Building component warehousing management system based on RFID

Abstract

The invention relates to the technical field of building informatization management, and discloses an RFID-based building component warehousing management system, which comprises the following modules: an RFID tag and sensor integration module, an intelligent quality evaluation algorithm module, a dynamic threshold setting and intelligent early warning module and a data visualization and management platform module. The RFID tag and sensor integration module is used for integrating an RFID tag and a sensor, acquiring continuous monitoring of the physical state of the building component, and uploading the data to a system background; and the intelligent quality evaluation algorithm module is used for automatically analyzing the data collected by the sensor in the RFID tag through a machine learning algorithm, and predicting and evaluating the current quality state of the component. The building component warehousing management system based on the RFID aims to solve the problem that an existing RFID system can only achieve article warehousing and warehousing identification and cannot effectively evaluate the quality of warehouse-out components.

Description

Technical Field

[0001] The present invention relates to the technical field of building information management, and in particular to a building component storage management system based on RFID. Background Art

[0002] In the construction industry, inventory management of building components plays a vital role in ensuring the progress and quality of the project. Traditional RFID systems are widely used in the warehousing and outbound management of building components due to their high efficiency and accuracy. RFID systems automatically identify building components passing through specific areas through wireless signals without manual intervention, greatly improving the efficiency and accuracy of logistics tracking. In the warehousing link, the RFID system can automatically record the warehousing time, quantity, type and other information of the components, and synchronize with the background database to ensure real-time update of inventory data. In the outbound link, the system can also automatically identify and record the outbound information of the components, effectively preventing the occurrence of problems such as wrong delivery and missing delivery. In addition, the RFID system can also realize the full tracking of building components, from warehousing to outbound, every link is under control, which not only helps managers to understand the inventory status at any time and make timely adjustments, but also provides strong support for subsequent project quality control.

[0003] Although traditional RFID systems have demonstrated high efficiency and accuracy in the management of warehousing and outbound delivery of building components, their functions are often limited to confirming the existence of components, and are unable to directly evaluate their physical state or quality. RFID systems can usually only identify label information on components, such as ID, type, quantity, etc., but are unable to obtain key physical parameters such as internal stress, temperature, and humidity. This means that even if some components are of good quality when they are put into storage, they may deteriorate or be damaged due to environmental factors during storage, and the RFID system cannot capture these changes in time. Therefore, some unqualified components may be shipped out of the warehouse and sent to the construction site without strict inspection, which may not only cause delays and rework during the construction process, but also pose a serious threat to the overall safety and stability of the construction project. Summary of the invention

[0004] The purpose of the present invention is to solve the problem that the existing RFID system can only realize the identification of goods entering and leaving the warehouse, but cannot effectively evaluate the quality of the outgoing components, and proposes an RFID-based building component warehouse management system.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] An RFID-based building component storage management system includes the following modules: an RFID tag and sensor integration module, an intelligent quality assessment algorithm module, a dynamic threshold setting and intelligent early warning module, and a data visualization and management platform module;

[0007] The RFID tag and sensor integration module is used to integrate the RFID tag with the sensor to obtain continuous monitoring of the physical status of the building components and upload this data to the system background;

[0008] The intelligent quality assessment algorithm module is used for automatically analyzing the data collected by the sensor in the RFID tag using a machine learning algorithm to predict and assess the current quality status of the component;

[0009] The dynamic threshold setting and intelligent early warning module is used to dynamically set the threshold of quality assessment according to different types of building components and their use environment, and automatically trigger an early warning when the component quality does not meet the preset standard;

[0010] The data visualization and management platform module is used to provide users with a data visualization interface and a management platform to achieve real-time monitoring and optimization of the inventory status and quality trends of building components.

[0011] Based on the above technical solution, the present invention can also be improved as follows.

[0012] Furthermore, in the RFID tag and sensor integration module, a special RFID tag is affixed to the building component before it is put into storage. A small sensor is also built into the module. The sensor type is selected according to the component characteristics and monitoring requirements, including but not limited to stress sensors, temperature sensors, humidity sensors, etc. The installation position of the sensor must ensure that it can accurately reflect the changes in the physical state of the component. The stress sensor should be installed at the stress-bearing part of the component, and the temperature sensor should be installed at a position inside or on the surface of the component that is easily affected by temperature.

[0013] Furthermore, during the installation process, the sensor must ensure close integration with the component, and the sensor data is uploaded to the system background in real time through the wireless transmission function of the RFID tag. The data transmission adopts low-power wireless communication technology to ensure that the sensor maintains stable data transmission capabilities during long-term use.

[0014] Furthermore, a set of quality assessment algorithms based on machine learning are integrated in the system background of the intelligent quality assessment algorithm module. The algorithm can automatically receive and process data collected by sensors in RFID tags, including but not limited to physical state information such as temperature, humidity, vibration, etc. of building components. Secondly, the algorithm combines basic information such as material, size, production date, etc. of building components, and uses historical data and actual quality feedback for training and optimization. During the training process, the algorithm continuously adjusts model parameters to improve the accuracy of component quality assessment. Finally, the algorithm uses one of the machine learning algorithms such as support vector machine, random forest or deep learning to classify and regress sensor data. Through continuous iterative learning, the algorithm gradually optimizes the evaluation model to improve the quality assessment capabilities of building components of different types, materials and sizes.

