Concrete mixing plant automatic control system based on intellectualization

Abstract

The invention discloses a concrete mixing plant automatic control system based on intelligence, and belongs to the technical field of automatic control. Comprising a multi-modal sensing data acquisition module, an intelligent batching optimization module, a digital twin simulation module, a self-adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-target collaborative scheduling module. Real-time synchronization of sensor data and a virtual model is realized in combination with an edge computing technology, dynamic and visual technical support is provided for full-flow simulation of the concrete mixing plant, and complex working conditions in production are reflected more truly; the system predicts a potential problem through a machine learning algorithm, triggers an early warning signal based on a multi-dimensional threshold rule, and generates a preventive maintenance plan in advance; the digital twin platform supports AR and VR interaction interfaces, so that an operator can visually observe the operation states of a virtual model and actual equipment.

Description

Technical Field

[0001] The present invention belongs to the technical field of automatic control, and in particular refers to an intelligent-based automatic control system for a concrete mixing station. Background Art

[0002] The intelligent concrete mixing plant automation control system integrates the Internet of Things, big data, artificial intelligence, and automated control technologies to build an intelligent management platform that integrates production process monitoring, resource optimization and scheduling, and precise quality control. The system relies on a sensor network to achieve real-time collection and transmission of key parameters such as the operating status of mixing plant equipment, raw material ratios, and energy consumption data. It uses industrial control computers and programmable controllers to build a closed-loop control core, combining fuzzy control theory and step-by-step fault-tolerance technology to ensure the safety and stability of the production process.

[0003] However, the existing automated control systems for concrete mixing plants still have certain defects. The existing model construction and maintenance costs are high. High-precision geometric modeling and physical property integration require reliance on professional software tools, which require high hardware computing power and software licensing fees, and small mixing plants may not be able to afford them. Model training relies on high-quality historical data. If the mixing plant is a new project or equipment is frequently replaced, there is a lack of sufficient historical energy consumption and operating condition data, which may lead to difficulty in algorithm convergence and poor initial optimization results. The reliability of sensor data directly affects the prediction accuracy. If the vibration sensor or infrared thermal imager generates noise due to environmental interference, the attention mechanism may mistakenly focus on non-critical features, resulting in residual life prediction deviations. For this reason, an intelligent automated control system for concrete mixing plants is proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent concrete mixing plant automation control system to solve the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: an intelligent concrete mixing plant automation control system, comprising a multimodal perception data acquisition module, an intelligent batching optimization module, a digital twin simulation module, an adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module, and a multi-objective collaborative scheduling module;

[0006] The multimodal sensing data acquisition module integrates high-precision sensors, industrial cameras, microwave hygrometers, and temperature and humidity sensors to collect aggregate particle size distribution, moisture content, ambient temperature and humidity, and mixer operating status data in real time.

[0007] The intelligent batching optimization module dynamically adjusts the batching ratio based on the collected dynamic data, combined with the historical production database and machine learning model;

[0008] The digital twin simulation module builds a virtual mirror model of the concrete mixing plant, simulates the production process in real time and predicts potential problems;

[0009] The adaptive energy consumption management module monitors the energy consumption of the equipment in real time and optimizes the operation strategy;

[0010] The fault self-diagnosis and predictive maintenance module predicts the failure risk of key components by monitoring equipment vibration, temperature, and current signals;

[0011] The dynamic quality traceability module records the production parameters of each batch of concrete and associates them with the quality test results;

[0012] Among them, the multi-objective collaborative scheduling module optimizes concrete supply and transportation scheduling based on real-time demand and resource status.

