A collaborative management system and method for the progress of forestry ecological restoration projects

Abstract

This invention discloses a collaborative management system and method for forestry ecological restoration engineering progress, belonging to the field of engineering progress management technology. The system includes: a multi-source environmental sensing module for real-time acquisition of environmental disturbance data and current construction progress data of the target restoration area; a nonlinear ergonomics deduction module for mapping environmental disturbance data to a process-environment response matrix, calculating ergonomic correction factors, dynamically iteratively simulating and generating a predicted total construction period, and identifying the risk level of progress deviation; a biological constraint collaborative decision-making module for responding to risk levels, coupling biological growth rhythms with construction window constraints to construct a set of alternative correction schemes, and selecting the optimal correction scheme through conflict resolution and marginal benefit analysis; and an adaptive progress update module for analyzing the optimal scheme and generating a dynamic collaborative progress plan. This invention achieves dynamic deduction and proactive collaborative correction of environmental disturbances, improving the timeliness and accuracy of forestry ecological restoration engineering progress management.

Description

Technical Field

[0001] This invention relates to the field of engineering progress management technology, and in particular to a collaborative management system and method for forestry ecological restoration engineering progress. Background Technology

[0002] The collaborative management of forestry ecological restoration projects enables scientific and rational resource allocation, effective progress monitoring and adjustment, collaborative work among all stakeholders, and real-time risk and quality control. In existing forestry ecological restoration project management, the commonly used progress management process includes the following steps: First, the forestry ecological restoration project is decomposed layer by layer into specific work units using work breakdown structure (WBS) technology, combined with GIS (Geographic Information System). Geographic Information System (GIS) technology is used to spatially vectorize and weight the work shifts, and a baseline schedule including each task node and critical path is compiled based on the afforestation season window. This schedule is then distributed to the terminals of all participating parties through a collaborative management platform. During the project implementation phase, construction personnel upload work forms and on-site images through mobile smart terminals. In conjunction with UAV low-altitude remote sensing or BeiDou positioning equipment, data from the work surface is automatically collected and transmitted back to the cloud database in real time. The platform automatically compares the actual progress with the planned progress, calculates the progress deviation value and performance index, and triggers an early warning immediately upon detecting any delay risk, highlighting the problem area on the GIS map. Subsequently, the management initiates collaborative scheduling based on the shared data on the platform, analyzes the causes of delays, and formulates corrective measures. Adjustment instructions and resource allocation plans are issued in real time through the platform to update subsequent plan nodes. Finally, the system automatically summarizes the progress data and change records of the entire process to generate electronic project archives, conducts post-evaluation of management efficiency, and feeds back optimization parameters to subsequent projects, thereby achieving full-process collaborative management from plan construction to closed-loop optimization.

[0003] For example, Chinese invention patent CN118428887B discloses a project progress management optimization and monitoring system in a collaborative design environment, which includes: a collaborative design integration module, a progress intelligent optimization module, a real-time monitoring and dashboard module, an alarm and early warning module, a data analysis and reporting module, an access control and permission module, and a notification and communication module. The collaborative design integration module achieves a high degree of integration of collaborative design tools; the progress intelligent optimization module can dynamically adjust the project schedule, optimize resources, and manage risks; the real-time monitoring and dashboard module provides intuitive visualization tools; the alarm and early warning module detects potential problems in real time; the data analysis and reporting module deeply mines project data and generates detailed reports; the access control and permission module ensures the security of project data; and the notification and communication module promotes timely communication among team members.

[0004] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: Forestry ecological restoration projects are highly susceptible to natural environmental factors (such as sudden droughts, torrential rains, and pests), exhibiting a high degree of uncertainty. Existing project schedules are mostly static or semi-static linear, lacking intelligent dynamic projection capabilities. When environmental disturbances cause delays, current technologies are largely limited to post-event alerts, lacking the ability to dynamically project and proactively coordinate to address environmental uncertainties. This results in project management being largely reactive, with corrective measures lacking scientific quantification, failing to meet the timeliness and precision management requirements of forestry ecological restoration projects. Summary of the Invention

[0005] To address the technical problem that existing technologies lack intelligent dynamic simulation capabilities, are unable to proactively coordinate and correct deviations in response to environmental uncertainties, and thus result in a long-term passive response state in progress management, making it difficult to meet the timeliness and accuracy management requirements of forestry ecological restoration projects, this invention provides a forestry ecological restoration project progress collaborative management system and method. The technical solution is as follows: On the one hand, a forestry ecological restoration project progress collaborative management system is provided, including: a multi-source environmental sensing module, used to acquire environmental disturbance data and current construction progress data of the target restoration area in real time, wherein the environmental disturbance data includes at least meteorological fluctuation data, pest and disease evolution stage data, and soil moisture monitoring values; and a nonlinear ergonomics simulation module, used to map the environmental disturbance data to a pre-set process-environment response matrix, calculate the sensitivity of environmental stress to different forestry processes and output ergonomics correction factors, and use the ergonomics correction factors to dynamically iteratively simulate the remaining process duration in the current construction progress data to generate a predicted total project duration, and based on... The system predicts the deviation between the total project duration and the planned total project duration to identify the risk level of schedule deviation. A biological constraint collaborative decision-making module responds to the risk level of schedule deviation by coupling forestry biological growth rhythms with suitable construction window constraints to construct a multi-dimensional set of alternative corrective measures. A conflict resolution algorithm is used to perform resource balance checks on the set of alternative corrective measures, and the optimal corrective measure is selected based on the marginal benefits of project recovery efficiency and additional costs. An adaptive schedule update module analyzes the optimal corrective measure to extract adjusted process logic parameters, reconstructs a dynamic schedule network diagram including the evolution of the critical path, and generates a dynamic collaborative schedule plan that is pushed to the field terminal.

