, including:

[0055] Real-time collection of current environmental data and actual conditions at the construction site;

[0056] By analyzing the collected historical and real-time data, key variables affecting the construction progress can be identified.

[0057] Construct a construction progress prediction model based on key variables;

[0058] The optimized construction plan and real-time environmental factors are input into the construction progress prediction model to generate a construction progress prediction within a preset time period in the future.

[0059] Real-time monitoring of actual progress and efficiency during construction, detection of deviations between actual construction progress and predicted values, identification and analysis of the causes of deviations;

[0060] Based on the prediction results and the identification results of the deviation analysis, the risks affecting the construction schedule are assessed.

[0061] Optionally, the risk management module, when assessing construction risks during the project's construction process and dynamically adjusting the optimized construction plan based on the predicted construction schedule, includes:

[0062] Collect comprehensive data on the construction project and format the comprehensive data of the construction project into a quantum computing input format;

[0063] The identified risk factors are converted into quantum bit representations;

[0064] The Hamming distance between the current construction status and historical risk event samples is calculated using quantum algorithms to identify the historical risk situation most similar to the current engineering conditions.

[0065] Based on the identification results, find the historical risk events with the minimum Hamming distance;

[0066] Based on the nearest neighbor risk events found, assess the current risk level of construction, analyze the impact of the risk level on construction progress and safety, and dynamically adjust the construction plan.

[0067] Simultaneously monitor construction activities and environmental changes based on the adjusted construction plan.

[0068] Optionally, a quantum algorithm can be used to calculate the Hamming distance between the current construction status and historical risk event samples to identify historical risk situations most similar to the current engineering conditions, including:

[0069] The states of qubits are used to represent various feature values ​​in construction status and historical risk data;

[0070] Quantum states are prepared for each current construction status and historical risk event sample;

[0071] Calculate the Hamming distance between the current state and each historical event using quantum circuits;

[0072] Based on the output of the quantum circuit, the specific value of the Hamming distance is obtained, the measurement results are analyzed, and the historical risk events with the smallest Hamming distance to the current state are identified.

[0073] According to another aspect of the present invention, a method for multi-dimensional monitoring of engineering project progress based on information technology is also provided, the method comprising the following steps:

[0074] S1. Develop an initial construction plan based on the design and planning of the project;

[0075] S2. Collect multi-dimensional data on construction progress at the construction site in real time and monitor environmental factors simultaneously;

[0076] S3. Analyze the multidimensional data of construction progress using the intuitionistic fuzzy reasoning algorithm, generate a construction project ranking table by combining construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time.

[0077] S4. Based on the optimized construction plan and real-time environmental factors, predict the construction progress and monitor the construction efficiency of the project.

[0078] S5. Assess the construction risks during the project's construction process and dynamically adjust and optimize the construction plan based on the predicted construction progress.

[0079] Compared with the prior art, this application has the following beneficial effects:

[0080] 1. This invention dynamically adjusts construction plans by collecting multidimensional data from the construction site in real time and applying an intuitionistic fuzzy reasoning algorithm to cope with constantly changing site conditions and external environmental factors. By analyzing the needs and bottlenecks of the construction project using the intuitionistic fuzzy reasoning algorithm, and combining construction logic and physical constraints, resources can be allocated more accurately. A fuzzy logic controller handles fuzzy and uncertain situations, and combined with expert experience and historical data, it provides scientific decision support. By continuously monitoring and analyzing key variables of the construction progress, it predicts and addresses potential risks and problems. Optimizing the construction plan using intuitionistic fuzzy reasoning ensures that the project complies with all relevant building and safety standards. Through continuous monitoring and timely adjustments, it avoids the risk of violating regulations and standards, guaranteeing project quality and safety.

[0081] 2. This invention collects environmental data and actual construction site conditions in real time and analyzes them in conjunction with historical data. The construction progress prediction module can accurately identify key variables affecting construction progress, which can significantly improve the accuracy of construction progress prediction. Through the progress forecast generated by the prediction model, the management team can allocate resources, such as manpower, materials and equipment, more rationally to match actual construction needs. By monitoring the deviation between the predicted and actual values ​​in real time, the project team can quickly identify risks and take corresponding risk mitigation measures. The integration and analysis of real-time data provides strong decision support for project management. Through continuous monitoring and analysis of environmental variables, the construction progress prediction module can predict and respond to construction delays caused by unforeseen events such as weather changes and equipment failures.

[0082] 3. This invention uses quantum algorithms to calculate Hamming distance, enabling precise comparison of the similarity between the current construction status and historical risk events, thus allowing for more accurate risk assessment. Based on the identified risks, dynamic adjustments to the construction plan can promptly address potential risks such as environmental changes and equipment failures, reducing construction delays and cost overruns. Utilizing quantum technology to update the risk database and construction plan in real time enables real-time and continuous risk management. Through accurate risk assessment, the project team can allocate resources more rationally and prepare contingency response strategies. The integrated quantum computing and risk management system provides strong decision support for project management, making the decision-making process more scientific and data-driven, reducing uncertain decisions based on experience or intuition. Attached Figure Description

[0083] The above-mentioned features, characteristics, and advantages of the present invention, as well as their implementation methods, will become clearer and more readily understood in conjunction with the following description of the embodiments, which are illustrated in detail with reference to the accompanying drawings. Schematic diagrams are shown here:

[0084] Figure 1 This is a schematic diagram of a multi-dimensional monitoring system for engineering project progress based on information technology, according to an embodiment of the present invention.

[0085] Figure 2 This is a flowchart of a multi-dimensional monitoring method for engineering project progress based on information technology, according to an embodiment of the present invention.

