Cement tank management system and method integrating intelligent scheduling and quality monitoring

Abstract

The invention discloses a cement tank management system integrating intelligent scheduling and quality monitoring. The cement tank management system comprises a cement quality detection module, a storage environment monitoring module, a cement tank weighing module, a data receiving and processing module, a central algorithm module, a man-machine interaction display module, a feedback control hardware module and a cloud platform module. The invention also discloses a method used by the cement tank management system integrating intelligent scheduling and quality monitoring, and the system integrates the cement quality detection module, the storage environment monitoring module, the weighing monitoring module, the data processing module, the intelligent scheduling module, the cloud management module and the like. A full-period management system covering warehousing, storage, discharging, scheduling and distribution is constructed, intelligent, automatic and refined management of cement tanks from quality monitoring to supply scheduling is achieved, the visualization level and management efficiency of a cement supply chain are greatly improved, and intelligent monitoring and management of the whole process of cement are achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field, and particularly relates to a cement tank management system and method integrating intelligent scheduling and quality monitoring. BACKGROUND

[0002] Cement is an important basic raw material in foundation engineering and is widely used in cement mixing pile, post grouting, grouting anchor, high-pressure jet grouting pile and other processes. Cement tank trucks transport bulk cement from cement plants to construction sites and store it through cement tanks. The cement tank delivers cement to the mixing back-end through a feeder to make cement slurry, and plays an important role as a temporary storage warehouse at the construction site. However, the traditional cement tank is only used for storage, and there are many management problems. First, the quality of cement cannot be monitored in real time, and even the situation of drivers stealing raw materials has occurred; second, the storage environment is unstable, and the cement in the tank may be damp and clumpy or deteriorated; in addition, the supply time is not accurate, and the inventory management is chaotic, and other problems often occur. These problems not only affect the efficiency of cement use, but also may cause construction quality problems and cost waste.

[0003] At present, the cement management at the construction site relies on manual quality monitoring and inventory management, but due to the lack of intelligent real-time scheduling and feedback mechanism, it is impossible to accurately predict cement demand and efficiently manage the supply chain. The traditional cement quality detection method mainly relies on manual sampling detection, often needs to conduct compression tests, and cannot evaluate the quality of cement in real time, nor can it comprehensively monitor the chemical composition of cement. In addition, the humidity, temperature and other environmental factors of the cement warehouse have a great influence on the quality of cement, often causing the cement to be damp, clumpy or deteriorated, thereby affecting the construction progress and engineering quality.

[0004] Therefore, an integrated intelligent scheduling and quality monitoring cement tank management system is urgently needed, which can improve the overall efficiency and response capability of the cement supply chain, reduce operating costs, and ensure the stability of cement quality, and accurately meet the construction demand through real-time monitoring, dynamic scheduling and intelligent algorithm optimization prediction. SUMMARY

[0005] The present application aims to provide an integrated intelligent scheduling and quality monitoring cement tank management system to solve the problems raised in the background. The integrated intelligent scheduling and quality monitoring cement tank management system provided by the present application has the characteristics of improving the overall efficiency and response capability of the cement supply chain, reducing operating costs, and ensuring the stability of cement quality, and accurately meeting the construction demand through real-time monitoring, dynamic scheduling and intelligent algorithm optimization prediction.

[0006] Another object of the present application is to provide a method used by the integrated intelligent scheduling and quality monitoring cement tank management system.

[0007] To achieve the above object, the application provides the following technical scheme: a cement tank management system integrating intelligent scheduling and quality monitoring, comprising:

[0008] a cement quality detection module, which is installed at the bottom of the cement tank, monitors the chemical components of cement in real time through near-infrared spectroscopy analysis technology (NIR), transmits data through a data receiving and processing module, and feeds back to the central algorithm module in real time to ensure that the cement quality meets the construction requirements and avoid affecting the project progress due to cement quality problems;

[0009] In this module, near-infrared spectroscopy analysis technology (NIR) is a modern analysis technology based on the differences in optical properties such as absorption, reflection, and transmission of near-infrared light (wavelength range 780nm to 2526nm) by substances to analyze the composition of substances and determine related properties. When near-infrared light shines on a cement sample, organic and inorganic molecules in the cement (such as C-H, N-H, O-H, etc.) will selectively absorb light of a specific wavelength, resulting in the generation of characteristic absorption peaks. When the difference between the photon energy of near-infrared light and the energy level of molecular vibration matches, the molecule will transition from the ground state to the excited state and absorb light of a specific wavelength. Common chemical components in cement, such as calcium oxide (CaO), silicon dioxide (SiO2), and aluminum oxide (Al2O3), all have significant near-infrared light absorption properties, making NIR technology an effective tool for analyzing the chemical composition of cement.

[0010] In this module, among the chemical components of cement raw materials, calcium carbonate, silicon dioxide, aluminum oxide, and iron oxide are the main components. To accurately detect these components, a near-infrared spectroscopy detection system is used, and strict sample loading specifications are followed. The collected near-infrared diffuse reflectance absorption spectrum data has a wavenumber range of 10000-4000cm -1 interval, a resolution of 4cm -1 , and a scanning frequency of 64 times, ensuring high-quality spectral data.

[0011] Specifically, the calcium oxide sample exhibits significant absorption peaks near 5110cm -1 and 7080cm -1 , and weak absorption characteristics near 4270cm -1 . The silicon dioxide sample has obvious absorption peaks at 5190cm -1 and 4500cm -1 , and only weak absorption near 7082cm -1 . The aluminum oxide sample shows absorption peaks near 5190cm -1 and 7200cm -1 , and weak absorption near 4470cm -1There is a weak absorption. The spectral characteristics of the iron trioxide sample are relatively simple, with only a weak absorption peak at 5160 cm -1 near 4520 cm -1 These characteristic absorption peaks provide key evidence for subsequent accurate identification and quantitative analysis of various oxide components in cement raw materials.

[0012] In this module, by collecting the near-infrared spectral data of the cement sample, and with the help of chemometrics methods (such as principal component analysis PCA and machine learning algorithms, etc.), quantitative analysis models between spectral data and cement chemical composition can be established. These models can accurately analyze the main components in cement, even simultaneously analyze multiple components, and fully grasp the chemical composition of cement. Compared with traditional single-component analysis methods, NIR technology has the advantages of high efficiency, rapidity, non-destructive, etc., greatly improving the accuracy and efficiency of cement quality detection.

[0013] Specifically, NIR technology has significant technical advantages:

[0014] (1) Non-destructive testing: NIR technology can perform real-time monitoring without damaging the physical structure of cement raw materials or cement samples, avoiding material waste caused by sampling damage.

[0015] (2) Rapid analysis: NIR technology can complete the analysis of cement samples in a few seconds to a few minutes, significantly shortening the analysis time compared to traditional chemical analysis methods, and has the ability of real-time and continuous monitoring, especially suitable for immediate component feedback and process adjustment in the cement production process.

[0016] (3) Multi-component analysis: NIR can simultaneously analyze multiple components such as calcium oxide (CaO), aluminum oxide (Al2O3), and silicon oxide (SiO2), improving analysis efficiency, reducing experimental steps, and reducing errors.

[0017] (4) Simple operation: NIR operation is simple, reducing the dependence on chemical reagents, and meeting the requirements of green and environmentally friendly production.

[0018] (5) Good adaptability and repeatability: NIR technology can provide reliable analysis data under different material states and environmental conditions, ensuring long-term stability and efficiency of quality control in cement production.

[0019] The storage environment monitoring module is composed of multiple sensors installed at different parts of the cement tank, which monitors the temperature, humidity, and pressure of the cement tank in real time, ensuring that the cement is not affected by changes in the external environment during storage, preventing the cement from being damp, caking, or deteriorating, thereby ensuring the long-term quality stability of the cement;

[0020] The cement tank weighing module is installed at the bottom of the supporting legs of the cement tank, and uses a high-precision weighing sensor to monitor the real-time weight of the cement tank, so as to obtain accurate weight data of the cement tank, ensure that the amount of each discharge meets the actual demand, avoid errors, and thus ensure the accuracy and stability of the cement supply.

