Bulk grain single-chute-tube intelligent train loading system

Abstract

The invention discloses a system for intelligently loading bulk grains into a train through a single chute barrel, and relates to the technical field of port intellectualization. The problems that in the prior art, the modern logistics efficient requirement for bulk grain large-scale transportation is difficult to meet, the effect on dust capture and control is limited, and the working environment is severe are solved. Key data such as bulk grain conveying flow, loading weight and environmental dust concentration are collected in real time through the sensing monitoring unit, a flow guide plate in the chute barrel is dynamically adjusted accordingly, it is guaranteed that bulk grains flow uniformly and are accurately loaded to a train carriage, meanwhile, the dust concentration is monitored to optimize the loading environment, safety of operators is guaranteed, and the working efficiency is improved. The intelligent management and control unit analyzes loading tasks, allocates resources, remotely controls equipment and automatically generates a report after operation is completed, so that loading efficiency is improved, collaborative operation optimization is achieved, loading operation efficiency is improved, the problem of dust control is effectively solved, and safety and high efficiency of the operation environment are ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent port technology, and in particular to an intelligent single-chute bulk grain loading train system. Background Art

[0002] As an important hub for bulk grain logistics, the loading and unloading efficiency of ports is directly related to the overall operating efficiency and economic benefits of the logistics chain. In the operation of loading bulk grain onto trains, traditional technical solutions mostly use a combination of multiple bucket scales and chutes to load bulk grain. Although it can achieve a certain degree of automation, there are many deficiencies in its operation, which limits the further improvement of loading efficiency and system intelligence level.

[0003] However, in the existing technology, the operating flow rate of each bulk hopper scale is only 100t / h, and the operating capacity of the entire loading line is 400t / h, which is difficult to meet the modern logistics efficiency requirements for large-scale transportation of bulk grain, and has limited effect in dust capture and control, resulting in a poor working environment and increasing the difficulty of subsequent cleaning work. Summary of the invention

[0004] The purpose of the present invention is to provide a single-chute intelligent train loading system for bulk grain, so as to realize coordinated cooperation and data fusion when multiple areas work together, centralized control, reduce the operation links and equipment maintenance, reduce the labor intensity of employees, improve the measurement accuracy, meet customer needs, improve the railway loading capacity, release port railway resources, 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 solutions: A single-chute intelligent train loading system for bulk grains, comprising: The sensor monitoring unit is used to collect key loading data such as bulk grain transportation flow, loading weight and ambient dust concentration in real time, and feed the collected key loading data back to the transportation and loading unit and the intelligent management and control unit; The conveying and loading unit is used to obtain the loading task instructions, and transport the bulk grain from the storage area to the loading area based on the loading task instructions. At the same time, the guide plate inside the chute is adjusted based on the monitoring feedback data obtained in real time; The conveying and loading unit is also used to extract the environmental dust concentration parameters from the key loading data, compare the environmental dust concentration parameters with the preset safety environment threshold, and determine whether to optimize the loading environment; The intelligent management and control unit is used to analyze loading operations, allocate resources based on the priority of loading operations, send loading operation task instructions based on the Internet of Things, remotely control each loading equipment, and generate a corresponding loading operation task report after the operation is completed.

[0006] Furthermore, the sensor monitoring unit comprises: The sensor data acquisition module is used to collect bulk grain flow data based on the flow sensor, the weighing sensor to monitor the loading weight in real time, and the vibration sensor to monitor the running status of the conveyor belt and chute to determine whether each loading equipment has abnormal vibration; The dust monitoring control module is used to monitor the dust concentration in the environment in real time based on the dust concentration sensor, and issue an alarm when the dust concentration exceeds the preset safety threshold, triggering the loading environment optimization process; The operation feedback module is used to obtain the key loading data collected by the sensor data acquisition module and the dust monitoring and control module, construct the monitoring feedback data during the loading operation task, monitor the operation progress and the status of each loading equipment, and transmit the monitoring feedback data to the conveying and loading unit.

[0007] Furthermore, the sensor monitoring unit also includes: a data quality inspection module, which obtains real-time monitoring data of the sensor data acquisition module and the dust monitoring control module, including key loading data of bulk grain transportation flow, loading weight and ambient dust concentration, performs quality inspection on the key loading data, and obtains qualified key loading data. The specific steps are as follows: According to the characteristics of each collection parameter, the key loading data is traversed, abnormal data that does not meet the requirements is extracted, and the collection time point of the abnormal data is recorded; Based on the collection time point of the abnormal data, determine whether the collection time interval of the abnormal data meets the preset normal collection interval threshold, and determine whether the collection quantity meets the normal collection frequency; If the time interval and quantity of abnormal data meet the normal standards, the abnormal data will be removed from the key loading data and the normal data will be retained. Otherwise, it will be determined whether the data source is faulty or abnormal, and an alarm will be triggered.

