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

Abstract

The invention discloses a material transmission control adjustment method and system based on the Internet of Things, particularly relates to the technical field of material transmission control, and is characterized in that the weight of a sensor is dynamically adjusted based on a Kalman filtering algorithm, and the positioning precision of a material barrel is optimized; identifying and positioning an abnormal risk area and predicting a high-risk time period based on spatial-temporal feature analysis of historical data; optimizing a transmission path and speed by combining a digital twinborn model and a path planning algorithm; dynamically adjusting a resource scheduling strategy and calculating an optimal intervention path according to an abnormal prediction result and a path planning parameter; the conveying state of the material barrel is monitored in real time, and the safety and efficiency of the conveying process are ensured; the fault probability calculation module dynamically evaluates the fault probability based on the real-time transmission state; accurate control and dynamic adjustment in the material conveying process are achieved, and the problems that in a traditional material conveying system, positioning precision is low, abnormal prediction is not timely, and path planning is not optimized are solved.

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 are the basic loading units, and their precise positioning and predetermined path control are crucial to the efficiency and safety of the system.

[0003] Traditional material transportation systems usually rely on a single positioning method (such as optical sensors, ultrasonic sensors, etc.), but existing material transportation systems face problems such as environmental interference and signal instability, such as equipment vibration, path curves or obstructions at intersections, etc. 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 transmission 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 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: Analyze the trajectory deviation of the material barrel based on sensor data, correct the positioning of the material barrel in real time, and adjust the control strategy to ensure the safety of material barrel transmission; combine the Kalman filter algorithm to fuse multi-sensor data, dynamically adjust the sensor weight to optimize positioning accuracy; monitor the environmental noise in real time and adjust the sensor weight according to the environmental noise level to reduce the impact of environmental noise on positioning;

[0007] Positioning anomaly prediction and resource scheduling: Analyze the positioning anomaly set to obtain the spatiotemporal characteristics of the positioning anomaly; identify the positioning anomaly risk area based on the spatiotemporal characteristics of historical data; build a positioning anomaly risk prediction model based on the spatiotemporal characteristics, and use prediction algorithms (such as time series analysis, machine learning models) to predict the probability of occurrence of positioning anomaly risk areas in each time period to obtain the high-risk period of the abnormal risk area; perform fault prevention and resource scheduling according to the high-risk period of the abnormal risk area;

[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 speed 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 according to the noise characteristics of the position sensor and the speed sensor; the value of the noise covariance matrix is ​​negatively correlated with the sensor weight; when the noise is large, the reliance on the sensor is reduced to ensure positioning accuracy;

[0011] Based on the Kalman filter, the data of the position sensor and the speed sensor are integrated, and the real-time data of the speed sensor and the integral prediction of the material barrel position are combined to dynamically adjust the weights of the position sensor and the speed sensor; the external noise source is monitored in real time through the environmental sensor, and the noise model and sensor weight in the Kalman filter are dynamically adjusted according to the noise level; in a high-noise environment, the dependence on the position sensor is reduced, the weight of the speed sensor is increased, and the 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 comprises the step of locating anomaly analysis:

[0015] When the positioning of the material barrel is abnormal, the trajectory deviation of the material barrel is analyzed in combination with the data from the speed and position sensors to determine whether the cause of the deviation is a change in the 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 the positioning correction is performed until the positioning of the material barrel is normal.

[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 the transmission priority and the path planning algorithm, wherein the transmission planning parameters include the transmission speed, the predetermined path and the timing data of the operation instruction;

[0019] The constraint function is that when the transmission safety meets the requirements, the weighted sum of the transmission efficiency and the transmission cost is minimized; transmission safety means that the physical distance between the material barrels does not exceed the safety 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, and the steering device includes a rotating device and a lifting device, and the steering device is used to ensure that the material barrel can be smoothly transferred from one transmission line to another transmission line;

[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 positioning, and transmit them to the data analysis platform via wireless communication;

