Intelligent scheduling management method and system for unmanned forklift

Abstract

The present application is suitable for the technical field of unmanned forklifts, and provides an intelligent scheduling management method and system for unmanned forklifts. The present application selects a cargo carrying unmanned forklift by performing basic operation monitoring on multiple online unmanned forklifts, calculates an unstable confidence value, calculates multiple assistance values when in an unstable dangerous state, selects an assistance unmanned forklift, and performs assistance control on the assistance unmanned forklift. The unstable confidence value of the cargo carrying unmanned forklift can be calculated to determine whether it is in an unstable dangerous state, and when in an unstable dangerous state, assistance analysis is performed, multiple assistance values are calculated, an assistance unmanned forklift is selected from multiple online unmanned forklifts, and assistance analysis and assistance control are performed, so that automatic assistance scheduling and control can be performed among multiple unmanned forklifts in the case of unstable cargo placement, manual cargo adjustment by humans is not required, and the continuity and efficiency of unmanned forklift operation are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of unmanned forklifts, and particularly relates to an intelligent scheduling management method and system for unmanned forklifts. BACKGROUND

[0002] An unmanned forklift is an intelligent logistics transportation device integrating sensor technology, automatic control technology, navigation positioning technology and intelligent decision-making algorithms. Its main function is to complete the transportation, stacking, loading and unloading and transfer of goods without direct human driving. Through the use of multi-source sensing devices such as laser radars, visual cameras and ultrasonic sensors, the unmanned forklift can sense the surrounding environment information in real time and realize autonomous path planning and dynamic obstacle avoidance in combination with high-precision maps and positioning systems.

[0003] In the prior art, the scheduling management of unmanned forklifts mainly focuses on basic transportation path planning and obstacle avoidance control, realizing dynamic adjustment of the driving path and obstacle avoidance behavior of a single unmanned forklift. However, there is a lack of effective technical intervention means for the problem of unstable placement of goods caused by factors such as road bumps, shifts in the center of gravity of goods or vibration of the fork arm during transportation. Usually, manual adjustment of goods by humans is relied on, and automatic assistance scheduling and control between multiple unmanned forklifts cannot be achieved, which restricts the continuity and efficiency of unmanned operation. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide an intelligent scheduling management method and system for unmanned forklifts, aiming to solve the technical problems existing in the prior art mentioned in the background.

[0005] The embodiments of the present application are implemented as follows:

[0006] An intelligent scheduling management method for unmanned forklifts, which specifically comprises the following steps:

[0007] Performing basic operation monitoring on multiple online unmanned forklifts to obtain multiple basic monitoring data, selecting a goods transportation unmanned forklift and obtaining goods transportation monitoring data of the goods transportation unmanned forklift;

[0008] Identifying the goods transportation monitoring data, calculating an unstable confidence value and determining whether an unstable dangerous state exists;

[0009] When the unstable dangerous state exists, performing assistance analysis on the multiple basic monitoring data, calculating multiple assistance value and selecting an assistance unmanned forklift from the multiple online unmanned forklifts;

[0010] Scheduling and obtaining assistance detection data transmitted by the assistance unmanned forklift, performing assistance analysis, generating an assistance action instruction and performing assistance control on the assistance unmanned forklift.

[0011] As a further limitation of the technical scheme of the embodiment of the application, the step of monitoring the basic operation of the plurality of online unmanned forklifts to obtain a plurality of basic monitoring data, selecting a delivery unmanned forklift, and obtaining delivery monitoring data of the delivery unmanned forklift specifically comprises the following steps:

[0012] Obtaining a plurality of online state information to determine a plurality of online unmanned forklifts;

[0013] Monitoring the basic operation of the plurality of online unmanned forklifts to obtain a plurality of basic monitoring data;

[0014] Identifying delivery of the plurality of basic monitoring data, and selecting a delivery unmanned forklift from the plurality of online unmanned forklifts;

[0015] Obtaining delivery monitoring data of the delivery unmanned forklift.

