Engineering project progress multi-dimensional monitoring system and method based on information technology

Abstract

The invention discloses an engineering project progress multi-dimensional monitoring system and method based on an information technology, and belongs to the field of monitoring. The engineering project progress multi-dimensional monitoring system comprises an initial construction plan construction module, a data acquisition and environment monitoring module, a data analysis and plan optimization module, a construction progress prediction module and a risk management module. By collecting multi-dimensional data of a construction site in real time and applying the intuitionistic fuzzy reasoning algorithm, a construction plan can be dynamically adjusted to cope with constantly changing site conditions and external environment factors, the intuitionistic fuzzy reasoning algorithm is utilized to analyze requirements and bottlenecks of a construction project, and the construction efficiency is improved by combining construction logic and physical constraints. Resource configuration can be carried out more accurately; fuzzy and uncertain conditions are processed through the fuzzy logic controller, it is ensured that a project meets all related building and safety standards, and project quality and safety are ensured through continuous monitoring and timely adjustment.

Description

Technical Field

[0001] The present invention relates to the field of monitoring, and in particular, to a multi-dimensional monitoring system and method for engineering project progress based on information technology. Background Art

[0002] Engineering projects involve complex design, planning, construction and management activities, the main purpose of which is to achieve specific building or infrastructure goals and meet the requirements of functional use, safety, efficiency and economy. Engineering project monitoring is to ensure the smooth progress and successful completion of the project.

[0003] Project monitoring is a key component of project management. It involves continuous tracking and control of the project's progress, cost, quality, scope, risk and resources during project implementation. The purpose of project monitoring is to ensure that the project can proceed smoothly according to the predetermined goals and plans, and to deal with problems and risks that arise during project implementation to ensure the successful completion of the project.

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

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In order to overcome the above problems, the present invention aims to propose a multi-dimensional monitoring system and method for engineering project progress based on information technology, with the aim of solving 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; such delayed emergency response may lead to safety risks and further delays in construction schedules.

[0007] To this end, the specific technical solution adopted by the present invention is as follows: According to one aspect of the present invention, there is provided a multi-dimensional monitoring system for the progress of an engineering project based on information technology, the multi-dimensional monitoring system for the progress of an engineering project comprising: an initial construction plan building module, a data collection and environment monitoring module, a data analysis and plan optimization module, a construction progress prediction module and a risk management module; The initial construction plan building module is used to develop an initial construction plan based on the design and planning of the project; Data collection and environmental monitoring module, used to collect multi-dimensional data of construction progress at the construction site in real time and simultaneously monitor environmental factors; The data analysis and planning optimization module is used to analyze the multi-dimensional data of the construction progress using the intuitive fuzzy reasoning algorithm, generate a construction project ranking table based on the construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time; The construction progress prediction module is used to predict the construction progress based on the optimized construction plan and real-time environmental factors, and monitor the construction efficiency of the project; The risk management module is used to evaluate the construction risks during the project construction process and dynamically adjust the optimized construction plan based on the forecast results of the construction progress; The data analysis and planning optimization module uses the intuitive fuzzy reasoning algorithm to analyze the multi-dimensional data of the construction progress, combines the construction logic and physical constraints to generate a construction project ranking table, and optimizes the initial construction plan based on the construction project ranking table and the expected completion time. Collect multi-dimensional data of construction progress, identify the actual needs of the construction project, and clarify the logical relationships and physical constraints based on the actual needs; The key input variables of the intuitive 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. Establish an intuitive fuzzy reasoning model, and adjust the crossover rate and mutation rate of the intuitive fuzzy reasoning algorithm based on the maximum fitness change rate and the average fitness change rate; According to the adjusted parameters of the intuitive fuzzy reasoning model, adaptive crossover and mutation operations are performed to iteratively update the population until the algorithm converges to the optimal construction project sequencing solution; A construction project ranking table is generated based on the optimal construction project ranking scheme, and the construction project ranking table is used in combination with the expected completion time to optimize the initial construction plan.

[0008] Optionally, establishing an intuitive fuzzy reasoning model, and adjusting the crossover rate and mutation rate of the intuitive fuzzy reasoning algorithm based on the maximum fitness change rate and the average fitness change rate include: The maximum fitness change rate and the average fitness change rate are used as input variables to establish the intuitive fuzzy reasoning model; Set language values ​​for input variables and assign appropriate membership functions to each language value to convert actual data into membership in fuzzy sets; Define fuzzy inference rules based on the combination of linguistic values ​​of input variables; Using fuzzy logic reasoning mechanism, the adjustment values ​​of crossover rate and mutation rate are calculated based on membership degree and fuzzy reasoning rules; Through the defuzzification method, the calculation results using the fuzzy logic reasoning mechanism are converted into adjustment values ​​of specific crossover rate and mutation rate, and the adjusted crossover rate and mutation rate are obtained.

[0009] Optionally, using a fuzzy logic reasoning mechanism, the adjustment values ​​of the crossover rate and the mutation rate are calculated based on the membership degree and the fuzzy reasoning rules, including: Calculate the membership of the maximum fitness change rate and the average fitness change rate, and determine the membership degree of the corresponding language value based on the membership; Matching fuzzy inference rules based on the degree of membership of linguistic values; Using fuzzy logic operations to calculate the influence of various fuzzy reasoning rules, a comprehensive fuzzy output is formed; According to the comprehensive fuzzy output, the fuzzy logic reasoning mechanism is used to generate the recommended adjustment values ​​of the crossover rate and mutation rate for each fuzzy reasoning rule; Aggregating all recommended adjustment values ​​to form a single fuzzy output value; The single fuzzy output value is converted into a specific numerical value through the defuzzification method to obtain the final adjustment values ​​of the crossover rate and mutation rate.

[0010] Optionally, the expression for calculating the membership of the maximum fitness change rate and the average fitness change rate is: ; In the formula, e represents the input variables including the maximum fitness change rate and the average fitness change rate; a Indicates the position where the degree of membership rises from 0; b Indicates the position where the membership reaches the highest value 1, indicating that the input value e conforming to predefined language categories at certain points; c Indicates the position where the degree of membership drops to 0; Represents input variables e The degree of membership to a fuzzy set.

