Ecological early warning and green evaluation system based on power construction cooperation algorithm

Abstract

The present application relates to the technical field of green ecological evaluation, and particularly relates to an ecological early warning green evaluation system based on a power construction collaborative algorithm, so as to solve the problem that the overall composite influence of construction activities on the ecological environment cannot be accurately reflected due to the fragmented evaluation mode, and finally the accuracy of the ecological evaluation result is insufficient; the present application generates standardized four types of ecological data by means of a data cleaning algorithm and a time synchronization technology to provide basic support by setting a data acquisition and processing module, and the accuracy of the four types of parameter weight proportions is accurately adjusted according to the construction stage characteristics by combining a dynamic weight distribution mechanism and establishing an evaluation model by adopting a multi-objective optimization algorithm, and then the evaluation result is visualized and the standardized report is generated by a data display and output module, the overall ecological influence of the power construction is evaluated by the multi-module collaborative empowerment of the core algorithm, and finally the accuracy of the ecological evaluation result is improved.

Description

Technical Field

[0001] This invention relates to the field of green ecological evaluation technology, and in particular to an ecological early warning green evaluation system based on a power construction collaborative algorithm. Background Technology

[0002] The ecological early warning green assessment system is a general-purpose tool for ecological environment monitoring and assessment. It collects environmental data such as air pollutant concentrations, soil physicochemical properties, and water quality indicators through methods such as manual sampling and fixed-point automatic monitoring. The collected data is then compared with preset fixed ecological thresholds, and finally a phased ecological impact assessment report is generated. When the monitored indicators exceed the fixed thresholds, corresponding early warning prompts are triggered, providing basic data support and reference for regional ecological protection.

[0003] Chinese patent CN116933042B discloses a water ecology evaluation method and system based on deep learning algorithms. The method includes the following steps: acquiring an aquatic organism monitoring dataset; dividing the dataset into three different datasets; adding the best and worst individuals to the three datasets to obtain three new datasets; standardizing the data in the three new datasets; establishing a variational autoencoder for the standardized datasets; obtaining the distance between each individual's point and the best individual, and between each individual's point and the point corresponding to the worst individual; obtaining the distance weights for different species; obtaining the projected distance; and obtaining a comprehensive evaluation score based on the distance weights and projected distances for different species. This application considers monitoring data from all biological groups and the influence of different species, making the evaluation results for aquatic organisms more accurate.

[0004] Regarding the above-mentioned and existing related technologies, the inventors believe that the following defects often exist: the current ecological and environmental impact assessment in power construction generally adopts the method of individual detection and assessment, which cannot accurately reflect the overall complex impact of construction activities on the ecological environment. This fragmented assessment model results in a lack of effective collaborative optimization mechanism between ecological protection measures and construction progress, ultimately leading to insufficient accuracy of ecological assessment results. Summary of the Invention

[0005] The purpose of this invention is to provide an ecological early warning and green evaluation system based on a collaborative algorithm for power construction, which solves the problem in the above-mentioned background technology that the fragmented evaluation mode cannot accurately reflect the overall complex impact of construction activities on the ecological environment, ultimately leading to insufficient accuracy of ecological evaluation results.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an ecological early warning and green evaluation system based on a power construction collaborative algorithm, comprising: According to the data acquisition and processing module, outliers are eliminated through data cleaning algorithms and time synchronization technology is used to collect and standardize the ecological monitoring data of four types of monitoring data: air quality, water quality, soil, and noise. This enables real-time acquisition and standardized processing of ecological monitoring data in the power construction area, providing an accurate and complete data foundation for subsequent collaborative analysis, ensuring that the data quality and timeliness meet the assessment requirements, and supporting the collaborative assessment of the four types of ecological parameters. The core algorithm and analysis module uses a multi-objective optimization algorithm to establish an evaluation model, and adjusts the weight ratio of four types of parameters according to the characteristics of the construction stage through a dynamic weight allocation mechanism. This enables collaborative analysis and overall evaluation of ecological monitoring data, balances construction efficiency and ecological protection requirements, provides scientific decision-making basis and early warning information support, and ensures the accuracy and practicality of the evaluation results. The data display and output module uses visualization interface technology to display data graphically and adopts report generation algorithms to automatically output standard evaluation documents. It displays evaluation results and monitoring data in a graphical way, provides real-time display and historical query functions, generates standardized reports to support user decision-making needs, and ensures the completeness and intuitiveness of information display. The data recording and storage module achieves systematic storage through a standardized database structure and adopts data encryption technology and regular backup mechanisms to store monitoring data, system logs, and early warning records, ensuring data integrity, security, and traceability. It supports subsequent data analysis and regulatory verification work, and guarantees the reliability and stability of system operation.

