Cyclic application management and control system for capital construction materials

Abstract

The invention relates to the technical field of material management, and discloses an infrastructure material cyclic application management and control system, which comprises the following modules: an intelligent material identification and real-time data acquisition module, which is used for carrying out automatic identification, classification and real-time data acquisition on infrastructure materials based on various sensor technologies (visual identification, RFID, laser scanning and NFC) and a deep learning algorithm, a digital material file is generated, and dynamic tracking and accurate positioning of materials are achieved; and the block chain-based full life cycle management module is used for carrying out data management and encrypted storage on the full life cycle (from purchasing, transportation, storage, use, maintenance, cyclic utilization to scrapping treatment) of the materials. Through close combination of a multi-mode sensor technology (visual identification, RFID, laser scanning and NFC) and a deep learning algorithm, all-directional and multi-dimensional identification and real-time data acquisition can be performed on capital construction materials in a complex and changeable environment.

Description

Technical Field

[0001] The present invention relates to the technical field of material management, and in particular to a capital construction material recycling application management and control system. Background Art

[0002] In modern infrastructure projects, the management and application of materials have a vital impact on the cost control, schedule management and quality assurance of the project. There are many types of infrastructure materials, usually including steel, cement, concrete, pipes, cables and other large materials. These materials are not only of many types and large quantities, but also need to go through multiple links such as procurement, transportation, storage, use, maintenance, recycling and scrapping during use. How to efficiently manage these materials is a complex and challenging task.

[0003] Most existing material management systems can only manage materials at a certain stage, but lack the ability to comprehensively manage and trace the entire life cycle of materials. Especially in the circulation of materials, data is easily tampered with or lost, resulting in the inability to obtain true and complete data in subsequent management links, affecting the rational use and effective maintenance of materials. At the same time, due to the lack of security of traditional systems, important material management data is prone to cyber attacks or internal leaks, bringing immeasurable risks to enterprises.

[0004] In traditional infrastructure material management, information between various management links is usually isolated, and data cannot be shared and synchronized in real time, resulting in fragmented management processes. For example, it is often difficult to synchronize material information between the procurement department, warehousing department, and construction site, resulting in untimely data updates, which in turn affects the effective allocation and use of materials. This information island phenomenon not only reduces management efficiency, but also increases management costs and the risk of errors, which may lead to problems such as over-purchasing of materials, inventory backlogs or shortages.

[0005] In view of the deficiencies in the prior art, the present invention proposes a construction material recycling application management and control system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an infrastructure material recycling application management and control system, which solves the problem that most existing material management systems can only manage materials at a certain stage, but lack the ability to comprehensively manage and trace the entire life cycle of materials.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a construction material recycling application management and control system, including the following modules: Intelligent material identification and real-time data collection module: It is used to automatically identify, classify and collect real-time data of infrastructure materials based on a variety of sensor technologies (visual recognition, RFID, laser scanning, NFC) and deep learning algorithms, generate digital material files, and realize dynamic tracking and precise positioning of materials; The blockchain-based full life cycle management module is used to manage and encrypt the data of the entire life cycle of materials (from procurement, transportation, storage, use, maintenance, recycling to scrapping). It automatically executes management tasks, warranty period monitoring, maintenance reminders and asset transfers through smart contracts to ensure the transparency of the management process and the immutability of data. Dynamic inventory optimization and supply chain intelligent collaboration module: Based on big data analysis and reinforcement learning algorithms, it monitors and optimizes the inventory status of materials, predicts future demand changes, automatically generates intelligent procurement plans, and collaborates with supplier systems to ensure efficient inventory management and stable supply chains; IoT-driven intelligent monitoring and predictive maintenance module: used to monitor the operating status of materials in real time through the IoT sensor network, and use big data analysis and deep learning models to predict the remaining life and potential failures of materials, and automatically generate maintenance plans and preventive maintenance recommendations; Multi-mode intelligent scheduling and route optimization module: used to generate the optimal transportation route and scheduling plan through a multi-objective optimization algorithm based on real-time traffic information, weather data, material demand priority and transportation tool status, supporting multi-mode transportation and optimizing transportation time, cost and carbon emissions; Recycling and remanufacturing intelligent decision-making module: It is used to automatically identify materials suitable for recycling or remanufacturing based on the life cycle assessment (LCA) method and remanufacturing process analysis, generate the optimal treatment plan, and dynamically adjust the remanufacturing strategy and recycling path in combination with market demand and environmental protection standards; Intelligent scrap management and environmental compliance module: used to automatically identify materials that need to be scrapped, execute the scrapping process through smart contracts, and combine environmental impact assessment and compliance inspection to generate environmental compliance treatment plans, and automatically connect to third-party environmental treatment platforms; Cross-project multi-dimensional data analysis and intelligent decision support module: used to integrate material management data of multiple projects and multiple regions, generate multi-dimensional data reports through big data analysis and machine learning algorithms, and provide decision support suggestions for material scheduling, inventory management, recycling and cost control; Intelligent compliance management and dynamic risk control module: used to automatically parse and update laws, regulations and industry standards through natural language processing (NLP) technology and rule engines, conduct compliance reviews and risk assessments, and generate compliance reports and risk response strategies.