[0021] In this module, the weighing sensor can be an electronic weighing sensor, which is usually a strain sensor and can accurately measure the weight of the cement in the cement tank by sensing pressure changes. The weighing data is transmitted in real time to the central algorithm module through the data receiving and processing module using Modbus, CAN or analog communication protocol. The central algorithm module further calibrates and analyzes the data to ensure the accuracy of the weighing data. The weighing module uses high-precision sensors with an accuracy of 0.1%, and is designed with protective measures to work stably in harsh environmental conditions such as high temperature, high humidity and high dust, ensuring the high reliability of the weighing results. Through this module, the system can accurately monitor the real-time weight change of the cement in the cement tank, so as to ensure that the amount of each discharge meets the actual demand and avoid waste or insufficient supply of cement due to errors.

[0022] The data receiving and processing module is used to receive and integrate the raw data from the cement quality detection module, the storage environment monitoring module and the cement tank weighing module. The interaction between hardware modules and software is carried out through Modbus TCP communication protocol for data transmission to ensure real-time and data reliability, and data preprocessing, denoising and verification operations are performed to transmit the processed data to the central algorithm module.

[0023] In this module, the spectral data processing of cement raw materials faces complex interactions, which may generate redundant information. These information mainly come from light absorption, scattering effects, particle non-uniformity, environmental interference and temperature fluctuations. These factors reduce the signal-to-noise ratio and may cause multicollinearity problems, which affect the accuracy of quantitative analysis. To solve these problems, spectral preprocessing technology is essential. Common preprocessing methods such as multiple scattering correction (MSC), standard normal variate (SNV), first derivative (FD) and smoothing (SG) each have their own advantages, but a single method often cannot cope with complex interference. Therefore, combining multiple preprocessing methods can achieve better results. The present invention integrates multiple preprocessing techniques and combines orthogonal sequential preprocessing (SO-PLS) to effectively eliminate redundant information and significantly improve the performance of the prediction model. This method can significantly reduce spectral noise interference, optimize feature wavelength selection, and enhance the robustness and generalization ability of the model.

[0024] The central algorithm module receives the processed data from the data receiving processing module, and performs real-time comprehensive analysis, optimization and prediction on multiple key data such as cement quality, storage environment, inventory and temperature and humidity changes based on a multi-dimensional algorithm model. Through a feedback control algorithm, the central algorithm module generates corresponding control signals according to the analysis results, and transmits the control instructions to the feedback control hardware module to form a closed-loop control. The central algorithm module also integrates a machine learning algorithm, which can continuously learn feedback from real-time operation data, automatically optimize cement dispatching and quality control, and generate dispatching instructions and warning information based on the optimization results, and guide the system to perform intelligent scheduling and operation, thereby improving the efficiency of cement supply and ensuring the stability and accuracy of system operation.

[0025] In this module, the selection and optimization of characteristic wavenumbers in near-infrared spectroscopy (NIR) for cement composition analysis is a key step to improve the prediction accuracy of the cement raw material composition analysis model. After spectral preprocessing, some wavenumber variables may have little contribution to the target component information or be redundant, affecting the performance of the model. Therefore, multiple feature wavelength selection methods are used to optimize the model. The successive projections algorithm (SPA) selects wavelengths that are linearly independent of the selected variables to construct the optimal feature wavelength subset, reducing redundant information and effectively reducing the dimensionality of the spectral data; the uninformative variable elimination method (UVE) identifies and removes uninformative variables that have little contribution to the composition analysis by introducing noise variables; the backward interval partial least squares-genetic algorithm (BPLS-GA) combines genetic algorithm to optimize feature selection, remove redundant and irrelevant variables, and further improve the stability and prediction performance of the model.

[0026] In the establishment and optimization process of the correction model, the present application combines the optimized feature wavenumber selection and spectral preprocessing method, and uses the partial least squares regression (PLS) model to fit the data. The specific optimization steps include: using the joint X-Y distance method to divide the cement raw material samples reasonably, ensuring the representativeness and diversity of the sample set, and improving the generalization ability of the model; using the absolute deviation F test method of cross-validation to exclude abnormal samples and reduce errors caused by sample quality problems; combining multiple preprocessing methods and feature selection algorithms to select the optimal combination, and evaluating the model performance through multiple cross-validation.

[0027] In this module, when the near-infrared spectroscopy detects that the cement composition or environmental conditions do not meet the preset values, the system will automatically trigger the anomaly detection mechanism and provide real-time feedback. The central algorithm module receives real-time spectral data and calculates the cement composition (such as calcium oxide (CaO), silicon dioxide (SiO2), aluminum oxide (Al2O3), and iron oxide (Fe2O3)) and the temperature, humidity, and pressure of the cement tank, and then compares these data with the preset standard values. If the values of key components in the cement, temperature, humidity, or pressure deviate from the normal range, the system will automatically trigger an alarm according to the set threshold. The alarm information will not only be displayed in real-time on the local human-machine interaction system, but also be pushed to the terminal operator through the cloud platform to ensure timely response. The system also conducts in-depth analysis of abnormal data to identify potential influencing factors such as raw material quality fluctuations, environmental changes, etc., and records these analysis results to provide references for subsequent quality control and production optimization decisions. Through this intelligent monitoring and control mechanism, the system can accurately control the cement production process, ensure the stability and consistency of product quality, improve production efficiency, and effectively reduce the risk of quality fluctuations.

[0028] In this module, when the system detects that the temperature, humidity, or pressure in the cement tank exceeds the set threshold, the central algorithm module will trigger the feedback control mechanism for timely adjustment. First, the central algorithm module receives data from the sensors and compares the real-time measured temperature, humidity, and pressure values with the preset standard range. If a certain environmental parameter exceeds the set upper and lower threshold, the system will calculate the deviation value, i.e., the difference between the actual measured value and the target value. According to these deviation values, the feedback control algorithm will generate corresponding control signals and transmit them to the feedback control hardware module, which will then adjust the temperature and humidity adjustment devices or pressure adjustment equipment in the cement tank. The control signals will guide these devices to operate to ensure that the temperature and humidity and pressure return to the predetermined range. This control process is a closed-loop process, meaning that whenever the control device makes an adjustment, the system will continue to monitor environmental changes and provide real-time feedback on the current situation. In this way, the system can continuously optimize adjustments to ensure that temperature and humidity and pressure changes always remain within the set range, avoiding negative impacts on the cement production process. Through this simple feedback control mechanism, the system can effectively respond to environmental fluctuations in the cement tank, maintain the stability of cement quality during storage, and improve production efficiency.

[0029] In this module, the system will automatically notify the administrator and generate specific cement delivery information after generating the scheduling instructions. By real-time calculation of demand, inventory status and production progress, the system can accurately determine the cement model, quantity and delivery time that needs to be delivered. After receiving the scheduling instructions, the administrator will automatically generate detailed delivery tasks, including specifying the cement model, weight, delivery location and time, and will immediately notify the relevant suppliers or manufacturers through the system. Suppliers will arrange production and transportation according to these instructions to ensure timely delivery of cement to the designated location. The system will also track the delivery status in real time and monitor the transportation process. Once any delay or abnormality is found, the system will immediately issue a warning to the administrator to ensure timely adjustments and avoid production delays. In addition, all delivery tasks and status data will be recorded and archived for future queries and optimization. Through this automated scheduling and delivery management system, it can ensure efficient and stable operation of the cement production and supply chain, minimize human intervention, and improve overall production efficiency and supply chain response speed.

