Intelligent food production management system

Abstract

The present application relates to the field of production transportation, and particularly relates to an intelligent food production management system, comprising: a region monitoring module, used to periodically detect the amount of tasks to be executed and the number of key task points of a target production region, so as to determine a production execution state; a region analysis module, used to determine a corresponding region analysis mode according to the production execution state; a first region setting module, used to determine the distribution state of each key task point according to the number of related key task points, and determine a set of each intensive task point and a key scheduling region of each discrete task point; a second region setting module, used to determine the key scheduling region according to the number of associated task routes and the task completion degree; and a device scheduling module, used to determine whether to perform task scheduling on a second type of execution vehicle according to the proportion of key route segments, and determine the task scheduling mode of a vehicle to be scheduled according to the proportion of a first type of execution vehicle; the present application improves the overall transportation efficiency in a food processing plant.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of production transportation, in particular to an intelligent food production management system. BACKGROUND

[0002] At present, a large number of AGV vehicles are often matched in the transportation link of part of large food intelligent production parks. The application of AGV vehicles reduces the use of human resources and improves the food production efficiency. However, in the actual use process, the task allocation of AGV is often allocated by the operator or the central control system, and the allocation criterion is often not fixed, usually taking power and distance as the criterion, without considering the actual running condition of the vehicle and the road condition in the scene, so that the scheduling efficiency of AGV is difficult to meet the actual use demand. Therefore, how to actually determine whether there is a easy-to-block area in the transportation scene which causes additional power consumption to the vehicle, so as to make task scheduling in advance and ensure the execution efficiency of the transportation process, is a problem to be solved by the person skilled in the art.

[0003] Patent No. CN117055505A discloses an AGV path optimization and real-time scheduling method. The AGV path optimization and real-time scheduling method comprises the following steps: obtaining warehouse map data, obtaining grid map data based on the warehouse map data; obtaining the motion scheduling data of the transport vehicle based on the first-come-first-served scheduling algorithm; obtaining the actual travel trajectory data based on the motion scheduling data of the transport vehicle, outputting the control data of the transport vehicle based on the actual travel trajectory data, and the control data controls the transport vehicle to travel based on the actual travel trajectory data. However, the above-mentioned scheme has the following problems: it does not consider whether there is an easy-to-block area in the actual transportation scene which causes additional power consumption to the transport vehicle, and there is a risk of vehicle stop, which further affects the overall transportation process. SUMMARY

[0004] Therefore, the present application provides an intelligent food production management system to overcome the problem that the prior art fails to effectively predict the area where the vehicle may be blocked according to the driving route of the transport vehicle in the actual scene, and to schedule the task for part of the transport vehicle with low power, resulting in the risk of vehicle stop and the low efficiency of the overall transportation process.

[0005] To achieve the above-mentioned purpose, the present application provides an intelligent food production management system, comprising:

[0006] The area monitoring module is used to periodically detect the amount of tasks to be executed in the target production area and the number of key task points, to determine the production execution state of the target production area, and to determine the category of the task execution vehicle in response to the residual power value corresponding to the vehicle category condition;

[0007] a region analysis module connected with the region monitoring module, configured to determine a corresponding region analysis mode in response to different production execution states, the region analysis mode comprising detecting a distribution state of each key task point and obtaining a key scheduling node of a target production region;

[0008] a first region setting module connected with the region analysis module, configured to determine the distribution state of each key task point and determine a key scheduling region of each intensive task point set and each discrete task point in response to the number of relevant key task points corresponding to the distribution condition;

[0009] a second region setting module connected with the region analysis module, configured to determine a key scheduling region according to the number of associated task routes passing through each route node and the task completion degree;

[0010] a device scheduling module connected with the region monitoring module, the first region setting module and the second region setting module, configured to determine whether to schedule a task for a second type of execution vehicle in response to a key route segment proportion corresponding to a vehicle scheduling condition, and determine a task scheduling mode of a vehicle to be scheduled in response to a first type of execution vehicle proportion corresponding to a task scheduling condition.

[0011] Further, the region monitoring module determines a production execution state of a target production region in response to a to-be-executed task amount and a number of key task points corresponding to an execution state condition;

[0012] The region monitoring module determines that the target production region is in a first production execution state in response to an execution state condition that the to-be-executed task amount is greater than a preset to-be-executed task amount and the number of key task points is greater than a preset number of key task points;

[0013] The region monitoring module determines that the target production region is in a second production execution state in response to an execution state condition that the to-be-executed task amount is greater than a preset to-be-executed task amount and the number of key task points is less than or equal to a preset number of key task points.

