An intelligent bulk grain single chute train loading system

Abstract

The present invention discloses an intelligent bulk grain single chute train loading system, which relates to the field of port intelligent technology. In order to solve the problems in the prior art that it is difficult to meet the efficient demand of modern logistics for large-scale bulk grain transportation, and the effect in dust capture and control is limited, resulting in a harsh working environment; the present invention collects key data such as bulk grain conveying flow rate, loading weight and ambient dust concentration in real time through a sensing and monitoring unit, and dynamically adjusts the deflector inside the chute accordingly to ensure that the bulk grain flows evenly and is precisely loaded into the train carriage. At the same time, the dust concentration is monitored to optimize the loading environment and ensure the safety of operators. The intelligent control unit analyzes the loading tasks, allocates resources, remotely controls equipment, and automatically generates a report after the operation is completed, thereby improving the loading efficiency and realizing the optimization of collaborative operation. It not only improves the loading operation efficiency, but also effectively solves the dust control problem, ensuring the safety and efficiency of the working environment.

Description

Technical Field

[0001] The present invention relates to the technical field of port intelligentization, and particularly relates to an intelligent bulk grain single chute train loading system. Background Art

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

[0003] However, in the prior art, the operation flow rate of each bulk material weighing scale is only 100 t / h, and the operation capacity of the entire loading line is 400 t / h, which is difficult to meet the high-efficiency requirements of modern logistics for large-scale transportation of bulk grain. Moreover, the effect in dust capture and control is limited, resulting in a poor operation environment and increasing the difficulty of subsequent cleaning work. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent bulk grain single chute train loading system, which realizes the collaborative cooperation, data fusion, and centralized management and control when multiple regions are working together, reduces the operation links and the quantity of equipment maintenance, reduces the labor intensity of workers, improves the metering accuracy, meets the customer requirements, improves the railway train loading capacity, and releases the port railway resources, so as to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] An intelligent bulk grain single chute train loading system, comprising:

[0007] A sensing and monitoring unit, which is used to collect key loading data such as the bulk grain conveying flow rate, the loading weight, and the environmental dust concentration in real time, and feedback the collected key loading data to the conveying and loading unit and the intelligent management and control unit;

[0008] A conveying and loading unit, which is used to obtain a loading operation task instruction, convey bulk grain from the storage area to the loading area based on the loading operation task instruction, and at the same time, adjust the internal deflector of the chute based on the monitoring feedback data obtained in real time;

[0009] The conveying and loading unit is also used to extract the environmental dust concentration parameter in the key loading data, compare the environmental dust concentration parameter with a preset safe environment threshold, and judge whether to perform loading environment optimization processing;

[0010] 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.

[0011] Furthermore, the sensor monitoring unit comprises:

[0012] 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;

[0013] 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;

[0014] 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.

[0015] 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:

[0016] 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;

[0017] 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;

[0018] 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.

[0019] Furthermore, the data quality inspection module further includes:

[0020] Classify the key loading data after removing abnormal data to obtain the equipment parameters and loading process parameters;

[0021] Based on the performance characteristics of each loading device itself, establish the first parameter association of the device itself; based on the actual operation rules of the loading process, establish the second parameter association of the loading process; based on the constraint rules between each loading device itself and the loading process, establish the third parameter association between the parameters of each loading device itself and the parameters of the loading process;

[0022] Judge whether the parameters of each loading device itself meet the first parameter association rule. If so, judge that the state of the loading device itself is normal; otherwise, judge that the state of the loading device itself is abnormal and give an alarm reminder;

[0023] Judge whether the parameters of the loading process meet the second parameter association rule. If so, judge that the state of the loading process is normal; otherwise, judge that the loading process is abnormal and give an alarm reminder;

[0024] If the state of the device itself is normal and the loading process is normal, then judge whether the parameters of each loading device itself and the parameters of the loading process meet the third parameter association rule. If so, judge that the control operation of the loading device is normal and use the normal collected parameters as qualified collected parameters; otherwise, judge that the control operation of the loading device is abnormal and give an alarm reminder.