[0015] Furthermore, the algorithm can perform real-time analysis on the collected sensor data according to preset quality standards or thresholds to determine whether the building components meet the quality requirements. The algorithm can also generate a quality assessment report, which includes but is not limited to information such as the quality grade of the components, potential quality problems and recommended measures. The algorithm also has self-learning and optimization capabilities. By continuously collecting new sensor data and actual quality feedback, the algorithm can continuously optimize the assessment model and improve the accuracy and reliability of the assessment. At the same time, the algorithm can also adapt to the quality assessment needs of building components of different types and different application scenarios.

[0016] Furthermore, the dynamic threshold setting and intelligent warning module is provided with a threshold setting interface, through which the user can input or select key information such as the material, size, usage environment, and service life of the component. The system automatically calculates and recommends a reasonable threshold range based on this information. At the same time, the user can also manually adjust the threshold according to actual conditions to ensure that the evaluation criteria are highly matched with actual needs. In addition, the system can continuously collect and analyze the usage and quality feedback of the component, and automatically adjust the threshold to adapt to the actual performance changes of the component.

[0017] Furthermore, the dynamic threshold setting and intelligent early warning module can automatically start the quality assessment process when the RFID system recognizes that the component is about to be shipped out. The assessment process includes reading the sensor data in the RFID tag, analyzing the key indicators of the component such as stress, temperature, humidity, etc., and comparing them with the preset thresholds. If the assessment result shows that the quality of the component does not meet the preset standards, the intelligent early warning module will immediately trigger the early warning mechanism, prevent the component from being shipped out, and notify relevant personnel through the system interface, text messages, emails, etc. for further inspection or processing. The early warning information includes but is not limited to the component's ID, location, quality problems and recommended measures, etc., so as to respond quickly and take measures. In addition, the intelligent early warning module also has the function of customizing early warning standards. Users can set specific early warning standards in the system according to actual needs, such as the percentage of stress exceeding the limit, the range of temperature being too high or too low, the threshold of excessive humidity, etc., to ensure that the early warning mechanism can accurately reflect the actual quality status of the component.

[0018] Furthermore, the data visualization and management platform module provides a user data visualization interface, which displays the entire life cycle records of building components such as warehousing, storage, quality assessment and outbound delivery in an intuitive and clear manner. Through various visualization tools such as charts, graphs, dashboards, etc., the inventory status of the components is fully displayed, including but not limited to key information such as inventory quantity, type distribution, storage location, etc. At the same time, the system can also display the quality trend of the components. In addition, the system also provides data screening and filtering functions to help users quickly locate component data of a specific time period, specific type or specific quality. The data visualization module can receive and update data from the RFID system in real time to ensure that the displayed content is always consistent with the actual situation. At the same time, the system can also provide real-time feedback functions, such as automatically issuing an alarm when the inventory quantity is lower than the preset threshold, reminding managers to take timely measures.

[0019] Furthermore, based on the data displayed by the data visualization module, the management platform can automatically analyze the inventory status and quality trends of components and provide managers with optimization suggestions for inventory management strategies. The management platform adopts cloud computing and big data technologies to realize real-time storage, processing and analysis of data. Through the elastic computing of cloud computing and the distributed storage capabilities of big data, the system can process massive data and quickly generate analysis results to ensure the accuracy and timeliness of the data. At the same time, the pay-as-you-go model of cloud computing can also reduce the operating costs of enterprises and improve resource utilization efficiency.

[0020] Compared with the prior art, the technical solution of this application has the following beneficial technical effects:

[0021] The present invention introduces RFID tags and sensor integration modules, and the system realizes continuous monitoring of the physical status of building components. This function overcomes the limitation of traditional RFID systems that can only confirm the existence of components, and enables the system to obtain key physical parameters such as stress, temperature, humidity, etc. of components in real time, so as to have a more comprehensive understanding of the storage status of components. This not only helps to timely discover and deal with potential quality problems, but also provides more accurate data support for subsequent engineering quality control. Secondly, the introduction of the intelligent quality assessment algorithm module enables the system to use machine learning algorithms to automatically analyze the data collected by sensors, predict and evaluate the current quality status of components. This function not only improves the accuracy and efficiency of quality assessment, but also reduces the cost and risk of manual intervention. Through the analysis of intelligent algorithms, the system can timely discover components with unqualified quality to avoid them being mistakenly shipped out and sent to construction site, thereby ensuring the smooth progress and safety of the construction project. Furthermore, the design of dynamic threshold setting and intelligent early warning module enables the system to dynamically set the threshold of quality assessment according to different types of building components and their use environment. This function enhances the flexibility and adaptability of the system, allowing the system to more accurately reflect the actual quality status of the components. At the same time, when the quality of the component does not meet the preset standards, the system can automatically trigger an early warning to remind managers to take timely measures, thereby effectively preventing the further expansion of quality problems. Finally, the data visualization and management platform module provides users with an intuitive and clear data visualization interface and management platform, allowing managers to monitor the inventory status and quality trends of building components in real time. This function not only improves management efficiency, but also enables managers to make decisions and adjustments more quickly, thereby optimizing inventory management strategies, reducing inventory costs, and improving resource utilization efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a system block diagram of an RFID-based building component warehousing management system of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the embodiments 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.

