Intelligent food production management system

Abstract

The invention relates to the field of production and transportation, in particular to an intelligent food production management system which comprises an area monitoring module used for periodically detecting the quantity of tasks to be executed and the quantity of key task points in a target production area so as to determine a production execution state; the regional analysis module is used for determining a corresponding regional analysis mode according to the production execution state; the first region setting module is used for determining the distribution state of each key task point according to the number of the related key task points, and determining a key scheduling region of each dense task point set and each discrete task point; the second area setting module is used for determining a key scheduling area according to the number of the associated task routes and the task completion degree; the equipment scheduling module is used for determining whether task scheduling is carried out on the second-class execution vehicles or not according to the proportion of the key route paragraphs, and determining a task scheduling mode of the to-be-scheduled vehicles according to the proportion of the first-class execution vehicles; the overall transportation efficiency in the food processing factory area is improved.

Description

Technical Field

[0001] The present invention relates to the field of production and transportation, and in particular to an intelligent food production management system. Background Art

[0002] At present, the transportation links of some large-scale smart food production parks are often equipped with a large number of AGV vehicles. The application of AGV vehicles reduces the use of human resources and improves food production efficiency. However, in actual use, the task allocation of AGV is often allocated by operators or central control systems. The allocation basis is often not fixed, usually based on power and distance, without considering the actual operation of the vehicle and road conditions in the scene, which makes it difficult for the AGV scheduling efficiency to meet actual usage needs. Therefore, how to actually determine whether there are areas prone to congestion in the transportation scene that cause additional power consumption for the vehicle, so as to make task scheduling in advance and ensure the execution efficiency of the transportation process, is a problem that technical personnel in this field need to solve urgently.

[0003] Patent publication number CN117055505A discloses a method for AGV path optimization and real-time scheduling. The method for AGV path optimization and real-time scheduling includes the following steps: obtaining warehouse map data, obtaining grid map data based on the warehouse map data; obtaining motion scheduling data of the transport vehicle based on a first-come, first-served scheduling algorithm; obtaining actual travel trajectory data based on the motion scheduling data of the transport vehicle, and outputting 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 scheme has the following problems: it does not take into account whether there are areas prone to congestion in the actual transportation scenario that cause additional power consumption for the transport vehicle, and there is a risk of vehicle shutdown, which in turn affects the subsequent overall transportation process. Summary of the invention

[0004] To this end, the present invention provides an intelligent food production management system to overcome the problem in the prior art that it fails to effectively predict areas where vehicle congestion may occur based on the driving routes of transport vehicles in actual scenarios, and fails to perform task scheduling for some transport vehicles with low battery power, resulting in the risk of vehicle shutdown, which in turn leads to inefficiency of the overall transportation process.

[0005] To achieve the above object, the present invention 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 and the number of key task points in the target production area to determine the production execution status of the target production area, and determine the category of the task execution vehicle in response to the remaining power value corresponding to the vehicle category condition;

[0007] A 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 state of each key task point and obtaining the key scheduling node of the target production area;

[0008] A first area setting module, which is connected to the area analysis module, is used to determine the distribution state of each key task point in response to the number of relevant key task points corresponding to the distribution condition, and to determine the key scheduling area of ​​each dense task point set and each discrete task point;

[0009] A second area setting module, which is connected to the area analysis module, is used to determine the key scheduling area according to the number of associated task routes passing through each route node and the task completion degree;

[0010] The equipment scheduling module is connected to the area monitoring module, the first area setting module and the second area setting module, and is used to respond to the proportion of key route sections corresponding to the vehicle scheduling conditions to determine whether to perform task scheduling for the second-class execution vehicles, and respond to the proportion of the first-class execution vehicles corresponding to the task scheduling conditions to determine the task scheduling method for the vehicles to be scheduled.

[0011] Further, the area monitoring module determines the production execution status of the target production area in response to the amount of tasks to be executed and the number of key task points of the execution status condition;

[0012] The execution state condition responded by the regional monitoring module is that the amount of tasks to be executed is greater than the preset amount of tasks to be executed and the number of key task points is greater than the preset number of key task points, then it is determined that the target production area is in the first production execution state;

[0013] The execution status condition responded by the regional monitoring module is that the amount of tasks to be executed is greater than the preset amount 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 it is determined that the target production area is in the second production execution state.

