Overall optimization system and method for full-process automatic production management of mixing station

Abstract

The application discloses a kind of mixing station whole-process automation production management global optimization system and method, system includes: the data acquisition module being configured for precision control to raw material proportioning and completing equipment operating state monitoring;Data processing module is configured for data preprocessing and data analysis to the data collected;Business logic module and application management module;The data acquisition module is arranged sensor module for precision control to raw material proportioning, and dynamic parameter correction model and self-adaptive adjustment mechanism are established by sensor module cooperation;The data processing module includes the data cleaning module being configured for denoising, error correction and format uniform processing to the data collected, and the data cleaning module establishes data exception alarm module by dynamic threshold management mechanism.Unified and efficient whole-process automation production management system is established.

Description

Technical Field

[0001] This invention belongs to the field of concrete production management technology, and more specifically, relates to a global optimization system and method for fully automated production management of a mixing plant. Background Technology

[0002] In modern engineering, mixing plants are key sites for the production of building materials such as concrete and asphalt, and their production management level directly affects the efficiency and quality of engineering construction. However, the traditional production model of mixing plants has long been constrained by low levels of informatization, making it difficult to adapt to the rapidly developing needs of the construction industry.

[0003] From a production process perspective, traditional mixing plants generally have a low level of information technology application across all stages, from raw material procurement and warehousing to batching and mixing and finished product output. Most production processes rely on manual operation and simple mechanical control, with inconsistent levels of information technology across different stages. Information management still primarily relies on traditional offline intervention methods, lacking standardized and unified execution criteria. This outdated management model results in a severe deficiency in data collection and integration capabilities during the production process. Production information cannot be fed back in a timely manner, making it difficult for managers to comprehensively and accurately grasp production dynamics, and real-time monitoring and optimization of the production process are simply out of the question.

[0004] Problems at the scheduling and management level are equally prominent. Equipment and personnel scheduling has long relied on experience-based judgment, lacking scientific data support, leading to frequent instances of idle equipment or unreasonable personnel allocation. This not only results in equipment idle rates as high as 15%-20%, but also keeps the scrap rate due to human error consistently in the range of 3%-5%, severely impacting production efficiency and product quality.

[0005] In terms of inventory management, traditional mixing plants struggle to accurately monitor the inventory status of raw materials and finished products due to the lack of intelligent production management systems. Production often suffers from raw material shortages affecting schedules, or finished products piling up and tying up capital and storage space. Furthermore, information resources cannot be shared between departments, production models are outdated, and existing information resources suffer from inaccuracies and delays, further hindering the production efficiency and economic benefits of the mixing plants.

[0006] With the rapid development of the construction industry, the market has placed higher demands on the production efficiency, product quality, and management level of batching plants. To improve production efficiency, it is necessary to achieve automated control of the production process, reduce manual intervention, and increase production speed and accuracy. To ensure product quality, real-time monitoring and precise control of each stage of production are required to ensure that products meet standards. To improve management level, information sharing and collaborative work among departments are needed to optimize the production process and reduce production costs. Against this backdrop, many problems of traditional batching plants urgently need to be solved through advanced automated production management systems and methods to drive the industry towards high efficiency, precision, and intelligence. Summary of the Invention

[0007] The present invention aims to construct a data acquisition module and sensor module system, and use sensors such as weight, flow rate, temperature and speed to accurately collect data from key parts of the mixing plant such as raw material silos, pipelines and mixers. By relying on the data acquisition equipment and the collaboration of various modules, the effective acquisition of data throughout the production process can be achieved, thus laying a solid data foundation for the overall optimization of automated production management.

[0008] To address the aforementioned deficiencies or improvement needs of existing technologies, as a first aspect of this invention, the present invention provides a fully automated production management and global optimization system for a mixing plant, comprising:

[0009] The system includes: a data acquisition module configured for precise control of raw material proportions and monitoring of equipment operating status; a data processing module configured for data preprocessing and analysis of the acquired data; a business logic module configured for production, scheduling, and inventory management; and an application management module configured for production information visualization, data-driven decision-making, and mobile management.

[0010] The data acquisition module uses sensor modules to perform precision control on the raw material ratio, and establishes a dynamic parameter correction model and adaptive adjustment mechanism in collaboration with the sensor modules.