[0011] Optionally, based on the integrated fuzzy output, the fuzzy logic reasoning mechanism is used to generate the recommended adjustment values ​​of the crossover rate and the mutation rate for each fuzzy reasoning rule, including: According to the set fuzzy logic reasoning mechanism, match the fuzzy reasoning rules; Using fuzzy logic operators to synthesize the effects of matched fuzzy reasoning rules to form a comprehensive fuzzy logic output; For each fuzzy rule, the output membership is calculated based on the result of the fuzzy logic operation; Based on the membership degree, the fuzzy logic reasoning mechanism is used to generate specific adjustment recommendations for the crossover rate and mutation rate according to the fuzzy logic operation results; Aggregate the recommended adjustment values ​​generated by all fuzzy inference rules.

[0012] Optionally, according to the adjusted parameters of the intuitionistic fuzzy reasoning model, adaptive crossover and mutation operations are performed, and the population is continuously updated iteratively until the algorithm converges to the optimal construction project sequencing scheme including: Set the initial size of the population, the baseline values ​​of the crossover rate and mutation rate, and the maximum number of iterations; Evaluate the fitness of individuals in the initial population according to the preset evaluation criteria; Using the intuitive fuzzy reasoning model, the adjustment values ​​of the crossover rate and mutation rate are calculated according to the fitness data of the current population; Perform crossover and mutation operations according to the calculated adjusted values ​​of crossover rate and mutation rate; 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 conditions are met, the iteration ends and the current optimal construction project sorting scheme is output; if not, the iteration continues and the fitness of individuals in the population is re-evaluated.

[0013] Optionally, the construction progress prediction module predicts the construction progress based on the optimized construction plan and real-time environmental factors, and monitors the construction efficiency of the project, including: Collect current environmental data and actual conditions of the construction site in real time; Analyze the collected historical and real-time data to identify key variables that affect the construction progress; Build a construction progress prediction model based on key variables; Input the optimized construction plan and real-time environmental factors into the construction progress prediction model to generate a construction progress prediction within a preset time in the future; Monitor the actual progress and efficiency of the construction process in real time, detect the deviation between the actual construction progress and the predicted value, and identify and analyze the causes of the deviation; Based on the prediction results and the identification results of the deviation analysis, the risks that may affect the construction progress are assessed.

[0014] Optionally, the risk management module includes the following when evaluating the construction risks during the construction of the project and dynamically adjusting the optimized construction plan based on the forecast results of the construction progress: Collect comprehensive data of construction projects and format the comprehensive data of construction projects into input form for quantum computing; Convert the identified risk factors into qubit representations; Using quantum algorithms to calculate the Hamming distance between the current construction status and historical risk event samples, we can identify the historical risk situations that are 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 risk events found, evaluate the risk level of the current construction, analyze the impact of the risk level on the construction progress and safety, and dynamically adjust the construction plan; Also monitor the implementation of construction activities and environmental changes according to the adjusted construction plan.

[0015] Optionally, a quantum algorithm is used to calculate the Hamming distance between the current construction status and the historical risk event samples to identify the historical risk situations that are most similar to the current engineering conditions, including: Using the state of quantum bits to represent each characteristic value in the construction status and historical risk data; Prepare quantum states for each current construction status and historical risk event sample; Use quantum circuits to calculate the Hamming distance between the current state and each historical event; According to 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.

[0016] According to another aspect of the present invention, a method for multi-dimensional monitoring of project progress based on information technology is provided. The method for multi-dimensional monitoring of project progress includes the following steps: S1. Develop an initial construction plan based on the design and planning of the project; S2. Collect multi-dimensional data of construction progress at the construction site in real time and monitor environmental factors simultaneously; S3. Analyze the multi-dimensional data of construction progress using intuitive fuzzy reasoning algorithm, generate a construction project ranking table based on construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and expected completion time; S4. Based on the optimized construction plan and real-time environmental factors, the construction progress is predicted and the construction efficiency of the project is monitored; S5. Evaluate the construction risks during the project construction process, and dynamically adjust the optimized construction plan based on the predicted results of the construction progress.

[0017] Compared with the prior art, this application has the following beneficial effects: 1. The present invention can dynamically adjust the construction plan to cope with the ever-changing site conditions and external environmental factors by collecting multi-dimensional data of the construction site in real time and applying the intuitive fuzzy reasoning algorithm. It uses the intuitive fuzzy reasoning algorithm to analyze the needs and bottlenecks of the construction project, and combines the construction logic and physical constraints to more accurately allocate resources. It uses the fuzzy logic controller to handle fuzzy and uncertain situations, and combines expert experience and historical data to provide scientific decision-making support. It continuously monitors and analyzes key variables of the construction progress, predicts and responds to potential risks and problems, and optimizes the construction plan using intuitive fuzzy reasoning, which can ensure that the project meets all relevant construction and safety standards. Through continuous monitoring and timely adjustments, the risk of violating regulations and standards can be avoided, ensuring the quality and safety of the project.

[0018] 2. The present invention collects environmental data and the actual conditions of the construction site in real time, and analyzes them in combination with historical data. The construction progress prediction module can accurately identify the key variables that affect the construction progress, and can greatly improve the accuracy of the construction progress prediction. Through the progress forecast generated by the prediction model, the management team can more reasonably allocate resources, such as manpower, materials and equipment, to match the actual construction needs. Through the deviation between real-time monitoring and predicted values, the project team can quickly identify risks and take corresponding risk mitigation measures. The integration and analysis of real-time data provides powerful decision-making support for project management. The construction progress prediction module can timely predict and respond to construction delays caused by emergencies such as weather changes and equipment failures through continuous monitoring and analysis of environmental variables.

[0019] 3. The present invention uses quantum algorithms to calculate the Hamming distance, which can accurately compare the similarity between the current construction status and historical risk events, thereby performing a more accurate risk assessment. Based on the identified risk situations, dynamic adjustment of the construction plan can respond to possible risks in a timely manner, such as environmental changes, equipment failures, etc., reducing construction delays and cost overruns. Using quantum technology to update the risk database and construction plan in real time can achieve real-time and continuity of risk management. Through accurate risk assessment, the project team can allocate resources more reasonably and prepare emergency response strategies. The integrated quantum computing and risk management system provides strong decision-making support for project management, making the decision-making process more scientific and data-driven, and reducing uncertain decisions based on experience or intuition. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which are described in detail in conjunction with the accompanying drawings. Herein, a schematic diagram is shown: Figure 1 It is a principle block diagram of a multi-dimensional monitoring system for engineering project progress based on information technology according to an embodiment of the present invention; Figure 2 The present invention is a flowchart of a method for multi-dimensional monitoring of engineering project progress based on information technology according to an embodiment of the present invention.