[0023] Step 3: Position verification and anomaly detection: If the material barrel’s trajectory deviation or state abnormality is detected, an alarm is triggered and a correction mechanism is initiated; state abnormality refers to the state of the material barrel (such as product attribute information) being abnormal during the transmission process;

[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 enabled; 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 transmission system (such as the positioning, speed, and path planning of the material barrel); each initial intervention plan has a different processing method (such as 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 transmission;

[0028] Selection, crossover and mutation: Screen intervention plans 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] Preferably, the fitness is obtained in the following manner:

[0030] The dynamic failure probability D(x) is obtained based on the change of transmission status and path iteration;

[0031] The risk elasticity measurement parameter R(x) is calculated by modeling the spatiotemporal correlation between the location of the material barrel and the fault location information;

[0032] The obtained dynamic failure probability D(x) and risk elasticity measurement parameter R(x) are input into the following formula to calculate the fitness F(x) of the intervention plan. The larger the value, the better the plan:

[0033]

[0034] Among them, α and β are adjustment parameters used to control the importance of each factor in fitness; exp is an exponential function used to introduce nonlinear effects, enhance the sensitivity of fitness value to each factor, and avoid the balance problem caused by linear weighting.

[0035] Preferably, the dynamic failure probability D(x) is obtained as follows:

[0036] Obtain the state changes of the material barrel during the transmission process, and the state changes during the transmission process include:

[0037] The material barrel deviates from the planned path ( Path error);

[0038] Delay in the delivery of the material barrel (difference from the scheduled time);

[0039] Conflicts between material barrels and other material barrels or equipment (path intersection, equipment interference, etc.);

[0040] Update the failure probability of the material bucket based on path error, time delay, path conflict, and changes in transmission status;

[0041]

[0042] Where n represents the number of material barrels, Δd i (t) represents the actual path of the material barrel i at time t and the predetermined path L i The deviation between i (t) represents the difference between the actual delivery time and the scheduled time of material barrel i at time t; T max Indicates the maximum time delay allowed; C conflict (t) represents the probability of material bucket i conflicting with other tasks or equipment at time t; C max Maximum path conflict probability; w path , w time , wconflict are the weight coefficients of path error, time delay and path conflict respectively, and w path +w time +w conflict =1.

[0043] Preferably, the risk elasticity measurement parameter R(x) is obtained as follows:

[0044]

[0045] Where ΔP fail (t) represents the change in failure probability at time t, d spatial (t) represents the spatial deviation of the material bucket i at time t (indicating the distance between the current position of the material bucket and the target position); d temporal (t) represents the time delay of material bucket i at time t (representing the difference between the actual delivery time of the material bucket and the scheduled time); F 1 , F 2 They are used to control the effects of spatial deviation and time delay on recovery capability respectively; K represents the recovery rate constant, which reflects the speed at which the system recovers from a fault state to a normal state; T represents the total evaluation time.

[0046] To achieve the purpose of the present invention, a material transmission control and regulation system based on the Internet of Things is provided, comprising:

[0047] The multi-sensor fusion module, based on the Kalman filter algorithm, fuses the data of the position sensor, speed sensor and environmental sensor, dynamically adjusts the sensor weight, and optimizes the positioning accuracy of the material barrel; monitors the environmental noise in real time, adjusts the sensor weight according to the noise level, and reduces the impact of noise on positioning;

[0048] The anomaly prediction module identifies and locates abnormal risk areas based on the spatiotemporal feature analysis of historical data, and builds an abnormal risk prediction model for positioning. It uses time series analysis or machine learning models to predict the probability of occurrence of abnormal risk areas in each time period and obtain high-risk periods.

[0049] The path optimization module matches the transmission planning parameters for the material bucket based on the digital twin model of the material transmission device, combined with the transmission priority and path planning algorithm; the constraint function is to minimize the weighted sum of transmission efficiency and transmission cost when the transmission security meets the requirements;

[0050] Dynamic scheduling module: dynamically adjusts resource scheduling strategies based on abnormal risk areas and high-risk time periods provided by the abnormal prediction module; predicts fault location information based on the digital twin model and calculates the optimal intervention path;

[0051] 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.