[0011] The data processing module includes a data cleaning module configured to perform noise reduction, error correction, and format unification processing on the collected data. The data cleaning module establishes a data anomaly alarm module through a dynamic threshold management mechanism.

[0012] Furthermore, the data acquisition module also includes a data acquisition device, which converts the analog signals acquired by the configured sensor module into digital signals and transmits them to the data processing module.

[0013] Furthermore, the sensor module includes: a weight sensor installed in the raw material silo, a flow sensor installed in the liquid raw material conveying pipeline, temperature sensors distributed inside and around the mixing equipment, and a speed sensor installed on the mixing shaft of the mixing equipment.

[0014] Furthermore, the dynamic parameter correction model is as follows:

[0015] The dynamic parameter correction model achieves dynamic adaptation of all factors in the production process through multi-dimensional mathematical modeling and real-time data interaction. Its core functions can be summarized as follows:

[0016] Based on real-time data collected by sensors, such as temperature, rotation speed, flow rate, and weight, environmental interference is compensated for using a temperature correction formula, as shown below:

[0017] ,

[0018] in, The corrected target temperature; The ambient temperature; For initial monitoring of the internal temperature of the equipment / materials; As a reference temperature standard, This is a temperature correction factor;

[0019] Optimize the mixing state of materials using the mixing uniformity index:

[0020] ,

[0021] in, The expression represents the mixing uniformity index, where k2 is the uniformity coefficient and k3 is the attenuation coefficient. To achieve the optimal rotational speed; This refers to the actual rotational speed;

[0022] Controlling raw material ratios using flow rate adjustment formulas and weight thresholds:

[0023] ,

[0024] ,

[0025] in, Indicates the real-time operating frequency of the device. The initial frequency, This indicates real-time traffic monitoring. For target traffic, , This includes two sets of weight measurements and the "theoretical batch quantity" and "actual weighing value" of the same material. This represents the maximum permissible weight deviation.

[0026] Furthermore, the adaptive adjustment mechanism is as follows:

[0027] After identifying anomalies through a dynamic parameter correction model, the target parameters of the actuator are calculated in real time based on the process mathematical model. The core is the mathematical mapping of "deviation-adjustment amount".

[0028] Known current flow deviation Calculated using proportional-integral methods:

[0029] ,

[0030] In the formula, Indicates a new frequency output; Indicates the initial reference frequency; This indicates the frequency adjustment intensity corresponding to a unit flow deviation; This represents the difference between the actual traffic volume and the target traffic volume. This indicates the frequency adjustment intensity corresponding to the cumulative flow deviation per unit time.

[0031] After compensating for ambient temperature, the heating power needs to be adjusted.

[0032] ,

[0033] In the formula, This indicates the actual power that the actuator needs to output; This indicates the initial operating power of the temperature control equipment; This indicates the power compensation ratio corresponding to a unit of "normalized temperature deviation"; The corrected target temperature; For initial monitoring of the internal temperature of the equipment / materials; As a reference temperature standard;

[0034] The adjustment command is sent to the actuator, and the physical quantity is changed through the electrical / hydraulic control module. The core is the accurate conversion of "command-action". After the adjustment action is completed, the parameters are re-collected through the feedback verification mechanism to verify whether the standard is met. If the standard is not met, a secondary adjustment is triggered.

[0035] Furthermore, the feedback verification mechanism specifically includes:

[0036] Feedback verification mechanisms are divided into static verification and dynamic verification; static verification is for situations after a single adjustment; for traffic scenarios, after adjustment... If the condition is met, the system will be deemed acceptable; otherwise, proportional-integral calculations will be triggered again for compensation adjustment. For temperature-related scenarios: If the condition is deemed satisfactory, the temperature compensation mechanism will be triggered again for adjustment.

