Material transmission control and adjustment method and system based on the Internet of Things

Abstract

The present invention discloses a material transmission control and adjustment method and system based on the Internet of Things, which specifically relates to the field of material transmission control technology. It dynamically adjusts sensor weights based on the Kalman filter algorithm to optimize the positioning accuracy of material barrels; identifies and locates abnormal risk areas and predicts high-risk periods based on the spatiotemporal feature analysis of historical data; combines a digital twin model and a path planning algorithm to optimize the transmission path and speed; dynamically adjusts resource scheduling strategies and calculates the optimal intervention path based on abnormal prediction results and path planning parameters; monitors the transmission status of material barrels in real time to ensure the safety and efficiency of the transmission process; a fault probability calculation module dynamically evaluates the fault probability based on the real-time transmission status; realizes precise control and dynamic adjustment of the material transmission process, and solves the problems of low positioning accuracy, untimely abnormal prediction, and unoptimized path planning in traditional material transmission systems.

Description

Technical Field

[0001] The present invention relates to the technical field of material transmission control, and more specifically, to a material transmission control and adjustment method and system based on the Internet of Things. Background Art

[0002] Material transfer systems are widely used in automated production lines, logistics warehousing, and material handling. In these systems, material barrels serve as basic loading units, and their precise positioning and predetermined path control are crucial to the efficiency and safety of the system.

[0003] Traditional material conveying systems usually rely on a single positioning method (such as optical sensors, ultrasonic sensors, etc.), but existing material conveying systems face problems such as environmental interference and signal instability, such as equipment vibration, path curves or obstruction of intersections. These factors will affect the accuracy of sensor signals and lead to misjudgment of the position of material barrels; at the same time, traditional positioning correction methods mostly rely on simple path correction and data weighted averaging, and fail to effectively utilize multi-sensor data fusion, resulting in low correction efficiency and slow response, making it difficult to provide accurate and real-time positioning information in complex environments. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a material transportation control and adjustment method and system based on the Internet of Things. By introducing advanced sensor data fusion technology, dynamic trajectory prediction and correction algorithm, and intelligent positioning verification mechanism, it can significantly improve the positioning accuracy and system stability of material barrels in complex environments, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a material transmission control and adjustment method based on the Internet of Things, comprising the following steps:

[0006] Positioning Correction: Analyzes the trajectory deviation of the material barrel based on sensor data, corrects the material barrel positioning in real time, and adjusts the control strategy to ensure the safe transmission of the material barrel. Combines the Kalman filter algorithm to fuse multi-sensor data and dynamically adjusts sensor weights to optimize positioning accuracy. Real-time monitoring of environmental noise and adjusts sensor weights according to the environmental noise level to reduce the impact of environmental noise on positioning.

[0007] Location anomaly prediction and resource scheduling: Analyze location anomaly collections to obtain the spatiotemporal characteristics of location anomalies; identify location anomaly risk areas based on spatiotemporal characteristics of historical data; build a location anomaly risk prediction model based on spatiotemporal characteristics, and use prediction algorithms (such as time series analysis and machine learning models) to predict the probability of occurrence of location anomaly risk areas in each time period to identify high-risk periods for anomaly risk areas; and perform fault prevention and resource scheduling based on high-risk periods for anomaly risk areas.

[0008] Path planning: Path planning is performed through the digital twin model of the material transfer device to optimize the transfer speed, predetermined path, and transfer safety.

[0009] Preferably, the positioning correction includes:

[0010] The initial position and velocity of the material barrel are obtained as the initial input of the Kalman filter, and the covariance matrix is ​​initialized. A smaller covariance value indicates a high degree of confidence in the initial state. The noise covariance matrix is ​​set based on the noise characteristics of the position sensor and velocity sensor. The value of the noise covariance matrix is ​​negatively correlated with the sensor weight. When the noise is high, the reliance on the sensor is reduced to ensure positioning accuracy.

