A logistics and warehousing management system based on big data

Abstract

The application relates to the field of warehouse management, in particular to a logistics warehouse management system based on big data, which comprises a division unit, a analysis unit and a control unit. The division unit is used for determining effective task points according to task update frequency, determining regional division modes according to the number and distribution coefficient of the effective task points, and determining the vehicle category of the carrying equipment according to the running efficiency and loading proportion. The analysis unit is used for determining analysis processing modes according to the vehicle matching degree of the carrying area. The control unit is used for determining the priority coefficient of each adjustable vehicle, determining the response vehicle according to the priority coefficient, and determining the moving route of the response vehicle according to the radiation demand degree of the movable route. The scheduling unit is used for determining the area state according to the task dispersion degree of the two types of carrying areas and the number of the response vehicles during vehicle scheduling analysis, determining the scheduling mode according to the area state, and determining the vehicle scheduling range according to the task fluctuation coefficient and the obstacle complexity. The logistics warehouse management efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of warehouse management, and in particular to a logistics warehouse management system based on big data. BACKGROUND

[0002] With the rapid development of e-commerce and the increasing demand for delivery timeliness, the traditional warehouse mode has been unable to meet the increasingly complex logistics demand. Modern logistics warehouse systems are equipped with a large number of automated mobile transport devices, which improve the efficiency of the logistics warehouse system. However, in the scenario of collaborative control of multiple transport devices, complex warehouse scenarios and irregular task allocation often lead to poor task allocation efficiency and route conflicts, thereby causing the overall operation efficiency to decline. Therefore, how to effectively allocate tasks according to the actual warehouse operation situation and improve the efficiency of warehouse transportation management is a problem that needs to be solved by those skilled in the art.

[0003] Chinese patent publication No. CN119376354A discloses an AGV robot scheduling management method and system, which includes: a data collection module for collecting AGV robot job data and establishing an AGV robot job environment topology graph G(V, E); a charging detection module for calculating the battery consumption of the AGV robot job task and performing charging operation; a task scheduling and path planning module for AGV robot job task scheduling and path planning; a data encryption storage module for encrypting and storing AGV robot job data in a database; a computer device including a memory and a processor; the memory stores a computer program, and the processor executes the computer program to realize the steps of the AGV robot scheduling management method. The above technical solution also discloses collecting AGV robot task data, using the node set and edge set in the environment topology graph G(V, E) to obtain the task path, and collecting the current task load weight w through a sensor, and setting the urgency of each task, so as to consider the characteristics of each task in the scheduling process. However, it does not consider the influence of the task distribution in different areas in the actual working scene and the randomness of task distribution on the working efficiency of the subsequent carrying equipment, which leads to the difficulty of scheduling control of the carrying equipment to meet the actual long-term task demand, and further leads to poor logistics warehouse management efficiency. SUMMARY

[0004] Therefore, the present application provides a logistics warehouse management system based on big data to overcome the problem that the prior art does not consider the influence of the task distribution in different areas in the actual working scene and the randomness of task distribution on the working efficiency of the subsequent carrying equipment, which leads to the difficulty of scheduling control of the carrying equipment to meet the actual long-term task demand, and further leads to poor logistics warehouse management efficiency.

[0005] To achieve the above object, the application provides a logistics warehouse management system based on big data, comprising:

[0006] The division unit is used to determine effective task points according to the task update frequency, determine the regional division mode as uniform division or aggregate division according to the number and distribution coefficient of the effective task points, and determine the vehicle category of the carrying equipment according to the operation efficiency and loading proportion.

[0007] The analysis unit is connected with the division unit and used to determine the analysis processing mode as vehicle control analysis or vehicle scheduling analysis according to the vehicle matching degree of the carrying region.

[0008] The control unit is connected with the division unit and the analysis unit respectively, used to determine the priority coefficient of each adjustable vehicle, determine the response vehicle according to the priority coefficient, and determine the moving route of the response vehicle according to the radiation demand degree of the movable route.

[0009] The scheduling unit is connected with the division unit, the analysis unit and the control unit respectively, used to determine the region state according to the task dispersion degree of the two types of carrying regions and the number of response vehicles when the vehicle scheduling analysis is performed, determine the scheduling mode according to the region state, and determine the vehicle scheduling range according to the task fluctuation coefficient and the obstacle complexity.

[0010] Further, the division unit determines effective task points according to the task update frequency, and determines the regional division mode according to the number and distribution coefficient of the effective task points.

[0011] If the number of effective task points is less than or equal to the preset number of effective task points or the distribution coefficient is less than or equal to the preset distribution coefficient, the regional division mode is uniform division.

[0012] If the number of effective task points is greater than the preset number of effective task points and the distribution coefficient is greater than the preset distribution coefficient, the regional division mode is aggregate division.