[0024] Combination Figure 1 As shown, an RFID-based building component storage management system of the present invention includes the following modules: an RFID tag and sensor integration module, an intelligent quality assessment algorithm module, a dynamic threshold setting and intelligent early warning module, and a data visualization and management platform module;

[0025] RFID tag and sensor integration module, used to integrate RFID tags with sensors to obtain continuous monitoring of the physical status of building components and upload this data to the system backend;

[0026] Intelligent quality assessment algorithm module, which uses machine learning algorithms to automatically analyze the data collected by sensors in RFID tags to predict and assess the current quality status of components;

[0027] Dynamic threshold setting and intelligent early warning module, which is used to dynamically set the threshold of quality assessment according to different types of building components and their use environment, and automatically trigger an early warning when the component quality does not meet the preset standards;

[0028] The data visualization and management platform module is used to provide users with a data visualization interface and management platform to achieve real-time monitoring and optimization of the inventory status and quality trends of building components.

[0029] In a preferred embodiment, the present invention can be further configured as follows: a special RFID tag is affixed to the building component before it is put into storage in the RFID tag and sensor integration module, which also has a small sensor built in it. The sensor type is selected according to the component characteristics and monitoring requirements, including but not limited to stress sensors, temperature sensors, humidity sensors, etc. The installation position of the sensor must ensure that it can accurately reflect the physical state changes of the component. The stress sensor should be installed at the stress-bearing part of the component, and the temperature sensor should be installed at the position inside or on the surface of the component that is easily affected by temperature. The attachment of special RFID tags is the basis for the system to identify and track components. These tags should be firmly affixed to a conspicuous and non-damaged position of the component to ensure that readability is always maintained during the circulation of the component. The design of the tag should take into account the actual environment of the construction site, such as dustproof, waterproof, wear-resistant and other characteristics to ensure that it can still work normally under harsh conditions. Secondly, the selection of sensors Selection and installation are crucial to accurately monitoring the physical state of components. According to the characteristics of the components and monitoring requirements, it is necessary to select from a variety of sensor types, including but not limited to stress sensors, temperature sensors, humidity sensors, etc. The installation location of each sensor needs to be carefully designed and considered to ensure that it can accurately reflect the changes in the physical state of the components. Specifically, stress sensors should be installed at the stress-bearing parts of the components. These parts are usually the areas where the components are most likely to deform or be damaged when subjected to stress. By monitoring the stress changes in these parts, abnormal stress conditions of the components can be discovered in a timely manner, thereby avoiding potential safety hazards. Temperature sensors should be installed at locations inside or on the surface of the components that are susceptible to temperature. These locations are usually the areas where the temperature fluctuates the most during storage or use of the components. By monitoring the temperature changes at these locations, the temperature adaptability of the components in different environments can be understood, and corresponding protective measures can be taken.

[0030] In a preferred embodiment, the present invention can be further configured as follows: during the installation process, the sensor must ensure that the sensor is tightly combined with the component, and the sensor data is uploaded to the system background in real time through the wireless transmission function of the RFID tag. The data transmission adopts low-power wireless communication technology to ensure that the sensor maintains stable data transmission capabilities during long-term use. Regarding the tight combination of the sensor and the component, this is the basis for ensuring data accuracy and system reliability. When installing the sensor, it must be ensured that the sensor can be firmly fixed on the component to avoid loosening or falling off due to vibration, temperature changes or other environmental factors. To achieve this, a special installation kit or adhesive can be used. These materials should have high strength, weather resistance and chemical stability to adapt to various environments that building components may face. At the same time, strict process standards should be followed during the installation process to ensure that the contact area between the sensor and the component is maximized, thereby reducing errors caused by poor contact. Secondly, the real-time uploading of sensor data is the key to the system realizing real-time monitoring and early warning functions. To achieve this, the sensor needs to be integrated with the RFID tag and the wireless transmission function of the RFID tag is used. In this process, the stability and security of data transmission must be ensured. In terms of stability, advanced low-power wireless communication technologies such as LoRa, Zigbee or Bluetooth low energy can be used. These technologies can significantly reduce energy consumption and extend the service life of sensors and RFID tags while ensuring the quality of data transmission. In terms of security, data encryption is required to ensure that it is not illegally intercepted or tampered with during transmission, and to protect the privacy and security of building components. Finally, regarding low-power wireless communication technology, this is the key to ensuring that sensors maintain stable data transmission capabilities during long-term use. When selecting communication technology, factors such as data transmission rate, coverage, energy consumption and cost need to be considered comprehensively. Low-power wireless communication technology can significantly reduce the energy consumption of sensors while ensuring the quality of data transmission, thereby extending their service life. In addition, energy consumption can be further reduced by optimizing data transmission strategies, such as periodically collecting and uploading data. At the same time, sensors need to be regularly maintained and calibrated to ensure their long-term stable operation and data accuracy.

