Dynamic optimization method, system, device and medium for transfer site

Abstract

The application discloses a kind of dynamic optimization method, system, equipment and medium of transfer site, comprising: the construction total construction period is decomposed into multiple construction time periods, to determine global capacity demand of transfer site based on the earthwork quantity-filling demand quantity time sequence balance equation of each construction time period;Based on global capacity demand of transfer site, a multi-objective optimization model is constructed for the configuration of transfer site and solved, and the configuration scheme of transfer site including optimal number, position, capacity and service area division is obtained;According to transfer site configuration scheme, multiple transfer sites are deployed, and the current soil storage capacity and actual filling progress of each transfer site are monitored in real time during construction, to realize the allocation of transfer site resources based on current soil storage capacity and actual filling progress;Global capacity demand prediction of transfer site and real-time matching of construction dynamic change are realized, the optimal site configuration scheme is obtained, and the site configuration can be adjusted according to the actual change of construction progress, and the construction efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of earthwork construction technology, and in particular to a dynamic optimization method, system, equipment and medium for transfer sites. Background Technology

[0002] In large-scale earthwork construction projects such as dike and river / lake improvement projects and highway projects, transfer sites (also known as temporary material yards or temporary soil stockpiles) are crucial facilities for achieving a balance between excavation and filling. Taking a certain dike project as an example, the construction area stretches for tens of kilometers, involving various types of work areas such as borrow pits, filling areas, transfer sites, and spoil disposal areas, with earthwork excavation volumes reaching millions of cubic meters. Because excavation and filling often occur at different times, the amount of excavated soil exceeds the direct filling demand at certain periods, requiring the excess soil to be temporarily stored at the transfer site; conversely, the filling demand exceeds the direct excavation demand, requiring soil to be borrowed from the transfer site. Therefore, the rational planning of transfer sites directly affects earthwork transportation costs, construction continuity, and site area occupancy.

[0003] In existing technologies, the planning methods for transfer sites mainly fall into the following categories: The first category is the earthwork allocation optimization method based on static linear programming. This method aims to minimize allocation costs and construction period, establishing an earthwork allocation optimization model that considers constraints such as material supply sources, receiving sources, and transportation routes. The second category is the joint optimization method of construction access roads and earthwork allocation based on cost distance. This method achieves coordinated optimization of access road routes and earthwork allocation by constructing a comprehensive cost grid and using GIS cost distance analysis, involving the location planning of transfer sites in the earthwork allocation scheme. The third category is the dynamic earthwork allocation method based on real-time monitoring. This method utilizes real-time data collected by IoT devices to adjust the earthwork allocation scheme.

[0004] Although traditional methods have improved earthwork allocation and transfer site planning from different perspectives, the number, location, capacity and service area division of transfer sites in traditional technologies are determined only by engineering experience or rough estimation, lacking systematic optimization design. Furthermore, traditional solutions lack the ability to dynamically adjust based on actual construction progress, ultimately resulting in a disconnect between site configuration and actual construction needs, affecting construction efficiency and cost control. Summary of the Invention

[0005] This invention provides a dynamic optimization method, system, equipment, and medium for transit sites, in order to solve the defect in the prior art of "the disconnect between site configuration and actual construction needs, which affects construction efficiency and cost control".

[0006] This invention provides a dynamic optimization method for transit sites, comprising: The total construction period is broken down into multiple construction periods, and the global capacity requirement of the transfer site is determined by the time-series balance equation based on the amount of excavated soil minus the amount of filling required in each construction period. Based on the global capacity requirements of the transit sites, a multi-objective optimization model for the configuration of transit sites is constructed and solved to obtain a transit site configuration scheme that includes the optimal number, location, capacity, and service area division. Multiple transfer sites are deployed according to the aforementioned transfer site configuration scheme. During the construction process, the current soil volume and actual filling progress of each transfer site are monitored in real time, so as to allocate transfer site resources based on the current soil volume and the actual filling progress.

[0007] According to the dynamic optimization method for a transfer site provided by the present invention, the step of determining the global capacity requirement of the transfer site based on the time-series balance equation of excavated soil volume - filling demand volume for each construction period specifically includes: For each construction period, the earthwork supply and demand difference for the current construction period is determined based on the corresponding earthwork output-filling demand time-series balance equation. The time-series balance equation represents the functional relationship between the sum of earthwork output in each excavation area, the sum of filling demand in each filling area, and the earthwork supply and demand difference within the current construction period. The cumulative earthwork volume for each construction period is calculated based on the difference between earthwork supply and demand, and the maximum value among the multiple cumulative earthwork volumes is taken as the global capacity requirement of the transfer site.