[0011] The Kalman filter fuses data from the position sensor and speed sensor, combines the real-time data from the speed sensor and integrals to predict the position of the material barrel, and dynamically adjusts the weights of the position sensor and speed sensor. Environmental sensors monitor external noise sources in real time, and dynamically adjust the noise model and sensor weights in the Kalman filter based on the noise level. In high-noise environments, the reliance on the position sensor is reduced, the weight of the speed sensor is increased, and position correction is optimized.

[0012] The sensor data is cross-validated to ensure data consistency. When anomalies or sensor failures are found, the error detection and correction mechanism is used to automatically adjust, thereby improving positioning accuracy and robustness.

[0013] Preferably, the material barrel transmission process is tracked at a fixed frequency, and in an unobstructed area, the trajectory deviation between the actual path of the material barrel and the predetermined path is verified by a laser device; when the cumulative value of the trajectory deviation is higher than a threshold, a start instruction for positioning anomaly prediction and resource scheduling is generated.

[0014] Preferably, the method includes the step of locating anomaly analysis:

[0015] When the material barrel is positioned abnormally, the trajectory deviation of the material barrel is analyzed in combination with the data from the speed and position sensors to determine whether the deviation is caused by a change in motion state, equipment failure, or sensor inaccuracy. Depending on the cause of the deviation, the control strategy is adjusted, the equipment failure is repaired, or positioning corrections are performed until the material barrel is positioned normally.

[0016] Preferably, the path planning includes:

[0017] Build a digital twin model of the material transfer device, which is used to realize the transfer, waiting, rotation, and lifting operations of the material barrel;

[0018] Matching transmission planning parameters for the material barrel based on transmission priority and path planning algorithm, wherein the transmission planning parameters include transmission speed, predetermined path and timing data of operation instructions;

[0019] The constraint function is that the weighted sum of transmission efficiency and transmission cost is minimized when the transmission safety requirements are met; transmission safety means that the physical distance between the material barrels does not exceed the safe distance during the transmission process.

[0020] Preferably, the method includes a fault avoidance step based on multiple transmission tasks, including:

[0021] Step 1: Divide the material transmission device into several independent transmission lines and assign a unique number to each transmission line; the transmission lines are connected by a steering device, which includes a rotating device and a lifting device. The steering device is used to ensure that the material barrel can be smoothly transferred from one transmission line to another;

[0022] Step 2: Material barrel information collection and transmission: Monitor the real-time status of each material barrel during the transmission task, obtain the material barrel preset information, material barrel weight, material barrel actual image, real-time location, and transmit it to the data analysis platform via wireless communication;

[0023] Step 3: Position Verification and Anomaly Detection: If a deviation in the barrel's trajectory or abnormal state is detected, an alarm is triggered and a correction mechanism is initiated. An abnormal state refers to an abnormal state of the barrel during transportation (such as product attribute information).

[0024] Step 4: Risk control adjustment: Based on the actual information of the material barrel and the pre-set transmission planning parameters, the digital twin model is used to predict the fault location information without intervention; based on the fault location information, the optimal intervention path is calculated or backup equipment is activated; the intervention path includes adjusting the transmission speed of the material barrel and adjusting the predetermined path.

[0025] Preferably, the process of obtaining the optimal intervention path to avoid failure by using a genetic algorithm includes:

[0026] Initialize the population: Set an initial set of intervention plans (i.e., "population") based on the current state of the transport system (e.g., positioning, speed, and path planning of the material barrels). Each initial intervention plan has a different treatment method (e.g., a different path or speed).

[0027] Fitness evaluation: By simulating the performance of each intervention plan in the current digital twin model, the fitness of the intervention plan is evaluated, that is, the effectiveness of the intervention plan in avoiding failures and optimizing material transportation;

[0028] Selection, crossover, and mutation: Screen intervention options based on fitness for crossover (combination) and mutation (modification) to generate new solutions, and continuously improve and optimize until the optimal intervention path is found.