[0007] Furthermore, the data acquisition and processing module includes a real-time data acquisition unit, a data caching unit, and a connection status monitoring unit, wherein: The real-time data acquisition unit is responsible for acquiring ecological data from four types of environmental monitoring instruments in the power construction area in real time. Through multi-threaded parallel acquisition technology and real-time data stream processing mechanism, it ensures the timely acquisition and transmission of monitoring data, provides the original data source for subsequent processing and analysis, and supports the system's continuous monitoring needs for the ecological status of the construction area. The data caching unit is used to temporarily store the collected ecological monitoring data. Through a circular buffer design and memory management algorithm, it realizes data caching and read / write operations, prevents data loss, balances differences in data processing rates, ensures data integrity and system stability during data transmission, and provides a smooth data flow for subsequent processing units. The connection status monitoring unit monitors the connection status between the monitoring instrument and the system in real time. Through a heartbeat detection mechanism and a timeout reconnection algorithm, it continuously detects the online status and communication quality of the equipment, promptly detects connection anomalies and triggers early warnings, ensuring the reliability of the data acquisition link and providing connection guarantee support for the stable operation of the system.

[0008] Furthermore, the data acquisition and processing module also includes a data cleaning unit and a data alignment unit, wherein: The data cleaning unit preprocesses the collected raw ecological data, identifies and removes erroneous data and noise interference through outlier detection algorithms and data validity verification rules, and uses data imputation technology to handle missing values, thereby improving data quality and accuracy and providing clean and well-organized data input for subsequent analysis and evaluation. The data alignment unit ensures the temporal consistency of the four types of ecological monitoring data. Through timestamp synchronization algorithms and clock calibration technology, it unifies the time base of different monitoring instruments, eliminates data asynchrony problems caused by time deviations, and realizes the temporal alignment of the four types of ecological parameter monitoring data, providing a time-consistent data foundation for collaborative analysis and overall assessment.

[0009] Furthermore, the core algorithm and analysis module includes a multi-parameter comprehensive evaluation unit, a multi-objective optimization calculation unit, and a dynamic weight allocation unit, wherein: The multi-parameter comprehensive evaluation unit is used to simultaneously conduct collaborative analysis and comprehensive evaluation of four types of ecological parameters: air quality, water quality, soil, and noise. By establishing a multi-parameter correlation analysis model and a comprehensive evaluation index system, it integrates the correlation effects of monitoring data of the four types of ecological parameters to achieve a comprehensive assessment of the ecological impact of power construction and provide a scientific basis for judging the overall ecological status. The multi-objective optimization calculation unit is used to balance the two conflicting objectives of maximizing construction efficiency and minimizing ecological impact. It uses a multi-objective genetic algorithm to find the best balance between construction scheme and ecological protection, realizes dual-objective synergistic optimization, provides the optimal scheme selection for construction decision-making, and ensures the unity of economic benefits and ecological benefits. The dynamic weight allocation unit dynamically adjusts the weight coefficients of four types of ecological parameters according to the characteristics of different construction stages. Through stage identification algorithms and weight adaptive adjustment mechanisms, it sets differentiated weight ratios for foundation construction, main construction, equipment installation, and finishing stages, thereby realizing the dynamic configuration of parameter importance and enhancing the pertinence and timeliness of the assessment.

[0010] Furthermore, the core algorithm and analysis module also includes a threshold comparison unit, an early warning judgment unit, and an algorithm parameter configuration unit, wherein: The threshold comparison unit is used to compare real-time monitoring data with preset ecological thresholds. Through hierarchical threshold setting technology, a two-level judgment standard of warning threshold and limit threshold is established. Real-time data comparison algorithm is used for continuous monitoring to promptly detect data anomalies and exceedances, providing basic data support for warning judgment and ensuring the sensitivity of monitoring. The early warning judgment unit generates ecological early warning signals of different levels based on threshold comparison results. Through a three-level early warning judgment logic and early warning triggering mechanism, combined with multi-parameter comprehensive evaluation results, it generates specific early warning information and disposal suggestions, providing timely risk warnings for construction management and supporting response and risk control. The algorithm parameter configuration unit contains various parameters and configuration information required by the core algorithm and analysis module for various evaluation algorithms. Through the parameter management interface and configuration file storage mechanism, it maintains system configuration data such as threshold settings, weight coefficients, and algorithm parameters, supports visual modification and version management of parameters, and ensures the flexibility and maintainability of algorithm operation.

[0011] Furthermore, the data display and output module includes a real-time data display unit and an evaluation result display unit, wherein: The real-time data display unit is used to graphically display the real-time monitoring data of four types of ecological parameters in the power construction area. Through data stream processing technology and real-time refresh mechanism, it dynamically displays the current monitoring values ​​in the form of curves, dashboards and digital panels. It supports the simultaneous display of multiple parameters on the same screen, providing operators with an intuitive real-time monitoring interface to ensure timely visualization and status awareness of monitoring data. The assessment results display unit is used to visually present the ecological impact assessment results and early warning level information. Through a chart rendering engine and color coding technology, it displays the comprehensive assessment score and the degree of impact of multiple parameters in the form of radar charts, bar charts and heat maps. It adopts a red, yellow and green three-color early warning label system to clearly display the ecological risk level of different regions, providing an intuitive display of assessment results for decision-making.