[0014] Further, the region analysis module determines that the first region setting module detects the distribution state of each key task point in response to the first production execution state;

[0015] The region analysis module determines that the second region setting module obtains a key scheduling node of a target production region in response to the second production execution state.

[0016] Further, the first region setting module detects the distribution state of each key task point in the target production region in response to a first analysis condition;

[0017] for a single key task point,

[0018] The first area setting module determines that the relevant key task point is in a dense state when the number of relevant key task points is greater than a preset number of relevant key task points, and records the key task point as a dense task point.

[0019] The first area setting module determines that the relevant key task point is in a discrete state when the number of relevant key task points is less than or equal to a preset number of relevant key task points, and records the key task point as a discrete task point.

[0020] The first analysis condition is that the area analysis module determines the distribution state of each key task point detected by the first analysis unit.

[0021] Further, the first area setting module determines a plurality of dense task point sets, and detects the task correlation degree of the dense task points in each dense task point set;

[0022] For a single dense task point set, the dense task point with the largest task correlation degree in the set is recorded as the region center point corresponding to the dense task point set, and the key scheduling area of the dense task point set is set according to the demand task amount of each dense task point in the set;

[0023] Any dense task point in each dense task point set is associated with at least one other dense task point through the same relevant key task point.

[0024] Further, the first area setting module sets the key length of the key scheduling area of the corresponding discrete task point according to the task richness of each discrete task point and the demand task amount of the corresponding target execution task;

[0025] The key length is positively correlated with the demand task amount and the task richness, respectively.

[0026] Further, the second area setting module responds to the second analysis condition to obtain the associated task route between each key task point and its associated task point;

[0027] The key scheduling node is determined according to the number of associated task routes passing through each route node, and the key length of the key scheduling area of the corresponding key scheduling node is determined according to the task completion degree of each key scheduling node, and the key length is positively correlated with the task completion degree;

[0028] If the number of associated task routes passing through a route node is greater than a preset number of routes, the route node is recorded as a key scheduling node;

[0029] The second analysis condition is that the area analysis module determines that the second area setting module obtains the key scheduling node of the target production area.

[0030] Furthermore, the area monitoring module responds to the remaining battery value of the vehicle category condition to determine the category of the vehicle performing the task;

[0031] For vehicles performing a single task.

[0032] If the vehicle category condition responded by the area monitoring module is that the remaining battery value is greater than the preset remaining battery value, then the vehicle executing the task is determined to be a Class I vehicle.

[0033] If the vehicle category condition responded by the area monitoring module is that the remaining battery value is less than or equal to the preset remaining battery value, then the vehicle performing the task is determined to be a Class II vehicle.

[0034] Furthermore, once the area settings are complete, the equipment scheduling module determines whether to perform task scheduling for Class II vehicles based on the proportion of key route segments in response to vehicle scheduling conditions.

[0035] For a single Class II execution vehicle, if the vehicle scheduling condition responded by the equipment scheduling module is that the proportion of key route segments is greater than the preset proportion of key route segments, then it is determined that task scheduling will be performed for the Class II execution vehicle, and the Class II execution vehicle will be recorded as a vehicle to be scheduled.

[0036] The condition for completing the area setting is that all key scheduling areas within the target production area have been determined.

[0037] Furthermore, the equipment scheduling module determines the task scheduling method for the vehicles to be scheduled by considering the proportion of vehicles of a certain type that respond to the task scheduling conditions.

[0038] For a single vehicle to be dispatched,

[0039] If the task scheduling condition responded by the equipment scheduling module is that the proportion of a certain type of vehicle is greater than the preset proportion of a certain type of vehicle, then it is determined that the vehicle to be scheduled will complete the corresponding target execution task according to the combined transportation method.

[0040] If the task scheduling condition responded by the equipment scheduling module is that the proportion of a certain type of vehicle is less than or equal to the preset proportion of a certain type of vehicle, then the module determines that the vehicle to be scheduled should be scheduled to complete other target execution tasks, and sets the scheduling priority coefficient for each target execution task based on the distance to the task point and the reference task energy consumption.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows: In the technical solution of the present invention, the production execution status is determined according to the amount of tasks to be executed in the target production area and the number of key task points, and the corresponding regional analysis method is determined according to the production execution status to identify the key scheduling area of ​​the target production area, that is, to identify the area where there is already vehicle congestion during transportation, and to timely schedule tasks for vehicles with slightly low battery power to avoid vehicle stoppage due to additional power consumption. The present invention improves the overall transportation efficiency in the food processing plant area.