[0013] Further, the division unit performs the regional division mode of uniform division, comprising:

[0014] The target scene region is divided into several sub-regions with equal areas, and each sub-region is recorded as a two-type carrying region.

[0015] Further, the division unit performs the regional division mode of aggregate division, comprising:

[0016] The positions of the effective task points in the latest monitoring period are detected, and aggregate analysis is performed for each effective task point to obtain several one-type carrying regions.

[0017] When the aggregated analysis is performed on a single effective task point, the effective task point is recorded as a target task point, a circular area meeting the demand condition is constructed with the target task point as the center, and the circular area meeting the demand condition and having the largest area is recorded as a first-class carrying area;

[0018] The demand condition is that the distance between the effective task point in the circular area and other effective task points in the circular area is less than a preset reference distance, and there is no overlapping area with other first-class carrying areas;

[0019] Among them, there is no overlap between each first-class carrying area;

[0020] The part of the target scene area other than the first-class carrying area is recorded as a second-class carrying area.

[0021] Further, the division unit determines the vehicle category of the carrying device according to the running efficiency and the loading proportion, and the vehicle category includes:

[0022] An adjustable vehicle with a running efficiency greater than a preset running efficiency and a loading proportion less than or equal to a preset loading proportion;

[0023] A non-adjustable vehicle with a running efficiency less than or equal to a running efficiency or a loading proportion greater than a preset loading proportion.

[0024] Further, the analysis unit determines the analysis processing mode according to the vehicle matching degree of the carrying area;

[0025] If the vehicle matching degree is greater than a preset vehicle matching degree, the analysis processing mode is vehicle control analysis;

[0026] If the vehicle matching degree is less than or equal to a preset vehicle matching degree, the analysis processing mode is vehicle scheduling analysis.

[0027] Further, the control unit determines the priority coefficient of each adjustable vehicle under a preset task response condition when performing vehicle control analysis, and records the adjustable vehicle with the largest priority coefficient as a response vehicle;

[0028] The priority coefficient is determined according to the route distance and the number of movable routes;

[0029] The preset task response condition is that there is a vehicle-mounted task release.

[0030] Further, the control unit determines the moving route of the response vehicle according to the radiation demand degree of the movable route;

[0031] For a single movable route, the corresponding radiation demand degree is determined according to the reference distance between the related route of the movable route and the movable route and the number of affected task points;

[0032] Corresponding to the maximum movable route of the radiation demand is recorded as the moving route of the response vehicle.

[0033] Further, the scheduling unit detects the task dispersion degree of the two-class carrying area and the number of response vehicles to determine the region state corresponding to each two-class carrying area during vehicle scheduling analysis, and determines the scheduling mode corresponding to each two-class carrying area according to the region state;

[0034] If the region state corresponding to the two-class carrying area is that the task dispersion degree is less than or equal to the preset task dispersion degree or the number of response vehicles is greater than the preset number of response vehicles, the scheduling mode is to select the response vehicle within the scheduling range corresponding to the two-class carrying area;

[0035] The region state is that the task dispersion degree is greater than the preset task dispersion degree and the number of response vehicles is less than the preset number of response vehicles, and the scheduling mode is to perform early warning and supplement for the two-class carrying area.

[0036] Further, the scheduling unit determines the vehicle scheduling range according to the task fluctuation coefficient and the obstacle complexity;

[0037] If the task fluctuation coefficient is less than or equal to the preset task fluctuation coefficient and the obstacle complexity is less than or equal to the preset obstacle complexity, the scheduling unit adopts the benchmark vehicle scheduling range;

[0038] If the task fluctuation coefficient is greater than the preset task fluctuation coefficient or the obstacle complexity is greater than the preset obstacle complexity, the scheduling unit adopts the adjusted vehicle scheduling range.

[0039] Compared with the prior art, the beneficial effects of the present application are that in the technical scheme of the present application, the division unit determines the region division mode according to the number and distribution coefficient of effective task points, and determines the vehicle category of the carrying equipment according to the running efficiency and loading proportion; the distribution of effective task points is reflected through the number and distribution coefficient of effective task points, the actual running state of the vehicle is reflected through the running efficiency and loading proportion, and is correspondingly divided into different categories, and the region state is determined according to the task dispersion degree of the two-class carrying area and the number of response vehicles, and the scheduling mode is determined according to the region state, which avoids the problem that the single vehicle scheduling mode in the prior art cannot effectively meet the actual complex transportation demand, so that the scheduling of the vehicle is more in line with the actual working scene, and the overall transportation efficiency of the vehicle is improved.

[0040] Further, in the technical scheme of the present application, the effective task points are determined according to the task update frequency, and the region division mode is determined according to the number and distribution coefficient of the effective task points. The task update frequency reflects the real-time and dynamic nature of the task demand. The number and distribution coefficient of the effective task points reflect the concentration of the task points in the scene. The classification of the effective task points is conducive to subsequent region division, and the region division mode is determined as uniform division or aggregation division, avoiding the irrationality of subsequent task allocation caused by a single classification mode, thereby improving the task allocation efficiency.

