A method for intelligent management and control of materials in large-scale hydropower station projects using the Internet of Things

Abstract

This invention discloses an IoT-based intelligent management and control method for engineering materials in large-scale hydropower stations, relating to the field of intelligent management and control technology. The method includes: acquiring the engineering materials required for the large-scale hydropower station, assigning a unique digital tag to each material, and establishing a full lifecycle dataset for each material; constructing a comprehensive digital map of the hydropower station project, integrating real-time road conditions, construction progress, transportation vehicle performance, and site constraints, calculating the optimal transportation route for engineering materials, comparing the actual transportation trajectory with the planned route in real time, calculating the real-time route deviation minus the estimated delay time, setting up a tiered early warning mechanism, and achieving automatic early warning for abnormal routes; implementing dynamic resource collaborative scheduling of engineering materials, comprehensively analyzing the status of materials en route, the urgency of needs at each construction site, the distribution of reserve inventory, and available transportation capacity, and generating a globally optimal emergency scheduling plan for the large-scale hydropower station. This invention enhances the resilience of the supply of engineering materials for large-scale hydropower stations.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, specifically to an Internet of Things (IoT) intelligent control method for materials in large-scale hydropower station projects. Background Technology

[0002] Existing technologies for managing materials in large-scale hydropower projects generally suffer from the following shortcomings: reliance on discrete manual records or single-point information systems leads to a break in the data chain throughout the entire lifecycle of materials, from procurement and transportation to consumption, creating information silos; monitoring of the transportation process is mostly limited to location tracking, lacking comprehensive analysis of real-time road conditions, vehicle performance, and site constraints, making it impossible to achieve dynamic route optimization and accurate prediction of delay risks; when transportation anomalies occur, emergency response mainly relies on manual experience for local coordination, making it difficult to quickly integrate multi-dimensional resources such as the status of materials en route, dynamic needs from multiple construction sites, distributed inventory, and real-time available transportation capacity, resulting in delayed scheduling decisions, low resource allocation efficiency, and an inability to form a globally optimal emergency plan to ensure the project's critical path. Summary of the Invention

[0003] To address the aforementioned technical issues, this paper presents an IoT-based intelligent management and control method for materials in large-scale hydropower station projects. This technical solution resolves the problem of failing to formulate a globally optimal emergency plan to ensure the critical path of the project.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0005] A method for intelligent management and control of materials in large-scale hydropower station projects via the Internet of Things includes:

[0006] S1. Obtain the engineering materials required for large-scale hydropower stations, assign a unique holographic digital tag to each engineering material, and establish a full life cycle dataset for each engineering material.

[0007] S2. Based on the full life cycle dataset of each engineering material, construct a digital map of the entire hydropower station project, integrate real-time road conditions, construction progress, transportation vehicle performance and site constraints, calculate the optimal transportation route of engineering materials, compare the actual transportation trajectory of engineering materials with the planned route in real time, calculate the real-time path deviation of engineering materials - estimated delay time, set up a hierarchical early warning mechanism to realize automatic early warning of abnormal paths;

[0008] S3. Based on the real-time path deviation of engineering materials and the estimated delay time, implement dynamic resource collaborative scheduling of engineering materials, comprehensively analyze the status of materials in transit, the urgency of demand at each construction site, the distribution of reserve inventory and available transportation capacity, and generate the global optimal emergency scheduling plan for large hydropower stations.

[0009] Preferably, step S1 specifically includes:

[0010] Before leaving the factory, the manufacturer generates a unique RFID tag based on the type of engineering materials. This unique RFID tag adopts a three-level coding system, consisting of a four-digit engineering material category code, a six-digit batch code, and a ten-digit serial number. The unique RFID tag is installed on each engineering material to read its pre-shipment data in real time, which serves as the basic attribute data of the engineering material.

[0011] The four-digit engineering material category code is used to identify the material type; the six-digit batch code is used to identify different batches of the same type of material; and the ten-digit serial number ensures the uniqueness of each material.

[0012] Pre-shipment data includes: manufacturer information, production date, warranty period, certificate of conformity number, electronic link to test report, and inspector information;

[0013] When engineering materials are issued from the warehouse, the RFID tags of the materials are scanned in batches to automatically verify the information of the issued list and the actual materials. If the issued quantity does not match the actual quantity or the unique RFID tag is damaged, an abnormal situation warning will be triggered immediately.

[0014] By scanning the RFID tags on transportation vehicles and combining them with the six-digit batch codes of engineering materials, a relationship between engineering material batches and transportation vehicles is established, and an electronic waybill for engineering materials is generated.

[0015] Preferably, step S1 further includes:

[0016] During transportation, for the vehicles carrying engineering materials, the vehicle-mounted Beidou positioning terminal is used to obtain the latitude, longitude, speed and direction information of the engineering materials in real time at a frequency of 30 seconds, as the transportation status data of the engineering materials.

[0017] Based on temperature and humidity sensors and vibration sensors deployed on transportation vehicles, temperature, humidity and vibration data are monitored in real time during transportation at a sampling frequency of 5 minutes, serving as environmental monitoring data for the transportation of engineering materials.

[0018] For each transportation transfer station, the RFID tags of the arriving engineering materials are scanned using handheld terminals. The planned delivery list is compared with the actual delivery list, and the handover time, the person in charge, and the current status of the engineering materials are recorded as verification data for the handover of engineering materials.

[0019] Preferably, step S1 further includes:

[0020] Based on the project schedule management system, the engineering material demand plan is obtained, the construction task code is associated, a demand time window early warning mechanism is set, and the arrival information of engineering materials is automatically matched with the construction plan to obtain engineering material construction-related data.