[0029] To achieve the purpose of the present invention, a material transportation control and regulation system based on the Internet of Things is provided, comprising:

[0030] The multi-sensor fusion module, based on the Kalman filter algorithm, fuses data from position sensors, velocity sensors, and environmental sensors, dynamically adjusts sensor weights, and optimizes the positioning accuracy of the material barrel. It also monitors environmental noise in real time and adjusts sensor weights based on noise levels to reduce the impact of noise on positioning.

[0031] The anomaly prediction module identifies and locates abnormal risk areas based on the spatiotemporal characteristics of historical data and builds an abnormal risk prediction model. It uses time series analysis or machine learning models to predict the probability of occurrence of abnormal risk areas in each time period and identify high-risk periods.

[0032] The path optimization module, based on the digital twin model of the material transmission device, combines transmission priority and path planning algorithms to match transmission planning parameters for material buckets. The constraint function is to minimize the weighted sum of transmission efficiency and transmission cost while meeting transmission safety requirements.

[0033] Dynamic Scheduling Module: Dynamically adjusts resource scheduling strategies based on abnormal risk areas and high-risk time periods provided by the abnormality prediction module; predicts fault location information based on the digital twin model and calculates the optimal intervention path;

[0034] The transmission execution module executes the material barrel transmission task according to the optimal intervention path provided by the dynamic scheduling module; it monitors the transmission status of the material barrel in real time to ensure the safety and efficiency of the transmission process.

[0035] The technical effects and advantages of the present invention are as follows:

[0036] The material transmission control and adjustment method based on the Internet of Things provided by the present invention, by combining the data of the speed sensor and the position sensor, corrects the positioning of the material barrel in real time, and optimizes the positioning accuracy through the Kalman filter algorithm; compared with the existing technology, the speed sensor is combined to predict the position of the material barrel and correct the error of the position sensor, which significantly improves the real-time and accuracy of positioning; based on the analysis of spatiotemporal characteristics, a positioning abnormality risk prediction model is constructed, which can dynamically predict the abnormal areas where the material barrel may appear and the probability of their occurrence; this predictive method can identify high-risk periods in advance and perform resource scheduling, such as enabling backup sensors or scheduling maintenance personnel, to effectively prevent the occurrence of system failures; the material transmission device is modeled through a digital twin model, and the predetermined path, speed and safety of the material barrel are dynamically optimized based on the transmission priority and path planning algorithm; by comprehensively considering the weighted and minimization of transmission efficiency and cost, the working efficiency and safety of the material transmission system are further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the material transmission control and adjustment method of the present invention.

[0038] Figure 2 This is a flow chart of the material transmission control and adjustment method based on fault avoidance of the present invention. DETAILED DESCRIPTION

[0039] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0040] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0041] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0042] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0043] Background technology, the process of material transmission can be summarized as follows: after receiving the path planning information, the process flow of parts is obtained according to the established process; the logistics barrels are transported according to the process flow, and the process from semi-finished products to finished products is realized through the process. The status of the material barrels is obtained by scanning the code. When the parts in the material barrels complete the target process, they are placed in the warehouse waiting for use; the material barrels are transported by conveyor belts, and the transmission process instructions include transmission, waiting, rotation, and lifting operations.

[0044] Example 1, see Figure 1 The material transmission control and adjustment method flow chart of the present invention provides the following Figure 1 A material transmission control and adjustment method based on the Internet of Things is shown, comprising:

[0045] Positioning correction: Analyze the trajectory deviation of the material barrel based on sensor data, correct the material barrel positioning in real time, and adjust the control strategy to ensure the safe transportation of the material barrel; combine the Kalman filter algorithm to fuse multi-sensor data and dynamically adjust sensor weights to optimize positioning accuracy; monitor the environmental noise in real time and adjust the sensor weights according to the environmental noise level to reduce the impact of environmental noise on positioning; the environmental noise includes at least temperature, humidity, and vibration;

[0046] Location anomaly prediction and resource scheduling: Analyze the location anomaly set to obtain the spatiotemporal characteristics of the location anomaly occurrence. Build a location anomaly risk prediction model based on the spatiotemporal characteristics. Use prediction algorithms (such as time series analysis and machine learning models) to predict the probability of occurrence of location anomaly risk areas in each time period, identifying high-risk periods for the anomaly risk areas. Perform fault prevention and resource scheduling based on the high-risk periods for the anomaly risk areas.