[0013] Among them, the multimodal perception data acquisition module monitors the aggregate particle size distribution in real time by deploying high-precision sensors, and the industrial camera captures the aggregate morphology through 3D point cloud modeling; the microwave hygrometer combines with infrared thermal imaging to perform non-contact moisture content and temperature measurement; the temperature and humidity sensor collects environmental parameters, and the vibration sensor and current monitoring are used to monitor the operation status of the mixer; the original data is synchronously collected through multimodal sensors, the Kalman filter is used to eliminate noise, the image data is denoised and enhanced and edge detected, the microwave humidity measurement signal is spectrally analyzed, and key feature parameters are extracted; a feature-level fusion strategy is adopted to associate the aggregate particle size distribution data with the image recognition results through a deep learning model, and the environmental temperature and humidity and mixer operation status data are combined to construct a dynamic perception feature vector; the main control circuit is used to perform high-speed collection and synchronization of multi-sensor data, and the 5G module is used to upload the data to the cloud platform.

[0014] Among them, the intelligent batching optimization module standardizes the dynamic data collected by the multimodal perception module, and extracts key features in combination with the recipe parameters in the historical production database; uses a time series model to train the prediction model, inputs real-time data and historical features, and outputs the optimal ratio scheme. At the same time, a reinforcement learning algorithm is introduced to dynamically adjust the model weights to adapt to raw material fluctuations; according to the current environmental parameters and equipment status, the water-cement ratio and admixture ratio parameters are adjusted through model calculation to generate a dynamic formula and verify its feasibility; the optimized ratio parameters are sent to the execution layer, the actual production data is collected and compared with the target value, and the model parameters are continuously updated using the online learning mechanism; combined with cost constraints, production efficiency and quality requirements, a Pareto optimal solution set is generated through a multi-objective optimization algorithm for operators or the upper system to select the final formula.

[0015] Among them, the digital twin simulation module clarifies the digital mapping requirements of the concrete mixing station, determines the areas and key parameters that need to be simulated, uses tools to build a high-precision three-dimensional geometric model, and integrates physical properties; uses the dynamic data collected by the multimodal perception module, combined with the equipment operation logs and process parameters in the historical production database, to build a hybrid simulation model through physical modeling and data-driven model.

[0016] Among them, the digital twin simulation module deploys a three-dimensional virtual mirror model through the platform, uses a physical engine to simulate the movement of mixer blades, the material mixing process and the stress distribution of the equipment, and combines edge computing technology to synchronize sensor data and model status at the millisecond level, supporting operators to interact in real time through AR and VR interfaces; based on the simulation model operation results, it uses machine learning algorithms to predict potential problems, triggers early warning signals through multi-dimensional threshold rules, and links the intelligent ingredient optimization module to dynamically adjust the formula and generate preventive maintenance plans.

[0017] Among them, the adaptive energy consumption management module monitors the equipment energy consumption, temperature, and vibration parameters in real time by deploying a high-precision sensor network, and combines environmental sensors and process parameters to standardize the raw data and extract key features through edge computing nodes; a hybrid neural network is used to predict the energy consumption trend of the equipment in the next few dozen minutes, and an optimization control strategy is generated by combining the reinforcement learning algorithm. The current operating parameters and historical energy consumption data are input to output the optimal operating parameters; the equipment operation mode is dynamically adjusted according to the prediction results, and energy efficiency and performance are balanced through the fuzzy control algorithm, and an adaptive compensation mechanism is triggered under abnormal working conditions; an integrated multi-objective optimization algorithm is used to solve the Pareto optimal solution between maximizing energy efficiency, minimizing costs and extending equipment life, and the optimal strategy is recommended to the operator through the human-computer interaction interface.

[0018] Among them, the fault self-diagnosis and predictive maintenance module installs high-precision vibration sensors, infrared thermal imagers and current transformers on key equipment to monitor the equipment's vibration spectrum, temperature distribution and current fluctuation signals in real time, synchronizes multi-source data through edge computing nodes, and constructs an equipment health status feature matrix based on environmental parameters; uses Kalman filtering to eliminate signal noise, performs frequency domain analysis on vibration signals to extract resonant frequency and harmonic features, performs trend decomposition on temperature time series data to identify abnormal temperature rise, and extracts time domain features and frequency domain features through a sliding window to form a standardized fault feature vector.