[0008] Preferably, the intelligent material identification and real-time data collection module includes the following units: Multimodal sensor unit: integrates multiple sensors such as vision, RFID, laser scanning, NFC, etc., to simultaneously collect multi-dimensional attribute information of materials (material, size, weight, location, etc.) to ensure the comprehensiveness and accuracy of material identification; Deep learning unit: It combines the collected data to perform image recognition and pattern analysis to improve the accuracy and classification efficiency of material identification and support the adaptive optimization of material identification algorithms. The core algorithm formula is: Among them, L(θ) is the loss function, x i and i are the input and label of the i-th sample, θ is the model parameter, and f(x i; θ) is the predicted output; Edge computing unit: used to process and analyze data in real time at the collection site, reduce data transmission delays, improve system response speed, and support instant decision-making on site.

[0009] Preferably, the blockchain-based full life cycle management module includes the following units: Blockchain data storage unit: used to encrypt and store data on the entire life cycle of materials, ensure the security, integrity and non-tamperability of data, and support multi-party data sharing; Smart contract unit: used to automatically execute contract tasks related to material management, warranty period monitoring, automated maintenance reminders and asset transfers, ensuring automation and process management of each link; Permission Control Unit: Manages access rights of multiple users, ensures secure data sharing, and supports data collaboration and transparent management among suppliers, contractors, and regulators.

[0010] Preferably, the dynamic inventory optimization and supply chain intelligent collaboration module includes the following units: Inventory monitoring unit: real-time monitoring of inventory status and consumption of materials, combined with historical data analysis, to predict future demand fluctuations; Intelligent procurement unit: Based on inventory status and supply chain dynamics, it automatically generates procurement plans and connects with supplier systems to achieve automated replenishment and inventory adjustments; Supply chain collaboration unit: used to synchronize data with supplier systems in real time, support supply chain resilience analysis and emergency response, and ensure the stability of the supply chain and the rationality of inventory.

[0011] Preferably, the IoT-driven intelligent monitoring and predictive maintenance module includes the following units: Sensor network unit: monitors the operating status (temperature, humidity, vibration) of materials in real time through IoT sensors, providing continuous status data input; Data analysis unit: Combines big data analysis and deep learning algorithms to process sensor data, predict the remaining life and potential failures of materials, and provide maintenance recommendations; Maintenance planning unit: Automatically generates maintenance plans based on analysis results, supports preventive maintenance and real-time adjustments, and improves the service life and operating efficiency of materials.

[0012] Preferably, the multi-mode intelligent scheduling and path optimization module includes the following units: Traffic information unit: real-time acquisition and analysis of traffic conditions, weather data, transportation tool status and other information, providing dynamic data support for scheduling and route optimization; Multi-objective optimization unit: using multi-objective optimization algorithms, comprehensively considering factors such as transportation time, cost, and carbon emissions, to generate the optimal scheduling and transportation plan; Scheduling execution unit: Based on the optimization results, it automatically generates transportation plans and allocates transportation resources to ensure timely and efficient distribution of materials.

[0013] Preferably, the recycling and remanufacturing intelligent decision-making module includes the following units: Life cycle assessment unit: Based on the LCA method, it evaluates the environmental impact of materials throughout their life cycle and determines materials suitable for recycling or remanufacturing. The core algorithm formula is: Among them, LCA is the result of life cycle assessment, E i is the environmental impact factor of the ith process, F i is the frequency of material use, I i is the input amount of materials; Remanufacturing process unit: Analyze the materials and structure of materials, generate the best process route and technical solution for remanufacturing, and improve remanufacturing efficiency and resource utilization; Market Analysis Unit: Dynamically adjust recycling and remanufacturing strategies based on real-time market demand to optimize the economic and environmental benefits of resources.