[0016] As a further limitation of the technical scheme of the embodiment of the application, the step of identifying the delivery monitoring data, calculating an unstable confidence value, and determining whether it is in an unstable dangerous state specifically comprises the following steps:

[0017] Identifying the delivery monitoring data to periodically extract unstable identification data;

[0018] Calculating an unstable evaluation value according to the unstable identification data;

[0019] Calculating an unstable confidence value based on the unstable evaluation value;

[0020] Comparing the unstable confidence value with a preset confidence threshold to determine whether it is in an unstable dangerous state.

[0021] As a further limitation of the technical scheme of the embodiment of the application, the calculation formula of the unstable evaluation value is:

[0022] ;

[0023] ;

[0024] wherein, is the unstable evaluation value, is a cargo inclination angle, is a preset maximum allowable inclination angle, is a cargo offset distance, is a preset maximum allowable offset distance, is a cargo shaking amplitude, is a preset maximum allowable shaking amplitude, , and is a preset evaluation coefficient.

[0025] The formula for calculating the unstable confidence value is:

[0026] ;

[0027] in, Unstable confidence values, The preset confidence midpoint, This is the preset adjustment coefficient.

[0028] As a further limitation of the technical solution of this embodiment of the invention, the step of assisting in the analysis of multiple basic monitoring data, calculating multiple assistance costs, and selecting an assisting unmanned forklift from multiple online unmanned forklifts specifically includes the following steps:

[0029] Select multiple idle automated forklifts from the multiple online automated forklifts;

[0030] Select idle monitoring data corresponding to multiple idle unmanned forklifts from multiple basic monitoring data;

[0031] Multiple idle monitoring data are identified, and multiple contributing data are extracted.

[0032] Based on multiple assistance impact data, fuzzy assistance routes for multiple idle unmanned forklifts are planned, and the corresponding assistance distances are determined;

[0033] Based on the multiple assisted routes and the multiple assisted impact data, calculate the arrival time of the multiple idle unmanned forklifts;

[0034] Calculate the assistance cost of multiple idle unmanned forklifts based on multiple arrival times and multiple assistance impact data;

[0035] The multiple assistance costs are compared, and an assistance unmanned forklift is selected from the multiple idle unmanned forklifts.

[0036] As a further limitation of the technical solution of this embodiment of the invention, the calculation formulas for the various arrival times are as follows:

[0037] ;

[0038] in, Representing the An idle unmanned forklift For the first The arrival time of an idle unmanned forklift. For the first The assistance of an idle unmanned forklift. For the first The travel speed of an idle, unmanned forklift. is a preset delay constant;

[0039] The calculation formula of the assistance value of the plurality of the assistance values is:

[0040]

[0041]

[0042] wherein, is an assistance value of an idle unmanned forklift, is a current power of the idle unmanned forklift, is a function matching degree of the idle unmanned forklift, is a risk degree of the idle unmanned forklift, , , and are preset cost coefficients. As a further limitation of the technical scheme of the embodiment of the present application, the scheduling and obtaining the assistance detection data transmitted by the assistance unmanned forklift, performing assistance analysis, generating an assistance action instruction, and assisting in controlling the assistance unmanned forklift specifically include the following steps:

[0043] Planning an assistance detection position;

[0044] According to the assistance detection position, planning a scheduling assistance route of the assistance unmanned forklift, and scheduling and controlling the assistance unmanned forklift;

[0045] Obtaining assistance detection data transmitted by the assistance unmanned forklift;

[0046] Performing assistance analysis on the assistance detection data, and generating an assistance action instruction;

[0047] According to the assistance action instruction, assisting in controlling the assistance unmanned forklift.

[0048] According to the assistance action instruction, assisting in controlling the assistance unmanned forklift.

[0049] An intelligent scheduling management system of an unmanned forklift, the system comprising a basic operation monitoring module, an unstable danger judgment module, an assistance analysis selection module, and a scheduling assistance control module, wherein:

[0050] The basic operation monitoring module is used for performing basic operation monitoring on a plurality of online unmanned forklifts, obtaining a plurality of basic monitoring data, selecting a cargo carrying unmanned forklift, and obtaining cargo carrying monitoring data of the cargo carrying unmanned forklift;

[0051] ​​​​​An unstable danger judgment module is configured to identify the freight monitoring data, calculate an unstable confidence value, and determine whether an unstable danger state exists.