[0030] Feedback control hardware module, which receives control instructions output by the central algorithm module, controls the ventilation device, heating system or humidity adjustment system to adjust the temperature and humidity in the cement tank;

[0031] Human-computer interaction display module, which is used to display the real-time status of the cement tank, including cement quality, inventory quantity and environmental data information. The operator can query, set and adjust data and operate equipment through this module. The module also supports real-time monitoring and control interface to ensure that the operator can obtain the latest data and status of the system operation at any time on site, facilitating timely adjustment and optimization of management;

[0032] Cloud platform module, which is used to receive data from all modules through 5G by the central algorithm module and transmit the data to the cloud. The cloud platform stores, backs up and analyzes the data and provides a remote access interface to support cross-device data sharing and intelligent analysis, forming a multi-dimensional data management system to realize full-cycle management of cement in and out of the warehouse and automatically generate detection reports to support subsequent analysis, tracking and decision-making, ensuring efficient management and continuous optimization of the cement supply chain.

[0033] In this module, the cloud platform module serves as the core data processing and management center of the system, responsible for data storage, analysis, and optimization. Through the 5G network, the cloud platform can transmit data such as cement quality analysis, inventory management, and environmental monitoring to the cloud in real time, ensuring high-speed synchronization and updating of data, providing real-time support for decision-making, and promoting seamless information integration and optimized processing. The platform also provides remote access interfaces, allowing operators and managers to query and monitor data at any time regardless of their location, breaking geographical limitations and enhancing work flexibility. It supports cross-device sharing and multi-terminal access, ensuring efficient collaboration among different roles. In addition, the cloud platform automatically generates detailed detection reports, aggregates production, quality control, and supply chain data, and supports trend prediction, problem tracking, and decision-making through intelligent analysis. Through automated reporting and real-time analysis, managers can quickly adjust production strategies, providing decision support for long-term optimization, ensuring smooth operation of the cement supply chain, and promoting continuous optimization of the production process.

[0034] Further in the present application, the cement quality detection module includes a near-infrared spectrum sensing unit, a data acquisition unit, and a spectrum analysis unit. The near-infrared spectrum sensing unit is installed at the inlet pipe of the cement tank or on the upper side wall of the cement tank, used for online spectrum scanning of the cement sample flowing through during the cement feeding process, to realize real-time monitoring and identification of the chemical composition of the cement. The spectrum analysis unit compares and extracts features of the near-infrared spectrum data based on a pre-set cement composition database, to identify the main chemical components and their proportions in the cement. The data acquisition unit transmits the analysis results to the data receiving and processing module, and the central algorithm module combines historical spectrum feature data to perform model fitting and trend analysis, to determine the cement quality and type. When the analysis results do not meet the system-set quality standards or cement type threshold, the central algorithm module automatically triggers a warning mechanism, sends abnormal prompt information to the human-computer interaction display module, and synchronizes the detection results to the remote monitoring end or the supplier system through the cloud platform module, for quality review and traceability.

[0035] Further in the present application, the storage environment monitoring module includes a temperature and humidity sensing unit, a pressure sensing unit, and an environmental data analysis unit. The temperature and humidity sensing unit is installed at the upper, middle, and lower positions of the cement tank body, used for real-time monitoring of the temperature and humidity distribution at different layers inside the cement tank, to obtain environmental change data. The pressure sensing unit is installed at the top of the cement tank, used for monitoring the tank pressure. The environmental data analysis unit comprehensively analyzes the collected temperature, humidity, and pressure data, to determine whether the cement storage environment is within the set safety range. When detecting that the humidity is out of standard, the temperature is abnormal, or the air pressure is unbalanced, the system automatically generates a warning signal through the central algorithm module and triggers the corresponding control strategy, including starting the tank ventilation device, suspending the feeding or discharging operation, to prevent the cement from being damp, caking, or deteriorating, and ensure the stability of the storage environment.

[0036] In this module, the arrangement of the temperature and humidity sensing unit is carefully designed to ensure accurate monitoring of the temperature and humidity changes at different levels within the cement tank. The sensing unit is installed at the upper, middle, and lower parts of the cement tank, effectively covering the entire tank. Through this layout, the system can monitor the temperature and humidity distribution from top to bottom in real time, avoiding the impact of local temperature and humidity anomalies on cement quality. For example, the top is more affected by the external environment, while the bottom may be different due to cement accumulation. Multi-point monitoring helps to timely discover these differences and avoid problems such as cement clumping or deterioration affecting project quality.

[0037] In this module, the pressure sensing unit is installed at the top of the cement tank to ensure accurate monitoring of the pressure changes within the tank. By detecting the air pressure in real time, the system can timely detect air pressure anomalies and respond to prevent storage problems caused by air pressure imbalance. Especially during the loading of the cement tank, it can effectively prevent the tank from exploding due to excessive pressure, and assist the tank ventilation device to maintain positive pressure inside the tank, ensuring the stability of the storage environment.

[0038] In the present application, further, the feedback control hardware module includes an adsorption drying unit and a hot air circulation unit arranged below the cement tank and connected to the top of the cement tank through a pipeline reflux; the adsorption drying unit absorbs the moisture in the cement tank to reduce humidity and accelerate evaporation of moisture; the hot air circulation unit uniformly distributes hot air to further promote evaporation of moisture, ensuring stable cement quality; at the same time, the cement tank is designed with a cold bridge prevention structure and a sealing device to reduce the entry of external moisture, ensuring stable cement quality.

[0039] In the present application, further, the central algorithm module automatically controls the following functions when it detects that the humidity inside the tank output by the storage environment monitoring module exceeds the set threshold, or when the ambient temperature is low and the risk of condensation on the tank wall is high:

[0040] (1) Start the adsorption drying unit to absorb the moisture in the cement tank, reduce the humidity, and accelerate the evaporation of moisture;

[0041] (2) Start the hot air circulation unit to uniformly distribute hot air in the cement tank, further accelerate the evaporation of moisture, and ensure stable cement quality;

[0042] (3) Through the pipeline reflux mechanism, the moisture at the top of the cement tank is circulated with the moisture below to ensure uniform distribution of hot air and accelerate the removal of moisture;

[0043] (4) Maintain a slight positive pressure in the cement tank to prevent external moisture from flowing back and ensure a stable environment inside the cement tank; if the humidity exceeds the threshold and meets the preset threshold, an early warning mechanism is automatically triggered, and the abnormal information is sent to the remote operator through the cloud platform, facilitating timely intervention and taking necessary measures to ensure that the environment inside the cement tank always meets the requirements.

[0044] Further in the present application, the central algorithm module adopts a long short-term memory artificial neural network model (LSTM) for cement demand prediction, which is based on the following input data: cement consumption data, construction progress and daily project planning, and historical cement usage construction data. By analyzing these historical data and real-time data, the future cement demand is predicted, and based on the prediction results, the cement supply plan is optimized, and the cement transportation is automatically scheduled, the inventory level is adjusted, and the delivery time arrangement is optimized;

[0045] In the model training phase, the system adopts a supervised learning method, taking the actual remaining amount in the cement tank when the cement tanker arrives as the target output value. This output reflects the instant cement consumption level and replenishment demand of the construction site, and is an important indicator for evaluating the rationality of the cement supply plan. By correlating the output value with historical consumption data, construction progress, weather conditions, and transportation records, the LSTM neural network model can effectively capture the time series characteristics and nonlinear change rules in the cement usage process, thereby achieving high-precision prediction of future cement demand and tank remaining amount.

[0046] To prevent overfitting and improve the generalization ability of the model, Dropout regularization and Batch Normalization strategies are introduced during training. At the same time, the model uses adaptive learning rate optimization algorithms such as Adam to speed up network convergence and improve training efficiency.

[0047] In the model deployment phase, the system inputs real-time construction data into the trained LSTM model, combines the latest inventory information, transportation status, and on-site material usage rate to dynamically generate cement demand prediction values for the future period. The central algorithm module automatically optimizes the supply chain scheduling strategy based on the prediction results, including cement dispatching plan and inventory replenishment scheme, thereby achieving dynamic balance between cement supply and on-site demand, improving resource utilization and supply efficiency.