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

[0015] The area analysis module responds to the second production execution state, and determines that the second area setting module obtains the key scheduling node of the target production area.

[0016] Furthermore, the first area setting module detects the distribution status of each key task point in the target production pipe area in response to the first analysis condition;

[0017] For a single key task point,

[0018] If the distribution condition responded by the first area setting module is that the number of relevant key task points is greater than the preset number of relevant key task points, the key task point is determined to be in a dense state, and the key task point is recorded as a dense task point;

[0019] If the distribution condition of the response of the first area setting module is that the number of relevant key task points is less than or equal to the preset number of relevant key task points, the key task point is determined to be in a discrete state, and the key task point is recorded as a dense task point;

[0020] The first analysis condition is that the regional analysis module determines that the first analysis unit detects the distribution status of each key task point.

[0021] Furthermore, the first area setting module determines a number of dense task point sets, and detects the task relevance 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 is recorded as the regional 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 required task volume of each dense task point in the dense task point set;

[0023] Any dense task point in each dense task point set has the same related key task point as at least another dense task point.

[0024] Furthermore, 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 required task volume of the corresponding target execution task;

[0025] The key length is positively correlated with the required task volume and task richness respectively.

[0026] Furthermore, the second area setting module obtains the associated task route between each key task point and its associated task point in response to the second analysis condition;

[0027] Determine the key dispatching node according to the number of associated task routes passing through each route node, and determine the key length of the key dispatching area corresponding to the key dispatching node according to the task completion degree of each key dispatching node, wherein 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 the preset number of routes, the route node is recorded as a key scheduling node;

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

[0030] Further, the area monitoring module is responsive to the remaining power value of the vehicle category condition to determine the category of the task execution vehicle;

[0031] For a single mission execution vehicle,

[0032] If the vehicle category condition responded by the area monitoring module is that the remaining power value is greater than the preset remaining power value, the task execution vehicle is determined to be a type 1 execution vehicle;

[0033] If the vehicle category condition responded by the area monitoring module is that the remaining power value is less than or equal to the preset remaining power value, the task execution vehicle is determined to be a second-category execution vehicle.

[0034] Further, under the condition that the regional setting is completed, the equipment scheduling module determines whether to perform task scheduling for the second-class execution vehicle in response to the proportion of key route sections of the vehicle scheduling condition;

[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 sections is greater than the preset proportion of key route sections, it is determined that the Class II execution vehicle is to be scheduled, and the Class II execution vehicle is recorded as a vehicle to be scheduled;

[0036] The completion condition for the area setting is that all key scheduling areas within the target production area are determined and completed.

[0037] Furthermore, the equipment scheduling module responds to the proportion of a type of executing vehicles of the task scheduling condition to determine the task scheduling mode of the vehicle to be scheduled;

[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 one type of execution vehicles is greater than the preset proportion of another type of execution vehicles, it is determined that the vehicle to be scheduled completes the corresponding target execution task according to the combined transportation mode;

[0040] The task scheduling condition responded by the equipment scheduling module is that the proportion of one type of execution vehicles is less than or equal to the preset proportion of one type of execution vehicles, then it is determined to schedule the vehicle to be scheduled to complete other target execution tasks, and the scheduling priority coefficient of each target execution task is set according to the distance of the task point and the reference task energy consumption.

[0041] Compared with the prior art, the beneficial effect of the present invention lies in that, in the technical scheme 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 determine the key scheduling area of ​​the target production area, that is, to determine the area where vehicle congestion already exists during the transportation process, and thereby timely task scheduling is carried out for task execution vehicles with slightly low power to avoid vehicle shutdown due to additional power consumption. The present invention improves the overall transportation efficiency within the food processing plant area.