[0047] It is worth noting that in the embodiments of the present invention, if the probability of occurrence exceeds a threshold, the high-risk period of the positioning abnormality risk area is marked and an alarm is issued in a timely manner to remind the operator or initiate an automatic adjustment program (including optimizing the predetermined path of the material barrel and automatically reducing the transmission speed) to avoid the occurrence of failures. Through continuous accumulation and training of spatiotemporal feature data, the positioning abnormality risk prediction model is continuously optimized to adapt to environmental changes.

[0048] It should be further explained that in the embodiments of the present invention, resource scheduling refers to automatically dispatching resources (such as backup sensors and maintenance personnel) to high-risk periods in areas with abnormal risk, thereby ensuring the stability of the transmission system; through intelligent redundancy management, backup equipment is automatically activated when equipment or sensors fail, maintaining the continuity of material transmission; and maintenance personnel are automatically dispatched to inspect and repair equipment or areas where abnormalities frequently occur, thereby reducing downtime and improving system efficiency.

[0049] Path planning: Path planning is performed through the digital twin model of the material transfer device to optimize the transfer speed, predetermined path, and transfer safety.

[0050] It needs to be further explained in the embodiment of the present invention that the positioning correction includes:

[0051] The initial position and velocity of the material barrel are obtained as the initial input of the Kalman filter, and the covariance matrix is ​​initialized. A smaller covariance value indicates a high degree of confidence in the initial state. The noise covariance matrix is ​​set based on the noise characteristics of the position sensor and velocity sensor. The value of the noise covariance matrix is ​​negatively correlated with the sensor weight. When the noise is high, the reliance on the sensor is reduced to ensure positioning accuracy.

[0052] The Kalman filter fuses data from the position sensor and speed sensor, combines the real-time data from the speed sensor and integrals to predict the position of the material barrel, and dynamically adjusts the weights of the position sensor and speed sensor. Environmental sensors monitor external noise sources in real time, and dynamically adjust the noise model and sensor weights in the Kalman filter based on the noise level. In high-noise environments, the reliance on the position sensor is reduced, the weight of the speed sensor is increased, and position correction is optimized.

[0053] The sensor data is cross-validated to ensure data consistency. When anomalies or sensor failures are found, the error detection and correction mechanism is used to automatically adjust, thereby improving positioning accuracy and robustness.

[0054] What needs to be further explained in the embodiment of the present invention is that the material barrel transmission process is tracked at a fixed frequency, and in an unobstructed area, the trajectory deviation between the actual path of the material barrel and the predetermined path is verified by a laser device; when the cumulative value of the trajectory deviation is higher than the threshold, a start instruction for positioning anomaly prediction and resource scheduling is generated.

[0055] Explanation: The process of verifying the deviation between the actual path of the material barrel and the predetermined path by using a laser device includes:

[0056] The laser transmitter emits a laser beam, which strikes the surface of the material barrel and reflects back to the receiver. By calculating the time difference between the transmitted and received signals (time of flight) and the deflection angle of the laser beam, the system can accurately measure the distance between the material barrel and the laser sensor, thereby determining the specific location of the material barrel. The laser transmitter is installed above or to the side of the conveyor track. The laser receiver is paired with the transmitter to receive the reflected laser signal and calculate the distance data.