[0019] The fault self-diagnosis and predictive maintenance module is based on a physical and data hybrid modeling method. It uses the historical equipment fault database to train a deep learning model, combines the attention mechanism to dynamically focus on key features, and introduces a Bayesian probability model to quantify the probability of failure. It outputs the remaining life prediction of the equipment and the fault type classification results. The prediction results are synchronized with the virtual equipment model through the digital twin platform. When abnormal features are detected, a graded warning is triggered and a maintenance plan is generated. The implementation formula is:

[0020]

[0021] Among them, P ft represents the equipment failure probability vector; α i represents the attention mechanism weight; X t Represents real-time monitoring signal; LSTM(X t ) represents the fault feature extraction result output by the deep learning model; F p (X t ) represents the residual correction term under the constraints of the physical model; W d 、W p Represents the fusion weight of data-driven and physical models.

[0022] Among them, the dynamic quality traceability module collects the key parameters of each batch of production in real time through the sensor network deployed in the concrete mixing station, assigns a unique identification code to each batch of concrete using QR code technology, and synchronously records the process parameters and production logs through the MES system; sets up automated testing equipment at the concrete discharge port, curing stage and finished product delivery link, binds the test results to the batch identification code, and realizes the time alignment of production parameters and quality data through edge computing nodes; adopts a distributed database to store structured production parameters and unstructured test reports, extracts key indicators through feature engineering, and uses machine learning models to establish the regression relationship between production parameters and quality indicators, monitors abnormal batches that deviate from the threshold in real time, and triggers early warning signals; visualizes the batch production process, parameter trends, and test results in the form of three-dimensional animation and heat map through the digital twin platform, supports forward and reverse tracing, and provides data-driven suggestions for process optimization in combination with similar historical cases.

[0023] Among them, the multi-objective collaborative scheduling module collects production demand, resource status and external factors in real time through the sensor network deployed in concrete mixing plants, transport vehicles and construction sites, performs data cleaning and time alignment through edge computing nodes, and constructs dynamic demand and resource status matrices; expresses the scheduling problem as a multi-objective mathematical model, and uses mixed integer linear programming to construct objective functions and constraints; based on real-time demand fluctuations and resource status changes, a rolling time domain optimization method is used to re-solve the scheduling plan within the time window, and combines the digital twin platform to simulate the scheduling results to generate scheduling instructions including task allocation, loading sequence, driving route and emergency alternative plans; the scheduling instructions are sent to the mixing plant PLC, transport vehicle terminal and construction management APP through the industrial Internet of Things platform, using the 5G network to achieve millisecond-level response, and an exception handling mechanism is deployed at the same time.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This invention builds a high-precision three-dimensional geometric model and a physical and data hybrid simulation model, combining it with edge computing technology to achieve real-time synchronization between sensor data and virtual models. This provides dynamic and visual technical support for the full-process simulation of concrete mixing plants. Compared with traditional static simulation or local modeling methods, this system can more realistically reflect the complex working conditions in production. The system predicts potential problems through machine learning algorithms and triggers early warning signals based on multi-dimensional threshold rules, generating preventive maintenance plans in advance, thereby reducing the risk of unplanned downtime. The digital twin platform also supports AR and VR interactive interfaces, allowing operators to intuitively observe the operating status of virtual models and actual equipment, improving decision-making efficiency.

[0026] 2. This invention uses a hybrid neural network and reinforcement learning algorithm, combined with real-time operating parameters and historical energy consumption data, to dynamically generate equipment operation strategies, significantly improving energy utilization efficiency. Compared with traditional fixed operating modes and simple PID control methods, this method can adaptively adjust operating parameters to achieve coordinated optimization of maximizing energy efficiency and extending equipment life. The system uses a fuzzy control algorithm to balance energy efficiency and performance requirements, reducing energy consumption while ensuring production efficiency. The human-computer interaction interface recommends optimal strategies to operators, avoiding the subjectivity of traditional empirical decision-making. This intelligent energy consumption management solution not only directly reduces electricity costs but also extends the service life of key components by reducing equipment overheating and mechanical wear.