[0041] Further, in the technical scheme of the present application, the vehicle category of the carrying equipment is determined according to the operation efficiency and the loading ratio. The operation efficiency and the loading ratio reflect the carrying capacity of the carrying equipment, avoiding resource waste and increasing operation cost caused by inefficient scheduling, thereby facilitating dynamic adjustment according to real-time tasks during vehicle scheduling, improving overall operation efficiency, and optimizing traffic flow during vehicle operation.

[0042] Further, in the technical scheme of the present application, the analysis processing mode is determined according to the vehicle matching degree of the carrying region. The vehicle matching degree of the carrying region reflects whether the carrying equipment in the carrying region meets the work demand of the carrying region, and different analysis processing modes are selected accordingly, so that the analysis processing mode can effectively cope with the actual work scene, avoiding the problem that a single control and scheduling mode cannot meet the actual demand.

[0043] Further, in the technical scheme of the present application, the moving route of the response vehicle is determined according to the radiation demand degree of the movable route. The radiation demand degree reflects the task demand degree of the related route of the movable route, thereby improving the task receiving efficiency of the response vehicle, avoiding the problem that a single task distribution mode in the prior art cannot consider the influence of subsequent task distribution on the carrying equipment, thereby facilitating the dispersion of vehicle flow in advance and improving the overall operation efficiency.

[0044] Further, in the technical scheme of the present application, the region state is determined according to the task dispersion degree and the number of response vehicles of the second type of carrying region, and the scheduling mode is determined according to the region state. The region state reflects the task distribution degree in time and the resource availability in the second type of carrying region, so that the scheduling mode is more suitable for the actual demand, avoiding the problem that a single scheduling mode cannot adapt to the actual scene, thereby improving the efficiency of vehicle scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0045] Fig. 1 The unit connection diagram of the logistics warehouse management system based on big data of the present application;

[0046] Fig. 2 The flowchart of determining the region division mode according to the number and distribution coefficient of the effective task points of the present application;

[0047] Fig. 3 A flow chart for determining the vehicle category according to the operation efficiency and the loading proportion in the present application;

[0048] Fig. 4 A flow chart for determining the analysis processing mode according to the vehicle matching degree of the carrying area in the present application. DETAILED DESCRIPTION

[0049] In order to make the objects and advantages of the present application clearer, the present application will be further described in conjunction with embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and should not be used to limit the present application.

[0050] The preferred embodiments of the present application will be 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 application and are not used to limit the protection scope of the present application.

[0051] It should be noted that, in the description of the present application, the terms of direction or position relationship such as "upper", "lower", "left", "right", "inner", "outer" and the like are based on the direction or position relationship shown in the drawings, which is only 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, and therefore cannot be understood as a limitation of the present application.

[0052] In addition, it should also be noted that, in the description of the present application, unless otherwise explicitly specified and limited, the terms "mounting", "connecting", "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through an intermediate medium, or internal communication of two elements. Those skilled in the art can understand the specific meaning of the above terms in the present application according to the specific circumstances.

[0053] Please refer to Figs. 1 to 4 As shown in the drawings, the present application provides a logistics warehouse management system based on big data, which comprises:

[0054] The division unit is used to determine the effective task points according to the task update frequency, and determine the area division mode as uniform division or aggregation division according to the number and distribution coefficient of the effective task points, and determine the category of the carrying equipment according to the operation efficiency and the loading proportion;

[0055] The analysis unit is connected with the division unit, and is used to determine the analysis processing mode as vehicle control analysis or vehicle scheduling analysis according to the vehicle matching degree of the carrying area;

[0056] a control unit connected with the division unit and the analysis unit respectively, used to determine the priority coefficient of each adjustable vehicle and determine the response vehicle according to the priority coefficient, and determine the moving route of the response vehicle according to the radiation demand degree of the movable route;

[0057] a scheduling unit connected with the division unit, the analysis unit and the control unit respectively, used to determine the region state according to the task dispersion degree of the two types of carrying regions and the number of response vehicles during the vehicle scheduling analysis, determine the scheduling mode according to the region state, and determine the vehicle scheduling range according to the task fluctuation coefficient and the obstacle complexity.

[0058] Specifically, the division unit determines the effective task point according to the task update frequency, and determines the region division mode according to the number of effective task points and the distribution coefficient;

[0059] If the number of effective task points is less than or equal to the preset number of effective task points or the distribution coefficient is less than or equal to the preset distribution coefficient, the region division mode is uniform division;

[0060] If the number of effective task points is greater than the preset number of effective task points and the distribution coefficient is greater than the preset distribution coefficient, the region division mode is aggregate division.