[0025] Further, the conveying and loading unit includes:

[0026] A task receiving module, which is used to obtain the loading operation task instruction of the intelligent control unit and obtain the operation status data of the corresponding conveying equipment based on the loading operation task instruction;

[0027] A bulk grain conveying module, which is used to convey bulk grain from the storage area to the loading area, adjust the initial running speed of the conveyor according to the preset target operation flow rate, and at the same time, adjust the running speed of the conveyor based on the monitored feedback data to control the actual operation flow rate within the target operation flow rate range;

[0028] A chute adjustment module, which is used to extract the flow state of the bulk grain inside the chute from the monitored feedback data, adjust the angle of the guide plate inside the chute based on the flow characteristics of the bulk grain, and evenly distribute the bulk grain and make it enter the chute outlet;

[0029] The chute adjustment module is also used to extract the position and loading height data of the train car body from the monitored feedback data, compare the current loading state of the car body with the target loading state, and adjust the height and direction of the chute outlet based on the comparison result.

[0030] Further, the intelligent control unit includes:

[0031] An intelligent loading module, which is used to allocate and schedule resources according to the importance of the loading operation tasks in each area. Among them, the scheduling logic takes into account the remaining loading volume and the estimated completion time in each area;

[0032] The remote control module is used to send loading operation task instructions to the conveying and loading unit based on the resource allocation and scheduling results, adjust the parameters of the loading operation task instructions based on the monitoring feedback data, and generate a loading operation report after the operation is completed.

[0033] Further, the intelligent loading module specifically includes:

[0034] Obtain the loading operation tasks in each area, read the loading operation tasks, and determine the loading task plan for the loading operation tasks to be executed, including the types of loading tasks, required resources, and estimated completion time;

[0035] Analyze the loading task plan, determine the priority of the loading task plan and sort the priorities. At the same time, mark the loading operation tasks in emergency tasks and key areas, and execute them preferentially based on the marking results;

[0036] Conduct resource scheduling based on the remaining loading volume and estimated completion time in each area to ensure that all loading operation tasks can be completed evenly and in a timely manner;

[0037] Adjust the resource allocation of the loading operation tasks in each area based on the priority, operation conditions, and remaining loading volume of the loading operation tasks in each area, and adjust the loading equipment and loading sequence according to the loading progress.

[0038] Further, the remote control module also includes:

[0039] Obtain the loading progress data of each area based on the monitoring feedback data, compare the loading operation tasks in each area with the loading progress data, and calculate the completion status of the loading operation tasks;

[0040] Compare the actual completion status of the loading operation tasks with the preset target, evaluate the efficiency, quality, and equipment status of the loading operation tasks, and analyze whether it is necessary to optimize and adjust the operation process, resource allocation, and loading equipment according to the evaluation results;

[0041] Calculate the satisfaction of task execution based on the data in the loading operation report (such as loading time, loading efficiency, loading weight, etc.);

[0042] Compare the calculated task satisfaction with the preset target satisfaction. If the satisfaction is less than the target value, trigger an alarm and perform equipment adjustment and re-arrange the loading operation tasks. If the task satisfaction is greater than or equal to the target value, it is considered that the loading operation task has been completed.

[0043] Further, the priority of the loading operation tasks is determined in the following way:

[0044] Determine the volume and weight of the object to be loaded according to each loading operation task, and determine the loading space requirements based on the volume and weight of the object to be loaded;

[0045] Based on the loading space requirements, determine the loading arrangement carriage for the object to be loaded for each loading operation task, and obtain the rated load-bearing weight and rated load-bearing volume of the loading arrangement carriage;

[0046] According to the rated load-bearing weight and rated load-bearing volume of each loading arrangement carriage, and the volume and weight of the object to be loaded for this loading operation task, determine the loading resource utilization index for each loading operation task:

[0047]