[0006] On the other hand, a method for collaborative management of forestry ecological restoration project progress is provided. This method includes: real-time acquisition of environmental disturbance data and current construction progress data of the target restoration area, wherein the environmental disturbance data includes at least meteorological fluctuation data, pest and disease evolution stage data, and soil moisture monitoring values; mapping the environmental disturbance data to a pre-set process-environment response matrix, calculating the sensitivity of environmental stress to different forestry processes and outputting efficiency correction factors, using the efficiency correction factors to dynamically iterate and simulate the remaining process duration in the current construction progress data to generate a predicted total project duration, and identifying the progress deviation risk level based on the deviation between the predicted total project duration and the planned total project duration; responding to the progress deviation risk level, coupling the forestry biological growth rhythm and suitable construction window constraints to construct a multi-dimensional set of alternative correction schemes, using a conflict resolution algorithm to perform resource balance detection on the set of alternative correction schemes, and selecting the optimal correction scheme based on the marginal benefits of project recovery efficiency and additional costs; parsing the optimal correction scheme to extract the adjusted process logic parameters, reconstructing a dynamic progress network diagram including the evolution of the critical path, and generating a dynamic collaborative progress plan to be pushed to the field terminal.

[0007] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: Compared with existing forestry ecological restoration project progress management technologies, this invention significantly improves and enhances environmental perception capabilities, dynamic progress prediction capabilities, and collaborative decision-making capabilities. By constructing modules such as multi-source environmental perception, nonlinear ergonomic prediction, biological constraint collaborative decision-making, and adaptive progress updates, it achieves dynamic perception, intelligent prediction, and proactive collaborative correction of environmental uncertainties. Compared with existing technologies, it significantly improves the real-time performance, intelligence level, and decision-making accuracy of forestry ecological restoration project progress management, and better meets the refined management needs of forestry ecological restoration projects under complex environmental conditions.

[0008] Specifically, firstly, in terms of environmental information acquisition, existing technologies typically rely on manual recording or a single meteorological information source. Environmental data acquisition is delayed and limited in dimensions, making it difficult to reflect the complex and ever-changing ecological environment of the construction area in a timely manner. This invention, by setting up a multi-source environmental sensing module, can acquire multi-dimensional environmental disturbance data in real time, including meteorological fluctuation data, pest and disease evolution stage data, and soil moisture monitoring values. This data is then simultaneously collected and fused with current construction progress data, thereby achieving continuous monitoring of ecological environment changes in the target restoration area. This improves the real-time nature and comprehensiveness of environmental information acquisition, providing a reliable data foundation for subsequent progress prediction and decision-making.

[0009] Secondly, in terms of schedule prediction and risk identification, existing technologies mostly employ static or semi-static linear schedules, typically only issuing alerts after delays occur, lacking the ability to dynamically extrapolate the impact of environmental disturbances. This invention, by setting up a nonlinear ergonomics extrapolation module, maps environmental disturbance data to a pre-defined process-environment response matrix, calculates the sensitivity of environmental stress to each forestry process, and generates ergonomics correction factors. Based on this, iteratively simulates the duration of remaining processes, thereby generating a predicted total project duration and identifying schedule deviation risk levels. Compared to traditional schedule management methods, this invention can predict potential schedule deviations in advance, shifting from passive response to proactive early warning, thus improving the foresight and scientific rigor of schedule management.

[0010] Furthermore, in terms of schedule correction and collaborative decision-making, existing technologies often rely on simple resource adjustments based on human experience, lacking a systematic consideration of forestry ecological characteristics and construction window constraints. This invention, by setting up a biological constraint collaborative decision-making module, incorporates ecological constraints such as forestry biological growth rhythms and suitable construction windows into the schedule optimization decision-making process. It constructs a multi-dimensional set of alternative correction schemes and uses a conflict resolution algorithm to test the resource balance of these schemes. Simultaneously, it comprehensively evaluates the marginal benefits of time recovery and additional costs, thereby selecting the optimal correction scheme. Compared to traditional manual decision-making methods, this invention achieves more scientific, reasonable, and quantifiable schedule correction decisions.

[0011] Finally, regarding schedule updates and collaborative execution, existing technologies typically only make simple adjustments to local processes, making it difficult to dynamically reflect changes in the critical path, and on-site execution information updates are lagging. This invention uses an adaptive schedule update module to analyze the optimal correction scheme, extract the adjusted process logic parameters, and reconstruct a dynamic schedule network diagram containing the evolution of the critical path. Simultaneously, it generates a new dynamic collaborative schedule plan and pushes it to the on-site terminal, achieving real-time updates of construction progress information and multi-party collaborative execution, thereby significantly improving the response speed and collaborative efficiency of schedule management.

[0012] Overall, this invention integrates multiple intelligent modules to achieve dynamic prediction, intelligent coordination, and proactive correction of the progress of forestry ecological restoration projects, thereby improving the timeliness and accuracy of project progress management. Attached Figure Description

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

[0014] Figure 1A schematic diagram of the structure of a forestry ecological restoration project progress collaborative management system provided in this application embodiment; Figure 2 A three-dimensional visualization interactive module interface diagram of a forestry ecological restoration project progress collaborative management system provided in this application embodiment; Figure 3 A flowchart illustrating a collaborative management method for the progress of forestry ecological restoration projects, provided as an embodiment of this application. Detailed Implementation

[0015] Embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present disclosure are shown in the drawings, it should be understood that embodiments of the present disclosure may be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure.

[0016] It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure. In the description of the embodiments of this disclosure, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "this embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects.

[0017] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0018] In forestry ecological restoration projects, the construction environment is complex and ever-changing, influenced by various natural factors such as weather fluctuations, pest and disease evolution, and soil physicochemical conditions. The project implementation process often faces environmental disturbances and unforeseen risks. Traditional forestry project schedule management often employs static or semi-static linear plans, lacking the ability to dynamically perceive and predict environmental changes, making it difficult to promptly detect and effectively correct schedule deviations. This not only leads to project delays and increased construction costs but may also adversely affect the ecological restoration effect, reducing the scientific rigor and precision of project management.