[0012] Furthermore, the data display and output module also includes a historical data query unit and a report generation unit, wherein: The historical data query unit provides query and retrieval functions for historical monitoring data and evaluation results. Through the time range filtering conditions and parameter type selection interface, it supports combined queries by date, monitoring point, and parameter type. It adopts pagination display technology and data export function to realize the rapid retrieval and result browsing of historical data, meeting the needs of post-event analysis and traceability. The report generation unit is used to automatically generate standardized ecological assessment reports and early warning record documents. Through the report template engine and data filling algorithm, it automatically generates complete reports containing monitoring data, assessment results and early warning information according to preset formats. It supports PDF and Excel format output and provides report customization and batch generation functions to meet the document needs of different users.

[0013] Furthermore, the data recording and storage module includes a historical data storage unit, a system log recording unit, and an early warning event recording unit, wherein: The historical data storage unit is used to store ecological monitoring data, assessment results data, and system operation data. It establishes a standardized data table structure and indexing mechanism through a relational database management system and partitioned storage technology. It adopts data compression and archiving strategies to achieve data storage and fast retrieval, and ensures data integrity and traceability. The system log recording unit is used to record system operating status, user operation records, and abnormal event information. Through the log collection framework and hierarchical recording mechanism, system activities are recorded according to three levels: information, warning, and error. Timestamp marking and log rotation technology are used to achieve standardized recording and management of operating logs, supporting system fault diagnosis and operating status monitoring. The early warning event recording unit stores information on the occurrence time, warning level, handling status, and handling results of all ecological early warning events. Through event recording tables and associated storage technology, a complete early warning event archive is established. A status tracking mechanism is used to record the entire process of an early warning from occurrence to cancellation, supporting statistical analysis and effect evaluation of early warning events, and providing data support for early warning management.

[0014] Furthermore, the specific process for establishing an evaluation model using a multi-objective optimization algorithm is as follows: S1. First, normalize the four types of ecological monitoring data to eliminate dimensional differences. At the same time, initialize the parameters of the multi-objective genetic algorithm, including population size, crossover probability, and mutation probability. If there are abnormal or missing data, trigger the data verification abnormality process and return to the data acquisition module to reacquire the data. S2. A multi-objective genetic algorithm framework based on the Pareto optimality concept is adopted. Population evolution is carried out through non-dominated sorting and crowding calculation. The construction efficiency objective function and the ecological impact objective function are calculated in each generation. The maximum number of iterations and the convergence threshold are set. When the improvement of the result obtained by ten consecutive calculations is less than the set threshold, it is determined that a satisfactory optimal solution has been found and the calculation is stopped. S3. Select the Pareto optimal solution set from the final population, verify the feasibility of each solution, and remove solutions that violate the constraints. If the optimal solution set is empty or insufficient, trigger the re-optimization process, adjust the algorithm parameters and recalculate. S4. Based on the characteristics of the construction stage and the requirements for ecological protection, the optimal solution selected by Pareto is used to generate ten feasible construction schemes. The objective function value and ecological impact assessment results of each scheme are output. At the same time, the algorithm running log and performance indicators are recorded to support subsequent algorithm optimization and parameter tuning.

[0015] Furthermore, the specific process of the dynamic weight allocation mechanism is as follows: S1. First, obtain the current construction stage information through the project progress management system, and at the same time read the real-time monitoring data to verify the accuracy of stage identification. If there is a stage identification conflict or data abnormality, trigger the manual confirmation process to ensure the reliability of stage judgment. S2. Based on the identified construction stage, retrieve the corresponding four types of ecological parameter weight configuration schemes from the preset weight rule library. Three alternative weight schemes are preset for each stage. The system selects the most suitable configuration based on the current environmental sensitivity. S3. Apply the selected weight configuration scheme, calculate the new weight coefficients of each parameter, and perform normalization verification. At the same time, perform sensitivity analysis to ensure that the weight adjustment will not cause fluctuations in the evaluation results. If the verification fails, use the backup weight scheme to recalculate. S4. Apply the verified weight configuration to the evaluation model, update the collaborative analysis and comprehensive evaluation logic of the multi-parameter comprehensive evaluation unit in real time, and record the time, stage, specific weight value and reason for the weight adjustment. Establish a complete weight change file to support subsequent weight optimization and effect analysis.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention proposes an ecological early warning and green evaluation system based on a collaborative algorithm for power construction. Upon system startup, the real-time data acquisition unit of the data acquisition and processing module first collects four types of ecological monitoring data—air quality, water quality, soil, and noise—from the power construction area. A data caching unit temporarily stores the data, and a connection status monitoring unit ensures stable device connections through heartbeat detection. Next, a data cleaning unit removes outliers and supplements missing values. A data alignment unit uses timestamp synchronization technology to achieve temporal alignment of the four types of ecological parameter monitoring data, providing a standardized data foundation for subsequent analysis. Subsequently, the multi-objective optimization calculation unit of the core algorithm and analysis module normalizes the data and initializes algorithm parameters, running a Pareto-optimal multi-objective genetic algorithm to find the optimal solution. A dynamic weight allocation unit retrieves the weight scheme according to the construction stage and completes verification and updates. A multi-parameter comprehensive evaluation unit conducts collaborative analysis, and a threshold comparison unit and an early warning judgment unit generate tiered early warnings. Finally, the data display and output module visualizes real-time data and evaluation results through each unit and automatically generates standard reports. The data recording and storage module synchronously stores monitoring data, system logs, and early warning records, ultimately improving the accuracy of the ecological assessment results. Attached Figure Description

