Intelligent scheduling and unattended acceptance method for construction materials of pumped storage power station

Abstract

The invention belongs to the technical field of pumped storage power station material scheduling and acceptance, and discloses an intelligent scheduling and unattended acceptance method for pumped storage power station construction materials. The initial scheduling instruction is generated based on the construction progress model, the material demand plan and the predefined scheduling rule, the latest demand time point and the scheduling trigger time point of the materials are scientifically calculated, it is ensured that the materials are supplied in time, stock overstock is avoided, the scheduling accuracy and efficiency are improved, and the scheduling cost is reduced. The problem that scheduling instruction generation is lagged and inaccurate in the prior art is solved. According to the method, the external dynamic influence factors are fused in real time in the acceptance process, the supplementary scheduling analysis is triggered in time, the supplementary scheduling demand is scientifically judged, the supplementary scheduling instruction is generated, the continuity of material supply is ensured, and the problem that the supplementary scheduling analysis is not timely and inaccurate in the prior art is effectively solved. And meanwhile, continuous optimization is performed by collecting historical data, so that the scheduling and acceptance performance is further improved.

Description

Technical Field

[0001] This invention belongs to the field of material scheduling and acceptance technology for pumped storage power stations, and relates to a method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations. Background Technology

[0002] Material scheduling and acceptance are crucial for the smooth progress of pumped storage power station construction. Pumped storage power station construction is characterized by a large workload, a wide variety of materials, a complex construction environment, and high requirements for material quality and timely supply.

[0003] Traditional material scheduling relies heavily on manual planning, which suffers from low efficiency and susceptibility to human factors. Acceptance work is also largely done manually, consuming significant manpower and resources, and potentially leading to inaccurate results due to the subjectivity of human judgment. Furthermore, it is difficult to achieve real-time monitoring and rapid response to anomalies during material supply. Therefore, research on intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations is of great significance.

[0004] In existing technologies, the generation of scheduling instructions often relies on manual methods based on experience and construction progress, lacking precise integration with construction progress models and material demand planning. This can easily lead to premature material supply resulting in inventory backlog, or delayed supply affecting construction progress. Furthermore, the calculation of the latest material demand time and scheduling trigger time is not scientifically sound, failing to fully consider factors such as supplier production cycles, logistics and transportation times, and safety stock periods.

[0005] In existing technologies, for bulk and individually packaged materials, quantity information is often obtained through manual estimation or simple weighing, which has low accuracy and cannot accurately reflect the actual quantity of the materials. Regarding quality information inspection, traditional methods rely on manual sampling and testing, which has a long testing cycle and may suffer from insufficient representativeness, making it impossible to comprehensively and promptly determine whether the material quality is up to standard.

[0006] In existing technologies, when situations arise during acceptance testing such as insufficient material quantity, substandard quality, or transportation delays, it is difficult to determine replenishment needs and generate replenishment instructions in real time. Replenishment analysis does not fully integrate external dynamic influencing factors, leading to unscientific replenishment decisions and potentially failing to replenish materials in a timely manner to meet construction requirements. Summary of the Invention

[0007] In view of this, in order to solve the problems mentioned in the background technology, a method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations is proposed.

[0008] The objective of this invention can be achieved through the following technical solution: a method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations, comprising: generating scheduling instructions, generating initial scheduling instructions based on predefined scheduling rules according to a preset construction progress model and material demand plan, wherein the initial scheduling instructions include material type, required quantity, quality standard and predetermined delivery time window.

[0009] Unmanned acceptance testing utilizes acceptance equipment to automatically execute the acceptance process, collecting real-time data on the materials' transit and arrival. The transit data includes the real-time location and estimated arrival time of the transport vehicles, while the arrival data includes the actual quantity and quality information of the materials.

[0010] The acceptance process involves real-time comparison of the on-site data collected in real time with the acceptance criteria included in the initial scheduling instructions, and dynamic generation of acceptance progress data. The acceptance progress data includes the number of qualified materials, the number of unqualified materials, and the remaining number of materials to be accepted.