[0008] Furthermore, the data quality inspection module further includes: Classify the key loading data after removing abnormal data to obtain the equipment parameters and loading process parameters; Based on the performance characteristics of each loading device, a first parameter association of the device is established; based on the actual operation rules of the loading process, a second parameter association of the loading process is established; based on the constraint rules of each loading device and the loading process, a third parameter association of the parameters of each loading device and the loading process is established; Determine whether the parameters of each loading device satisfy the first parameter association rule. If yes, determine that the loading device is in a normal state; otherwise, determine that the loading device is in an abnormal state and issue an alarm. Determine whether the loading process parameters satisfy the second parameter association rule; if so, determine that the loading process is normal; otherwise, determine that the loading process is abnormal and issue an alarm; If the status of the equipment itself is normal and the loading process is normal, then determine whether the parameters of each loading equipment itself and the loading process parameters meet the third parameter association rule. If so, determine that the loading equipment control operation is normal, and use the normal collection parameters as qualified collection parameters. Otherwise, determine that the loading equipment control operation is abnormal, and issue an alarm.

[0009] Furthermore, the conveying and loading unit comprises: A task receiving module is used to obtain the loading task instruction of the intelligent control unit, and obtain the operation status data of the corresponding conveying equipment based on the loading task instruction; The bulk grain conveying module is used to transport bulk grain from the storage area to the loading area. The initial operating speed of the conveyor is adjusted according to the preset target operating flow. At the same time, the operating speed of the conveyor is adjusted based on the monitoring feedback data to control the actual operating flow within the target operating flow range. The chute adjustment module is used to extract the flow state of bulk grain inside the chute from the monitoring feedback data, and adjust the angle of the guide plate inside the chute based on the flow characteristics of the bulk grain to evenly distribute the bulk grain and allow it to enter the chute outlet; The chute adjustment module is also used to extract the train cargo compartment position and loading height data from the monitoring feedback data, compare the current loading status of the cargo compartment with the target loading status, and adjust the height and direction of the chute outlet based on the comparison results.

[0010] Furthermore, the intelligent management and control unit includes: Intelligent loading module, which is used to allocate resources and schedule loading tasks according to the importance of each area, where the scheduling logic takes into account the remaining load and estimated completion time of each area; The remote control module is used to send loading operation task instructions to the transportation and loading units based on resource allocation and scheduling results, adjust the loading operation task instruction parameters based on monitoring feedback data, and generate a loading operation report after the operation is completed.

[0011] Furthermore, the intelligent loading module specifically includes: Obtained loading tasks for each area, and read the loading tasks to determine the loading task plan for the loading tasks to be executed, including the type of loading task, required resources and estimated completion time; Analyze the loading task plan, determine the priority of the loading task plan and sort the priorities. At the same time, mark the loading tasks of urgent tasks and key areas, and prioritize them based on the marking results; Scheduling resources based on the remaining load and estimated completion time of each area to ensure that all loading tasks can be completed in a balanced and timely manner; Based on the priority, operating conditions and remaining load volume of the loading tasks in each area, the resource allocation of the loading tasks in each area is adjusted, and the loading equipment and loading sequence are adjusted according to the loading progress.

[0012] Furthermore, the remote control module also includes: Obtain loading progress data for each area based on monitoring feedback data, compare loading tasks in each area with loading progress data, and calculate the completion status of loading tasks; Compare the actual completion of loading tasks with the preset goals, evaluate the efficiency, quality and equipment status of loading tasks, and analyze whether the operation process, resource allocation and loading equipment need to be optimized and adjusted based on the evaluation results; Calculate the satisfaction of task execution based on various data in the loading operation report (such as loading time, loading efficiency, loading weight, etc.); The calculated task satisfaction is compared with the preset target satisfaction. If the satisfaction is less than the target value, an alarm is triggered and the equipment is adjusted and the loading task is rearranged. If the task satisfaction is greater than or equal to the target value, the loading task is considered to be completed.