[0057] The deviation between the actual path and the planned path is calculated through the real-time distance data and the real-time positioning of the material barrel;

[0058] The laser device continuously monitors the real-time position of the material barrel and compares it with the position of the predetermined path. The system generates a trajectory deviation value during each sampling period. If the accumulated trajectory deviation value exceeds a set threshold (such as the total deviation within a certain distance range or time period), it means that the material barrel has deviated from the predetermined path.

[0059] Furthermore, the threshold value depends on the transmission accuracy of the material barrel, the error range of the transmission equipment, and the physical characteristics of the material barrel (such as weight and shape).

[0060] What needs to be further explained in the embodiment of the present invention is that the positioning anomaly analysis step includes:

[0061] When the material barrel is positioned abnormally, the trajectory deviation of the material barrel is analyzed in combination with the data from the speed and position sensors to determine whether the deviation is caused by a change in motion state, equipment failure, or sensor inaccuracy. Depending on the cause of the deviation, the control strategy is adjusted, the equipment failure is repaired, or positioning corrections are performed until the material barrel is positioned normally.

[0062] The explanation states that the deviation cause determination process includes: analyzing the material barrel's trajectory deviation by combining data from the speed sensor and position sensor to determine the cause of its deviation from the planned path; distinguishing whether the deviation is caused by a change in the material barrel's motion state (such as a sudden change in speed or acceleration) or an error in the planned path itself (such as equipment failure, conveyor belt curve problems, sensor inaccuracy);

[0063] Abnormal motion state handling: If the deviation is caused by a change in the motion state of the material barrel (such as a change in speed, a sudden increase in acceleration, etc.), adjust the speed control strategy and optimize the control parameters to restore the normal path of the material barrel and ensure its stable operation;

[0064] Equipment correction: If the deviation is caused by equipment failure (such as drive system failure) or conveyor belt curve problems (such as conveyor belt deviation, inconsistent curvature, etc.), adjust the operating status of the conveyor belt or recalibrate the equipment to ensure that the material barrel is smoothly transported along the predetermined track;

[0065] Positioning correction: If the deviation is caused by sensor inaccuracy (such as sensor failure or long-term drift), the positioning correction module is activated, or redundant sensors are introduced for data fusion to ensure the accuracy of sensor measurement data and restore the precise positioning of the material barrel;

[0066] If the failure risk index of the transmission line exceeds the threshold, the movement trajectory of the material barrel is adjusted to avoid the risky transmission line and ensure the stable operation of the material barrel.

[0067] It needs to be further explained in the embodiment of the present invention that the path planning includes:

[0068] Build a digital twin model of the material transfer device, which is used to realize the transfer, waiting, rotation, and lifting operations of the material barrel;

[0069] Matching transmission planning parameters for the material barrel based on transmission priority and path planning algorithm, wherein the transmission planning parameters include transmission speed, predetermined path and timing data of operation instructions;

[0070] The constraint function is that the weighted sum of transmission efficiency and transmission cost is minimized when the transmission safety requirements are met; transmission safety means that the physical distance between the material barrels does not exceed the safe distance during the transmission process.

[0071] Summary: The material transmission control and adjustment method based on the Internet of Things provided by the embodiment of the invention realizes the precise positioning and dynamic path optimization of the material barrel during the transmission process by combining sensor data analysis, Kalman filtering algorithm, multi-sensor data fusion, environmental noise monitoring, positioning anomaly prediction and resource scheduling technologies; by correcting the trajectory deviation of the material barrel in real time, adjusting the predetermined path, optimizing the transmission speed and ensuring the transmission safety, it solves the problems of low positioning accuracy of the material barrel in a complex environment, possible failure and equipment overload during the transmission process, thereby improving the efficiency, stability and safety of the system; at the same time, it adopts intelligent redundancy management and resource scheduling to ensure automatic switching in the event of equipment failure and timely response of maintenance personnel, effectively reducing downtime and optimizing the overall performance of the material transmission system.