[0037] Dynamic verification is designed for continuous production scenarios; it uses a sliding window to perform statistical analysis on various aspects. Make the following adjustments:

[0038] ,

[0039] In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; This represents the standard deviation of the error within the sliding window; Indicates the target standard deviation allowed by the process; Indicates the fluctuation amplification factor;

[0040] ,

[0041] In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; Indicates a sliding window for temperature deviation; A sliding window representing the rate of change of ambient temperature; Indicates the maximum fluctuation threshold of the target. Indicates the environmental interference coefficient;

[0042] ,

[0043] In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; Indicates the integral correction factor; This represents the cumulative sum of temperature deviations; Indicates the number of window cycles; This represents the target steady-state error.

[0044] Furthermore, the dynamic threshold management mechanism is as follows:

[0045] For any production data sequence Based on sliding window The dynamic threshold can be expressed as:

[0046] Upper threshold: ;

[0047] Lower threshold: ;

[0048] in, This is the average of the data within the window, reflecting the current operating condition baseline; The standard deviation of the data within the window reflects the degree of fluctuation. Indicates window size; Represents the confidence level coefficient;

[0049] Let the newly collected data be The abnormal alarm triggering conditions are:

[0050] ,

[0051] Combined with the alarm classification mechanism, it can be expanded to:

[0052] Level 1 alarm: And continuously m times;

[0053] Level 2 alarm: Or n consecutive Level 1 alarms; The threshold for the number of times an alarm level is triggered;

[0054] To adapt to changes in operating conditions, the confidence coefficient of the dynamic threshold is... Self-optimization based on historical anomaly data:

[0055] ,

[0056] In the formula, This represents the actual anomaly detection rate; Indicates the target anomaly detection rate; This represents the optimization step size coefficient.

[0057] Furthermore, the business logic module includes: a production management module configured to formulate production plans based on the results of the data analysis module and monitor the production process in real time; a scheduling management module configured to schedule equipment and personnel and optimize resource allocation through preset methods based on the production plan and the real-time status of equipment and personnel; and an inventory management module configured to monitor raw material inventory and finished product inventory in real time and automatically make purchase requests and sales warnings based on preset raw material and finished product inventory thresholds.

[0058] Furthermore, the application management module includes: a monitoring visualization interface, a report generation module, and a mobile application; the monitoring visualization interface displays the production process and equipment operating status in an intuitive graphical way; the report generation module automatically generates various reports, including daily, monthly, and annual production reports, according to a preset time period; the mobile application is configured to allow users to view production information at any time via mobile devices.

[0059] As a second aspect of the present invention, a global optimization method for the fully automated production management of a mixing plant is also provided, comprising:

[0060] S1. In unmanned loader operation scenarios, sensor modules are deployed to perform precision control of raw material ratios and monitor equipment operating status; a dynamic parameter correction model and adaptive adjustment mechanism are established through sensor collaboration.

[0061] S2. Configure an adaptive sliding window for different production data, calculate the mean and standard deviation of the data within the window in real time, and establish a dynamic threshold management mechanism to complete data anomaly alarms;

[0062] S3. After the raw material transport vehicle arrives, the system completes the raw material information identification, automatically records and updates the inventory data; the scheduling module arranges tasks according to orders and production plans, allocates equipment and raw materials, and generates production instructions;

[0063] S4. After generation and discharge are completed, record discharge information including discharge time, quantity and quality to enable traceability and management of the production process; and regularly conduct comprehensive analysis of production data, dynamically optimize the production process, and adjust production parameters and scheduling rules based on the analysis results.

[0064] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0065] 1. The present invention provides a global optimization method for the fully automated production management of a mixing plant, achieving precise data governance through a dynamic threshold management mechanism. In the data acquisition phase, sliding window technology is used to analyze the central tendency and dispersion of production data, thereby dynamically setting the boundaries of the normal data range. The data cleaning module determines anomalies based on this threshold. When data exceeds the range, different levels of anomalies are addressed with measures such as data correction, marking as invalid, or triggering shutdown for maintenance. This mechanism effectively identifies and promptly handles data anomalies, replacing manual intervention to correct data, ensuring the accuracy and reliability of the data input into the system, and laying a solid foundation for production decisions.