[0052] Technical effects and advantages of the present invention:

[0053] 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 prior art, the speed sensor is combined to predict the position of the material barrel, and the error of the position sensor is corrected, which significantly improves the real-time and accuracy of the 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 spare 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

[0054] Figure 1 This is a flow chart of the material transmission control and adjustment method of the present invention.

[0055] Figure 2 This is a flow chart of the material transport control and adjustment method based on fault avoidance of the present invention. DETAILED DESCRIPTION

[0056] The 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. On the contrary, 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.

[0057] 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.

[0058] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present application, its application, or uses.

[0059] 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 as part of the specification.

[0060] 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 barrel is transmitted according to the process flow, and the process from semi-finished products to finished products is realized through the process, and the status of the material barrel is obtained by scanning the code. When the parts in the material barrel complete the target process, they are placed in the warehouse waiting for use; the material barrel is transmitted through a conveyor belt, and the transmission process instructions include transmission, waiting, rotation, and lifting operations.

[0061] 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:

[0062] Positioning correction: Analyze the trajectory deviation of the material barrel based on sensor data, correct the positioning of the material barrel in real time, and adjust the control strategy to ensure the safety of material barrel transmission; combine the Kalman filter algorithm to fuse multi-sensor data, dynamically adjust the sensor weight to optimize the positioning accuracy; monitor the environmental noise in real time and adjust the sensor weight 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;

[0063] Positioning anomaly prediction and resource scheduling: Analyze the positioning anomaly set to obtain the spatiotemporal characteristics of the positioning anomaly; build a positioning anomaly risk prediction model based on the spatiotemporal characteristics, use prediction algorithms (such as time series analysis, machine learning models) to predict the probability of occurrence of positioning anomaly risk areas in each time period, and obtain the high-risk period of the abnormal risk area; perform fault prevention and resource scheduling according to the high-risk period of the abnormal risk area;

[0064] It is necessary to further explain in the embodiments of the present invention that if the probability of occurrence exceeds the threshold, the high-risk period of the abnormal risk area is marked and an alarm is issued in time to remind the operator or start the 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 abnormal risk prediction model is continuously optimized to adapt to environmental changes;

[0065] It needs to be further explained in the embodiments of the present invention that the resource scheduling refers to automatically scheduling resources (such as backup sensors and maintenance personnel) to locate high-risk periods of abnormal risk areas to ensure the stability of the transmission system; through intelligent redundancy management, automatically enabling backup equipment when equipment or sensors fail to maintain the continuity of material transmission; automatically scheduling maintenance personnel to inspect and repair equipment or areas where abnormalities frequently occur, reducing downtime and improving system efficiency;

[0066] 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.

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

[0068] The initial position and speed 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 according to the noise characteristics of the position sensor and the speed sensor; the value of the noise covariance matrix is ​​negatively correlated with the sensor weight; when the noise is large, the reliance on the sensor is reduced to ensure positioning accuracy;

[0069] Based on the Kalman filter, the data of the position sensor and the speed sensor are integrated, and the real-time data of the speed sensor and the integral prediction of the material barrel position are combined to dynamically adjust the weights of the position sensor and the speed sensor; the external noise source is monitored in real time through the environmental sensor, and the noise model and sensor weight in the Kalman filter are dynamically adjusted according to the noise level; in a high-noise environment, the dependence on the position sensor is reduced, the weight of the speed sensor is increased, and the position correction is optimized;

[0070] 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.

[0071] 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.

[0072] Explanation: The process of verifying the deviation of the actual path of the material barrel from the predetermined path by the laser device includes:

[0073] The laser transmitter emits a laser beam, which hits the surface of the material barrel and reflects back to the receiver. By calculating the time difference (flight time) between the transmitted and received signals 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 position of the material barrel; the laser transmitter is installed above or on the side of the transmission track, and the laser receiver is paired with the transmitter to receive the reflected laser signal and calculate the distance data;

[0074] The deviation between the actual path and the planned path is calculated through the real-time acquired distance data and the real-time positioning of the material barrel;

[0075] 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 in each sampling period. If the accumulated trajectory deviation value is higher than the 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 trajectory.