[0042] Furthermore, in this invention, the production execution status of the target production area is determined based on the amount of tasks to be executed and the number of key task points. The area analysis module responds to different production execution statuses to determine the corresponding area analysis method, making the setting of key scheduling areas for the target production area more in line with the actual transportation scenario. This invention improves the accuracy of the determined key scheduling areas.

[0043] Furthermore, in the first production execution state of this invention, there are many key task points in the target production area. Based on the task correlation and required task volume of each dense task point, a corresponding key scheduling area is determined for the divided set of dense task points. The dense task point with the most dense task points in the vicinity of the set of dense task points is selected as the center of the key scheduling area. Moreover, if the sum of the required task volume corresponding to each dense task point in the set of dense task points is larger, that is, if more task execution vehicles need to perform transportation tasks in this vicinity, the coverage area of ​​the set key scheduling area is larger. This invention improves the effectiveness of the determined key scheduling area.

[0044] Furthermore, in the second production execution state of this invention, there are fewer critical task points in the target production area, but the demand for tasks corresponding to each critical task point is large. Key scheduling nodes are determined based on the intersection of the associated task routes in the target production area. There are always many target execution vehicles in the vicinity of the key scheduling nodes. The corresponding key length is determined based on the task completion rate of each key scheduling node. If the demand for tasks corresponding to the associated task routes of the key scheduling node is large, the probability of congestion in the vicinity of the key scheduling node is greater, which may lead to adjustments in the transportation routes for task execution vehicles. This may easily affect a larger area surrounding the key scheduling node. Therefore, the coverage area of ​​the corresponding key scheduling area is larger. This invention improves the effectiveness of the determined key scheduling area.

[0045] Furthermore, in this invention, task scheduling for corresponding Category II vehicles is determined based on the proportion of key route segments. If the proportion of key route segments for Category I and Category II vehicles is large, meaning that the transportation routes of these Category II vehicles pass through many areas with a high probability of congestion, there is a risk of additional energy consumption. To avoid the impact of Category II vehicles stopping on the overall transportation process, task scheduling is performed for these Category II vehicles, and a targeted task scheduling method is determined based on the proportion of Category I vehicles. This makes the task scheduling method more in line with the actual transportation situation, and this invention improves the overall transportation efficiency within the food processing plant area. Attached Figure Description

[0046] Fig. 1 This is a module connection diagram of the intelligent food production management system of the present invention;

[0047] Fig. 2 A flowchart illustrating how the region analysis module of this invention determines the corresponding region analysis method in response to different production execution states;

[0048] Fig. 3 This is a flowchart illustrating the process of determining whether to perform task scheduling for Category II vehicles based on the proportion of key route segments in response to vehicle scheduling conditions by the equipment scheduling module of this invention.

[0049] Fig. 4 This is a flowchart illustrating a task scheduling method for determining the proportion of vehicles to be scheduled based on the response of the equipment scheduling module of this invention to task scheduling conditions. Detailed Implementation

[0050] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0051] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0053] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0054] Please see Figs. 1 to 4 As shown, the present invention provides an intelligent food production management system, comprising:

[0055] The area monitoring module is used to periodically detect the amount of tasks to be executed and the number of key task points in the target production area to determine the production execution status of the target production area, and to determine the category of the task execution vehicle in response to the remaining power value corresponding to the vehicle category condition.

[0056] The regional analysis module, which is connected to the regional monitoring module, is used to respond to different production execution states to determine the corresponding regional analysis method. The regional analysis method includes detecting the distribution status of each key task point and obtaining the key scheduling nodes of the target production area.

[0057] The first region setting module is connected to the region analysis module. It is used to determine the distribution status of each key task point in response to the number of relevant key task points corresponding to the distribution conditions, and to determine the key scheduling areas of each dense task point set and each discrete task point.

[0058] The second region setting module, which is connected to the region analysis module, is used to determine the key scheduling region based on the number of associated task routes passing through each route node and the task completion rate.