[0027] 3. This invention achieves accurate prediction and graded early warning of equipment failure risks through hybrid physical and data modeling and Bayesian probability models, combined with attention mechanisms and edge computing technologies. It identifies potential failures in advance and outputs predictions of the equipment's remaining life through vibration spectrum analysis, temperature trend decomposition, and current fluctuation monitoring. The system synchronizes virtual equipment models with actual operating status via a digital twin platform. When abnormal features are detected, graded early warnings are triggered and maintenance plans are generated. These plans are then distributed to mobile devices via the MES system, ensuring a rapid response from maintenance personnel.

[0028] 4. The present invention achieves traceability of data across the entire concrete production chain through unique identification codes and distributed database technology. By binding automated testing equipment to batch identification codes, the system ensures the temporal alignment of production parameters and quality test results. The system extracts key indicators through feature engineering and uses machine learning models to establish a regression relationship between production parameters and quality indicators, enabling real-time monitoring of abnormal batches. The digital twin platform visualizes production processes and test results in the form of three-dimensional animations and heat maps, supports forward and reverse tracing, and provides data-driven recommendations for process optimization. The dynamic tracing mechanism not only improves the accuracy of quality control, but also enhances the systematic nature of problem analysis by associating historical cases through knowledge graph technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a schematic diagram of the structure of the intelligent concrete mixing station automation control system of the present invention;

[0030] Figure 2 The operation process of the intelligent concrete mixing station automatic control system of the present invention is as follows Figure 1 ;

[0031] Figure 3 The operation process of the intelligent concrete mixing station automation control system of the present invention is as follows Figure 2 . DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0033] Example

[0034] See also Figure 1-Figure 3As shown, the present invention provides a technical solution: including a multimodal perception data acquisition module, an intelligent batching optimization module, a digital twin simulation module, an adaptive energy consumption management module, a fault self-diagnosis and predictive maintenance module, a dynamic quality tracing module and a multi-objective collaborative scheduling module;

[0035] The multimodal sensing data acquisition module integrates high-precision sensors, industrial cameras, microwave hygrometers, and temperature and humidity sensors to collect aggregate particle size distribution, moisture content, ambient temperature and humidity, and mixer operating status data in real time.

[0036] The intelligent batching optimization module dynamically adjusts the batching ratio based on the collected dynamic data, combined with the historical production database and machine learning model;

[0037] The digital twin simulation module builds a virtual mirror model of the concrete mixing plant, simulates the production process in real time and predicts potential problems;

[0038] The adaptive energy consumption management module monitors the energy consumption of the equipment in real time and optimizes the operation strategy;

[0039] The fault self-diagnosis and predictive maintenance module predicts the failure risk of key components by monitoring equipment vibration, temperature, and current signals;

[0040] The dynamic quality traceability module records the production parameters of each batch of concrete and associates them with the quality test results;

[0041] Among them, the multi-objective collaborative scheduling module optimizes concrete supply and transportation scheduling based on real-time demand and resource status.

[0042] Among them, the multimodal perception data acquisition module monitors the aggregate particle size distribution in real time by deploying high-precision sensors, and the industrial camera captures the aggregate morphology through 3D point cloud modeling; the microwave hygrometer combines with infrared thermal imaging to perform non-contact moisture content and temperature measurement; the temperature and humidity sensor collects environmental parameters, and the vibration sensor and current monitoring are used to monitor the operation status of the mixer; the original data is synchronously collected through multimodal sensors, the Kalman filter is used to eliminate noise, the image data is denoised and enhanced and edge detected, the microwave humidity measurement signal is spectrally analyzed, and key feature parameters are extracted; a feature-level fusion strategy is adopted to associate the aggregate particle size distribution data with the image recognition results through a deep learning model, and the environmental temperature and humidity and mixer operation status data are combined to construct a dynamic perception feature vector; the main control circuit is used to perform high-speed collection and synchronization of multi-sensor data, and the 5G module is used to upload the data to the cloud platform.