[0076] 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).

[0077] What needs to be further explained in the embodiment of the present invention is that the location abnormality analysis step includes:

[0078] When the positioning of the material barrel is abnormal, the trajectory deviation of the material barrel is analyzed in combination with the data from the speed and position sensors to determine whether the cause of the deviation is a change in the 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 the positioning correction is performed until the positioning of the material barrel is normal.

[0079] Explanation: The deviation cause determination process includes: analyzing the trajectory deviation of the material barrel by combining the data of the speed sensor and the position sensor to determine the reason for its deviation from the predetermined path; distinguishing whether the deviation is caused by a change in the motion state of the material barrel (such as a sudden change in speed or a sudden increase in acceleration), or by an error in the predetermined path itself (such as equipment failure, conveyor belt curve problem, sensor inaccuracy);

[0080] Abnormal motion state processing: If the deviation is caused by the change of the motion state of the material barrel (such as speed change, sudden increase in acceleration, etc.), adjust the speed control strategy and optimize the control parameters to restore the normal path of the material barrel to ensure its stable operation;

[0081] 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 running status of the conveyor belt or recalibrate the equipment to ensure that the material barrel is smoothly transmitted along the predetermined track;

[0082] Positioning correction: If the deviation is caused by sensor inaccuracy (such as sensor failure or long-term drift), start the positioning correction module, or introduce redundant sensors for data fusion to ensure the accuracy of sensor measurement data and restore the precise positioning of the material barrel;

[0083] 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.

[0084] It needs to be further explained in the embodiment of the present invention that the path planning includes:

[0085] 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;

[0086] Matching transmission planning parameters for the material barrel based on the transmission priority and the path planning algorithm, wherein the transmission planning parameters include the transmission speed, the predetermined path and the timing data of the operation instruction;

[0087] The constraint function is that when the transmission safety meets the requirements, the weighted sum of the transmission efficiency and the transmission cost is minimized; transmission safety means that the physical distance between the material barrels does not exceed the safety distance during the transmission process.

[0088] 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 real-time correction of the trajectory deviation of the material barrel, adjustment of the predetermined path, optimization of the transmission speed and guarantee of transmission safety, the problems of low positioning accuracy of the material barrel in a complex environment, possible failure and equipment overload during the transmission process are solved, thereby improving the efficiency, stability and safety of the system; at the same time, the use of intelligent redundancy management and resource scheduling ensures automatic switching in the event of equipment failure and timely response of maintenance personnel, effectively reduces downtime and optimizes the overall performance of the material transmission system.

[0089] Background technology: In the context of multiple material barrels being 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 attribute 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 will not only bring direct failure risks, but also increase the difficulty of managing material transmission, especially when multiple material barrels are transmitted at the same time. How to handle abnormalities efficiently and accurately becomes a key challenge to improve the efficiency and safety of material transmission. In order to solve this problem, an embodiment of the present invention proposes a material transmission control and adjustment method based on the Internet of Things, which intelligently adjusts the transmission process of the material barrel through real-time monitoring, data collection, positioning correction, risk prediction and other means, 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.

[0090] Example 2, see Figure 2 The embodiment of the present invention provides a material transmission control and adjustment method based on the Internet of Things, which is different from the embodiment 1 in that it includes a fault avoidance step, including:

[0091] 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, and the steering device includes a rotating device and a lifting device, and the steering device is used to ensure that the material barrel can be smoothly transferred from one transmission line to another transmission line;

[0092] 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 positioning, and transmit them to the data analysis platform via wireless communication;

[0093] What needs to be further explained in the embodiments of the present invention is 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, used to acquire image information of the material barrel, and analyze the image information to obtain product attribute information) are installed at key positions (such as the starting section, the intersection section, and the key section) of the transmission line to acquire the weight of the material barrel, the preset information of the material barrel and the actual image of the material barrel. When the material barrel approaches the intelligent sensing device, the sensing device reads the corresponding sensing code to obtain the preset information of the material barrel.