[0021] In the figure: 1. Initial construction plan building module; 2. Data collection and environmental monitoring module; 3. Data analysis and plan optimization module; 4. Construction progress prediction module; 5. Risk management module. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0023] According to an embodiment of the present invention, a system and method for multi-dimensional monitoring of engineering project progress based on information technology are provided.

[0024] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to one embodiment of the present invention, a multi-dimensional monitoring system for project progress based on information technology is provided, and the multi-dimensional monitoring system for project progress includes: an initial construction plan building module 1, a data collection and environment monitoring module 2, a data analysis and plan optimization module 3, a construction progress prediction module 4 and a risk management module 5; The initial construction plan building module 1 is used to formulate an initial construction plan based on the design and planning of the engineering project.

[0025] It needs to be explained that all documents related to the project, including design drawings, plans, customer requirements, etc., are collected, meetings are held with the project management team, designers, and customers to understand the project goals and expectations in detail. Requirements analysis is the key to ensuring 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 major work packages and milestones, and develop a preliminary resource allocation plan, including estimates of manpower, material and equipment requirements. The preliminary plan is presented to the project team, customers and other key stakeholders, feedback is collected, especially feedback on the schedule, resource allocation and budget, the plan is adjusted according to the feedback received, any potential conflicts or problems are resolved, the final approved construction plan is issued, and any deviations during implementation are monitored.

[0026] The data collection and environment monitoring module 2 is used to collect multi-dimensional data of the construction progress at the construction site in real time and simultaneously monitor environmental factors.

[0027] It is necessary to explain that appropriate sensors and data acquisition equipment should be selected, such as temperature and humidity sensors, and GPS systems for monitoring the location of workers and the status of equipment, GPS systems should be installed on the construction site, and they should be able to cover all key areas and environmental parameters, and the technical characteristics and applicable environments of various sensors should be understood in depth to select the most suitable equipment, central monitoring should be implemented to receive and process data from on-site sensors in real time, data synchronization technology should be configured to ensure that all data is updated in real time and accessible to the project management team, and environmental impact assessments should be conducted using collected environmental data (such as weather conditions, temperature, humidity, etc.), and environmental risks that may affect construction progress or safety should be identified through data analysis, and 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, and the changing trends of construction progress and environmental factors should be analyzed, and suggestions for adjusting construction plans and resource allocation should be made based on data analysis results.

[0028] The data analysis and planning optimization module 3 is used to analyze the multi-dimensional data of the construction progress using the intuitive fuzzy reasoning algorithm, generate a construction project ranking table based on the construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time.

[0029] The data analysis and plan optimization module 3 uses the intuitive fuzzy reasoning algorithm to analyze the multi-dimensional data of the construction progress, combines the construction logic and physical constraints to generate a construction project ranking table, and optimizes the initial construction plan based on the construction project ranking table and the expected completion time. Collect multi-dimensional data on construction progress, identify the actual needs of the construction project, and clarify logical relationships and physical constraints based on the actual needs.

[0030] The key input variables of the intuitive 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.

[0031] It should be explained that a variety of sensors and data acquisition equipment are deployed at the construction site to monitor and record multi-dimensional data related to the construction progress in real time, such as working hours, material usage, equipment efficiency, etc.; data are automatically collected using a data acquisition system and transmitted to a central database in real time via a wireless network; the collected data are analyzed to identify key requirements and bottlenecks in the construction project, such as resource shortages, delay nodes, etc.; demand analysis meetings are held to discuss the data analysis results with the project management team and the construction team to jointly determine the priority of improvement and possible adjustment strategies; according to the project design and construction specifications, the logical relationship between the various tasks and activities in the project, such as sequence, parallel relationship, etc.; considering the actual physical constraints, such as the space limitations of the construction site, equipment capacity and other factors, determine the various The implementation scope of the task and possible conflicts, the logical relationships and physical constraints in project planning are the key to ensuring the smooth progress of construction, which involves knowledge in the fields of engineering management and operation research; based on the determined logical relationships and physical constraints, the key input variables in the intuitive fuzzy reasoning 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 plan schemes; fuzzy logic controllers are used to process these variables to model and reason about uncertainty and fuzzy conditions. Intuitive fuzzy reasoning combines the advantages of fuzzy logic and intuitive judgment, and is suitable for dealing with uncertainty and complexity in the decision-making process. When setting the algorithm input variables, what needs to be considered is how to optimize the construction plan through changes in these variables and improve its adaptability to changes in real conditions.

[0032] An intuitive fuzzy reasoning model is established, and the crossover rate and mutation rate of the intuitive fuzzy reasoning algorithm are adjusted based on the maximum fitness change rate and the average fitness change rate.

[0033] Preferably, establishing an intuitive fuzzy reasoning model, and adjusting the crossover rate and mutation rate of the intuitive fuzzy reasoning algorithm based on the maximum fitness change rate and the average fitness change rate includes: The maximum fitness change rate and the average fitness change rate are used as input variables to establish the intuitive fuzzy reasoning model; Set language values ​​for input variables and assign appropriate membership functions to each language value to convert actual data into membership in fuzzy sets; Define fuzzy inference rules based on the combination of linguistic values ​​of input variables; Using fuzzy logic reasoning mechanism, the adjustment values ​​of crossover rate and mutation rate are calculated based on membership degree and fuzzy reasoning rules; Through the defuzzification method, the calculation results using the fuzzy logic reasoning mechanism are converted into adjustment values ​​of specific crossover rate and mutation rate, and the adjusted crossover rate and mutation rate are obtained.

[0034] It needs to be explained that the maximum fitness change rate and the average fitness change rate are first determined as input variables. These two indicators reflect the dynamic characteristics of construction progress and condition changes. Relevant data, such as historical records of past construction projects, are collected to estimate the actual values ​​of these variables.