[0052] An assistance analysis selection module is configured to perform assistance analysis on the plurality of basic monitoring data when the unstable danger state exists, calculate a plurality of assistance evaluation values, and select an assistance unmanned forklift from the plurality of online unmanned forklifts.

[0053] A dispatch assistance control module is configured to dispatch and acquire assistance detection data transmitted by the assistance unmanned forklift, perform assistance analysis, generate an assistance action instruction, and perform assistance control on the assistance unmanned forklift.

[0054] As a further limitation of the technical scheme of the embodiment of the present application, the unstable danger judgment module specifically includes:

[0055] An unstable identification data extraction unit is configured to identify the freight monitoring data and periodically extract unstable identification data.

[0056] An evaluation value calculation unit is configured to calculate an unstable evaluation value based on the unstable identification data.

[0057] A confidence value calculation unit is configured to calculate an unstable confidence value based on the unstable evaluation value.

[0058] An unstable danger state judgment unit is configured to compare the unstable confidence value with a preset confidence threshold value and determine whether an unstable danger state exists.

[0059] As a further limitation of the technical scheme of the embodiment of the present application, the assistance analysis selection module specifically includes:

[0060] An idle unmanned forklift selection unit is configured to select a plurality of idle unmanned forklifts from the plurality of online unmanned forklifts.

[0061] An idle monitoring data selection unit is configured to select idle monitoring data corresponding to the idle unmanned forklifts from the plurality of basic monitoring data.

[0062] An assistance influence data extraction unit is configured to identify the plurality of idle monitoring data and extract a plurality of assistance influence data.

[0063] An assistance route determination unit is configured to plan fuzzy assistance routes of the plurality of idle unmanned forklifts and determine corresponding assistance routes based on the plurality of assistance influence data.

[0064] An arrival time calculation unit is configured to calculate arrival times of the plurality of idle unmanned forklifts based on the plurality of assistance routes and the plurality of assistance influence data.

[0065] An assistance generation value calculation unit is configured to calculate assistance generation values of the idle unmanned forklifts according to the plurality of arrival times and the plurality of assistance influence data;

[0066] An assistance unmanned forklift selection unit is configured to compare the plurality of assistance generation values and select the assistance unmanned forklift from the plurality of idle unmanned forklifts.

[0067] Compared with the prior art, the present application has the following beneficial effects:

[0068] (1) The present application can calculate the unstable confidence value of the goods-carrying unmanned forklift, determine whether it is in an unstable dangerous state, and when it is in the unstable dangerous state, select the assistance unmanned forklift from the plurality of online unmanned forklifts and perform assistance analysis and assistance control, so as to automatically schedule and control among the plurality of unmanned forklifts in the case of unstable goods placement, without manual goods adjustment, thereby effectively improving the continuity and efficiency of unmanned forklift operation;

[0069] (2) The present application can periodically extract unstable identification data from the goods-carrying monitoring data, calculate the unstable evaluation value, and then calculate the unstable confidence value based on the unstable evaluation value, compare the unstable confidence value with the preset confidence threshold value to determine whether it is in an unstable dangerous state, thereby realizing automatic determination of the unstable dangerous state of the goods-carrying unmanned forklift, which is fast and effective;

[0070] (3) The present application can extract a plurality of assistance influence data from a plurality of idle monitoring data, plan a fuzzy assistance route of a plurality of idle unmanned forklifts, calculate a plurality of arrival times, and then calculate a plurality of assistance generation values of the idle unmanned forklifts according to the plurality of arrival times and the plurality of assistance influence data, compare the plurality of assistance generation values, and select the assistance unmanned forklift from the plurality of idle unmanned forklifts, thereby realizing globally optimal scheduling and resource allocation in the collaborative operation environment of the plurality of unmanned forklifts. BRIEF DESCRIPTION OF DRAWINGS

[0071] Figure 1 A flowchart of the intelligent scheduling management method of the unmanned forklift provided by the embodiment of the present application is shown;