[0048] Among them, represents the loading resource utilization index for the i-th loading operation task, represents the weight of the object to be loaded for the i-th loading operation task, represents the rated load-bearing weight of the loading arrangement carriage for the object to be loaded for the i-th loading operation task, represents the load-bearing constraint factor of the loading arrangement carriage for the object to be loaded for the i-th loading operation task, represents the volume of the object to be loaded for the i-th loading operation task, represents the rated load-bearing volume of the loading arrangement carriage for the object to be loaded for the i-th loading operation task, represents the space utilization coefficient of the loading arrangement carriage for the object to be loaded for the i-th loading operation task, ln represents the natural logarithm, represents the loading rate of the loading arrangement carriage for the object to be loaded for the i-th loading operation task, represents the weight balance constraint factor of the loading arrangement carriage for the object to be loaded for the i-th loading operation task;

[0049] Sort the loading resource utilization indexes of each loading operation task in descending order to determine the priority of the loading operation tasks.

[0050] Furthermore, before the intelligent control unit analyzes the loading operation tasks, the system is also used for:

[0051] Obtain the task description information of the loading operation task, and perform parameter decomposition on the task description information to obtain the task objective information, task type information, and task operation information;

[0052] Based on the task objective information, task type information, and task operation information, determine the task load factor for each loading operation task;

[0053] Divide all loading operation tasks into light-load tasks and heavy-load tasks according to the task load factor, and determine the stage task indicators and overall task indicators for each divided loading operation task in the light-load task type and the heavy-load task type;

[0054] Construct a state transition matrix for each divided loading operation task according to the stage task indicators, the overall task indicators, and the index change range;

[0055] Determine the standard deduction task process parameters for each divided loading operation task according to the state transition matrix, and determine a number of monitoring indicators according to the standard deduction task process parameters;

[0056] Obtain the response sub-task items corresponding to each monitoring indicator, determine the scheduling resources for the response sub-task items, and judge whether there are synchronous domain task items according to the scheduling resources;

[0057] If so, obtain the loading program for each divided loading operation task, and determine the space interleaving information of the synchronous domain task items according to the loading program for each divided loading operation task;

[0058] Determine the loading space requirements for each divided loading operation task according to the space interleaving information, and sort the divided loading operation tasks in the light-load task type and the heavy-load task type based on the loading space requirements;

[0059] Determine the analysis order of the loading operation tasks according to the sorting result, and analyze the loading operation tasks in sequence according to the analysis order.

[0060] Compared with the prior art, the beneficial effects of the present invention are:

[0061] The key data such as the flow rate of bulk grain transportation, the loading weight, and the environmental dust concentration are collected in real time through the sensing monitoring unit, and the internal deflector of the chute is dynamically adjusted accordingly to ensure that the bulk grain flows evenly and is accurately loaded into the train carriage. At the same time, the dust concentration is monitored to optimize the loading environment and ensure the safety of the operators. The intelligent control unit analyzes the loading tasks, allocates resources, remotely controls the equipment, and automatically generates a report after the operation is completed, thereby improving the loading efficiency. It not only improves the loading operation efficiency, but also effectively solves the dust control problem, ensuring the safety and high efficiency of the operation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a module diagram of the intelligent bulk grain single-chute train loading system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0064] To solve the technical problems of low efficiency in intelligent train loading and dust control during operation, please refer to Figure 1 , the following technical solutions are provided in this embodiment:

[0065] An intelligent single chute train loading system for bulk grain includes:

[0066] A sensing and monitoring unit for real-time collecting key loading data such as bulk grain conveying flow rate, loading weight, and environmental dust concentration, and feeding back the collected key loading data to the conveying and loading unit and the intelligent control unit;

[0067] A conveying and loading unit for obtaining a loading operation task instruction, conveying bulk grain from the storage area to the loading area based on the loading operation task instruction, and at the same time, adjusting the internal deflector of the chute based on the monitored feedback data obtained in real time to achieve precise loading and dynamic adjustment, so as to ensure that the flow of bulk grain is uniform and precisely loaded into the target area of the train carriage;

[0068] The conveying and loading unit is also used to extract the environmental dust concentration parameter in the key loading data, compare the environmental dust concentration parameter with a preset safe environment threshold, and determine whether to perform loading environment optimization processing;

[0069] An intelligent control unit for analyzing the loading operation task, allocating resources based on the priority of the loading operation task, sending a loading operation task instruction based on the Internet of Things, remotely controlling each loading device, and generating a corresponding loading operation task report after the operation is completed to achieve automation, collaborative optimization, and precise report generation of the operation.