[0019] Furthermore, forestry ecological restoration projects are subject to unique constraints such as biological growth rhythms and seasonal construction windows. Construction arrangements must be coordinated with ecological laws, and simply relying on linear schedules cannot fully account for the complexity of these biological constraints and resource limitations. As the scale of forestry projects expands and ecological restoration goals become more sophisticated, the limitations of traditional schedule management methods in addressing multi-source environmental disturbances, optimizing resource allocation, and ensuring project quality are becoming increasingly apparent.

[0020] Therefore, there is an urgent need for a forestry ecological restoration project progress management mechanism that can sense environmental changes in real time, dynamically assess progress risks, intelligently generate optimization and correction plans, and achieve on-site collaborative execution, so as to improve the scientific nature, accuracy and overall efficiency of project progress management and achieve high-quality implementation of ecological restoration projects.

[0021] like Figure 1 The diagram shown is a structural schematic of a forestry ecological restoration project progress collaborative management system provided in an embodiment of this application. The system includes: a multi-source environmental perception module, a nonlinear ergonomics deduction module, a biological constraint collaborative decision-making module, and an adaptive progress update module.

[0022] Specifically, firstly, a multi-source environmental perception module is used to acquire real-time environmental disturbance data and current construction progress data of the target restoration area. The environmental disturbance data includes at least meteorological fluctuation data, pest and disease evolution stage data, and soil moisture monitoring values. Secondly, a nonlinear ergonomics simulation module is used to map the environmental disturbance data to a pre-set process-environment response matrix, calculate the sensitivity of environmental stress to different forestry processes, and output ergonomics correction factors. The ergonomics correction factors are used to dynamically iteratively simulate the remaining process duration in the current construction progress data to generate a predicted total construction period, and the deviation between the predicted total construction period and the planned total construction period is used to identify the progress deviation risk level.

[0023] Secondly, the biological constraint collaborative decision-making module responds to schedule deviation risk levels, couples forestry biological growth rhythms with suitable construction window constraints, constructs a multi-dimensional set of alternative corrective measures, uses conflict resolution algorithms to perform resource balance checks on the set of alternative corrective measures, and selects the optimal corrective measure based on the marginal benefits of schedule recovery and additional costs. Finally, the adaptive schedule update module analyzes the optimal corrective measure to extract adjusted process logic parameters, reconstructs a dynamic schedule network diagram including the evolution of the critical path, and generates a dynamic collaborative schedule plan that is pushed to the field terminal.

[0024] Through close collaboration among the above modules, this invention constructs a dynamic closed-loop management mechanism of "environmental perception - progress prediction - risk identification - corrective decision-making - progress update", which enables the progress of forestry ecological restoration projects to be adjusted in real time according to environmental changes. Compared with the traditional static progress management method, it can significantly improve the foresight, accuracy and collaborative efficiency of project progress control, and fundamentally solve the technical problem that existing technologies lack intelligent dynamic prediction capabilities and proactive collaborative correction capabilities.

[0025] It should be added that the specific composition and implementation methods of the multi-source environmental perception module are as follows: I. Deployment and Data Acquisition Mechanism of the IoT Sensor Group: The IoT sensor group adopts a grid-based deployment strategy distributed across the target remediation area, specifically including: Soil Monitoring Unit: A tubular soil moisture sensor is used, buried at the root activity layer depth (e.g., 20cm-60cm underground) in the afforestation pit to collect real-time data on soil volumetric water content, temperature, and conductivity, reflecting the water stress status of seedling root growth. Micrometeorological Monitoring Unit: Set up in a typical open area of ​​the forest, integrating air temperature and humidity sensors, anemometers, and rain gauges to acquire local micrometeorological data. For data transmission, each sensor node uses a low-power wide-area network (LPWAN, such as LoRa or NB-IoT) communication protocol to package and send the collected physicochemical parameters to the system server at a preset frequency (e.g., once every 30 minutes), realizing real-time digital mapping of habitat status.

[0026] II. Construction and Operation of the Meteorological Database Communication Interface: The communication interface for accessing the meteorological database is interconnected by calling the open API interface of meteorological service providers (such as the National Meteorological Administration or professional meteorological service agencies). Specific implementation methods include: the system periodically sending HTTP requests to obtain numerical weather forecast data for a future set time period (e.g., the next 7 to 15 days) for the repair area. The system focuses on analyzing key construction influencing factors in meteorological fluctuation data, including rainfall forecasts (used to determine whether land preparation and planting conditions are suitable), extreme temperature warnings (such as high-temperature heat damage or low-temperature freezing damage warnings), and wind speed levels (affecting the safety of high-altitude operations and drone spraying). The interface incorporates abnormal data cleaning logic to ensure the timeliness and accuracy of meteorological data, providing a macro-environmental background for subsequent nonlinear ergonomic simulations.

[0027] III. Operation and Identification Process of the UAV Inspection Unit: The UAV inspection unit uses industrial-grade UAVs equipped with multispectral or hyperspectral cameras as carriers. The specific implementation methods are as follows: Flight Route Planning: Based on the topography and operational zones of the restoration area, pre-set UAV inspection routes ensure that the aerial coverage and overlap meet the image stitching requirements. Data Acquisition: Low-altitude remote sensing operations are conducted according to a set cycle (e.g., weekly or bi-weekly) or triggered by specific weather events to collect hyperspectral image data of the restoration area. Intelligent Identification: Using edge computing or cloud processing, a pre-set image recognition model (e.g., a pest and disease identification model based on a convolutional neural network (CNN)) is run. By analyzing the spectral reflectance and texture characteristics of vegetation, spectral indices in specific bands (e.g., NDVI normalized vegetation index) are extracted to retrieve the physiological growth status data of vegetation; simultaneously, a pest and disease spectral feature library is matched to accurately identify pest and disease evolution stages (e.g., early stage of disease onset, outbreak stage), thereby achieving non-contact rapid monitoring of biological disturbances.