[0021] By integrating basic attribute data of engineering materials, transportation status data, environmental monitoring data during transportation, and construction-related data, aligning timestamps, and performing data preprocessing, a full lifecycle dataset for each engineering material is established.

[0022] Preferably, step S2 specifically includes:

[0023] By applying for enterprise-level permissions based on the map software platform API and setting a fixed update frequency, we can obtain data on the road network and waterway around large hydropower stations, map them to a unified coordinate system, obtain the basic map coordinate system around large hydropower stations, and extract the attribute features of the road network and waterway corresponding to the level, speed limit, number of lanes, bridge height limit and tunnel width limit.

[0024] Based on the coordinate system of the basic map around the large hydropower station, the coordinates of formal roads, temporary access roads and loading and unloading points in the construction area of ​​the large hydropower station are extracted using the building information model. The temporary facility paths not covered by the building information model are supplemented by the CAD file of the construction layout plan. The coordinates and paths are converted into vector layers that can be recognized by the map information system, and business attributes are bound to each unloading point.

[0025] The business attributes include: available time period, maximum number of vehicles in queue, weighbridge opening hours, and contact information of the person in charge.

[0026] Based on real-time traffic data from the access map service provider, the system obtains real-time traffic speed, congestion level, construction closure areas, and temporary traffic control data for various road sections around the large hydropower station. The data refresh frequency is set to once per minute to construct a digital map of the entire hydropower station project area. Different colors are used to highlight the data: congested road sections are marked in red, construction road sections are marked in gray, and restricted areas are marked in yellow.

[0027] Preferably, step S2 further includes:

[0028] Based on the full life cycle dataset of each engineering material and the digital map of the entire hydropower station project, the engineering material transportation task is obtained. The latitude and longitude of the supplier warehouse is used as the starting point, the entrance coordinates of the specific construction face of the large hydropower station are used as the ending point, the type of transportation vehicle, load capacity and average speed are used as the transportation vehicle attributes, the required arrival time and the opening time of the unloading point are used as the task constraints, and the real-time road condition data around the large hydropower station at the current time is used as the dynamic environment data.

[0029] Based on the digital map of the entire hydropower station project, an evaluation model for the transportation route of engineering materials was established with the shortest transportation distance and the shortest expected arrival time as dual objective functions. The maximum load limit of the transportation vehicle, the matching degree between the size of the transportation vehicle and the height and width restrictions of the road, the available time period of the unloading point, the real-time road hardness coefficient and the road passage permit of the construction section were used as constraints to generate several candidate routes for the transportation of engineering materials. The comprehensive evaluation value of each candidate route was calculated, sorted by elevation and descent, and the route with the lowest comprehensive evaluation value was selected as the optimal transportation route for engineering materials.

[0030] The real-time road condition hardness coefficient is the ratio of free-flow time to real-time estimated travel time.

[0031] Overall evaluation value = 0.6 × (estimated arrival time of the task - required arrival time of the task) + 0.4 × transportation distance.

[0032] Preferably, step S2 further includes:

[0033] Based on the real-time acquisition of the latitude and longitude of engineering materials through vehicle-mounted Beidou positioning terminals during the transportation of engineering materials, the coordinates of the materials are dynamically mapped onto the digital map of the entire hydropower station project area. Discrete positioning points are matched to the road network in real time to obtain the actual transportation trajectory of the engineering materials. A fixed sliding time window of five minutes is set, and the overlap between the actual transportation trajectory of the engineering materials and the optimal transportation path of the engineering materials within the time window is calculated. If the road segments matched by three consecutive positioning points are not within the preset corridor zone t of the optimal transportation path of the engineering materials, it is determined that a path deviation has occurred. The real-time path deviation of the engineering materials is calculated as the estimated delay time. t is the width of the strip buffer area generated by the optimal transportation path, and the value of t is 20-50m.

[0034] The real-time path deviation of the engineering materials = (actual mileage - optimal transport path mileage) / optimal transport path mileage × 100%;

[0035] Estimated delay time = (Remaining time from current location to destination under real-time traffic conditions) - (Remaining time from location corresponding to the optimal delivery route to destination under real-time traffic conditions).

[0036] Based on the real-time deviation of the engineering materials' route and the estimated delay time, a tiered early warning mechanism is set up to automatically warn of abnormal routes. If the real-time deviation of the engineering materials' route is between 5% and 10% or the estimated delay time is between 15 and 30 minutes, a Level 1 warning is triggered, and a voice prompt is automatically sent to the driver to remind them to pay attention to the driving route. If the real-time deviation of the engineering materials' route is between 10% and 20% or the estimated delay time is between 30 and 60 minutes, a Level 2 warning is triggered, and an alternative route is automatically pushed to the vehicle terminal to guide the driver to choose a better route. If the real-time deviation of the engineering materials' route is greater than 20% or the estimated delay time is greater than 60 minutes, a Level 3 warning is triggered, and an emergency handling mechanism is activated to contact the driver to inquire about the situation, determine whether the driver has had an accident or abnormality, and manually plan an emergency route or coordinate on-site assistance.

[0037] Preferably, step S3 specifically includes:

[0038] Based on the graded early warning triggered by the real-time path deviation of engineering materials and the estimated delay time, emergency dispatch analysis is immediately initiated. According to the unique RFID tag information of the early warning engineering materials, all other engineering material transportation units of the same type and destination that are in transit are selected from the full life cycle dataset of each engineering material. Based on the latest estimated arrival time, the top 3 materials of the same type that are expected to arrive earliest are identified and marked as affected materials.