[0031] In a preferred embodiment, the present invention can be further configured as follows: a set of quality assessment algorithms based on machine learning are integrated in the system background of the intelligent quality assessment algorithm module. The algorithm can automatically receive and process data collected from sensors in RFID tags, including but not limited to physical state information such as temperature, humidity, and vibration of building components. Secondly, the algorithm combines basic information such as material, size, and production date of building components, and uses historical data and actual quality feedback for training and optimization. During the training process, the algorithm continuously adjusts model parameters to improve the accuracy of component quality assessment. Finally, the algorithm uses one of the machine learning algorithms such as support vector machines, random forests, or deep learning to classify and regress sensor data. Through continuous iterative learning, the algorithm gradually optimizes the evaluation model to improve the quality evaluation ability of building components of different types, materials and sizes. Regarding the machine learning algorithm in the intelligent quality evaluation algorithm module, which is the core part of the entire system, the algorithm can automatically receive and process the data collected by the sensors in the RFID tags. These data cover the physical state information of the building components, such as temperature, humidity, vibration, etc., as well as the basic information of the components, such as material, size, and production date. In order to accurately process these complex data, advanced machine learning technologies such as support vector machines, random forests or deep learning are required. These algorithms have powerful data processing and pattern recognition capabilities and can extract useful information from large amounts of data. Features, and build an accurate evaluation model. Secondly, regarding the data processing process, it is necessary to ensure the accuracy and completeness of the data. In the data collection stage, the sensor will collect the physical status information of the building components in real time, and upload it to the system background through the wireless transmission function of the RFID tag. In the data preprocessing stage, the collected data needs to be cleaned, denoised and normalized to eliminate outliers and noise in the data and improve the quality of the data. In the feature extraction stage, features related to component quality will be extracted from the processed data, such as temperature fluctuation range, humidity change trend, vibration frequency, etc. These features will be used as inputs to the machine learning algorithm to build an evaluation model. Next, regarding model training and optimization, this is to improve the accuracy of the algorithm. The key step is to use historical data and actual quality feedback to train and optimize the algorithm during the training process. By constantly adjusting the model parameters, the algorithm can better adapt to building components of different types, materials and sizes. At the same time, it is also necessary to adopt cross-validation, regularization and other strategies to prevent model overfitting and improve the generalization ability of the model. In the optimization process, grid search, random search and other algorithms can be used to find the optimal combination of model parameters to further improve the accuracy of the algorithm. Finally, regarding algorithm selection, it is necessary to select a suitable machine learning algorithm based on the specific application scenario and data characteristics. Support vector machines are suitable for processing high-dimensional data and nonlinear problems; random forests have strong anti-overfitting capabilities and parallel computing capabilities;Deep learning can handle more complex data structures and patterns. When selecting an algorithm, it is necessary to comprehensively consider factors such as the accuracy, computational efficiency, and interpretability of the algorithm to ensure the performance and reliability of the entire system.

[0032] In a preferred embodiment, the present invention can be further configured as follows: the algorithm can analyze the collected sensor data in real time according to preset quality standards or thresholds to determine whether the building components meet the quality requirements. The algorithm can also generate a quality assessment report, which includes but is not limited to information such as the quality level of the component, potential quality problems and recommended measures. The algorithm also has self-learning and optimization capabilities. By continuously collecting new sensor data and actual quality feedback, the algorithm can continuously optimize the assessment model to improve the accuracy and reliability of the assessment. At the same time, the algorithm can also adapt to the quality assessment needs of building components of different types and different application scenarios. Regarding the real-time analysis capability of the algorithm, this is the key to ensuring that the system can respond to and process sensor data in a timely manner. The algorithm can analyze the collected sensor data in real time according to preset quality standards or thresholds. These standards or thresholds can be set according to the specific type of building components, application scenarios and industry standards. For example, for concrete components, reasonable ranges of parameters such as temperature, humidity and vibration can be set;For steel structure components, we can pay attention to their stress, deformation and other parameters. By comparing these real-time data with the preset standards, the algorithm can quickly determine whether the building components meet the quality requirements, and trigger the early warning mechanism when necessary to notify relevant personnel to take corresponding measures. Secondly, regarding the generation of quality assessment reports, this is an important way for the system to display the assessment results to users. The algorithm can not only determine whether the building components meet the quality requirements, but also generate a detailed quality assessment report. The report should include information such as the quality grade of the components, potential quality problems and recommended measures. The quality grade can be divided according to the actual assessment results, such as excellent, good, qualified, unqualified, etc. Potential quality problems should be based on the algorithm's in-depth analysis of sensor data, which may include abnormal physical parameters of certain components, material aging and other problems. Recommended measures should propose specific solutions or preventive measures based on the nature of the potential quality problems. The presentation of this information should be intuitive and easy to understand, so that users can quickly understand the quality status of the components and take corresponding actions. Next, regarding the self-learning and optimization capabilities of the algorithm, this is to ensure that the system can continuously improve the assessment accuracy. The key to accuracy and reliability is that the algorithm can continuously optimize the evaluation model by continuously collecting new sensor data and actual quality feedback. This optimization process can include adjusting model parameters, adding new feature variables, improving algorithm structure, etc. Through continuous iterative learning, the algorithm can gradually adapt to the quality evaluation needs of building components of different types and different application scenarios. At the same time, the algorithm should also have the ability to automatically detect and process abnormal data to ensure the accuracy and stability of the evaluation results. Finally, the ability of the algorithm to adapt to different types and application scenarios is the key to ensuring the wide applicability of the system. In actual applications, the types and application scenarios of building components are diverse, such as residential, commercial buildings, bridges, tunnels, etc. Therefore, the algorithm needs to have a high degree of flexibility and scalability to adapt to the quality evaluation needs of different types and application scenarios. This can be achieved by adopting modular design, providing configurable parameters and options, etc. In addition, the algorithm should be able to integrate and interact with other systems, such as BIM (Building Information Modeling) system, ERP (Enterprise Resource Planning) system, etc., to achieve more comprehensive and efficient quality management. ;