[0008] According to a dynamic optimization method for transit sites provided by the present invention, the step of constructing and solving a multi-objective optimization model for transit site configuration based on the global capacity requirements of the transit site, and obtaining a transit site configuration scheme including optimal quantity, location, capacity, and service area division, specifically includes: With the optimization objectives of minimizing transportation costs, minimizing the area occupied by the transfer site, and maximizing construction continuity, a multi-objective optimization model is constructed by combining multiple constraints, including supply-demand difference constraints set based on the global capacity demand of the transfer site. A multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a Pareto optimal solution set, and a set of solutions is selected from the Pareto optimal solution set as the transit site configuration scheme.

[0009] According to the present invention, a dynamic optimization method for transit sites is provided, wherein the supply-demand difference constraint is that the total design capacity of all transit sites is greater than or equal to the global capacity demand of the transit sites.

[0010] According to the dynamic optimization method for transit sites provided by the present invention, the multiple constraints also include a capacity limit constraint, a minimum distance constraint between transit sites, and an environmental distance constraint. The capacity limit constraint is that the design capacity of each transfer site shall not exceed the topographic capacity limit of the corresponding site. The minimum distance constraint between transfer sites is that the spatial distance between any two transfer sites shall not be less than the preset safety distance. The environmental protection distance constraint is that the distance between each transfer site and the preset environmentally sensitive area shall not be less than the preset environmental protection distance.

[0011] According to the dynamic optimization method for transfer sites provided by the present invention, the step of allocating transfer site resources based on the current soil inventory specifically includes: For each of the aforementioned transfer sites: calculate the capacity utilization rate of the current transfer site based on the current stock volume and design capacity; When the capacity utilization rate reaches the first preset threshold, a first-level warning is triggered, reducing the soil excavation intensity of the service area corresponding to the current transfer site, and transferring part of the soil excavation to other transfer sites. When the capacity utilization rate reaches the second preset threshold, a second-level warning is triggered, the transport of earthwork to the current transfer site is stopped, and the earthwork planned to be allocated to the current transfer site is redistributed to other transfer sites, wherein the second preset threshold is greater than the first preset threshold.

[0012] According to the present invention, a dynamic optimization method for a transfer site includes a step of allocating transfer site resources based on the actual filling progress, specifically comprising: The actual filling volume of each filling area is obtained periodically, and the actual filling volume is compared with the corresponding planned filling volume to calculate the filling progress deviation rate. When the filling progress deviation rate exceeds the preset deviation threshold, it is determined that there is a progress deviation. The supply and demand difference and the global capacity demand of the transfer site for the remaining time periods are updated according to the remaining filling volume and the adjusted filling intensity. The resource allocation of transit sites is adjusted based on the updated global capacity requirements of the transit sites.

[0013] The present invention also provides a dynamic optimization system for transit sites, comprising: The time-series prediction module is used to decompose the total construction period into multiple construction periods, and to determine the global capacity demand of the transfer site based on the time-series balance equation of excavation volume - filling demand for each construction period. The configuration scheme determination module is used to construct and solve a multi-objective optimization model for the configuration of the transit site based on the global capacity requirements of the transit site, so as to obtain a transit site configuration scheme that includes the optimal number, location, capacity and service area division. The dynamic monitoring and allocation module is used to deploy multiple transfer sites according to the transfer site configuration plan, and to monitor the current soil volume and actual filling progress of each transfer site in real time during the construction process, so as to allocate transfer site resources based on the current soil volume and the actual filling progress.

[0014] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the dynamic optimization method for transit sites as described above.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a dynamic optimization method for transit sites as described above.