[0014] Preferably, the intelligent scrap management and environmental compliance module includes the following units: Scrap identification unit: Automatically identify the scrap status of materials through IoT and AI technology and evaluate their reuse value; Environmental treatment unit: Combine environmental impact assessment and compliance inspection to generate the best scrap treatment plan to ensure environmental compliance during the treatment process; Platform docking unit: docking with the third-party environmental protection processing platform, automatically generating and submitting processing applications, tracking processing status, and ensuring the legal and compliant processing of scrapped materials.

[0015] Preferably, the cross-project multidimensional data analysis and intelligent decision support module includes the following units: Data Integration Unit: Integrate material management data from multiple projects and different regions to form a unified data analysis platform; Intelligent analysis unit: Generates multi-dimensional data reports on material scheduling, inventory management, recycling and cost control through big data analysis, machine learning and data mining technologies; Decision Support Unit: Provides strategic decision support suggestions based on analysis results to help managers perform global optimization and resource allocation across projects.

[0016] Preferably, the intelligent compliance management and dynamic risk control module includes the following units: Regulatory parsing unit: Use NLP technology to automatically parse and update laws, regulations and industry standards related to material management to ensure real-time compliance of the system. The core algorithm formula is:

[0017] Among them, Compliance is the compliance score, R j is the compliance level of the jth regulation or standard, W j The importance weight of the regulation or standard; Risk Assessment Unit: Conducts real-time risk assessment through the rule engine, identifies potential legal risks and compliance issues, and generates risk response strategies; Compliance reporting unit: Generates compliance reports to record the compliance status of management operations and provide a basis for auditing and supervision.

[0018] The present invention provides a construction material recycling and application management system, which has the following beneficial effects: 1. The present invention can perform all-round, multi-dimensional identification and real-time data collection of infrastructure materials in complex and changeable environments through the close combination of multimodal sensor technology (visual recognition, RFID, laser scanning, NFC) and deep learning algorithms. The adaptive optimization function of the deep learning algorithm ensures that the system can always maintain high-precision identification and classification when the environment changes and the data is updated. The introduction of edge computing technology further reduces data transmission delays and supports real-time decision-making on site, allowing the system to maintain efficient and stable material management capabilities even when the network is unstable or the amount of data is huge. The integration of these technical means has greatly improved the accuracy and timeliness of material identification and dynamic monitoring.

[0019] 2. The present invention integrates blockchain technology to achieve full life cycle management of materials from procurement to scrapping. Data is encrypted and stored through blockchain and management tasks are automatically executed by smart contracts, ensuring the transparency and immutability of the entire management process. All parties (suppliers, contractors and regulators) can share and verify data in real time to avoid management risks caused by information asymmetry or data tampering. This not only improves the transparency of material management, but also ensures the compliance of all parties' operations and the security of data, fundamentally optimizing the management efficiency of materials.

[0020] 3. Through the application of big data analysis, reinforcement learning algorithms and supply chain digital twin technology, the present invention can monitor and predict the inventory status of materials in real time, automatically generate and optimize procurement plans, seamlessly connect with supplier systems, and realize intelligent collaborative management of the supply chain. This intelligent supply chain collaboration mechanism can not only cope with fluctuations in market demand, but also quickly adjust inventory and procurement strategies in the event of supply chain disruptions or emergencies to ensure the continuity of material supply and the rationality of inventory management. By optimizing resource allocation and improving supply chain flexibility, while reducing inventory costs, the continuity and operational efficiency of infrastructure projects are ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the structure of the intelligent material identification and real-time data acquisition module of the present invention; Figure 3 This is a schematic diagram of the structure of the blockchain-based full life cycle management module of the present invention; Figure 4 It is a schematic diagram of the structure of the dynamic inventory optimization and supply chain intelligent collaboration module of the present invention; Figure 5 This is a schematic diagram of the structure of the intelligent monitoring and predictive maintenance module driven by the Internet of Things of the present invention; Figure 6 It is a schematic diagram of the structure of the multi-mode intelligent scheduling and path optimization module of the present invention; Figure 7 This is a schematic diagram of the structure of the recycling and remanufacturing intelligent decision-making module of the present invention; Figure 8 It is a schematic diagram of the structure of the intelligent scrap management and environmental compliance module of the present invention; Fig. 9 This is a schematic diagram of the structure of the cross-project multi-dimensional data analysis and intelligent decision support module of the present invention; Fig.10 It is a schematic diagram of the structure of the intelligent compliance management and dynamic risk control module of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Embodiment:

[0024] Please refer to the attached Figure 1 - Attachment Fig.10 The embodiment of the present invention provides a construction material recycling application management and control system, including the following modules: Intelligent material identification and real-time data collection module: It is used to automatically identify, classify and collect real-time data of infrastructure materials based on a variety of sensor technologies (visual recognition, RFID, laser scanning, NFC) and deep learning algorithms, generate digital material files, and realize dynamic tracking and precise positioning of materials; The blockchain-based full life cycle management module is used to manage and encrypt the data of the entire life cycle of materials (from procurement, transportation, storage, use, maintenance, recycling to scrapping). It automatically executes management tasks, warranty period monitoring, maintenance reminders and asset transfers through smart contracts to ensure the transparency of the management process and the immutability of data. Dynamic inventory optimization and supply chain intelligent collaboration module: Based on big data analysis and reinforcement learning algorithms, it monitors and optimizes the inventory status of materials, predicts future demand changes, automatically generates intelligent procurement plans, and collaborates with supplier systems to ensure efficient inventory management and stable supply chains; IoT-driven intelligent monitoring and predictive maintenance module: used to monitor the operating status of materials in real time through the IoT sensor network, and use big data analysis and deep learning models to predict the remaining life and potential failures of materials, and automatically generate maintenance plans and preventive maintenance recommendations; Multi-mode intelligent scheduling and route optimization module: used to generate the optimal transportation route and scheduling plan through a multi-objective optimization algorithm based on real-time traffic information, weather data, material demand priority and transportation tool status, supporting multi-mode transportation and optimizing transportation time, cost and carbon emissions; Recycling and remanufacturing intelligent decision-making module: It is used to automatically identify materials suitable for recycling or remanufacturing based on the life cycle assessment (LCA) method and remanufacturing process analysis, generate the optimal treatment plan, and dynamically adjust the remanufacturing strategy and recycling path in combination with market demand and environmental protection standards; Intelligent scrap management and environmental compliance module: used to automatically identify materials that need to be scrapped, execute the scrapping process through smart contracts, and combine environmental impact assessment and compliance inspection to generate environmental compliance treatment plans, and automatically connect to third-party environmental treatment platforms; Cross-project multi-dimensional data analysis and intelligent decision support module: used to integrate material management data of multiple projects and multiple regions, generate multi-dimensional data reports through big data analysis and machine learning algorithms, and provide decision support suggestions for material scheduling, inventory management, recycling and cost control; Intelligent compliance management and dynamic risk control module: used to automatically parse and update laws, regulations and industry standards through natural language processing (NLP) technology and rule engines, conduct compliance reviews and risk assessments, and generate compliance reports and risk response strategies.

[0025] The intelligent material identification and real-time data acquisition module includes the following units: Multimodal sensor unit: integrates multiple sensors such as vision, RFID, laser scanning, NFC, etc., to simultaneously collect multi-dimensional attribute information of materials (material, size, weight, location, etc.) to ensure the comprehensiveness and accuracy of material identification; Deep learning unit: It combines the collected data to perform image recognition and pattern analysis to improve the accuracy and classification efficiency of material identification and support the adaptive optimization of material identification algorithms. The core algorithm formula is:

[0026] Among them, L(θ) is the loss function, x i and i are the input and label of the i-th sample, θ is the model parameter, and f(x i; θ) is the predicted output; Edge computing unit: used to process and analyze data in real time at the collection site, reduce data transmission delays, improve system response speed, and support instant decision-making on site.

[0027] Specifically, first, the multimodal sensor unit integrates multiple sensors such as visual recognition, RFID, laser scanning and NFC, which can simultaneously collect multi-dimensional attribute information of materials, such as material, size, weight, location, etc. This multi-sensor fusion technology not only ensures the comprehensiveness and accuracy of material identification, but also provides reliable identification results in complex environments with many obstructions and insufficient light. Secondly, the deep learning unit uses these collected data for complex image recognition and pattern analysis. By training deep neural networks, the system can automatically extract the characteristics of materials and perform accurate classification and identification. The adaptive optimization function of the deep learning algorithm can also dynamically adjust according to environmental changes and new data to improve recognition accuracy. Finally, the edge computing unit greatly reduces data transmission delay and supports localized instant decision-making by processing data in real time at the collection site. This architecture not only improves the response speed of the system, but also reduces the burden of cloud computing, ensuring the continuity and reliability of the system in the case of unstable network or large data volume. Overall, this module realizes efficient, accurate and real-time management of material identification through the integration of multiple technologies, and is suitable for various complex infrastructure environments.