[0048] In addition, the system has self-learning ability and will continuously collect the deviation data between actual consumption and prediction value, periodically retrain and update the LSTM model, ensuring long-term stable operation of the model and maintaining high-precision prediction performance.

[0049] According to the prediction results, the central algorithm module automatically adjusts the cement distribution, transportation, and inventory management strategy, which improves the cement supply efficiency in the following ways:

[0050] (1) Automatically scheduling cement transportation arrangements to ensure that cement transportation and supply are synchronized with construction progress;

[0051] (2) Dynamically adjusting inventory levels to avoid cement overstock or shortage and ensure continuous delivery;

[0052] (3) Optimize the delivery schedule, reduce delays, and ensure that the construction is completed on time;

[0053] At the same time, the central algorithm module transmits data to the cloud platform in real time through 5G, and the cloud platform continuously optimizes the long short-term memory artificial neural network model by receiving real-time data and using a feedback control mechanism; when new cement consumption data arrives, the cloud platform will adjust the parameters of the long short-term memory artificial neural network model in real time, and update the model prediction results according to real-time data; through learning from historical data and real-time feedback, the cloud platform continuously improves the prediction accuracy of the model, and real-time issues the optimized strategy to the central algorithm module to ensure that the cement supply system can effectively respond to demand fluctuations; when the system detects demand fluctuations or environmental changes, the long short-term memory artificial neural network model can quickly adjust the prediction and optimize the supply chain strategy accordingly, ensuring that the cement supply system is always in the best state, thereby maximizing resource utilization and reducing costs.

[0054] Further in the present application, a method for a cement tank management system integrated with intelligent scheduling and quality monitoring, characterized by comprising the following steps:

[0055] S1, when bulk cement enters the warehouse through the inlet pipeline, the near-infrared spectrum sensing unit installed at the inlet pipeline monitors the chemical composition of the cement in real time and captures the spectral data of the cement sample; the real-time analysis result is transmitted to the central algorithm module for further processing and quality evaluation; if the analysis result does not meet the set quality standard or cement type threshold, the central algorithm module will automatically trigger the early warning mechanism, send an abnormal prompt to the human-computer interaction display module, and synchronize the detection result to the remote monitoring end or the supplier system through the cloud platform for quality review and traceability processing;

[0056] S2, the storage environment monitoring module monitors the temperature, humidity and pressure data inside the cement tank in real time, and automatically adjusts the humidity control system according to these environmental data; the system will judge the environmental state inside the cement tank according to real-time data, and automatically start the corresponding humidity control measures such as activating the ventilation device or adjusting the heating system when detecting that the humidity is out of standard, the temperature is abnormal or the air pressure is unbalanced, to keep the environment inside the cement tank stable and prevent the cement from being damp or caking;

[0057] S3, According to the real-time acquisition of cement tank weighing module, the cement weight data is combined with the engineering progress, daily engineering planning and historical cement usage information, and the central algorithm module predicts the future cement demand through a long short-term memory artificial neural network model; the long short-term memory artificial neural network model automatically generates a cement demand prediction result by analyzing the time sequence mode of historical cement consumption data, real-time construction progress and daily cement usage plan; the system automatically optimizes the cement supply plan according to the predicted cement demand, and generates accurate transportation and inventory management strategies;

[0058] S4, Data is transmitted to the cloud platform in real time through the 5G network, and the cloud platform uses historical data and real-time feedback to continuously optimize prediction accuracy and improve the adaptive ability of supply chain management strategies; the optimized strategy will be quickly fed back to the central algorithm module to ensure efficient operation of the cement supply chain; in addition, the cloud platform is also responsible for data storage, backup and analysis, provides a remote access interface, supports cross-device data sharing and intelligent analysis, builds a multi-dimensional data management system, and fully realizes the whole cycle management of cement in and out of the warehouse, and automatically generates detection reports to support subsequent analysis, tracking and decision-making.

[0059] Further in the present application, S3, according to the predicted cement demand, the cement supply plan is automatically optimized, and accurate transportation and inventory management strategies are generated, including the following steps:

[0060] S31, Prediction and scheduling:

[0061] S311, Inventory optimization: According to the predicted cement demand, the system automatically adjusts the inventory level to avoid cement overstock or shortage, and ensures that the construction is carried out on time; the system will analyze historical data, construction plans and current inventory to generate dynamic inventory scheduling instructions;

[0062] S312, Transportation arrangement: According to the predicted demand, the system automatically schedules cement transportation arrangement to ensure that cement distribution and construction progress are synchronized; for example, when the demand is large, the system will preferentially schedule more transport vehicles;

[0063] S313, Supply time optimization: The system will optimize the supply time arrangement according to the predicted demand and construction progress to ensure that each supply can meet the construction demand on time;

[0064] S32, Real-time data support:

[0065] S321, Operator interface: The system provides real-time data support, and through the man-machine interaction display module, the operator can view the predicted cement demand, current inventory and transportation plan information;

[0066] S322, supply and transportation data: operators can understand the progress of supply, transportation status and estimated arrival time data through real-time monitoring interface, to ensure the transparency and response speed of system operation.

[0067] Further in the application, in S4, the implementation method of subsequent analysis, tracking and decision-making includes the following steps:

[0068] S41, real-time adjustment and optimization:

[0069] S411, feedback mechanism: the system transmits real-time data to the cloud platform through 5G, the cloud platform analyzes the newly acquired cement consumption data and construction progress data, and optimizes the long short-term memory artificial neural network model according to the analysis result; after each new data feedback, the long short-term memory artificial neural network model automatically adjusts the prediction result to improve the accuracy and reliability of subsequent prediction;

[0070] S412, cloud platform function: the cloud platform continuously optimizes the prediction accuracy to ensure that the prediction model is adjusted and updated according to real-time feedback; the cloud platform also provides data storage, analysis and backup functions, supports cross-device data sharing and intelligent analysis, real-time monitoring of cement supply chain status, and ensures that the supply system runs in the best state;

[0071] S413, early warning and emergency response: when there is a problem with cement supply or the system detects an anomaly, the central algorithm module will automatically analyze the relevant data and trigger the early warning mechanism through the cloud platform; the cloud platform will analyze the cement inventory, transportation progress and construction demand data in real time and generate detailed warning information; the warning information will be immediately pushed to the mobile devices or computer terminals of the remote operators through the 5G network; after receiving the warning, the operator can view the problem details in real time, including inventory level, transportation delay and inaccurate demand prediction information.

[0072] Compared with the prior art, the application has the following advantages:

[0073] 1. The application integrates cement quality detection, storage environment monitoring, weighing monitoring, data processing, intelligent scheduling and cloud management modules to build a full-cycle management system covering "storage-in, storage, discharge, scheduling and distribution", realizing intelligent, automated and fine management of cement tanks from quality monitoring to supply scheduling, greatly improving the visualization level and management efficiency of the cement supply chain, and realizing intelligent monitoring and management of the whole cement process.

[0074] 2、The cement chemical composition online detection and real-time evaluation are realized by adopting near-infrared spectrum analysis technology (NIR) combined with chemometrics algorithm, abnormal batches or quality fluctuations can be identified in time, when the cement quality deviates from the standard, the system automatically sends out early warning and links the cloud platform, avoids unqualified materials flowing into the construction link, improves the engineering quality and safety, realizes the real-time detection and automatic early warning of cement quality.

[0075] 3、The cement tank environment is accurately monitored by the multi-point distributed temperature, humidity and pressure sensing units, combined with the adsorption drying and hot air circulation device, the tank environment can be automatically adjusted when the humidity exceeds the standard or the risk of condensation appears, the feedback control ensures the stability of the tank temperature and humidity, prevents the cement from being damp, caking or deteriorating, prolongs the storage period and guarantees the discharge quality, realizes the environment monitoring and dynamic regulation and control to ensure the storage stability.