[0017] Figure 1 This is a diagram of the overall architecture of the present invention; Figure 2 This is a diagram of the data acquisition and processing module architecture of the present invention; Figure 3 This is a diagram illustrating the core algorithm and analysis module architecture of the present invention. Figure 4 This is a diagram illustrating the architecture of the data display and output module of the present invention. Figure 5 This is a diagram illustrating the architecture of the data recording and storage module of the present invention. Figure 6 A flowchart for establishing an evaluation model for the multi-objective optimization algorithm of this invention; Figure 7 This is a flowchart of the dynamic weight allocation mechanism of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To resolve the existing issues, please refer to Figure 1 - Figure 7 The following preferred technical solutions are provided: An ecological early warning and green assessment system based on a collaborative algorithm for power construction includes: The data acquisition and processing module eliminates outliers through data cleaning algorithms and uses time synchronization technology to process four types of monitoring data: air quality, water quality, soil, and noise. This enables real-time acquisition and standardized processing of ecological monitoring data in power construction areas, providing an accurate and complete data foundation for subsequent collaborative analysis. It ensures that the data quality and timeliness meet the assessment requirements and supports the collaborative assessment of the four types of ecological parameters. The core algorithm and analysis module uses a multi-objective optimization algorithm to establish an evaluation model, and adjusts the weight ratio of four types of parameters according to the characteristics of the construction stage through a dynamic weight allocation mechanism. This enables collaborative analysis and overall evaluation of ecological monitoring data, balances construction efficiency and ecological protection requirements, provides scientific decision-making basis and early warning information support, and ensures the accuracy and practicality of the evaluation results. The data display and output module uses visualization interface technology to display data graphically and adopts report generation algorithms to automatically output standard evaluation documents. It displays evaluation results and monitoring data in a graphical way, provides real-time display and historical query functions, generates standardized reports to support user decision-making needs, and ensures the completeness and intuitiveness of information display. The data recording and storage module achieves systematic storage through a standardized database structure and adopts data encryption technology and regular backup mechanisms to store monitoring data, system logs, and early warning records, ensuring data integrity, security, and traceability. It supports subsequent data analysis and regulatory verification work, and guarantees the reliability and stability of system operation.

[0020] The data acquisition and processing module includes a real-time data acquisition unit, a data caching unit, and a connection status monitoring unit, wherein: The real-time data acquisition unit is responsible for acquiring ecological data from four types of environmental monitoring instruments in the power construction area in real time. Through multi-threaded parallel acquisition technology and real-time data stream processing mechanism, it ensures the timely acquisition and transmission of monitoring data, provides the original data source for subsequent processing and analysis, and supports the system's continuous monitoring needs for the ecological status of the construction area. The data caching unit is used to temporarily store the collected ecological monitoring data. Through a circular buffer design and memory management algorithm, it realizes data caching and read / write operations, prevents data loss, balances differences in data processing rates, ensures data integrity and system stability during data transmission, and provides a smooth data flow for subsequent processing units. The connection status monitoring unit monitors the connection status between the monitoring instrument and the system in real time. Through a heartbeat detection mechanism and a timeout reconnection algorithm, it continuously detects the online status and communication quality of the equipment, promptly detects connection anomalies and triggers early warnings, ensuring the reliability of the data acquisition link and providing connection guarantee support for the stable operation of the system.

[0021] The data acquisition and processing module also includes a data cleaning unit and a data alignment unit, wherein: The data cleaning unit preprocesses the collected raw ecological data, identifies and removes erroneous data and noise interference through outlier detection algorithms and data validity verification rules, and uses data imputation technology to handle missing values, thereby improving data quality and accuracy and providing clean and well-organized data input for subsequent analysis and evaluation. The data alignment unit ensures the temporal consistency of the four types of ecological monitoring data. Through timestamp synchronization algorithms and clock calibration technology, it unifies the time base of different monitoring instruments, eliminates data asynchrony problems caused by time deviations, and realizes the temporal alignment of the four types of ecological parameter monitoring data, providing a time-consistent data foundation for collaborative analysis and overall assessment.