[0072] Figure 2 An application architecture diagram of the intelligent scheduling management system of the unmanned forklift provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0074] It can be understood that in the prior art, the scheduling management of unmanned forklifts mainly focuses on basic transportation path planning and obstacle avoidance control, realizing dynamic adjustment of the driving path and obstacle avoidance behavior of a single unmanned forklift. However, for the problem of unstable placement of goods caused by factors such as road bumps, shift of the center of gravity of goods or vibration of the fork arm during transportation of the unmanned forklift, there is a lack of effective technical intervention means, and usually only manual adjustment of goods by manual labor can be relied on, and automatic assistance scheduling and control cannot be performed between multiple unmanned forklifts, thereby restricting the continuity and efficiency of unmanned operation.

[0075] To solve the above problems, the embodiment of the present application discloses an intelligent scheduling management method and system for unmanned forklifts. The method comprises the following steps: performing basic operation monitoring on multiple online unmanned forklifts to obtain multiple basic monitoring data, selecting a goods transportation unmanned forklift, and obtaining goods transportation monitoring data of the goods transportation unmanned forklift; identifying the goods transportation monitoring data, calculating an unstable confidence value, and determining whether it is in an unstable dangerous state; when it is in the unstable dangerous state, performing assistance analysis on the multiple basic monitoring data, calculating multiple assistance value, selecting an assistance unmanned forklift from the multiple online unmanned forklifts; scheduling and obtaining assistance detection data transmitted by the assistance unmanned forklift, performing assistance analysis, generating an assistance action instruction, and performing assistance control on the assistance unmanned forklift. The method can calculate the unstable confidence value of the goods transportation unmanned forklift, determine whether it is in the unstable dangerous state, and when it is in the unstable dangerous state, perform assistance analysis, calculate multiple assistance value, select an assistance unmanned forklift from the multiple online unmanned forklifts, and perform assistance analysis and assistance control, so that automatic assistance scheduling and control can be performed between multiple unmanned forklifts in the case of unstable placement of goods, manual adjustment of goods by manual labor is not required, and the continuity and efficiency of unmanned operation of the forklift are effectively improved.

[0076] Specifically, Figure 1 A flowchart of an intelligent scheduling management method for unmanned forklifts provided by the embodiment of the present application is shown.

[0077] In one preferred embodiment provided by the present application, an intelligent scheduling management method for unmanned forklifts comprises the following steps:

[0078] In step S101, basic operation monitoring is performed on multiple online unmanned forklifts to obtain multiple basic monitoring data, a goods transportation unmanned forklift is selected, and goods transportation monitoring data of the goods transportation unmanned forklift is obtained.

[0079] In the embodiment of the present application, by acquiring a plurality of online state information, a plurality of online unmanned forklifts are determined, and then the plurality of online unmanned forklifts are subjected to basic operation monitoring of factors such as position, speed, power, function, and load, a plurality of basic monitoring data are acquired, and then the plurality of basic monitoring data are subjected to cargo carrying identification, when the load of the basic monitoring data is greater than a preset standard value, it is determined that the corresponding online unmanned forklift is in a cargo carrying state, at this time, it is determined as a cargo carrying unmanned forklift, and cargo carrying monitoring data of the cargo carrying unmanned forklift are acquired.

[0080] It can be understood that the cargo carrying monitoring data is video data obtained by monitoring and shooting the cargo during the cargo carrying process of the cargo carrying unmanned forklift.

[0081] Specifically, in another preferred embodiment provided by the present application, the basic operation monitoring of the plurality of online unmanned forklifts, the acquisition of the plurality of basic monitoring data, the selection of the cargo carrying unmanned forklift, and the acquisition of the cargo carrying monitoring data of the cargo carrying unmanned forklift specifically include the following steps:

[0082] acquiring a plurality of online state information to determine a plurality of online unmanned forklifts;

[0083] monitoring the plurality of online unmanned forklifts for basic operation to acquire a plurality of basic monitoring data;

[0084] identifying the plurality of basic monitoring data for cargo carrying, and selecting a cargo carrying unmanned forklift from the plurality of online unmanned forklifts;

[0085] acquiring cargo carrying monitoring data of the cargo carrying unmanned forklift.