[0035] Among them, the maximum fitness change rate usually refers to the maximum change in fitness (such as work efficiency or progress completion rate) within a single cycle, and the average fitness change rate indicates the average level of fitness change over a period of time. Language values ​​are defined for the maximum fitness change rate and the average fitness change rate, such as high, medium, and low. Membership functions are designed to map these language values ​​to fuzzy sets, such as using triangular or trapezoidal membership functions. Membership functions are used in fuzzy logic to quantify the applicability of language values ​​at a specific value.

[0036] According to project requirements and historical data analysis, define how to adjust the crossover rate and mutation rate based on the combination of the maximum fitness change rate and the average fitness change rate, and compile fuzzy rules. For example, if the maximum fitness change rate is high and the average fitness change rate is low, increase the crossover rate. Fuzzy rules are based on expert experience and historical data to predict the optimal response in different situations; apply fuzzy reasoning mechanism, combine membership and pre-defined fuzzy rules to calculate the adjustment value of crossover rate and mutation rate, use fuzzy logic controller to integrate input, and obtain output through fuzzy operation. Fuzzy logic controller processes information through three processes: fuzzified input, rule evaluation and fuzzy reasoning. The output of fuzzy logic reasoning is converted into precise values ​​through defuzzification methods (such as centroid method and maximum membership method). The specific crossover rate and mutation rate values ​​obtained are used to adjust the parameters of the intuitive fuzzy reasoning algorithm. Defuzzification is the process of converting fuzzy output into clear values ​​in fuzzy logic system. The key is to ensure the practicality and accuracy of the conversion result.

[0037] Preferably, using a fuzzy logic reasoning mechanism, calculating the adjustment values ​​of the crossover rate and the mutation rate based on the membership degree and the fuzzy reasoning rules includes: Calculate the membership of the maximum fitness change rate and the average fitness change rate, and determine the membership degree of the corresponding language value based on the membership; Matching fuzzy inference rules based on the degree of membership of linguistic values; Using fuzzy logic operations to calculate the influence of various fuzzy reasoning rules, a comprehensive fuzzy output is formed; According to the comprehensive fuzzy output, the fuzzy logic reasoning mechanism is used to generate the recommended adjustment values ​​of the crossover rate and mutation rate for each fuzzy reasoning rule; Aggregating all recommended adjustment values ​​to form a single fuzzy output value; The single fuzzy output value is converted into a specific numerical value through the defuzzification method to obtain the final adjustment values ​​of the crossover rate and mutation rate.

[0038] Preferably, the expression for calculating the membership of the maximum fitness change rate and the average fitness change rate is: ; In the formula, e represents the input variables including the maximum fitness change rate and the average fitness change rate; a Indicates the position where the degree of membership rises from 0; b Indicates the position where the membership reaches the highest value 1, indicating that the input value e conforming to predefined language categories at certain points; c Indicates the position where the degree of membership drops to 0; Represents input variables e The degree of membership to a fuzzy set.

[0039] It should be explained that the designed membership function is used to calculate the membership of the maximum fitness change rate and the average fitness change rate, and determine the membership degree of the language value (such as low, medium, and high) corresponding to each membership. The membership function, such as the trapezoidal or triangular function, is used to convert the actual numerical value into the degree in the fuzzy set, which is the basis of fuzzy logic. According to the calculated language value membership, the appropriate fuzzy reasoning rule is selected for matching. For example, if the membership of the maximum fitness change rate points to "high", the relevant fuzzy rule is matched for the next calculation. The fuzzy reasoning rules are usually in the form of "if-then" statements, which are used to determine the response of the output variable according to the membership of the input variable. These rules are based on expert experience or historical data analysis, and are designed to simulate the decision-making process. Fuzzy logic operations are performed to infer the impact of each rule to form a comprehensive fuzzy output. Fuzzy operation rules (such as Fuzzy AND operation or fuzzy OR operation), combined with the output of all related rules, fuzzy logic operations allow 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, aggregate the outputs of all fuzzy reasoning rules to form a single fuzzy output value, and provide a decision basis for the adjustment of crossover rate and mutation rate. Aggregation methods such as fuzzy summation or fuzzy maximum method are used to ensure the comprehensiveness of the output. Aggregation is the core step in fuzzy logic and is used to integrate multiple fuzzy outputs to form a unified decision output; apply defuzzification methods such as the centroid method or the maximum membership method to convert fuzzy output values ​​into specific crossover rate and mutation rate adjustment values. These adjustment values ​​will be directly used to update the parameters of the intuitive fuzzy reasoning algorithm. Defuzzification is the process of converting the output of a fuzzy logic system into precise operating instructions. The key lies in practical application.

[0040] Preferably, based on the comprehensive fuzzy output, the recommended adjustment values ​​of the crossover rate and the mutation rate for each fuzzy inference rule are generated by using the fuzzy logic inference mechanism, including: According to the set fuzzy logic reasoning mechanism, match the fuzzy reasoning rules; Using fuzzy logic operators to synthesize the effects of matched fuzzy reasoning rules to form a comprehensive fuzzy logic output; For each fuzzy rule, the output membership is calculated based on the result of the fuzzy logic operation; Based on the membership degree, the fuzzy logic reasoning mechanism is used to generate specific adjustment recommendations for the crossover rate and mutation rate according to the fuzzy logic operation results; Aggregate the recommended adjustment values ​​generated by all fuzzy inference rules.

[0041] It should be explained that, first, based on the membership of the input variables (calculated in the previous step), the appropriate fuzzy inference rules are selected; these rules are pre-defined, such as: if the maximum fitness change rate is high and the average fitness change rate is low, then the crossover rate is increased; ensure that all relevant fuzzy rules are considered and matched in order to fully evaluate the situation, and use fuzzy logic operators (such as AND and OR operators) to combine the influence of all matched fuzzy inference rules to form a comprehensive fuzzy logic output, which involves weighting and synthesizing the influence of each rule according to its importance and applicability. For each fuzzy rule, the membership of each output (crossover rate and mutation rate) is calculated based on the results of the fuzzy logic operation, which involves mapping the fuzzy logic output Return to the specific membership function to determine the membership degree of each output value. According to each output membership, use the fuzzy logic reasoning mechanism, combine the membership and the importance of the rule, generate specific crossover rate and mutation rate adjustment recommendation values, evaluate the recommended values ​​generated by all relevant rules, and ensure that the most appropriate adjustment recommendations are provided. The output membership provides a quantitative measurement that indicates the degree of consistency between each recommended adjustment value and the fuzzy output. Aggregate the recommended adjustment values ​​generated by all fuzzy inference rules to form a single, comprehensive output value. Use aggregation techniques such as weighted average or maximum / minimum value to ensure that all valid information is considered. Aggregation processing is necessary because it combines recommendations from different rules to provide a unified decision result.