[0076] 4、The central algorithm module of the application introduces a long short-term memory artificial neural network model, analyzes multi-source data such as historical consumption data, construction progress, engineering plan and weather conditions, takes the actual remaining amount when the cement tank truck arrives as the supervision target, realizes high-precision prediction of future cement demand and inventory changes, the model combines a self-learning mechanism to continuously optimize parameters, makes the supply plan dynamically adapt to the construction rhythm, avoids shortage or overstock, realizes intelligent demand prediction and scheduling optimization based on the long short-term memory artificial neural network model.

[0077] 5、The application automatically generates the warehouse and distribution plan based on the long short-term memory artificial neural network model prediction result, realizes intelligent optimization of cement transportation arrangement, inventory replenishment and supply time, through linkage with the cloud platform, the transportation progress can be monitored in real time and automatically warned in abnormal conditions, the supply chain response speed and coordination efficiency are significantly improved, realizing automatic supply and transportation scheduling.

[0078] 6、The application uploads the module data to the cloud platform in real time through the 5G network, realizes remote access, data storage, trend analysis and report generation, the cloud platform analyzes the real-time and historical data, provides data support and decision basis for construction site management and supply chain optimization, builds a high-reliability intelligent cement management system, realizes remote data collaboration and intelligent decision-making supported by the cloud platform. BRIEF DESCRIPTION OF DRAWINGS

[0079] Figure 1 The system block diagram of the application.

[0080] Figure 2 The method flowchart of the application.

[0081] Figure 3 The structure schematic diagram of the application.

[0082] In the figure: 1, cement tank; 2, cement tank weighing platform; 3, cement quality detection module; 4, pressure sensor; 5, temperature and humidity sensor; 6, man-machine interaction display module; 7, hot air circulation unit; 8, air compressor; 9, adsorption drying unit; 10, air storage tank. DETAILED DESCRIPTION

[0083] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0084] In the description of the present application, unless explicitly specified and limited, the terms "connected", "connected", "fixed" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or it can be integrated; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium; it can be the internal communication of two elements or the interaction relationship between two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0085] In the present application, unless explicitly specified and limited, the first feature "on" or "below" the second feature can include that the first and second features are in direct contact, or that the first and second features are not in direct contact but are in contact through another feature between them. Moreover, the first feature "on", "above" and "above" the second feature includes that the first feature is directly above and obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "below" and "below" the second feature includes that the first feature is directly below and obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0086] In the description of the present application, the terms "up", "down", "right", "left" and other orientation or position relationships are based on the orientation or position relationship shown in the drawings, and are only for the convenience of description and simplification of operation, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" are only used to distinguish in description, and have no special meaning.

[0087] Example 1

[0088] Please refer to Figures 1-2The embodiment provides the following technical scheme: a cement tank management system integrating intelligent scheduling and quality monitoring, comprising a cement quality detection module, a storage environment monitoring module, a cement tank weighing module, a data receiving and processing module, a central algorithm module, a man-machine interaction display module, a feedback control hardware module, and a cloud platform module. Specifically, through the coordinated work of near-infrared spectroscopy analysis technology (NIR), the storage environment monitoring module, the weighing module, the central algorithm module, and various other technologies, the system can realize real-time monitoring of cement quality, accurate control of storage environment, real-time management of inventory, and automatic intelligent scheduling according to demand prediction of the construction site.

[0089] Specifically, at the construction site, the cement truck first pours the cement into the cement tank 1 at the preset position. The cement tank 1 is stably placed on the cement tank weighing platform 2 or the cement tank leg is provided with a weighing sensor. At this time, the operator performs zero setting operation on the weighing platform and the cement tank through the handheld terminal module, to ensure that the system is in the initial state.

[0090] The weighing sensor is usually a strain sensor, which can accurately measure the weight of the cement in the cement tank by sensing the change in pressure. The weighing data is connected with the data receiving and processing module, and is transmitted in real time to the central algorithm module after being preprocessed by the data receiving and processing module using Modbus, CAN, or analog communication protocols. The central algorithm module further calibrates and analyzes the data to ensure the accuracy of the weighing data. The weighing module uses a high-precision sensor with an accuracy of 0.1%, and is designed with protective measures to work stably in harsh environmental conditions such as high temperature, high humidity, and high dust, ensuring high reliability of the weighing results.

[0091] The system monitors the weight of the cement tank 1 in real time, to ensure that the amount of cement delivered does not exceed the capacity of the cement tank 1. For example, if the capacity of the cement tank is 80 tons, when the cement truck delivers cement, the system displays that the weight in the cement tank is 75 tons and issues an alarm to remind the operator to avoid overloading and reduce potential risks.

[0092] Specifically, a near-infrared spectroscopy sensing unit 3 is installed at the inlet pipeline of the cement tank 1. When the cement is poured into the pre-set position of the cement tank 1, the near-infrared spectroscopy sensing unit 3 starts to monitor the chemical composition of the cement in real time. The spectral data of the cement is transmitted to the central algorithm module for processing through the data acquisition unit. The central algorithm module analyzes the main components of the cement in real time and compares them with the cement quality standards. If the spectral analysis result does not meet the set cement quality standards or model requirements, for example, the order shows that the required cement is 42.5 type, but the actual detection result shows that it is 32.5 type, the system will automatically trigger the early warning mechanism, send an abnormal prompt to the man-machine interaction display module 6, and synchronize the detection result to the remote monitoring end or the supplier system through the cloud platform for quality review and traceability processing.

[0093] Inside the cement tank 1, the temperature and humidity sensing unit 5 is installed at the upper, middle and lower positions respectively, and the pressure sensing unit 4 is installed at the top of the tank for real-time monitoring of the temperature and humidity distribution and pressure change inside the cement tank. The data collected by all sensors will be transmitted to the central algorithm module in real time. The system analyzes the environmental state in the cement tank according to these data and judges whether it is within the safe range to ensure the stability of the cement storage environment.

[0094] Before the cement tank truck delivers the cement to the cement tank 1, the system monitors the humidity of the cement entering the tank in real time to ensure that it meets the standard and prevent the cement from getting wet. If the humidity in the cement tank is detected to exceed the pre-set value, the system will automatically start the drying process to ensure that the quality of the cement is not affected. At the same time, the system continuously monitors the pressure in the cement tank during the delivery process to ensure that the pressure is maintained within a safe range. When the pressure exceeds the pre-set threshold, the system immediately issues an alarm to prevent the cement tank from exploding.

[0095] Specifically, during the cement storage process, the environmental data analysis unit analyzes the collected temperature, humidity and pressure data comprehensively to determine whether the cement storage environment is within the set safe range. Taking temperature as an example, if the relative humidity inside the cement tank 1 is higher than 70% and the temperature is lower than 10°C, the system will determine that the environment is not suitable for cement storage and trigger the early warning mechanism. For example, suppose that in a certain monitoring, the humidity in the cement tank 1 is higher than 72% and the temperature is 8°C, which exceeds the set safe range. At this time, the environmental data analysis unit will transmit these data to the central algorithm module, and the central algorithm module will generate a warning signal according to the analysis result and start the pre-set control strategy.

[0096] These strategies include:

[0097] 1. Suspend feeding or discharging operation: To prevent the cement from getting wet, caking or deteriorating, the system will suspend the feeding or discharging operation of the cement until the environmental conditions in the cement tank 1 return to the safe range.

[0098] 2. Activate the adsorption drying unit 9: This unit rapidly reduces humidity and accelerates moisture evaporation by adsorbing moisture from the cement tank 1. This process helps to quickly remove excess moisture and prevent the cement from becoming damp.

[0099] 3. Activate hot air circulation unit 7: This unit further accelerates the evaporation of moisture inside the cement tank by evenly distributing hot air, ensuring that the cement maintains stable quality during storage.

[0100] 4. Maintain a slight positive pressure inside the cement silo: The system maintains a slight positive pressure inside the cement silo to prevent backflow of external moisture and ensure a stable environment inside the silo. When the humidity continuously exceeds the standard and reaches the preset threshold, the system will automatically trigger an early warning mechanism and send the abnormal information to the remote operator via the cloud platform so that timely intervention measures can be taken to ensure that the environment inside the cement silo always meets the requirements and to ensure the stability of cement quality.