[0011] Real-time supplementary scheduling analysis, based on the external dynamic influencing factors obtained in real time through the fusion of the acceptance process data, determines the supplementary scheduling needs and generates supplementary scheduling instructions, and completes the supplementary scheduling of the next batch of materials before the end of the initial scheduling acceptance process.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention generates initial scheduling instructions based on the construction progress model, material demand plan and predefined scheduling rules. By scientifically calculating the latest demand time of materials and the scheduling trigger time, it ensures timely material supply and avoids inventory backlog, improves the accuracy and efficiency of scheduling, and solves the problem of delayed and inaccurate generation of scheduling instructions in the prior art.

[0013] (2) This invention employs advanced scanning and weighing technologies for both bulk and individually packaged materials, thereby improving the accuracy of quantity detection. Simultaneously, it utilizes multispectral imaging equipment and non-contact sensors for detection, and combines this with pre-trained models for analysis, achieving comprehensive, timely, and accurate acquisition of quality information, overcoming the shortcomings of outdated methods for acquiring quantity and quality information in existing technologies.

[0014] (3) This invention integrates external dynamic influencing factors in real time during the acceptance process, triggers timely supplementary scheduling analysis, scientifically determines supplementary scheduling needs and generates supplementary scheduling instructions, ensuring the continuity of material supply and effectively solving the problems of untimely and inaccurate supplementary scheduling analysis in the prior art. At the same time, by collecting historical data for continuous optimization, the performance of scheduling and acceptance is further improved. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram illustrating the implementation steps of the method of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 As shown, the present invention provides a method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations, including: generating scheduling instructions, generating initial scheduling instructions based on predefined scheduling rules according to a preset construction progress model and material demand plan, wherein the initial scheduling instructions include material type, required quantity, quality standard and predetermined delivery time window.

[0019] It should be noted that the preset construction progress model is a digital model based on the overall planning of the pumped storage power station project. Specifically, it can be constructed using the industry-renowned waterfall project management method combined with digital modeling technology. It includes the work sequence of each construction stage, key nodes such as excavation, pouring, and equipment installation, as well as the process connection relationship and time requirements, providing a basic framework for the time matching of material needs.

[0020] The material requirements plan refers to the specific list of materials required for each construction stage, which is decomposed from the construction schedule model. These materials include steel bar types, cement grades, and cable specifications. It is the direct source of material types and required quantities in scheduling instructions. The material requirements plan is decomposed from the construction schedule model using the industry-renowned top-down decomposition method of engineering decomposition structure.

[0021] The predefined scheduling rules are algorithmic logic developed by combining engineering experience and supply chain characteristics. They cover supplier response time, transportation route optimization, safety stock thresholds, etc., and are used to transform demand into executable supply instructions.

[0022] In a preferred embodiment of the present invention, the initial scheduling instruction is generated in the following manner: the engineering breakdown structure associated with the current construction stage is retrieved from the building information model, and the bill of materials for the current construction stage is obtained by parsing.

[0023] For example, if the current stage is the underground plant construction phase, the engineering breakdown structure can be broken down to identify the specific materials required for this stage, such as the type of steel reinforcement, the concrete strength grade, and the formwork specifications.

[0024] Building Information Model (BIM) is a standardized BIM model that has been completed during the design phase of pumped storage power station projects. The design model is completed by the engineering design unit in accordance with the industry standard (GB / T 51235-2017 "Standard for Construction Application of Building Information Model").

[0025] Based on the start time and duration of the work activities determined in the current construction phase plan, calculate the latest demand time for each material.

[0026] It should be noted that, based on the start time and duration of the work activities determined in the current construction phase plan, the latest demand time for each material is calculated in reverse. For example, if a pouring process is planned to start on October 1st and is expected to last 5 days, and if the rebar tying needs to be completed one day before the pouring, then the latest demand time for the rebar is September 30th.

[0027] Taking into account the production cycle of material suppliers, logistics and transportation time, and the preset safety stock period, the scheduling trigger time point of each material is calculated.

[0028] It should be noted that the material scheduling initiation time is determined by considering three key time factors: Supplier production cycle: the time from order placement to production completion; Logistics and transportation time: the transportation time from the supplier to the construction site; and Pre-set safety stock period: the buffer time reserved to cope with emergencies.

[0029] For example, the formula for calculating the start time of material scheduling is: scheduling trigger time point = latest demand time point minus the sum of production cycle, transportation time and safety stock cycle.

[0030] When the scheduling trigger time for any material arrives, a structured initial scheduling instruction data packet containing the material identifier, required quantity, quality standard, designated acceptance area, and scheduled delivery time window is automatically generated and sent to the designated material supplier.