[0086] Further, the intelligent scheduling management method of the unmanned forklift further includes the following steps:

[0087] Step S102, identifying the cargo carrying monitoring data, calculating an unstable confidence value, and determining whether it is in an unstable dangerous state.

[0088] In the embodiment of the present application, the cargo carrying monitoring data is identified, and unstable identification data such as cargo tilt, cargo offset, and cargo jitter are periodically extracted, then an unstable evaluation value is calculated according to the unstable identification data, and on the basis of the unstable evaluation value, an unstable confidence value is calculated, by comparing the unstable confidence value with a preset confidence threshold, whether it is in an unstable dangerous state is determined, specifically, the calculation formula of the unstable evaluation value is:

[0089]

[0090]

[0091] wherein, is the unstable evaluation value,​​ is an inclination angle of the goods, is a preset maximum allowable inclination angle, is a deviation distance of the goods, is a preset maximum allowable deviation distance, is a shaking amplitude of the goods, is a preset maximum allowable shaking amplitude, , and is a preset evaluation coefficient;

[0092] The calculation formula of the instability confidence value is:

[0093]

[0094] wherein, is the instability confidence value, is a preset confidence midpoint, is a preset adjustment coefficient.

[0095] It can be understood that when the instability confidence value is greater than the confidence threshold, it is determined to be in an unstable dangerous state; and when the instability confidence value is not greater than the confidence threshold, it is determined not to be in an unstable dangerous state.

[0096] It can be understood that the instability evaluation value is a comprehensive index, and the greater the value, the more unstable the goods are. However, for different unmanned forklifts, due to the differences in size, function and scene, even the same instability evaluation value may correspond to different instability risks, and the instability evaluation value has a numerical jump. Therefore, on the basis of the instability evaluation value, by calculating the instability confidence value, the instability evaluation values of different unmanned forklifts can all provide a smooth transition from “safe” to “dangerous”, so that different unmanned forklifts can all be judged for the unstable dangerous state through a unified confidence threshold.

[0097] Specifically, in another preferred embodiment provided by the present application, the identifying the freight monitoring data, calculating the instability confidence value, and judging whether it is in an unstable dangerous state specifically includes the following steps:

[0098] identifying the freight monitoring data, and periodically extracting instability identification data;

[0099] calculating an instability evaluation value according to the instability identification data;

[0100] calculating an instability confidence value based on the instability evaluation value;

[0101] comparing the instability confidence value with a preset confidence threshold, and judging whether it is in an unstable dangerous state. ​

[0102] Further, the intelligent scheduling management method of the unmanned forklift further includes the following steps:

[0103] Step S103, when in an unstable dangerous state, assisting analysis is performed on the plurality of basic monitoring data, a plurality of assistance generation values are calculated, and an assistance unmanned forklift is selected from the plurality of online unmanned forklifts.

[0104] In the embodiment of the present application, a plurality of idle unmanned forklifts are selected from the plurality of online unmanned forklifts, a plurality of idle monitoring data corresponding to the plurality of idle unmanned forklifts are selected from the plurality of basic monitoring data, assistance influence data including factors such as position, speed, power, and function are extracted by identifying the plurality of idle monitoring data, the plurality of assistance influence data correspond to the plurality of idle unmanned forklifts, the position data in the plurality of assistance influence data are combined with the position data of the delivery unmanned forklift to plan a fuzzy assistance route of the plurality of idle unmanned forklifts and determine a corresponding assistance distance, then the arrival time of the plurality of idle unmanned forklifts is calculated according to the plurality of assistance distances and the plurality of assistance influence data, and the assistance generation value of the plurality of idle unmanned forklifts is calculated according to the plurality of arrival times and the plurality of assistance influence data, and the assistance unmanned forklift with the smallest assistance generation value is selected from the plurality of idle unmanned forklifts by comparing the plurality of assistance generation values. Specifically, the calculation formula of the plurality of arrival times is:

[0105] ;

[0106] Among them, represents the i-th idle unmanned forklift, is the arrival time of the i-th idle unmanned forklift, is the assistance distance of the i-th idle unmanned forklift, is the driving speed of the i-th idle unmanned forklift, is a preset delay constant; The calculation formula of the plurality of assistance generation values is: ;

[0107]

[0108] ;

[0109] ; Among them,

[0110] is the assistance generation value of the i-th idle unmanned forklift, is the current power of the i-th idle unmanned forklift, is the assistance distance of the i-th idle unmanned forklift, is the driving speed of the i-th idle unmanned forklift, is a preset delay constant; ​​​a function matching degree of the idle unmanned forklift, a first a risk degree of the idle unmanned forklift, and a preset cost coefficient.