[0028] The design of the multi-source environmental perception module fully considers the multi-dimensional characteristics of environmental disturbances and the dynamic changes in restoration progress. By collecting and accurately analyzing various environmental data in the restoration area in real time, it forms a powerful support for the progress management of forestry ecological restoration projects and significantly improves the system's intelligent perception and decision-making capabilities.

[0029] As a further step, it should be noted that the nonlinear ergonomics simulation module calculates the ergonomics correction factor in the following way to achieve a quantitative assessment of the impact of environmental disturbances: First, a process-environment response matrix is ​​constructed. The row vectors of this matrix represent different forestry processes, the column vectors represent different environmental disturbance factors, and the matrix elements characterize the influence coefficient of each process on the environment. The system has a pre-set process-environment response matrix. This matrix is ​​constructed based on regression analysis of historical construction data and an expert experience database. The specific construction method is as follows: Define the row vectors of the matrix. Representing the Forestry processes (e.g., land preparation, seedling planting, young forest tending, pest and disease control, etc.); define the column vectors of the matrix. Representing the Various environmental disturbance factors (e.g., rainfall intensity, soil moisture content, temperature deviation, pest and disease index, etc.); matrix elements Characterizing the first The first process is affected by the The influence coefficient of each environmental factor ranges from [0,1]. 0 indicates that the environmental factor has no impact on the process, while 1 indicates that an extreme anomaly in the environmental factor will cause the process to completely stop. For example, for the "seedling planting" process, the influence coefficient corresponding to "rainfall intensity" is relatively high, while for the "land preparation" process, the influence coefficient corresponding to "pest and disease index" is 0 or extremely low.

[0030] Next, the real-time environmental disturbance data input from the multi-source environmental sensing module is received, and the comprehensive sensitivity of each process is calculated through the following steps: (1) Generate environmental stress vector: The values ​​of various environmental disturbance factors collected in real time are normalized and converted into a dimensionless environmental stress vector. .in, Representing the The stress level of each environmental factor, the larger the value, the more severe the environmental conditions. (2) Matrix operation: The process-environment response matrix is ​​transformed into a matrix of environmental factors. With environmental stress vector Perform matrix multiplication to obtain the process comprehensive sensitivity vector. : Among them, components That is, the first The sensitivity score of each process under the current comprehensive environmental stress takes into account the superimposed influence of multiple environmental factors.

[0031] Then, the system has a pre-set nonlinear efficiency decay function f(x) to simulate the nonlinear characteristics of efficiency changes with environmental pressure under real construction conditions (such as the "threshold effect": when the environmental pressure is low, the effect on efficiency is not obvious, but when the threshold is exceeded, efficiency drops sharply). The specific implementation method is as follows: call the nonlinear efficiency decay function (e.g., the Sigmoid function or the piecewise exponential decay function), and adjust the overall sensitivity... Mapped as input variables: Output efficiency correction factor Its value range is usually (0,1). This is used to characterize the degree of decline in actual construction efficiency relative to planned efficiency. For example, if the efficiency correction factor for the "young forest tending" process is calculated... This means that under the current environmental disturbances, the actual construction efficiency of this process is only 70% of the planned efficiency, and the process duration needs to be extended accordingly. Through the above implementation methods, the nonlinear ergonomics simulation module can accurately quantify the differentiated impact of complex environmental disturbances on different forestry processes, providing precise parameter support for subsequent progress prediction and risk identification.

[0032] The nonlinear ergonomics simulation module, through precise construction of process-environment response matrices, real-time environmental data acquisition, and application of nonlinear ergonomics attenuation models, achieves dynamic perception of environmental disturbances and precise adjustment of construction efficiency. This significantly improves the accuracy, flexibility, and intelligence of progress prediction, providing strong support for the progress management of forestry ecological restoration projects.

[0033] As a further explanation, it should be noted that the dynamic iterative simulation process of the nonlinear ergonomics derivation module includes: First, extract all remaining processes currently in the project schedule database that are either "not started" or "in progress" and obtain their planned duration. Subsequently, the efficiency correction factor calculated in the aforementioned steps is called. The planned duration is adjusted using the following formula to obtain the actual duration reflecting the actual construction environment. : In the formula, This is the corrected actual duration. This represents the efficiency correction factor for the corresponding work process. As the formula shows, the actual duration is inversely proportional to the efficiency correction factor: when environmental disturbances lead to a decrease in construction efficiency (i.e., ... Actual duration It will last longer than the planned duration. When environmental conditions are ideal and undisturbed (i.e. The actual duration is equal to the planned duration. For example, if the planned duration of a seedling planting process is 5 days, and the efficiency correction factor under the current environment is 0.8, then the corrected actual duration is 5 / 0.8 = 6.25 days.

[0034] After obtaining the actual duration of all remaining processes, construct or update the schedule network diagram based on the critical path method, and perform forward calculation. The specific steps include: (1) Topology sorting and node initialization: Sort the schedule network diagram composed of the remaining processes in the topology to ensure that the calculation order conforms to the logical relationship between processes (such as the predecessor-successor relationship). Set the earliest start time of the starting node of the network diagram (i.e., the current monitoring time). This represents the current time as a zeroed-out representation.

[0035] (2) Forward recursive calculation: Calculate the earliest start time (ES) and earliest end time (EF) of each process sequentially along the arrows in the network diagram. The calculation rules are as follows: For any process i, its earliest start time... It equals the maximum value of the earliest finish times of all its immediate preceding processes; its earliest finish time The calculation formula is: ;in, This is the actual duration of the process after correction.