[0031] It should be noted that this invention generates initial scheduling instructions based on a construction progress model, material demand plan, and predefined scheduling rules. By scientifically calculating the latest demand time of materials and the scheduling trigger time, it ensures timely material supply and avoids inventory backlog, thereby improving the accuracy and efficiency of scheduling and solving the problem of delayed and inaccurate scheduling instruction generation in the prior art.

[0032] Unmanned acceptance testing utilizes acceptance equipment to automatically execute the acceptance process, collecting real-time data on the materials' transit and arrival. The transit data includes the real-time location and estimated arrival time of the transport vehicles, while the arrival data includes the actual quantity and quality information of the materials.

[0033] In a preferred embodiment of the present invention, the specific analysis method of the quantity information is as follows: For bulk materials, a UAV three-dimensional laser scanning system deployed at the entrance and exit of the acceptance area is used to scan the compartment of the transport vehicle before and after the material is unloaded, and a three-dimensional point cloud model of the compartment before and after unloading is constructed.

[0034] The volume of the unloaded material is calculated based on the 3D point cloud model of the interior of the carriage before and after unloading, and the weight of the material is calculated as the actual quantity information based on the preset material density database.

[0035] It should be noted that for bulk materials, such as sand, gravel, cement, and earthwork, which lack fixed packaging and are loose in shape, 3D scanning is used to calculate their actual quantity. Laser scanning can capture the spatial details inside the vehicle compartment, and by comparing the differences between the models before and after unloading, the volume of space occupied by the materials can be accurately calculated.

[0036] For individually packaged materials, a high-precision dynamic weighing sensor array deployed on the conveyor belt or unloading platform is used to weigh each material that passes through, and the weights of each material are added together to obtain the total weight of the materials as the actual quantity information.

[0037] High-frequency RFID readers are used to identify electronic tags attached to individual materials, and the number of materials passing through is counted as auxiliary actual quantity information.

[0038] It should be noted that for individually packaged materials, such as prefabricated components, equipment parts, and packaging building materials, which have independent packaging or fixed forms, a dual verification method of weighing accumulation and RFID counting is adopted: the sensor has a fast response speed, can adapt to dynamic scenarios where materials pass through continuously, and meets the requirements of high-precision measurement. RFID-assisted counting and weighing results form cross-verification to avoid misjudgment of the total quantity due to abnormal weight of a single material, thus improving data reliability.

[0039] In a preferred embodiment of the present invention, the specific analysis method of the quality information is as follows: during the material unloading process, a multispectral imaging device is used to continuously photograph the material sample to obtain the reflectance data of the material in different spectral bands.

[0040] The reflectance data is input into a pre-trained material quality classification model. The model is constructed based on the spectral data of historical material samples and the corresponding laboratory physicochemical test results. It is used to identify whether the material has abnormal composition, excessive impurities, or physical damage. The pre-trained material quality classification model is constructed using existing supervised machine learning algorithms.

[0041] It should be noted that materials of different compositions and states exhibit significant differences in reflectance in specific spectral bands, and these microscopic features can be captured through multispectral data.

[0042] For specific materials, non-contact sensors are used to perform point-to-point or scanning detection of the material flow to obtain the content data of key chemical components.

[0043] It should be noted that for certain materials with strict requirements on chemical composition, such as special concrete and alloy materials, non-contact sensors are used to perform fixed-point or scanning detection on the material flow to directly obtain the content data of key chemical components.

[0044] The quality-related parameters obtained through the above methods are integrated into structured quality information and stored in association with the unique identifier of the material.

[0045] For example, the quality information includes: the determination result of whether the material quality is qualified.

[0046] The specific values ​​for each quality indicator.

[0047] Detailed description of the exception.

[0048] It should be noted that this invention employs advanced scanning and weighing technologies for both bulk and individually packaged materials, improving the accuracy of quantity detection. Simultaneously, it utilizes multispectral imaging equipment and non-contact sensors for detection, combined with pre-trained models for analysis, achieving comprehensive, timely, and accurate acquisition of quality information, overcoming the shortcomings of outdated methods for acquiring quantity and quality information in existing technologies.

[0049] The acceptance process involves real-time comparison of the on-site data collected in real time with the acceptance criteria included in the initial scheduling instructions, and dynamic generation of acceptance progress data. The acceptance progress data includes the number of qualified materials, the number of unqualified materials, and the remaining number of materials to be accepted.