[0042] Furthermore, in the present invention, the production execution status of the target production area is determined according to the amount of tasks to be executed and the number of key task points. The regional analysis module determines the corresponding regional analysis method in response to different production execution statuses, so that the setting method of the key scheduling area of ​​the target production area is more in line with the actual transportation scenario. The present invention improves the accuracy of the determined key scheduling area.

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

[0044] Furthermore, in the second production execution state of the present invention, there are fewer key task points in the target production area, but the demand task volume corresponding to each key task point is large. The key scheduling node is determined according to the intersection of each related task route in the target production area. There are always many target execution vehicles in the vicinity of the key scheduling node. The corresponding key length is determined according to the task completion degree of each key scheduling node. If the demand task volume of the target execution task corresponding to the related task route of the key scheduling node is large, the congestion probability of the vicinity of the key scheduling node is greater, which may lead to adjustments to the transportation routes of the task execution vehicles, and easily affect a larger range of surrounding areas of the key scheduling node. Therefore, the corresponding key scheduling area has a larger coverage area. The present invention improves the effectiveness of the determined key scheduling area.

[0045] Furthermore, the present invention determines whether to perform task scheduling for the corresponding Class II execution vehicle based on the proportion of key route sections. If the proportion of key route sections for Class I and Class II execution vehicles is relatively large, that is, the transportation route of the Class II execution vehicle passes through more areas with a high probability of congestion, there is likely to be additional power consumption. In order to avoid the impact of the shutdown of the Class II execution vehicle on the overall transportation process, task scheduling is performed for the Class II execution vehicle, and a targeted task scheduling method is determined based on the proportion of Class I execution vehicles, so that the task scheduling method is more in line with the actual transportation situation. The present invention improves the overall transportation efficiency within the food processing plant area. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a module connection diagram of the intelligent food production management system of the present invention;

[0047] Figure 2 A flow chart of the regional analysis module of the present invention determining the corresponding regional analysis method in response to different production execution states;

[0048] Figure 3 A flow chart of the device scheduling module of the present invention responding to the proportion of key route sections of vehicle scheduling conditions to determine whether to perform task scheduling for the second type of execution vehicles;

[0049] Figure 4 A flow chart of a task scheduling method in which a device scheduling module of the present invention determines a proportion of a type of executing vehicles to be scheduled in response to a task scheduling condition. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0052] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0053] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] See also Figures 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 determine the category of the task execution vehicle in response to the remaining power value corresponding to the vehicle category condition;

[0056] A 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 state of each key task point and obtaining the key scheduling node of the target production area;

[0057] A first area setting module, which is connected to the area analysis module, is used to determine the distribution state of each key task point in response to the number of relevant key task points corresponding to the distribution condition, and to determine the key scheduling area of ​​each dense task point set and each discrete task point;

[0058] A second area setting module, which is connected to the area analysis module, is used to determine the key scheduling area according to the number of associated task routes passing through each route node and the task completion degree;

[0059] The equipment scheduling module is connected to the area monitoring module, the first area setting module and the second area setting module, and is used to respond to the proportion of key route sections corresponding to the vehicle scheduling conditions to determine whether to perform task scheduling for the second-class execution vehicles, and respond to the proportion of the first-class execution vehicles corresponding to the task scheduling conditions to determine the task scheduling method for the vehicles to be scheduled.

[0060] Among them, the present invention is applied to a meat food processing plant area, and the food processing plant area where the transportation task is performed is recorded as the target production area. There are several target execution tasks in the present invention. Any target execution task corresponds to the weight of the transfer product that needs to be transported, the transportation starting position, the transportation end position and the transportation route. The transportation starting position and the transportation end position of each target execution task are recorded as the target task point of the target production area. A target task point can correspond to multiple target execution tasks, but a target execution task only has two target task points. The transportation route is a collection of route sections that need to be passed through to complete the corresponding target execution task. The transportation route of each target execution task is the shortest route required to complete the corresponding target execution task. Each target execution task is matched with several task execution vehicles. The categories of transfer products in the present invention include but are not limited to: target prepared finished products, preparation raw materials and intermediate products. The target prepared finished products are the final products prepared in the target production area, the preparation raw materials are all materials used to prepare the target prepared finished products, and the intermediate products are the intermediate products produced in the process of preparing the target prepared finished products.