[0016] The dynamic optimization method for transfer sites provided by this invention decomposes the total construction period into multiple construction periods and establishes a time-series balance equation for the amount of excavated soil versus the amount of filling required in each construction period. This allows for the prediction of the peak global capacity demand of transfer sites throughout the entire construction cycle. Based on the global capacity demand of transfer sites, a multi-objective optimization model for transfer site configuration is constructed and solved. This model determines the number, location, capacity, and service area division of transfer sites, avoiding the limitations of existing technologies that rely solely on engineering experience or rough estimations to determine site configuration. By monitoring the current amount of excavated soil and the actual filling progress of each transfer site in real time during construction, and dynamically allocating transfer site resources based on the monitoring data, this invention enables the perception of excavated soil and early warning of capacity overruns during construction, as well as adaptive adjustments to site configuration driven by filling progress deviations. Therefore, this invention achieves real-time matching between the prediction of global capacity demand for transfer sites and dynamic changes in construction. By obtaining the optimal site configuration scheme and allowing the site configuration to be adjusted according to actual changes in construction progress, it improves construction efficiency. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating the dynamic optimization method for transit sites provided by the present invention. Figure 2 This is a schematic diagram of the construction sequence decomposition and capacity prediction process of the dynamic optimization method for transfer sites provided by the present invention. Figure 3This is a schematic diagram of the transfer site configuration scheme design process based on a multi-objective genetic algorithm, which is the dynamic optimization method for transfer sites provided by this invention. Figure 4 This is a schematic diagram of the IoT real-time monitoring and hierarchical early warning mechanism of the dynamic optimization method for transit sites provided by this invention; Figure 5 This is a flowchart of the closed-loop dynamic adjustment mechanism of the dynamic optimization method for transit sites provided by the present invention; Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0019] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and should not be construed as limiting the scope of the invention.

[0020] In the description of this invention, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0021] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0022] See Figure 1 This application provides a dynamic optimization method for transit sites, comprising the following steps: S110: Decompose the total construction period into multiple construction periods, and determine the global capacity requirement of the transfer site based on the time-series balance equation of excavation volume - filling demand volume for each construction period. S120: Based on the global capacity requirements of transit sites, construct and solve a multi-objective optimization model for transit site configuration to obtain a transit site configuration scheme that includes the optimal number, location, capacity, and service area division. S130: Deploy multiple transfer sites according to the transfer site configuration plan, and monitor the current soil volume and actual filling progress of each transfer site in real time during the construction process, so as to allocate transfer site resources based on the current soil volume and actual filling progress.

[0023] This application, by decomposing the total construction period into multiple construction periods and establishing a time-series balance equation for the amount of excavated soil versus the amount of filling required in each construction period, can predict the peak global capacity demand of transfer sites throughout the entire construction cycle. Based on the global capacity demand of transfer sites, a multi-objective optimization model for transfer site configuration is constructed and solved, which can determine the number, location, capacity, and service area division of transfer sites, avoiding the limitations of existing technologies that rely solely on engineering experience or rough estimations to determine site configuration. By monitoring the current amount of excavated soil and the actual filling progress of each transfer site in real time during construction, and dynamically allocating transfer site resources based on the monitoring data, this invention can achieve the perception of excavated soil and early warning of capacity overruns during construction, as well as adaptive adjustments to site configuration driven by filling progress deviations. Thus, this invention achieves real-time matching between the prediction of global capacity demand for transfer sites and dynamic changes in construction, obtains the optimal site configuration scheme, and enables the site configuration to be adjusted according to actual changes in construction progress, thereby improving construction efficiency.

[0024] See S110. Figure 2 The total construction period is broken down into multiple construction periods according to the filling schedule, which is a timetable for the filling volume to be completed in each construction area within each period. Taking a certain embankment project as an example, the total construction period is 180 days. According to the filling rhythm, the construction process is divided into three stages: peak period (days 1-60), stable period (days 61-150), and final period (days 151-180). Each stage is further subdivided into several weeks or months as basic calculation periods.

[0025] The steps for determining the global capacity requirement of the transfer site based on the time-series balance equation of excavation volume - filling demand for each construction period include: for each construction period, determining the earthwork supply and demand difference for the current construction period based on the corresponding excavation volume - filling demand time-series balance equation, wherein the time-series balance equation represents the functional relationship between the sum of excavation volume of each excavation area, the sum of filling demand of each filling area, and the earthwork supply and demand difference within the current construction period; calculating the cumulative earthwork volume for each construction period based on the earthwork supply and demand difference, and taking the maximum value among multiple cumulative earthwork volumes as the global capacity requirement of the transfer site.

[0026] Establish time-series balance equations for the amount of excavated soil and the amount of fill required for each construction period. For each construction period... Calculate the difference between earthwork supply and demand during this period. ,in, This represents the sum of the excavated soil volume from each excavation zone during that period. This represents the sum of the filling requirements for each fill zone during that period. A value greater than zero indicates that the amount of excavated soil during that period exceeds the filling requirement, and the excess soil needs to be temporarily stored at the transfer site; when When the value is less than zero, it means that the filling demand during that period is greater than the excavation demand, and soil needs to be taken from the temporary site.