[0094] Step 3: Position verification and anomaly detection: If the material barrel’s trajectory deviation or state abnormality is detected, an alarm is triggered and a correction mechanism is initiated; state abnormality refers to the state of the material barrel (such as product attribute information) being abnormal during the transmission process;

[0095] What needs to be further explained in the embodiment of the present invention is that the actual information of the material barrel is obtained based on the actual image of the material barrel, and if the preset information of the material barrel does not match the actual information of the material barrel, it is judged as a transportation abnormality and an alarm is triggered; the real-time positioning of each logistics barrel is obtained, and after the data analysis platform receives the status information and real-time positioning, if the real-time positioning of the material barrel does not match the current line number or the preset positioning, it is judged as a positioning abnormality and an alarm is triggered, and the positioning correction is performed;

[0096] 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 enabled; the intervention path includes adjusting the transmission speed of the material barrel and adjusting the predetermined path.

[0097] It needs to be further explained 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:

[0098] Initialize the population: set a set of initial intervention plans (i.e., "population") according to the current state of the transmission system (such as the positioning, speed, and path planning of the material barrel); each initial intervention plan has a different processing method (such as a different path or speed);

[0099] 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;

[0100] Selection, crossover and mutation: Screen intervention plans 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.

[0101] It needs to be further explained in the embodiment of the present invention that the fitness is obtained in the following manner:

[0102] The dynamic failure probability D(x) is obtained based on the change of transmission status and path iteration;

[0103] The risk elasticity measurement parameter R(x) is calculated by modeling the spatiotemporal correlation between the location of the material barrel and the fault location information;

[0104] The obtained dynamic failure probability D(x) and risk elasticity measurement parameter R(x) are input into the following formula to calculate the fitness F(x) of the intervention plan. The larger the value, the better the plan:

[0105]

[0106] Among them, α and β are adjustment parameters used to control the importance of each factor in fitness; exp is an exponential function used to introduce nonlinear effects, enhance the sensitivity of fitness value to each factor, and avoid the balance problem caused by linear weighting.

[0107] Explanation: In the multi-material barrel transmission task, the dynamic failure probability is calculated based on the real-time transmission state changes of the material barrels and the path iteration process; the transmission state of the material barrels changes continuously with the execution of the task, the change of the path and the influence of environmental conditions. The transmission path of the material barrels will iterate with the change of the real-time state (such as speed adjustment, path optimization, etc.), and each path iteration will update the path error (such as position deviation), time delay and possible conflict risk;

[0108] Therefore, the failure probability is also adjusted dynamically. The calculation of the dynamic failure probability comprehensively considers the optimization of the path, the status of the material bucket, and the interaction with other material bucket tasks.

[0109] It needs to be further explained in the embodiment of the present invention that the dynamic failure probability D(x) is obtained as follows:

[0110] Obtain the state changes of the material barrel during the transmission process, and the state changes during the transmission process include:

[0111] The material barrel deviates from the predetermined path (path error);

[0112] Delay in the delivery of the material barrel (difference from the scheduled time);

[0113] Conflicts between material barrels and other material barrels or equipment (path intersection, equipment interference, etc.);

[0114] Update the failure probability of the material bucket based on path error, time delay, path conflict, and changes in transmission status;

[0115]

[0116] Where n represents the number of material barrels, Δd i (t) represents the actual path of the material barrel i at time t and the predetermined path L i The deviation between i (t) represents the difference between the actual delivery time and the scheduled time of material barrel i at time t; T max Indicates the maximum time delay allowed; C conflict (t) represents the probability of material bucket i conflicting with other tasks or equipment at time t; C max Maximum path conflict probability; w path , w time , w conflict are the weight coefficients of path error, time delay and path conflict respectively, and w path +w time +w conflict =1.