[0061] There are several transport vehicles in the present invention, and all of them are AGVs that run on electricity. Only transport vehicles with a remaining power value greater than a minimum power value can be used to perform target execution tasks. The value of the minimum power value can be determined by the user according to the actual usage scenario. A minimum power value is provided, and 5% of the maximum power of the AGV is recorded as the minimum power value. The AGVs used in the present invention can be connected to each other to assist in performing target execution tasks. The present invention does not limit the specific model of the AGV used, and the user can set it according to the actual situation; there are several route nodes in the present invention, and each route node is an intersection within the target production area. The types of intersections include but are not limited to: crossroads, T-shaped intersections, Y-shaped intersections, and roundabouts. There are several route sections in the present invention, and any route section has route nodes only at both ends;

[0062] Several historical management records are applied in the present invention, and any historical management record records the required task volume, the task volume to be executed, the number of related key task points, the number of related task routes, the remaining power value of each task execution vehicle, the proportion of key route sections and the key area change coefficient of each target task point in at least one management record for the target production area, and each historical management record corresponds to a qualified mark, which records whether the overall transportation efficiency requirements for the target production area meet user needs.

[0063] Specifically, the area monitoring module determines the production execution status of the target production area in response to the amount of tasks to be executed and the number of key task points of the execution status condition;

[0064] The execution state condition responded by the regional monitoring module is that the amount of tasks to be executed is greater than the preset amount of tasks to be executed and the number of key task points is greater than the preset number of key task points, then it is determined that the target production area is in the first production execution state;

[0065] The execution status condition responded by the regional monitoring module is that the amount of tasks to be executed is greater than the preset amount 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 it is determined that the target production area is in the second production execution state.

[0066] Among them, the present invention applies a cyclic status monitoring cycle, and 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 is provided, and the status monitoring cycle is 15 minutes. 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 task volume to be executed is the sum of the required task volumes of each target execution task in the target production area. For a single target execution task, the required task volume is the weight of the part of the transferred products that needs to be transported to the transportation key position of the corresponding target execution task but has not reached the transportation terminal position of the corresponding target execution task. The key task point is the target task point of the target execution task corresponding to the required task volume that is greater than the preset required task volume. The value of the preset required task volume can be determined by the user according to the actual work scenario. For example, the user can set it according to the historical management records. The higher the user's requirements for the overall transportation efficiency of the target production area, the smaller the value of the preset required task volume. A method for determining the value of the preset required task volume is provided, and the minimum value of the required task volume of the key task point in the historical management records that meets the user's requirements for the overall transportation efficiency of the target production area is recorded as the preset required task volume;

[0068] The values ​​of the preset amount of tasks to be performed 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 setting the preset amount of tasks to be performed is provided, and the historical management records of the key scheduling area of ​​the corresponding set of dense task points set according to the required task volume of each dense task point are recorded as status reference records. The minimum value of each amount of tasks to be performed in the status reference record that meets the user's requirements for the overall transportation efficiency of the target production area is recorded as the preset amount of tasks to be performed. A method for setting the preset number of key task points is provided, and the minimum value of the number of key task points in the status reference record that meets the user's requirements for the overall transportation efficiency of the target production area is recorded as the preset number of key task points.

[0069] Specifically, the region analysis module responds to the first production execution status, and then determines that the first region setting module detects the distribution status of each key task point;

[0070] The area analysis module responds to the second production execution state, and determines that the second area setting module obtains the key scheduling node of the target production area.

[0071] Specifically, the first area setting module detects the distribution status of each key task point in the target production pipe area in response to the first analysis condition;

[0072] For a single key task point,

[0073] If the distribution condition responded by the first area setting module is that the number of relevant key task points is greater than the preset number of relevant key task points, the key task point is determined to be in a dense state, and the key task point is recorded as a dense task point;

[0074] If the distribution condition of the response of the first area setting module is that the number of relevant key task points is less than or equal to the preset number of relevant key task points, the key task point is determined to be in a discrete state, and the key task point is recorded as a dense task point;

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

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

[0077] The value of the preset number of relevant key task points 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 requirement for the overall transportation efficiency of 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, and the minimum value of the number of relevant key task points of each intensive task point in the state reference record that meets the user's requirement for the overall transportation efficiency of the target production area is recorded as the preset number of relevant key task points.