[0050] In a preferred embodiment of the present invention, the specific analysis method for dynamically generating acceptance process data is as follows: the actual quantity information collected in real time is compared with the required quantity in the initial scheduling instruction, and the quantity deviation rate is calculated.

[0051] The collected structured quality information is compared item by item with the quality standards in the initial scheduling instructions to determine whether each quality indicator is qualified or not, and the number of unqualified indicators is counted.

[0052] Based on the quantity deviation rate and the number of non-conforming indicators, a preset anomaly level matrix is ​​queried, and the corresponding acceptance anomaly level is output. The anomaly level is divided into three levels: minor, moderate, and severe.

[0053] When an acceptance test is deemed unqualified, a timestamp record is created containing the material identifier, details of the unqualified item, and the level of the anomaly.

[0054] It should be noted that the core basis for determining acceptance failure is the difference between the on-site data and the acceptance standard in the initial scheduling instruction. This mainly includes the comparison of two types of key data: 1. Quantity information comparison: Compare the actual quantity information, such as the weight of bulk materials, the total weight or number of individually packaged materials, with the required quantity in the initial scheduling instruction. If the quantity deviation rate exceeds the acceptable range, the quantity is determined to be unqualified.

[0055] 2. Quality information comparison: The structured quality information, such as the impurity level of multispectral analysis and the content of chemical components detected by the sensor, is compared with the quality standards in the initial scheduling instructions item by item. If one or more quality indicators fail to meet the standards, the quality is deemed unqualified.

[0056] Real-time supplementary scheduling analysis, based on the external dynamic influencing factors obtained in real time through the fusion of the acceptance process data, determines the supplementary scheduling needs and generates supplementary scheduling instructions, and completes the supplementary scheduling of the next batch of materials before the end of the initial scheduling acceptance process.

[0057] In a preferred embodiment of the present invention, the real-time adjustment analysis includes adjustment command triggering, and the specific analysis method is as follows: after the unattended acceptance process is started, the system monitors in real time the ratio of the number of qualified materials accepted to the time used for acceptance in the acceptance process data stream to obtain the real-time acceptance rate.

[0058] The real-time acceptance rate is compared with the standard acceptance rate calculated based on the initial scheduling instructions and historical data. If the real-time acceptance rate is lower than the preset rate threshold, supplementary scheduling analysis is triggered in advance.

[0059] It should be noted that the real-time acceptance rate is compared with the standard acceptance rate calculated based on the initial scheduling instructions and historical data. If the real-time acceptance rate is lower than the preset rate threshold, the current acceptance progress is determined to be lagging behind, which may result in the final qualified materials not meeting the demand on time. This triggers a supplementary scheduling analysis in advance, allowing sufficient time for supplementary adjustments.

[0060] During the acceptance process, if the number of non-conforming items accumulated in the acceptance process data stream exceeds the preset first-level tolerance limit, supplementary scheduling analysis will be triggered immediately.

[0061] It should be noted that during the acceptance process, the system continuously accumulates the quantity of non-conforming materials in the acceptance process data stream, including the difference in quantity and all or part of the materials that do not meet quality standards. Once this accumulated value exceeds the preset first-level tolerance limit, a replenishment analysis is immediately triggered to prevent the supply gap from widening due to excessive non-conforming materials. The first-level tolerance limit is set according to the importance of the materials; for example, the tolerance limit for critical structural components is 0, and for ordinary building materials it is 5% of the required quantity.

[0062] After receiving the on-the-way data of the material transport vehicle, the estimated arrival time is compared with the scheduled delivery time window. If the estimated delay time exceeds the preset second-level tolerance limit, a supplementary scheduling analysis is triggered before the material arrives.

[0063] In a preferred embodiment of the present invention, the specific analysis method of the external dynamic influencing factors is as follows: by accessing the national meteorological data service interface, the weather forecast for the construction site within a specified time period is obtained, including rainfall, wind force level, temperature, etc.

[0064] It should be noted that meteorological data is converted into a quantitative risk index. For example, heavy rain may cause transportation road interruptions, so the risk index is recorded as high; light wind weather has no impact, so the risk index is recorded as low. This is used to assess the potential impact of meteorological conditions on material transportation, on-site storage, and construction progress. For example, rain may delay concrete pouring, indirectly changing the rhythm of material demand.