[0061] In the present application, the warehouse management personnel can issue vehicle-mounted tasks through the central control device or the wireless communication control device, the vehicle-mounted task corresponds to a point that needs to be reached by a vehicle, and the point is recorded as a task point. The number of tasks of each task point in the latest monitoring period is detected, the number of tasks is the number of times of demand carrying equipment reaching the task point in the latest monitoring period, the carrying equipment is a movable equipment for executing the vehicle-mounted task, the carrying equipment in the present application is AGV, the non-carrying equipment is a movable equipment other than AGV, including but not limited to manual small forklift and movable robot, etc., the task update frequency = the number of tasks in the latest monitoring period / the time length of a single monitoring period, the task point with a task update frequency greater than a preset task update frequency is recorded as an effective task point. The value of the preset task update frequency can be adaptively set by the user according to the actual demand, and a value of the preset task update frequency is provided, the preset task update frequency is the average value of the update frequencies corresponding to all task points, the monitoring period is a continuous cycle, the time length of a single monitoring period is set according to the actual demand of the user, the greater the efficiency demand of the user for logistics warehouse management, the smaller the time length of a single monitoring period.

[0062] The application has a plurality of historical records, and each historical record at least includes a detection value corresponding to a historical use process of the logistics warehouse management system for a period of time. The detection value includes but is not limited to reference neighbor distance, effective task point quantity, distribution coefficient, reference distance, moving time length, loading proportion, running efficiency, vehicle matching degree, path transformation reference value, non-adjustable vehicle quantity, task dispersion degree, task fluctuation coefficient, and obstacle complexity. The historical record is provided with a qualified mark, and the qualified mark records whether the historical record meets the user demand. Whether the historical record meets the user demand can be determined according to whether the completion time of each vehicle-mounted task meets the user demand for the task completion speed, but is not limited to this. Whether the historical record meets the user demand is determined according to a self-set index, which is a content mastered by a person skilled in the art and is not limited herein.

[0063] The distribution coefficient = the reference neighbor distance / the preset reference neighbor distance.

[0064] The reference neighbor distance is confirmed by distance analysis of each effective task point to obtain the minimum neighbor distance corresponding to each effective task point. When distance analysis is performed on a single effective task point, the neighbor distance between the effective task point and other effective task points is detected, and the minimum value of the neighbor distance is recorded as the minimum neighbor distance corresponding to the effective task point. For any two effective task points, the calculation formula of the corresponding neighbor distance is:

[0065] ;

[0066] wherein the two effective task points are randomly recorded as effective task point A and effective task point B, is the horizontal coordinate of effective task point A, is the vertical coordinate of effective task point A, is the horizontal coordinate of effective task point B, is the vertical coordinate of effective task point B.

[0067] The average value of the minimum neighbor distances of all task points is calculated and recorded as the reference neighbor distance.

[0068] The value of the preset reference neighbor distance can be adaptively set by the user according to the actual application scene. A method for extracting the expected distance in the historical record meeting the user demand, screening the abnormal value, and recording the average value of the expected distance after removing the abnormal value as the preset expected distance is provided.

[0069] In the present application, the concentration degree of the task point in the scene is reflected by the effective task point quantity and the distribution coefficient. The higher the spatial efficiency of the system required by the user, the smaller the preset effective task point quantity, and the larger the preset distribution coefficient. The values of the preset effective task point quantity and the preset distribution coefficient can be adaptively set according to the actual application scene. A method for determining the values of the preset effective task point quantity and the preset distribution coefficient is provided. The corresponding effective task point quantity and the distribution coefficient in the historical record meeting the user's demand are extracted, the outliers are screened out, and the average values of the effective task point quantity and the distribution coefficient after removing the outliers are respectively recorded as the preset effective task point quantity and the preset distribution coefficient. The outlier screening method can be, but is not limited to, 3 sigma criterion method or IQR method.

[0070] Specifically, the division unit performs a uniform division region division mode, including:

[0071] The target scene region is divided into a plurality of sub-regions with equal areas, and each sub-region is recorded as a two-class carrying region.

[0072] The target scene region is the main working area of the carrying device vehicle, including but not limited to the storage area, the sorting area, the handover area, and the charging and maintenance area.

[0073] Specifically, the division unit performs an aggregation division region division mode, including:

[0074] The positions of the effective task points in the latest monitoring period are detected, and the aggregation analysis is performed for each effective task point to obtain a plurality of one-class carrying regions;

[0075] When performing the aggregation analysis for a single effective task point, the effective task point is recorded as a target task point, a circular region meeting the demand condition is constructed with the target task point as the center, and the circular region meeting the demand condition and having the largest area is recorded as a one-class carrying region;

[0076] The demand condition is that the distance between the effective task point in the circular region and other effective task points also in the circular region is less than a preset reference distance, and there is no overlapping region with other one-class carrying regions;

[0077] Among them, there is no overlap between each one-class carrying region;

[0078] The part of the target scene region other than the one-class carrying region is recorded as a two-class carrying region.