[0078] Specifically, the first area setting module determines a number of dense task point sets, and detects the task relevance of the dense task points in each dense task point set;

[0079] For a single dense task point set, the dense task point with the largest task correlation is recorded as the regional 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 required task volume of each dense task point in the dense task point set;

[0080] Any dense task point in each dense task point set has the same related key task point as at least another dense task point.

[0081] Among them, for a single dense task point, the task association degree is positively correlated with the number of dense task points in the key task points related to the dense task point. For a single dense task point set, the key length of its key scheduling area is positively correlated with the sum of the required task quantities of the target execution tasks corresponding to each dense task point in the dense task point set. The key scheduling area is a circular area with the area center point of the dense task point set as the center and the key length corresponding to the dense task point set as the radius.

[0082] Specifically, 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 required task volume of the corresponding target execution task;

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

[0084] Among them, the key scheduling coefficient of the corresponding discrete task point is determined according to the task richness of each discrete task point and the required task volume of each corresponding target execution task. The key scheduling coefficient = ln (task richness of the discrete task point × the sum of the required task volumes 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 area of ​​the discrete task point is positively correlated with the key scheduling coefficient of the discrete task point. The key scheduling area is a circular area with the position 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 obtains the associated task route between each key task point and its associated task point in response to the second analysis condition;

[0086] Determine the key dispatching node according to the number of associated task routes passing through each route node, and determine the key length of the key dispatching area corresponding to the key dispatching node according to the task completion degree of each key dispatching node, wherein the key length is positively correlated with the task completion degree;

[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 recorded as a key scheduling node;

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

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

[0090] For a single key scheduling node, the task completion rate is the average value of the proportion of the required task volume of the target execution tasks corresponding to each associated task route passing through the key scheduling node. The key scheduling area of ​​the key scheduling node is a circular area with the position of the key scheduling node as the center and the key length corresponding to the key scheduling node as the radius. The proportion of the required task volume of any target execution task = the required task volume of the target execution task / the weight of the transferred products that need to be transported for the target execution task.

[0091] Specifically, the area monitoring module responds to the remaining power value of the vehicle category condition to determine the category of the task execution vehicle;

[0092] For a single mission execution vehicle,

[0093] If the vehicle category condition responded by the area monitoring module is that the remaining power value is greater than the preset remaining power value, the task execution vehicle is determined to be a type 1 execution vehicle;

[0094] If the vehicle category condition responded by the area monitoring module is that the remaining power value is less than or equal to the preset remaining power value, the task execution vehicle is determined to be a second-category execution vehicle.

[0095] Among them, at the end time of each status monitoring cycle, the power value of each task execution vehicle is detected, and the detected power value is recorded as the remaining power value of the corresponding task execution vehicle. The value of the preset remaining power value can be determined by the user according to the actual working 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 larger the value of the preset remaining power value. A method for setting the preset remaining power value is provided, and the maximum value of the remaining power value of each type of second execution vehicle for task scheduling in the historical management records that meets the user's overall transportation efficiency requirement for the target production area is recorded as the preset remaining power value.

[0096] Specifically, under the condition that the regional setting is completed, the equipment scheduling module determines whether to perform task scheduling for the second-class execution vehicle in response to the proportion of key route sections of the vehicle scheduling condition;

[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 sections is greater than the preset proportion of key route sections, it is determined that the Class II execution vehicle is to be scheduled, and the Class II execution vehicle is recorded as a vehicle to be scheduled;

[0098] The completion condition for the area setting is that all key scheduling areas within the target production area are determined and completed.