[0039] Based on the construction progress management system, the status of all related construction tasks is extracted. The urgency of each construction surface is assessed according to the importance of the construction tasks. Construction tasks on the main project path, tasks whose delays will directly affect the total project duration, or tasks with a buffer time of less than 4 hours are marked as first priority. Construction tasks on the non-main path but with limited free float, tasks with a buffer time of 4-8 hours, or tasks requiring coordination of multiple engineering materials are marked as second priority. Tasks involving routine material replenishment, tasks with a buffer time of more than 8 hours, or non-continuous operations are marked as third priority.

[0040] Preferably, step S3 further includes:

[0041] Integrate the WMS systems of each field warehouse with the central warehouse management system to obtain the real-time available inventory, specific location and access convenience of engineering materials, distinguish between locked inventory and available inventory, assess picking and loading time based on the characteristics of engineering materials and warehouse operation capacity, calculate their outbound preparation time, and establish a backup inventory distribution map.

[0042] The system queries all available empty vehicles of contracted transportation companies within a 30-kilometer radius of the warning incident location, obtains vehicle type, load capacity and current location, confirms driver contact information and working hour restrictions, and obtains a pool of available transportation vehicles. It also compiles statistics on available unloading equipment and personnel at each outbound warehouse, obtains the current working status and estimated available time of the equipment, and obtains a pool of available loading and unloading resources. A comprehensive assessment is then conducted to obtain the available transportation capacity for engineering materials.

[0043] Preferably, step S3 further includes:

[0044] Using the urgency level of each affected construction site, the distribution map of backup inventory, and the available transportation capacity of engineering materials as inputs, and minimizing total delay losses, additional transportation costs, and implementation complexity as optimization objectives, and supply, demand, vehicle capacity, working time, and loading and unloading capacity as constraints, a multi-objective optimization model for emergency dispatching schemes is established.

[0045] Fifty initial emergency dispatch schemes that meet the constraints are randomly generated. Each scheme is encoded with a specific warehouse-vehicle-construction site material allocation relationship. The total delay loss, additional transportation cost, and implementation complexity of each scheme are calculated, normalized, and combined into a fitness value. Using the roulette wheel selection method, the top 30% of emergency dispatch schemes are selected and retained. The selected emergency dispatch schemes are cross-referenced in pairs, exchanging the allocation targets of some engineering materials. The selected emergency dispatch schemes are mutated with a 10% probability. The selection, cross-reference, and mutation process is repeated until the number of iterations is met, and the globally optimal emergency dispatch scheme for the large hydropower station is obtained.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] This invention proposes an IoT-based intelligent management and control method for engineering materials in large-scale hydropower stations. By assigning holographic digital tags to each engineering material and constructing a global digital map, this invention achieves transparent management and control of the entire lifecycle of engineering materials from procurement to consumption. It calculates path deviation and estimated delay time in real time and triggers automatic early warnings. It dynamically integrates multi-dimensional data such as materials in transit, construction needs, reserve inventory, and available transportation capacity. Through a multi-objective optimization model and intelligent algorithms, it generates a globally optimal emergency dispatch plan, effectively overcoming the shortcomings of traditional management such as information silos between systems, delayed response, and localized resource allocation. This improves the resilience of the supply of engineering materials for large-scale hydropower stations, the efficiency of collaborative dispatch, and the ability to ensure project schedule. Attached Figure Description

[0048] Figure 1 This is a flowchart of an IoT-based intelligent management and control method for materials in a large-scale hydropower station project. Detailed Implementation

[0049] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0050] Reference Figure 1 As shown, a method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things includes:

[0051] S1. Obtain the engineering materials required for large-scale hydropower stations, assign a unique holographic digital tag to each engineering material, and establish a full life cycle dataset for each engineering material.

[0052] Step S1 specifically includes:

[0053] Before leaving the factory, the manufacturer generates a unique RFID tag based on the type of engineering materials. This unique RFID tag adopts a three-level coding system, consisting of a four-digit engineering material category code, a six-digit batch code, and a ten-digit serial number. The unique RFID tag is installed on each engineering material to read its pre-shipment data in real time, which serves as the basic attribute data of the engineering material.

[0054] The four-digit engineering material category code is used to identify the material type; the six-digit batch code is used to identify different batches of the same type of material; and the ten-digit serial number ensures the uniqueness of each material.

[0055] Pre-shipment data includes: manufacturer information, production date, warranty period, certificate of conformity number, electronic link to test report, and inspector information;

[0056] When engineering materials are issued from the warehouse, the RFID tags of the materials are scanned in batches to automatically verify the information of the issued list and the actual materials. If the issued quantity does not match the actual quantity or the unique RFID tag is damaged, an abnormal situation warning will be triggered immediately.

[0057] By scanning the RFID tags on transportation vehicles and combining them with the six-digit batch codes of engineering materials, a relationship between engineering material batches and transportation vehicles is established, and an electronic waybill for engineering materials is generated.

[0058] Step S1 also includes:

[0059] During transportation, for the vehicles carrying engineering materials, the vehicle-mounted Beidou positioning terminal is used to obtain the latitude, longitude, speed and direction information of the engineering materials in real time at a frequency of 30 seconds, as the transportation status data of the engineering materials.

[0060] Based on temperature and humidity sensors and vibration sensors deployed on transportation vehicles, temperature, humidity and vibration data are monitored in real time during transportation at a sampling frequency of 5 minutes, serving as environmental monitoring data for the transportation of engineering materials.

[0061] For each transportation transfer station, the RFID tags of the arriving engineering materials are scanned using handheld terminals. The planned delivery list is compared with the actual delivery list, and the handover time, the person in charge, and the current status of the engineering materials are recorded as verification data for the handover of engineering materials.