[0079] The preset reference distance can be set by the user according to the actual application area, and the smaller the value of the preset reference distance is, the higher the accuracy requirement of the user for the area division is, the preset reference distance can effectively reflect the distribution state of the effective task points, and a value taking mode is provided, the average value of the reference distances of the historical records meeting the user's demand is recorded as the preset reference distance.

[0080] Specifically, the division unit determines the vehicle category of the carrying device according to the operation efficiency and the loading proportion, and the vehicle category includes:

[0081] an adjustable vehicle with an operation efficiency greater than a preset operation efficiency and a loading proportion less than or equal to a preset loading proportion;

[0082] a non-adjustable vehicle with an operation efficiency less than or equal to a preset operation efficiency or a loading proportion greater than a preset loading proportion.

[0083] For a single carrying device, the operation efficiency = the effective moving time length / the time length of a single monitoring period, the moving time length of the single carrying device in each monitoring period is detected, the time length with a moving time length greater than a preset moving time length is extracted, the time length with a moving time length greater than a preset moving time length in all monitoring periods is recorded as the effective time length, the value of the preset moving time length can be set by the warehouse management personnel according to the actual working scene, the smaller the congestion degree of the road is, the greater the preset moving time length is, a value taking mode is provided, the average value of the moving time length corresponding to the historical records meeting the user's demand is recorded as the preset moving time length, the loading proportion = the current loading weight / the maximum loadable weight, the current loading weight can be measured in real time by the weight sensor arranged on the carrying device, and the maximum loadable weight is the maximum weight that can be loaded by each carrying device.

[0084] In the application, the carrying capacity of the carrying device is represented by the operation efficiency and the loading proportion, the greater the carrying capacity demand of the user for the carrying device required for subsequent scheduling is, the greater the preset operation efficiency is, and the smaller the value of the preset loading proportion is, a value taking mode is provided, the operation efficiency and the loading proportion corresponding to the historical records meeting the user's demand are extracted from the historical records of the user, the abnormal values are removed, and the average values of the operation efficiency and the loading proportion after removing the abnormal values are respectively recorded as the preset operation efficiency and the preset loading proportion.

[0085] Specifically, the analysis unit determines the analysis processing mode according to the vehicle matching degree of the carrying area.

[0086] If the vehicle matching degree is greater than a preset vehicle matching degree, the analysis processing mode is vehicle control analysis.

[0087] If the vehicle matching degree is less than or equal to a preset vehicle matching degree, the analysis processing mode is vehicle scheduling analysis.

[0088] The vehicle matching degree = A1 × A2; wherein A1 is the number of adjustable vehicles in the carrying area, and A2 is the number of tasks; for a single carrying area, the total number of tasks in the nearest period in the carrying area is detected, which is recorded as the number of tasks, wherein the carrying area is a first type of carrying area or a second type of carrying area, is a task adjustment coefficient, it can be understood that the greater the overall demand degree of the task for the carrying capacity of the carrying device, the greater the value of In the specific implementation of the present application is 0.5, and the present application characterizes whether the carrying device corresponding to the carrying area can effectively meet the task demand of the carrying area through the vehicle matching degree.

[0089] The value of the preset vehicle matching degree can be adaptively set by the user according to the actual application scene, a method for setting the value of the preset vehicle matching degree is provided, the vehicle matching degree in the historical record meeting the user's demand is extracted, the abnormal values are screened out, and the average value of the vehicle matching degree after removing the abnormal values is recorded as the preset vehicle matching degree.

[0090] Specifically, the control unit determines the priority coefficient of each adjustable vehicle under the preset task response condition when analyzing the vehicle control, and records the adjustable vehicle with the largest priority coefficient as the response vehicle;

[0091] The priority coefficient is determined according to the route distance and the number of movable routes;

[0092] The preset task response condition is that there is a vehicle-mounted task publishing.

[0093] The priority coefficient and the route distance are in a negative correlation relationship, and the priority coefficient and the number of movable routes are in a positive correlation relationship, the present application provides a calculation method of the priority coefficient, the priority coefficient = the number of movable routes - k x route distance, wherein k is a reference coefficient, it can be understood that the value of the route distance is usually greater than the number of movable routes, k is used to adjust the proportion degree of the route distance, the user can set the value of k according to the area of the target scene area, the greater the area of the target scene area, the greater the value of k, and in the specific implementation of the present application, the value of k is 0.3.