[0065] The planned progress in the building information model is compared with the actual construction progress data collected by on-site sensors in real time, and the percentage of progress deviation on the critical path is calculated as the adjustment coefficient for project schedule risk.

[0066] It should be noted that the aforementioned schedule risk adjustment coefficient is the relative deviation between the actual completed amount and the planned completed amount. For example, a deviation of 10% indicates a 10% delay in progress, and the pace of make-up adjustments can be appropriately slowed down; a deviation of +5% indicates that the progress is ahead of schedule, and the urgency of material supply needs to be increased.

[0067] The actual average loss rate of similar materials in each stage of construction is statistically analyzed and compared with the standard loss rate estimated in the design stage. The dynamic loss deviation coefficient is then calculated to correct subsequent material demand forecasts.

[0068] It should be noted that the dynamic loss deviation coefficient is the ratio of the actual average loss rate to the standard loss rate.

[0069] In a preferred embodiment of the present invention, the specific analysis method for determining the supplementary scheduling demand is as follows: based on the acceptance process data stream, the gap between the expected final qualified material quantity and the initial demand quantity is calculated.

[0070] The material requirements for subsequent construction stages are corrected using the dynamic loss deviation coefficient to obtain the corrected future demand.

[0071] Based on the aforementioned gap, the corrected future demand, the quantitative risk index of weather conditions, and the schedule risk adjustment coefficient of project progress deviation, a pre-built model is used to output a supplementary scheduling priority score and a suggested supplementary scheduling quantity. When the priority score exceeds a preset execution threshold, it is determined that supplementary scheduling is required.

[0072] It should be noted that the preset execution threshold is a numerical critical standard used to determine whether the supplementary scheduling priority score meets the conditions for initiating supplementary scheduling. When the supplementary scheduling priority score calculated by the pre-built model is greater than or equal to the execution threshold, the system determines that the supplementary scheduling process needs to be initiated immediately; if the score is less than the threshold, supplementary scheduling will not be initiated for the time being, and the system will continue to monitor the acceptance process and changes in external influencing factors.

[0073] It should be further explained that the setting of the execution threshold should take into account the following factors to ensure that it is neither too high, which would cause delays in replenishment, nor too low, which would cause unnecessary waste in replenishment: 1. Importance of materials: The execution threshold for critical materials, such as steel and concrete that affect structural safety, is lower, that is, replenishment is initiated as soon as there is a slight risk of shortage; the threshold for non-critical materials, such as auxiliary building materials, is higher to reduce ineffective replenishment.

[0074] 2. Construction urgency: For processes on the critical path, such as power plant building pouring, the execution threshold is lower to ensure that material supply is not delayed; for non-critical processes, the threshold can be appropriately increased.

[0075] 3. Supply chain resilience: If suppliers respond quickly and have sufficient inventory, the execution threshold can be higher; if the supply chain is fragile, the threshold needs to be lowered and replenishment should be initiated in advance to allow time.

[0076] In a preferred embodiment of the present invention, the specific analysis method for generating the supplementary scheduling instruction is as follows: based on the suggested supplementary scheduling quantity, combined with the real-time inventory, production capacity and quotation information of different suppliers, the optimal supplementary scheduling supplier is determined through a multi-objective optimization algorithm.

[0077] The multi-objective optimization algorithm includes minimizing procurement costs, minimizing the arrival time of replenishment scheduling, and maximizing supplier reliability scores.

[0078] Based on the output of the optimization algorithm, a structured supplementary scheduling instruction is generated. In addition to the fields of the initial scheduling instruction, this instruction also includes the original scheduling instruction association identifier and the reason for supplementary scheduling.

[0079] Before the acceptance process for the initial batch of materials is completed, the supplementary dispatch instruction is sent to one or more selected suppliers, and the material delivery plan in the building information model is updated synchronously.

[0080] In a preferred embodiment of the present invention, the real-time supplementary scheduling analysis further includes: after each initial scheduling and supplementary scheduling cycle, collecting complete scheduling execution data, including initial instruction data, acceptance result data, supplementary scheduling instruction data, actual material arrival time, final construction and usage status, etc.

[0081] The collected data is saved as historical data samples in the historical scheduling database to facilitate continuous optimization and analysis.