[0033] In a preferred embodiment, the present invention can be further configured as follows: a threshold setting interface is provided in the dynamic threshold setting and intelligent early warning module, through which the user can input or select key information such as the material, size, use environment, and service life of the component, and the system automatically calculates and recommends a reasonable threshold range based on this information. At the same time, the user can also manually adjust the threshold according to the actual situation to ensure that the evaluation standard is highly matched with the actual needs. In addition, the system can continuously collect and analyze the usage and quality feedback of the component, and automatically adjust the threshold to adapt to the actual performance changes of the component. Regarding the threshold setting interface, this is an important window for the user to interact with the system. The dynamic threshold setting and intelligent early warning module is provided with an intuitive An easy-to-use threshold setting interface, through which users can conveniently input or select key information such as the material, size, use environment, and service life of building components. This information is the basis for the system to calculate and recommend reasonable thresholds. The interface design should be concise and clear, and provide necessary guidance and prompts to reduce the user's learning cost. At the same time, the interface should also support multiple input methods, such as drop-down menus, text boxes, sliders, etc., to meet the operating habits and needs of different users. Secondly, regarding the automatic calculation and recommendation of threshold mechanisms, this is an important manifestation of the intelligence of the system. Based on the key component information input by the user, the system uses built-in algorithms and databases to automatically calculate and recommend reasonable threshold ranges. These threshold ranges It should be set based on industry standards, historical data and specific properties of components to ensure that the evaluation criteria are highly matched with actual needs. At the same time, the system should also provide explanations and basis for threshold recommendations to help users understand the rationality and necessity of threshold settings. Next, regarding the manual adjustment of threshold functions, this is an important means for users to flexibly adjust according to actual needs. Users can manually adjust the thresholds recommended by the system in the threshold setting interface to adapt to specific application scenarios or special needs. For example, for some key components or components in special environments, users may need to set a stricter threshold range to ensure safety. The system should support users to make fine-tuning and provide real-time feedback and preview functions to help Help users make reasonable decisions. Finally, regarding the automatic adjustment of threshold strategies based on usage and quality feedback, this is an important manifestation of the system's adaptability and self-learning ability. The system can continuously collect and analyze the usage and quality feedback of components, such as real-time data of physical parameters such as temperature, humidity, vibration, and component maintenance records, fault reports and other information. Based on these data, the system can automatically adjust the threshold to adapt to the actual performance changes of the components. This adjustment can be real-time or periodic, depending on the system configuration and user needs. Through continuous learning and optimization, the system can gradually improve the accuracy and adaptability of threshold settings, thereby more accurately reflecting the actual quality status of components.

[0034] In a preferred embodiment of the present invention, the dynamic threshold setting and intelligent early warning module can automatically start the quality assessment process when the RFID system recognizes that the component is about to be shipped out. The assessment process includes reading the sensor data in the RFID tag, analyzing the key indicators of the component such as stress, temperature, humidity, etc., and comparing them with the preset thresholds. If the assessment result shows that the quality of the component does not meet the preset standard, the intelligent early warning module will immediately trigger the early warning mechanism to prevent the component from being shipped out, and notify relevant personnel through the system interface, SMS, email, etc. for further inspection or processing. The early warning information includes but is not limited to the component's ID, location, quality problems and recommended measures, so as to respond quickly and take measures. In addition, the intelligent early warning module also has an early warning mechanism. Standard custom function, users can set specific warning standards in the system according to actual needs, such as the percentage of stress exceeding the limit, the range of temperature being too high or too low, the threshold of humidity being too high, etc., to ensure that the warning mechanism can accurately reflect the actual quality status of the component. Regarding the dynamic threshold setting and the automatic start of the quality assessment process when the RFID system identifies the component leaving the warehouse, this is an important link to ensure the quality and safety of the component before leaving the warehouse. When the RFID system identifies that the component is about to leave the warehouse, the module will automatically start the quality assessment process. This process includes reading the sensor data stored in the RFID tag. These data cover key indicators such as stress, temperature, and humidity of the component. The system will then conduct an in-depth analysis of these data to evaluate the component. The current quality status of the component. Secondly, regarding the evaluation details, the system will compare and analyze these key indicators based on preset thresholds. These thresholds may be based on industry standards, historical data, or user-defined. The system will compare the relationship between the actual measured values ​​and the thresholds to determine whether the component meets the quality requirements. For example, if the stress of the component exceeds the preset safety range, or parameters such as temperature and humidity exceed the normal fluctuation range, the system will consider that the component quality does not meet the preset standards. Next, regarding the triggering conditions of the early warning mechanism, once the evaluation results show that the component quality does not meet the preset standards, the intelligent early warning module will immediately trigger the early warning mechanism. This mechanism is designed to prevent the component from leaving the warehouse to prevent potential safety hazards. At the same time, the system will notify the user through various notifications. The warning information can be conveyed to relevant personnel in a timely manner through methods such as system interface prompts, SMS notifications, and email reminders for further inspection or processing. Regarding the content of the warning information, this information should fully and accurately reflect the quality problems and processing suggestions of the components. The warning information includes but is not limited to the component ID (i.e. the unique identifier of the RFID tag), location information (such as warehouse number, shelf number, etc.), detailed description of the quality problem (such as the specific value of the stress exceeding the limit, the range of temperature or humidity abnormality, etc.) and recommended measures (such as re-testing, repair or replacement, etc.). This information will help relevant personnel quickly locate the problem, understand the severity of the problem, and take corresponding corrective measures. Finally, regarding the customized warning standard function, in order to meet the actual needs of different users,The intelligent early warning module also provides a customized early warning standard function. Users can set specific early warning standards in the system according to specific application scenarios or safety requirements. For example, users can set the percentage of stress exceeding the limit, the range of temperature being too high or too low, the threshold of humidity being too high, etc. These customized standards will more accurately reflect the actual quality status of the components and improve the accuracy and practicality of the early warning mechanism.