[0027] The excavation volume for each time period is calculated based on the total excavation volume of each excavation zone, the average daily excavation intensity, and the construction day coefficient. The filling demand is calculated based on the total filling volume of each filling zone, the average daily filling intensity, and the construction day coefficient. The construction day coefficient is determined based on the construction schedule of each construction area within the corresponding time period, and its value ranges from zero to one. Specifically, the construction day coefficient is the proportion of the actual construction days in a certain construction period to the total number of days in that period.

[0028] In one example, the total excavation volume of each excavation zone is used as the baseline. Dividing this by the planned total number of construction days yields the average daily excavation intensity. This intensity is then multiplied by the actual effective number of construction days for the current period (i.e., the product of calendar days and the construction day coefficient) to obtain the excavated volume for the current construction period. The calculation method for filling requirements is similar. The above is merely an example; those skilled in the art can flexibly adjust the calculation method according to the actual project situation, and are not limited to the above example.

[0029] Furthermore, based on the supply-demand difference in each construction period, the cumulative earthwork volume in each period is calculated (only periods with a positive supply-demand difference are counted), and the global capacity requirement of the transfer site is the maximum value of the cumulative earthwork volume in all periods.

[0030] Specifically, the difference between the excavated and filled volumes is calculated for each construction period to obtain the earthwork supply-demand difference for each period. Starting from the first construction period, the supply-demand difference for each period is accumulated segment by segment. When the supply-demand difference is positive, the accumulated earthwork volume increases accordingly; when the supply-demand difference is negative, the accumulated earthwork volume decreases accordingly (the minimum is zero, indicating no earthwork on site). This yields the accumulated earthwork volume for each period. After obtaining the accumulated earthwork volume for each period, the maximum value among all periods is taken as the global capacity requirement of the transfer site to ensure that the site can accommodate the maximum earthwork surplus peak during construction. In some examples, in actual engineering applications, a safety margin can be added to the global capacity requirement of the transfer site based on factors such as project importance and construction uncertainties to enhance the risk resistance during construction.

[0031] It should be noted that in the process of calculating the cumulative soil volume segment by segment, the cumulative soil volume will only increase when the supply-demand difference is positive. This positive value represents the excess soil volume generated in that period. Negative value periods will consume existing soil and cause the cumulative value to decrease.

[0032] Unlike existing static capacity calculation methods, the time-series capacity prediction of this invention can reflect the dynamic fluctuations in earthwork supply and demand during construction. At certain times, there may be peak excavation periods with multiple excavation zones operating simultaneously, and at other times, there may be peak filling periods with multiple filling zones operating simultaneously. These fluctuations determine that the actual demand for the transfer site's capacity is far greater than the simple difference between total excavation and total filling. Through time-series decomposition, this invention can more accurately predict the capacity demand of the transfer site.

[0033] See S120. Figure 3 Based on the global capacity requirements of transit sites, a multi-objective optimization model for transit site configuration is constructed and solved to obtain a transit site configuration scheme that includes optimal quantity, location, capacity, and service area division. Specifically, this includes: using minimizing transportation costs, minimizing transit site occupancy area, and maximizing construction continuity as optimization objectives, and combining multiple constraints to construct a multi-objective optimization model, including supply-demand difference constraints set based on the global capacity requirements of transit sites; using a multi-objective genetic algorithm to solve the multi-objective optimization model to obtain a Pareto optimal solution set, and selecting a set of solutions from the Pareto optimal solution set as the transit site configuration scheme.

[0034] The optimization model focuses on three engineering objectives: minimizing transportation costs, i.e., minimizing the sum of weighted transportation distances from each transfer site to the cut-and-fill points in its service area; minimizing the area occupied by transfer sites, i.e., minimizing the total area of ​​transfer sites (capacity divided by the design stack height and multiplied by the terrain correction factor); and maximizing construction continuity, i.e., minimizing the probability of insufficient transfer site capacity during construction, and minimizing the probability of construction interruption due to insufficient transfer site capacity by rationally allocating transfer sites.

[0035] The constraints of the optimization model include: supply and demand difference constraints, ensuring that the total capacity of the transfer sites meets the cumulative soil storage demand in each time period; capacity limit constraints for each transfer site, ensuring that the capacity does not exceed the terrain capacity limit of the site; minimum distance constraints between transfer sites, ensuring that the distance between any two transfer sites is not less than the safe distance; and environmental distance constraints, ensuring that the distance from the transfer site to the environmentally sensitive area is not less than the environmental protection requirement distance.

[0036] Specifically, regarding the supply-demand gap constraint, the actual total design capacity of all transfer sites must be greater than or equal to the maximum cumulative earthwork volume calculated in the above steps for any time period during construction. This constraint ensures that any candidate configuration scheme generated by the optimization algorithm has the total capacity to accommodate all excess earthwork throughout the entire construction cycle.