[0022] The core algorithm and analysis module includes a multi-parameter comprehensive evaluation unit, a multi-objective optimization calculation unit, and a dynamic weight allocation unit, among which: The multi-parameter comprehensive evaluation unit is used to simultaneously conduct collaborative analysis and comprehensive evaluation of four types of ecological parameters: air quality, water quality, soil, and noise. By establishing a multi-parameter correlation analysis model and a comprehensive evaluation index system, it integrates the correlation effects of monitoring data of the four types of ecological parameters to achieve a comprehensive assessment of the ecological impact of power construction and provide a scientific basis for judging the overall ecological status. The multi-objective optimization calculation unit is used to balance the two conflicting objectives of maximizing construction efficiency and minimizing ecological impact. It uses a multi-objective genetic algorithm to find the best balance between construction scheme and ecological protection, realizes dual-objective synergistic optimization, provides the optimal scheme selection for construction decision-making, and ensures the unity of economic benefits and ecological benefits. The dynamic weight allocation unit dynamically adjusts the weight coefficients of four types of ecological parameters according to the characteristics of different construction stages. Through stage identification algorithms and weight adaptive adjustment mechanisms, it sets differentiated weight ratios for foundation construction, main construction, equipment installation, and finishing stages, thereby realizing the dynamic configuration of parameter importance and enhancing the pertinence and timeliness of the assessment.

[0023] The core algorithm and analysis module also includes a threshold comparison unit, an early warning judgment unit, and an algorithm parameter configuration unit, among which: The threshold comparison unit is used to compare real-time monitoring data with preset ecological thresholds. Through hierarchical threshold setting technology, a two-level judgment standard of warning threshold and limit threshold is established. Real-time data comparison algorithm is used for continuous monitoring to promptly detect data anomalies and exceedances, providing basic data support for warning judgment and ensuring the sensitivity of monitoring. The early warning judgment unit generates ecological early warning signals of different levels based on threshold comparison results. Through a three-level early warning judgment logic and early warning triggering mechanism, combined with multi-parameter comprehensive evaluation results, it generates specific early warning information and disposal suggestions, providing timely risk warnings for construction management and supporting response and risk control. The algorithm parameter configuration unit contains various parameters and configuration information required by the core algorithm and analysis module for various evaluation algorithms. Through the parameter management interface and configuration file storage mechanism, it maintains system configuration data such as threshold settings, weight coefficients, and algorithm parameters, supports visual modification and version management of parameters, and ensures the flexibility and maintainability of algorithm operation.

[0024] The data display and output module includes a real-time data display unit and an evaluation result display unit, wherein: The real-time data display unit is used to graphically display the real-time monitoring data of four types of ecological parameters in the power construction area. Through data stream processing technology and real-time refresh mechanism, it dynamically displays the current monitoring values ​​in the form of curves, dashboards and digital panels. It supports the simultaneous display of multiple parameters on the same screen, providing operators with an intuitive real-time monitoring interface to ensure timely visualization and status awareness of monitoring data. The assessment results display unit is used to visually present the ecological impact assessment results and early warning level information. Through a chart rendering engine and color coding technology, it displays the comprehensive assessment score and the degree of impact of multiple parameters in the form of radar charts, bar charts and heat maps. It adopts a red, yellow and green three-color early warning label system to clearly display the ecological risk level of different regions, providing an intuitive display of assessment results for decision-making.

[0025] The data display and output module also includes a historical data query unit and a report generation unit, among which: The historical data query unit provides query and retrieval functions for historical monitoring data and evaluation results. Through the time range filtering conditions and parameter type selection interface, it supports combined queries by date, monitoring point, and parameter type. It adopts pagination display technology and data export function to realize the rapid retrieval and result browsing of historical data, meeting the needs of post-event analysis and traceability. The report generation unit is used to automatically generate standardized ecological assessment reports and early warning record documents. Through the report template engine and data filling algorithm, it automatically generates complete reports containing monitoring data, assessment results and early warning information according to preset formats. It supports PDF and Excel format output and provides report customization and batch generation functions to meet the document needs of different users.

[0026] The data recording and storage module includes a historical data storage unit, a system log recording unit, and an early warning event recording unit, among which: The historical data storage unit is used to store ecological monitoring data, assessment results data, and system operation data. It establishes a standardized data table structure and indexing mechanism through a relational database management system and partitioned storage technology. It adopts data compression and archiving strategies to achieve data storage and fast retrieval, and ensures data integrity and traceability. The system log recording unit is used to record system operating status, user operation records, and abnormal event information. Through the log collection framework and hierarchical recording mechanism, system activities are recorded according to three levels: information, warning, and error. Timestamp marking and log rotation technology are used to achieve standardized recording and management of operating logs, supporting system fault diagnosis and operating status monitoring. The early warning event recording unit stores information on the occurrence time, warning level, handling status, and handling results of all ecological early warning events. Through event recording tables and associated storage technology, a complete early warning event archive is established. A status tracking mechanism is used to record the entire process of an early warning from occurrence to cancellation, supporting statistical analysis and effect evaluation of early warning events, and providing data support for early warning management.