[0035] In a preferred embodiment, the present invention can be further configured as follows: a user data visualization interface is provided in the data visualization and management platform module, which displays the entire life cycle records of the warehousing, storage, quality assessment and delivery of building components in an intuitive and clear manner, and comprehensively displays the inventory status of components through various visualization tools such as charts, graphs, dashboards, etc., including but not limited to key information such as inventory quantity, type distribution, storage location, etc. At the same time, the system can also display the quality trend of components, such as the distribution of quality assessment results, the frequency of occurrence of quality problems, etc., so that management personnel can quickly understand the overall status of components. The data visualization interface supports users to perform interactive operations, such as clicking, dragging, zooming, etc., so that users can customize views according to their needs, and more In addition, the system also provides data screening and filtering functions to help users quickly locate component data of a specific time period, specific type or specific quality. The data visualization module can receive and update data from the RFID system in real time to ensure that the displayed content is always consistent with the actual situation. At the same time, the system can also provide real-time feedback functions, such as automatically issuing an alarm when the inventory quantity is lower than the preset threshold, reminding managers to take timely measures. Regarding the user data visualization interface, this is an important window for managers to obtain information on the entire life cycle of building components. The interface displays the records of key links such as warehousing, storage, quality assessment and outbound delivery of building components in an intuitive and clear manner. The interface design should be concise and clear, with reasonable color matching to ensure To ensure that the information is clear at a glance, the interface should have a good response speed to provide a smooth user experience. Secondly, regarding the application of visualization tools, the system uses a variety of visualization tools such as charts, graphs, and dashboards to fully display the inventory status of components. These tools can intuitively reflect key information such as inventory quantity, type distribution, and storage location, helping managers quickly understand the overall inventory situation of components. For example, the inventory quantity of different types of components can be displayed through a bar chart, the type distribution of components can be displayed through a pie chart, and the storage location of components can be displayed through a map or floor plan. Next, regarding the comprehensive display of inventory status, in addition to the basic inventory quantity, type distribution, and storage location information, the system should also provide other key information, such as the entry of components. The information such as warehouse time, validity period, batch number, etc., can help managers to have a more comprehensive understanding of the inventory status of components and provide a basis for subsequent decision-making. In terms of the display of quality trends, the system can display key information such as the distribution of component quality assessment results and the frequency of quality problems. Through visualization tools such as line graphs and scatter graphs, managers can intuitively see the changing trend of component quality, so as to timely discover potential quality problems and take corresponding measures to improve them. Regarding the interactive operation function, the data visualization interface supports users to perform interactive operations such as clicking, dragging, and zooming. These operations enable users to customize views according to their needs and obtain the required information more flexibly. For example, users can adjust the size and position of the chart by dragging.Zoom in to view more detailed or macro information. In terms of data screening and filtering functions, the system provides a variety of screening and filtering conditions to help users quickly locate component data of a specific time period, specific type or specific quality. These functions can greatly improve the efficiency of data query, allowing users to find the required information faster. Regarding real-time data reception and update, the data visualization module can receive and update data from the RFID system in real time, which means that the displayed content is always consistent with the actual situation. Managers can obtain the latest inventory status and quality information at any time. This function is crucial to ensure the accuracy and timeliness of information. Finally, regarding the real-time feedback function, the system provides real-time alarm functions, such as automatically issuing alarms when the inventory quantity is lower than the preset threshold. These alarms can remind managers to take timely measures, such as replenishing inventory, adjusting storage strategies, etc., to ensure that the supply and storage of components are always in the best state. ,