[0036] (3) Determining the total project duration: Traverse the entire network diagram to the endpoint node. The maximum value of the earliest completion time (EF) among all processes pointing to the endpoint node is the total project duration. Through the aforementioned implementation methods, the system can quantify the impact of real-time environmental disturbances and transmit them to all subsequent processes. By dynamically iteratively simulating and accurately predicting the project completion time, it provides a reliable quantitative basis for identifying the risk level of schedule deviations.

[0037] Through this dynamic iterative simulation mechanism, the present invention can realize real-time prediction of construction progress and dynamic identification of critical paths, effectively improving the accuracy and response speed of forestry ecological restoration project progress prediction, and providing important support for proactive management and intelligent decision-making of project progress.

[0038] As a further solution, the specific implementation methods for constructing a set of alternative correction schemes for the biological constraint collaborative decision-making module can be described in detail as follows: In this embodiment of the invention, the specific process by which the biological constraint collaborative decision-making module constructs a set of alternative correction schemes is as follows: First, the system has a pre-set forestry biological growth rhythm database, which stores biological characteristic data of various tree species at different growth stages within the target restoration area. Specific content includes: suitable planting windows for each tree species (e.g., before spring budding or after autumn leaf fall), periods to avoid construction (e.g., the new shoot growth period, periods of frequent extreme weather), and the optimal timeframe for land improvement and maintenance operations. The module provides biological constraint benchmarks for progress adjustments by calling this database.

[0039] Next, obtain the information on the key processes that are currently lagging behind, and extract the tree species identifier associated with the process. Based on the current time node and the predicted delay time node, execute the following judgment logic: (1) Calculate the predicted process execution time period. (2) Query the forestry biological growth rhythm database to obtain the suitable construction window period for the associated tree species. (3) Calculate the spatiotemporal matching degree .like and If there is an overlap and the coverage rate meets the preset threshold (e.g., coverage rate > 80%), it is determined that the construction is "within a suitable construction window". Otherwise, if there is no overlap or the coverage rate is below the threshold, it is determined that the construction is "not within a suitable construction window".

[0040] Then, the branch generates the execution methods of the correction plan, specifically for example: Scenario 1: Within the suitable construction window – resource-enhanced correction plan; when the judgment result is a match, it indicates that the current environmental conditions allow for high-intensity operations. The module automatically generates a resource-enhanced correction plan. The specific implementation method is: the system searches the current idle resource pool and generates scheduling instructions to increase construction teams, deploy backup machinery and equipment, or extend daily working hours (such as adopting a "work-while-rests" mode). This plan aims to compress the duration of the process through the resource superposition effect, directly making up for the early construction period delays, while ensuring that the construction activities do not cause physiological damage to the seedlings.

[0041] Scenario 2: Not within the suitable construction window period -- Process logic reorganization scheme; When the judgment result is mismatched (for example, the delay causes the planting process to be postponed to the summer high temperature period), forced construction will lead to serious ecological losses. The module automatically executes the process logic reorganization strategy, and the specific steps are as follows: (1) Process postponement: Identify the affected planting process, force the planned execution time to be postponed to the next suitable construction window period, and lock the time period. (2) Window period filling: Calculate the time period "window period" generated after postponement (that is, the time period between the original planned planting time and the next suitable window period). (3) Compensation process insertion: The system traverses the non-seasonal strong constraint process library, selects compensation processes such as land improvement (such as deep plowing and soil improvement, application of base fertilizer), water conservancy facility maintenance or phased maintenance, and inserts them into the above "window period". (4) Scheme generation: Adjust the immediate preceding and immediate following logical relationship between processes (for example, set "land improvement" as a new preceding process of "planting"), and generate a set of alternative correction schemes that both conform to the biological growth law and make full use of the idle construction period. Through the above implementation methods, the module can ensure that no matter when the construction delay occurs, the generated correction plan can strictly follow the laws of forestry biology and avoid ecological restoration failure caused by "rushing to meet the deadline".

[0042] The process of constructing the alternative correction scheme set of the biological constraint collaborative decision-making module, by considering the matching between forestry ecological environment and construction timing, can provide accurate and reasonable correction schemes for schedule deviations, ensuring that ecological restoration projects meet schedule requirements while taking into account ecological health and restoration effects. It has high scientificity, flexibility and operability.

[0043] Furthermore, the specific implementation method of the conflict resolution algorithm in the biological constraint collaborative decision-making module can be described in detail as follows: In this embodiment of the invention, the conflict resolution algorithm is based on multi-objective optimization logic, and the specific process of resource balance detection and adjustment of the candidate correction scheme set is as follows: First, the resource demand vectors (including labor quantity, machinery and equipment shifts, seedling materials, etc.) of each process in the candidate correction scheme set are analyzed, and a resource demand pulse curve distributed with the time axis is generated by combining the time parameters of the process. Simultaneously, the maximum available resource capacity boundary is retrieved from the on-site resource database. (e.g., maximum daily workforce limit, total number of mechanical equipment).

[0044] The system calculates resource utilization by superimposing and fitting resource demand pulses with capacity boundaries. If within any time period... Internal appearance If the situation is as described above, then that time period is determined to be a resource conflict point and marked as an infeasible period.