[0062] Step S1 also includes:

[0063] Based on the project schedule management system, the engineering material demand plan is obtained, the construction task code is associated, a demand time window early warning mechanism is set, and the arrival information of engineering materials is automatically matched with the construction plan to obtain engineering material construction-related data.

[0064] By integrating basic attribute data of engineering materials, transportation status data, environmental monitoring data during transportation, and construction-related data, aligning timestamps, and performing data preprocessing, a full lifecycle dataset for each engineering material is established.

[0065] When using it, please refer to the steps outlined above:

[0066] Traditional large-scale hydropower station engineering material management relies heavily on manual recording and barcode scanning, which suffers from problems such as inconsistent coding systems, fragmented data, and poor real-time performance. In particular, the lack of full-process dynamic monitoring and anomaly early warning capabilities in the transportation stage leads to difficulties in material traceability, information opacity, and low collaborative efficiency, making it difficult to meet the needs of refined, full life-cycle management. This step introduces unique RFID tags with a three-level coding system and integrates multi-source data to achieve unique identification, real-time tracking, and automatic verification of engineering materials throughout the entire process from production and transportation to on-site delivery. This improves the accuracy, traceability, and management collaboration efficiency of material data, laying a data foundation for subsequent intelligent scheduling and risk early warning.

[0067] S2. Based on the full life cycle dataset of each engineering material, construct a digital map of the entire hydropower station project, integrate real-time road conditions, construction progress, transportation vehicle performance and site constraints, calculate the optimal transportation route of engineering materials, compare the actual transportation trajectory of engineering materials with the planned route in real time, calculate the real-time path deviation of engineering materials - estimated delay time, set up a hierarchical early warning mechanism to realize automatic early warning of abnormal paths;

[0068] Step S2 specifically includes:

[0069] By applying for enterprise-level permissions based on the map software platform API and setting a fixed update frequency, we can obtain data on the road network and waterway around large hydropower stations, map them to a unified coordinate system, obtain the basic map coordinate system around large hydropower stations, and extract the attribute features of the road network and waterway corresponding to the level, speed limit, number of lanes, bridge height limit and tunnel width limit.

[0070] Based on the coordinate system of the basic map around the large hydropower station, the coordinates of formal roads, temporary access roads and loading and unloading points in the construction area of ​​the large hydropower station are extracted using the building information model. The temporary facility paths not covered by the building information model are supplemented by the CAD file of the construction layout plan. The coordinates and paths are converted into vector layers that can be recognized by the map information system, and business attributes are bound to each unloading point.

[0071] The business attributes include: available time period, maximum number of vehicles in queue, weighbridge opening hours, and contact information of the person in charge.

[0072] Based on real-time traffic data from the access map service provider, the system obtains real-time traffic speed, congestion level, construction closure areas, and temporary traffic control data for various road sections around the large hydropower station. The data refresh frequency is set to once per minute to construct a digital map of the entire hydropower station project area. Different colors are used to highlight the data: congested road sections are marked in red, construction road sections are marked in gray, and restricted areas are marked in yellow.

[0073] Step S2 also includes:

[0074] Based on the full life cycle dataset of each engineering material and the digital map of the entire hydropower station project, the engineering material transportation task is obtained. The latitude and longitude of the supplier warehouse is used as the starting point, the entrance coordinates of the specific construction face of the large hydropower station are used as the ending point, the type of transportation vehicle, load capacity and average speed are used as the transportation vehicle attributes, the required arrival time and the opening time of the unloading point are used as the task constraints, and the real-time road condition data around the large hydropower station at the current time is used as the dynamic environment data.

[0075] Based on the digital map of the entire hydropower station project, an evaluation model for the transportation route of engineering materials was established with the shortest transportation distance and the shortest expected arrival time as dual objective functions. The maximum load limit of the transportation vehicle, the matching degree between the size of the transportation vehicle and the height and width restrictions of the road, the available time period of the unloading point, the real-time road hardness coefficient and the road passage permit of the construction section were used as constraints to generate several candidate routes for the transportation of engineering materials. The comprehensive evaluation value of each candidate route was calculated, sorted by elevation and descent, and the route with the lowest comprehensive evaluation value was selected as the optimal transportation route for engineering materials.

[0076] The real-time road condition hardness coefficient is the ratio of free-flow time to real-time estimated travel time.

[0077] Overall evaluation value = 0.6 × (estimated arrival time of the task - required arrival time of the task) + 0.4 × transportation distance.

[0078] Step S2 also includes:

[0079] Based on the real-time acquisition of the latitude and longitude of engineering materials through vehicle-mounted Beidou positioning terminals during the transportation of engineering materials, the coordinates of the materials are dynamically mapped onto the digital map of the entire hydropower station project area. Discrete positioning points are matched to the road network in real time to obtain the actual transportation trajectory of the engineering materials. A fixed sliding time window of five minutes is set, and the overlap between the actual transportation trajectory of the engineering materials and the optimal transportation path of the engineering materials within the time window is calculated. If the road segments matched by three consecutive positioning points are not within the preset corridor zone t of the optimal transportation path of the engineering materials, it is determined that a path deviation has occurred. The real-time path deviation of the engineering materials is calculated as the estimated delay time. t is the width of the strip buffer area generated by the optimal transportation path, and the value of t is 20-50m.

[0080] The real-time path deviation of the engineering materials = (actual mileage - optimal transport path mileage) / optimal transport path mileage × 100%;

[0081] Estimated delay time = (Remaining time from current location to destination under real-time traffic conditions) - (Remaining time from location corresponding to the optimal delivery route to destination under real-time traffic conditions).