[0070] In this embodiment, the sensing and monitoring unit collects key data such as the flow rate of bulk grain transportation, the loading weight, and the ambient dust concentration in real time, and dynamically adjusts the guide plate inside the chute accordingly to ensure that the bulk grain flows evenly and is precisely loaded into the train carriage, achieving efficient transportation and loading at a flow rate of 1000 t / h. It ensures that the flow of bulk grain is uniform, the loading process is smooth, and the system operates stably under high load. At the same time, it monitors the dust concentration to optimize the loading environment and ensure the safety of operators. The intelligent control unit analyzes the loading tasks, allocates resources, remotely controls equipment, and automatically generates a report after the operation is completed, thereby improving the loading efficiency, reducing the operating costs, and achieving optimized collaborative operation. It not only improves the efficiency of the loading operation but also effectively solves the dust control problem, ensuring a safe and efficient operating environment.

[0071] In this embodiment, the sensing and monitoring unit includes:

[0072] A sensing data acquisition module for collecting bulk grain flow data based on a flow sensor, real-time monitoring of the loading weight using a weighing sensor, and monitoring the operating status of the conveyor belt and chute with a vibration sensor to determine whether there is abnormal vibration in each loading device;

[0073] A dust monitoring and control module for real-time monitoring of the dust concentration in the environment based on a dust concentration sensor, issuing an alarm when the dust concentration exceeds the preset safety threshold, triggering the optimization process of the loading environment, and automatically adjusting the dust removal air volume, air pressure, and exhaust time according to the change of the dust concentration to ensure that the operating environment remains within the safety standard range;

[0074] An operation feedback module for obtaining the key loading data collected by the sensing data acquisition module and the dust monitoring and control module, constructing the monitoring feedback data during the loading operation task, including data such as flow rate, weight, operation progress, equipment status, train carriage position, and loading height, monitoring the operation progress and the status of each loading device, and transmitting the monitoring feedback data to the transportation and loading unit.

[0075] In this embodiment, by collecting flow rate, weight, and vibration data in real time, it is possible to detect and handle abnormal vibration of the loading equipment in a timely manner, ensuring the stability and safety of the loading process. The real-time monitoring and automatic adjustment functions of the dust monitoring and control module effectively control the dust concentration in the operating environment, protect the health of operators, and reduce environmental pollution. The operation feedback module, by constructing comprehensive monitoring feedback data, not only monitors the operation progress and equipment status in real time but also provides key information such as the train carriage position and loading height, enabling the transportation and loading unit to make precise adjustments based on these data, greatly improving the automation level and efficiency of the loading operation, reducing the operation error rate, and thus improving the overall operation quality and management level.

[0076] 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:

[0077] 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;

[0078] 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;

[0079] 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;

[0080] 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.

[0081] 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;

[0082] 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.

[0083] 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;

[0084] 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.

[0085] In this embodiment, by traversing the key data of loading and extracting abnormal data, the accuracy and reliability of the data are ensured, operation errors caused by data errors are reduced, the time interval and quantity of abnormal data are judged to automatically identify and eliminate abnormal data that does not meet the normal standards, or the data source failure is promptly identified and an alarm is triggered, improving the system's self-diagnosis and maintenance capabilities. Classifying the data and establishing parameter association rules can analyze the equipment status and loading process more deeply, ensure the normal operation of the loading equipment and the smoothness of the loading process. By comprehensively judging the parameters of the equipment itself, the parameters of the loading process, and the association rules between them, it is possible to accurately judge whether the control operation of the loading equipment is normal, thus ensuring the stability and safety of the entire loading operation, improving the operation efficiency, and ensuring the efficient, accurate, and intelligent management of the bulk grain loading operation.