[0066] 2. The global optimization method for the fully automated production management of a mixing plant of the present invention achieves adaptive production through a closed-loop collaborative mechanism throughout the entire process. During production operation, dynamic thresholds are updated in real time according to changes in operating conditions, and the information is fed back to the data cleaning, alarm, and optimization modules. The optimization model adjusts the production strategy based on the operating condition benchmark and fluctuations reflected by the dynamic thresholds. The execution layer controls the batching, mixing, and conveying equipment in conjunction with the strategy. The results of anomaly handling will reversely correct the calculation basis of the dynamic thresholds, thus forming a complete closed loop of "data acquisition - threshold calculation - optimization decision - equipment execution - feedback iteration". This mechanism enables the system to respond in real time to changes in material characteristics and fluctuations in equipment status, ensuring that the production management of the mixing plant continuously adapts to actual operating conditions. Attached Figure Description

[0067] Figure 1This is a module structure diagram of a fully automated production management global optimization system for a mixing plant according to an embodiment of the present invention;

[0068] Figure 2 This is a diagram illustrating the configuration of the data acquisition module according to an embodiment of the present invention;

[0069] Figure 3 This is a diagram illustrating the configuration of the data processing module according to an embodiment of the present invention.

[0070] Figure 4 This is a diagram illustrating the business logic module structure of an embodiment of the present invention;

[0071] Figure 5 This is a diagram illustrating the application demonstration module configuration of an embodiment of the present invention;

[0072] Figure 6 This is a schematic diagram of the raw material silo and weight sensor according to an embodiment of the present invention;

[0073] Figure 7 This is a schematic diagram of a solid material conveying device according to an embodiment of the present invention;

[0074] Figure 8 This is a flowchart of a global optimization method for fully automated production management of a mixing plant, according to an embodiment of the present invention. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0076] Example 1

[0077] Please refer to Figure 1 This embodiment 1 provides a global optimization system for fully automated production management of a mixing plant, including:

[0078] The system includes: a data acquisition module configured for precise control of raw material proportions and monitoring of equipment operating status; a data processing module configured for data preprocessing and analysis of the acquired data; a business logic module configured for production, scheduling, and inventory management; and an application management module configured for production information visualization, data-driven decision-making, and mobile management.

[0079] The data acquisition module uses sensor modules to perform precision control on the raw material ratio, and establishes a dynamic parameter correction model and adaptive adjustment mechanism in collaboration with the sensor modules.

[0080] The data processing module includes a data cleaning module configured to perform noise reduction, error correction, and format unification processing on the collected data. The data cleaning module establishes a data anomaly alarm module through a dynamic threshold management mechanism.

[0081] The business logic modules include: a production management module configured to formulate production plans based on the results of the data analysis module and monitor the production process in real time; a scheduling management module configured to schedule equipment and personnel and optimize resource allocation based on the production plan and the real-time status of equipment and personnel through preset methods; and an inventory management module configured to monitor raw material inventory and finished product inventory in real time and automatically make purchase requests and sales warnings based on preset raw material and finished product inventory thresholds.

[0082] The application management module includes: a monitoring visualization interface, a report generation module, and a mobile application; the monitoring visualization interface displays the production process and equipment operating status in an intuitive graphical way; the report generation module automatically generates various reports, including daily, monthly, and annual production reports, according to a preset time period; the mobile application is configured to allow users to view production information at any time via mobile devices.

[0083] (1) Data acquisition module

[0084] Please refer to Figure 2 In unmanned loader operation scenarios, the sensor modules and equipment of the data acquisition module need to address core issues such as accurate control of raw material proportioning and monitoring of equipment operating status to achieve precise data collection and application. The data acquisition module includes sensor modules and data acquisition equipment.