[0101] The cement silo weighing module 2 monitors the cement weight in real time using precise weighing sensors. The initial weight of cement silo 1 is 78 tons. The system continuously monitors changes in cement weight and combines this with construction progress, historical usage, and daily cement demand. Using a Long Short-Term Memory (LSTM) neural network model, it accurately predicts future cement demand. Historical cement consumption data for a construction project shows that cement usage over the past three days was 12.5 tons, 13.5 tons, and 13.3 tons respectively. Based on current weather conditions and construction progress, the LSTM model predicts that 13.2 tons of cement will be needed in the next 24 hours. The system automatically generates future cement demand based on this prediction and adjusts inventory levels in real time to ensure cement supply meets on-site needs. When the system detects that the current inventory is 10 tons while the predicted demand is 12 tons, it automatically activates a scheduling mechanism, adjusting transportation arrangements and prioritizing transport vehicles to ensure timely cement supply. By dynamically adjusting inventory levels, the system can avoid cement shortages, ensure continuous material supply to the construction site, reduce excessive cement reserves, and optimize inventory management.

[0102] The system uploads all data in real time through an IoT cloud platform, which continuously optimizes the prediction model using historical data and real-time feedback. Whenever new cement warehousing data enters the system, the cloud platform automatically updates the parameters of the LSTM model and adjusts the prediction results based on the latest data, ensuring a dynamic balance between cement supply and construction demand.

[0103] In the construction project, the system monitors in real-time that the daily estimated cement demand is 13 tons, and based on historical data and real-time weather conditions, it predicts that the remaining amount of cement at 8:00 am is 5 tons. The system immediately notifies the cement supplier A to complete the supply before noon. However, the system detects during monitoring that the transportation of supplier A is delayed, and the originally scheduled 70 tons of cement will arrive 8 hours later, causing the estimated afternoon downtime of 3 hours. To respond to this unexpected situation, the cloud platform immediately adjusts the transportation plan. According to real-time feedback, the system automatically starts the backup plan, prioritizing the supply vehicles of other cement tanks to this device, ensuring timely supply of cement while ensuring that the replaced cement tank still has enough remaining amount to support the workload of the day. Through this intelligent scheduling, the system effectively ensures that the cement demand at the construction site is not affected by the transportation delay, avoiding the risk of construction downtime or material shortage, minimizing the delay time, and ensuring the construction progress.

[0104] The system uploads the data of each module to the cloud platform in real-time through the 5G network. The cloud platform stores and backs up the data, which can record the data of each cement entering the warehouse, discharging, transportation, and quality monitoring, and generate detailed reports. The report content includes the quality analysis of each batch of cement, inventory changes, and discharge amount, which is convenient for subsequent query, audit, and optimization decision. At the construction site, the cement tank receives 75 tons of cement delivered by supplier A on that day. The system automatically generates an entry report when the cement enters the warehouse, which records the entry amount, composition analysis, transportation information, and entry time of each batch of cement in detail. For example, the system shows that the cement entry amount is 75 tons, and the near-infrared spectrum analysis shows that the main components of the cement are calcium oxide (CaO) 60%, silicon dioxide (SiO2) 25%, and aluminum oxide (Al2O3) 10%, which meets the standard of 42.5 type. The report also includes transportation information, which shows that the cement took 4 hours to transport from supplier A to the construction site and arrived 8 hours later. All these data are transmitted to the remote management end in real-time through the cloud platform, ensuring the accuracy and timeliness of the data. Through the remote platform, management personnel can view the detailed entry report at any time and conduct quality monitoring to ensure that the cement quality meets the construction requirements and adjust future inventory and supply plans based on real-time data.

[0105] To ensure the long-term stable operation of the system, the cloud platform continuously collects the deviation data between the actual remaining amount of cement in the cement tank and the predicted value, and performs periodic retraining and parameter updating. The LSTM model improves the accuracy of cement demand prediction through learning from historical data and real-time feedback, thereby optimizing the supply chain management and scheduling strategy, reducing costs, and improving resource utilization efficiency.

[0106] This embodiment enables precise monitoring of cement quality, automatic environmental adjustment, intelligent inventory management, and real-time transportation scheduling, thereby improving the overall efficiency of the cement supply chain and ensuring stable cement quality and timely completion of construction projects. Through prediction and dynamic optimization using an LSTM neural network model, the system achieves accurate cement demand forecasting and efficient supply chain scheduling, significantly reducing human intervention, improving resource utilization, and lowering costs.

[0107] Example 2

[0108] Furthermore, the method used in the cement silo management system integrating intelligent scheduling and quality monitoring described in this invention includes the following steps:

[0109] S1. When bulk cement enters the warehouse through the inlet pipe, the near-infrared spectral sensing unit installed at the inlet pipe monitors the chemical composition of the cement in real time and captures the spectral data of the cement sample. The real-time analysis results are transmitted to the central algorithm module for further processing and quality assessment. If the analysis results do not meet the set quality standards or cement type thresholds, the central algorithm module will automatically trigger the early warning mechanism, send an abnormal prompt to the human-machine interaction display module, and synchronize the test results to the remote monitoring terminal or supplier system through the cloud platform for quality review and traceability.

[0110] S2. The system monitors the temperature, humidity and pressure data inside the cement tank in real time through the storage environment monitoring module, and automatically adjusts the humidity control system based on this environmental data. The system will judge the environmental status inside the cement tank based on real-time data, and automatically start corresponding humidity control measures when it detects excessive humidity, abnormal temperature or unbalanced air pressure, such as activating the ventilation device or adjusting the heating system, in order to maintain the stability of the environment inside the cement tank and prevent the cement from getting damp or clumping.

[0111] S3. Based on the real-time cement weight data collected by the cement silo weighing module, combined with the project progress, daily project plan, and historical cement usage information, the central algorithm module predicts future cement demand using a long short-term memory artificial neural network model. The long short-term memory artificial neural network model automatically generates cement demand forecast results by analyzing the time-series patterns of historical cement consumption data, real-time construction progress, and daily cement usage plans. Based on the predicted cement demand, the system automatically optimizes the cement supply plan and generates accurate transportation and inventory management strategies.

[0112] S4, the data is transmitted to the cloud platform in real time through the 5G network, the cloud platform uses historical data and real-time feedback to continuously optimize prediction accuracy and improve the adaptive ability of supply chain management strategies; the optimized strategies are quickly fed back to the central algorithm module to ensure efficient operation of the cement supply chain; in addition, the cloud platform is also responsible for data storage, backup and analysis, provides a remote access interface, supports cross-device data sharing and intelligent analysis, builds a multi-dimensional data management system, fully realizes the whole cycle management of cement warehouse in and out, and automatically generates detection reports to support subsequent analysis, tracking and decision-making.

[0113] Further in the present application, in S3, according to the predicted cement demand, the cement supply plan is automatically optimized, and accurate transportation and inventory management strategies are generated, including the following steps:

[0114] S31, prediction and scheduling:

[0115] S311, inventory optimization: according to the predicted cement demand, the system automatically adjusts the inventory level to avoid cement overstock or shortage and ensure that the construction is on schedule; the system will analyze historical data, construction plans and current inventory to generate dynamic inventory scheduling instructions;

[0116] S312, transportation arrangement: according to the predicted demand, the system automatically schedules cement transportation arrangement to ensure that cement distribution is synchronized with construction progress; for example, when the demand is large, the system will prioritize more transportation vehicles;

[0117] S313, supply time optimization: the system will optimize the supply time arrangement according to the predicted demand and construction progress to ensure that each supply can meet the construction demand on time;

[0118] S32, real-time data support:

[0119] S321, operator interface: the system provides real-time data support, through the man-machine interaction display module, the operator can view the predicted cement demand, current inventory and transportation plan information;

[0120] S322, supply and transportation data: the operator understands the supply progress, transportation status and estimated arrival time data through the real-time monitoring interface to ensure the transparency and response speed of the system.