[0036] In a preferred embodiment, the present invention can be further configured as follows: based on the data displayed by the data visualization module, the management platform can automatically analyze the inventory status and quality trends of the components, and provide optimization suggestions for inventory management strategies for managers. These suggestions include but are not limited to inventory adjustments based on quality assessment results, procurement plan adjustments, etc., to help managers improve inventory turnover and reduce inventory costs. The management platform provides a wealth of data analysis tools, such as data mining, statistical analysis, trend prediction, etc., to help managers deeply explore the laws and trends behind the data. These analysis tools can reveal the root causes of component quality problems, predict future trends in inventory demand, etc., and provide strong support for managers' decision-making. The management platform adopts cloud computing and big data technology to achieve real-time storage, processing and analysis of data. Through the elastic computing of cloud computing and the distributed storage capabilities of big data, the system can process massive data and quickly generate analysis results to ensure the accuracy and timeliness of the data. At the same time, the on-demand payment model of cloud computing can also reduce the operating costs of enterprises and improve resource utilization efficiency. First of all, the management platform automatically analyzes the inventory status and quality trends of components, which is the key to improving inventory turnover and reducing inventory costs. The management platform can automatically analyze the inventory quantity, The information such as type distribution, storage location and quality assessment results can be used to accurately judge the inventory status and quality trend. These analyses include not only the assessment of the current inventory status, but also the prediction of future inventory demand changes. In terms of providing optimization suggestions for inventory management strategies, the management platform can provide managers with a series of specific optimization suggestions based on the analysis results. These suggestions include but are not limited to inventory adjustments based on quality assessment results (such as giving priority to components with poor quality to avoid long-term backlogs leading to further quality decline), procurement plan adjustments (such as adjusting procurement quantity and frequency based on future inventory demand forecasts to ensure inventory adequacy and economy), etc. These suggestions are designed to help managers improve inventory turnover, reduce inventory costs, and ensure the quality and supply of components. Next, regarding the specific application of data analysis tools, the management platform provides a wealth of data analysis tools, such as data mining, statistical analysis, trend prediction, etc. These tools can deeply explore the laws and trends behind the data, reveal the root causes of component quality problems, and predict future inventory demand changes. For example, through data mining technology, it can be found which types of components are more likely to have quality problems, so that corresponding preventive measures can be taken; through statistical analysis technology, the impact of different inventory strategies on inventory turnover can be evaluated;Through trend prediction technology, the inventory demand of components in the future can be predicted, providing a scientific basis for procurement plans. In terms of the implementation of cloud computing and big data technology, the management platform uses cloud computing and big data technology to support real-time storage, processing and analysis of data. The elastic computing power of cloud computing enables the system to dynamically adjust computing resources as needed to ensure high efficiency and stability when processing massive data. The distributed storage capacity of big data enables the system to store and manage large amounts of data while ensuring data reliability and security. Through the combination of cloud computing and big data technology, the management platform can quickly generate analysis results to ensure data accuracy and timeliness. In addition, the on-demand payment model of cloud computing can also reduce the operating costs of enterprises. Enterprises can pay according to actual usage, avoiding the high investment and maintenance costs of traditional IT infrastructure. This flexible payment model enables enterprises to use resources more efficiently and improve resource utilization efficiency. ;

[0037] The RFID tag and sensor integration module is the data acquisition front end of the entire system. By integrating RFID tags with various sensors (such as temperature sensors, humidity sensors, etc.) and attaching them to each building component, the system can capture the physical state changes of components during storage, transportation and use in real time, such as temperature fluctuations, humidity changes and other key parameters. These monitoring data are then automatically uploaded to the system background to provide a basis for subsequent analysis and evaluation. Next, the intelligent quality assessment algorithm module uses advanced machine learning algorithms to conduct in-depth mining and automatic analysis of the data collected by sensors in RFID tags. Through the trained model, the system can predict and evaluate the current quality status of building components and identify potential quality problems or performance degradation trends. This process not only improves the accuracy and efficiency of quality assessment, but also enables the system to detect and deal with quality problems in a timely manner to avoid further losses. Dynamic threshold setting and The intelligent early warning module dynamically adjusts the threshold of quality assessment according to different types of building components and their specific usage environment. This means that the system can flexibly set the upper and lower limits of quality standards according to the actual needs and conditions of the components. Once the quality status of the components is monitored to be lower or higher than the preset threshold, the system will immediately trigger the early warning mechanism, and promptly remind managers to take corresponding measures through text messages, emails or system notifications to ensure the quality and safety of the components. Finally, the data visualization and management platform module, as the core of the system, provides users with an intuitive and easy-to-use data visualization interface and management tools. Through visualization elements such as charts and dashboards, the system can comprehensively display key data such as inventory status, quality trends and early warning information of building components, helping managers to quickly understand the overall situation. At the same time, the management platform also supports real-time monitoring of data, historical data query, report generation and other functions, providing strong data support for decision-making.

[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0039] 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 building component storage management system based on RFID, characterized in that: It includes the following modules: RFID tag and sensor integration module, intelligent quality assessment algorithm module, dynamic threshold setting and intelligent early warning module and data visualization and management platform module; The RFID tag and sensor integration module is used to integrate the RFID tag with the sensor to obtain continuous monitoring of the physical status of the building components and upload this data to the system background; The intelligent quality assessment algorithm module is used for automatically analyzing the data collected by the sensor in the RFID tag using a machine learning algorithm to predict and assess the current quality status of the component; The dynamic threshold setting and intelligent early warning module is used to dynamically set the threshold of quality assessment according to different types of building components and their use environment, and automatically trigger an early warning when the quality of the component does not meet the preset standard; The data visualization and management platform module is used to provide users with a data visualization interface and a management platform to achieve real-time monitoring and optimization of the inventory status and quality trends of building components.