[0037] Regarding the capacity limit constraint, the design capacity of any site shall not exceed the maximum earth-fill capacity allowed by the terrain conditions at that site. For example, the maximum earth-fill capacity can be determined by a combination of factors such as the available area of ​​the site, the allowable earth-fill height, and the bearing capacity of the foundation.

[0038] To address the minimum spacing constraints between transit sites, the spatial distance between any two transit sites must not be less than a preset safety threshold, in order to prevent safety risks such as construction interference and slope instability between adjacent sites, while also avoiding regional traffic congestion caused by the concentrated layout of multiple sites.

[0039] In response to environmental distance constraints, the distance between any candidate transfer site and surrounding environmentally sensitive areas (such as water source protection areas, residential areas, basic farmland, ecological red line areas, etc.) shall not be less than the minimum safe distance stipulated by national or local environmental protection regulations, so as to ensure that the selection of transfer site will not have an adverse impact on environmental protection objectives.

[0040] Furthermore, a multi-objective genetic algorithm is used to solve the above multi-objective optimization model. Decision variables include the number of transit sites, their location coordinates, capacity, and service area division. The algorithm outputs a set of Pareto optimal solutions, allowing decision-makers to select the final solution based on actual engineering needs. In engineering applications, algorithm parameters can be set according to the project scale, typically with a population size of 50 to 200 and an iteration count of 100 to 300. Those skilled in the art can also set other parameters based on actual conditions.

[0041] The decision variables include the number of transfer sites, the location coordinates of each site, the design capacity of each site, and the service area division of each site. The number determines how many transfer sites should be set up; the location coordinates determine the specific location of each site; the capacity allocation determines the temporary earthwork storage scale of each site; and the service area division clarifies which excavation and fill areas each site is responsible for supplying. The algorithm, by adjusting the combination of these variables, searches for a transfer site configuration scheme that achieves the optimal balance between transportation costs, land area, and construction continuity, while satisfying various constraints.

[0042] In S130, the step of allocating transfer site resources based on the current soil volume specifically includes: for each transfer site: calculating the capacity utilization rate of the current transfer site based on the current soil volume and design capacity; when the capacity utilization rate reaches a first preset threshold, triggering a first-level warning, reducing the soil excavation intensity of the service area corresponding to the current transfer site, and transferring some of the excavated soil to other transfer sites; when the capacity utilization rate reaches a second preset threshold, triggering a second-level warning, stopping the transport of soil to the current transfer site, and redistributing the soil planned to be allocated to the current transfer site to other transfer sites, wherein the second preset threshold is greater than the first preset threshold.

[0043] In this step, see Figure 4 This application establishes a real-time monitoring and hierarchical early warning mechanism for the capacity utilization of transfer sites based on digital Internet of Things weighbridges.

[0044] In terms of hardware deployment, digital IoT weighbridges are installed at the entrances and exits of each transfer site. The weighbridge system includes a weighing platform, a license plate recognition camera, a data transmission module, and a management terminal. The weighing platform is used to measure the weight of transport vehicles entering and leaving the site; the license plate recognition camera is used to automatically identify vehicle license plate numbers and record entry and exit times; the data transmission module uploads weight and license plate data to the management platform in real time via a cellular network; and the management terminal is used for data display, threshold setting, and early warning management.

[0045] In terms of data collection and processing, when a vehicle enters the site (carrying excavated soil), the weighbridge system records the vehicle's gross weight and entry time, and identifies the vehicle information through license plate recognition. When a vehicle leaves the site (empty or after excavation), the system records the vehicle's gross weight and exit time. The management platform calculates the volume of excavated soil transported per trip based on the difference between the entry and exit weights, combined with soil density parameters. The real-time soil storage volume at the transfer site is the cumulative entry volume minus the cumulative exit volume.

[0046] Regarding capacity utilization calculation and early warning, the capacity utilization rate is calculated by dividing the current stocked soil volume by the designed capacity. The current stocked soil volume refers to the total amount of earthwork actually stockpiled at the current transfer site. Three levels of status are set based on the capacity utilization rate: Normal status (utilization rate below 70%), the site is operating normally; Yellow warning (utilization rate greater than or equal to 70% but less than 85%), the system reduces the excavation intensity of the corresponding service area and transfers some earthwork to other transfer sites; Red warning (utilization rate greater than or equal to 85%), the system stops transporting earthwork to the transfer site, transfers the earthwork originally planned for this site to other transfer sites or directly connects with the filling area for transportation, and simultaneously sends a red warning notification to management personnel. The above threshold settings are only examples; those skilled in the art can set other values ​​for the thresholds according to actual circumstances.