[0072] Background technology: In a scenario where multiple material barrels are synchronously transmitted according to predetermined planning parameters during the material transmission process, if an abnormality occurs in a material barrel (such as positioning deviation, product property change, or equipment failure), it may increase the risk of the entire transmission system, thereby affecting the normal transmission of other material barrels. This abnormality not only brings direct failure risks, but also increases the difficulty of managing material transmission. In particular, when multiple material barrels are transmitted simultaneously, how to efficiently and accurately handle abnormalities becomes a key challenge to improving the efficiency and safety of material transmission. To solve this problem, an embodiment of the present invention proposes a material transmission control and adjustment method based on the Internet of Things. Through real-time monitoring, data collection, positioning correction, risk prediction, etc., the transmission process of the material barrels is intelligently adjusted, thereby reducing the system risk caused by the abnormality of a single material barrel and ensuring the efficiency, safety, and stability of the transmission process.

[0073] Example 2, see Figure 2 Flowchart of a material transmission control and adjustment method based on fault avoidance. This embodiment of the present invention provides a material transmission control and adjustment method based on the Internet of Things. The difference from Example 1 is that it includes a fault avoidance step, including:

[0074] Step 1: Divide the material transmission device into several independent transmission lines and assign a unique number to each transmission line; the transmission lines are connected by a steering device, which includes a rotating device and a lifting device. The steering device is used to ensure that the material barrel can be smoothly transferred from one transmission line to another;

[0075] Step 2: Material barrel information collection and transmission: Monitor the real-time status of each material barrel during the transmission task, obtain the material barrel preset information, material barrel weight, material barrel actual image, real-time location, and transmit it to the data analysis platform via wireless communication;

[0076] It is necessary to further explain in the embodiments of the present invention that a gravity sensing device, an intelligent sensing device (such as an NFC or RFID sensor), and an image acquisition device (arranged above the key position and used to acquire image information of the material barrel and analyze the image information to obtain product attribute information) are installed at key locations of the transmission line (such as the starting section, the intersection section, and the key section). The weight of the material barrel, the preset information of the material barrel, and the actual image of the material barrel are acquired. When the material barrel approaches the intelligent sensing device, the sensing device reads the corresponding sensing code and obtains the preset information of the material barrel.

[0077] Step 3: Position Verification and Anomaly Detection: If a deviation in the barrel's trajectory or abnormal state is detected, an alarm is triggered and a correction mechanism is initiated. An abnormal state refers to an abnormal state of the barrel during transportation (such as product attribute information).

[0078] It is necessary to further explain in the embodiments of the present invention that the actual information of the material barrel is obtained based on the actual image of the material barrel. If the preset information of the material barrel does not match the actual information of the material barrel, it is determined that it is a transportation abnormality and an alarm is triggered; the real-time location of each logistics barrel is obtained. After the data analysis platform receives the status information and real-time location, if the real-time location of the material barrel does not match the current route number or the preset location, it is determined that it is a positioning abnormality and an alarm is triggered, and the positioning correction is performed;

[0079] Step 4: Risk control adjustment: Based on the actual information of the material barrel and the pre-set transmission planning parameters, the digital twin model is used to predict the fault location information without intervention; based on the fault location information, the optimal intervention path is calculated or backup equipment is activated; the intervention path includes adjusting the transmission speed of the material barrel and adjusting the predetermined path.

[0080] It is necessary to further explain in the embodiment of the present invention that the process of obtaining the optimal intervention path to avoid the occurrence of a fault by using a genetic algorithm includes:

[0081] Initialize the population: Set an initial set of intervention plans (i.e., "population") based on the current state of the transport system (e.g., positioning of the material barrel, speed, path planning, etc.); each initial intervention plan has a different processing method (e.g., a different path or speed);

[0082] Fitness evaluation: By simulating the performance of each intervention plan in the current digital twin model, the fitness of the intervention plan is evaluated, that is, the effectiveness of the intervention plan in avoiding failures and optimizing material transportation;

[0083] Selection, crossover, and mutation: Screen intervention options based on fitness for crossover (combination) and mutation (modification) to generate new solutions, and continuously improve and optimize until the optimal intervention path is found.