[0082] For example, the collected data is used as training samples and input into a reinforcement learning model. The reward function of the model is related to the timeliness of material supply, cost control, and final construction quality. Through continuous training, the reinforcement learning model continuously optimizes the parameters in the scheduling instruction generation step, including the setting of the safety stock cycle, the advance of the scheduling trigger time point, and the dynamic adjustment of various tolerance limits and thresholds in the supplementary scheduling analysis. This enables the entire scheduling and acceptance system to learn autonomously and adapt to the ever-changing construction environment, continuously improving the accuracy and foresight of scheduling decisions.

[0083] It should be noted that this invention integrates external dynamic influencing factors in real time during the acceptance process, promptly triggers supplementary scheduling analysis, scientifically determines supplementary scheduling needs, and generates supplementary scheduling instructions, ensuring the continuity of material supply and effectively solving the problems of untimely and inaccurate supplementary scheduling analysis in existing technologies. Simultaneously, by collecting historical data for continuous optimization, the performance of scheduling and acceptance is further improved.

[0084] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations, characterized in that, include: The scheduling instruction is generated based on the preset construction progress model and material demand plan and predefined scheduling rules. The initial scheduling instruction includes the material type, required quantity, quality standard and scheduled delivery time window. Unmanned acceptance testing utilizes acceptance equipment to automatically execute the acceptance process, collecting real-time data on the materials' transit and arrival. The transit data includes the real-time location and estimated arrival time of the transport vehicles, while the arrival data includes the actual quantity and quality information of the materials. The acceptance process involves real-time comparison of the on-site data collected in real time with the acceptance criteria included in the initial scheduling instructions, and dynamic generation of acceptance progress data. The acceptance progress data includes the number of qualified materials, the number of unqualified materials, and the remaining number of materials to be accepted. Real-time supplementary scheduling analysis, based on the external dynamic influencing factors obtained in real time through the fusion of the acceptance process data, determines the supplementary scheduling needs and generates supplementary scheduling instructions, and completes the supplementary scheduling of the next batch of materials before the end of the initial scheduling acceptance process.

2. The method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations as described in claim 1, characterized in that: The specific method for generating the initial scheduling instruction is as follows: Retrieve the engineering breakdown structure associated with the current construction phase from the building information model, and parse it to obtain the bill of materials for the current construction phase; Based on the start time and duration of the work activities determined in the current construction phase plan, calculate the latest demand time for each material; Taking into account the production cycle of material suppliers, logistics and transportation time, and the preset safety stock period, the scheduling trigger time point of each material is calculated. When the scheduling trigger time for any material arrives, a structured initial scheduling instruction data packet containing the material identifier, required quantity, quality standard, designated acceptance area, and scheduled delivery time window is automatically generated and sent to the designated material supplier.

3. The method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations as described in claim 1, characterized in that: The specific analysis method for the quantity information is as follows: For bulk materials, a UAV 3D laser scanning system deployed at the entrance and exit of the acceptance area is used to scan the cargo compartment of the transport vehicle before and after unloading the materials, and to construct a 3D point cloud model of the interior of the cargo compartment before and after unloading. The volume of the unloaded material is calculated based on the three-dimensional point cloud model of the interior of the carriage before and after unloading, and the weight of the material is calculated as the actual quantity information based on the preset material density database. For individually packaged materials, a high-precision dynamic weighing sensor array deployed on the conveyor belt or unloading platform is used to weigh the materials passing through one by one, and the weights of each material are added together to obtain the total weight of the materials as the actual quantity information. High-frequency RFID readers are used to identify electronic tags attached to individual materials, and the number of materials passing through is counted as auxiliary actual quantity information.

4. The method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations as described in claim 3, characterized in that: The specific analysis method for the quality information is as follows: During the material unloading process, multispectral imaging equipment is used to continuously photograph material samples to obtain reflectance data of the material in different spectral bands; The reflectance data is input into a pre-trained material quality classification model, which is built based on the spectral data of historical material samples and the corresponding laboratory physicochemical test results. The model is used to identify whether the material has abnormal composition, excessive impurities, or physical damage. For specific materials, non-contact sensors are used to perform point-to-point or scanning detection of the material flow to obtain the content data of key chemical components. The quality-related parameters obtained through the above methods are integrated into structured quality information and stored in association with the unique identifier of the material.