[0086] In the picture:

[0087] 1. Initial construction plan construction module; 2. Data acquisition and environmental monitoring module; 3. Data analysis and plan optimization module; 4. Construction progress prediction module; 5. Risk management module. Detailed Implementation

[0088] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0089] According to embodiments of the present invention, a multi-dimensional monitoring system and method for engineering project progress based on information technology is provided.

[0090] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments, such as... Figure 1 As shown, according to an embodiment of the present invention, a multi-dimensional monitoring system for engineering project progress based on information technology is provided. The multi-dimensional monitoring system for engineering project progress includes: an initial construction plan construction module 1, a data acquisition and environmental monitoring module 2, a data analysis and plan optimization module 3, a construction progress prediction module 4, and a risk management module 5.

[0091] Initial Construction Plan Module 1 is used to develop an initial construction plan based on the design and planning of the project.

[0092] It's important to clarify that the process involves collecting all project-related documents, including design drawings, planning documents, and client requirements; holding meetings with the project management team, designers, and clients to gain a detailed understanding of the project's goals and expectations; and conducting requirements analysis, which is crucial for project success and involves communication and document analysis skills. Based on the requirements analysis, project management software such as Microsoft Project or Primavera is used to create a timeline, define key work packages and milestones, develop an initial resource allocation plan, including estimates of manpower, materials, and equipment needs, present the initial plan to the project team, clients, and other key stakeholders, collect feedback, especially regarding schedules, resource allocation, and budgets, adjust the plan based on the feedback received, resolve any potential conflicts or problems, release the final approved construction plan, and monitor any deviations during implementation.

[0093] The data acquisition and environmental monitoring module 2 is used to collect multi-dimensional data on the construction progress at the construction site in real time and to monitor environmental factors simultaneously.

[0094] It is important to explain that selecting suitable sensors and data acquisition equipment, such as temperature and humidity sensors, and GPS systems for monitoring worker locations and equipment status, is crucial. This includes installing GPS systems on the construction site and ensuring they cover all critical areas and environmental parameters. A thorough understanding of the technical characteristics and applicable environments of various sensors is essential for selecting the most suitable equipment. Centralized monitoring is necessary to receive and process data from on-site sensors in real time. Data synchronization technology must be configured to ensure all data is updated in real time and accessible to the project management team. Environmental impact assessments should be conducted using collected environmental data (such as weather conditions, temperature, and humidity). Data analysis should identify environmental risks that may affect construction progress or safety. Environmental science and construction safety knowledge should be used to assess the specific impact of environmental data on construction activities. Real-time data should be compared with historical data to analyze trends in construction progress and environmental factors. Based on the data analysis results, recommendations for adjusting construction plans and resource allocation should be proposed.

[0095] The data analysis and planning optimization module 3 is used to analyze multidimensional data of construction progress using an intuitionistic fuzzy reasoning algorithm, generate a construction project ranking table by combining construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time.

[0096] The data analysis and planning optimization module 3, in particular, utilizes an intuitionistic fuzzy reasoning algorithm to analyze multidimensional data on construction progress, combines construction logic and physical constraints to generate a construction project ranking table, and optimizes the initial construction plan based on the ranking table and expected completion time.

[0097] Collect multidimensional data on construction progress, identify the actual needs of the construction project, and clarify logical relationships and physical constraints based on the actual needs.

[0098] The key input variables of the intuitionistic fuzzy reasoning algorithm are set based on logical relationships and physical constraints. The key input variables include the maximum fitness change rate and the average fitness change rate.

[0099] It needs to be explained that multiple sensors and data acquisition devices are deployed at the construction site to monitor and record multi-dimensional data related to construction progress in real time, such as working hours, material usage, and equipment efficiency. A data acquisition system automatically collects data and transmits it to a central database in real time via a wireless network. The collected data is analyzed to identify key needs and bottlenecks in the construction project, such as resource shortages and delayed nodes. Needs analysis meetings are held to discuss the data analysis results with the project management team and the construction team, jointly determining improvement priorities and possible adjustment strategies. Based on project design and construction specifications, the logical relationships between various tasks and activities in the project are clarified, such as their sequence and parallel relationships. Considering actual physical constraints, such as space limitations of the construction site and equipment capabilities, the specific tasks and activities are determined. The scope of the task and potential conflicts, as well as the logical relationships and physical constraints in the project planning, are crucial to ensuring smooth construction. This involves knowledge from the fields of engineering management and operational research. Based on the established logical relationships and physical constraints, key input variables in the intuitionistic fuzzy inference algorithm are set, such as the maximum fitness change rate and the average fitness change rate. These variables help evaluate the adaptability of different construction plans. A fuzzy logic controller is used to handle these variables, modeling and reasoning about uncertainties and fuzzy conditions. Intuitionistic fuzzy inference combines the advantages of fuzzy logic and intuitive judgment, making it suitable for handling uncertainties and complexities in the decision-making process. When setting the algorithm's input variables, it is necessary to consider how to optimize the construction plan through changes in these variables and improve its adaptability to changes in real-world conditions.

[0100] An intuitionistic fuzzy inference model is established, and the crossover rate and mutation rate of the intuitionistic fuzzy inference algorithm are adjusted based on the maximum fitness change rate and the average fitness change rate.