[0042] According to the adjusted parameters of the intuitive fuzzy reasoning model, adaptive crossover and mutation operations are performed, and the population is continuously updated iteratively until the algorithm converges to the optimal construction project sequencing solution.

[0043] Preferably, according to the adjusted parameters of the intuitive fuzzy reasoning model, adaptive crossover and mutation operations are performed, and the population is continuously updated iteratively until the algorithm converges to the optimal construction project sorting scheme including: Set the initial size of the population, the baseline values ​​of the crossover rate and mutation rate, and the maximum number of iterations; Evaluate the fitness of individuals in the initial population according to the preset evaluation criteria; Using the intuitive fuzzy reasoning model, the adjustment values ​​of the crossover rate and mutation rate are calculated according to the fitness data of the current population; Perform crossover and mutation operations according to the calculated adjusted values ​​of crossover rate and mutation rate; 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 conditions are met, the iteration ends and the current optimal construction project sorting scheme is output; if not, the iteration continues and the fitness of individuals in the population is re-evaluated.

[0044] It should be explained that setting the initial size of the population usually depends on the complexity of the problem and the availability of resources; initially setting the baseline values ​​of the crossover rate and mutation rate, these parameters will affect the algorithm's exploration and development capabilities; defining the maximum number of iterations to limit the algorithm's running time to ensure efficiency, the population size and parameter settings need to balance computing resources and solution accuracy; using preset evaluation criteria, such as project time, cost and resource utilization, to evaluate the fitness of each individual (a possible sorting scheme for construction projects), the evaluation results are used to guide subsequent selection, crossover and mutation operations, fitness evaluation is a key step in genetic algorithms, ensuring that high-quality individuals are retained and optimized, based on the current population's fitness data, using the intuitive fuzzy reasoning model to calculate the crossover rate and mutation rate adjustment values, the adjustment values ​​should reflect the population Diversity and fitness change, optimize the search process, perform crossover and mutation operations according to the adjusted crossover rate and mutation rate to generate new individuals. Crossover operations usually involve the exchange of some genes of two individuals, and mutation randomly changes a gene of an individual. Crossover and mutation are basic operations used to generate new solutions in genetic algorithms, which help the algorithm to jump out of the local optimal solution. Based on the fitness of individuals, individuals suitable for forming the next generation of the population are selected. Roulette, tournament selection and other methods are usually used. Individuals with high fitness have a greater probability of being selected to inherit to the next generation. 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 conditions are met (such as fitness is no longer significantly improved), the iteration is terminated and the current optimal construction project sorting plan is output; if not, the iteration continues.

[0045] A construction project ranking table is generated based on the optimal construction project ranking scheme, and the construction project ranking table is used in combination with the expected completion time to optimize the initial construction plan.

[0046] It should be explained that, based on the optimal construction project sequencing plan obtained after optimization, a detailed construction project sequencing table is generated, which should include a detailed description of each construction project, scheduled start and end time, resource allocation and any related dependencies; the generated construction project sequencing table is compared with the expected completion time of the project, the differences and potential risks between the existing plan and the goal are analyzed, and the key time nodes, resource allocation and process flow in the initial construction plan are adjusted to ensure that each stage can be effectively connected and the use of time and resources is optimized. According to the adjusted construction plan, resource requirements and time allocation are re-evaluated to ensure that each resource can be effectively utilized within the appropriate time, and more flexible resource management strategies are considered, such as cross-project resource sharing, to improve resource utilization efficiency. The optimized construction plan is recorded in detail in an official document, including all updated timelines, resource allocations and risk management strategies, to ensure that all relevant stakeholders (such as project management teams, contractors, customers, etc.) have access to the final construction plan.

[0047] The construction progress prediction module 4 is used to predict the construction progress based on the optimized construction plan and real-time environmental factors, and monitor the construction efficiency of the project.

[0048] Preferably, the construction progress prediction module 4 predicts the construction progress based on the optimized construction plan and real-time environmental factors, and monitors the construction efficiency of the project, including: Collect current environmental data and actual conditions of the construction site in real time; Analyze the collected historical and real-time data to identify key variables that affect the construction progress; Build a construction progress prediction model based on key variables; Input the optimized construction plan and real-time environmental factors into the construction progress prediction model to generate a construction progress prediction within a preset time in the future; Monitor the actual progress and efficiency of the construction process in real time, detect the deviation between the actual construction progress and the predicted value, and identify and analyze the causes of the deviation; Based on the prediction results and the identification results of the deviation analysis, the risks that may affect the construction progress are assessed.

[0049] It should be explained that environmental monitoring sensors and on-site monitoring equipment, such as temperature and humidity sensors, anemometers, and video monitoring equipment, are deployed to collect environmental data and construction site conditions in real time, and the collected data is transmitted to the central database in real time using the Internet of Things technology; data integration and preprocessing, including data cleaning and normalization, are performed 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 that affect the construction progress from historical and real-time data; appropriate prediction model frameworks are selected, such as time series analysis, machine learning models, etc., and models are trained to predict future construction progress based on the identified key variables. The prediction model needs to be selected based on the characteristics of the construction and the nature of the data. Selection to ensure the accuracy and reliability of the prediction. Model training involves the selection of algorithms, parameter adjustment and verification processes. The currently collected real-time environmental data and the optimized construction plan are used as input to run the construction progress prediction model. The generated prediction report should show the construction progress within the preset time range in detail, and track and compare the actual progress of the construction site with the predicted progress in real time. When deviations are detected, the cause analysis is immediately carried out, which may include resource allocation errors, changes in environmental factors, etc. The deviation analysis results are used to evaluate the risks that may affect the project progress, and update the risk management plan, including possible mitigation measures and emergency response strategies. Risk assessment is an important part of project management and helps prevent potential problems and mitigate their impact.