[0086] In this embodiment, the conveying and loading unit includes:

[0087] The task receiving module is used to obtain the loading operation task instructions of the intelligent control unit, including task parameters such as target flow rate, task time, and loading location, and obtain the operation status data of the corresponding conveying equipment based on the loading operation task instructions;

[0088] The bulk grain conveying module is used to convey bulk grain from the storage area to the loading area, adjust the initial running speed of the conveyor according to the preset target operation flow rate, and at the same time, adjust the running speed of the conveyor based on the monitored feedback data to control the actual operation flow rate within the target operation flow rate range;

[0089] The chute adjustment module is used to extract the flow state of bulk grain inside the chute in the monitored 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 enter the chute outlet, ensuring uniform flow direction of the bulk grain and avoiding local accumulation or uneven loading;

[0090] The chute adjustment module is also used to extract the train car position and loading height data in the monitored feedback data, compare the current loading state of the car with the target loading state, and adjust the height and direction of the chute outlet based on the comparison result to ensure that the grain can be accurately loaded into the target area.

[0091] In this embodiment, the bulk grain conveying module combines real-time monitoring and dynamic regulation functions to ensure accurate and stable conveying flow rate, and to avoid conveying interruption or overload to the greatest extent. The dynamic guide plate and precise outlet regulation functions of the chute adjustment module effectively improve the loading uniformity and accuracy. By comparing the current loading state of the car with the target loading state, it is ensured that the bulk grain accurately enters the designated area of the car, avoiding overflow or omission. Combining real-time sensor feedback, the shape and position of the chute outlet are adaptively adjusted to fit carriages of different sizes and specifications. The conveying and loading process responds in real time to changes in the operating environment and equipment status, and has strong adaptability.

[0092] In this embodiment, the intelligent control unit includes:

[0093] An intelligent loading module, which is used to allocate and schedule resources according to the importance of the loading operation tasks in each area, and give priority to ensuring the execution of urgent tasks. Among them, the scheduling logic takes into account the remaining loading capacity and the estimated completion time of each area to ensure the balanced completion of all loading operation tasks. Specifically, it includes:

[0094] Obtain the loading operation tasks in each area, read the loading operation tasks, and determine the loading task plan for the loading operation tasks to be executed, including the types of loading tasks, the required resources, and the estimated completion time;

[0095] Analyze the loading task plan, determine the priority of the loading task plan and perform priority sorting. At the same time, mark the loading operation tasks of urgent tasks and key areas, and perform priority execution based on the marking results;

[0096] Perform resource scheduling based on the remaining loading capacity and the estimated completion time of each area to ensure that all loading operation tasks can be completed evenly and in a timely manner, and avoid excessive concentration or waste of resources;

[0097] Adjust the resource allocation of the loading operation tasks in each area based on the priority, operation conditions, and remaining loading capacity of the loading operation tasks in each area, and adjust the loading equipment and the loading sequence according to the loading progress;

[0098] In this embodiment, through the intelligent loading module, the optimal execution of the operation process is ensured, resource waste is reduced, the operation efficiency is guaranteed, and through refined resource allocation and scheduling, the efficiency and balance of the loading operation are significantly improved, and the priority execution of urgent tasks is ensured, thereby improving the automation level and response speed of the overall loading operation;

[0099] A remote control module, which is used to send loading operation task instructions to the conveying and loading unit based on the resource allocation and scheduling results, and adjust the parameter of the loading operation task such as the conveying speed, the chute angle, and the outlet position based on the monitoring feedback data. After the operation is completed, a loading operation report is generated, including the loading time, the loading efficiency, the total loading weight, the weight error, and the dust control effect, etc. 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 operation;

[0100] In this embodiment, the remote control module further includes:

[0101] Obtain the loading progress data for each area based on the monitored feedback data, including real-time loading weight, time, loading rate, etc. Compare the loading operation tasks in each area with the loading progress data, such as loading time, loading rate, loading weight, and equipment operation status, and calculate the completion status of the loading operation tasks;

[0102] Compare the actual completion status of the loading operation tasks with the preset goals, evaluate the efficiency, quality, and equipment status of the loading operation tasks. Based on the evaluation results, analyze whether it is necessary to optimize and adjust the operation process, resource allocation, and loading equipment to ensure the efficient execution of the tasks;

[0103] Calculate the satisfaction of task execution based on the data in the loading operation report (such as loading time, loading efficiency, loading weight, etc.);

[0104] Compare the calculated task satisfaction with the preset target satisfaction. If the satisfaction is less than the target value, trigger an alarm and perform equipment adjustment and re-arrange the loading operation tasks. If the task satisfaction is greater than or equal to the target value, it is considered that the loading operation task is completed, and the relevant data is used for subsequent loading report generation, system optimization, and operation process adjustment.