[0059] The equipment scheduling module, which is connected to the area monitoring module, the first area setting module and the second area setting module, is used to determine whether to schedule tasks for Class II vehicles in response to the proportion of key route segments corresponding to vehicle scheduling conditions, and to determine the task scheduling method for vehicles to be scheduled in response to the proportion of Class I vehicles corresponding to task scheduling conditions.

[0060] This invention is applied to a meat processing plant area. The food processing plant area where transportation tasks are performed is designated as the target production area. This invention involves several target tasks. Each target task corresponds to the weight of the transported product, the starting and ending points of the transport, and the transport route. The starting and ending points of each target task are designated as target task points within the target production area. One target task point can correspond to multiple target tasks, but each target task has only two target task points. The transport route is the set of route segments required to complete the corresponding target task. Each target task's transport route is the shortest route required to complete the corresponding target task. Each target task is matched with several task vehicles. The types of transported products in this invention include, but are not limited to: target finished products, raw materials, and intermediate products. Target finished products are the final products prepared within the target production area. Raw materials are all materials used to prepare the target finished products. Intermediate products are intermediate products generated during the preparation of the target finished products.

[0061] This invention includes several transport vehicles, all of which are AGVs (Automated Guided Vehicles) powered by electricity. Only transport vehicles with a remaining power value greater than a minimum power value can be used to perform the target task. The minimum power value can be determined by the user based on the actual usage scenario. One possible minimum power value is 5% of the maximum power of the AGV. The AGVs used in this invention can be interconnected to assist in performing the target task. This invention does not limit the specific model of the AGV used; the user can set it according to the actual situation. This invention also includes several route nodes, each of which is an intersection within the target production area. Intersection types include, but are not limited to, crossroads, T-shaped intersections, Y-shaped intersections, and roundabouts. This invention also includes several route segments, with each route segment having route nodes at only two ends.

[0062] This invention utilizes several historical management records. Each historical management record contains at least one management record for the target production area, including the required task quantity, the number of tasks to be executed, the number of related key task points, the number of associated task routes, the remaining battery power of each task execution vehicle, the proportion of key route segments, and the key area change coefficient. Each historical management record also has a corresponding qualification mark, which records whether the overall transportation efficiency requirements for the target production area meet the user's needs.

[0063] Specifically, the regional monitoring module determines the production execution status of the target production area based on the number of tasks to be executed and the number of key task points in response to the execution status conditions.

[0064] If the execution status conditions of the area monitoring module are that the number of tasks to be executed is greater than the preset number of tasks to be executed and the number of key task points is greater than the preset number of key task points, then the target production area is determined to be in the first production execution state.

[0065] If the execution status condition of the area monitoring module is that the number of tasks to be executed is greater than the preset number of tasks to be executed and the number of key task points is less than or equal to the preset number of key task points, then the target production area is determined to be in the second production execution state.

[0066] In this invention, a cyclical status monitoring cycle is applied. The duration of the status monitoring cycle can be determined by the user. The higher the user's requirements for the overall transportation efficiency of the target production area, the shorter the duration of the status monitoring cycle. A status monitoring cycle duration of 15 minutes is provided. At the end of each status monitoring cycle, the amount of tasks to be executed and the number of key task points in the target production area are detected to determine the production execution status of the target production area, and the corresponding regional analysis method is determined according to different production execution statuses.

[0067] The quantity of tasks to be executed is the sum of the required task quantities of each target execution task within the target production area. For a single target execution task, the required task quantity is the weight of the transferred products that need to be transported to the key transportation location of the corresponding target execution task but have not yet reached the transportation destination of the corresponding target execution task. The key task point is the target task point where there is a target execution task with a required task quantity greater than the preset required task quantity. The value of the preset required task quantity can be determined by the user according to the actual work scenario. For example, the user can set it according to historical management records. The higher the user's overall transportation efficiency requirement for the target production area, the smaller the value of the preset required task quantity. A method for determining the value of the preset required task quantity is provided, which records the minimum value of the required task quantity of the key task point in the historical management records that meets the user's overall transportation efficiency requirement for the target production area as the preset required task quantity.

[0068] The values ​​of the preset number of tasks to be executed and the preset number of key task points can be determined by the user according to the actual work scenario. For example, the user can set them according to historical management records. A method for determining the value of the preset number of tasks to be executed is provided, in which the historical management records of the key scheduling area of ​​the set of dense task points corresponding to the demand task volume of each dense task point are recorded as status reference records, and the minimum value of each task to be executed in the status reference records that meets the user's overall transportation efficiency requirements for the target production area is recorded as the preset number of tasks to be executed. A method for determining the value of the preset number of key task points is provided, in which the minimum value of the number of key task points in the status reference records that meets the user's overall transportation efficiency requirements for the target production area is recorded as the preset number of key task points.