[0050] The risk management module 5 is used to evaluate the construction risks during the construction of the project and dynamically adjust the optimized construction plan based on the forecast results of the construction progress.

[0051] Preferably, the risk management module 5 includes the following steps when evaluating the construction risks during the project construction process and dynamically adjusting the optimized construction plan in combination with the predicted results of the construction progress: Collect comprehensive data of construction projects and format the comprehensive data of construction projects into input form for quantum computing; Convert the identified risk factors into qubit representations; Using quantum algorithms to calculate the Hamming distance between the current construction status and historical risk event samples, we can identify the historical risk situations that are most similar to the current engineering conditions. Preferably, the quantum algorithm is used to calculate the Hamming distance between the current construction status and the historical risk event samples, and the identification of the historical risk situation most similar to the current engineering conditions includes: Using the state of quantum bits to represent each characteristic value in the construction status and historical risk data; Prepare quantum states for each current construction status and historical risk event sample; Use quantum circuits to calculate the Hamming distance between the current state and each historical event; According to 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.

[0052] It should be explained that first, comprehensive data related to the construction project is collected, including engineering parameters, environmental factors, staffing, historical risk events, etc. These data are pre-processed and standardized and converted into a format suitable for quantum computing input; this may involve data encoding and quantum state preparation; quantum encoding of identified risk factors, converting the characteristic value of each risk factor into the state of quantum bits (qubits), designing quantum circuits so that they can process these qubits to simulate the dynamic changes of risk situations. Quantum bits can represent multiple possibilities in a hyper-position state, which is very useful for simulating complex risk factors, and preparing quantum states representing the current construction status and historical risk data; this involves initialization and manipulation of quantum states, using quantum circuits to calculate the Hamming distance between the current construction status and each historical risk event sample; Hamming distance is a method of measuring the difference between two strings, which can be implemented in quantum computing through specific quantum logic gates, and the specific value of the Hamming distance is obtained based on the output of the quantum circuit; this usually involves quantum measurement and data interpretation, analyzing the measurement results, and identifying the historical risk events with the smallest Hamming distance from the current state; this information will be used to assess the current risk level and adjust the construction plan.

[0053] To use quantum circuits to calculate the Hamming distance between the current construction status and each historical risk event, it is first necessary to encode the data of the construction status and historical events into quantum bits, then design and implement a quantum circuit that performs comparisons through appropriate quantum gate operations (such as CNOT gates and Pauli-Z gates), and finally obtain the differences between the quantum bits through quantum measurements to calculate the Hamming distance. This process can quickly and efficiently identify the historical risk events that are most similar to the current construction conditions, providing a basis for risk assessment and construction plan adjustments.

[0054] Based on the identification results, find the historical risk events with the minimum Hamming distance; Based on the nearest risk events found, evaluate the risk level of the current construction, analyze the impact of the risk level on the construction progress and safety, and dynamically adjust the construction plan; Also monitor the implementation of construction activities and environmental changes according to the adjusted construction plan.

[0055] It should be explained that the Hamming distance calculated by the quantum algorithm is used to find the event with the minimum Hamming distance from the current construction status from the historical risk database, which involves querying the database and comparing data to ensure that the most relevant historical risk events are found; the risk level of the current construction is evaluated based on the identified nearest neighbor risk events, the results of the historical events, the measures taken and the final impact are analyzed, and this information is used to estimate the potential risks of the current project, and the actual impact of historical risk events with high risk levels on the progress and safety of the corresponding construction projects is analyzed. These data are used to predict specific problems that may be encountered in the current project and evaluate possible delays or safety risks. According to the results of the risk assessment, the construction plan is dynamically adjusted, including but not limited to changing the construction period, reallocating resources or modifying working methods, updating the risk management plan and emergency response strategy, ensuring that all potential problems can be effectively managed and resolved, real-time monitoring of the status of the construction site and changes in the external environment, ensuring that the adjusted plan can adapt to the new working environment, and using the on-site monitoring system and environmental monitoring equipment to collect real-time data and compare it with the preset risk management parameters.

[0056] In addition, when evaluating the risk level of the 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.

[0057] The specific steps for evaluating the risk level of the current construction based on the nearest neighbor risk events found, analyzing the impact of the risk level on the construction progress and safety, and dynamically adjusting the construction plan are as follows: Use geographic information systems (GIS) and historical accident databases to find historical risk events that are geographically adjacent to the current construction project, and analyze the types, frequencies, and severity of these events to determine their relevance to the current project; Based on the data of the nearest 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, construction period constraints and the impact of historical risk events; Compare risk levels with the construction schedule, predict possible delays and cost overruns caused by risks, and consider the potential impact of risks on site safety, such as possible accidents or injuries; Based on the risk level and its predicted impact on the construction schedule, adjust key nodes of the construction plan and resource allocation, and implement risk mitigation measures such as increasing safety inspections, adjusting work methods, or introducing additional safety equipment.

[0058] Suppose there is a large construction project, and the project site is located in an area where earthquakes often occur. In this case, the multi-dimensional monitoring system mentioned above is used to ensure construction safety and progress control; the following are specific application examples: 1) Initial construction plan building blocks Implementation: At the outset of the project, the project management team uses historical data and seismic risk analysis to develop a preliminary construction plan; this includes seismic requirements for building design, necessary engineering measures and timelines; Tools and Methods: Use Primavera software to develop detailed work packages and milestones to ensure that all building components take into account earthquake risks; 2) Data acquisition and environmental monitoring module Implementation: Install earthquake monitoring instruments and other environmental sensors, such as vibration sensors and inclinometers, at the construction site to monitor changes in the environment and construction status in real time; Tools and methods: Deployment of high-precision GPS systems and seismic monitoring equipment to monitor possible seismic activities and their impact on the construction site; 3) Data analysis and planning optimization module Implementation: Use the collected data to analyze the potential impact of seismic activity on construction progress and structural safety; optimize the construction plan based on data feedback; Tools and methods: Use intuitive fuzzy reasoning algorithms to process environmental and construction data, predict the impact of earthquakes on construction progress, and adjust construction schedules and resource allocation; 4) Construction progress prediction module Implementation: Predict future construction progress based on optimized construction plans and real-time environmental data, especially for rapid recovery after an earthquake; Tools and methods: Combine machine learning models and time series analysis to predict construction progress and adjust work plans to minimize the impact of earthquakes; 5) Risk management module Implementation: Assess risks of earthquakes and other relevant environmental factors and dynamically adjust construction plans based on real-time data; Tools and Methods: Use quantum algorithms to calculate the Hamming distance to historical earthquake events, identify potential high-risk periods, and take preventive measures, such as reinforcing already constructed structural parts or suspending high-risk operations during periods of active earthquakes.