[0028] The blockchain-based full life cycle management module includes the following units: Blockchain data storage unit: used to encrypt and store data on the entire life cycle of materials, ensure the security, integrity and non-tamperability of data, and support multi-party data sharing; Smart contract unit: used to automatically execute contract tasks related to material management, warranty period monitoring, automated maintenance reminders and asset transfers, ensuring automation and process management of each link; Permission Control Unit: Manages access rights of multiple users, ensures secure data sharing, and supports data collaboration and transparent management among suppliers, contractors, and regulators.

[0029] The dynamic inventory optimization and supply chain intelligent collaboration module includes the following units: Inventory monitoring unit: real-time monitoring of inventory status and consumption of materials, combined with historical data analysis, to predict future demand fluctuations; Intelligent procurement unit: Based on inventory status and supply chain dynamics, it automatically generates procurement plans and connects with supplier systems to achieve automated replenishment and inventory adjustments; Supply chain collaboration unit: used to synchronize data with supplier systems in real time, support supply chain resilience analysis and emergency response, and ensure the stability of the supply chain and the rationality of inventory.

[0030] Specifically, the blockchain-based full life cycle management module uses the blockchain data storage unit to encrypt and store the data of the entire life cycle of materials from procurement, transportation, storage, use, maintenance, recycling to scrapping. This unit ensures the security, integrity and immutability of the data, supports multi-party data sharing, and thus improves the transparency and credibility of the entire management process. The immutability of data not only prevents malicious modification of data, but also ensures that all parties can obtain true and reliable information during the management process.

[0031] At the same time, the smart contract unit automatically executes tasks related to material management, such as warranty period monitoring, automated maintenance reminders, and asset transfers, through preset contract logic. The introduction of smart contracts has greatly improved the automation level of the management process, reduced the possibility of human intervention, and thus improved the efficiency and accuracy of the process. This automated process management makes the life cycle management of materials more efficient and reliable, and can effectively reduce operational risks and management costs.

[0032] In addition, the authority control unit ensures that different users (suppliers, contractors and regulators) can securely access and share data according to their respective permissions through a strict authority management mechanism. This unit supports multi-party collaboration, improves data transparency and sharing efficiency, and ensures the security of sensitive data, preventing unauthorized access and data leakage.

[0033] In the dynamic inventory optimization and supply chain intelligent collaboration module, the inventory monitoring unit monitors the inventory status and consumption of materials in real time, analyzes historical data and real-time consumption trends, and predicts future demand fluctuations. In this way, the system can promptly identify potential inventory shortages or surpluses and take countermeasures in advance.

[0034] The IoT-driven intelligent monitoring and predictive maintenance module includes the following units: Sensor network unit: monitors the operating status (temperature, humidity, vibration) of materials in real time through IoT sensors, providing continuous status data input; Data analysis unit: Combines big data analysis and deep learning algorithms to process sensor data, predict the remaining life and potential failures of materials, and provide maintenance recommendations; Maintenance planning unit: Automatically generates maintenance plans based on analysis results, supports preventive maintenance and real-time adjustments, and improves the service life and operating efficiency of materials.

[0035] The multi-mode intelligent scheduling and path optimization module includes the following units: Traffic information unit: real-time acquisition and analysis of traffic conditions, weather data, transportation tool status and other information, providing dynamic data support for scheduling and route optimization; Multi-objective optimization unit: using multi-objective optimization algorithms, comprehensively considering factors such as transportation time, cost, and carbon emissions, to generate the optimal scheduling and transportation plan; Scheduling execution unit: Based on the optimization results, it automatically generates transportation plans and allocates transportation resources to ensure timely and efficient distribution of materials.

[0036] Specifically, the sensor network unit monitors the key operating status of materials, such as temperature, humidity, vibration, etc., in real time through IoT sensors deployed on materials. These sensors can provide continuous status data input, allowing the system to fully grasp the real-time operating status of materials. Through continuous monitoring of these data, the system can detect anomalies in a timely manner, prevent potential failures, and ensure that materials remain in good operating condition during use.

[0037] Next, the data analysis unit combines big data analysis technology and deep learning algorithms to process and analyze the massive data collected by the sensor network. By establishing complex prediction models, the unit can accurately predict the remaining life of materials and potential failure points, thereby identifying possible problems in advance. Based on these prediction results, the system can automatically generate maintenance suggestions, replace parts, adjust operating conditions, etc., to avoid failures and improve the service life and reliability of materials.

[0038] The maintenance planning unit automatically generates a detailed maintenance plan based on the prediction results provided by the data analysis unit. The plan not only includes regular preventive maintenance tasks, but also can be dynamically adjusted based on the real-time monitored status data to ensure that maintenance work can be carried out in a timely and effective manner. Through such an active maintenance mechanism, the service life of materials can be extended, the operating efficiency can be significantly improved, and the downtime and operating costs caused by equipment failure can be reduced.