[0013] Furthermore, the priority of the loading task is determined as follows: Determine the volume and weight of the object to be loaded according to each loading operation task, and determine the loading space requirement according to the volume and weight of the object to be loaded; Determine a loading arrangement carriage for the object to be loaded for each loading operation task based on the loading space requirement, and obtain the rated load-bearing weight and rated load-bearing volume of the loading arrangement carriage; The loading resource utilization index of each loading task is determined according to the rated load weight and rated load volume of each loading arrangement carriage, the volume and weight of the object to be loaded in the loading task:

[0014] in, It is represented as the loading resource utilization index of the i-th loading task, It is represented as the weight of the object to be loaded in the i-th loading task, It represents the rated load weight of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is expressed as the load constraint factor of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is represented as the volume of the object to be loaded in the i-th loading task, It represents the rated carrying volume of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is expressed as the space utilization coefficient of the loading arrangement compartment of the object to be loaded in the i-th loading task, ln is expressed as the natural logarithm, It is expressed as the loading rate of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is represented as the weight balance constraint factor of the loading arrangement carriage of the object to be loaded in the i-th loading task; The priority of each loading task is determined by sorting the loading resource utilization index of each loading task from large to small.

[0015] Furthermore, before the intelligent management and control unit analyzes the loading operation task, the system is also used to: Obtaining task description information of the loading operation task, and performing parameter decomposition on the task description information to obtain task target information, task type information, and task operation information; Determine a task load factor for each loading operation task based on task target information, task type information, and task operation information; Divide all loading tasks into light loading tasks and heavy loading tasks according to the task load factor, and determine the stage task index and overall task index of each divided loading task in the light loading task type and the heavy loading task type; According to the stage task index, the overall task index and the index change range, a state transfer matrix of each divided loading task is constructed; Determine the standard deduction task process parameters for each divided loading operation task according to the state transfer matrix, and determine multiple monitoring indicators according to the standard deduction task process parameters; Obtain the response subtask item corresponding to each monitoring indicator, determine the scheduling resources of the response subtask item, and determine whether there is a synchronization domain task item based on the scheduling resources; If yes, obtain the loading program of each divided loading operation task, and determine the spatial interleaving information of the synchronization domain task item according to the loading program of each divided loading operation task; Determine the loading space requirement of each divided loading operation task according to the spatial interleaving information, and sort the divided loading operation tasks in the light loading task type and the heavy loading task type based on the loading space requirement; The analysis order of the loading operation tasks is determined according to the sorting results, and the loading operation tasks are analyzed in sequence according to the analysis order.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The sensor monitoring unit collects key data such as bulk grain conveying flow, loading weight and environmental dust concentration in real time, and dynamically adjusts the internal guide plate of the chute accordingly to ensure that the bulk grain flows evenly and is accurately loaded into the train cargo compartment. At the same time, the dust concentration is monitored to optimize the loading environment and ensure the safety of operators. The intelligent management and control unit analyzes loading tasks, allocates resources, and remotely controls equipment, and automatically generates reports after the operation is completed, thereby improving loading efficiency. This not only improves the efficiency of loading operations, but also effectively solves the dust control problem, ensuring a safe and efficient working environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a module diagram of the single-chute intelligent train loading system for bulk grain of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] In order to solve the technical problems of low efficiency of intelligent train loading and dust control during operation, please refer to Figure 1 , this embodiment provides the following technical solutions: A single-chute intelligent train loading system for bulk grains, comprising: The sensor monitoring unit is used to collect key loading data such as bulk grain transportation flow, loading weight and ambient dust concentration in real time, and feed the collected key loading data back to the transportation and loading unit and the intelligent management and control unit; The conveying and loading unit is used to obtain loading task instructions, and transport the bulk grain from the storage area to the loading area based on the loading task instructions. At the same time, based on the real-time monitoring feedback data, the guide plate inside the chute is adjusted to achieve precise loading and dynamic adjustment to ensure that the bulk grain flows evenly and is accurately loaded into the target area of ​​the train cargo compartment; The conveying and loading unit is also used to extract the environmental dust concentration parameters from the key loading data, compare the environmental dust concentration parameters with the preset safety environment threshold, and determine whether to optimize the loading environment; The intelligent management and control unit is used to analyze loading operations, allocate resources based on the priority of loading operations, send loading operation task instructions based on the Internet of Things, remotely control each loading equipment, and generate corresponding loading operation task reports after the operation is completed, thereby realizing automation, collaborative optimization and accurate report generation of operations.

[0020] In this embodiment, the key data such as bulk grain conveying flow rate, loading weight and environmental dust concentration are collected in real time through the sensor monitoring unit, and the internal guide plate of the chute is dynamically adjusted accordingly to ensure that the bulk grain flows evenly and is accurately loaded into the train cargo compartment, thereby achieving efficient transportation and loading of 1000t / h flow rate, ensuring uniform flow of bulk grain, smooth loading process, and stable operation of the system under high load. At the same time, the dust concentration is monitored to optimize the loading environment and ensure the safety of operators. The intelligent management and control unit analyzes loading tasks, allocates resources, and remotely controls equipment, and automatically generates reports after the operation is completed, thereby improving loading efficiency, reducing operating costs, and realizing collaborative operation optimization. It not only improves the loading operation efficiency, but also effectively solves the dust control problem, ensuring a safe and efficient working environment.