[0059] According to another embodiment of the present invention, Figure 2 As shown, a multi-dimensional monitoring method for project progress based on information technology is also provided, and the multi-dimensional monitoring method for project progress includes the following steps: S1. Develop an initial construction plan based on the design and planning of the project; S2. Collect multi-dimensional data of construction progress at the construction site in real time and monitor environmental factors simultaneously; S3. Analyze the multi-dimensional data of construction progress using intuitive fuzzy reasoning algorithm, generate a construction project ranking table based on construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and expected completion time; S4. Based on the optimized construction plan and real-time environmental factors, the construction progress is predicted and the construction efficiency of the project is monitored; S5. Evaluate the construction risks during the project construction process, and dynamically adjust the optimized construction plan based on the predicted results of the construction progress.

[0060] In summary, with the help of the above technical solutions of the present invention, the present invention can dynamically adjust the construction plan to cope with the changing site conditions and external environmental factors by collecting multi-dimensional data (such as working hours, material usage and equipment efficiency) of the construction site in real time and applying the intuitive fuzzy reasoning algorithm. The intuitive fuzzy reasoning algorithm is used to analyze the needs and bottlenecks of the construction project. In combination with construction logic and physical constraints, resources (such as manpower, materials and mechanical equipment) can be configured more accurately; the fuzzy logic controller is used to handle fuzzy and uncertain situations, and scientific decision support is provided in combination with expert experience and historical data; by continuously monitoring and analyzing the key variables of the construction progress (such as the maximum fitness change rate and the average fitness change rate), potential risks and problems are predicted and responded to, and the intuitive fuzzy reasoning is used to optimize the construction plan, which can ensure that the project meets all relevant building and safety standards. Through continuous monitoring and timely adjustment, the risk of violating regulations and standards can be avoided, and the quality and safety of the project can be guaranteed; the present invention collects environmental data and the actual situation of the construction site in real time, and analyzes it in combination with historical data. The construction progress prediction module can accurately identify the key variables that affect the construction progress, which can greatly improve the accuracy of the construction progress prediction; through the prediction model The project team can more reasonably allocate resources, such as manpower, materials and equipment, to match the actual construction needs by using the progress forecast generated by the model. By monitoring the deviation between the real-time and predicted values, the project team can quickly identify risks and take corresponding risk mitigation measures. The integration and analysis of real-time data provide strong decision-making support for project management. The construction progress prediction module can timely predict and respond to construction delays caused by emergencies such as weather changes and equipment failures through continuous monitoring and analysis of environmental variables. The present invention uses quantum algorithms to calculate the Hamming distance, which can accurately compare the similarity between the current construction status and historical risk events, so as to conduct more accurate risk assessment. Based on the identified risk conditions, the construction plan can be dynamically adjusted to respond to possible risks in a timely manner, such as environmental changes, equipment failures, etc., to reduce construction delays and cost overruns. Using quantum technology to update the risk database and construction plan in real time can achieve real-time and continuity of risk management. Through accurate risk assessment, the project team can more reasonably allocate resources and prepare emergency response strategies. The integrated quantum computing and risk management system provides strong decision-making support for project management, making the decision-making process more scientific and data-driven, and reducing uncertain decisions based on experience or intuition.

[0061] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are merely examples for the convenience of description and are not intended to limit the present invention. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be based on the claims.

Claims

1. A multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The multi-dimensional project progress monitoring system includes: initial construction plan building module, data collection and environmental monitoring module, data analysis and plan optimization module, construction progress prediction module and risk management module; The initial construction plan building module is used to develop an initial construction plan based on the design and planning of the project; Data collection and environmental monitoring module, used to collect multi-dimensional data of construction progress at the construction site in real time and simultaneously monitor environmental factors; The data analysis and planning optimization module is used to analyze the multi-dimensional data of the construction progress using the intuitive fuzzy reasoning algorithm, generate a construction project ranking table based on the construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and the expected completion time; The construction progress prediction module is used to predict the construction progress based on the optimized construction plan and real-time environmental factors, and monitor the construction efficiency of the project; The risk management module is used to evaluate the construction risks during the project construction process and dynamically adjust the optimized construction plan based on the forecast results of the construction progress; The data analysis and plan optimization module uses the intuitive fuzzy reasoning algorithm to analyze the multi-dimensional data of the construction progress, generates a construction project ranking table in combination with the construction logic and physical constraints, and optimizes the initial construction plan based on the construction project ranking table and the expected completion time, including: Collect multi-dimensional data of construction progress, identify the actual needs of the construction project, and clarify the logical relationships and physical constraints based on the actual needs; Setting key input variables of the intuitive fuzzy reasoning algorithm based on logical relationships and physical constraints, wherein the key input variables include a maximum fitness change rate and an average fitness change rate; Establish an intuitive fuzzy reasoning model, and adjust the crossover rate and mutation rate of the intuitive fuzzy reasoning algorithm based on the maximum fitness change rate and the average fitness change rate; According to the adjusted parameters of the intuitive fuzzy reasoning model, adaptive crossover and mutation operations are performed to iteratively update the population until the algorithm converges to the optimal construction project sequencing solution; A construction project ranking table is generated based on the optimal construction project ranking scheme, and the construction project ranking table is used in combination with the expected completion time to optimize the initial construction plan.