[0085] In the sensor module, please refer to... Figure 6 A weight sensor (Mettler-Toledo PW15) is installed in the raw material silo to monitor real-time weight changes of the raw materials. By monitoring these weight changes in real time and combining the amount (G) grabbed by the loader in a single operation, a mixing formula is established: Target mixing ratio = Preset ratio × Total demand. When the weight difference exceeds a threshold (e.g., ±5kg), the system automatically triggers a replenishment or reduction command to ensure mixing accuracy is controlled within ±2%. Please refer to [reference needed]. Figure 7Solid materials are transported to the mixing tube via a belt conveyor. Flow sensors are installed in the liquid raw material conveying pipeline to accurately measure the flow rate of the liquid raw material. Real-time flow data (Q, unit L / min) is collected and, combined with the mixing tank volume (V) and mixing time (t), the real-time injection volume is calculated. When flow fluctuations exceed the set range (e.g., ±3%), the system automatically adjusts the pump frequency (f). Temperature sensors (PT100 three-wire system) are distributed inside the mixing equipment and in the surrounding environment to collect temperature data in real time during the mixing process. When the internal temperature (Tinternal) exceeds the process upper limit (e.g., 60℃), the cooling system is triggered to start and correct the mixing temperature control parameters in real time. Speed ​​sensors are installed on the mixing shaft of the mixing equipment to obtain the shaft speed information, monitor the speed (n, unit r / min) in real time, and establish a mixing uniformity model. The data acquisition equipment converts the analog signals collected by various sensors into digital signals and transmits them to the data processing module.

[0086] In a preferred embodiment, a dynamic parameter correction model is established based on the above sensor settings, specifically as follows:

[0087] The dynamic parameter correction model achieves dynamic adaptation of all factors in the production process through multi-dimensional mathematical modeling and real-time data interaction. Its core functions can be summarized as follows:

[0088] Based on real-time data collected by sensors, such as temperature, rotation speed, flow rate, and weight, environmental interference is compensated for using a temperature correction formula, as shown below:

[0089] ,

[0090] in, The corrected target temperature; The ambient temperature; For initial monitoring of the internal temperature of the equipment / materials; As a reference temperature standard, This is a temperature correction factor;

[0091] Optimize the mixing state of materials using the mixing uniformity index:

[0092] ,

[0093] in, The expression represents the mixing uniformity index, where k2 is the uniformity coefficient and k3 is the attenuation coefficient. To achieve the optimal rotational speed; This refers to the actual rotational speed;

[0094] Controlling raw material ratios using flow rate adjustment formulas and weight thresholds:

[0095] ,

[0096] ,

[0097] in, Indicates the real-time operating frequency of the device. The initial frequency, This indicates real-time traffic monitoring. For target traffic, , This includes two sets of weight measurements and the "theoretical batch quantity" and "actual weighing value" of the same material. This represents the maximum permissible weight deviation.

[0098] In a preferred embodiment, an adaptive adjustment mechanism is also established, specifically as follows:

[0099] After identifying anomalies through a dynamic parameter correction model, the target parameters of the actuator are calculated in real time based on the process mathematical model. The core is the mathematical mapping of "deviation-adjustment amount".

[0100] Known current flow deviation Calculated using proportional-integral methods:

[0101] ,

[0102] In the formula, Indicates a new frequency output; Indicates the initial reference frequency; This indicates the frequency adjustment intensity corresponding to a unit flow deviation; This represents the difference between the actual traffic volume and the target traffic volume. This indicates the frequency adjustment intensity corresponding to the cumulative flow deviation per unit time.

[0103] After compensating for ambient temperature, the heating power needs to be adjusted.

[0104] ,

[0105] In the formula, This indicates the actual power that the actuator needs to output; This indicates the initial operating power of the temperature control equipment; This indicates the power compensation ratio corresponding to a unit of "normalized temperature deviation"; The corrected target temperature; For initial monitoring of the internal temperature of the equipment / materials; As a reference temperature standard;

[0106] The adjustment command is sent to the actuator, and the physical quantity is changed through the electrical / hydraulic control module. The core is the accurate conversion of "command-action". After the adjustment action is completed, the parameters are re-collected through the feedback verification mechanism to verify whether the standard is met. If the standard is not met, a secondary adjustment is triggered.

[0107] In a specific preferred embodiment, the feedback verification mechanism is as follows:

[0108] Feedback verification mechanisms are divided into static verification and dynamic verification; static verification is for situations after a single adjustment; for traffic scenarios, after adjustment... If the condition is met, the system will be deemed acceptable; otherwise, proportional-integral calculations will be triggered again for compensation adjustment. For temperature-related scenarios: If the condition is deemed satisfactory, the temperature compensation mechanism will be triggered again for adjustment.