[0043] Among them, the intelligent batching optimization module standardizes the dynamic data collected by the multimodal perception module, and extracts key features in combination with the recipe parameters in the historical production database; uses a time series model to train the prediction model, inputs real-time data and historical features, and outputs the optimal ratio scheme. At the same time, a reinforcement learning algorithm is introduced to dynamically adjust the model weights to adapt to raw material fluctuations; according to the current environmental parameters and equipment status, the water-cement ratio and admixture ratio parameters are adjusted through model calculation to generate a dynamic formula and verify its feasibility; the optimized ratio parameters are sent to the execution layer, the actual production data is collected and compared with the target value, and the model parameters are continuously updated using the online learning mechanism; combined with cost constraints, production efficiency and quality requirements, a Pareto optimal solution set is generated through a multi-objective optimization algorithm for operators or the upper system to select the final formula.

[0044] Among them, the digital twin simulation module clarifies the digital mapping requirements of the concrete mixing station, determines the areas and key parameters that need to be simulated, uses tools to build a high-precision three-dimensional geometric model, and integrates physical properties; uses the dynamic data collected by the multimodal perception module, combined with the equipment operation logs and process parameters in the historical production database, to build a hybrid simulation model through physical modeling and data-driven model.

[0045] Among them, the digital twin simulation module deploys a three-dimensional virtual mirror model through the platform, uses a physical engine to simulate the movement of mixer blades, the material mixing process and the stress distribution of the equipment, and combines edge computing technology to synchronize sensor data and model status at the millisecond level, supporting operators to interact in real time through AR and VR interfaces; based on the simulation model operation results, it uses machine learning algorithms to predict potential problems, triggers early warning signals through multi-dimensional threshold rules, and links the intelligent ingredient optimization module to dynamically adjust the formula and generate preventive maintenance plans.

[0046] Among them, the adaptive energy consumption management module monitors the equipment energy consumption, temperature, and vibration parameters in real time by deploying a high-precision sensor network, and combines environmental sensors and process parameters to standardize the raw data and extract key features through edge computing nodes; a hybrid neural network is used to predict the energy consumption trend of the equipment in the next few dozen minutes, and an optimization control strategy is generated by combining the reinforcement learning algorithm. The current operating parameters and historical energy consumption data are input to output the optimal operating parameters; the equipment operation mode is dynamically adjusted according to the prediction results, and energy efficiency and performance are balanced through the fuzzy control algorithm, and an adaptive compensation mechanism is triggered under abnormal working conditions; an integrated multi-objective optimization algorithm is used to solve the Pareto optimal solution between maximizing energy efficiency, minimizing costs and extending equipment life, and the optimal strategy is recommended to the operator through the human-computer interaction interface.

[0047] Among them, the fault self-diagnosis and predictive maintenance module installs high-precision vibration sensors, infrared thermal imagers and current transformers on key equipment to monitor the equipment's vibration spectrum, temperature distribution and current fluctuation signals in real time, synchronizes multi-source data through edge computing nodes, and constructs an equipment health status feature matrix based on environmental parameters; uses Kalman filtering to eliminate signal noise, performs frequency domain analysis on vibration signals to extract resonant frequency and harmonic features, performs trend decomposition on temperature time series data to identify abnormal temperature rise, and extracts time domain features and frequency domain features through a sliding window to form a standardized fault feature vector.

[0048] The fault self-diagnosis and predictive maintenance module is based on a physical and data hybrid modeling method. It uses the historical equipment fault database to train a deep learning model, combines the attention mechanism to dynamically focus on key features, and introduces a Bayesian probability model to quantify the probability of failure. It outputs the remaining life prediction of the equipment and the fault type classification results. The prediction results are synchronized with the virtual equipment model through the digital twin platform. When abnormal features are detected, a graded warning is triggered and a maintenance plan is generated. The implementation formula is:

[0049]

[0050] Among them, P ft represents the equipment failure probability vector; α i represents the attention mechanism weight; X t Represents real-time monitoring signal; LSTM(X t ) represents the fault feature extraction result output by the deep learning model; F p (X t ) represents the residual correction term under the constraints of the physical model; W d 、W p Represents the fusion weight of data-driven and physical models.