[0084] Summary: The IoT-based material transfer control and adjustment method provided by the present invention embodiment integrates sensors, image acquisition, and intelligent sensing devices into the material transfer device, enabling precise monitoring and positioning correction of material barrels. By optimizing intervention paths using a digital twin model and genetic algorithms, this method addresses positioning anomalies, trajectory deviations, and potential failures during the material barrel transfer process, improving system efficiency and stability while reducing the risk of failures and ensuring the safety and continuity of the material transfer process.

[0085] In embodiment 3, the present invention provides a material transport control and regulation system based on the Internet of Things, including:

[0086] The multi-sensor fusion module, based on the Kalman filter algorithm, fuses data from position sensors, velocity sensors, and environmental sensors, dynamically adjusts sensor weights, and optimizes the positioning accuracy of the material barrel. It also monitors environmental noise in real time and adjusts sensor weights based on noise levels to reduce the impact of noise on positioning.

[0087] The anomaly prediction module identifies and locates abnormal risk areas based on the spatiotemporal characteristics of historical data and builds an abnormal risk prediction model. It uses time series analysis or machine learning models to predict the probability of occurrence of abnormal risk areas in each time period and identify high-risk periods.

[0088] The path optimization module, based on the digital twin model of the material transmission device, combines transmission priority and path planning algorithms to match transmission planning parameters for material buckets. The constraint function is to minimize the weighted sum of transmission efficiency and transmission cost while meeting transmission safety requirements.

[0089] Dynamic Scheduling Module: Dynamically adjusts resource scheduling strategies based on abnormal risk areas and high-risk time periods provided by the abnormality prediction module; predicts fault location information based on the digital twin model and calculates the optimal intervention path;

[0090] The transmission execution module executes the material barrel transmission task according to the optimal intervention path provided by the dynamic scheduling module; it monitors the transmission status of the material barrel in real time to ensure the safety and efficiency of the transmission process.

[0091] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A material transmission control and adjustment method based on the Internet of Things, characterized in that: include: Positioning correction: Analyze the trajectory deviation of the material barrel based on sensor data, correct the material barrel positioning in real time, and adjust the control strategy to ensure the safe transmission of the material barrel; Combined with the Kalman filter algorithm to fuse multi-sensor data, dynamically adjust sensor weights to optimize positioning accuracy; real-time monitoring of environmental noise and adjust sensor weights according to the environmental noise level to reduce the impact of environmental noise on positioning; Positioning anomaly prediction and resource scheduling: Analyze the location anomaly set to obtain the spatiotemporal characteristics of the location anomaly occurrence; identify location anomaly risk areas based on the spatiotemporal characteristics of historical data; build a location anomaly risk prediction model based on the spatiotemporal characteristics, and use a prediction algorithm to predict the probability of occurrence of location anomaly risk areas in each time period to determine the high-risk periods of the anomaly risk areas; perform fault prevention and resource scheduling based on the high-risk periods of the anomaly risk areas; Path planning: Path planning is performed using a digital twin model of the material transfer device to optimize transfer speed, predetermined path, and transfer safety. The troubleshooting steps based on multi-transmission tasks include: Step 1: Divide the material transmission device into several independent transmission lines and assign a unique number to each transmission line; the transmission lines are connected by a steering device, which includes a rotating device and a lifting device. The steering device is used to ensure that the material barrel can be smoothly transferred from one transmission line to another; Step 2: Material barrel information collection and transmission: Monitor the real-time status of each material barrel during the transmission task, obtain the material barrel preset information, material barrel weight, material barrel actual image, real-time location, and transmit it to the data analysis platform via wireless communication; Step 3: Position Verification and Anomaly Detection: If a deviation in the barrel's trajectory or abnormal state is detected, an alarm is triggered and a correction mechanism is initiated. An abnormal state refers to an abnormal state of the barrel during transportation. Step 4: Risk control adjustment: Based on the actual information of the material barrel and the pre-set transmission planning parameters, the digital twin model is used to predict the fault location information without intervention; based on the fault location information, the optimal intervention path is calculated or backup equipment is activated; the intervention path includes adjusting the transmission speed of the material barrel and adjusting the predetermined path.