[0109] Dynamic verification is designed for continuous production scenarios; it uses a sliding window to perform statistical analysis on various aspects. Make the following adjustments:

[0110] ,

[0111] In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; This represents the standard deviation of the error within the sliding window; Indicates the target standard deviation allowed by the process; Indicates the fluctuation amplification factor;

[0112] ,

[0113] In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; Indicates a sliding window for temperature deviation; A sliding window representing the rate of change of ambient temperature; Indicates the maximum fluctuation threshold of the target. Indicates the environmental interference coefficient;

[0114] ,

[0115] In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; Indicates the integral correction factor; This represents the cumulative sum of temperature deviations; Indicates the number of window cycles; This represents the target steady-state error.

[0116] (2) Data processing module

[0117] Please refer to Figure 3 The data processing module consists of a data cleaning module, a data storage module, and a data analysis module. The data cleaning module performs noise reduction, error correction, and format standardization on the collected data to ensure accuracy and consistency; error correction employs a dynamic threshold management mechanism.

[0118] The data storage module uses a combination of relational and non-relational databases to classify and store the cleaned data. The relational database is used to store structured production data, such as production plans and equipment operating parameters, while the non-relational database is used to store unstructured data, such as real-time data sequences collected by sensors. The data analysis module uses big data analytics and machine learning algorithms to perform in-depth analysis on the stored data, uncovering patterns and potential problems behind the data.

[0119] In a preferred embodiment, the dynamic threshold management mechanism is as follows:

[0120] For any production data sequence Based on sliding window The dynamic threshold can be expressed as:

[0121] Upper threshold: ;

[0122] Lower threshold: ;

[0123] in, This is the average of the data within the window, reflecting the current operating condition baseline; The standard deviation of the data within the window reflects the degree of fluctuation. Indicates window size; Represents the confidence level coefficient;

[0124] Let the newly collected data be The abnormal alarm triggering conditions are:

[0125] ,

[0126] Combined with the alarm classification mechanism, it can be expanded to:

[0127] Level 1 alarm: And continuously m times;

[0128] Level 2 alarm: Or n consecutive Level 1 alarms; The threshold for the number of times an alarm level is triggered;

[0129] To adapt to changes in operating conditions, the confidence coefficient of the dynamic threshold is... Self-optimization based on historical anomaly data:

[0130] ,

[0131] In the formula, This represents the actual anomaly detection rate; Indicates the target anomaly detection rate; This represents the optimization step size coefficient.

[0132] (3) Business logic module

[0133] Please refer to Figure 4 The business logic modules encompass production management, scheduling management, and inventory management. The production management module formulates scientific and reasonable production plans based on the results of the data analysis module and monitors the production process in real time. Upon detecting production anomalies, such as deviations in ingredient ratios or insufficient mixing time, it promptly issues alarms and automatically adjusts production parameters to achieve quality control. The scheduling management module, based on the production plan and the real-time status of equipment and personnel, uses intelligent algorithms to schedule equipment and personnel, optimizing resource allocation and improving equipment utilization and personnel efficiency. The inventory management module monitors raw material and finished product inventory in real time. When raw material inventory falls below a preset threshold, it automatically generates a purchase request; when finished product inventory reaches a certain level, it promptly reminds sales personnel to make sales arrangements.

[0134] (4) Application Management Module

[0135] Please refer to Figure 5 The application management module includes a monitoring interface, a report generation module, and a mobile application. The Unity3D development interface displays the production process and equipment operating status in an intuitive graphical way, with a red flashing alarm for abnormal points (response time <1s) to facilitate operators' real-time monitoring of the production site. The report generation module automatically generates various reports such as daily, monthly, and annual production reports according to preset time periods, providing management personnel with decision-making support. The mobile application allows management personnel to view production information anytime, anywhere via mobile phone or tablet, enabling remote management.

[0136] Example 2

[0137] Please refer to Figure 8 This embodiment 2 provides a global optimization method for the fully automated production management of a mixing plant, including:

[0138] S1. In unmanned loader operation scenarios, sensor modules are deployed to perform precision control of raw material ratios and monitor equipment operating status; a dynamic parameter correction model and adaptive adjustment mechanism are established through sensor collaboration.