[0111] It can be understood that the idle unmanned forklift is an online unmanned forklift not in a cargo carrying state.

[0112] It can be understood that the fuzzy assistance route is a route planned only according to the position data of the idle unmanned forklift and the unmanned forklift carrying cargo, and the actual route needs to consider the assistance detection position. Therefore, the fuzzy assistance route is not the actual route.

[0113] It can be understood that when the idle unmanned forklift calculates the assistance distance according to the fuzzy assistance route, the turning time, acceleration time and deceleration time of the idle unmanned forklift need to be considered. Therefore, a time delay constant needs to be introduced to ensure that the arrival time calculated conforms to the actual situation.

[0114] Specifically, in another preferred embodiment provided by the present application, the assistance analysis on the plurality of basic monitoring data and the calculation of a plurality of assistance cost values, and the selection of an assistance unmanned forklift from the plurality of online unmanned forklifts specifically include the following steps:

[0115] selecting a plurality of idle unmanned forklifts from the plurality of online unmanned forklifts;

[0116] selecting idle monitoring data corresponding to the plurality of idle unmanned forklifts from the plurality of basic monitoring data;

[0117] identifying the plurality of idle monitoring data and extracting a plurality of assistance influence data;

[0118] planning a fuzzy assistance route of the plurality of idle unmanned forklifts according to the plurality of assistance influence data and determining a corresponding assistance distance;

[0119] calculating an arrival time of the plurality of idle unmanned forklifts according to the plurality of assistance distances and the plurality of assistance influence data;

[0120] calculating an assistance cost value of the plurality of idle unmanned forklifts according to the plurality of arrival times and the plurality of assistance influence data;

[0121] comparing the plurality of assistance cost values and selecting an assistance unmanned forklift from the plurality of idle unmanned forklifts.

[0122] Further, the intelligent scheduling management method of the unmanned forklift further includes the following steps: ​​

[0123] Step S104, scheduling and acquiring the assistance detection data transmitted by the assistance forklift, performing assistance analysis, generating assistance action instructions, and assisting the control of the assistance forklift.

[0124] In the embodiment of the present application, on the side of the goods of the goods forklift, the assistance detection position is planned, and then the scheduling assistance route of the assistance forklift is planned according to the assistance detection position. The assistance forklift is controlled according to the scheduling assistance route, so that the assistance forklift automatically travels to the assistance detection position. Then, the assistance forklift detects and feeds back the goods of the goods forklift, acquires the assistance detection data transmitted by the assistance forklift, plans the assistance position, the assistance angle and the assistance force through assistance analysis of the assistance detection data, generates the corresponding assistance action instructions, and controls the assistance forklift through the assistance action instructions. The assistance forklift adjusts the assistance position of the goods of the goods forklift according to the assistance angle and the assistance force.

[0125] Specifically, in another preferred embodiment provided by the present application, the scheduling and acquiring the assistance detection data transmitted by the assistance forklift, performing assistance analysis, generating assistance action instructions, and assisting the control of the assistance forklift specifically includes the following steps:

[0126] Planning the assistance detection position;

[0127] Planning the scheduling assistance route of the assistance forklift according to the assistance detection position, and scheduling the control of the assistance forklift;

[0128] Acquiring the assistance detection data transmitted by the assistance forklift;

[0129] Performing assistance analysis on the assistance detection data, and generating assistance action instructions;

[0130] Controlling the assistance forklift according to the assistance action instructions.

[0131] Further, Figure 2 The application architecture diagram of the intelligent scheduling management system of the forklift provided by the embodiment of the present application is shown.