[0021] In this embodiment, the sensor monitoring unit includes: The sensor data acquisition module is used to collect bulk grain flow data based on the flow sensor, the weighing sensor to monitor the loading weight in real time, and the vibration sensor to monitor the running status of the conveyor belt and chute to determine whether each loading equipment has abnormal vibration; The dust monitoring control module is used to monitor the dust concentration in the environment in real time based on the dust concentration sensor, and issue an alarm when the dust concentration exceeds the preset safety threshold, triggering the loading environment optimization process. According to the change of dust concentration, the dust removal air volume, air pressure and exhaust time are automatically adjusted to ensure that the working environment remains within the safety standard range; The operation feedback module is used to obtain the key loading data collected by the sensor data acquisition module and the dust monitoring and control module, and construct the monitoring feedback data during the loading operation task, including flow, weight, operation progress, equipment status, train cargo compartment position and loading height data, etc., monitor the operation progress and the status of each loading equipment, and transmit the monitoring feedback data to the conveying and loading unit.

[0022] In this embodiment, by collecting flow, weight and vibration data in real time, abnormal vibration of the loading equipment can be discovered and handled in time to ensure the stability and safety of the loading process. The real-time monitoring and automatic adjustment functions of the dust monitoring control module effectively control the dust concentration in the working environment, protect the health of the operators, and reduce environmental pollution. The operation feedback module constructs comprehensive monitoring feedback data, which not only monitors the operation progress and equipment status in real time, but also provides key information such as the position of the train cargo compartment and the loading height, so that the conveying and loading units can be accurately adjusted according to these data, which greatly improves the automation level and efficiency of the loading operation, reduces the operating error rate, and thus improves the overall operation quality and management level.

[0023] In this embodiment, the sensor monitoring unit further includes: a data quality inspection module, which obtains real-time monitoring data of the sensor data acquisition module and the dust monitoring control module, including key loading data of bulk grain transportation flow, loading weight and ambient dust concentration, performs quality inspection on the key loading data, and obtains qualified key loading data. The specific steps are as follows: According to the characteristics of each collection parameter, the key loading data is traversed, abnormal data that does not meet the requirements is extracted, and the collection time point of the abnormal data is recorded; Based on the collection time point of the abnormal data, determine whether the collection time interval of the abnormal data meets the preset normal collection interval threshold, and determine whether the collection quantity meets the normal collection frequency; If the time interval and quantity of abnormal data meet the normal standards, the abnormal data will be removed from the key loading data and the normal data will be retained. Otherwise, it will be determined whether the data source is faulty or abnormal, and an alarm will be triggered to remind the operator to conduct inspection or maintenance; After removing abnormal data, the key loading data are classified to obtain the equipment parameters and loading process parameters. The equipment parameters include bulk grain flow, loading weight, equipment vibration, temperature, etc. The loading process parameters include flow rate, loading amount, dust concentration, etc. Based on the performance characteristics of each loading device, a first parameter association of the device is established; based on the actual operation rules of the loading process, a second parameter association of the loading process is established; based on the constraint rules of each loading device and the loading process, a third parameter association of the parameters of each loading device and the loading process is established; Determine whether the parameters of each loading device satisfy the first parameter association rule. If yes, determine that the loading device is in a normal state; otherwise, determine that the loading device is in an abnormal state and issue an alarm. Determine whether the loading process parameters satisfy the second parameter association rule; if so, determine that the loading process is normal; otherwise, determine that the loading process is abnormal and issue an alarm; If the status of the equipment itself is normal and the loading process is normal, then determine whether the parameters of each loading equipment itself and the loading process parameters meet the third parameter association rule. If so, determine that the loading equipment control operation is normal, and use the normal collection parameters as qualified collection parameters for subsequent data analysis; otherwise, determine that the loading equipment control operation is abnormal, and issue an alarm.

[0024] In this embodiment, by traversing the key loading data and extracting abnormal data, the accuracy and reliability of the data are ensured, operational errors caused by data errors are reduced, the time interval and number of abnormal data are judged to automatically identify and eliminate abnormal data that does not meet normal standards, or data source failures are identified in time and alarms are triggered, thereby improving the self-diagnosis and maintenance capabilities of the system, classifying data and establishing parameter association rules, and being able to more deeply analyze the equipment status and loading process to ensure the normal operation of the loading equipment and the smoothness of the loading process. By comprehensively judging the equipment's own parameters, the loading process parameters and the association rules between them, it is possible to accurately judge whether the control operation of the loading equipment is normal, thereby ensuring the stability and safety of the entire loading operation, improving operating efficiency, and ensuring efficient, accurate and intelligent management of bulk grain loading operations.