[0047] Unlike existing technologies that rely on manual inspections to estimate soil volume, the IoT (Internet of Things) monitoring mechanism of this invention enables automated, real-time, and precise monitoring of soil volume at transfer sites, eliminating subjective errors in manual estimation and providing a reliable data foundation for dynamic adjustments.

[0048] In this embodiment, the step of allocating transfer site resources based on actual filling progress specifically includes: periodically obtaining the actual filling completion volume of each filling area, comparing the actual filling completion volume with the corresponding planned filling completion volume, and calculating the filling progress deviation rate; when the filling progress deviation rate exceeds a preset deviation threshold, it is determined that there is a progress deviation, and the supply and demand difference and the global capacity demand of the transfer site for each remaining time period are updated according to the remaining filling volume and the adjusted filling intensity; and the resource allocation of the transfer site is adjusted based on the updated global capacity demand of the transfer site.

[0049] In this step, see Figure 5 This application establishes a closed-loop dynamic adjustment mechanism based on the rolling update of the time-series balance equation.

[0050] Regarding schedule deviation detection, the system periodically (e.g., weekly or monthly) compares the actual amount of filling completed with the planned amount of filling completed, and calculates the filling schedule deviation rate. When the absolute value of the deviation rate exceeds the preset deviation threshold (usually 10%), a closed-loop adjustment process is triggered.

[0051] The closed-loop adjustment process includes the following steps: schedule deviation analysis, analyzing the causes of deviations and determining whether the deviations are persistent; rolling update of the time balance equation, keeping the data of completed construction periods unchanged, recalculating the time balance equation for the remaining periods based on the remaining filling volume and the adjusted filling intensity, and updating the supply-demand difference and capacity demand for each period.

[0052] In a specific example, the data for the completed construction period remains unchanged, while the filling volume for the remaining periods is recalculated using the corrected daily average filling intensity to redetermine the supply-demand difference for each period. Starting from the current time point, the supply-demand difference for the remaining periods is cumulatively added segment by segment to update the cumulative soil volume for each subsequent period, and the maximum value is extracted again as the adjusted global capacity requirement for the transfer site.

[0053] For example, the configuration of transit sites is dynamically adjusted. If the updated capacity demand increases and the existing transit sites cannot meet it, a backup transit site is activated or the service area of ​​the existing site is expanded. If the capacity demand decreases, the service area of ​​the transit site is reduced. The service area is re-divided. Based on the updated transit site configuration, the service area of ​​each transit site is re-divided and the transportation route is optimized.

[0054] This closed-loop mechanism enables the configuration of transfer sites to be adaptively adjusted according to the actual changes in the construction progress, effectively solving the problem of mismatch between site capacity and actual demand caused by the fixed configuration after the planning is completed in the existing technology.

[0055] In this embodiment of the application, a visualization display function for the configuration scheme of the transit site is also provided to assist engineering management personnel in making decisions and reviews.

[0056] For map visualization, the location of transfer sites, service area boundaries, and capacity status (green / yellow / red) are marked on the construction area map or 3D reality model. Layered display is supported, allowing users to view the configuration status of transfer sites at different time periods according to construction stages.

[0057] The data dashboard provides real-time data displays for each transfer site, including real-time soil storage volume, design capacity, capacity utilization rate, service area name, and number of vehicles entering and leaving today. It also provides construction progress comparison charts, showing the comparison curve and deviation rate between planned and actual filling volumes.

[0058] In terms of early warning management, when a yellow or red alert is triggered, the system highlights the alert information on the data panel and simultaneously notifies relevant management personnel via SMS or app push notifications. The early warning processing record includes the alert time, alert level, triggering reason, measures taken, and processing result.

[0059] This application adopts a three-tier architecture design: "data layer - decision layer - application layer". The data layer is responsible for the collection, fusion, and standardized storage of multi-source data, including: construction plan data (filling schedule, division of construction periods for each area, excavation intensity and filling intensity parameters), topographic data (DEM digital elevation model, geological survey data, engineering boundary data, environmental constraint data), and IoT real-time data (vehicle weight, entry and exit direction, and timestamp data collected by digital IoT weighbridges). The data layer interacts with external systems through standardized data interfaces, including receiving access road path data and earthwork allocation plans from external construction access road planning systems, and outputting real-time soil storage volume and capacity utilization data for each transfer site to external construction management systems.