[0082] Based on the real-time deviation of the engineering materials' route and the estimated delay time, a tiered early warning mechanism is set up to automatically warn of abnormal routes. If the real-time deviation of the engineering materials' route is between 5% and 10% or the estimated delay time is between 15 and 30 minutes, a Level 1 warning is triggered, and a voice prompt is automatically sent to the driver to remind them to pay attention to the driving route. If the real-time deviation of the engineering materials' route is between 10% and 20% or the estimated delay time is between 30 and 60 minutes, a Level 2 warning is triggered, and an alternative route is automatically pushed to the vehicle terminal to guide the driver to choose a better route. If the real-time deviation of the engineering materials' route is greater than 20% or the estimated delay time is greater than 60 minutes, a Level 3 warning is triggered, and an emergency handling mechanism is activated to contact the driver to inquire about the situation, determine whether the driver has had an accident or abnormality, and manually plan an emergency route or coordinate on-site assistance.

[0083] When using it, please refer to the steps outlined above:

[0084] Traditional hydropower station engineering material transportation route planning relies heavily on static maps and experience-based judgments, lacking the integration of dynamic data from the entire road network. This makes it impossible to respond in real time to changes in road conditions, construction progress, and site constraints. The route planning models are simplistic, failing to comprehensively consider multi-dimensional constraints such as vehicle performance and unloading point business attributes. Transportation process monitoring is weak, primarily relying on manual reporting, making it difficult to promptly detect route deviations and delays. The absence or delayed response of early warning mechanisms leads to untimely material delivery and construction delays. This step addresses this by constructing a comprehensive digital map integrating real-time road conditions, construction progress, vehicle performance, and site constraints, achieving dynamic optimization and precise planning of engineering material transportation routes. Optimal routes are generated based on dual objective functions and multiple constraints, improving transportation efficiency and reliability. Real-time trajectory tracking and deviation calculation, combined with a tiered early warning mechanism, enable automatic early warning and rapid response to abnormal routes, effectively reducing the risk of transportation delays and ensuring the intelligent management of hydropower station construction progress and material scheduling.

[0085] S3. Based on the real-time path deviation of engineering materials and the estimated delay time, implement dynamic resource collaborative scheduling of engineering materials, comprehensively analyze the status of materials in transit, the urgency of demand at each construction site, the distribution of reserve inventory and available transportation capacity, and generate the global optimal emergency scheduling plan for large hydropower stations.

[0086] Step S3 specifically includes:

[0087] Based on the tiered early warning triggered by the real-time path deviation of engineering materials and the estimated delay time, emergency dispatch analysis is immediately initiated. According to the unique RFID tag information of the early warning engineering materials, all other engineering material transportation units of the same type and destination that are in transit are screened from the full life cycle dataset of each engineering material. Based on the latest estimated arrival time, the top 3 materials of the same type that are expected to arrive earliest are identified and marked as affected materials. The specific values ​​of the top 3 materials of the same type are pre-set by project management rules and dispatch strategies.

[0088] Based on the construction progress management system, the status of all related construction tasks is extracted. The urgency of each construction surface is assessed according to the importance of the construction tasks. Construction tasks on the main project path, tasks whose delays will directly affect the total project duration, or tasks with a buffer time of less than 4 hours are marked as first priority. Construction tasks on the non-main path but with limited free float, tasks with a buffer time of 4-8 hours, or tasks requiring coordination of multiple engineering materials are marked as second priority. Tasks involving routine material replenishment, tasks with a buffer time of more than 8 hours, or non-continuous operations are marked as third priority.

[0089] Step S3 also includes:

[0090] Integrate the WMS systems of each field warehouse with the central warehouse management system to obtain the real-time available inventory, specific location and access convenience of engineering materials, distinguish between locked inventory and available inventory, assess picking and loading time based on the characteristics of engineering materials and warehouse operation capacity, calculate their outbound preparation time, and establish a backup inventory distribution map.

[0091] The system queries all available empty vehicles of contracted transportation companies within a 30-kilometer radius of the warning incident location, obtains vehicle type, load capacity and current location, confirms driver contact information and working hour restrictions, and obtains a pool of available transportation vehicles. It also compiles statistics on available unloading equipment and personnel at each outbound warehouse, obtains the current working status and estimated available time of the equipment, and obtains a pool of available loading and unloading resources. A comprehensive assessment is then conducted to obtain the available transportation capacity for engineering materials.

[0092] Step S3 also includes:

[0093] Using the urgency level of each affected construction site, the distribution map of backup inventory, and the available transportation capacity of engineering materials as inputs, and minimizing total delay losses, additional transportation costs, and implementation complexity as optimization objectives, and supply, demand, vehicle capacity, working time, and loading and unloading capacity as constraints, a multi-objective optimization model for emergency dispatching schemes is established.

[0094] Fifty initial emergency dispatch schemes that meet the constraints are randomly generated. Each scheme is encoded with a specific warehouse-vehicle-construction site material allocation relationship. The total delay loss, additional transportation cost, and implementation complexity of each scheme are calculated, normalized, and combined into a fitness value. Using the roulette wheel selection method, the top 30% of emergency dispatch schemes are selected and retained. The selected emergency dispatch schemes are cross-referenced in pairs, exchanging the allocation targets of some engineering materials. The selected emergency dispatch schemes are mutated with a 10% probability. The selection, cross-reference, and mutation process is repeated until the number of iterations is met, and the globally optimal emergency dispatch scheme for the large hydropower station is obtained.