[0121] Further in the present application, in S4, the implementation method of subsequent analysis, tracking and decision-making includes the following steps:

[0122] S41, real-time adjustment and optimization:

[0123] S411, feedback mechanism: the system transmits real-time data to the cloud platform through 5G, the cloud platform analyzes the newly acquired cement consumption data and construction progress data, and optimizes the long short-term memory artificial neural network model according to the analysis result; after each new data feedback, the long short-term memory artificial neural network model automatically adjusts the prediction result to improve the accuracy and reliability of subsequent prediction;

[0124] S412, cloud platform function: the cloud platform continuously optimizes the prediction accuracy to ensure that the prediction model is adjusted and updated according to real-time feedback; the cloud platform also provides data storage, analysis and backup functions, supports cross-device data sharing and intelligent analysis, monitors the cement supply chain state in real time, and ensures that the supply system runs in the best state;

[0125] S413, early warning and emergency response: when there is a problem in cement supply or the system detects an anomaly, the central algorithm module will automatically analyze the relevant data and trigger the early warning mechanism through the cloud platform; the cloud platform will analyze the cement inventory, transportation progress and construction demand data in real time and generate detailed warning information; the warning information will be immediately pushed to the mobile device or computer terminal of the remote operator through the 5G network; after receiving the warning, the operator can view the problem details in real time, including inventory level, transportation delay and inaccurate demand prediction information.

[0126] In summary, the present application integrates cement quality detection, storage environment monitoring, weighing monitoring, data processing, intelligent scheduling and cloud management modules, and builds a full-cycle management system covering "storage-in, storage, discharge, scheduling and distribution", realizes intelligent, automated and fine management of cement tanks from quality monitoring to supply scheduling, greatly improves the visualization level and management efficiency of the cement supply chain, and realizes intelligent monitoring and management of the whole process of cement. The present application uses near infrared spectroscopy analysis technology (NIR) combined with chemometrics algorithm to realize online detection and real-time evaluation of cement chemical composition, which can timely identify abnormal batches or quality fluctuations, and when the cement quality deviates from the standard, the system automatically issues a warning and links the cloud platform, avoids unqualified materials flowing into the construction link, improves the engineering quality and safety, realizes real-time detection and automatic warning of cement quality. The present application realizes accurate monitoring of the environment in the cement tank through the multi-point distributed temperature, humidity and pressure sensing unit, combines the adsorption drying and hot air circulation device, can automatically adjust the tank environment when the humidity exceeds the standard or the risk of condensation appears, feedback control ensures the stability of the tank temperature and humidity, prevents cement from being damp, caked or deteriorated, prolongs the storage period and guarantees the discharge quality, realizes 3.

[0127] The environmental monitoring and dynamic regulation guarantee the storage stability. The central algorithm module of the present application introduces a long short-term memory artificial neural network model, analyzes multi-source data such as historical consumption data, construction progress, engineering plan and meteorological conditions, takes the actual remaining amount when the cement truck arrives as the supervision target, realizes high-precision prediction of future cement demand and inventory change, the model combines a self-learning mechanism to continuously optimize parameters, makes the supply plan dynamically adapt to the construction rhythm, avoids shortage or overstock, realizes intelligent demand prediction and scheduling optimization based on the long short-term memory artificial neural network model. The present application automatically generates the warehouse and distribution plan based on the long short-term memory artificial neural network model prediction result, realizes intelligent optimization of cement transportation arrangement, inventory replenishment and supply time, through linkage with the cloud platform, the transportation progress can be monitored in real time and automatic early warning can be realized under abnormal conditions, the supply chain response speed and coordination efficiency are significantly improved, automatic supply and transportation scheduling are realized. The present application uploads the data of each module to the cloud platform in real time through the 5G network, realizes remote access, data storage, trend analysis and report generation, the cloud platform analyzes the real-time and historical data, provides data support and decision basis for construction site management and supply chain optimization, builds a high-reliability intelligent cement management system, realizes remote data collaboration and intelligent decision-making supported by the cloud platform.

[0128] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, the scope of the present application being defined by the appended claims and equivalents thereof.

Claims

1. A cement silo management system integrating intelligent scheduling and quality monitoring, characterized in that, include: The cement quality testing module is installed at the inlet pipe or side wall of the cement silo to collect near-infrared spectra of the flowing cement samples and output spectral data. The storage environment monitoring module is installed at different heights of the cement tank to collect data on temperature, humidity and pressure inside the tank. The cement silo weighing module is installed at the cement silo support structure and is used to collect the weight data of the cement silo. The data receiving and processing module is used to receive and integrate data from the cement quality testing module, the storage environment monitoring module, and the cement silo weighing module. It communicates with external devices through the communication interface, preprocesses the received data, and outputs the processed data to the control algorithm unit. The central algorithm module is used to receive data output from the data receiving and processing module, analyze and process it, generate control commands, and output them to the feedback control hardware module. The feedback control hardware module receives control commands from the central algorithm module and controls the ventilation device, heating system, or humidity control system to regulate the temperature and humidity inside the cement tank. The human-machine interaction display module is used to display cement silo operating status information, including cement quality, inventory, and environmental parameters, and to receive user input. The cloud platform module is used to receive system operation data, store, back up and analyze the data, and provide a data access interface.

2. The cement silo management system integrating intelligent scheduling and quality monitoring according to claim 1, characterized in that: The cement quality testing module includes near-infrared spectroscopy transmission. The unit consists of a sensing unit, a data acquisition unit, and a spectral analysis unit. The near-infrared spectral sensing unit is installed at the feed pipe of the cement silo or on the upper side wall of the cement silo to perform online spectral scanning of the cement sample flowing through it and collect the reflected spectral data. The spectral analysis unit uses a comparison algorithm to analyze near-infrared spectral data in real time based on a pre-set cement composition database, and extracts the main chemical components and their proportions in the cement. The data acquisition unit transmits the analysis results to the data receiving and processing module via industrial communication protocol, and the central algorithm module combines historical spectral feature data to perform model fitting and trend analysis in order to determine the quality and type of cement. When the analysis results do not match the quality standards or cement type thresholds set by the system, the central algorithm module adjusts the system operation through control signals and synchronizes the relevant test data to the remote monitoring system through the cloud platform module for quality verification and traceability.

3. The cement silo management system integrating intelligent scheduling and quality monitoring according to claim 1, characterized in that: The storage environment monitoring module includes a temperature and humidity sensing unit, a pressure sensing unit, and an environmental data analysis unit, wherein... Temperature and humidity sensing units are installed at the top, middle and bottom of the cement tank to monitor the temperature and humidity of each layer in real time and collect environmental data. The pressure sensing unit is installed on the top of the cement tank to monitor the gas pressure inside the tank; The environmental data analysis unit analyzes the collected temperature, humidity and pressure data based on a preset multi-parameter analysis algorithm to determine whether the cement storage environment meets the set safety standards. The environmental data analysis unit performs comprehensive analysis on the collected temperature, humidity and pressure data to determine whether the cement storage environment is within the set safe range. When excessive humidity, abnormal temperature or unbalanced air pressure is detected, the system automatically generates an early warning signal through the central algorithm module and triggers corresponding control strategies, including starting the tank ventilation device and suspending feeding or discharging operations, to ensure that the cement does not become damp, clump or deteriorate, and to ensure a stable storage environment.

4. A cement silo management system integrating intelligent scheduling and quality monitoring according to claim 1, characterized in that: The feedback control hardware module includes an adsorption drying unit and a hot air circulation unit located below the cement tank, and is connected to the top of the cement tank via a pipe for return flow. The adsorption drying unit reduces humidity and accelerates moisture evaporation by adsorbing moisture inside the cement tank. The hot air circulation unit further promotes moisture evaporation by evenly distributing hot air to maintain stable temperature and humidity inside the cement tank. At the same time, the cement tank is designed with a cold bridge prevention structure and a sealing device to reduce the entry of external moisture and prevent condensation caused by temperature differences, further ensuring a stable cement storage environment and preventing the cement from becoming damp, clumping, or deteriorating.