[0060] The decision-making layer includes: a time-series capacity forecasting component, used to decompose and calculate the difference between earthwork supply and demand and the cumulative earthwork storage demand according to the construction period; a multi-objective optimization design component, used to solve the optimal configuration scheme of the transfer site; and a closed-loop dynamic adjustment component, used to automatically adjust the site configuration when schedule deviations or early warnings are triggered.

[0061] The application layer is responsible for the functional implementation and visualization of engineering management personnel, including: generation and display of transfer site configuration schemes (map visualization of the number, location, capacity, and service area of ​​transfer sites), IoT real-time monitoring panel (status display of soil storage and capacity utilization rate of each transfer site), hierarchical early warning and alarm management (triggering, push and processing records of yellow and red early warnings), construction progress deviation analysis (comparison charts of planned and actual values, deviation rate calculation), and closed-loop adjustment scheme output (adjusted transfer site configuration scheme and adjustment suggestion report), etc.

[0062] Compared with the prior art, this application solves the problem that the static balance calculation using the total excavation volume and total filling volume in the prior art cannot reflect the dynamic fluctuations of earthwork supply and demand during construction. By decomposing the total construction period into multiple time periods according to the filling rhythm and establishing the time-series balance equation of excavation volume-filling demand for each time period, it realizes the accurate prediction of the global capacity demand of the transfer site, avoiding site overcapacity due to insufficient capacity design or site idleness due to excessive capacity design.

[0063] The system optimization design method for transfer sites based on multi-objective genetic algorithms solves the problem that the number, location, capacity and service area of ​​transfer sites in the existing technology are determined only by engineering experience and lack system optimization. By establishing three engineering optimization objectives of minimizing transportation costs, minimizing site occupation area and maximizing construction continuity, and a constraint system including supply and demand difference constraints, capacity upper limit constraints, minimum spacing constraints and environmental distance constraints, the global optimum of the transfer site configuration scheme is achieved.

[0064] By adopting a real-time monitoring and tiered early warning technology for the capacity utilization of transfer sites based on digital IoT weighbridges, the problem of existing technologies relying on manual inspection and estimation of soil storage in transfer sites and lacking automatic monitoring and early warning is solved. By deploying digital IoT weighbridges at the site entrances and exits, automatically collecting weight data of vehicles entering and exiting, calculating soil storage and capacity utilization in real time, and setting two-level thresholds for yellow and red early warnings and triggering different adjustment measures respectively, the automation and intelligence of transfer site capacity management are realized.

[0065] By adopting a closed-loop dynamic adjustment technology based on rolling updates of the time-series balance equation, the problem of the transfer site configuration remaining unchanged during construction and the inability to automatically correct deviations in filling progress in existing technologies is solved. By comparing the actual filling volume with the planned filling volume to calculate the progress deviation rate, the rolling updates of the time-series balance equation and the dynamic adjustment of the transfer site configuration are triggered, thus achieving adaptive matching between the transfer site capacity and actual demand.

[0066] The dynamic optimization system for transit sites provided by this invention will be described below. The dynamic optimization system for transit sites described below can be referred to in correspondence with the dynamic optimization method for transit sites described above.

[0067] This application provides a dynamic optimization system for transit sites, including: The time-series prediction module is used to decompose the total construction period into multiple construction periods, and to determine the global capacity demand of the transfer site based on the time-series balance equation of excavation volume - filling demand for each construction period. The configuration scheme determination module is used to construct and solve a multi-objective optimization model for the configuration of transit sites based on the global capacity requirements of transit sites, and obtain a transit site configuration scheme that includes the optimal number, location, capacity, and service area division. The dynamic monitoring and allocation module is used to deploy multiple transfer sites according to the transfer site configuration plan. During the construction process, it monitors the current soil volume and actual filling progress of each transfer site in real time, so as to allocate transfer site resources based on the current soil volume and actual filling progress.

[0068] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 830, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute a dynamic optimization method for the transfer site.

[0069] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. 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.

[0070] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the dynamic optimization method for transit sites provided by the methods described above.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0072] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic optimization method for transit sites, characterized in that, include: The total construction period is broken down into multiple construction periods, and the global capacity requirement of the transfer site is determined by the time-series balance equation based on the amount of excavated soil minus the amount of filling required in each construction period. Based on the global capacity requirements of the transit sites, a multi-objective optimization model for the configuration of transit sites is constructed and solved to obtain a transit site configuration scheme that includes the optimal number, location, capacity, and service area division. Multiple transfer sites are deployed according to the aforementioned transfer site configuration scheme. During the construction process, the current soil volume and actual filling progress of each transfer site are monitored in real time, so as to allocate transfer site resources based on the current soil volume and the actual filling progress.