[0025] In this embodiment, the conveying and loading unit includes: The task receiving module is used to obtain the loading task instructions of the intelligent control unit, including task parameters such as target flow, task time and loading location, and obtain the operating status data of the corresponding conveying equipment based on the loading task instructions; The bulk grain conveying module is used to transport bulk grain from the storage area to the loading area. The initial operating speed of the conveyor is adjusted according to the preset target operating flow. At the same time, the operating speed of the conveyor is adjusted based on the monitoring feedback data to control the actual operating flow within the target operating flow range. The chute adjustment module is used to extract the flow state of bulk grain inside the chute from the monitoring feedback data, including flow velocity and flow direction, and adjust the angle of the guide plate inside the chute based on the flow characteristics of the bulk grain to evenly distribute the bulk grain and allow it to enter the chute outlet, ensuring that the bulk grain flows evenly and avoids local accumulation or uneven loading; The chute adjustment module is also used to extract the train cargo compartment position and loading height data from the monitoring feedback data, compare the current loading status of the cargo compartment with the target loading status, and adjust the height and direction of the chute outlet based on the comparison results to ensure that the grain can be accurately loaded into the target area.

[0026] In this embodiment, the bulk grain conveying module combines real-time monitoring and dynamic control functions to ensure that the conveying flow is accurate and stable, and to minimize conveying interruptions or overloads. The dynamic guide plate and precise outlet control functions of the chute adjustment module effectively improve loading uniformity and accuracy. By comparing the current loading status of the cargo compartment with the target loading status, it ensures that the bulk grain accurately enters the designated area of ​​the cargo compartment to avoid overflow or omission. Combined with real-time sensor feedback, the shape and position of the chute outlet are adaptively adjusted to adapt to cargo compartments of different sizes and specifications. The conveying and loading process responds to changes in the operating environment and equipment status in real time, and has strong adaptability.

[0027] In this embodiment, the intelligent management and control unit includes: The intelligent loading module is used to allocate and schedule resources according to the importance of loading tasks in each area, giving priority to the execution of urgent tasks. The scheduling logic considers the remaining load and estimated completion time of each area to ensure that all loading tasks are completed evenly, including: Obtained loading tasks for each area, and read the loading tasks to determine the loading task plan for the loading tasks to be executed, including the type of loading task, required resources and estimated completion time; Analyze the loading task plan, determine the priority of the loading task plan and sort the priorities. At the same time, mark the loading tasks of urgent tasks and key areas, and prioritize them based on the marking results; Resource scheduling is performed based on the remaining load capacity and estimated completion time of each area to ensure that all loading tasks can be completed in a balanced and timely manner, avoiding excessive concentration or waste of resources; Adjust resource allocation for loading tasks in each area based on the priority, operating conditions and remaining load of each area, and adjust loading equipment and loading sequence according to loading progress; In this embodiment, the intelligent loading module is used to ensure the optimal execution of the operation process, reduce resource waste, and ensure operation efficiency. Through refined resource allocation and scheduling, the efficiency and balance of the loading operation are significantly improved, ensuring the priority execution of emergency tasks, thereby improving the automation level and response speed of the overall loading operation; The remote control module is used to send loading task instructions to the conveying and loading units based on resource allocation and scheduling results, and adjust the loading task instruction parameters of conveying speed, chute angle and exit position based on monitoring feedback data. After the operation is completed, a loading operation report is generated, including loading time, loading efficiency, total loading weight, weight error and dust control effect. According to the analysis results in the report, the management personnel optimize the system parameters or adjust the operation process to improve the efficiency and accuracy of the next round of operations; In this embodiment, the remote control module further includes: Based on the monitoring feedback data, the loading progress data of each area is obtained, including real-time loading weight, time, loading rate, etc. The loading operation tasks of each area are compared with the loading progress data, such as loading time, loading rate, loading weight and equipment operation status, to calculate the completion status of the loading operation tasks; Compare the actual completion of loading tasks with the preset goals, evaluate the efficiency, quality and equipment status of loading tasks, and analyze whether the operation process, resource allocation and loading equipment need to be optimized and adjusted based on the evaluation results to ensure efficient execution of tasks; Calculate the satisfaction of task execution based on various data in the loading operation report (such as loading time, loading efficiency, loading weight, etc.); The calculated task satisfaction is compared with the preset target satisfaction. If the satisfaction is less than the target value, an alarm is triggered and the equipment is adjusted and the loading task is rearranged. If the task satisfaction is greater than or equal to the target value, the loading task is considered to be completed, and the relevant data is used for subsequent loading report generation, system optimization and operation process adjustment.