[0027] The specific process for establishing an evaluation model using a multi-objective optimization algorithm is as follows: S1. First, normalize the four types of ecological monitoring data to eliminate dimensional differences. At the same time, initialize the parameters of the multi-objective genetic algorithm, including population size, crossover probability, and mutation probability. If there are abnormal or missing data, trigger the data verification abnormality process and return to the data acquisition module to reacquire the data. S2. A multi-objective genetic algorithm framework based on the Pareto optimality concept is adopted. Population evolution is carried out through non-dominated sorting and crowding calculation. The construction efficiency objective function and the ecological impact objective function are calculated in each generation. The maximum number of iterations and the convergence threshold are set. When the improvement of the result obtained by ten consecutive calculations is less than the set threshold, it is determined that a satisfactory optimal solution has been found and the calculation is stopped. S3. Select the Pareto optimal solution set from the final population, verify the feasibility of each solution, and remove solutions that violate the constraints. If the optimal solution set is empty or insufficient, trigger the re-optimization process, adjust the algorithm parameters and recalculate. S4. Based on the characteristics of the construction stage and the requirements for ecological protection, the optimal solution selected by Pareto is used to generate ten feasible construction schemes. The objective function value and ecological impact assessment results of each scheme are output. At the same time, the algorithm running log and performance indicators are recorded to support subsequent algorithm optimization and parameter tuning.

[0028] The specific process of the dynamic weight allocation mechanism is as follows: S1. First, obtain the current construction stage information through the project progress management system, and at the same time read the real-time monitoring data to verify the accuracy of stage identification. If there is a stage identification conflict or data abnormality, trigger the manual confirmation process to ensure the reliability of stage judgment. S2. Based on the identified construction stage, retrieve the corresponding four types of ecological parameter weight configuration schemes from the preset weight rule library. Three alternative weight schemes are preset for each stage. The system selects the most suitable configuration based on the current environmental sensitivity. S3. Apply the selected weight configuration scheme, calculate the new weight coefficients of each parameter, and perform normalization verification. At the same time, perform sensitivity analysis to ensure that the weight adjustment will not cause fluctuations in the evaluation results. If the verification fails, use the backup weight scheme to recalculate. S4. Apply the verified weight configuration to the evaluation model, update the collaborative analysis and comprehensive evaluation logic of the multi-parameter comprehensive evaluation unit in real time, and record the time, stage, specific weight value and reason for the weight adjustment. Establish a complete weight change file to support subsequent weight optimization and effect analysis.

[0029] Specifically, after the system starts, the data acquisition and processing module first begins operation. The real-time data acquisition unit uses multi-threaded parallel technology to collect four types of ecological monitoring data—air quality, water quality, soil, and noise—from the power construction area. The data caching unit uses a circular buffer to temporarily store data to balance differences in processing speed. The connection status monitoring unit ensures the reliability of the connection between the monitoring instruments and the system through a heartbeat detection mechanism and a timeout reconnection algorithm. Next, the data cleaning unit uses an outlier detection algorithm to remove erroneous data and uses data interpolation technology to handle missing values. The data alignment unit uses a timestamp synchronization algorithm and clock calibration technology to unify the time base, achieving time sequence alignment of the four types of data and outputting standardized data. Subsequently, the core algorithm and analysis module intervenes. The multi-objective optimization calculation unit first normalizes the four types of ecological monitoring data to eliminate dimensional differences and initializes the parameters of the multi-objective genetic algorithm. If the data is abnormal, it returns to re-acquisition and then runs the Pareto-optimal genetic algorithm, using non-dominated sorting and dominant sorting. The crowding calculation completes population evolution, selects the optimal solution set and verifies feasibility. Simultaneously, the dynamic weight allocation unit obtains construction stage information through the engineering progress system, retrieves the corresponding weight scheme, and updates it to the evaluation model after normalization verification and sensitivity analysis. The multi-parameter comprehensive evaluation unit conducts collaborative analysis, the threshold comparison unit compares the data with preset thresholds, and the early warning judgment unit generates graded early warnings and handling suggestions. Then, the real-time data display unit of the data display and output module presents real-time data in chart form, the evaluation result display unit uses color coding to indicate the early warning level, the historical data query unit supports retrieval by combined dimensions of date, monitoring point, and parameter type, the report generation unit automatically generates standardized reports and supports PDF and Excel format output, and finally, the historical data storage unit of the data recording and storage module efficiently stores data through partitioned storage technology, the system log recording unit records operational information in a hierarchical manner, and the early warning event recording unit retains the entire early warning process archive, ultimately improving the accuracy of the ecological assessment results.

[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0031] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An ecological early warning and green evaluation system based on a collaborative algorithm for power construction, characterized in that, The data acquisition and processing module eliminates outliers through data cleaning algorithms and uses time synchronization technology to process four types of monitoring data: air quality, water quality, soil, and noise. This enables real-time acquisition and standardized processing of ecological monitoring data in power construction areas, providing an accurate and complete data foundation for subsequent collaborative analysis. It ensures that the data quality and timeliness meet the assessment requirements and supports the collaborative assessment of the four types of ecological parameters. The core algorithm and analysis module uses a multi-objective optimization algorithm to establish an evaluation model, and adjusts the weight ratio of four types of parameters according to the characteristics of the construction stage through a dynamic weight allocation mechanism. This enables collaborative analysis and overall evaluation of ecological monitoring data, balances construction efficiency and ecological protection requirements, provides scientific decision-making basis and early warning information support, and ensures the accuracy and practicality of the evaluation results. The data display and output module uses visualization interface technology to display data graphically and adopts report generation algorithms to automatically output standard evaluation documents. It displays evaluation results and monitoring data in a graphical way, provides real-time display and historical query functions, generates standardized reports to support user decision-making needs, and ensures the completeness and intuitiveness of information display. The data recording and storage module achieves systematic storage through a standardized database structure and adopts data encryption technology and regular backup mechanisms to store monitoring data, system logs, and early warning records, ensuring data integrity, security, and traceability. It supports subsequent data analysis and regulatory verification work, and guarantees the reliability and stability of system operation.

2. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 1, characterized in that: The data acquisition and processing module includes a real-time data acquisition unit, a data caching unit, and a connection status monitoring unit, wherein: The real-time data acquisition unit is responsible for acquiring ecological data from four types of environmental monitoring instruments in the power construction area in real time. Through multi-threaded parallel acquisition technology and real-time data stream processing mechanism, it ensures the timely acquisition and transmission of monitoring data, provides the original data source for subsequent processing and analysis, and supports the system's continuous monitoring needs for the ecological status of the construction area. The data caching unit is used to temporarily store the collected ecological monitoring data. Through a circular buffer design and memory management algorithm, it realizes data caching and read / write operations, prevents data loss, balances differences in data processing rates, ensures data integrity and system stability during data transmission, and provides a smooth data flow for subsequent processing units. The connection status monitoring unit monitors the connection status between the monitoring instrument and the system in real time. Through a heartbeat detection mechanism and a timeout reconnection algorithm, it continuously detects the online status and communication quality of the equipment, promptly detects connection anomalies and triggers early warnings, ensuring the reliability of the data acquisition link and providing connection guarantee support for the stable operation of the system.

3. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 2, characterized in that: The data acquisition and processing module further includes a data cleaning unit and a data alignment unit, wherein: The data cleaning unit preprocesses the collected raw ecological data, identifies and removes erroneous data and noise interference through outlier detection algorithms and data validity verification rules, and uses data imputation technology to handle missing values, thereby improving data quality and accuracy and providing clean and well-organized data input for subsequent analysis and evaluation. The data alignment unit ensures the temporal consistency of the four types of ecological monitoring data. Through timestamp synchronization algorithms and clock calibration technology, it unifies the time base of different monitoring instruments, eliminates data asynchrony problems caused by time deviations, and realizes the temporal alignment of the four types of ecological parameter monitoring data, providing a time-consistent data foundation for collaborative analysis and overall assessment.

4. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 1, characterized in that: The core algorithm and analysis module includes a multi-parameter comprehensive evaluation unit, a multi-objective optimization calculation unit, and a dynamic weight allocation unit, wherein: The multi-parameter comprehensive evaluation unit is used to simultaneously conduct collaborative analysis and comprehensive evaluation of four types of ecological parameters: air quality, water quality, soil, and noise. By establishing a multi-parameter correlation analysis model and a comprehensive evaluation index system, it integrates the correlation effects of monitoring data of the four types of ecological parameters to achieve a comprehensive assessment of the ecological impact of power construction and provide a scientific basis for judging the overall ecological status. The multi-objective optimization calculation unit is used to balance the two conflicting objectives of maximizing construction efficiency and minimizing ecological impact. It uses a multi-objective genetic algorithm to find the best balance between construction scheme and ecological protection, realizes dual-objective synergistic optimization, provides the optimal scheme selection for construction decision-making, and ensures the unity of economic benefits and ecological benefits. The dynamic weight allocation unit dynamically adjusts the weight coefficients of four types of ecological parameters according to the characteristics of different construction stages. Through stage identification algorithms and weight adaptive adjustment mechanisms, it sets differentiated weight ratios for foundation construction, main construction, equipment installation, and finishing stages, thereby realizing the dynamic configuration of parameter importance and enhancing the pertinence and timeliness of the assessment.

5. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 4, characterized in that: The core algorithm and analysis module also includes a threshold comparison unit, an early warning judgment unit, and an algorithm parameter configuration unit, wherein: The threshold comparison unit is used to compare real-time monitoring data with preset ecological thresholds. Through hierarchical threshold setting technology, a two-level judgment standard of warning threshold and limit threshold is established. Real-time data comparison algorithm is used for continuous monitoring to promptly detect data anomalies and exceedances, providing basic data support for warning judgment and ensuring the sensitivity of monitoring. The early warning judgment unit generates ecological early warning signals of different levels based on threshold comparison results. Through a three-level early warning judgment logic and early warning triggering mechanism, combined with multi-parameter comprehensive evaluation results, it generates specific early warning information and disposal suggestions, providing timely risk warnings for construction management and supporting response and risk control. The algorithm parameter configuration unit contains various parameters and configuration information required by the core algorithm and analysis module for various evaluation algorithms. Through the parameter management interface and configuration file storage mechanism, it maintains system configuration data such as threshold settings, weight coefficients, and algorithm parameters, supports visual modification and version management of parameters, and ensures the flexibility and maintainability of algorithm operation.

6. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 1, characterized in that: The data display and output module includes a real-time data display unit and an evaluation result display unit, wherein: The real-time data display unit is used to graphically display the real-time monitoring data of four types of ecological parameters in the power construction area. Through data stream processing technology and real-time refresh mechanism, it dynamically displays the current monitoring values ​​in the form of curves, dashboards and digital panels. It supports the simultaneous display of multiple parameters on the same screen, providing operators with an intuitive real-time monitoring interface to ensure timely visualization and status awareness of monitoring data. The assessment results display unit is used to visually present the ecological impact assessment results and early warning level information. Through a chart rendering engine and color coding technology, it displays the comprehensive assessment score and the degree of impact of multiple parameters in the form of radar charts, bar charts and heat maps. It adopts a red, yellow and green three-color early warning label system to clearly display the ecological risk level of different regions, providing an intuitive display of assessment results for decision-making.

7. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 6, characterized in that: The data display and output module also includes a historical data query unit and a report generation unit, wherein: The historical data query unit provides query and retrieval functions for historical monitoring data and evaluation results. Through the time range filtering conditions and parameter type selection interface, it supports combined queries by date, monitoring point, and parameter type. It adopts pagination display technology and data export function to realize the rapid retrieval and result browsing of historical data, meeting the needs of post-event analysis and traceability. The report generation unit is used to automatically generate standardized ecological assessment reports and early warning record documents. Through the report template engine and data filling algorithm, it automatically generates complete reports containing monitoring data, assessment results and early warning information according to preset formats. It supports PDF and Excel format output and provides report customization and batch generation functions to meet the document needs of different users.

8. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 1, characterized in that: The data recording and storage module includes a historical data storage unit, a system log recording unit, and an early warning event recording unit, wherein: The historical data storage unit is used to store ecological monitoring data, assessment results data, and system operation data. It establishes a standardized data table structure and indexing mechanism through a relational database management system and partitioned storage technology. It adopts data compression and archiving strategies to achieve data storage and fast retrieval, and ensures data integrity and traceability. The system log recording unit is used to record system operating status, user operation records, and abnormal event information. Through the log collection framework and hierarchical recording mechanism, system activities are recorded according to three levels: information, warning, and error. Timestamp marking and log rotation technology are used to achieve standardized recording and management of operating logs, supporting system fault diagnosis and operating status monitoring. The early warning event recording unit stores information on the occurrence time, warning level, handling status, and handling results of all ecological early warning events. Through event recording tables and associated storage technology, a complete early warning event archive is established. A status tracking mechanism is used to record the entire process of an early warning from occurrence to cancellation, supporting statistical analysis and effect evaluation of early warning events, and providing data support for early warning management.

9. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 1, characterized in that: The specific process for establishing the evaluation model using the multi-objective optimization algorithm is as follows: S1. First, normalize the four types of ecological monitoring data to eliminate dimensional differences. At the same time, initialize the parameters of the multi-objective genetic algorithm, including population size, crossover probability, and mutation probability. If there are abnormal or missing data, trigger the data verification abnormality process and return to the data acquisition module to reacquire the data. S2. A multi-objective genetic algorithm framework based on the Pareto optimality concept is adopted. Population evolution is carried out through non-dominated sorting and crowding calculation. The construction efficiency objective function and the ecological impact objective function are calculated in each generation. The maximum number of iterations and the convergence threshold are set. When the improvement of the result obtained by ten consecutive calculations is less than the set threshold, it is determined that a satisfactory optimal solution has been found and the calculation is stopped. S3. Select the Pareto optimal solution set from the final population, verify the feasibility of each solution, and remove solutions that violate the constraints. If the optimal solution set is empty or insufficient, trigger the re-optimization process, adjust the algorithm parameters and recalculate. S4. Based on the characteristics of the construction stage and the requirements for ecological protection, the optimal solution selected by Pareto is used to generate ten feasible construction schemes. The objective function value and ecological impact assessment results of each scheme are output. At the same time, the algorithm running log and performance indicators are recorded to support subsequent algorithm optimization and parameter tuning.

10. The ecological early warning and green evaluation system based on power construction collaborative algorithm as described in claim 9, characterized in that: The specific process of the dynamic weight allocation mechanism is as follows: S1. First, obtain the current construction stage information through the project progress management system, and at the same time read the real-time monitoring data to verify the accuracy of stage identification. If there is a stage identification conflict or data abnormality, trigger the manual confirmation process to ensure the reliability of stage judgment. S2. Based on the identified construction stage, retrieve the corresponding four types of ecological parameter weight configuration schemes from the preset weight rule library. Three alternative weight schemes are preset for each stage. The system selects the most suitable configuration based on the current environmental sensitivity. S3. Apply the selected weight configuration scheme, calculate the new weight coefficients of each parameter, and perform normalization verification. At the same time, perform sensitivity analysis to ensure that the weight adjustment will not cause fluctuations in the evaluation results. If the verification fails, use the backup weight scheme to recalculate. S4. Apply the verified weight configuration to the evaluation model, update the collaborative analysis and comprehensive evaluation logic of the multi-parameter comprehensive evaluation unit in real time, and record the time, stage, specific weight value and reason for the weight adjustment. Establish a complete weight change file to support subsequent weight optimization and effect analysis.

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