2. The dynamic optimization method for transit sites according to claim 1, characterized in that, The steps for determining the global capacity requirement of the transfer site based on the time-series balance equation of excavated soil volume minus filling demand for each construction period specifically include: For each construction period, the earthwork supply and demand difference for the current construction period is determined based on the corresponding earthwork output-filling demand time-series balance equation. The time-series balance equation represents the functional relationship between the sum of earthwork output in each excavation area, the sum of filling demand in each filling area, and the earthwork supply and demand difference within the current construction period. The cumulative earthwork volume for each construction period is calculated based on the difference between earthwork supply and demand, and the maximum value among the multiple cumulative earthwork volumes is taken as the global capacity requirement of the transfer site.

3. The dynamic optimization method for transit sites according to claim 1, characterized in that, The step of constructing and solving a multi-objective optimization model for the configuration of transit sites based on the global capacity requirements of the transit sites, and obtaining a transit site configuration scheme that includes optimal quantity, location, capacity, and service area division, specifically includes: With the optimization objectives of minimizing transportation costs, minimizing the area occupied by the transfer site, and maximizing construction continuity, a multi-objective optimization model is constructed by combining multiple constraints, including supply-demand difference constraints set based on the global capacity demand of the transfer site. A multi-objective genetic algorithm is used to solve the multi-objective optimization model to obtain a Pareto optimal solution set, and a set of solutions is selected from the Pareto optimal solution set as the transit site configuration scheme.

4. The dynamic optimization method for transit sites according to claim 3, characterized in that, The supply-demand gap constraint is that the total design capacity of all transit sites is greater than or equal to the global capacity demand of the transit sites.

5. The dynamic optimization method for transit sites according to claim 4, characterized in that, The constraints also include capacity limits, minimum distances between transit sites, and environmental distance constraints. The capacity limit constraint is that the design capacity of each transfer site shall not exceed the topographic capacity limit of the corresponding site. The minimum distance constraint between transfer sites is that the spatial distance between any two transfer sites shall not be less than the preset safety distance. The environmental protection distance constraint is that the distance between each transfer site and the preset environmentally sensitive area shall not be less than the preset environmental protection distance.

6. The dynamic optimization method for transit sites according to claim 1, characterized in that, The steps for allocating transfer site resources based on the current soil inventory specifically include: For each of the aforementioned transfer sites: calculate the capacity utilization rate of the current transfer site based on the current stock volume and design capacity; When the capacity utilization rate reaches the first preset threshold, a first-level warning is triggered, reducing the soil excavation intensity of the service area corresponding to the current transfer site, and transferring part of the soil excavation to other transfer sites. When the capacity utilization rate reaches the second preset threshold, a second-level warning is triggered, the transport of earthwork to the current transfer site is stopped, and the earthwork planned to be allocated to the current transfer site is redistributed to other transfer sites, wherein the second preset threshold is greater than the first preset threshold.

7. The dynamic optimization method for transit sites according to claim 1, characterized in that, The steps for allocating transfer site resources based on the actual filling progress include: The actual filling volume of each filling area is obtained periodically, and the actual filling volume is compared with the corresponding planned filling volume to calculate the filling progress deviation rate. When the filling progress deviation rate exceeds the preset deviation threshold, it is determined that there is a progress deviation. The supply and demand difference and the global capacity demand of the transfer site for the remaining time periods are updated according to the remaining filling volume and the adjusted filling intensity. Adjust the resource allocation of transit sites based on the updated global capacity requirements.

8. A dynamic optimization system for a transfer site, characterized in that, include: The time-series prediction module is used to decompose the total construction period into multiple construction periods, and to determine the global capacity demand of the transfer site based on the time-series balance equation of excavation volume - filling demand for each construction period. The configuration scheme determination module is used to construct and solve a multi-objective optimization model for the configuration of the transit site based on the global capacity requirements of the transit site, so as to obtain a transit site configuration scheme that includes the optimal number, location, capacity and service area division. The dynamic monitoring and allocation module is used to deploy multiple transfer sites according to the transfer site configuration plan, and to monitor the current soil volume and actual filling progress of each transfer site in real time during the construction process, so as to allocate transfer site resources based on the current soil volume and the actual filling progress.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the dynamic optimization method for transit sites as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the dynamic optimization method for transit sites as described in any one of claims 1 to 7.

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