[0099] Among them, for a single Class II execution vehicle, the route section in the transportation route of its corresponding target execution task that passes through the key scheduling area of ​​the target production area is recorded as a key route section, and the key route section ratio = the sum of the distances of each key route section in the transportation route of the target execution task corresponding to the Class II execution vehicle / the sum of the distances of each route section in the transportation route of the target execution task corresponding to the Class II execution vehicle. The value of the preset key route section ratio 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 requirement for the overall transportation efficiency of the target production area, the smaller the value of the preset key route section ratio. A method for determining the preset key route section ratio is provided, and the average value of the key route section ratio of each Class II execution vehicle that performs task scheduling in the historical management records that meet the user's requirement for the overall transportation efficiency of the target production area is recorded as the preset key route section ratio.

[0100] Specifically, the equipment scheduling module responds to the proportion of a type of executing vehicles in the task scheduling condition to determine the task scheduling method of the vehicle to be scheduled;

[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 one type of execution vehicles is greater than the preset proportion of another type of execution vehicles, it is determined that the vehicle to be scheduled completes the corresponding target execution task according to the combined transportation mode;

[0103] The task scheduling condition responded by the equipment scheduling module is that the proportion of one type of execution vehicles is less than or equal to the preset proportion of one type of execution vehicles, then it is determined to schedule the vehicle to be scheduled to complete other target execution tasks, and the scheduling priority coefficient of each target execution task is set according to the distance of the task point and the reference task energy consumption.

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

[0105] If the proportion of a type of execution vehicle of a to-be-dispatched vehicle is greater than the preset proportion of a type of execution vehicle, a combined transportation method is used to complete its corresponding target execution task, and the type of execution vehicle with the shortest interval between the task execution vehicle of its corresponding target execution task and the vehicle to be dispatched is recorded as a combination vehicle of the vehicle to be dispatched, and is recorded as a transportation combination with the vehicle to be dispatched to complete the corresponding target execution task. The interval distance between the dispatch vehicle and the type of execution vehicle of its corresponding target execution task is the distance that the vehicle to be dispatched travels along its corresponding transportation route to reach the type of execution vehicle. If the vehicle to be dispatched has additional power consumption due to a congestion area, resulting in the vehicle to be dispatched being unable to continue to perform the corresponding target execution task, the type of execution vehicle in the transportation combination can be connected to the vehicle to be dispatched to assist in completing the target execution task, so as to avoid the impact of the shutdown of the vehicle to be dispatched on the overall transportation efficiency.

[0106] If the proportion of a type of execution vehicles of a to-be-scheduled vehicle is less than or equal to the preset proportion of a type of execution vehicles, the to-be-scheduled vehicle is dispatched to complete other target execution tasks. The scheduling priority coefficient of any target execution task = 1 / (task point distance + reference task energy consumption). The task point distance is the shortest distance required for the to-be-scheduled vehicle to reach the transportation starting position of the target execution task. The reference task energy consumption is the average value of the amount of electricity consumed by each task execution vehicle corresponding to the target execution task during a transportation process. The starting time of the transportation process is the corresponding time when the task execution vehicle leaves the transportation starting position, and the end time of the transportation process is the corresponding time when the task execution vehicle arrives at the transportation end position. The amount of electricity consumed by the task execution vehicle during a transportation process = the remaining power value of the task execution vehicle corresponding to the starting time of the transportation process - the remaining power value of the task execution vehicle corresponding to the end time of the transportation process. The target execution tasks of the to-be-scheduled vehicles are arranged in a timely and reasonable manner to ensure that the to-be-scheduled vehicles complete their tasks smoothly.

[0107] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle 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 fall within the protection scope of the present invention.

[0108] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope 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 determine the category of the task execution vehicle in response to the remaining power value corresponding to the vehicle category condition; A 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 state of each key task point and obtaining the key scheduling node of the target production area; A first area setting module, which is connected to the area analysis module, is used to determine the distribution state of each key task point in response to the number of relevant key task points corresponding to the distribution condition, and to determine the key scheduling area of ​​each dense task point set and each discrete task point; A second area setting module, which is connected to the area analysis module, is used to determine the key scheduling area according to the number of associated task routes passing through each route node and the task completion degree; The equipment scheduling module is connected to the area monitoring module, the first area setting module and the second area setting module, and is used to respond to the proportion of key route sections corresponding to the vehicle scheduling conditions to determine whether to perform task scheduling for the second-class execution vehicles, and respond to the proportion of the first-class execution vehicles corresponding to the task scheduling conditions to determine the task scheduling method for the vehicles to be scheduled.