[0101] Preferably, establishing an intuitionistic fuzzy inference model and adjusting the crossover and mutation rates of the intuitionistic fuzzy inference algorithm based on the maximum fitness change rate and the average fitness change rate includes:

[0102] The maximum fitness change rate and the average fitness change rate are used as input variables for building the intuitionistic fuzzy reasoning model;

[0103] Set linguistic values ​​for the input variables and assign appropriate membership functions to each linguistic value to convert the actual data into membership degrees in a fuzzy set;

[0104] Fuzzy inference rules are defined based on the combination of linguistic values ​​of input variables;

[0105] Using fuzzy logic reasoning mechanism, the adjustment values ​​of crossover rate and mutation rate are calculated based on membership degree and fuzzy reasoning rules;

[0106] By using the defuzzification method, the calculation results using the fuzzy logic reasoning mechanism are converted into adjusted values ​​for specific crossover and mutation rates, thus obtaining the adjusted crossover and mutation rates.

[0107] It should be explained that, firstly, the maximum fitness change rate and the average fitness change rate are determined as input variables. These two indicators reflect the dynamic characteristics of construction progress and changes in conditions. Relevant data, such as historical records of past construction projects, are collected to estimate the actual values ​​of these variables.

[0108] The maximum fitness change rate usually refers to the maximum change in fitness (such as work efficiency or progress completion rate) within a single period, while the average fitness change rate represents the average level of fitness change over a period of time. Linguistic values, such as high, medium, and low, are defined for the maximum fitness change rate and the average fitness change rate. Membership functions are designed to map these linguistic values ​​to fuzzy sets, such as using triangular or trapezoidal membership functions. Membership functions are used in fuzzy logic to quantify the applicability of linguistic values ​​to a specific value.

[0109] Based on project requirements and historical data analysis, this paper defines how to adjust the crossover rate and mutation rate according to the combination of the maximum fitness change rate and the average fitness change rate, and compiles fuzzy rules. For example, if the maximum fitness change rate is high and the average fitness change rate is low, the crossover rate is increased. The fuzzy rules predict the optimal response under different conditions based on expert experience and historical data. A fuzzy inference mechanism is applied, combining membership degree and predefined fuzzy rules, to calculate the adjustment values ​​of the crossover rate and mutation rate. A fuzzy logic controller is used to integrate the inputs and derive the output through fuzzy operations. The fuzzy logic controller processes information through three processes: fuzzification of inputs, rule evaluation, and fuzzy inference. The output of the fuzzy logic inference is converted into precise values ​​through defuzzification methods (such as the centroid method and the maximum membership degree method). The obtained specific crossover rate and mutation rate values ​​are used to adjust the parameters of the intuitionistic fuzzy inference algorithm. Defuzzification is the process of converting fuzzy outputs into explicit values ​​in the fuzzy logic system. The key is to ensure the practicality and accuracy of the conversion results.

[0110] Preferably, the adjustment values ​​for crossover rate and mutation rate are calculated based on membership degree and fuzzy inference rules using a fuzzy logic reasoning mechanism, including:

[0111] Calculate the membership degree of the maximum fitness change rate and the average fitness change rate, and determine the degree of membership of the corresponding language value based on the membership degree;

[0112] Fuzzy inference rules based on the degree of membership of linguistic values;

[0113] By utilizing the influence of various fuzzy inference rules in fuzzy logic operations, a comprehensive fuzzy output is formed;

[0114] Based on the comprehensive fuzzy output, the recommended adjustment values ​​for crossover rate and mutation rate for each fuzzy inference rule are generated using the fuzzy logic inference mechanism.

[0115] All recommended adjustment values ​​are aggregated to form a single fuzzy output value;

[0116] The single fuzzy output value is converted into a specific numerical value through defuzzification, resulting in the final adjusted values ​​for crossover rate and mutation rate.

[0117] Preferably, the expression for calculating the membership degree of the maximum fitness change rate and the average fitness change rate is as follows:

[0118] ;

[0119] In the formula, e This represents the input variables, including the maximum rate of change in fitness and the average rate of change in fitness.

[0120] a Indicates the position where the membership degree starts to increase from 0;

[0121] b This indicates the position where the membership degree reaches its highest value of 1, representing the input value. e It conforms to a predefined language category at a specific point;

[0122] c This indicates the position where the membership degree drops to 0;

[0123] Indicates input variables e The membership degree of a fuzzy set.

[0124] It needs to be explained that, using a pre-designed membership function, the membership degrees of the maximum fitness change rate and the average fitness change rate are calculated to determine the degree of membership of the linguistic value (e.g., low, medium, high) corresponding to each membership degree. Membership functions, such as trapezoidal or triangular functions, are used to convert actual numerical values ​​into degrees within a fuzzy set. This is the foundation of fuzzy logic. Based on the calculated linguistic value membership degrees, appropriate fuzzy inference rules are selected for matching. For example, if the membership degree of the maximum fitness change rate points to "high," then relevant fuzzy rules are matched for the next calculation. Fuzzy inference rules are typically in the form of "if-then" statements, used to determine the response of the output variable based on the membership degree of the input variable. These rules are formulated based on expert experience or historical data analysis, aiming to simulate the decision-making process. Fuzzy logic operations are performed to infer the impact of each rule, forming a comprehensive fuzzy output. Fuzzy arithmetic rules (such as...) are then applied. Fuzzy logic operations (also known as fuzzy AND operations or fuzzy OR operations) combine the outputs of all relevant rules, allowing fuzzy outputs to be derived from multiple fuzzy inputs to handle complex decision-making scenarios. These operations simulate the interaction of multiple conditions and influencing factors in actual decision-making, aggregating the outputs of all fuzzy inference rules to form a single fuzzy output value, providing a decision basis for adjusting crossover and mutation rates. Aggregation methods such as fuzzy summation or fuzzy maximum value methods are used to ensure the comprehensiveness of the output. Aggregation is a core step in fuzzy logic, used to integrate multiple fuzzy outputs to form a unified decision output. Defuzzification methods, such as centroid method or maximum membership method, are applied to convert the fuzzy output value into specific crossover and mutation rate adjustment values. These adjustment values ​​will be directly used to update the parameters of the intuitionistic fuzzy inference algorithm. Defuzzification is the process of converting the output of the fuzzy logic system into precise operation instructions, and its key lies in practical application.