[0095] When using it, please refer to the steps outlined above:

[0096] In existing hydropower station material dispatching systems, responses to transportation delays rely on manual experience, lacking automated early warning and triggering mechanisms based on real-time path deviation and estimated delay time. Information such as the status of materials en route, the urgency of construction needs, inventory distribution, and available transport capacity are scattered across independent systems, making dynamic collaborative analysis and global decision-making difficult. Emergency dispatching is often limited to local adjustments, lacking an overall optimization model that comprehensively considers the priorities of multiple construction fronts, multiple sources of reserve inventory, and multiple constraints on transport capacity, resulting in delayed response, low resource allocation efficiency, and high costs. This step introduces a hierarchical early warning and triggering mechanism based on real-time path deviation and estimated delay time, and integrates multi-source data such as materials en route, the urgency of construction tasks, reserve inventory distribution, and available transport capacity. It constructs a multi-objective optimization model aimed at minimizing total delay losses, transportation costs, and implementation complexity. Combined with a genetic algorithm for global solution optimization, this achieves dynamic collaborative dispatching of engineering materials for large-scale hydropower stations and rapid reallocation of emergency resources, significantly improving the real-time performance of dispatching responses, the efficiency of global resource utilization, and the ability to ensure project schedule.

[0097] Based on the above, the specific implementation method is as follows:

[0098] A large hydropower station needs to purchase a batch of special steel plates for dam construction. At the manufacturer, a unique RFID tag is generated for each steel plate according to the type of material, with the code "STLB-220101-0000000001" ("STLB" represents steel plate, and "220101" represents the first batch in 2022). The tag records the manufacturer "a certain steel group", the production date, the 10-year warranty period, the certificate number, and the link to the cloud test report.

[0099] When the goods are shipped out, the labels are scanned in batches and the outbound order is automatically checked. If it is found that 100 items are planned but 99 items are actually scanned, an alert is immediately issued. It is found that one label is damaged. After manual re-entry, the outbound is completed.

[0100] After loading is completed, scan the RFID of the transport truck, associate the truck code "TRK-005" with the batch code "220101" of this batch of steel plates, and generate an electronic waybill;

[0101] During transportation, the vehicle-mounted Beidou terminal transmits location and speed information every 30 seconds, and temperature, humidity and vibration sensors report environmental data every 5 minutes. When the vehicle passes through a transfer station, staff use handheld terminals to scan the labels on the arriving steel plates, automatically compare them with the plan list, record the handover time and the person in charge, and release the vehicle after confirming that everything is correct. All data is preprocessed and integrated into the full life cycle dataset of this batch of steel plates.

[0102] Special steel plates need to be transported from the supplier's warehouse (point A) to the entrance of the No. 3 construction face of the hydropower station dam (point B). Based on the constructed full-area digital map, route planning is carried out. The map integrates the public road network, the coordinates of the temporary access road in the construction area, and the attributes of each unloading point (such as point B being open only from 8:00 to 18:00 daily). Real-time road conditions are updated every minute. With the goals of "shortest transportation distance" and "shortest estimated arrival time", considering the truck's load size, road height and width restrictions, the opening time of point B, and the current road conditions (a main road is marked in red for congestion), the optimal transportation route is calculated to be "A-National Highway GX-Construction Access Road S2-B" through a multi-constraint path evaluation model, with an estimated transportation time of 4.5 hours.

[0103] Once transportation begins, the truck's BeiDou positioning point is matched to the road network in real time to generate the actual trajectory. During the journey, if the driver detours a county road due to temporary traffic control, and three positioning points are detected deviating from the preset route corridor in a 5-minute window, the deviation is immediately calculated to be 15% (actually traveling an extra 12 kilometers), with an estimated delay of 45 minutes. Based on the graded early warning mechanism (a level 2 early warning is triggered if the deviation is 10%-20% or the delay is 30-60 minutes), an alternative route "A-Highway SYS-Construction Access Road S4-B" that takes into account real-time road conditions is automatically pushed to the vehicle terminal to guide the driver to adjust the route.

[0104] Because the aforementioned path deviation is expected to cause a 45-minute delay in the delivery of the steel plate to point B, and the dam pouring task associated with point B is on the critical path of the project with a buffer time of only 3 hours, it is marked as the first priority. After the system triggers a level 3 warning (delay > 30 minutes), emergency dispatch analysis is immediately initiated.

[0105] The system filtered out all in-transit materials of the same type (steel plates) and destination (dam area), and found that another truckload of steel plates (batch "220102") was expected to arrive at the nearby No. 4 construction site (point C, priority third) in 1 hour. The inventory system showed that the central warehouse had sufficient spare steel plates, the preparation time for dispatch was 30 minutes, and there were 2 empty trucks available near the warehouse. This information was integrated: the affected high-priority demand at point B, the low-priority in-transit materials at point C that could be allocated, the central warehouse's spare inventory, and the available transportation capacity.

[0106] With the goals of "minimizing delay losses at point B", "controlling additional transportation costs", and "reducing the complexity of scheduling operations", a multi-objective optimization model was established. Through iterative optimization using a genetic algorithm, a globally optimal emergency scheduling plan was generated: the vehicle about to arrive at point C was instructed to change its destination and be prioritized for direct delivery to point B (adding only 15 minutes to the journey); the instruction center warehouse immediately prepared spare steel plates and dispatched an empty truck to point C as a supplement. After the plan was confirmed, it was automatically issued, and the system simultaneously updated the relevant waybills, inventory lock status, and notified the responsible parties. The global resource reallocation was completed within 10 minutes to ensure uninterrupted supply of materials to key construction sites.