2. The RFID-based building component storage management system according to claim 1, characterized in that: In the RFID tag and sensor integration module, a special RFID tag is affixed to the building component before it is put into storage. A small sensor is also built in the module. The sensor type is selected according to the component characteristics and monitoring requirements, including but not limited to stress sensors, temperature sensors, humidity sensors, etc. The installation position of the sensor must ensure that it can accurately reflect the changes in the physical state of the component. The stress sensor should be installed at the stress-bearing part of the component, and the temperature sensor should be installed at a position inside or on the surface of the component that is easily affected by temperature.

3. The RFID-based building component storage management system according to claim 2, characterized in that: During the installation process, the sensor must ensure close integration with the component, and the sensor data is uploaded to the system background in real time through the wireless transmission function of the RFID tag. The data transmission adopts low-power wireless communication technology to ensure that the sensor maintains stable data transmission capabilities during long-term use.

4. The RFID-based building component storage management system according to claim 1, characterized in that: In the intelligent quality assessment algorithm module, a set of quality assessment algorithms based on machine learning are integrated in the system background. The algorithm can automatically receive and process data collected by sensors in RFID tags, including but not limited to physical state information such as temperature, humidity, vibration, etc. of building components. Secondly, the algorithm combines basic information such as material, size, production date, etc. of building components, and uses historical data and actual quality feedback for training and optimization. During the training process, the algorithm continuously adjusts model parameters to improve the accuracy of component quality assessment. Finally, the algorithm uses one of the machine learning algorithms such as support vector machine, random forest or deep learning to classify and regress sensor data. Through continuous iterative learning, the algorithm gradually optimizes the assessment model to improve the quality assessment ability of building components of different types, materials and sizes.

5. The RFID-based building component storage management system according to claim 4, characterized in that: The algorithm can analyze the collected sensor data in real time according to preset quality standards or thresholds to determine whether the building components meet the quality requirements. The algorithm can also generate a quality assessment report, which includes but is not limited to information such as the quality grade of the components, potential quality problems and recommended measures. The algorithm also has self-learning and optimization capabilities. By continuously collecting new sensor data and actual quality feedback, the algorithm can continuously optimize the assessment model and improve the accuracy and reliability of the assessment. At the same time, the algorithm can also adapt to the quality assessment needs of building components of different types and different application scenarios.

6. The RFID-based building component storage management system according to claim 5, characterized in that: The dynamic threshold setting and intelligent early warning module is provided with a threshold setting interface, through which the user can input or select key information such as the material, size, use environment, and service life of the component. The system automatically calculates and recommends a reasonable threshold range based on this information. At the same time, the user can also manually adjust the threshold according to actual conditions to ensure that the evaluation criteria are highly matched with actual needs. In addition, the system can continuously collect and analyze the usage and quality feedback of the component, and automatically adjust the threshold to adapt to the actual performance changes of the component.

7. The RFID-based building component storage management system according to claim 6, characterized in that: The dynamic threshold setting and intelligent early warning module can automatically start the quality assessment process when the RFID system recognizes that the component is about to be shipped out. The assessment process includes reading the sensor data in the RFID tag, analyzing the key indicators of the component such as stress, temperature, humidity, etc., and comparing them with the preset thresholds. If the assessment result shows that the quality of the component does not meet the preset standards, the intelligent early warning module will immediately trigger the early warning mechanism, prevent the component from being shipped out, and notify relevant personnel through the system interface, text messages, emails, etc. for further inspection or processing. The early warning information includes but is not limited to the component's ID, location, quality problems and recommended measures, etc., so as to respond quickly and take measures. In addition, the intelligent early warning module also has the function of customizing early warning standards. Users can set specific early warning standards in the system according to actual needs, such as the percentage of stress exceeding the limit, the range of temperature being too high or too low, the threshold of excessive humidity, etc., to ensure that the early warning mechanism can accurately reflect the actual quality status of the component.

8. The RFID-based building component storage management system according to claim 1, characterized in that: The data visualization and management platform module provides a user data visualization interface, which displays the entire life cycle records of building components such as warehousing, storage, quality assessment and delivery in an intuitive and clear manner. Through various visualization tools such as charts, graphs, dashboards, etc., the inventory status of components is fully displayed, including but not limited to key information such as inventory quantity, type distribution, storage location, etc. At the same time, the system can also display the quality trend of components. In addition, the system also provides data screening and filtering functions to help users quickly locate component data of a specific time period, specific type or specific quality. The data visualization module can receive and update data from the RFID system in real time to ensure that the displayed content is always consistent with the actual situation. At the same time, the system can also provide real-time feedback functions, such as automatically issuing an alarm when the inventory quantity is lower than the preset threshold, reminding management personnel to take timely measures.

9. The RFID-based building component storage management system according to claim 1, characterized in that: Based on the data displayed by the data visualization module, the management platform can automatically analyze the inventory status and quality trends of components, and provide managers with optimization suggestions for inventory management strategies. The management platform adopts cloud computing and big data technologies to realize real-time storage, processing and analysis of data. Through the elastic computing of cloud computing and the distributed storage capabilities of big data, the system can process massive data and quickly generate analysis results to ensure the accuracy and timeliness of the data. At the same time, the pay-as-you-go model of cloud computing can also reduce the operating costs of enterprises and improve resource utilization efficiency.

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