[0045] For identified resource conflict points, the algorithm prioritizes retrieving non-critical path processes within the conflict period. Specifically, it calculates the total float and free float for each non-critical process. Without delaying the overall project schedule, the float is used as a buffer, shifting the start time of non-critical processes forward or backward in specific steps (e.g., half a day or a full day). This shifting operation removes peak resource demands from conflict periods and fills in periods of low resource demand, thus redistributing resource demands over time. If process shifting alone cannot completely resolve the conflict, or if critical path processes also involve conflicts, the algorithm implements a resource peak-shaving and valley-filling strategy. Peak shaving: During resource conflict periods, if processes can be split for execution, the resource input intensity during that period is reduced, and the process duration is extended (ensuring the extended total duration does not exceed the constraint limit of biological growth rhythms), thereby reducing peak resource demand. Valley filling: During resource idle periods, the resource input intensity is increased to compress the duration of non-conflicting processes, improving resource utilization. The algorithm introduces resource balance evaluation metrics (such as resource demand variance or imbalance coefficient) and continuously adjusts these parameters through iterative calculations. The algorithm assumes that the adjusted resource demand curve satisfies... Furthermore, when the balance index reaches a preset threshold, the optimized solution that has passed the verification is output. Through the above implementation methods, the conflict resolution algorithm can ensure that the alternative correction solutions meet the project schedule requirements while being strictly limited by the actual carrying capacity of on-site resources, thus avoiding resource allocation bottlenecks or cost overruns caused by blind correction.

[0046] The conflict resolution algorithm achieves a scientific balance of construction resources and conflict resolution by optimizing and dynamically adjusting the resource requirements of alternative correction schemes through multiple objectives. This not only ensures the feasibility of the schedule and the efficiency of resource utilization, but also enhances the intelligence, flexibility and reliability of the schedule management of forestry ecological restoration projects.

[0047] It should also be noted that the specific implementation methods for the biological constraint collaborative decision-making module to select the optimal correction scheme can be described in detail as follows: In this embodiment of the invention, the specific process of the biological constraint collaborative decision-making module selecting the optimal correction scheme is as follows: First, a multi-objective decision-making model is constructed, with its objective function being... Defined as a function that maximizes overall benefits. Its specific form is as follows: ;in, It represents a specific solution from a set of alternative corrective measures. This represents the timeliness value of the plan; This represents the economic loss of the plan; and These are preset timeliness and economic factors, used to adjust the balance between the two based on the actual management needs of the project (such as the proportion of penalties for breach of contract due to project deadlines and budget constraints).

[0048] For each alternative corrective action that passes the resource balance test The module performs the following calculations: Schedule recovery efficiency calculation: obtain the predicted total project duration after implementing this plan. Predicted total project duration under uncorrected conditions Calculate the amount of time saved. : Time-sensitive value conversion: Converting the amount of time saved into monetary value or normalized score. The calculation formula is as follows: ;in, This is the unit construction period value coefficient (such as the penalty for daily construction delays or the converted value of losses due to ecological restoration delays).

[0049] Analyze the resource allocation list of alternative corrective actions and calculate the additional costs incurred in implementing the proposed corrective action. Specifically, this includes: direct cost increases, such as overtime pay for additional construction teams, rental fees for extra machinery and equipment, and costs associated with expedited work. Indirect cost increases, such as extended management cycles due to work process adjustments. This cost value represents the economic loss corresponding to this plan.

[0050] The calculated and Substitute into the decision objective function and calculate the overall benefit score. : The system iterates through all the alternative corrective measures in the set and compares their overall benefit scores. Select The solution with the highest score is selected as the optimal correction solution. If solutions with the same score exist, a secondary selection is performed based on preset priority rules (such as prioritizing the shortest construction period or the lowest cost) to ensure that the final output solution achieves the best balance between timeliness and economy.

[0051] Through the aforementioned decision objective function and comprehensive benefit evaluation mechanism, this invention can quickly select the optimal solution from multiple alternative correction schemes that balances progress recovery capability and economic investment rationality, thereby improving the scientific, economic and intelligent level of decision-making on progress correction in forestry ecological restoration projects.

[0052] It should also be noted that the process of reconstructing the dynamic progress network diagram by the adaptive progress update module includes: The system receives the optimal correction scheme from the biological constraint collaborative decision-making module and extracts the adjusted process logic parameters using preset parsing rules. The specific implementation method is as follows: Node change identification: It traverses the correction scheme to identify newly added compensation processes (such as "land improvement"), deleted or merged redundant processes, and change records of existing process attributes (such as duration and resource allocation).

[0053] Logical restructuring: Extract new preceding and succeeding relationships (FS, SS, etc.) between processes. For example, if the correction scheme postpones the "planting seedlings" process and inserts the "soil disinfection" process before it, the module automatically constructs "soil disinfection" as the connecting edge of the new preceding process of "planting seedlings".

[0054] Using Dynamic Network Planning (Dynamic CPM) technology, based on the updated logical relationships and revised durations, time parameter calculations are performed: Forward calculation: Starting from the starting node, the earliest start time (ES) and earliest end time (EF) of each process are calculated. Backward calculation: Starting from the end node, the latest start time (LS) and latest end time (LF) of each process are calculated. Float calculation: The total float (TF = LS - ES) is calculated to determine the flexibility of the processes. The module performs critical path calibration: Path calibration: All process nodes with a total float TF = 0 are selected and connected to form a new critical path. Trajectory recording: The system compares the original critical path with the new critical path, identifies drift or jump phenomena on the critical path (e.g., environmental disturbances causing the "maintenance" process on the original non-critical path to become a critical process), and generates a critical path evolution log. Plan generation: The above change information is integrated to generate a dynamic collaborative schedule data package containing time nodes, resource allocation, and logical relationships, which is then packaged and pushed to the field terminal.

[0055] As a further option, such as Figure 2 The diagram shown is a 3D visualization interactive module interface of a forestry ecological restoration project progress collaborative management system provided in this application embodiment. Specifically, the system also includes a 3D visualization interactive module, used for: constructing a 3D terrain model of the target restoration area based on a geographic information system; mapping the process nodes in the dynamic collaborative progress plan to the spatial coordinates of the 3D terrain model; rendering the construction progress status of different areas in the form of a heat map in the 3D terrain model; and highlighting the affected areas and the recommended optimal correction scheme when the progress deviation risk level exceeds a preset threshold.