[0105] In this embodiment, the remote control module realizes the precise control and optimization adjustment of the loading operation tasks through real-time monitoring feedback data, dynamically evaluates the operation efficiency and quality, discovers and solves potential problems in a timely manner to ensure the smooth progress of the loading operation. By calculating the task satisfaction and comparing it with the target satisfaction, it realizes the quantitative evaluation of the completion status of the loading operation tasks, ensures the high-quality completion of the operation tasks, and improves the overall automation level and customer satisfaction of the operation.

[0106] In one embodiment, the priority of the loading operation tasks is determined as follows:

[0107] Determine the volume and weight of the object to be loaded according to each loading operation task, and determine the loading space requirements based on the volume and weight of the object to be loaded;

[0108] Based on the loading space requirements, determine the loading arrangement carriage for the object to be loaded for each loading operation task, and obtain the rated carrying weight and rated carrying volume of the loading arrangement carriage;

[0109] Determine the loading resource utilization index for each loading operation task according to the rated carrying weight and rated carrying volume of each loading arrangement carriage, and the volume and weight of the object to be loaded for this loading operation task:

[0110]

[0111] Wherein, represents the loading resource utilization index of the i-th loading operation task, denotes the weight of the object to be loaded for the \(i\)-th loading operation task, denotes the rated load-bearing weight of the loading arrangement carriage for the object to be loaded for the \(i\)-th loading operation task, denotes the load weight constraint factor of the loading arrangement carriage for the object to be loaded for the \(i\)-th loading operation task, denotes the volume of the object to be loaded for the \(i\)-th loading operation task, denotes the rated load-bearing volume of the loading arrangement carriage for the object to be loaded for the \(i\)-th loading operation task, denotes the space utilization coefficient of the loading arrangement carriage for the object to be loaded for the \(i\)-th loading operation task, where \(\ln\) denotes the natural logarithm, denotes the loading rate of the loading arrangement carriage for the object to be loaded for the \(i\)-th loading operation task, denotes the weight balance constraint factor of the loading arrangement carriage for the object to be loaded for the \(i\)-th loading operation task;

[0112] Sort the loading resource utilization indices of each loading operation task in descending order to determine the priority of the loading operation tasks.

[0113] The beneficial effects of the above technical solution are as follows: By calculating the loading resource utilization index of each loading operation task and then sorting them in descending order to determine the priority of the loading operation tasks, the priority can be sorted according to the space utilization rate of each loading operation task, thereby maximizing the utilization rate of the loading space, improving the efficiency of single loading while reducing the transportation cost, achieving the maximum single loading volume, and improving the practicality.

[0114] In one embodiment, before the intelligent control unit analyzes the loading operation tasks, the system is further configured to:

[0115] Obtain the task description information of the loading operation task, decompose the task description information to obtain the task objective information, task type information, and task operation information;

[0116] Determine the task load factor of each loading operation task based on the task objective information, task type information, and task operation information;

[0117] Divide all loading operation tasks into light loading tasks and heavy loading tasks according to the task load factor, and determine the stage task indicators and overall task indicators of each divided loading operation task in the light loading task type and heavy loading task type;

[0118] Construct the state transition matrix of each divided loading operation task according to the stage task indicators, overall task indicators, and the amplitude of indicator changes;

[0119] Determine the standard deduction task progress parameters for each partition loading operation task according to the state transition matrix, and determine multiple monitoring indicators according to the standard deduction task progress parameters;

[0120] Obtain the response subtask items corresponding to each monitoring indicator, determine the scheduling resources of the response subtask items, and judge whether there are synchronous domain task items according to the scheduling resources;

[0121] If so, obtain the loading program for each partition loading operation task, and determine the space interleaving information of the synchronous domain task items according to the loading program of each partition loading operation task;

[0122] Determine the loading space requirements for each partition loading operation task according to the space interleaving information, and sort the partition loading operation tasks in the light loading task type and the heavy loading task type based on the loading space requirements;

[0123] Determine the analysis order of the loading operation tasks according to the sorting result, and analyze the loading operation tasks in sequence according to the analysis order.