[0125] Preferably, based on the comprehensive fuzzy output, the recommended adjustment values ​​for the crossover rate and mutation rate for each fuzzy inference rule are generated using a fuzzy logic inference mechanism, including:

[0126] Based on the established fuzzy logic reasoning mechanism, match the fuzzy reasoning rules;

[0127] By utilizing the influence of fuzzy logic operators on the fuzzy inference rules of the comprehensive matching, a comprehensive fuzzy logic output is formed;

[0128] For each fuzzy rule, calculate and output the membership degree based on the result of the fuzzy logic operation;

[0129] Based on membership degree, specific adjustment suggestions for crossover rate and mutation rate are generated according to the results of fuzzy logic operation using fuzzy logic reasoning mechanism.

[0130] The recommended adjustment values ​​generated by all fuzzy inference rules are aggregated.

[0131] It needs to be explained that, firstly, based on the membership degrees of the input variables (calculated in previous steps), suitable fuzzy inference rules are selected; these rules are predefined, such as increasing the crossover rate if the maximum fitness change rate is high and the average fitness change rate is low; ensuring that all relevant fuzzy rules are considered and matched for a comprehensive evaluation, the influence of all matched fuzzy inference rules is synthesized using fuzzy logic operators (such as AND and OR operators) to form a comprehensive fuzzy logic output. This involves weighting and synthesizing the influence of each rule, and calculating the membership degree of each output (crossover rate and mutation rate) based on the results of the fuzzy logic operations for each fuzzy rule according to its importance and applicability. This involves mapping the fuzzy logic output. Returning to the specific membership function, the membership degree of each output value is determined. Based on each output membership degree, fuzzy logic inference is used to generate specific adjustment suggestions for crossover rate and mutation rate, combining membership degree and rule importance. The recommended values ​​generated by all relevant rules are evaluated to ensure that the most appropriate adjustment suggestions are provided. The output membership degree provides a quantitative measure indicating the degree of fit between each recommended adjustment value and the fuzzy output. The recommended adjustment values ​​generated by all fuzzy inference rules are aggregated to form a single, comprehensive output value. Aggregation techniques such as weighted average or maximum / minimum are used to ensure that all valid information is considered. Aggregation is necessary because it combines recommendations from different rules to provide a unified decision result.

[0132] Based on the parameters of the adjusted intuitionistic fuzzy reasoning model, adaptive crossover and mutation operations are performed to iteratively update the population until the algorithm converges to the optimal construction project ranking scheme.

[0133] Preferably, based on the parameters of the adjusted intuitionistic fuzzy inference model, adaptive crossover and mutation operations are performed to iteratively update the population until the algorithm converges to the optimal construction project ranking scheme, including:

[0134] Set the initial population size, baseline values ​​for crossover rate and mutation rate, and the maximum number of iterations;

[0135] The fitness of individuals in the initial population is assessed according to the preset evaluation criteria.

[0136] Using an intuitionistic fuzzy reasoning model, the adjusted values ​​for crossover rate and mutation rate are calculated based on the fitness data of the current population;

[0137] Perform crossover and mutation operations based on the adjusted values ​​of the calculated crossover and mutation rates;

[0138] Based on the fitness of individuals in the population, select individuals suitable for forming the next generation of the population;

[0139] After each iteration, check whether the maximum number of iterations has been reached or whether the algorithm has converged to the optimal solution;

[0140] If the preset convergence condition is met, the iteration ends and the current optimal construction project ranking scheme is output; if not, the iteration continues and the fitness of individuals in the population is re-evaluated.

[0141] It's important to explain that setting the initial population size typically depends on the complexity of the problem and the availability of resources; initial setting of baseline values ​​for crossover and mutation rates will affect the algorithm's exploration and development capabilities; defining the maximum number of iterations to limit the algorithm's runtime and ensure efficiency; population size and parameter settings need to balance computational resources and solution accuracy; using pre-defined evaluation criteria, such as project time, cost, and resource utilization, the fitness of each individual (a possible sorting scheme for the construction project) is evaluated. The evaluation results guide subsequent selection, crossover, and mutation operations. Fitness evaluation is a crucial step in genetic algorithms, ensuring that high-quality individuals are preserved and optimized. Based on the current population fitness data, an intuitionistic fuzzy inference model is used to calculate adjustment values ​​for the crossover and mutation rates. These adjustment values ​​should reflect the population's... The algorithm optimizes the search process by adjusting the crossover and mutation rates based on changes in diversity and fitness. Crossover and mutation operations are performed to generate new individuals. Crossover typically involves exchanging a portion of the genes between two individuals, while mutation randomly alters a gene in an individual. Crossover and mutation are fundamental operations in genetic algorithms used to generate new solutions, helping the algorithm escape local optima. Based on the fitness of individuals, suitable individuals for the next generation are selected, often using methods like roulette wheel selection or tournament selection. Individuals with high fitness have a greater probability of being selected to inherit into the next generation. After each iteration, the algorithm checks whether the maximum number of iterations has been reached or whether it has converged to the optimal solution. If the preset convergence condition is met (e.g., fitness no longer significantly increases), the iteration ends and the current optimal construction project sorting scheme is output; otherwise, the iteration continues.