2. The material transmission control and adjustment method based on the Internet of Things according to claim 1 is characterized in that: The material barrel transfer process is tracked at a fixed frequency. In an unobstructed area, the trajectory deviation between the actual path of the material barrel and the predetermined path is verified by a laser device. When the cumulative value of the trajectory deviation exceeds the threshold, a start instruction for positioning anomaly prediction and resource scheduling is generated.

3. The material transmission control and adjustment method based on the Internet of Things according to claim 1 is characterized in that: The following steps are included to locate the abnormality analysis: When the material barrel is positioned abnormally, the trajectory deviation of the material barrel is analyzed in combination with the data from the speed and position sensors to determine whether the deviation is caused by a change in motion state, equipment failure, or sensor inaccuracy. Depending on the cause of the deviation, the control strategy is adjusted, the equipment failure is repaired, or positioning corrections are performed until the material barrel is positioned normally.

4. The material transmission control and adjustment method based on the Internet of Things according to claim 1 is characterized in that: The path planning includes: Build a digital twin model of the material transfer device, which is used to realize the transfer, waiting, rotation, and lifting operations of the material barrel; Matching transmission planning parameters for the material barrel based on transmission priority and path planning algorithm, wherein the transmission planning parameters include transmission speed, predetermined path and timing data of operation instructions; The constraint function is that the weighted sum of transmission efficiency and transmission cost is minimized when the transmission safety requirements are met; transmission safety means that the physical distance between the material barrels does not exceed the safe distance during the transmission process.

5. The material transmission control and adjustment method based on the Internet of Things according to claim 4 is characterized in that: The process of obtaining the optimal intervention path to avoid failure through genetic algorithm includes: Initialize the population: set an initial set of intervention plans based on the current state of the transmission system; Fitness evaluation: By simulating the performance of each intervention plan in the current digital twin model, the fitness of the intervention plan is evaluated, that is, the effectiveness of the intervention plan in avoiding failures and optimizing material transportation; Selection, crossover, and mutation: Screen intervention plans based on fitness for crossover and mutation to generate new solutions, and continuously improve and optimize until the optimal intervention path is found.

6. A material transport control and regulation system based on the Internet of Things, used to implement the method described in any one of claims 1 to 5, characterized in that: include: The multi-sensor fusion module, based on the Kalman filter algorithm, fuses the data of position sensors, speed sensors, and environmental sensors, dynamically adjusts sensor weights, and optimizes the positioning accuracy of the material barrel; Monitor environmental noise in real time and adjust sensor weights according to noise levels to reduce the impact of noise on positioning; The anomaly prediction module identifies and locates abnormal risk areas based on the spatiotemporal characteristics of historical data and builds an abnormal risk prediction model. It uses time series analysis or machine learning models to predict the probability of occurrence of abnormal risk areas in each time period and identify high-risk periods. The path optimization module, based on the digital twin model of the material transmission device, combines transmission priority and path planning algorithm to match the transmission planning parameters for the material barrel; The constraint function is that the weighted sum of transmission efficiency and transmission cost is minimized when transmission security meets the requirements; Dynamic scheduling module: Dynamically adjusts resource scheduling strategies based on abnormal risk areas and high-risk time periods provided by the abnormal prediction module; Predict fault location information based on the digital twin model and calculate the optimal intervention path; The transmission execution module executes the material barrel transmission task according to the optimal intervention path provided by the dynamic scheduling module; it monitors the transmission status of the material barrel in real time to ensure the safety and efficiency of the transmission process.

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