[0117] It needs to be further explained in the embodiment of the present invention that the risk elasticity measurement parameter R(x) is obtained in the following manner:

[0118]

[0119] Among them, ΔP fail (t) represents the change in failure probability at time t, d spatial (t) represents the spatial deviation of the material bucket i at time t (indicating the distance between the current position of the material bucket and the target position); d temporal (t) represents the time delay of material bucket i at time t (representing the difference between the actual delivery time of the material bucket and the scheduled time); F 1 , F 2They are used to control the effects of spatial deviation and time delay on recovery capability respectively; K represents the recovery rate constant, which reflects the speed at which the system recovers from a fault state to a normal state; T represents the total evaluation time.

[0120] Summary: The material transmission control and adjustment method based on the Internet of Things provided by the embodiment of the invention realizes accurate monitoring and positioning correction of the material barrel by integrating sensors, image acquisition and intelligent sensing equipment in the material transmission device. By optimizing the intervention path through the digital twin model and genetic algorithm, the positioning anomaly, trajectory deviation and potential fault problems in the material barrel transmission process are solved, the efficiency and stability of the system are improved, the risk of fault occurrence is reduced, and the safety and continuity of the material transmission process are ensured.

[0121] Embodiment 3, the embodiment of the present invention provides a material transmission control and regulation system based on the Internet of Things, including:

[0122] The multi-sensor fusion module, based on the Kalman filter algorithm, fuses the data of the position sensor, speed sensor and environmental sensor, dynamically adjusts the sensor weight, and optimizes the positioning accuracy of the material barrel; monitors the environmental noise in real time, adjusts the sensor weight according to the noise level, and reduces the impact of noise on positioning;

[0123] The anomaly prediction module identifies and locates abnormal risk areas based on the spatiotemporal feature analysis of historical data, and builds an abnormal risk prediction model for positioning. It uses time series analysis or machine learning models to predict the probability of occurrence of abnormal risk areas in each time period and obtain high-risk periods.

[0124] The path optimization module matches the transmission planning parameters for the material bucket based on the digital twin model of the material transmission device, combined with the transmission priority and path planning algorithm; the constraint function is to minimize the weighted sum of transmission efficiency and transmission cost when the transmission security meets the requirements;

[0125] Dynamic scheduling module: dynamically adjusts resource scheduling strategies based on abnormal risk areas and high-risk time periods provided by the abnormal prediction module; predicts fault location information based on the digital twin model and calculates the optimal intervention path;

[0126] 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.

[0127] 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 protection scope 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 positioning of the material barrel in real time, and adjust the control strategy to ensure the safe transmission of the material barrel; Combine Kalman filter algorithm to fuse multi-sensor data and dynamically adjust sensor weights to optimize positioning accuracy; monitor environmental noise in real time and adjust sensor weights according to environmental noise levels to reduce the impact of environmental noise on positioning; Positioning anomaly prediction and resource scheduling: Analyze the positioning anomaly set to obtain the spatiotemporal characteristics of the positioning anomaly; identify the positioning anomaly risk area based on the spatiotemporal characteristics of historical data; build a positioning anomaly risk prediction model based on the spatiotemporal characteristics, use the prediction algorithm to predict the probability of occurrence of the positioning anomaly risk area in each time period, and obtain the high-risk period of the abnormal risk area; perform fault prevention and resource scheduling according to the high-risk period of the abnormal risk area; 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.

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 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.

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 positioning of the material barrel is abnormal, the trajectory deviation of the material barrel is analyzed in combination with the data from the speed and position sensors to determine whether the cause of the deviation is a change in the 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 the positioning correction is performed until the positioning of the material barrel is normal.

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 the transmission priority and the path planning algorithm, wherein the transmission planning parameters include the transmission speed, the predetermined path and the timing data of the operation instruction; The constraint function is that when the transmission safety meets the requirements, the weighted sum of the transmission efficiency and the transmission cost is minimized; transmission safety means that the physical distance between the material barrels does not exceed the safety distance during the transmission process.