[0094] The confirmation manner of the movable route is to obtain a target task point, the target task point being a task point corresponding to a current issued vehicle task, and to perform route analysis on each adjustable vehicle in a carrying area where the target task point is located to determine a movable route corresponding to each adjustable vehicle. When performing route analysis on a single adjustable vehicle, the current position of the adjustable vehicle is detected and all passable paths from the current position to the target task point are generated, the number L of non-adjustable vehicles on each passable path with a path transformation reference value greater than a preset path transformation reference value is obtained, and the passable path with L greater than a preset number of non-adjustable vehicles is recorded as the movable route corresponding to the adjustable vehicle. For a single non-adjustable vehicle, the confirmation manner of the path transformation reference value corresponding to the non-adjustable vehicle is to detect the task points of the vehicle task corresponding to the non-adjustable vehicle, to extract the task points in the carrying area where the target task point is located, and to obtain task point distances, to calculate the average of the task distances, and to record the average as the path transformation reference value. The task point distance includes the distance between the non-adjustable vehicle and the task point currently going to and the distance between task points adjacent in task publishing order, wherein the task publishing order is the order from early to late according to the time of vehicle task publishing. Any passable path can satisfy the movement of the adjustable vehicle from the current position to the target task point.

[0095] The values of the preset path transformation reference value and the preset number of non-adjustable vehicles can be adaptively set by the user according to the actual application scene. A method for determining the values of the preset path transformation reference value and the preset number of non-adjustable vehicles is provided. The path transformation reference values and the number of non-adjustable vehicles in the historical records meeting the user's requirements are extracted, the abnormal values are screened out, and the average values of the path transformation reference values and the number of non-adjustable vehicles after removing the abnormal values are recorded as the preset path transformation reference value and the preset number of non-adjustable vehicles, respectively. In the specific implementation of the present application, the area of the target scene area is 6000m², the preset path transformation reference value is 100m, and the preset number of non-adjustable vehicles is the average value of L corresponding to each passable path.

[0096] Specifically, the control unit determines the moving route of the response vehicle according to the radiation demand degree of the movable route.

[0097] For a single movable route, the radiation demand degree thereof is determined according to the related route of the movable route, the reference distance of the movable route, and the number of affected task points.

[0098] Radiation demand degree = number of affected task points / preset number of affected task points - reference distance / preset reference distance.

[0099] The radiation demand degree is negatively correlated with the reference distance, and the radiation demand degree is positively correlated with the number of influence task points. For a single movable route, the reference distance is determined by detecting the fuzzy distance between the movable route and each related route, arranging the fuzzy distances in descending order, extracting the first two fuzzy distances, and taking the average of the two fuzzy distances as the reference distance. For example, there are fuzzy distances H1, H2, and H3, where H1 < H2 < H3, and the average of H1 and H2 is the reference distance. The related route is the passable path other than the movable route. The number of influence task points is the total number of effective task points on the related route corresponding to the first two fuzzy distances.

[0100] For the fuzzy distance between the movable route and any related route, the fuzzy distance is determined by randomly extracting a number of points on the movable route, calculating the nearest distance between each point and the related route, and taking the average of the nearest distances as the fuzzy distance. The number of points can be set by the user according to the actual application scenario. It can be understood that the user can determine the number of points according to the determination requirement of the fuzzy distance. The greater the user's requirement for the accuracy of the fuzzy distance, the more the number of points.

[0101] The movable route with the largest corresponding radiation demand degree is recorded as the moving route of the response vehicle.

[0102] Specifically, the scheduling unit detects the task dispersion degree of the second type of carrying area and the number of response vehicles to determine the region state corresponding to each second type of carrying area, and determines the scheduling mode corresponding to each second type of carrying area according to the region state.

[0103] If the region state corresponding to the second type of carrying area is that the task dispersion degree is less than or equal to the preset task dispersion degree or the number of response vehicles is greater than the preset number of response vehicles, the scheduling mode is to select the response vehicle within the scheduling range corresponding to the second type of carrying area.

[0104] If the region state is that the task dispersion degree is greater than the preset task dispersion degree and the number of response vehicles is less than the preset number of response vehicles, the scheduling mode is to perform early warning and supplement for the second type of carrying area.

[0105] Extract the time points of each vehicle task release in the last monitoring period Calculate the between adjacent tasks in the order of release time, for example, is the interval between t2 and t1, is the interval between and , i = 1, 2, 3, …, n; and the task dispersion degree is the average of .

[0106] The preset task dispersion value can be set by the user according to an actual application scene, the preset task dispersion value can effectively reflect a time interval of publishing a task by an effective task point, a value taking mode is provided, a task dispersion value in a historical record meeting a user demand is counted, an abnormal value is eliminated, and an average value of the task dispersion value after eliminating the abnormal value is recorded as the preset task dispersion value.

[0107] The early warning supplement is that the system sends an early warning report to the user to remind the user to participate manually to independently adjust the control distribution of the carrying device, wherein the form of the early warning report sent by the system is not limited, and the early warning report can be sent to a mobile device such as a mobile phone of the user through remote communication, or the system is also provided with a display screen to display the early warning report.