[0142] A construction project sequencing table is generated based on the optimal construction project sequencing scheme, and the initial construction plan is optimized by combining the construction project sequencing table with the expected completion time.

[0143] It should be explained that, based on the optimized construction project sequencing scheme, a detailed construction project sequencing table is generated. This table should include a detailed description of each construction project, its scheduled start and end times, resource allocation, and any related dependencies. The generated project sequencing table is then compared with the project's expected completion time to analyze the differences between the existing plan and the objectives, as well as potential risks. Key time nodes, resource allocation, and work processes in the initial construction plan are adjusted to ensure effective alignment at each stage, optimize time and resource utilization, and reassess resource requirements and time allocation based on the adjusted plan. This ensures that each resource is effectively utilized within the appropriate timeframe, and more flexible resource management strategies, such as cross-project resource sharing, are considered to improve resource utilization efficiency. The optimized construction plan is then meticulously recorded in an official document, including all updated timelines, resource allocations, and risk management strategies, ensuring that all relevant stakeholders (such as the project management team, contractors, and clients) can access this final construction plan.

[0144] The construction progress prediction module 4 is used to predict the construction progress based on the optimized construction plan and real-time environmental factors, while monitoring the construction efficiency of the project.

[0145] Preferably, the construction progress prediction module 4, when predicting the construction progress based on the optimized construction plan and real-time environmental factors, and simultaneously monitoring the construction efficiency of the project, includes:

[0146] Real-time collection of current environmental data and actual conditions at the construction site;

[0147] By analyzing the collected historical and real-time data, key variables affecting the construction progress can be identified.

[0148] Construct a construction progress prediction model based on key variables;

[0149] The optimized construction plan and real-time environmental factors are input into the construction progress prediction model to generate a construction progress prediction within a preset time period in the future.

[0150] Real-time monitoring of actual progress and efficiency during construction, detection of deviations between actual construction progress and predicted values, identification and analysis of the causes of deviations;

[0151] Based on the prediction results and the identification results of the deviation analysis, the risks affecting the construction schedule are assessed.

[0152] It needs to be explained that the process involves deploying environmental monitoring sensors and on-site monitoring equipment, such as temperature and humidity sensors, anemometers, and video surveillance equipment, to collect environmental data and construction site conditions in real time. IoT technology is used to transmit the collected data to a central database in real time. Data integration and preprocessing are then performed, including data cleaning and normalization, to improve the accuracy of the analysis. Statistical analysis and data mining techniques, such as regression analysis and factor analysis, are used to identify key variables affecting construction progress from historical and real-time data. Finally, a suitable predictive model framework, such as time series analysis or machine learning models, is selected. Based on the identified key variables, the model is trained to predict future construction progress. The predictive model needs to be selected based on the characteristics of the construction and the nature of the data. To ensure the accuracy and reliability of predictions, model training involves algorithm selection, parameter adjustment, and verification. Using currently collected real-time environmental data and optimized construction plans as input, the construction progress prediction model is run. The generated prediction report should detail the construction progress within a preset timeframe. The actual progress at the construction site is tracked and compared with the predicted progress in real time. When deviations are detected, root cause analysis is immediately conducted, which may include resource allocation errors, changes in environmental factors, etc. Using the deviation analysis results, risks that may affect project progress are assessed, and the risk management plan is updated, including possible mitigation measures and contingency response strategies. Risk assessment is a crucial component of project management, helping to prevent potential problems and mitigate their impact.

[0153] Risk Management Module 5 is used to assess construction risks during the project construction process and dynamically adjust and optimize the construction plan based on the predicted construction progress.

[0154] Preferably, the risk management module 5, when assessing construction risks during the project construction process and dynamically adjusting the optimized construction plan based on the predicted construction progress, includes:

[0155] Collect comprehensive data on the construction project and format the comprehensive data of the construction project into a quantum computing input format;

[0156] The identified risk factors are converted into quantum bit representations;

[0157] The Hamming distance between the current construction status and historical risk event samples is calculated using quantum algorithms to identify the historical risk situation most similar to the current engineering conditions.

[0158] Preferably, the Hamming distance between the current construction status and historical risk event samples is calculated using a quantum algorithm, and the historical risk situations most similar to the current engineering conditions are identified, including:

[0159] The states of qubits are used to represent various feature values ​​in construction status and historical risk data;

[0160] Quantum states are prepared for each current construction status and historical risk event sample;

[0161] Calculate the Hamming distance between the current state and each historical event using quantum circuits;

[0162] Based on the output of the quantum circuit, the specific value of the Hamming distance is obtained, the measurement results are analyzed, and the historical risk events with the smallest Hamming distance to the current state are identified.

[0163] It needs to be explained that the first step is to collect comprehensive data related to the construction project, including engineering parameters, environmental factors, personnel allocation, and historical risk events. This data is then preprocessed and standardized to transform it into a format suitable for quantum computing input; this may involve data encoding and quantum state preparation. Identified risk factors are quantum-encoded, converting the characteristic values ​​of each risk factor into the states of qubits. Quantum circuits are designed to process these qubits to simulate the dynamic changes in risk situations. Qubits can represent multiple possibilities in hyperposition states, which is very useful for simulating complex risk factors. Quantum states representing the current construction state and historical risk data are then prepared; this involves the initialization and manipulation of quantum states. The Hamming distance between the current construction state and each historical risk event sample is calculated using quantum circuits. Hamming distance is a method for measuring the difference between two strings, which can be implemented in quantum computing using specific quantum logic gates. The specific value of the Hamming distance is obtained based on the output of the quantum circuit; this typically involves quantum measurement and data interpretation. The measurement results are analyzed to identify the historical risk event with the smallest Hamming distance to the current state. This information will be used to assess the current risk level and adjust the construction plan.