5. The method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations as described in claim 4, characterized in that: The specific analysis method for dynamically generating acceptance process data is as follows: The actual quantity information collected in real time is compared with the required quantity in the initial scheduling instruction, and the quantity deviation rate is calculated. The collected structured quality information is compared item by item with the quality standards in the initial scheduling instructions to determine whether each quality indicator is qualified or not, and the number of unqualified indicators is counted. Based on the quantity deviation rate and the number of non-conforming indicators, the preset anomaly level matrix is ​​queried, and the corresponding acceptance anomaly level is output. The anomaly level is divided into three levels: minor, moderate, and severe. When an acceptance test is deemed unqualified, a timestamp record is created containing the material identifier, details of the unqualified item, and the level of the anomaly.

6. The method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations as described in claim 5, characterized in that: The real-time adjustment analysis includes adjustment command triggering, and its specific analysis method is as follows: After the unattended acceptance process is started, the system monitors the ratio of the number of qualified materials accepted to the time taken for acceptance in the acceptance process data stream in real time to obtain the real-time acceptance rate. The real-time acceptance rate is compared with the standard acceptance rate calculated based on the initial scheduling instructions and historical data. If the real-time acceptance rate is lower than the preset rate threshold, supplementary scheduling analysis is triggered in advance. During the acceptance process, if the number of non-conforming items accumulated in the acceptance process data stream exceeds the preset first-level tolerance limit, supplementary scheduling analysis will be triggered immediately. After receiving the on-the-way data of the material transport vehicle, the estimated arrival time is compared with the scheduled delivery time window. If the estimated delay time exceeds the preset second-level tolerance limit, a supplementary scheduling analysis is triggered before the material arrives.

7. The method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations as described in claim 1, characterized in that: The specific analysis method for the external dynamic influencing factors is as follows: By accessing the national meteorological data service interface, weather forecasts for the construction site for a specified period of time can be obtained, including rainfall, wind speed, and temperature. Real-time comparison of the planned progress in the building information model with the actual construction progress data collected by on-site sensors; calculation of the progress deviation percentage on the critical path as a schedule risk adjustment coefficient. The actual average loss rate of similar materials in each stage of construction is statistically analyzed and compared with the standard loss rate estimated in the design stage. The dynamic loss deviation coefficient is then calculated to correct subsequent material demand forecasts.

8. The method for intelligent scheduling and unmanned acceptance of construction materials for pumped storage power stations as described in claim 7, characterized in that: The specific analysis method for determining the supplementary scheduling requirement is as follows: Based on the acceptance process data flow, the gap between the expected final qualified material quantity and the initial demand quantity is calculated; The material requirements for subsequent construction stages are corrected using the dynamic loss deviation coefficient to obtain the corrected future demand. Based on the aforementioned gap, the corrected future demand, the quantitative risk index of weather conditions, and the schedule risk adjustment coefficient of project progress deviation, a pre-built model is used to output a supplementary scheduling priority score and a suggested supplementary scheduling quantity. When the priority score exceeds a preset execution threshold, it is determined that supplementary scheduling is required.

9. The method for intelligent scheduling and unmanned acceptance of construction materials for a pumped storage power station as described in claim 8, characterized in that: The specific analysis method for generating the supplementary scheduling instruction is as follows: Based on the suggested replenishment quantity, and combined with the real-time inventory, production capacity and quotation information of different suppliers, the optimal replenishment supplier is determined through a multi-objective optimization algorithm. The multi-objective optimization algorithm includes minimizing procurement costs, minimizing the replenishment and dispatch arrival time, and maximizing supplier reliability scores. Based on the output of the optimization algorithm, a structured supplementary scheduling instruction is generated. In addition to the fields of the initial scheduling instruction, the instruction also includes the original scheduling instruction association identifier and the reason for supplementary scheduling. Before the acceptance process for the initial batch of materials is completed, the supplementary dispatch instruction is sent to one or more selected suppliers, and the material delivery plan in the building information model is updated synchronously.

10. The method for intelligent scheduling and unmanned acceptance of construction materials for a pumped storage power station as described in claim 1, characterized in that: The real-time adjustment analysis also includes: After each initial scheduling and supplementary scheduling cycle, complete scheduling execution data is collected, including initial instruction data, acceptance result data, supplementary scheduling instruction data, actual material arrival time, and final construction usage. The collected data is saved as historical data samples in the historical scheduling database to facilitate continuous optimization and analysis.

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