[0051] Among them, the dynamic quality traceability module collects the key parameters of each batch of production in real time through the sensor network deployed in the concrete mixing station, assigns a unique identification code to each batch of concrete using QR code technology, and synchronously records the process parameters and production logs through the MES system; sets up automated testing equipment at the concrete discharge port, curing stage and finished product delivery link, binds the test results to the batch identification code, and realizes the time alignment of production parameters and quality data through edge computing nodes; adopts a distributed database to store structured production parameters and unstructured test reports, extracts key indicators through feature engineering, and uses machine learning models to establish the regression relationship between production parameters and quality indicators, monitors abnormal batches that deviate from the threshold in real time, and triggers early warning signals; visualizes the batch production process, parameter trends, and test results in the form of three-dimensional animation and heat map through the digital twin platform, supports forward and reverse tracing, and provides data-driven suggestions for process optimization in combination with similar historical cases.

[0052] Among them, the multi-objective collaborative scheduling module collects production demand, resource status and external factors in real time through the sensor network deployed in concrete mixing plants, transport vehicles and construction sites, performs data cleaning and time alignment through edge computing nodes, and constructs dynamic demand and resource status matrices; expresses the scheduling problem as a multi-objective mathematical model, and uses mixed integer linear programming to construct objective functions and constraints; based on real-time demand fluctuations and resource status changes, a rolling time domain optimization method is used to re-solve the scheduling plan within the time window, and combines the digital twin platform to simulate the scheduling results to generate scheduling instructions including task allocation, loading sequence, driving route and emergency alternative plans; the scheduling instructions are sent to the mixing plant PLC, transport vehicle terminal and construction management APP through the industrial Internet of Things platform, using the 5G network to achieve millisecond-level response, and an exception handling mechanism is deployed at the same time.

[0053] Working principle: With a high-precision sensor network as the core, multi-source heterogeneous data is collected in real time, and Kalman filtering technology is used to eliminate noise and extract key features; based on dynamic perception feature vectors, combined with historical production databases and machine learning models, the ingredient ratio is dynamically adjusted, the water-cement ratio and admixture ratio are optimized, and a Pareto optimal solution set is generated for decision-making reference; at the same time, the digital twin platform builds a virtual mirror model, simulates the mixer blade movement, material mixing process and stress distribution through a physical engine, and achieves millisecond-level synchronization of sensor data and model status based on edge computing, combines attention mechanism with Bayesian probability model to predict potential faults and remaining equipment life, and triggers graded pre-emptive measures. The system generates maintenance plans through alerts and linkage; through hybrid neural networks and fuzzy control algorithms, combined with real-time operating conditions and historical energy consumption data, it dynamically optimizes equipment operation strategies and balances energy efficiency and performance requirements; the dynamic quality traceability module relies on unique identification codes and distributed databases to associate production parameters, test results and quality indicators, and uses regression models to monitor abnormal batches in real time and visualize full-process data, supporting forward tracing and reverse analysis; the multi-objective collaborative scheduling module is based on mixed integer linear programming and rolling horizon optimization methods, integrating production demand, resource status and external factors to generate scheduling instructions including task allocation, transportation routes and emergency plans, and achieve millisecond-level response through the 5G network.

[0054] While 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 invention, and that the scope of the invention is defined by the appended claims and their equivalents.