[0139] S2. Configure an adaptive sliding window for different production data, calculate the mean and standard deviation of the data within the window in real time, and establish a dynamic threshold management mechanism to complete data anomaly alarms;

[0140] S3. After the raw material transport vehicle arrives, the system completes the raw material information identification, automatically records and updates the inventory data; the scheduling module arranges tasks according to orders and production plans, allocates equipment and raw materials, and generates production instructions;

[0141] S4. After generation and discharge are completed, record discharge information including discharge time, quantity and quality to enable traceability and management of the production process; and regularly conduct comprehensive analysis of production data, dynamically optimize the production process, and adjust production parameters and scheduling rules based on the analysis results.

[0142] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fully automated production management and global optimization system for a mixing plant, characterized in that, include: A data acquisition module configured to precisely control the raw material ratio and monitor the equipment operating status; A data processing module configured to perform data preprocessing and data analysis on the collected data; The business logic module is configured for production, scheduling, and inventory management, and the application management module is configured for production information visualization, data-driven decision-making, and mobile management. The data acquisition module uses sensor modules to perform precision control on the raw material ratio, and establishes a dynamic parameter correction model and adaptive adjustment mechanism in collaboration with the sensor modules. The data processing module includes a data cleaning module configured to perform noise reduction, error correction, and format unification processing on the collected data. The data cleaning module establishes a data anomaly alarm module through a dynamic threshold management mechanism. The dynamic parameter correction model is as follows: The dynamic parameter correction model achieves dynamic adaptation of all factors in the production process through multi-dimensional mathematical modeling and real-time data interaction, specifically as follows: Based on real-time data collected by sensors, including temperature, rotational speed, flow rate, and weight, environmental interference is compensated for using a temperature correction formula, as shown below: ， in, The corrected target temperature; The ambient temperature; For initial monitoring of the internal temperature of the equipment / materials; As a reference temperature standard, This is a temperature correction factor; Optimize the mixing state of materials using the mixing uniformity index: ， in, This indicates the mixing uniformity index. This is the uniformity coefficient. The attenuation coefficient is... To achieve the optimal rotational speed; This refers to the actual rotational speed; Controlling raw material ratios using flow rate adjustment formulas and weight thresholds: ， ， in, Indicates the real-time operating frequency of the device. The initial frequency, This indicates real-time traffic monitoring. For target traffic, , This includes two sets of weight measurements and the "theoretical batch quantity" and "actual weighing value" of the same material. This is the maximum permissible weight deviation; Meanwhile, the adaptive adjustment mechanism is as follows: After identifying anomalies through a dynamic parameter correction model, the target parameters of the actuator are calculated in real time based on the process mathematical model. The core is the mathematical mapping of "deviation-adjustment amount". Known current flow deviation Calculated using proportional-integral methods: ， In the formula, Indicates a new frequency output; Indicates the initial reference frequency; This indicates the frequency adjustment intensity corresponding to a unit flow deviation; This indicates real-time traffic monitoring; For target traffic; This represents the difference between the actual traffic volume and the target traffic volume. This indicates the frequency adjustment intensity corresponding to the cumulative flow deviation per unit time. After compensating for ambient temperature, the heating power needs to be adjusted. ， In the formula, This indicates the actual power that the actuator needs to output; This indicates the initial operating power of the temperature control equipment; This indicates the power compensation ratio corresponding to a unit of "normalized temperature deviation"; The corrected target temperature; For initial monitoring of the internal temperature of the equipment / materials; As a reference temperature standard; The adjustment command is sent to the actuator, and the physical quantity is changed through the electrical / hydraulic control module. The core is the accurate conversion of "command-action". After the adjustment action is completed, the parameters are re-collected through the feedback verification mechanism to verify whether the standard is met. If the standard is not met, a secondary adjustment is triggered.

2. The fully automated production management global optimization system for a mixing plant according to claim 1, characterized in that, The data acquisition module also includes a data acquisition device, which converts the analog signals acquired by the configured sensor module into digital signals and transmits them to the data processing module.

3. The fully automated production management global optimization system for a mixing plant according to claim 1, characterized in that, The sensor module includes: a weight sensor installed in the raw material silo, a flow sensor installed in the liquid raw material conveying pipeline, temperature sensors distributed inside and around the mixing equipment, and a speed sensor installed on the mixing shaft of the mixing equipment.