[0108] Specifically, the scheduling unit determines a vehicle scheduling range according to a task fluctuation coefficient and an obstacle complexity;

[0109] If the task fluctuation coefficient is less than or equal to a preset task fluctuation coefficient and the obstacle complexity is less than or equal to a preset obstacle complexity, the scheduling unit adopts a benchmark vehicle scheduling range;

[0110] If the task fluctuation coefficient is greater than the preset task fluctuation coefficient or the obstacle complexity is greater than the preset obstacle complexity, the scheduling unit adopts an adjusted vehicle scheduling range.

[0111] The benchmark vehicle scheduling range is a range of the second-class carrying area, and the adjusted vehicle scheduling range is a circular area with the area center point of the second-class carrying area as the center and a radius greater than the radius of the second-class carrying area.

[0112] The confirmation method of the task fluctuation coefficient is that vehicle tasks published by each task point in the second-class carrying area in the nearest monitoring period are extracted, the nearest monitoring period is evenly divided into 5 sub-periods with the same time length, the total number of vehicle tasks published by each task point in the second-class carrying area corresponding to each sub-period is recorded as a vehicle task sub-number, the absolute value of the difference between the vehicle task sub-numbers corresponding to each sub-period in time sequence is calculated, the number of absolute values greater than a preset absolute value is recorded as the task fluctuation coefficient, and the obstacle complexity = the number of non-carrying devices in the current second-class carrying area range + the number of personnel in the current second-class carrying area.

[0113] The preset task fluctuation coefficient and the preset obstacle complexity can be set according to an actual application scene, the preset task fluctuation coefficient and the preset obstacle complexity can effectively reflect the demand of a user for the vehicle scheduling efficiency in a target scene area, a value mode is provided, the task fluctuation coefficient and the obstacle complexity in the historical record meeting the demand of the user are counted, the abnormal values are removed, and the average values after removing the abnormal values are recorded as the preset task fluctuation coefficient and the preset obstacle complexity respectively.

[0114] So far, the technical solutions of the present application have been described in combination with the preferred embodiments shown in the drawings, but those skilled in the art can easily understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical solutions after the changes or replacements will fall within the protection scope of the present application.