[0124] The beneficial effects of the above technical solution are as follows: By dividing the loading operation tasks into light loading tasks and heavy loading tasks, technicians can plan the loading costs in advance according to the task labels, improving the user experience and practicality. Further, by determining the loading space requirements for each partition loading operation task and then determining the analysis order of the loading operation tasks, the configured loading space requirements required for each loading operation task can be met, thereby ensuring the completion degree of each loading operation task and further improving the practicality and stability.

[0125] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution of the present invention and its inventive concept, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent bulk grain single chute train loading system, characterized in that, Including: A sensing and monitoring unit, which is used to collect key data of the loading operation in real time, including the flow rate of bulk grain transportation, the loading weight, and the environmental dust concentration, and feed back the collected key data of the loading operation to the transportation and loading unit and the intelligent control unit; A transportation and loading unit, which is used to obtain a loading operation task instruction, transport bulk grain from the storage area to the loading area based on the loading operation task instruction. At the same time, based on the monitoring feedback data obtained in real time, it adjusts the deflector inside the chute; The transportation and loading unit is also used to extract the environmental dust concentration parameter in the key data of the loading operation, compare the environmental dust concentration parameter with a preset safe environment threshold, and judge whether to perform loading environment optimization treatment; An intelligent control unit, which is used to analyze the loading operation task, allocate resources based on the priority of the loading operation task, send a loading operation task instruction based on the Internet of Things, remotely control each loading device, and generate a corresponding loading operation task report after the operation is completed; The priority of the loading operation task is determined in the following way: 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; Based on the loading space requirement, determine the loading arrangement carriage for the object to be loaded for each loading operation task, and obtain the rated load weight and rated load volume of the loading arrangement carriage; Determine the loading resource utilization index of each loading operation task 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 for this loading operation task; Among them, represents the loading resource utilization index of the i-th loading operation task, represents the weight of the object to be loaded in the i-th loading operation task, represents the rated carrying weight of the carriage for the loading arrangement of the object to be loaded in the i-th loading operation task, represents the load constraint factor of the carriage for the loading arrangement of the object to be loaded in the i-th loading operation task, represents the volume of the object to be loaded in the i-th loading operation task, represents the rated carrying volume of the carriage for the loading arrangement of the object to be loaded in the i-th loading operation task, represents the space utilization coefficient of the carriage for the loading arrangement of the object to be loaded in the i-th loading operation task, represents the natural logarithm, represents the loading rate of the carriage for the loading arrangement of the object to be loaded in the i-th loading operation task, represents the weight balance constraint factor of the carriage for the loading arrangement of the object to be loaded in the i-th loading operation task; Sort according to the loading resource utilization index of each loading operation task in descending order to determine the priority of the loading operation task.

2. The intelligent bulk grain single chute train loading system according to claim 1, wherein Before the intelligent control unit analyzes the loading operation task, the system is also used to: Obtain the task description information of the loading operation task, decompose the task description information into parameter to obtain task objective information, task type information, and task operation information; Determine the task load factor of each loading operation task based on the task objective information, task type information, and task operation information; Divide all loading operation tasks into light loading tasks and heavy loading tasks according to the task load factor, and determine the stage task indicators and overall task indicators of each divided loading operation task in the light loading task type and the heavy loading task type; Construct a state transition matrix for each divided loading operation task according to the stage task indicators, overall task indicators, and the amplitude of indicator changes; Determine the standard deduction task process parameters for each divided loading operation task according to the state transition matrix, and determine multiple monitoring indicators according to the standard deduction task process parameters; Obtain the corresponding response sub-task items for each monitoring indicator, determine the scheduling resources of the response sub-task items, and judge whether there are synchronous domain task items according to the scheduling resources; If so, obtain the loading program of each divided loading operation task, and determine the space interleaving information of the synchronous domain task items 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 space 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.