[0039] In the multi-mode intelligent scheduling and route optimization module, the traffic information unit acquires and analyzes various dynamic information in real time, including traffic conditions, weather data, and transportation tool status. Through comprehensive analysis of these data, the system can grasp various influencing factors during transportation in real time and provide accurate data support for scheduling and route optimization. This acquisition of real-time dynamic data ensures that the scheduling plan can adapt to changes in actual conditions in a timely manner and reduce the risk of transportation delays.

[0040] The multi-objective optimization unit uses a multi-objective optimization algorithm to comprehensively consider multiple factors such as transportation time, cost, and carbon emissions to generate the optimal scheduling and transportation plan. By balancing various requirements and finding the optimal solution, this unit can not only reduce transportation costs and shorten transportation time, but also reduce carbon emissions during transportation, achieving the dual goals of environmental protection and efficiency.

[0041] Finally, the dispatch execution unit automatically generates a specific transportation plan based on the optimization results provided by the multi-objective optimization unit, and rationally allocates transportation resources to ensure that materials can be delivered to the destination in a timely and accurate manner. The unit's automated dispatch function greatly reduces the need for manual intervention and improves dispatch efficiency and execution results. At the same time, it can also dynamically adjust the transportation plan to respond to emergencies and ensure the reliability and continuity of material transportation.

[0042] The recycling and remanufacturing intelligent decision-making module includes the following units: Life cycle assessment unit: Based on the LCA method, it evaluates the environmental impact of materials throughout their life cycle and determines materials suitable for recycling or remanufacturing. The core algorithm formula is:

[0043] Among them, LCA is the result of life cycle assessment, E i is the environmental impact factor of the ith process, F i is the frequency of material use, I i is the input amount of materials; Remanufacturing process unit: Analyze the materials and structure of materials, generate the best process route and technical solution for remanufacturing, and improve remanufacturing efficiency and resource utilization; Market Analysis Unit: Dynamically adjust recycling and remanufacturing strategies based on real-time market demand to optimize the economic and environmental benefits of resources.

[0044] The intelligent waste management and environmental compliance module includes the following units: Scrap identification unit: Automatically identify the scrap status of materials through IoT and AI technology and evaluate their reuse value; Environmental treatment unit: Combine environmental impact assessment and compliance inspection to generate the best scrap treatment plan to ensure environmental compliance during the treatment process; Platform docking unit: docking with the third-party environmental protection processing platform, automatically generating and submitting processing applications, tracking processing status, and ensuring the legal and compliant processing of scrapped materials.

[0045] The cross-project multidimensional data analysis and intelligent decision support module includes the following units: Data Integration Unit: Integrate material management data from multiple projects and different regions to form a unified data analysis platform; Intelligent analysis unit: Generates multi-dimensional data reports on material scheduling, inventory management, recycling and cost control through big data analysis, machine learning and data mining technologies; Decision Support Unit: Provides strategic decision support suggestions based on analysis results to help managers perform global optimization and resource allocation across projects.

[0046] The intelligent compliance management and dynamic risk control module includes the following units: Regulatory parsing unit: Use NLP technology to automatically parse and update laws, regulations and industry standards related to material management to ensure real-time compliance of the system. The core algorithm formula is:

[0047] Among them, Compliance is the compliance score, R j is the compliance level of the jth regulation or standard, W j The importance weight of the regulation or standard; Risk Assessment Unit: Conducts real-time risk assessment through the rule engine, identifies potential legal risks and compliance issues, and generates risk response strategies; Compliance reporting unit: Generates compliance reports to record the compliance status of management operations and provide a basis for auditing and supervision.

[0048] Specifically, the Recycling and Remanufacturing Intelligent Decision-making Module uses the Life Cycle Assessment (LCA) method through its Life Cycle Assessment Unit to comprehensively evaluate the environmental impact of materials throughout their entire life cycle. The unit conducts an in-depth analysis of the environmental load of materials, from procurement, use to final disposal, assesses the environmental impact of each link, and determines which materials are suitable for recycling or remanufacturing. In this way, the system is able to identify and prioritize materials with high reuse potential or remanufacturing value, thereby minimizing resource waste and environmental impact.

[0049] Next, the remanufacturing process unit conducts a detailed material and structural analysis of the evaluated materials. The unit develops the optimal remanufacturing process route and technical solution to improve the efficiency and resource utilization of the remanufacturing process. The design of the remanufacturing process not only considers the physical and chemical properties of the materials, but also combines the current technical capabilities and economic conditions to ensure that the remanufacturing process is both feasible and economical. At the same time, this optimized process route can effectively extend the service life of the materials and enhance their competitiveness in the market.