[0164] To calculate the Hamming distance between the current construction status and each historical risk event using quantum circuits, the data for both the construction status and historical events must first be encoded into qubits. Then, a quantum circuit is designed and implemented that performs comparisons using appropriate quantum gate operations (such as CNOT and Pauli-Z gates). Finally, the differences between each qubit are obtained through quantum measurement, thus calculating the Hamming distance. This process can quickly and efficiently identify historical risk events most similar to the current construction conditions, providing a basis for risk assessment and adjustments to construction plans.

[0165] Based on the identification results, find the historical risk events with the minimum Hamming distance;

[0166] Based on the nearest neighbor risk events found, assess the current risk level of construction, analyze the impact of the risk level on construction progress and safety, and dynamically adjust the construction plan.

[0167] Simultaneously monitor construction activities and environmental changes based on the adjusted construction plan.

[0168] It needs to be explained that the Hamming distance calculated using quantum algorithms is used to search for events with the smallest Hamming distance to the current construction status in the historical risk database. This involves querying and comparing data in the database to ensure that the most relevant historical risk events are found. The risk level of the current construction is assessed based on the identified nearest neighbor risk events. The results of the historical events, the measures taken, and the final impact are analyzed. This information is used to estimate the potential risks of the current project. The actual impact of historical risk events with high risk levels on the progress and safety of the corresponding construction projects is analyzed. This data is used to predict specific problems that the current project may encounter and assess possible delays or safety risks. Based on the risk assessment results, the construction plan is dynamically adjusted, including but not limited to changing the schedule, reallocating resources, or modifying work methods. The risk management plan and emergency response strategy are updated to ensure that all potential problems are effectively managed and resolved. The status of the construction site and changes in the external environment are monitored in real time to ensure that the adjusted plan can adapt to the new working environment. Real-time data is collected using on-site monitoring systems and environmental monitoring equipment and compared with preset risk management parameters.

[0169] In addition, when assessing the risk level of current construction, the "nearest neighbor risk event" refers to finding the historical risk event that is most similar to the current situation by comparing the similarity between the current construction status and historical risk events.

[0170] Based on the identified nearest-neighbor risk events, the following steps are taken to assess the current construction risk level, analyze the impact of the risk level on construction progress and safety, and dynamically adjust the construction plan:

[0171] Using Geographic Information System (GIS) and historical accident database, identify historical risk events geographically adjacent to the current construction project, analyze the type, frequency, and severity of these events to determine their relevance to the current project;

[0172] Based on the data of nearest neighbor risk events, a risk assessment model is used to quantitatively assess the risk level of the current construction, taking into account various factors such as construction technology, site conditions, schedule constraints and the impact of historical risk events.

[0173] Compare the risk level with the construction schedule, predict the delays and cost overruns that the risks may cause, and consider the potential impact of the risks on on-site safety, such as possible accidents or injuries.

[0174] Based on the risk level and its predicted impact on construction progress, adjust the key milestones and resource allocation of the construction plan, and implement risk mitigation measures, such as increasing safety inspections, adjusting work methods, or introducing additional safety equipment.

[0175] Suppose there is a large construction project located in an area prone to earthquakes. In this case, the aforementioned multi-dimensional monitoring system is used to ensure construction safety and schedule control; the following is a specific application example:

[0176] 1) Initial Construction Plan Construction Module

[0177] Implementation: At the project initiation phase, the project management team utilizes historical data and seismic risk analysis to develop a preliminary construction plan; this includes the seismic requirements of the building design, necessary engineering measures, and timelines;

[0178] Tools and Methods: Use Primavera software to develop detailed work packages and milestones to ensure that all building components take earthquake risks into account;

[0179] 2) Data Acquisition and Environmental Monitoring Module

[0180] Implementation: Install seismic monitoring instruments and other environmental sensors, such as vibration sensors and tiltmeters, at the construction site to monitor changes in the environment and construction status in real time;

[0181] Tools and methods: Deploy high-precision GPS systems and seismic monitoring equipment to monitor potential seismic activity and its impact on the construction site;

[0182] 3) Data Analysis and Planning Optimization Module

[0183] Implementation: Using the collected data, analyze the potential impact of seismic activity on construction progress and structural safety; optimize the construction plan based on data feedback;

[0184] Tools and methods: Intuitive fuzzy reasoning algorithms are used to process environmental and construction data, predict the impact of earthquakes on construction progress, and adjust the construction period and resource allocation;

[0185] 4) Construction progress prediction module

[0186] Implementation: Based on the optimized construction plan and real-time environmental data, predict future construction progress, especially for rapid recovery after an earthquake;

[0187] Tools and methods: Combining machine learning models and time series analysis, we predict construction progress and adjust work plans to minimize the impact of earthquakes;

[0188] 5) Risk Management Module

[0189] Implementation: Assess the risks of earthquakes and other relevant environmental factors, and dynamically adjust the construction plan based on real-time data;

[0190] Tools and methods: Utilize quantum algorithms to calculate Hamming distances to historical seismic events, identify potentially high-risk periods, and take preventative measures such as reinforcing existing structures or suspending high-risk operations during periods of seismic activity.