[0107] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things, characterized in that, include: S1. Obtain the engineering materials required for large-scale hydropower stations, assign a unique holographic digital tag to each engineering material, and establish a full life cycle dataset for each engineering material. S2. Based on the full life cycle dataset of each engineering material, construct a digital map of the entire hydropower station project, integrate real-time road conditions, construction progress, transportation vehicle performance and site constraints, calculate the optimal transportation route of engineering materials, compare the actual transportation trajectory of engineering materials with the planned route in real time, calculate the real-time path deviation of engineering materials - estimated delay time, set up a hierarchical early warning mechanism to realize automatic early warning of abnormal paths; S3. Based on the real-time path deviation of engineering materials and the estimated delay time, implement dynamic resource collaborative scheduling of engineering materials, comprehensively analyze the status of materials in transit, the urgency of demand at each construction site, the distribution of reserve inventory and available transportation capacity, and generate the global optimal emergency scheduling plan for large hydropower stations.

2. The method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things according to claim 1, characterized in that, Step S1 specifically includes: Before leaving the factory, the manufacturer generates a unique RFID tag based on the type of engineering materials. This unique RFID tag adopts a three-level coding system, consisting of a four-digit engineering material category code, a six-digit batch code, and a ten-digit serial number. The unique RFID tag is installed on each engineering material to read its pre-shipment data in real time, which serves as the basic attribute data of the engineering material. The four-digit engineering material category code is used to identify the material type; the six-digit batch code is used to identify different batches of the same type of material; and the ten-digit serial number ensures the uniqueness of each material. Pre-shipment data includes: manufacturer information, production date, warranty period, certificate of conformity number, electronic link to test report, and inspector information; When engineering materials are issued from the warehouse, the RFID tags of the materials are scanned in batches to automatically verify the information of the issued list and the actual materials. If the issued quantity does not match the actual quantity or the unique RFID tag is damaged, an abnormal situation warning will be triggered immediately. By scanning the RFID tags on transportation vehicles and combining them with the six-digit batch codes of engineering materials, a relationship between engineering material batches and transportation vehicles is established, and an electronic waybill for engineering materials is generated.

3. The method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things according to claim 2, characterized in that, Step S1 also includes: During transportation, for the vehicles carrying engineering materials, the vehicle-mounted Beidou positioning terminal is used to obtain the latitude, longitude, speed and direction information of the engineering materials in real time at a frequency of 30 seconds, as the transportation status data of the engineering materials. Based on temperature and humidity sensors and vibration sensors deployed on transportation vehicles, temperature, humidity and vibration data are monitored in real time during transportation at a sampling frequency of 5 minutes, serving as environmental monitoring data for the transportation of engineering materials. For each transportation transfer station, the RFID tags of the arriving engineering materials are scanned using handheld terminals. The planned delivery list is compared with the actual delivery list, and the handover time, the person in charge, and the current status of the engineering materials are recorded as verification data for the handover of engineering materials.

4. The method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things according to claim 3, characterized in that, Step S1 also includes: Based on the project schedule management system, the engineering material demand plan is obtained, the construction task code is associated, a demand time window early warning mechanism is set, and the arrival information of engineering materials is automatically matched with the construction plan to obtain engineering material construction-related data. By integrating basic attribute data of engineering materials, transportation status data, environmental monitoring data during transportation, and construction-related data, aligning timestamps, and performing data preprocessing, a full lifecycle dataset for each engineering material is established.

5. The method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things according to claim 4, characterized in that, Step S2 specifically includes: By applying for enterprise-level permissions based on the map software platform API and setting a fixed update frequency, we can obtain data on the road network and waterway around large hydropower stations, map them to a unified coordinate system, obtain the basic map coordinate system around large hydropower stations, and extract the attribute features of the road network and waterway corresponding to the level, speed limit, number of lanes, bridge height limit and tunnel width limit. Based on the coordinate system of the basic map around the large hydropower station, the coordinates of formal roads, temporary access roads and loading and unloading points in the construction area of ​​the large hydropower station are extracted using the building information model. The temporary facility paths not covered by the building information model are supplemented by the CAD file of the construction layout plan. The coordinates and paths are converted into vector layers that can be recognized by the map information system, and business attributes are bound to each unloading point. The business attributes include: available time period, maximum number of vehicles in queue, weighbridge opening hours, and contact information of the person in charge. Based on real-time traffic data from the access map service provider, the system obtains real-time traffic speed, congestion level, construction closure areas, and temporary traffic control data for various road sections around the large hydropower station. The data refresh frequency is set to once per minute to construct a digital map of the entire hydropower station project area. Different colors are used to highlight the data: congested road sections are marked in red, construction road sections are marked in gray, and restricted areas are marked in yellow.

6. The method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things according to claim 5, characterized in that, Step S2 also includes: Based on the full life cycle dataset of each engineering material and the digital map of the entire hydropower station project, the engineering material transportation task is obtained. The latitude and longitude of the supplier warehouse is used as the starting point, the entrance coordinates of the specific construction face of the large hydropower station are used as the ending point, the type of transportation vehicle, load capacity and average speed are used as the transportation vehicle attributes, the required arrival time and the opening time of the unloading point are used as the task constraints, and the real-time road condition data around the large hydropower station at the current time is used as the dynamic environment data. Based on the digital map of the entire hydropower station project, an evaluation model for the transportation route of engineering materials was established with the shortest transportation distance and the shortest expected arrival time as dual objective functions. The maximum load limit of the transportation vehicle, the matching degree between the size of the transportation vehicle and the height and width restrictions of the road, the available time period of the unloading point, the real-time road hardness coefficient and the road passage permit of the construction section were used as constraints to generate several candidate routes for the transportation of engineering materials. The comprehensive evaluation value of each candidate route was calculated, sorted by elevation and descent, and the route with the lowest comprehensive evaluation value was selected as the optimal transportation route for engineering materials. The real-time road condition hardness coefficient is the ratio of free-flow time to real-time estimated travel time. Overall evaluation value = 0.6 × (estimated arrival time of the task - required arrival time of the task) + 0.4 × transportation distance.