2. According to claim 1, a multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The method of establishing an intuitive fuzzy reasoning model and adjusting the crossover rate and mutation rate of the intuitive fuzzy reasoning algorithm based on the maximum fitness change rate and the average fitness change rate includes: The maximum fitness change rate and the average fitness change rate are used as input variables to establish the intuitive fuzzy reasoning model; Set language values ​​for input variables and assign appropriate membership functions to each language value to convert actual data into membership in fuzzy sets; Define fuzzy inference rules based on the combination of linguistic values ​​of input variables; Utilizing a fuzzy logic reasoning mechanism, based on the membership degree and the fuzzy reasoning rule, calculate the adjustment values ​​of the crossover rate and the mutation rate; Through the defuzzification method, the calculation results using the fuzzy logic reasoning mechanism are converted into adjustment values ​​of specific crossover rate and mutation rate, and the adjusted crossover rate and mutation rate are obtained.

3. According to claim 2, a multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The method of using a fuzzy logic reasoning mechanism to calculate the adjustment values ​​of the crossover rate and the mutation rate based on the membership degree and the fuzzy reasoning rule includes: Calculate the membership of the maximum fitness change rate and the average fitness change rate, and determine the membership degree of the corresponding language value based on the membership; Matching fuzzy inference rules based on the degree of membership of linguistic values; Using fuzzy logic operations to calculate the influence of various fuzzy reasoning rules, a comprehensive fuzzy output is formed; According to the comprehensive fuzzy output, the fuzzy logic reasoning mechanism is used to generate the recommended adjustment values ​​of the crossover rate and mutation rate for each fuzzy reasoning rule; Aggregating all recommended adjustment values ​​to form a single fuzzy output value; The single fuzzy output value is converted into a specific numerical value through the defuzzification method to obtain the final adjustment values ​​of the crossover rate and mutation rate.

4. According to claim 3, a multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The expression for calculating the membership of the maximum fitness change rate and the average fitness change rate is: ； In the formula, e represents the input variables including the maximum fitness change rate and the average fitness change rate; a Indicates the position where the degree of membership rises from 0; b Indicates the position where the membership reaches the highest value 1, indicating that the input value e conforming to predefined language categories at certain points; c Indicates the position where the degree of membership drops to 0; Represents input variables e The degree of membership to a fuzzy set.

5. According to claim 4, a multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The method of generating the recommended adjustment values ​​of the crossover rate and the mutation rate for each fuzzy inference rule by using the fuzzy logic inference mechanism according to the comprehensive fuzzy output includes: According to the set fuzzy logic reasoning mechanism, match the fuzzy reasoning rules; Using fuzzy logic operators to synthesize the effects of matched fuzzy reasoning rules to form a comprehensive fuzzy logic output; For each fuzzy rule, the output membership is calculated based on the result of the fuzzy logic operation; Based on the membership degree, the fuzzy logic reasoning mechanism is used to generate specific adjustment recommendations for the crossover rate and mutation rate according to the fuzzy logic operation results; Aggregate the recommended adjustment values ​​generated by all fuzzy inference rules.

6. According to claim 5, a multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The method of performing adaptive crossover and mutation operations based on the adjusted parameters of the intuitive fuzzy reasoning model and continuously iterating and updating the population until the algorithm converges to the optimal construction project sorting scheme includes: Set the initial size of the population, the baseline values ​​of the crossover rate and mutation rate, and the maximum number of iterations; Evaluate the fitness of individuals in the initial population according to the preset evaluation criteria; Using the intuitive fuzzy reasoning model, the adjustment values ​​of the crossover rate and mutation rate are calculated according to the fitness data of the current population; Perform crossover and mutation operations according to the calculated adjusted values ​​of crossover rate and mutation rate; 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 conditions are met, the iteration ends and the current optimal construction project sorting scheme is output; if not, the iteration continues and the fitness of individuals in the population is re-evaluated.

7. According to claim 1, a multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The construction progress prediction module predicts the construction progress based on the optimized construction plan and real-time environmental factors, and monitors the construction efficiency of the project, including: Collect current environmental data and actual conditions of the construction site in real time; Analyze the collected historical and real-time data to identify key variables that affect the construction progress; Build a construction progress prediction model based on key variables; Input the optimized construction plan and real-time environmental factors into the construction progress prediction model to generate a construction progress prediction within a preset time in the future; Monitor the actual progress and efficiency of the construction process in real time, detect the deviation between the actual construction progress and the predicted value, and identify and analyze the causes of the deviation; Based on the prediction results and the identification results of the deviation analysis, the risks that may affect the construction progress are assessed.

8. According to claim 1, a multi-dimensional monitoring system for engineering project progress based on information technology, It is characterized in that The risk management module includes the following when evaluating the construction risks during the project construction process and dynamically adjusting the optimized construction plan in combination with the forecast results of the construction progress: Collect comprehensive data of construction projects and format the comprehensive data of construction projects into input form for quantum computing; Convert the identified risk factors into qubit representations; Using quantum algorithms to calculate the Hamming distance between the current construction status and historical risk event samples, we can identify the historical risk situations that are 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 risk events found, evaluate the risk level of the current construction, analyze the impact of the risk level on the construction progress and safety, and dynamically adjust the construction plan; Also monitor the implementation of construction activities and environmental changes according to the adjusted construction plan.

9. According to claim 8, a multi-dimensional monitoring system for project progress based on information technology, It is characterized in that The method of using quantum algorithms to calculate the Hamming distance between the current construction status and the historical risk event samples and identifying the historical risk situation most similar to the current engineering conditions includes: Using the state of quantum bits to represent each characteristic value in the construction status and historical risk data; Prepare quantum states for each current construction status and historical risk event sample; Use quantum circuits to calculate the Hamming distance between the current state and each historical event; According to 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.

10. 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 claimed in any one of claims 1 to 9, It is characterized in that The multi-dimensional monitoring method for the progress of the engineering 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 of construction progress at the construction site in real time and monitor environmental factors simultaneously; S3. Analyze the multi-dimensional data of construction progress using intuitive fuzzy reasoning algorithm, generate a construction project ranking table based on construction logic and physical constraints, and optimize the initial construction plan based on the construction project ranking table and expected completion time; S4. Based on the optimized construction plan and real-time environmental factors, the construction progress is predicted and the construction efficiency of the project is monitored; S5. Evaluate the construction risks during the project construction process, and dynamically adjust the optimized construction plan based on the predicted results of the construction progress.

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