2. The intelligent food production management system according to claim 1, characterized in that: The area monitoring module determines the production execution status of the target production area in response to the amount of tasks to be executed and the number of key task points of the execution status condition; The execution state condition responded by the regional monitoring module is that the amount of tasks to be executed is greater than the preset amount of tasks to be executed and the number of key task points is greater than the preset number of key task points, then it is determined that the target production area is in the first production execution state; The execution status condition responded by the regional monitoring module is that the amount of tasks to be executed is greater than the preset amount 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 it is determined that the target production area is in the second production execution state.

3. The intelligent food production management system according to claim 2, characterized in that: The region analysis module responds to the first production execution status, and then determines that the first region setting module detects the distribution status of each key task point; The area analysis module responds to the second production execution state, and determines that the second area setting module obtains the key scheduling node of the target production area.

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

5. The intelligent food production management system according to claim 4, characterized in that: The first area setting module determines a number of dense task point sets, and detects the task relevance of the dense task points in each dense task point set; For a single dense task point set, the dense task point with the largest task correlation is recorded as the regional 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 required task volume of each dense task point in the dense task point set; Any dense task point in each dense task point set has the same related key task point as at least another dense task point.

6. The intelligent food production management system according to claim 3, characterized in that: 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 required task volume of the corresponding target execution task; The key length is positively correlated with the required task volume and task richness respectively.

7. The intelligent food production management system according to claim 6, characterized in that: The second area setting module obtains the associated task route between each key task point and its associated task point in response to the second analysis condition; Determine the key dispatching node according to the number of associated task routes passing through each route node, and determine the key length of the key dispatching area corresponding to the key dispatching node according to the task completion degree of each key dispatching node, wherein the key length is positively correlated with the task completion degree; If the number of associated task routes passing through a route node is greater than the preset number of routes, the route node is recorded as a key scheduling node; The second analysis condition is that the regional analysis module determines that the second regional setting module obtains the key scheduling node of the target production area.

8. The intelligent food production management system according to claim 7, characterized in that: The area monitoring module responds to the remaining power value of the vehicle category condition to determine the category of the task execution vehicle; For a single mission execution vehicle, If the vehicle category condition responded by the area monitoring module is that the remaining power value is greater than the preset remaining power value, the task execution vehicle is determined to be a type 1 execution vehicle; If the vehicle category condition responded by the area monitoring module is that the remaining power value is less than or equal to the preset remaining power value, the task execution vehicle is determined to be a second-category execution vehicle.

9. The intelligent food production management system according to claim 8, characterized in that: Under the condition that the regional setting is completed, the equipment scheduling module determines whether to perform task scheduling for the second-class execution vehicle in response to the proportion of key route sections of the vehicle scheduling condition; 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 sections is greater than the preset proportion of key route sections, it is determined that the Class II execution vehicle is to be scheduled, and the Class II execution vehicle is recorded as a vehicle to be scheduled; The completion condition for the area setting is that all key scheduling areas within the target production area are determined and completed.

10. The intelligent food production management system according to claim 9, characterized in that: The equipment scheduling module responds to a proportion of a type of executing vehicles in the task scheduling condition to determine a 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 one type of execution vehicles is greater than the preset proportion of another type of execution vehicles, it is determined that the vehicle to be scheduled completes the corresponding target execution task according to the combined transportation mode; The task scheduling condition responded by the equipment scheduling module is that the proportion of one type of execution vehicles is less than or equal to the preset proportion of one type of execution vehicles, then it is determined to schedule the vehicle to be scheduled to complete other target execution tasks, and the scheduling priority coefficient of each target execution task is set according to the task point distance and the reference task energy consumption.

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