7. The method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things according to claim 6, characterized in that, Step S2 also includes: Based on the real-time acquisition of the latitude and longitude of engineering materials through vehicle-mounted Beidou positioning terminals during the transportation of engineering materials, the coordinates of the engineering materials are dynamically mapped to the digital map of the entire hydropower station project area. Discrete positioning points are matched to the road network in real time to obtain the actual transportation trajectory of the engineering materials. A fixed sliding time window of five minutes is set, and the overlap between the actual transportation trajectory of the engineering materials and the optimal transportation path of the engineering materials within the time window is calculated. If the road segments matched by three consecutive positioning points are not within the preset corridor zone t of the optimal transportation path of the engineering materials, it is determined that a path deviation has occurred, and the real-time path deviation of the engineering materials is calculated as the estimated delay time. The real-time path deviation of the engineering materials = (actual mileage - optimal transport path mileage) / optimal transport path mileage × 100%; Estimated delay time = (Remaining time from current location to destination under real-time traffic conditions) - (Remaining time from location corresponding to the optimal delivery route to destination under real-time traffic conditions). Based on the real-time deviation of the engineering materials' route and the estimated delay time, a tiered early warning mechanism is set up to automatically warn of abnormal routes. If the real-time deviation of the engineering materials' route is between 5% and 10% or the estimated delay time is between 15 and 30 minutes, a Level 1 warning is triggered, and a voice prompt is automatically sent to the driver to remind them to pay attention to the driving route. If the real-time deviation of the engineering materials' route is between 10% and 20% or the estimated delay time is between 30 and 60 minutes, a Level 2 warning is triggered, and an alternative route is automatically pushed to the vehicle terminal to guide the driver to choose a better route. If the real-time deviation of the engineering materials' route is greater than 20% or the estimated delay time is greater than 60 minutes, a Level 3 warning is triggered, and an emergency handling mechanism is activated to contact the driver to inquire about the situation, determine whether the driver has had an accident or abnormality, and manually plan an emergency route or coordinate on-site assistance.

8. The method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things according to claim 7, characterized in that, Step S3 specifically includes: Based on the graded early warning triggered by the real-time path deviation of engineering materials and the estimated delay time, emergency dispatch analysis is immediately initiated. According to the unique RFID tag information of the early warning engineering materials, all other engineering material transportation units of the same type and destination that are in transit are selected from the full life cycle dataset of each engineering material. Based on the latest estimated arrival time, the top 3 materials of the same type that are expected to arrive earliest are identified and marked as affected materials. Based on the construction progress management system, the status of all related construction tasks is extracted. The urgency of each construction surface is assessed according to the importance of the construction tasks. Construction tasks on the main project path, tasks whose delays will directly affect the total project duration, or tasks with a buffer time of less than 4 hours are marked as first priority. Construction tasks on the non-main path but with limited free float, tasks with a buffer time of 4-8 hours, or tasks requiring coordination of multiple engineering materials are marked as second priority. Tasks involving routine material replenishment, tasks with a buffer time of more than 8 hours, or non-continuous operations are marked as third priority.

9. A method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things, as described in claim 8, is characterized in that... Step S3 also includes: Integrate the WMS systems of each field warehouse with the central warehouse management system to obtain the real-time available inventory, specific location and access convenience of engineering materials, distinguish between locked inventory and available inventory, assess picking and loading time based on the characteristics of engineering materials and warehouse operation capacity, calculate their outbound preparation time, and establish a backup inventory distribution map. The system queries all available empty vehicles of contracted transportation companies within a 30-kilometer radius of the warning incident location, obtains vehicle type, load capacity and current location, confirms driver contact information and working hour restrictions, and obtains a pool of available transportation vehicles. It also compiles statistics on available unloading equipment and personnel at each outbound warehouse, obtains the current working status and estimated available time of the equipment, and obtains a pool of available loading and unloading resources. A comprehensive assessment is then conducted to obtain the available transportation capacity for engineering materials.

10. A method for intelligent management and control of materials in a large-scale hydropower station project via the Internet of Things, as described in claim 9, is characterized in that... Step S3 also includes: Using the urgency level of each affected construction site, the distribution map of backup inventory, and the available transportation capacity of engineering materials as inputs, and minimizing total delay losses, additional transportation costs, and implementation complexity as optimization objectives, and supply, demand, vehicle capacity, working time, and loading and unloading capacity as constraints, a multi-objective optimization model for emergency dispatching schemes is established. Fifty initial emergency dispatch schemes that meet the constraints are randomly generated. Each scheme is encoded with a specific warehouse-vehicle-construction site material allocation relationship. The total delay loss, additional transportation cost, and implementation complexity of each scheme are calculated, normalized, and combined into a fitness value. Using the roulette wheel selection method, the top 30% of emergency dispatch schemes are selected and retained. The selected emergency dispatch schemes are cross-referenced in pairs, exchanging the allocation targets of some engineering materials. The selected emergency dispatch schemes are mutated with a 10% probability. The selection, cross-reference, and mutation process is repeated until the number of iterations is met, and the globally optimal emergency dispatch scheme for the large hydropower station is obtained.

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