4. The fully automated production management global optimization system for a mixing plant according to claim 1, characterized in that, The feedback verification mechanism is specifically as follows: Feedback verification mechanisms are divided into static verification and dynamic verification; static verification is for situations after a single adjustment; for traffic scenarios, after adjustment... If the condition is met, the system will be deemed acceptable; otherwise, proportional-integral calculations will be triggered again for compensation adjustment. For temperature-related scenarios: If the condition is deemed satisfactory, the temperature compensation mechanism will be triggered again for adjustment. Dynamic verification is designed for continuous production scenarios; it uses a sliding window to perform statistical analysis on various aspects. Make the following adjustments: ， In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; This represents the standard deviation of the error within the sliding window; Indicates the target standard deviation allowed by the process; Indicates the fluctuation amplification factor; ， In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; Indicates a sliding window for temperature deviation; A sliding window representing the rate of change of ambient temperature; Indicates the maximum fluctuation threshold of the target. Indicates the environmental interference coefficient; ， In the formula, express The new proportionality coefficient; express The baseline proportional coefficient; Indicates the integral correction factor; This represents the cumulative sum of temperature deviations; Indicates the number of window cycles; This represents the target steady-state error.

5. The fully automated production management global optimization system for a mixing plant according to claim 1, characterized in that, The dynamic threshold management mechanism is as follows: For any production data sequence Based on sliding window The dynamic threshold is specifically as follows: Upper threshold: ; Lower threshold: ; in, This is the average of the data within the window, reflecting the current operating condition baseline; The standard deviation of the data within the window reflects the degree of fluctuation. Indicates window size; Represents the confidence level coefficient; Let the newly collected data be The abnormal alarm triggering conditions are: ， In conjunction with the alarm classification mechanism, specifically: Level 1 alarm: And continuously m times; Level 2 alarm: Or n consecutive Level 1 alarms; The threshold for the number of times an alarm level is triggered; To adapt to changes in operating conditions, the confidence coefficient of the dynamic threshold is... Self-optimization based on historical anomaly data: ， In the formula, This represents the actual anomaly detection rate; Indicates the target anomaly detection rate; This represents the optimization step size coefficient.

6. The fully automated production management global optimization system for a mixing plant according to claim 1, characterized in that, The business logic modules include: a production management module configured to formulate production plans based on the results of the data analysis module and monitor the production process in real time; a scheduling management module configured to schedule equipment and personnel and optimize resource allocation based on the production plan and the real-time status of equipment and personnel through preset methods; and an inventory management module configured to monitor raw material inventory and finished product inventory in real time and automatically make purchase requests and sales warnings based on preset raw material and finished product inventory thresholds.

7. The fully automated production management global optimization system for a mixing plant according to claim 1, characterized in that, The application management module includes: a monitoring visualization interface, a report generation module, and a mobile application; the monitoring visualization interface displays the production process and equipment operating status in an intuitive graphical way; the report generation module automatically generates various reports, including daily, monthly, and annual production reports, according to a preset time period; the mobile application is configured to allow users to view production information at any time via mobile devices.

8. A global optimization method for fully automated production management of a mixing plant, applied to the implementation of a fully automated production management global optimization system for a mixing plant as described in any one of claims 1-7, characterized in that, include: S1. In unmanned loader operation scenarios, sensor modules are deployed to perform precision control of raw material ratios and monitor equipment operating status; a dynamic parameter correction model and adaptive adjustment mechanism are established through sensor collaboration. S2. Configure an adaptive sliding window for different production data, calculate the mean and standard deviation of the data within the window in real time, and establish a dynamic threshold management mechanism to complete data anomaly alarms; S3. After the raw material transport vehicle arrives, the system completes the raw material information identification, automatically records and updates the inventory data; the scheduling module arranges tasks according to orders and production plans, allocates equipment and raw materials, and generates production instructions; S4. After generation and discharge are completed, record discharge information including discharge time, quantity and quality to enable traceability and management of the production process; and regularly conduct comprehensive analysis of production data, dynamically optimize the production process, and adjust production parameters and scheduling rules based on the analysis results.

Images