[0055] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. Based on intelligent concrete mixing plant automation control system, the characteristics are: It includes multimodal perception data acquisition module, intelligent batching optimization module, digital twin simulation module, adaptive energy consumption management module, fault self-diagnosis and predictive maintenance module, dynamic quality traceability module and multi-objective collaborative scheduling module; The multimodal sensing data acquisition module integrates high-precision sensors, industrial cameras, microwave hygrometers, and temperature and humidity sensors to collect aggregate particle size distribution, moisture content, ambient temperature and humidity, and mixer operating status data in real time. The intelligent batching optimization module dynamically adjusts the batching ratio based on the collected dynamic data, combined with the historical production database and machine learning model; The digital twin simulation module builds a virtual mirror model of the concrete mixing plant, simulates the production process in real time and predicts potential problems; The adaptive energy consumption management module monitors the energy consumption of the equipment in real time and optimizes the operation strategy; The fault self-diagnosis and predictive maintenance module predicts the failure risk of key components by monitoring equipment vibration, temperature, and current signals; The dynamic quality traceability module records the production parameters of each batch of concrete and associates them with the quality test results; Among them, the multi-objective collaborative scheduling module optimizes concrete supply and transportation scheduling based on real-time demand and resource status.

2. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The multimodal perception data acquisition module monitors the aggregate particle size distribution in real time by deploying high-precision sensors, and the industrial camera captures the aggregate morphology through 3D point cloud modeling; The microwave moisture meter combines infrared thermal imaging for non-contact moisture and temperature measurement. Temperature and humidity sensors collect environmental parameters, while vibration sensors and current monitoring are used to monitor the mixer's operating status. Multimodal sensors simultaneously collect raw data, using Kalman filtering to eliminate noise, perform denoising enhancement and edge detection on image data, and perform spectrum analysis on the microwave moisture measurement signal to extract key characteristic parameters. A feature-level fusion strategy is adopted to associate the aggregate particle size distribution data with the image recognition results through a deep learning model, and a dynamic perception feature vector is constructed by combining the ambient temperature and humidity with the mixer operation status data. The main control circuit is used to collect and synchronize multi-sensor data at high speed, and the 5G module is used to upload the data to the cloud platform.

3. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The intelligent batching optimization module standardizes the dynamic data collected by the multimodal perception module and extracts key features based on the recipe parameters in the historical production database. It uses a time series model to train the prediction model, inputs real-time data and historical features, and outputs the optimal ratio scheme. It also introduces a reinforcement learning algorithm to dynamically adjust the model weights to adapt to raw material fluctuations. Based on the current environmental parameters and equipment status, the model calculates and adjusts the water-cement ratio and admixture ratio parameters to generate a dynamic recipe and verify its feasibility. The optimized ratio parameters are sent to the execution layer, actual production data is collected and compared with the target values, and the model parameters are continuously updated using the online learning mechanism; combined with cost constraints, production efficiency and quality requirements, a Pareto optimal solution set is generated through a multi-objective optimization algorithm for operators to select the final formula.

4. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The digital twin simulation module clarifies the digital mapping requirements of the concrete mixing station, uses tools to build a high-precision three-dimensional geometric model, and integrates physical properties; uses dynamic data collected by the multimodal perception module, combined with equipment operation logs and process parameters in the historical production database, to build a hybrid simulation model through physical modeling and data-driven modeling.

5. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The digital twin simulation module deploys a three-dimensional virtual mirror model through the platform, uses a physical engine to simulate the movement of mixer blades, the material mixing process, and the stress distribution of the equipment, and combines edge computing technology to synchronize sensor data and model status at the millisecond level, supporting real-time interaction between operators through AR and VR interfaces. Based on the simulation model operation results, it uses machine learning algorithms to predict potential problems, triggers early warning signals through multi-dimensional threshold rules, and links with the intelligent ingredient optimization module to dynamically adjust the recipe and generate preventive maintenance plans.

6. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The adaptive energy consumption management module deploys a high-precision sensor network to monitor equipment energy consumption, temperature, and vibration parameters in real time. In combination with environmental sensors and process parameters, it standardizes the raw data and extracts key features through edge computing nodes. It uses a hybrid neural network to predict the energy consumption trend of the equipment in the next few dozen minutes, and combines it with a reinforcement learning algorithm to generate an optimization control strategy. It inputs current operating parameters and historical energy consumption data and outputs optimal operating parameters. It dynamically adjusts the equipment operation mode based on the prediction results, balances energy efficiency and performance through a fuzzy control algorithm, and triggers an adaptive compensation mechanism under abnormal operating conditions. It integrates a multi-objective optimization algorithm to solve the Pareto optimal solution between maximizing energy efficiency, minimizing costs, and extending equipment life, and recommends the optimal strategy to the operator through a human-computer interaction interface.

7. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The fault self-diagnosis and predictive maintenance module installs high-precision vibration sensors, infrared thermal imagers and current transformers on key equipment to monitor the equipment's vibration spectrum, temperature distribution and current fluctuation signals in real time, synchronizes multi-source data through edge computing nodes, and constructs an equipment health status feature matrix based on environmental parameters. It uses Kalman filtering to eliminate signal noise, performs frequency domain analysis on vibration signals to extract resonance frequency and harmonic features, performs trend decomposition on temperature time series data to identify abnormal temperature rise, and extracts time domain features and frequency domain features through a sliding window to form a standardized fault feature vector.

8. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The fault self-diagnosis and predictive maintenance module is based on a hybrid physical and data modeling approach. It uses a historical equipment fault database to train a deep learning model, combines an attention mechanism to dynamically focus on key features, and introduces a Bayesian probability model to quantify the probability of failure. It outputs a prediction of the remaining equipment life and a classification of the fault type. The prediction results are synchronized with the virtual equipment model through the digital twin platform. When abnormal features are detected, a graded warning is triggered and a maintenance plan is generated. The implementation formula is: Among them, P ft represents the equipment failure probability vector; α i represents the attention mechanism weight; X t Represents real-time monitoring signal; LSTM(X t ) represents the fault feature extraction result output by the deep learning model; F p (X t ) represents the residual correction term under the constraints of the physical model; W d 、W p Represents the fusion weight of data-driven and physical models.

9. The intelligent concrete mixing plant automation control system according to claim 1 is characterized in that: The dynamic quality traceability module collects key parameters of each batch of production in real time through a sensor network deployed at the concrete mixing plant. It uses QR code technology to assign a unique identification code to each batch of concrete, and simultaneously records process parameters and production logs through the MES system. Automated testing equipment is installed at the concrete discharge port, during the curing stage, and when finished products leave the factory. The test results are bound to the batch identification code, and the time sequence of production parameters and quality data is aligned through edge computing nodes. A distributed database is used to store structured production parameters and unstructured test reports. Key indicators are extracted through feature engineering, and a machine learning model is used to establish a regression relationship between production parameters and quality indicators. Abnormal batches that deviate from the threshold are monitored in real time and early warning signals are triggered. The digital twin platform is used to visualize batch production processes, parameter trends, and test results in the form of three-dimensional animations and heat maps, supporting forward and reverse tracing. Combined with similar historical cases, data-driven suggestions are provided for process optimization.

10. The intelligent concrete mixing plant automation control system according to claim 1, characterized in that: The multi-objective collaborative scheduling module collects production demand, resource status, and external factors in real time through a sensor network deployed at concrete mixing plants, transport vehicles, and construction sites. It then uses edge computing nodes to cleanse and align data, and construct a dynamic demand and resource status matrix. The scheduling problem is expressed as a multi-objective mathematical model, and mixed integer linear programming is used to construct the objective function and constraints. Based on real-time demand fluctuations and changes in resource status, a rolling time domain optimization method is adopted to re-solve the scheduling plan within the time window. Combined with the scheduling results simulated on the digital twin platform, scheduling instructions are generated that include task allocation, loading sequence, driving route and emergency alternative plans; the scheduling instructions are sent to the mixing station PLC, transport vehicle terminals and construction management APP through the industrial Internet of Things platform, using the 5G network to achieve millisecond-level response, and an exception handling mechanism is deployed at the same time.

Images