[0069] Specifically, when the region analysis module responds to the first production execution state, it determines that the first region setting module detects the distribution status of each key task point.

[0070] When the regional analysis module responds to the second production execution status, it determines that the second regional setting module obtains the key scheduling nodes of the target production region.

[0071] Specifically, the first area setting module responds to the first analysis condition and detects the distribution status of each key task point within the target production management area;

[0072] For a single critical task point,

[0073] The distribution condition for the first area setting module response is that the number of related key task points is greater than the preset number of related key task points. Then, the key task point is determined to be in a dense state and is recorded as a dense task point.

[0074] The distribution condition for the first region setting module response is that if the number of related key task points is less than or equal to the preset number of related key task points, then the key task point is determined to be in a discrete state and is recorded as a discrete task point.

[0075] The first analysis condition is that the regional analysis module determines the distribution status of each key task point by the first analysis unit.

[0076] Among them, for a single critical task point, the relevant critical task point is a critical task point whose route distance from the critical task point is less than the preset route distance of the critical task point. The route distance is the sum of the distances of each route segment required to reach the critical task point. The value of the preset route distance of the critical task point is positively correlated with the required task volume of the target execution task corresponding to the critical task point.

[0077] The value of the preset number of relevant key task points can be determined by the user based on the actual work scenario. For example, the user can set it based on historical management records. The higher the user's overall transportation efficiency requirements for the target production area, the smaller the value of the preset number of relevant key task points. A method for determining the value of the preset number of relevant key task points is provided, which records the minimum value of the number of relevant key task points for each dense task point in the status reference record that meets the user's overall transportation efficiency requirements for the target production area as the preset number of relevant key task points.

[0078] Specifically, the first region setting module determines several sets of dense task points and detects the task correlation of dense task points within each set of dense task points.

[0079] For a single set of dense task points, the dense task point with the highest task correlation is recorded as the center point of the region corresponding to the set of dense task points. The key scheduling region of the set of dense task points is set according to the task demand of each dense task point in the set of dense task points.

[0080] Within each set of dense task points, any one dense task point has at least one shared critical task point with another dense task point.

[0081] Specifically, for a single dense task point, the task correlation is positively correlated with the number of dense task points among the related key task points. For a single set of dense task points, the key length of its key scheduling region is positively correlated with the sum of the required task volume of the target execution tasks corresponding to each dense task point in the set. Its key scheduling region is a circular area defined by the center point of the region of the set of dense task points as the center and the key length corresponding to the set of dense task points as the radius.

[0082] Specifically, the first region setting module sets the key length of the key scheduling region for each discrete task point according to the task richness of each discrete task point and the required task volume of the corresponding target execution task.

[0083] The key length is positively correlated with the required task volume and task richness.

[0084] Specifically, the key scheduling coefficient of each discrete task point is determined based on the task richness of each discrete task point and the required task quantity of each target execution task. The key scheduling coefficient = ln(task richness of discrete task point × sum of required task quantity of each target execution task corresponding to the discrete task point). For a single discrete task point, the task richness is the number of target execution tasks corresponding to the discrete task point. The key length of the key scheduling region of the discrete task point is positively correlated with the key scheduling coefficient of the discrete task point. The key scheduling region is a circular area defined by the location of the discrete task point as the center and the key length corresponding to the discrete task point as the radius.

[0085] Specifically, the second area setting module responds to the second analysis condition and obtains the associated task routes between each key task point and its associated task points;

[0086] The key scheduling nodes are determined based on the number of associated task routes passing through each route node, and the key length of the key scheduling area of ​​the corresponding key scheduling node is determined based on the task completion rate of each key scheduling node. The key length is positively correlated with the task completion rate.

[0087] If the number of associated task routes passing through a route node is greater than the preset number of routes, the route node is marked as a key scheduling node.

[0088] The second analysis condition is that the regional analysis module determines the key scheduling nodes of the target production area by the second regional setting module.