3. The intelligent bulk grain single chute train loading system according to claim 1, characterized in that 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.

4. The intelligent bulk grain single chute train loading system according to claim 3, 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.

5. The intelligent bulk grain single chute train loading system according to claim 4, 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 itself meet the first parameter association, if so, determine that the state of the loading device itself is normal; otherwise, determine that the state of the loading device itself is abnormal, and issue an alarm reminder; Determine whether the loading process parameters satisfy the second parameter association, if so, determine that the loading process is normal; otherwise, determine that the loading process is abnormal, and issue an alarm reminder; 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 satisfy the third parameter association. 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.

6. The intelligent bulk grain single chute train loading system according to claim 5, characterized in that, Conveying and loading unit, comprising: A task receiving module, which is used to obtain the loading operation task instruction of the intelligent control unit and obtain the operation status data of the corresponding conveying equipment based on the loading operation task instruction; A bulk grain conveying module, which is used to convey bulk grain from the storage area to the loading area, adjust the initial running speed of the conveyor according to the preset target operation flow rate. At the same time, based on the monitoring feedback data, adjust the running speed of the conveyor to control the actual operation flow rate within the target operation flow rate range; A chute adjusting module, which is used to extract the flow state of bulk grain inside the chute from the monitoring feedback data, and adjust the angle of the deflector inside the chute based on the flow characteristics of the bulk grain to evenly distribute the bulk grain and make it enter the chute outlet; The chute adjusting module is also used to extract the train car position and loading height data from the monitoring feedback data, compare the current loading state of the car with the target loading state, and adjust the height and direction of the chute outlet based on the comparison result.

7. The intelligent system for loading bulk grain into trains with a single chute as claimed in claim 1, wherein The intelligent control unit includes: An intelligent loading module, which is used to allocate and schedule resources according to the importance of the loading operation tasks in each area. Among them, the scheduling logic considers the remaining loading amount and the estimated completion time in each area; A remote control module, which is used to send the loading operation task instruction to the conveying and loading unit based on the resource allocation and scheduling results, and adjust the parameters of the loading operation task instruction based on the monitoring feedback data, and generate a loading operation report after the operation is completed.

8. The intelligent bulk grain single chute train loading system according to claim 7, characterized in that, The intelligent loading module specifically includes: Obtain the loading operation tasks in each area, read the loading operation tasks, and determine the loading task plan for the loading operation tasks to be executed, including the type of loading tasks, the required resources and the 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 operation tasks in emergency tasks and key areas, and execute them preferentially based on the marking results; Conduct resource scheduling based on the remaining loading amount and the estimated completion time in each area to ensure that all loading operation tasks can be completed evenly and in a timely manner; Based on the priority, operation conditions and remaining loading amount of the loading operation tasks in each area, adjust the resource allocation of the loading operation tasks in each area, and adjust the loading equipment and loading sequence according to the loading progress.

9. The intelligent bulk grain single chute train loading system according to claim 8, characterized in that The remote control module also includes: Obtain the loading progress data of each area based on the monitoring feedback data, compare the loading operation tasks in each area with the loading progress data, and calculate the completion of the loading operation tasks; Compare the actual completion of the loading operation tasks with the preset target, evaluate the efficiency, quality and equipment status of the loading operation tasks, and based on the evaluation results, analyze whether it is necessary to optimize and adjust the operation process, resource allocation and loading equipment; Calculate the satisfaction of task execution based on the data in the loading operation report; Compare the calculated task satisfaction with the preset target satisfaction. If the satisfaction is less than the target value, trigger an alarm and perform equipment adjustment and re-arrange the loading operation tasks. If the task satisfaction is greater than or equal to the target value, it is considered that the loading operation task is completed.

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