[0050] In addition, the market analysis unit monitors the dynamic changes of market demand in real time, and dynamically adjusts the recycling and remanufacturing strategies by analyzing market trends and demand fluctuations. This unit integrates market data with remanufacturing strategies, allowing the system to flexibly respond to market changes and ensure that the remanufacturing and recycling process of materials always has economic and environmental benefits. When market demand is strong, the system can speed up the remanufacturing process, and when market demand is low, it can adjust the production rhythm to avoid wasting resources.

[0051] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A construction material recycling application management and control system, characterized in that: Includes the following modules: Intelligent material identification and real-time data collection module: It is used to automatically identify, classify and collect real-time data of infrastructure materials based on a variety of sensor technologies (visual recognition, RFID, laser scanning, NFC) and deep learning algorithms, generate digital material files, and realize dynamic tracking and precise positioning of materials; The blockchain-based full life cycle management module is used to manage and encrypt the data of the entire life cycle of materials (from procurement, transportation, storage, use, maintenance, recycling to scrapping). It automatically executes management tasks, warranty period monitoring, maintenance reminders and asset transfers through smart contracts to ensure the transparency of the management process and the immutability of data. Dynamic inventory optimization and supply chain intelligent collaboration module: Based on big data analysis and reinforcement learning algorithms, it monitors and optimizes the inventory status of materials, predicts future demand changes, automatically generates intelligent procurement plans, and collaborates with supplier systems to ensure efficient inventory management and stable supply chains; IoT-driven intelligent monitoring and predictive maintenance module: used to monitor the operating status of materials in real time through the IoT sensor network, and use big data analysis and deep learning models to predict the remaining life and potential failures of materials, and automatically generate maintenance plans and preventive maintenance recommendations; Multi-mode intelligent scheduling and route optimization module: used to generate the optimal transportation route and scheduling plan through a multi-objective optimization algorithm based on real-time traffic information, weather data, material demand priority and transportation tool status, supporting multi-mode transportation and optimizing transportation time, cost and carbon emissions; Recycling and remanufacturing intelligent decision-making module: It is used to automatically identify materials suitable for recycling or remanufacturing based on the life cycle assessment (LCA) method and remanufacturing process analysis, generate the optimal treatment plan, and dynamically adjust the remanufacturing strategy and recycling path in combination with market demand and environmental protection standards; Intelligent scrap management and environmental compliance module: used to automatically identify materials that need to be scrapped, execute the scrapping process through smart contracts, and combine environmental impact assessment and compliance inspection to generate environmental compliance treatment plans, and automatically connect to third-party environmental treatment platforms; Cross-project multi-dimensional data analysis and intelligent decision support module: used to integrate material management data of multiple projects and multiple regions, generate multi-dimensional data reports through big data analysis and machine learning algorithms, and provide decision support suggestions for material scheduling, inventory management, recycling and cost control; Intelligent compliance management and dynamic risk control module: used to automatically parse and update laws, regulations and industry standards through natural language processing (NLP) technology and rule engines, conduct compliance reviews and risk assessments, and generate compliance reports and risk response strategies.

2. A construction material recycling management and control system according to claim 1, characterized in that: The intelligent material identification and real-time data acquisition module includes the following units: Multimodal sensor unit: integrates multiple sensors such as vision, RFID, laser scanning, NFC, etc., to simultaneously collect multi-dimensional attribute information of materials (material, size, weight, location, etc.) to ensure the comprehensiveness and accuracy of material identification; Deep learning unit: It combines the collected data to perform image recognition and pattern analysis to improve the accuracy and classification efficiency of material identification and support the adaptive optimization of material identification algorithms. The core algorithm formula is: Among them, L(θ) is the loss function, x i and i are the input and label of the i-th sample, θ is the model parameter, and f(x i; θ) is the predicted output; Edge computing unit: used to process and analyze data in real time at the collection site, reduce data transmission delays, improve system response speed, and support instant decision-making on site.

3. The infrastructure material recycling application management and control system according to claim 1 is characterized in that: The blockchain-based full life cycle management module includes the following units: Blockchain data storage unit: used to encrypt and store data on the entire life cycle of materials, ensure the security, integrity and non-tamperability of data, and support multi-party data sharing; Smart contract unit: used to automatically execute contract tasks related to material management, warranty period monitoring, automated maintenance reminders and asset transfers, ensuring automation and process management of each link; Permission Control Unit: Manages access rights of multiple users, ensures secure data sharing, and supports data collaboration and transparent management among suppliers, contractors, and regulators.