[0089] Specifically, for any critical task point, if a target task point and the critical task point belong to the same target execution task, then the target task point is recorded as the associated task point of the critical task point, and the transportation route corresponding to the target execution task of the critical task point and any associated task point is recorded as an associated task route. The value of the preset number of routes can be determined by the user according to the actual work scenario. For example, the user can set it according to historical management records. The higher the user's overall transportation efficiency requirements for the target production area, the smaller the value of the preset number of routes. A method for determining the value of the preset number of routes is provided, which records the key length of the key scheduling area of ​​the corresponding key scheduling node based on the task completion degree of each key scheduling node as the key reference record, and records the minimum value of the number of associated task routes passing through each key scheduling node in the key reference record that meets the user's overall transportation efficiency requirements for the target production area as the preset number of routes.

[0090] For a single key scheduling node, the task completion rate is the average percentage of the required task volume of each associated task route passing through the key scheduling node. The key scheduling area of ​​the key scheduling node is a circular area defined by the location of the key scheduling node as the center and the key length corresponding to the key scheduling node as the radius. The percentage of the required task volume of any target execution task = the required task volume of the target execution task / the weight of the transshipment products that the target execution task needs to transport.

[0091] Specifically, the area monitoring module responds to the remaining battery level of the vehicle category condition to determine the category of the vehicle performing the task;

[0092] For vehicles performing a single task.

[0093] If the vehicle category condition responded by the area monitoring module is that the remaining battery value is greater than the preset remaining battery value, then the vehicle executing the task is determined to be a Class I vehicle.

[0094] If the vehicle category condition responded by the area monitoring module is that the remaining battery value is less than or equal to the preset remaining battery value, then the vehicle performing the task is determined to be a Class II vehicle.

[0095] At the end of each status monitoring cycle, the battery level of each task-executing vehicle is detected, and the detected battery level is recorded as the remaining battery level of the corresponding task-executing vehicle. The preset remaining battery level can be determined by the user based on the actual work scenario. For example, the user can set it based on historical management records. The higher the user's overall transportation efficiency requirements for the target production area, the larger the preset remaining battery level will be. A method for setting the preset remaining battery level is provided, which records the maximum remaining battery level of each type of task-scheduling vehicle in the historical management records that meets the user's overall transportation efficiency requirements for the target production area as the preset remaining battery level.

[0096] Specifically, once the area setting is complete, the equipment scheduling module determines whether to perform task scheduling for Class II vehicles based on the proportion of key route segments in response to vehicle scheduling conditions.

[0097] For a single Class II execution vehicle, if the vehicle scheduling condition responded by the equipment scheduling module is that the proportion of key route segments is greater than the preset proportion of key route segments, then it is determined that task scheduling will be performed for the Class II execution vehicle, and the Class II execution vehicle will be recorded as a vehicle to be scheduled.

[0098] The condition for completing the area setting is that all key scheduling areas within the target production area have been determined.

[0099] Specifically, for a single Class II vehicle, the route segment passing through the key scheduling area of ​​the target production area in the transportation route of its corresponding target task is recorded as the key route segment. The proportion of the key route segment is equal to the sum of the distances of all key route segments in the transportation route of the target task corresponding to the Class II vehicle / the sum of the distances of all route segments in the transportation route of the target task corresponding to the Class II vehicle. The value of the preset key route segment proportion can be determined by the user according to the actual work scenario. For example, the user can set it based on historical management records. The higher the user's overall transportation efficiency requirements for the target production area, the smaller the value of the preset key route segment proportion. A method for determining the preset key route segment proportion is provided, which is to record the average value of the key route segment proportions of each Class II vehicle that performs task scheduling in the historical management records that meet the user's overall transportation efficiency requirements for the target production area as the preset key route segment proportion.

[0100] Specifically, the equipment scheduling module determines the task scheduling method of the vehicles to be scheduled by responding to the task scheduling conditions based on the proportion of a certain type of vehicles.

[0101] For a single vehicle to be dispatched,

[0102] If the task scheduling condition responded by the equipment scheduling module is that the proportion of a certain type of vehicle is greater than the preset proportion of a certain type of vehicle, then it is determined that the vehicle to be scheduled will complete the corresponding target execution task according to the combined transportation method.

[0103] If the task scheduling condition responded by the equipment scheduling module is that the proportion of a certain type of vehicle is less than or equal to the preset proportion of a certain type of vehicle, then the module determines that the vehicle to be scheduled should be scheduled to complete other target execution tasks, and sets the scheduling priority coefficient for each target execution task based on the distance to the task point and the reference task energy consumption.