[0028] In this embodiment, the remote management and control module realizes precise control and optimization adjustment of the loading operation task through real-time monitoring feedback data, dynamically evaluates the operation efficiency and quality, promptly discovers and solves potential problems, ensures the smooth progress of the loading operation, and realizes quantitative evaluation of the completion of the loading operation task by calculating the task satisfaction and comparing it with the target satisfaction, thereby ensuring the high-quality completion of the operation task and improving the overall operation automation level and customer satisfaction.

[0029] In one embodiment, the priority of the loading task is determined as follows: Determine the volume and weight of the object to be loaded according to each loading operation task, and determine the loading space requirement according to the volume and weight of the object to be loaded; Determine a loading arrangement carriage for the object to be loaded for each loading operation task based on the loading space requirement, and obtain the rated load-bearing weight and rated load-bearing volume of the loading arrangement carriage; The loading resource utilization index of each loading task is determined according to the rated load weight and rated load volume of each loading arrangement carriage, the volume and weight of the object to be loaded in the loading task:

[0030] in, It is represented as the loading resource utilization index of the i-th loading task, It is represented as the weight of the object to be loaded in the i-th loading task, It represents the rated load weight of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is expressed as the load constraint factor of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is represented as the volume of the object to be loaded in the i-th loading task, It represents the rated carrying volume of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is expressed as the space utilization coefficient of the loading arrangement compartment of the object to be loaded in the i-th loading task, ln is expressed as the natural logarithm, It is expressed as the loading rate of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is represented as the weight balance constraint factor of the loading arrangement carriage of the object to be loaded in the i-th loading task; The priority of each loading task is determined by sorting the loading resource utilization index of each loading task from large to small.

[0031] The beneficial effects of the above technical solution are: by calculating the loading resource utilization index of each loading operation task and then sorting them in order from large to small to determine the priority of the loading operation task, the space utilization rate of each loading operation task can be prioritized to maximize the utilization of the loading space, thereby improving the efficiency of single loading while reducing transportation costs, achieving the maximum single loading capacity, and improving practicality.

[0032] In one embodiment, before the intelligent management and control unit analyzes the loading operation task, the system is further used to: Obtaining task description information of the loading operation task, and performing parameter decomposition on the task description information to obtain task target information, task type information, and task operation information; Determine a task load factor for each loading operation task based on task target information, task type information, and task operation information; Divide all loading tasks into light loading tasks and heavy loading tasks according to the task load factor, and determine the stage task index and overall task index of each divided loading task in the light loading task type and the heavy loading task type; According to the stage task index, the overall task index and the index change range, a state transfer matrix of each divided loading task is constructed; Determine the standard deduction task process parameters for each divided loading operation task according to the state transfer matrix, and determine multiple monitoring indicators according to the standard deduction task process parameters; Obtain the response subtask item corresponding to each monitoring indicator, determine the scheduling resources of the response subtask item, and determine whether there is a synchronization domain task item based on the scheduling resources; If yes, obtain the loading program of each divided loading operation task, and determine the spatial interleaving information of the synchronization domain task item according to the loading program of each divided loading operation task; Determine the loading space requirement of each divided loading operation task according to the spatial interleaving information, and sort the divided loading operation tasks in the light loading task type and the heavy loading task type based on the loading space requirement; The analysis order of the loading operation tasks is determined according to the sorting results, and the loading operation tasks are analyzed in sequence according to the analysis order.

[0033] The beneficial effects of the above technical solution are: by dividing the loading operation tasks into light loading tasks and heavy loading tasks, technical personnel can make advance loading cost planning according to the task labels, thereby improving the user experience and practicality. Furthermore, by determining the loading space requirements of each divided loading operation task and then determining the analysis order of the loading operation tasks, the configuration loading space requirements required for each loading operation task can be met, thereby ensuring the completion of each loading operation task, further improving practicality and stability.