5. The material transmission control and adjustment method based on the Internet of Things according to claim 1 is characterized in that: Includes multi-transmission task-based fault avoidance steps, including: 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, and the steering device includes a rotating device and a lifting device, and the steering device is used to ensure that the material barrel can be smoothly transferred from one transmission line to another transmission line; 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 positioning, and transmit them to the data analysis platform via wireless communication; Step 3: Position verification and anomaly detection: If the material barrel’s trajectory deviation or abnormal state is detected, an alarm is triggered and a correction mechanism is started; abnormal state refers to an abnormal state of the material barrel during the transmission process; 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 enabled; the intervention path includes adjusting the transmission speed of the material barrel and adjusting the predetermined path.

6. The material transmission control and adjustment method based on the Internet of Things according to claim 5 is characterized in that: The process of obtaining the optimal intervention path to avoid failures through genetic algorithms 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 transmission; 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.

7. The material transmission control and adjustment method based on the Internet of Things according to claim 6 is characterized in that: The fitness is obtained in the following way: The dynamic failure probability D is obtained based on the change of transmission status and path iteration ( x ) ; The risk elasticity measurement parameter R is calculated by modeling the spatiotemporal correlation between the location of the material barrel and the fault location information. ( x ) ; The dynamic failure probability D ( x ) , Risk elasticity measurement parameter R9x ) Enter the following formula to calculate the fitness F of the intervention plan ( x ) : Among them, α and β are adjustment parameters used to control the importance of each factor in fitness.

8. The material transmission control and adjustment method based on the Internet of Things according to claim 7 is characterized in that: The dynamic failure probability D ( x ) The way to obtain is: Obtain the state changes of the material barrel during the transmission process, and the state changes during the transmission process include: Path error of the material barrel deviating from the predetermined path; Delay in delivery of material barrels; The material barrel conflicts with other material barrels or equipment; Update the failure probability of the material bucket based on path error, time delay, path conflict, and changes in transmission status; Where n represents the number of material barrels, Δd i( t ) Represents the actual path of the material barrel i at time t and the predetermined path L i The deviation between i( t ) represents the difference between the actual delivery time and the scheduled time of material barrel i at time t; T max Indicates the maximum time delay allowed; C conflict( t ) represents the probability of conflict between material bucket i at time t; C max Maximum path conflict probability; w path , w time , w conflict are the weight coefficients of path error, time delay and path conflict respectively, and w path +w time +w conflict =1.

9. The material transmission control and adjustment method based on the Internet of Things according to claim 8 is characterized in that: The risk elasticity measurement parameter R ( x ) The way to obtain is: Among them, ΔP fail (t) represents the change in failure probability at time t, d spatial (t) represents the spatial deviation of material bucket i at time t; d temporal (t) represents the time delay of material bucket i at time t; F1 and F2 are used to control the effects of spatial deviation and time delay on recovery ability, respectively, K represents the recovery rate constant, and T represents the total time of evaluation.

10. A material transmission control and regulation system based on the Internet of Things, used to implement the method described in any one of claims 1 to 6, characterized in that: include: The multi-sensor fusion module, based on the Kalman filter algorithm, fuses the data of the position sensor, speed sensor and environmental sensor, dynamically adjusts the sensor weight, and optimizes the positioning accuracy of the material barrel; Monitor environmental noise in real time, adjust sensor weights according to noise levels, and reduce the impact of noise on positioning; The anomaly prediction module identifies and locates abnormal risk areas based on the spatiotemporal feature analysis of historical data, and builds an abnormal risk prediction model for positioning. It uses time series analysis or machine learning models to predict the probability of occurrence of abnormal risk areas in each time period and obtain high-risk periods. The path optimization module matches the transmission planning parameters for the material barrels based on the digital twin model of the material transmission device and combines the transmission priority and path planning algorithm; The constraint function is that when the transmission security meets the requirements, the weighted sum of transmission efficiency and transmission cost is minimized; Dynamic scheduling module: dynamically adjusts resource scheduling strategies based on abnormal risk areas and high-risk time period information 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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