[0056] In this embodiment of the invention, the specific implementation method of the three-dimensional visualization interaction module is as follows: Access the geographic information system data interface to read the digital elevation model and digital orthophoto map of the target restoration area. Utilize a three-dimensional rendering engine (such as WebGL or OpenGL) to construct a three-dimensional terrain model that corresponds one-to-one with the actual geographic coordinates, realistically restoring the geomorphic features, slope, aspect, and current vegetation cover of the restoration area.

[0057] Establish a mapping relationship between schedule data and 3D spatial coordinates. Specifically, bind spatial attributes (such as work site ID and station range) to each work node in the dynamic collaborative schedule. Based on the work scope of each work process, extract the corresponding 3D spatial coordinate data and "land" the work node onto the corresponding location in the 3D terrain model, forming a virtual construction scene that integrates "time and space".

[0058] Based on real-time monitoring data and calculation results, the following rendering logic is executed: Progress Heatmap Rendering: Define color mapping rules to render different colored heatmaps in the corresponding areas of the 3D terrain model according to the completion percentage or progress deviation status of each area's work process. For example, areas with normal progress are rendered in green, areas with lagging progress are rendered in red, and areas that have not yet started are rendered in gray. Risk Warning and Solution Highlighting: When the system detects that the progress deviation risk level of a certain area exceeds a preset threshold, the module automatically triggers a highlighting alarm mechanism, highlighting the affected area in the form of a flashing or glowing outline. At the same time, layer overlay technology is used to display the recommended optimal correction solution information (such as "It is recommended to add 2 groups of construction personnel") above the area, assisting managers in making intuitive decisions.

[0059] By using the adaptive progress update module to dynamically reconstruct the progress network and the 3D visualization interaction module to spatially display the construction status, this invention not only achieves real-time updates of the project schedule and dynamic tracking of the critical path, but also significantly improves the intuitive expression of progress information and the efficiency of on-site collaborative decision-making, thereby effectively improving the intelligent level and overall management efficiency of forestry ecological restoration project progress management.

[0060] like Figure 3The diagram shows a flowchart of a forestry ecological restoration project progress collaborative management method provided in this application embodiment. This method includes: real-time acquisition of environmental disturbance data and current construction progress data for the target restoration area; the environmental disturbance data including at least meteorological fluctuation data, pest and disease evolution stage data, and soil moisture monitoring values; mapping the environmental disturbance data to a pre-set process-environment response matrix; calculating the sensitivity of environmental stress to different forestry processes and outputting a work efficiency correction factor; and using the work efficiency correction factor to continuously manage the remaining processes in the current construction progress data. The process involves dynamic iterative simulation over time to generate a predicted total project duration, and identifying the risk level of schedule deviation based on the discrepancy between the predicted and planned total project duration. In response to the risk level of schedule deviation, a multi-dimensional set of alternative corrective measures is constructed by coupling forestry biological growth rhythms with suitable construction window constraints. A conflict resolution algorithm is used to perform resource balance checks on the alternative corrective measures set, and the optimal corrective measure is selected based on the marginal benefits of project recovery efficiency and additional costs. The optimal corrective measure is analyzed to extract adjusted process logic parameters, a dynamic schedule network diagram including the evolution of the critical path is reconstructed, and a dynamic collaborative schedule plan is generated and pushed to the field terminal.

[0061] In summary, this invention achieves dynamic perception of environmental disturbances, proactive prediction of schedule deviations, and intelligent optimization of correction schemes through the close collaboration of modules such as multi-source environmental perception, nonlinear ergonomics simulation, biological constraint collaborative decision-making, and adaptive schedule updates. A closed-loop management mechanism is formed between each step through data flow and logical connections, enabling the construction schedule to respond to environmental changes in real time, taking into account forestry biological growth rhythms, construction windows, and resource balance, thereby improving the scientific, accurate, and intelligent level of project schedule management. Compared with existing technologies, this invention breaks through the limitations of traditional static or semi-static linear schedule planning, realizing dynamic prediction and proactive collaborative management of forestry ecological restoration projects under complex environmental conditions, and providing an efficient, quantifiable, and reliable schedule management solution for forestry ecological restoration projects.

[0062] Through the above description of the implementation methods, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the above functions can be divided into different functional modules to complete all or part of the functions described above.

[0063] In the embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units, located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0065] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0066] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the solution, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0067] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A collaborative management system for the progress of forestry ecological restoration projects, characterized in that, The system includes: The multi-source environmental sensing module is used to acquire environmental disturbance data and current construction progress data of the target remediation area in real time. The environmental disturbance data includes at least meteorological fluctuation data, pest and disease evolution stage data, and soil moisture monitoring values. The nonlinear ergonomics simulation module is used to map environmental disturbance data to a pre-set process-environment response matrix, calculate the sensitivity of environmental stress to different forestry processes and output ergonomics correction factors. The ergonomics correction factors are used to dynamically iterate and simulate the remaining process duration in the current construction progress data to generate the predicted total construction period, and the progress deviation risk level is identified based on the deviation between the predicted total construction period and the planned total construction period. The biological constraint collaborative decision-making module is used to respond to the schedule deviation risk level, couple the forestry biological growth rhythm with the suitable construction window constraint, construct a multi-dimensional set of alternative correction schemes, use a conflict resolution algorithm to perform resource balance detection on the set of alternative correction schemes, and select the optimal correction scheme based on the marginal benefits of schedule recovery efficiency and additional costs. The adaptive schedule update module is used to parse the optimal correction scheme to extract the adjusted process logic parameters, reconstruct the dynamic schedule network diagram including the evolution of the critical path, and generate a dynamic collaborative schedule plan to push to the field terminal.