4. The infrastructure material recycling and application management and control system according to claim 1 is characterized in that: The dynamic inventory optimization and supply chain intelligent collaboration module includes the following units: Inventory monitoring unit: real-time monitoring of inventory status and consumption of materials, combined with historical data analysis, to predict future demand fluctuations; Intelligent procurement unit: Based on inventory status and supply chain dynamics, it automatically generates procurement plans and connects with supplier systems to achieve automated replenishment and inventory adjustments; Supply chain collaboration unit: used to synchronize data with supplier systems in real time, support supply chain resilience analysis and emergency response, and ensure the stability of the supply chain and the rationality of inventory.

5. The infrastructure material recycling application management and control system according to claim 1 is characterized in that: The IoT-driven intelligent monitoring and predictive maintenance module includes the following units: Sensor network unit: monitors the operating status (temperature, humidity, vibration) of materials in real time through IoT sensors, providing continuous status data input; Data analysis unit: Combines big data analysis and deep learning algorithms to process sensor data, predict the remaining life and potential failures of materials, and provide maintenance recommendations; Maintenance planning unit: Automatically generates maintenance plans based on analysis results, supports preventive maintenance and real-time adjustments, and improves the service life and operating efficiency of materials.

6. The infrastructure material recycling application management and control system according to claim 1 is characterized in that: The multi-mode intelligent scheduling and path optimization module includes the following units: Traffic information unit: real-time acquisition and analysis of traffic conditions, weather data, transportation tool status and other information, providing dynamic data support for scheduling and route optimization; Multi-objective optimization unit: using multi-objective optimization algorithms, comprehensively considering factors such as transportation time, cost, and carbon emissions, to generate the optimal scheduling and transportation plan; Scheduling execution unit: Based on the optimization results, it automatically generates transportation plans and allocates transportation resources to ensure timely and efficient distribution of materials.

7. The infrastructure material recycling and application management and control system according to claim 1 is characterized in that: The recycling and remanufacturing intelligent decision-making module includes the following units: Life cycle assessment unit: Based on the LCA method, it evaluates the environmental impact of materials throughout their life cycle and determines materials suitable for recycling or remanufacturing. The core algorithm formula is: Among them, LCA is the result of life cycle assessment, E i is the environmental impact factor of the ith process, F i is the frequency of material use, I i is the input amount of materials; Remanufacturing process unit: Analyze the materials and structures of materials, generate the best process route and technical solutions for remanufacturing, and improve remanufacturing efficiency and resource utilization; Market Analysis Unit: Combined with real-time market demand, dynamically adjust recycling and remanufacturing strategies to optimize the economic and environmental benefits of resources.

8. The infrastructure material recycling and application management and control system according to claim 1 is characterized in that: The intelligent waste management and environmental compliance module includes the following units: Scrap identification unit: Automatically identify the scrap status of materials through IoT and AI technology and evaluate their reuse value; Environmental treatment unit: Combine environmental impact assessment and compliance inspection to generate the best scrap treatment plan to ensure environmental compliance during the treatment process; Platform docking unit: docking with the third-party environmental protection processing platform, automatically generating and submitting processing applications, tracking processing status, and ensuring the legal and compliant processing of scrapped materials.

9. The infrastructure material recycling and application management and control system according to claim 1 is characterized in that: The cross-project multidimensional data analysis and intelligent decision support module includes the following units: Data Integration Unit: Integrate material management data from multiple projects and different regions to form a unified data analysis platform; Intelligent analysis unit: Generates multi-dimensional data reports on material scheduling, inventory management, recycling and cost control through big data analysis, machine learning and data mining technologies; Decision Support Unit: Provides strategic decision support suggestions based on analysis results to help managers perform global optimization and resource allocation across projects.

10. The infrastructure material recycling application management and control system according to claim 1, characterized in that: The intelligent compliance management and dynamic risk control module includes the following units: Regulatory parsing unit: Use NLP technology to automatically parse and update laws, regulations and industry standards related to material management to ensure real-time compliance of the system. The core algorithm formula is: Among them, Compliance is the compliance score, R j is the compliance level of the jth regulation or standard, W j The importance weight of the regulation or standard; Risk Assessment Unit: Conducts real-time risk assessment through the rule engine, identifies potential legal risks and compliance issues, and generates risk response strategies; Compliance reporting unit: Generates compliance reports to record the compliance status of management operations and provide a basis for auditing and supervision.

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