[0034] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An intelligent single-drum bulk grain loading system, characterized in that: include: The sensor monitoring unit is used to collect key loading data such as bulk grain transportation flow, loading weight and ambient dust concentration in real time, and feed the collected key loading data back to the transportation and loading unit and the intelligent management and control unit; The conveying and loading unit is used to obtain the loading task instructions, and transport the bulk grain from the storage area to the loading area based on the loading task instructions. At the same time, the guide plate inside the chute is adjusted based on the monitoring feedback data obtained in real time; The conveying and loading unit is also used to extract the environmental dust concentration parameters from the key loading data, compare the environmental dust concentration parameters with the preset safety environment threshold, and determine whether to optimize the loading environment; The intelligent management and control unit is used to analyze loading operations, allocate resources based on the priority of loading operations, send loading operation task instructions based on the Internet of Things, remotely control each loading equipment, and generate a corresponding loading operation task report after the operation is completed.

2. The single-drum intelligent loading system for bulk grain as claimed in claim 1, characterized in that: The priority of loading job tasks is determined as follows: Determine the volume and weight of the object to be loaded according to each loading task, and determine the loading space requirement according to the volume and weight of the object to be loaded; Determine a loading arrangement carriage for the object to be loaded for each loading operation task based on the loading space requirement, and obtain the rated load-bearing weight and rated load-bearing volume of the loading arrangement carriage; The loading resource utilization index of each loading task is determined according to the rated load weight and rated load volume of each loading arrangement carriage, the volume and weight of the object to be loaded in the loading task: ； in, It is represented as the loading resource utilization index of the i-th loading task, It is represented as the weight of the object to be loaded in the i-th loading task, It represents the rated load weight of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is expressed as the load constraint factor of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is represented as the volume of the object to be loaded in the i-th loading task, It represents the rated carrying volume of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is expressed as the space utilization coefficient of the loading arrangement compartment of the object to be loaded in the i-th loading task, ln is expressed as the natural logarithm, It is expressed as the loading rate of the loading arrangement carriage for the object to be loaded in the i-th loading task, It is represented as the weight balance constraint factor of the loading arrangement carriage of the object to be loaded in the i-th loading task; The priority of each loading task is determined by sorting the loading resource utilization index of each loading task from large to small.

3. The single-drum intelligent loading system for bulk grain as claimed in claim 1, characterized in that: Before the intelligent control unit analyzes the loading task, the system is also used to: Obtaining task description information of the loading operation task, and performing parameter decomposition on the task description information to obtain task target information, task type information, and task operation information; Determine a task load factor for each loading operation task based on task target information, task type information, and task operation information; Divide all loading tasks into light loading tasks and heavy loading tasks according to the task load factor, and determine the stage task index and overall task index of each divided loading task in the light loading task type and the heavy loading task type; According to the stage task index, the overall task index and the index change range, a state transfer matrix of each divided loading task is constructed; Determine the standard deduction task process parameters for each divided loading operation task according to the state transfer matrix, and determine multiple monitoring indicators according to the standard deduction task process parameters; Obtain the response subtask item corresponding to each monitoring indicator, determine the scheduling resources of the response subtask item, and determine whether there is a synchronization domain task item based on the scheduling resources; If yes, obtain the loading program of each divided loading operation task, and determine the spatial interleaving information of the synchronization domain task item according to the loading program of each divided loading operation task; Determine the loading space requirement of each divided loading operation task according to the spatial interleaving information, and sort the divided loading operation tasks in the light loading task type and the heavy loading task type based on the loading space requirement; The analysis order of the loading operation tasks is determined according to the sorting results, and the loading operation tasks are analyzed in sequence according to the analysis order.

4. The single-drum intelligent loading system for bulk grain as claimed in claim 1, characterized in that: Sensor monitoring unit, including: The sensor data acquisition module is used to collect bulk grain flow data based on the flow sensor, the weighing sensor to monitor the loading weight in real time, and the vibration sensor to monitor the running status of the conveyor belt and chute to determine whether each loading equipment has abnormal vibration; The dust monitoring control module is used to monitor the dust concentration in the environment in real time based on the dust concentration sensor, and issue an alarm when the dust concentration exceeds the preset safety threshold, triggering the loading environment optimization process; The operation feedback module is used to obtain the key loading data collected by the sensor data acquisition module and the dust monitoring and control module, construct the monitoring feedback data during the loading operation task, monitor the operation progress and the status of each loading equipment, and transmit the monitoring feedback data to the conveying and loading unit.

5. The single-drum intelligent loading system for bulk grain as claimed in claim 4, characterized in that: The sensor monitoring unit also includes: a data quality inspection module, which obtains real-time monitoring data from the sensor data acquisition module and the dust monitoring control module, including key loading data of bulk grain transportation flow, loading weight and ambient dust concentration, and performs quality inspection on the key loading data to obtain qualified key loading data. The specific steps are as follows: According to the characteristics of each collection parameter, the key loading data is traversed, abnormal data that does not meet the requirements is extracted, and the collection time point of the abnormal data is recorded; Based on the collection time point of the abnormal data, determine whether the collection time interval of the abnormal data meets the preset normal collection interval threshold, and determine whether the collection quantity meets the normal collection frequency; If the time interval and quantity of abnormal data meet the normal standards, the abnormal data will be removed from the key loading data and the normal data will be retained. Otherwise, it will be determined whether the data source is faulty or abnormal, and an alarm will be triggered.