[0191] According to another embodiment of the invention, such as Figure 2 As shown, a multi-dimensional monitoring method for engineering project progress based on information technology is also provided. This method includes the following steps:

[0192] S1. Develop an initial construction plan based on the design and planning of the project;

[0193] S2. Collect multi-dimensional data on construction progress at the construction site in real time and monitor environmental factors simultaneously;

[0194] S3. Analyze the multidimensional data of construction progress using the intuitionistic fuzzy reasoning algorithm, generate a construction project ranking table by combining construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time.

[0195] S4. Based on the optimized construction plan and real-time environmental factors, predict the construction progress and monitor the construction efficiency of the project.

[0196] S5. Assess the construction risks during the project's construction process and dynamically adjust and optimize the construction plan based on the predicted construction progress.

[0197] In summary, by utilizing the above-mentioned technical solutions of this invention, the present invention can dynamically adjust the construction plan to cope with constantly changing site conditions and external environmental factors by collecting multi-dimensional data (such as working hours, material usage, and equipment efficiency) from the construction site in real time and applying an intuitionistic fuzzy reasoning algorithm. It analyzes the needs and bottlenecks of the construction project using the intuitionistic fuzzy reasoning algorithm, and, combined with construction logic and physical constraints, can more accurately allocate resources (such as manpower, materials, and machinery). By handling fuzzy and uncertain situations through a fuzzy logic controller, and combining expert experience and historical data, it provides scientific decision support. By continuously monitoring and analyzing key variables of construction progress (such as the maximum fitness rate of change and the average fitness rate of change), it predicts and addresses potential risks and problems. Optimizing the construction plan using intuitionistic fuzzy reasoning ensures that the project complies with all relevant building and safety standards. Through continuous monitoring and timely adjustments, it avoids the risk of violating regulations and standards, guaranteeing project quality and safety. By collecting environmental data and the actual conditions of the construction site in real time and analyzing historical data, the construction progress prediction module of this invention can accurately identify key variables affecting construction progress, significantly improving the accuracy of construction progress prediction. The generated progress forecasts allow management teams to allocate resources, such as manpower, materials, and equipment, more rationally to match actual construction needs. By monitoring the deviation between the forecast and actual values ​​in real time, project teams can quickly identify risks and take corresponding mitigation measures. The integration and analysis of real-time data provides powerful decision support for project management. The construction progress prediction module, through continuous monitoring and analysis of environmental variables, can predict and respond to construction delays caused by unforeseen events such as weather changes and equipment failures. This invention uses quantum algorithms to calculate Hamming distance, which can accurately compare the similarity between the current construction status and historical risk events, thereby conducting more accurate risk assessments. Based on the identified risk situations, dynamic adjustments to the construction plan can promptly address potential risks, such as environmental changes and equipment failures, reducing construction delays and cost overruns. Utilizing quantum technology to update the risk database and construction plan in real time enables real-time and continuous risk management. Through accurate risk assessment, project teams can allocate resources more rationally and prepare emergency response strategies. The integrated quantum computing and risk management system provides powerful decision support for project management, making the decision-making process more scientific and data-driven, and reducing uncertain decisions based on experience or intuition.

[0198] Although the present invention has been disclosed above with reference to preferred embodiments, the embodiments are merely examples for illustrative purposes and are not intended to limit the present invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention should be determined by the claims.

Claims

1. A multi-dimensional monitoring system for engineering project progress based on information technology, characterized in that, include: The initial construction plan building module is used to develop an initial construction plan based on the design and planning of the engineering project; The data acquisition and environmental monitoring module is used to collect multi-dimensional data on the construction progress at the construction site in real time and to monitor environmental factors simultaneously. The data analysis and planning optimization module utilizes intuitionistic fuzzy inference algorithms to analyze multidimensional data on construction progress. It generates a construction project ranking table by combining construction logic and physical constraints, and optimizes the initial construction plan based on the ranking table and expected completion times. This includes collecting multidimensional data on construction progress, identifying the actual needs of construction projects, and clarifying logical relationships and physical constraints based on these needs. Key input variables for the intuitionistic fuzzy inference algorithm are set based on these logical relationships and physical constraints, including the maximum fitness change rate and the average fitness change rate. An intuitionistic fuzzy inference model is established, and the crossover and mutation rates of the algorithm are adjusted based on the maximum and average fitness change rates, including using these rates as input variables for building the model. The algorithm sets linguistic values ​​for input variables and assigns suitable membership functions to each linguistic value, converting actual data into membership degrees in a fuzzy set. It defines fuzzy inference rules based on the combination of linguistic values ​​of the input variables. Using a fuzzy logic inference mechanism, it calculates adjusted values ​​for crossover and mutation rates based on membership degrees and fuzzy inference rules. Through defuzzification, it converts the calculation results using the fuzzy logic inference mechanism into adjusted values ​​for specific crossover and mutation rates, obtaining the adjusted crossover and mutation rates. Based on the adjusted parameters of the intuitionistic fuzzy inference model, it performs adaptive crossover and mutation operations, iteratively updating the population until the algorithm converges to the optimal construction project ranking scheme. Based on the optimal construction project ranking scheme, it generates a construction project ranking table and uses the ranking table combined with the expected completion time to optimize the initial construction plan. The construction progress prediction module is used to predict the construction progress based on the optimized construction plan and real-time environmental factors, while monitoring the construction efficiency of the project. This includes: real-time collection of current environmental data and actual conditions at the construction site; analysis of collected historical and real-time data to identify key variables affecting construction progress; construction of a construction progress prediction model based on these key variables; inputting the optimized construction plan and real-time environmental factors into the construction progress prediction model to generate a construction progress prediction for a preset future timeframe; real-time monitoring of actual progress and efficiency during construction, detecting deviations between actual and predicted progress, identifying and analyzing the causes of these deviations; and assessing the risks affecting construction progress based on the prediction results and the results of the deviation analysis. The risk management module is used to assess construction risks during the project construction process and dynamically adjust and optimize the construction plan based on the predicted construction progress. Using fuzzy logic reasoning mechanism, the adjustment values ​​for crossover rate and mutation rate are calculated based on membership degree and fuzzy reasoning rules, including: calculating the membership degree of the maximum fitness change rate and the average fitness change rate, and determining the degree of membership of the corresponding language value based on the membership degree; The fuzzy inference rules are matched based on the membership degree of linguistic values; the influence of each fuzzy inference rule is inferred using fuzzy logic operations to form a comprehensive fuzzy output; based on the comprehensive fuzzy output, the recommended adjustment values ​​for crossover rate and mutation rate for each fuzzy inference rule are generated using the fuzzy logic inference mechanism; all recommended adjustment values ​​are aggregated to form a single fuzzy output value; the single fuzzy output value is converted into specific numerical values ​​through defuzzification to obtain the final adjustment values ​​for crossover rate and mutation rate. The risk management module, when assessing construction risks during the project's construction process and dynamically adjusting the optimized construction plan based on the predicted construction progress, includes: Collect comprehensive data on the construction project and format the comprehensive data of the construction project into a quantum computing input format; The identified risk factors are converted into quantum bit representations; The Hamming distance between the current construction status and historical risk event samples is calculated using quantum algorithms to identify the historical risk situation most similar to the current engineering conditions. Based on the identification results, find the historical risk events with the minimum Hamming distance; Based on the nearest neighbor risk events found, assess the current risk level of construction, analyze the impact of the risk level on construction progress and safety, and dynamically adjust the construction plan. Simultaneously monitor construction activities and environmental changes in accordance with the adjusted construction plan; The step of using quantum algorithms to calculate the Hamming distance between the current construction status and historical risk event samples, and identifying the historical risk situation most similar to the current engineering conditions, includes: The states of qubits are used to represent various feature values ​​in construction status and historical risk data; Quantum states are prepared for each current construction status and historical risk event sample; Calculate the Hamming distance between the current state and each historical event using quantum circuits; Based on the output of the quantum circuit, the specific value of the Hamming distance is obtained, the measurement results are analyzed, and the historical risk events with the smallest Hamming distance to the current state are identified.