[0104] Specifically, for a single vehicle to be dispatched, the percentage of vehicles of type I execution = the number of vehicles of type I execution in the task execution vehicles corresponding to the target task of the vehicle to be dispatched / the number of vehicles of type I execution in the task execution vehicles corresponding to the target task of the vehicle to be dispatched. The value of the preset percentage of vehicles of type I execution can be determined by the user according to the actual work scenario. For example, the user can set it according to historical management records. The higher the user's requirements for the overall transportation efficiency of the target production area, the larger the value of the preset percentage of vehicles of type I execution. One preset value of the percentage of vehicles of type I execution is provided, which is 0.5.

[0105] If the proportion of a vehicle of type I to be dispatched is greater than the preset proportion of a vehicle of type I to be dispatched, a combined transportation method is adopted to complete its corresponding target execution task. The vehicle of type I with the shortest interval distance with the vehicle to be dispatched is recorded as the combined vehicle of the vehicle to be dispatched, and together with the vehicle to be dispatched, they are recorded as a transportation combination to complete the corresponding target execution task. The interval distance between the dispatched vehicle and the vehicle of type I to be dispatched is the distance that the vehicle to be dispatched travels along its corresponding transportation route to reach the vehicle of type I to be dispatched. If the vehicle to be dispatched has additional power consumption due to the congestion area, causing the vehicle to be dispatched to be unable to continue to perform the corresponding target execution task, the vehicle of type I in the transportation combination can connect with the vehicle to be dispatched to assist in completing the target execution task and avoid the vehicle to be dispatched from stopping and affecting the overall transportation efficiency.

[0106] If the proportion of a vehicle of a certain type to be dispatched is less than or equal to the preset proportion of a vehicle of that type, the vehicle to be dispatched will be dispatched to complete other target tasks. The dispatch priority coefficient of any target task is 1 / (task point distance + reference task energy consumption). The task point distance is the shortest distance required for the vehicle to be dispatched to reach the starting position of the transportation of the target task. The reference task energy consumption is the average amount of electricity consumed by each task vehicle corresponding to the target task during one transportation process. The start time of the transportation process is the time when the task vehicle leaves the starting position of the transportation process, and the end time of the transportation process is the time when the task vehicle arrives at the destination position of the transportation process. The amount of electricity consumed by the task vehicle during one transportation process is equal to the remaining amount of electricity of the task vehicle at the start time of the transportation process minus the remaining amount of electricity of the task vehicle at the end time of the transportation process. Timely and reasonable arrangement of the target tasks of the vehicles to be dispatched will ensure that the vehicles to be dispatched can complete their tasks smoothly.

[0107] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0108] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An intelligent food production management system, characterized in that, include: The area monitoring module is used to periodically detect the amount of tasks to be executed and the number of key task points in the target production area to determine the production execution status of the target production area, and to determine the category of the task execution vehicle in response to the remaining power value corresponding to the vehicle category condition. The regional analysis module, which is connected to the regional monitoring module, is used to respond to different production execution states to determine the corresponding regional analysis method in order to obtain key scheduling areas, i.e. areas with the risk of vehicle congestion. The regional analysis method includes detecting the distribution status of each key task point and obtaining key scheduling nodes of the target production area. The first region setting module, connected to the region analysis module, is used to determine the distribution status of each key task point in response to the number of relevant key task points corresponding to the distribution conditions, and to determine the setting method of the key task point's priority scheduling region based on the distribution status of each key task point, thereby determining the priority scheduling region of each dense task point set and each discrete task point. The key scheduling area for each dense task point set is set according to the required task volume of each dense task point within the corresponding dense task point set. The key scheduling area for each discrete task point is set according to the task richness of the corresponding discrete task point and the task volume required for the target task to be executed. The second region setting module, which is connected to the region analysis module, is used to determine the key scheduling region based on the number of associated task routes passing through each route node and the task completion rate. The equipment scheduling module is connected to the area monitoring module, the first area setting module and the second area setting module. It is used to determine whether to schedule tasks for vehicles of type II in response to the proportion of key route segments corresponding to vehicle scheduling conditions, and to determine the task scheduling method for vehicles to be scheduled in response to the proportion of vehicles of type I in response to task scheduling conditions. For vehicles performing a single task. If the vehicle category condition responded by the area monitoring module is that the remaining battery value is greater than the preset remaining battery value, then the vehicle executing the task is determined to be a Class I vehicle. If the vehicle category condition responded by the area monitoring module is that the remaining battery value is less than or equal to the preset remaining battery value, then the vehicle performing the task is determined to be a Class II vehicle.