2. The forestry ecological restoration project progress collaborative management system as described in claim 1, characterized in that, The multi-source environment sensing module includes: An IoT sensor array deployed in the target remediation area is used to collect physicochemical parameters characterizing the habitat status in real time, including soil moisture monitoring values ​​and micrometeorological data; A communication interface for accessing a meteorological database is used to acquire meteorological fluctuation data of the repair area, including rainfall forecasts, extreme temperature warnings, and wind force levels. And a drone inspection unit, used to periodically collect hyperspectral images of the restoration area, and use a pre-set image recognition model to analyze the hyperspectral images to obtain data on the evolution stage of pests and diseases and the physiological growth status of vegetation.

3. The forestry ecological restoration project progress collaborative management system as described in claim 1, characterized in that, The nonlinear ergonomics derivation module calculates the ergonomics correction factor in the following manner: Construct a process-environment response matrix, wherein the row vectors of the process-environment response matrix represent different forestry processes, the column vectors represent different environmental disturbance factors, and the matrix elements characterize the influence coefficient of the process on the environment; An environmental stress vector is generated based on real-time collected environmental disturbance data, and the comprehensive sensitivity of each process is calculated by combining the process-environment response matrix. A preset nonlinear efficiency decay function is invoked to map the comprehensive sensitivity into an efficiency correction factor, which is used to characterize the degree of decay of actual construction efficiency relative to planned efficiency.

4. The forestry ecological restoration project progress collaborative management system as described in claim 3, characterized in that, The dynamic iterative simulation process performed by the nonlinear ergonomics derivation module includes: Obtain the planned duration of the remaining processes, and adjust the planned duration according to the efficiency correction factor to obtain the adjusted actual duration. The actual duration is inversely proportional to the efficiency correction factor. Based on the critical path method, the schedule network diagram is forward extrapolated using the actual duration of all remaining processes, and the predicted total project duration is calculated.

5. The forestry ecological restoration project progress collaborative management system as described in claim 1, characterized in that, The process by which the biologically constrained collaborative decision-making module constructs a set of alternative correction schemes includes: Based on a pre-established database of forestry biological growth rhythms, the spatiotemporal matching degree between the lagging processes and the suitable construction window periods for related tree species is determined. If the construction window is within a suitable period, a resource-enhancing corrective action plan will be generated. Otherwise, the process logic will be automatically reorganized, and a set of alternative corrective action plans will be generated by postponing planting processes and inserting land improvement or maintenance compensation processes into the vacated time period.

6. The forestry ecological restoration project progress collaborative management system as described in claim 1, characterized in that, The conflict resolution algorithm is based on multi-objective optimization logic, which fits and verifies the resource demand pulse of the alternative correction scheme with the maximum carrying capacity boundary of the on-site resources. For resource conflict points that exceed the carrying capacity boundary, the resource peaks and valleys are smoothed by shifting the start time of non-critical path processes or between execution processes until the resource demand curve meets the preset balance index.

7. The forestry ecological restoration project progress collaborative management system as described in claim 1, characterized in that, The specific process for selecting the optimal correction scheme is as follows: Construct a decision objective function that includes timeliness and economic indicators, and calculate the time recovery efficiency and additional costs after the implementation of each alternative corrective action plan. Based on the decision objective function, a weighted balance calculation is performed on the timeliness value corresponding to the project time recovery effectiveness and the economic loss corresponding to the additional cost, and the scheme that maximizes the overall benefits is selected as the optimal correction scheme.

8. The forestry ecological restoration project progress collaborative management system as described in claim 1, characterized in that, The process of reconstructing the dynamic progress network graph by the adaptive progress update module includes: Analyze the optimal correction scheme and identify the changed process nodes and their logical connections; Using dynamic network planning technology, the earliest start time, latest finish time, and total float of each process are recalculated; The critical path is recalibrated based on the process chain with zero total float, and the displacement trajectory of the critical path from the original path to the new path due to environmental disturbances is recorded to generate a dynamic collaborative schedule containing logical change information.

9. The forestry ecological restoration project progress collaborative management system as described in claim 1, characterized in that, The system also includes a 3D visualization interaction module for: A three-dimensional terrain model of the target restoration area is constructed based on a geographic information system. Map the process nodes in the dynamic collaborative schedule to the spatial coordinates of the 3D terrain model; The construction progress status of different areas is rendered in the form of heat maps in the 3D terrain model. When the risk level of progress deviation exceeds the preset threshold, the affected area and the recommended optimal correction scheme are highlighted.

10. A method for collaborative management of forestry ecological restoration project progress, applied to the forestry ecological restoration project progress collaborative management system as described in any one of claims 1-9, characterized in that, The method includes the following steps: Real-time acquisition of environmental disturbance data and current construction progress data of the target remediation area, wherein the environmental disturbance data includes at least meteorological fluctuation data, pest and disease evolution stage data, and soil moisture monitoring values; The environmental disturbance data is mapped to a pre-set process-environment response matrix. The sensitivity of environmental stress to different forestry processes is calculated and the efficiency correction factor is output. The efficiency correction factor is used to dynamically iterate and simulate the duration of the remaining processes in the current construction progress data to generate the predicted total construction period. The risk level of progress deviation is identified based on the deviation between the predicted total construction period and the planned total construction period. In response to the aforementioned schedule deviation risk level, a multi-dimensional set of alternative correction schemes is constructed by coupling the growth rhythm of forestry organisms with the constraints of the suitable construction window. The resource balance of the alternative correction schemes is tested using a conflict resolution algorithm, and the optimal correction scheme is selected based on the marginal benefits of the project recovery efficiency and the additional costs. The optimal correction scheme is analyzed to extract the adjusted process logic parameters, a dynamic schedule network diagram including the evolution of the critical path is reconstructed, and a dynamic collaborative schedule plan is generated and pushed to the field terminal.

Images