2. The multi-dimensional monitoring system for engineering project progress based on information technology according to claim 1, characterized in that, The expressions for the membership degree used to calculate the maximum fitness change rate and the average fitness change rate are as follows: ; In the formula, e represents the input variable including the maximum fitness change rate and the average fitness change rate; a represents the position where the membership degree starts to increase from 0; b represents the position where the membership degree reaches the highest value of 1, indicating that the input value e conforms to the predefined language category at a specific point; c represents the position where the membership degree drops to 0; This represents the membership degree of the input variable e to the fuzzy set.

3. The multi-dimensional monitoring system for engineering project progress based on information technology according to claim 2, characterized in that, Based on the comprehensive fuzzy output, the recommended adjustment values ​​for crossover rate and mutation rate for each fuzzy inference rule are generated using the fuzzy logic inference mechanism, including: Based on the established fuzzy logic reasoning mechanism, match the fuzzy reasoning rules; By utilizing the influence of fuzzy logic operators on the fuzzy inference rules of the comprehensive matching, a comprehensive fuzzy logic output is formed; For each fuzzy rule, calculate and output the membership degree based on the result of the fuzzy logic operation; Based on membership degree, specific adjustment suggestions for crossover rate and mutation rate are generated according to the results of fuzzy logic operation using fuzzy logic reasoning mechanism. The recommended adjustment values ​​generated by all fuzzy inference rules are aggregated.

4. The multi-dimensional monitoring system for engineering project progress based on information technology according to claim 3, characterized in that, Based on the parameters of the adjusted intuitionistic fuzzy inference model, adaptive crossover and mutation operations are performed to iteratively update the population until the algorithm converges to the optimal construction project ranking scheme, which includes: Set the initial population size, baseline values ​​for crossover rate and mutation rate, and the maximum number of iterations; The fitness of individuals in the initial population is assessed according to the preset evaluation criteria. Using an intuitionistic fuzzy reasoning model, the adjusted values ​​for crossover rate and mutation rate are calculated based on the fitness data of the current population; Perform crossover and mutation operations based on the adjusted values ​​of the calculated crossover and mutation rates; Based on the fitness of individuals in the population, select individuals suitable for forming the next generation of the population; After each iteration, check whether the maximum number of iterations has been reached or whether the algorithm has converged to the optimal solution; If the preset convergence condition is met, the iteration ends and the current optimal construction project ranking scheme is output; if not, the iteration continues and the fitness of individuals in the population is re-evaluated.

5. A method for multi-dimensional monitoring of engineering project progress based on information technology, used to implement the multi-dimensional monitoring system for engineering project progress based on information technology as described in any one of claims 1-4, characterized in that, The multi-dimensional monitoring method for the progress of this project includes the following steps: S1. Develop an initial construction plan based on the design and planning of the project; S2. Collect multi-dimensional data on construction progress at the construction site in real time and monitor environmental factors simultaneously; S3. Analyze the multidimensional data of construction progress using the intuitionistic fuzzy reasoning algorithm, generate a construction project ranking table by combining construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time. S4. Based on the optimized construction plan and real-time environmental factors, predict the construction progress and monitor the construction efficiency of the project. S5. Assess the construction risks during the project's construction process and dynamically adjust and optimize the construction plan based on the predicted construction progress.

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