[0115] The above description is only the preferred embodiments of the present application and is not used to limit the present application; for those skilled in the art, the present application can have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A big data based logistics warehouse management system, characterized in that, The system comprises: a division unit configured to determine effective task points according to a task update frequency, determine a region division mode as uniform division or aggregated division according to a number and a distribution coefficient of the effective task points, and determine a vehicle type of a carrying device according to a running efficiency and a loading proportion; an analysis unit connected to the division unit and configured to determine an analysis processing mode as vehicle control analysis or vehicle dispatching analysis according to a vehicle matching degree of a carrying region; a control unit connected to the division unit and the analysis unit, and configured to determine a priority coefficient of each adjustable vehicle, determine a response vehicle according to the priority coefficient, and determine a moving route of the response vehicle according to a radiation demand degree of the moving route; a dispatching unit connected to the division unit, the analysis unit, and the control unit, and configured to, when the vehicle dispatching analysis is performed, determine a region state according to a task dispersion degree of a second type of carrying region and a number of response vehicles, determine a dispatching mode according to the region state, and determine a vehicle dispatching range according to a task fluctuation coefficient and an obstacle complexity; the division unit determines effective task points according to a task update frequency, and determines a region division mode according to a number and a distribution coefficient of the effective task points; if the number of the effective task points is less than or equal to a preset number of effective task points, or the distribution coefficient is less than or equal to a preset distribution coefficient, the region division mode is uniform division; if the number of the effective task points is greater than the preset number of effective task points and the distribution coefficient is greater than the preset distribution coefficient, the region division mode is aggregated division; the analysis unit determines an analysis processing mode according to a vehicle matching degree of a carrying region; if the vehicle matching degree is greater than a preset vehicle matching degree, the analysis processing mode is vehicle control analysis; if the vehicle matching degree is less than or equal to the preset vehicle matching degree, the analysis processing mode is vehicle dispatching analysis; the control unit, when the vehicle control analysis is performed, determines a priority coefficient of each adjustable vehicle under a preset task response condition, and records an adjustable vehicle with the largest priority coefficient as a response vehicle; the priority coefficient is determined according to a route distance and a number of movable routes; the preset task response condition is that there is a vehicle-mounted task release; the dispatching unit, when the vehicle dispatching analysis is performed, detects a task dispersion degree of a second type of carrying region and a number of response vehicles to determine a region state corresponding to each second type of carrying region, and determines a dispatching mode corresponding to each second type of carrying region according to the region state; if the region state corresponding to the second type of carrying region is that the task dispersion degree is less than or equal to a preset task dispersion degree, or the number of the response vehicles is greater than a preset number of response vehicles, the dispatching mode is to select the response vehicle in a dispatching range corresponding to the second type of carrying region; if the region state is that the task dispersion degree is greater than the preset task dispersion degree and the number of the response vehicles is less than the preset number of response vehicles, the dispatching mode is to perform early warning and supplement for the second type of carrying region; the distribution coefficient = reference neighbor distance / preset reference neighbor distance. The confirmation manner of the reference distance is that distance analysis is performed on each effective task point to obtain the minimum neighbor distance corresponding to each effective task point. When the distance analysis is performed on a single effective task point, the neighbor distance between the effective task point and each of the other effective task points is detected, and the minimum value of the neighbor distances is recorded as the minimum neighbor distance corresponding to the effective task point. For any two effective task points, the calculation formula of the corresponding neighbor distance is: ; wherein the two effective task points are randomly recorded as effective task point A and effective task point B respectively, is the abscissa of effective task point A, is the ordinate of effective task point A, is the abscissa of effective task point B, is the ordinate of effective task point B; the average value of the minimum adjacent distances of all task points is calculated and recorded as the reference adjacent distance; Vehicle matching degree ; wherein A1 is the number of adjustable vehicles in the carrying area, A2 is the number of tasks, is the task adjustment coefficient; Radiation demand degree = number of influence task points / preset number of influence task points - reference distance / preset reference distance; The number of influence task points is the total number of effective task points on the relevant routes corresponding to the first two fuzzy distances. For a single movable route, the confirmation manner of the reference distance is that the fuzzy distances between the movable route and each relevant route are detected, the fuzzy distances are arranged in descending order of the fuzzy distances, the first two fuzzy distances are extracted, and the average value of the two fuzzy distances is recorded as the reference distance. Extract the time points of each vehicle-mounted task release within the most recent monitoring period. Calculate the corresponding tasks in the order of their release times. , The interval between t2 and t1 is the duration. for and The interval between tasks, i = 1, 2, 3, ..., n; the task dispersion is... The average value; The confirmation manner of the task fluctuation coefficient is that the vehicle-mounted tasks published by each task point in the last monitoring period of the second type of carrying area are extracted, the last monitoring period is evenly divided into five sub-periods with the same time length, the total number of the vehicle-mounted tasks published by each task point in the second type of carrying area in each sub-period is recorded as the vehicle-mounted task sub-number, the absolute values of the differences between the vehicle-mounted task sub-numbers corresponding to each sub-period in time sequence are calculated, and the number of the absolute values greater than a preset absolute value is recorded as the task fluctuation coefficient. Obstacle complexity = number of non-carrying devices in the current second type of carrying area + number of personnel in the current second type of carrying area. 2.The big data-based logistics warehouse management system according to claim 1, wherein, The division unit performs the uniform division area division manner, including: The target scene area is divided into a plurality of sub-areas with equal areas, and each sub-area is recorded as a second type of carrying area. 3.The big data-based logistics warehouse management system according to claim 2, characterized in that, The division unit performs the aggregation division area division manner, including: The positions of the effective task points in the last monitoring period are detected, and aggregation analysis is performed on each effective task point to obtain a plurality of first type of carrying areas; When the aggregation analysis is performed on a single effective task point, the effective task point is recorded as a target task point, a circular area meeting a demand condition is constructed with the target task point as the center, and the circular area meeting the demand condition and having the largest area is recorded as a first type of carrying area; The demand condition is that the distance between the effective task point in the circular area and other effective task points also in the circular area is less than a preset reference distance, and there is no overlapping area with other first type of carrying areas; Wherein, there is no overlapping between each first type of carrying area; The part of the target scene area other than the first type of carrying areas is recorded as a second type of carrying area. 4.The big data-based logistics warehouse management system according to claim 3, characterized in that, The division unit determines the vehicle type of the carrying device according to the running efficiency and the loading proportion. The vehicle type includes: An adjustable vehicle with a running efficiency greater than a preset running efficiency and a loading proportion less than or equal to a preset loading proportion; A non-adjustable vehicle with a running efficiency less than or equal to a preset running efficiency or a loading proportion greater than a preset loading proportion. 5.The big data-based logistics warehouse management system according to claim 1, wherein, The control unit determines the moving route of the response vehicle according to the radiation demand degree of the movable route; For a single movable route, the corresponding radiation demand degree is determined according to the reference distance of the relevant route of the movable route and the number of influence task points; The movable route with the maximum corresponding radiation demand degree is recorded as the moving route of the response vehicle. 6.The big data-based logistics warehouse management system according to claim 1, wherein, The scheduling unit determines the vehicle scheduling range according to the task fluctuation coefficient and the obstacle complexity; If the task fluctuation coefficient is less than or equal to the preset task fluctuation coefficient and the obstacle complexity is less than or equal to the preset obstacle complexity, the scheduling unit adopts the benchmark vehicle scheduling range; If the task fluctuation coefficient is greater than the preset task fluctuation coefficient or the obstacle complexity is greater than the preset obstacle complexity, the scheduling unit adopts the adjusted vehicle scheduling range.

Images