5. A cement silo management system integrating intelligent scheduling and quality monitoring according to claim 1, characterized in that: When the central algorithm module detects that the humidity inside the tank output by the storage environment monitoring module exceeds a set threshold, or when the ambient temperature is low and the risk of condensation on the tank wall is high, the following functions are automatically controlled: (1) Start the adsorption drying unit to reduce humidity and accelerate the evaporation of moisture by adsorbing the moisture in the cement tank. (2) Start the hot air circulation unit to evenly distribute hot air in the cement tank, further accelerate the evaporation of moisture, and ensure stable cement quality. (3) The moisture at the top and bottom of the cement tank is circulated through the pipeline reflux mechanism to ensure uniform distribution of hot air and accelerate the removal of moisture. (4) Maintain a slight positive pressure inside the cement tank to prevent backflow of external moisture and ensure a stable environment inside the cement tank. If the humidity exceeds the standard for a continuous period and meets the preset threshold, an early warning mechanism will be automatically triggered, and abnormal information will be sent to the remote operator through the cloud platform to facilitate timely intervention and take necessary measures to ensure that the environment inside the cement tank always meets the requirements.

6. A cement silo management system integrating intelligent scheduling and quality monitoring according to claim 1, characterized in that: The central algorithm module uses a long short-term memory artificial neural network model to predict cement demand. This model is based on the following input data: cement consumption data, construction progress and daily project planning, and historical cement consumption construction data. By analyzing these historical and real-time data, it predicts future cement demand and optimizes the cement supply plan based on the prediction results, automatically scheduling cement transportation, adjusting inventory levels, and optimizing delivery time arrangements. Based on the forecast results, the central algorithm module automatically adjusts cement distribution, transportation, and inventory management strategies. This optimization strategy improves cement supply efficiency in the following ways: (1) Automatically schedule cement transportation to ensure that cement transportation and supply are synchronized with the construction progress; (2) Dynamically adjust inventory levels to avoid cement stockpiling or shortages and ensure continuous supply; (3) Optimize the supply schedule to reduce delays and ensure that the construction is completed on time; Meanwhile, the central algorithm module transmits data to the cloud platform in real time via 5G. The cloud platform receives the real-time data and uses a feedback control mechanism to continuously optimize the Long Short-Term Memory (LSTM) artificial neural network model. Each time new cement consumption data arrives, the cloud platform adjusts the parameters of the LTM model in real time and updates the model's prediction results based on the real-time data. By learning from historical data and real-time feedback, the cloud platform continuously improves the model's prediction accuracy and sends the optimized strategies to the central algorithm module in real time to ensure that the cement supply system can effectively cope with demand fluctuations. When the system detects demand fluctuations or environmental changes, the LTM model can quickly adjust its predictions and optimize the supply chain strategy accordingly to ensure that the cement supply system is always in optimal condition, thereby maximizing resource utilization and reducing costs.

7. The method used in a cement silo management system integrating intelligent scheduling and quality monitoring according to any one of claims 1-6, characterized in that, Includes the following steps: S1. When bulk cement enters the warehouse through the inlet pipe, the near-infrared spectral sensing unit installed at the inlet pipe monitors the chemical composition of the cement in real time and captures the spectral data of the cement sample. The real-time analysis results are transmitted to the central algorithm module for further processing and quality assessment. If the analysis results do not meet the set quality standards or cement type thresholds, the central algorithm module will automatically trigger the early warning mechanism, send an abnormal prompt to the human-machine interaction display module, and synchronize the test results to the remote monitoring terminal or supplier system through the cloud platform for quality review and traceability. S2. The system monitors the temperature, humidity and pressure data inside the cement tank in real time through the storage environment monitoring module, and automatically adjusts the humidity control system based on this environmental data. The system will judge the environmental status inside the cement tank based on real-time data, and automatically start corresponding humidity control measures when it detects excessive humidity, abnormal temperature or unbalanced air pressure, such as activating the ventilation device or adjusting the heating system, in order to maintain the stability of the environment inside the cement tank and prevent the cement from getting damp or clumping. S3. Based on the real-time cement weight data collected by the cement silo weighing module, combined with the project progress, daily project plan, and historical cement usage information, the central algorithm module predicts future cement demand using a long short-term memory artificial neural network model. The long short-term memory artificial neural network model automatically generates cement demand forecast results by analyzing the time-series patterns of historical cement consumption data, real-time construction progress, and daily cement usage plans. Based on the predicted cement demand, the system automatically optimizes the cement supply plan and generates accurate transportation and inventory management strategies. S4. Data is transmitted to the cloud platform in real time via the 5G network. The cloud platform uses historical data and real-time feedback to continuously optimize prediction accuracy and improve the adaptability of supply chain management strategies. The optimized strategies are quickly fed back to the central algorithm module to ensure the efficient operation of the cement supply chain. In addition, the cloud platform is also responsible for data storage, backup and analysis, provides remote access interfaces, supports cross-device data sharing and intelligent analysis, builds a multi-dimensional data management system, fully realizes the full life cycle management of cement entering and leaving the warehouse, and automatically generates test reports to support subsequent analysis, tracking and decision-making.

8. The method used in the cement silo management system integrating intelligent scheduling and quality monitoring according to claim 7, characterized in that: In step S3, based on the predicted cement demand, the cement supply plan is automatically optimized, and precise transportation and inventory management strategies are generated, including the following steps: S31. Prediction and Scheduling: S311. Inventory Optimization: Based on the predicted cement demand, the system automatically adjusts the inventory level to avoid cement stockpiling or shortages and ensure that construction proceeds on time; the system analyzes historical data, construction plans and current inventory to generate dynamic inventory scheduling instructions. S312. Transportation Arrangement: Based on the anticipated demand, the system automatically schedules cement transportation to ensure that cement delivery is synchronized with the construction progress; for example, when the demand is large, the system will prioritize dispatching more transportation vehicles. S313, Delivery Time Optimization: The system will optimize the delivery time arrangement based on the predicted demand and construction progress to ensure that each delivery can meet the construction needs on time. S32, Real-time data support: S321, Operator Interface: The system provides real-time data support. Through the human-machine interaction display module, operators can view the predicted cement demand, current inventory, and transportation plan information. S322, Supply and Transportation Data: Operators can understand the supply progress, transportation status and estimated arrival time data through a real-time monitoring interface, ensuring the transparency and responsiveness of the system operation.

9. The method used in the cement silo management system integrating intelligent scheduling and quality monitoring according to claim 7, characterized in that: In step S4, the subsequent analysis, tracking, and decision-making processes include the following steps: S41. Real-time adjustment and optimization: S411 Feedback Mechanism: The system transmits real-time data to the cloud platform via 5G. The cloud platform analyzes the newly acquired cement consumption data and construction progress data, and optimizes the long short-term memory artificial neural network model based on the analysis results. After each new data feedback, the long short-term memory artificial neural network model automatically adjusts the prediction results to improve the accuracy and reliability of subsequent predictions. S412. Functions of the cloud platform: The cloud platform continuously optimizes forecast accuracy, ensuring that the forecast model is adjusted and updated based on real-time feedback; the cloud platform also provides data storage, analysis and backup functions, supports cross-device data sharing and intelligent analysis, monitors the status of the cement supply chain in real time, and ensures that the supply system operates in the best condition. S413. Early Warning and Emergency Response: When cement supply issues arise or the system detects an anomaly, the central algorithm module will automatically analyze relevant data and trigger an early warning mechanism through the cloud platform. The cloud platform will analyze cement inventory, transportation progress, and construction demand data in real time and generate detailed warning information. The warning information will be immediately pushed to the remote operator's mobile device or computer terminal via the 5G network. After receiving the warning, the operator can view the details of the problem in real time, including inventory levels, transportation delays, and inaccurate demand forecasts.

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