6. The single-drum intelligent loading system for bulk grain as claimed in claim 5, characterized in that: The data quality inspection module further includes: Classify the key loading data after removing abnormal data to obtain the equipment parameters and loading process parameters; Based on the performance characteristics of each loading device, a first parameter association of the device is established; based on the actual operation rules of the loading process, a second parameter association of the loading process is established; based on the constraint rules of each loading device and the loading process, a third parameter association of the parameters of each loading device and the loading process is established; Determine whether the parameters of each loading device satisfy the first parameter association rule. If yes, determine that the loading device is in a normal state; otherwise, determine that the loading device is in an abnormal state and issue an alarm. Determine whether the loading process parameters satisfy the second parameter association rule; if so, determine that the loading process is normal; otherwise, determine that the loading process is abnormal and issue an alarm; If the status of the equipment itself is normal and the loading process is normal, then determine whether the parameters of each loading equipment itself and the loading process parameters meet the third parameter association rule. If so, determine that the loading equipment control operation is normal, and use the normal collection parameters as qualified collection parameters. Otherwise, determine that the loading equipment control operation is abnormal, and issue an alarm.

7. The single-drum intelligent loading system for bulk grain as claimed in claim 4, characterized in that: Conveying and loading unit, comprising: A task receiving module is used to obtain the loading task instruction of the intelligent control unit, and obtain the operation status data of the corresponding conveying equipment based on the loading task instruction; The bulk grain conveying module is used to transport bulk grain from the storage area to the loading area. The initial operating speed of the conveyor is adjusted according to the preset target operating flow. At the same time, the operating speed of the conveyor is adjusted based on the monitoring feedback data to control the actual operating flow within the target operating flow range. The chute adjustment module is used to extract the flow state of bulk grain inside the chute from the monitoring feedback data, and adjust the angle of the guide plate inside the chute based on the flow characteristics of the bulk grain to evenly distribute the bulk grain and allow it to enter the chute outlet; The chute adjustment module is also used to extract the train cargo compartment position and loading height data from the monitoring feedback data, compare the current loading status of the cargo compartment with the target loading status, and adjust the height and direction of the chute outlet based on the comparison results.

8. The single-drum intelligent loading system for bulk grain as claimed in claim 1, characterized in that: Intelligent management and control unit, including: Intelligent loading module, which is used to allocate resources and schedule loading tasks according to the importance of each area, where the scheduling logic takes into account the remaining load and estimated completion time of each area; The remote control module is used to send loading operation task instructions to the transportation and loading units based on resource allocation and scheduling results, adjust the loading operation task instruction parameters based on monitoring feedback data, and generate a loading operation report after the operation is completed.

9. The single-drum intelligent loading system for bulk grain as claimed in claim 8, characterized in that: Intelligent loading module, including: Obtained loading tasks for each area, and read the loading tasks to determine the loading task plan for the loading tasks to be executed, including the type of loading task, required resources and estimated completion time; Analyze the loading task plan, determine the priority of the loading task plan and sort the priorities. At the same time, mark the loading tasks of urgent tasks and key areas, and prioritize them based on the marking results; Scheduling resources based on the remaining load and estimated completion time of each area to ensure that all loading tasks can be completed in a balanced and timely manner; Based on the priority, operating conditions and remaining load volume of the loading tasks in each area, the resource allocation of the loading tasks in each area is adjusted, and the loading equipment and loading sequence are adjusted according to the loading progress.

10. The single-drum intelligent loading system for bulk grain as claimed in claim 8, characterized in that: The remote control module also includes: Obtain loading progress data for each area based on monitoring feedback data, compare loading tasks in each area with loading progress data, and calculate the completion status of loading tasks; Compare the actual completion of loading tasks with the preset goals, evaluate the efficiency, quality and equipment status of loading tasks, and analyze whether the operation process, resource allocation and loading equipment need to be optimized and adjusted based on the evaluation results; Calculate the satisfaction of task execution based on various data in the loading operation report; The calculated task satisfaction is compared with the preset target satisfaction. If the satisfaction is less than the target value, an alarm is triggered and the equipment is adjusted and the loading task is rearranged. If the task satisfaction is greater than or equal to the target value, the loading task is considered to be completed.

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