2. The intelligent food production management system according to claim 1, characterized in that, The regional monitoring module determines the production execution status of the target production area by responding to the number of pending tasks and the number of key task points in the execution status conditions. If the execution status conditions of the area monitoring module are that the number of tasks to be executed is greater than the preset number of tasks to be executed and the number of key task points is greater than the preset number of key task points, then the target production area is determined to be in the first production execution state. If the execution status condition of the area monitoring module is that the number of tasks to be executed is greater than the preset number of tasks to be executed and the number of key task points is less than or equal to the preset number of key task points, then the target production area is determined to be in the second production execution state.

3. The intelligent food production management system according to claim 2, characterized in that, When the region analysis module responds to the first production execution state, it determines that the first region setting module detects the distribution status of each key task point. When the regional analysis module responds to the second production execution status, it determines that the second regional setting module obtains the key scheduling nodes of the target production region.

4. The intelligent food production management system according to claim 3, characterized in that, The first region setting module responds to the first analysis condition and detects the distribution status of each key task point within the target production area; For a single critical task point, The distribution condition for the first area setting module response is that the number of related key task points is greater than the preset number of related key task points. Then, the key task point is determined to be in a dense state and is recorded as a dense task point. The distribution condition for the first region setting module response is that if the number of related key task points is less than or equal to the preset number of related key task points, then the key task point is determined to be in a discrete state and is recorded as a discrete task point. The first analysis condition is that the regional analysis module determines the distribution status of each key task point by the first analysis unit.

5. The intelligent food production management system according to claim 4, characterized in that, The first region setting module determines several sets of dense task points and detects the task correlation of dense task points within each set of dense task points. For a single set of dense task points, the dense task point with the highest task correlation is recorded as the center point of the region corresponding to the set of dense task points. Within each set of dense task points, any one dense task point has at least one shared critical task point with another dense task point.

6. The intelligent food production management system according to claim 3, characterized in that, The key length of the key scheduling region for each discrete task point is positively correlated with the required task quantity and task richness of the corresponding discrete task point.

7. The intelligent food production management system according to claim 6, characterized in that, The second region setting module responds to the second analysis condition and obtains the associated task routes between each key task point and its associated task points; The key scheduling nodes are determined based on the number of associated task routes passing through each route node, and the key length of the key scheduling area of ​​the corresponding key scheduling node is determined based on the task completion rate of each key scheduling node. The key length is positively correlated with the task completion rate. If the number of associated task routes passing through a route node is greater than the preset number of routes, the route node is marked as a key scheduling node. The second analysis condition is that the regional analysis module determines the key scheduling nodes of the target production area by the second regional setting module.

8. The intelligent food production management system according to claim 7, characterized in that, Once the area settings are complete, the equipment scheduling module determines whether to perform task scheduling for Class II vehicles based on the proportion of key route segments in response to vehicle scheduling conditions. For a single Class II execution vehicle, if the vehicle scheduling condition responded by the equipment scheduling module is that the proportion of key route segments is greater than the preset proportion of key route segments, then it is determined that task scheduling will be performed for the Class II execution vehicle, and the Class II execution vehicle will be recorded as a vehicle to be scheduled. The condition for completing the area setting is that all key scheduling areas within the target production area have been determined.

9. The intelligent food production management system according to claim 8, characterized in that, The equipment scheduling module responds to the task scheduling conditions by determining the proportion of a certain type of vehicle to be scheduled, thereby determining the task scheduling method for the vehicle to be scheduled. For a single vehicle to be dispatched, If the task scheduling condition responded by the equipment scheduling module is that the proportion of a certain type of vehicle is greater than the preset proportion of a certain type of vehicle, then it is determined that the vehicle to be scheduled will complete the corresponding target execution task according to the combined transportation method. If the task scheduling condition responded by the equipment scheduling module is that the proportion of a certain type of vehicle is less than or equal to the preset proportion of a certain type of vehicle, then the module determines that the vehicle to be scheduled should be scheduled to complete other target execution tasks, and sets the scheduling priority coefficient for each target execution task based on the distance to the task point and the reference task energy consumption.

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