[0132] Specifically, in another preferred embodiment provided by the present application, an intelligent scheduling management system of a forklift includes:

[0133] The basic operation monitoring module 101 is used for monitoring the basic operation of the plurality of online forklifts, acquiring a plurality of basic monitoring data, selecting a goods forklift, and acquiring the goods monitoring data of the goods forklift.

[0134] In the embodiment of the present application, the basic operation monitoring module 101 determines a plurality of online unmanned forklifts by acquiring a plurality of online state information, and then performs basic operation monitoring on the plurality of online unmanned forklifts in terms of position, speed, power, function, load, etc., to obtain a plurality of basic monitoring data. Then, the plurality of basic monitoring data is subjected to cargo carrying identification. When the load of the basic monitoring data is greater than a preset standard value, it is determined that the corresponding online unmanned forklift is in a cargo carrying state. At this time, it is determined as a cargo carrying unmanned forklift, and cargo carrying monitoring data of the cargo carrying unmanned forklift is obtained.

[0135] The unstable danger judgment module 102 is configured to identify the cargo carrying monitoring data, calculate an unstable confidence value, and judge whether it is in an unstable danger state.

[0136] In the embodiment of the present application, the unstable danger judgment module 102 identifies the cargo carrying monitoring data, periodically extracts unstable identification data such as cargo tilt, cargo offset, and cargo jitter, calculates an unstable evaluation value according to the unstable identification data, and calculates an unstable confidence value on the basis of the unstable evaluation value. By comparing the unstable confidence value with a preset confidence threshold, it is judged whether it is in an unstable danger state. Specifically, the calculation formula of the unstable evaluation value is:

[0137] ;

[0138] ;

[0139] Among them, is the unstable evaluation value, is the cargo tilt angle, is a preset maximum allowable tilt angle, is the cargo offset distance, is a preset maximum allowable offset distance, is the cargo jitter amplitude, is a preset maximum allowable jitter amplitude, , and are preset evaluation coefficients.

[0140] The calculation formula of the unstable confidence value is:

[0141] ;

[0142] Among them, is the unstable confidence value, is a preset confidence midpoint, is a preset adjustment coefficient.

[0143] Specifically, in another preferred embodiment provided by the present invention, the instability risk judgment module 102 specifically includes:

[0144] An unstable identification data extraction unit is used to identify the freight monitoring data and periodically extract unstable identification data.

[0145] An evaluation value calculation unit is used to calculate an instability evaluation value based on the instability identification data.

[0146] A confidence value calculation unit is used to calculate an instability confidence value based on the instability evaluation value;

[0147] An unstable danger state determination unit is used to compare the instability confidence value with a preset confidence threshold to determine whether the state is unstable or dangerous.

[0148] Furthermore, the intelligent scheduling and management system for the unmanned forklift also includes:

[0149] The assistance analysis selection module 103 is used to perform assistance analysis on multiple basic monitoring data when the situation is unstable and dangerous, calculate multiple assistance costs, and select an assistance unmanned forklift from multiple online unmanned forklifts.

[0150] In this embodiment of the invention, the assistance analysis and selection module 103 selects multiple idle unmanned forklifts from multiple online unmanned forklifts, and then selects idle monitoring data corresponding to multiple idle unmanned forklifts from multiple basic monitoring data. By identifying multiple idle monitoring data, assistance impact data including factors such as location, speed, battery level, and function are extracted. Multiple assistance impact data correspond to multiple idle unmanned forklifts. Then, based on the location data in the multiple assistance impact data and combined with the location data of the delivery unmanned forklifts, fuzzy assistance routes for multiple idle unmanned forklifts are planned, and the corresponding assistance distances are determined. Then, based on the multiple assistance distances and multiple assistance impact data, the arrival times of multiple idle unmanned forklifts are calculated. Then, based on the multiple arrival times and multiple assistance impact data, the assistance cost of multiple idle unmanned forklifts is calculated. By comparing multiple assistance cost values, the assisting unmanned forklift with the lowest assistance cost value is selected from multiple idle unmanned forklifts. Specifically, the calculation formula for multiple arrival times is as follows:

[0151] ;

[0152] in, Representing the An idle unmanned forklift For the first The arrival time of an idle unmanned forklift. For the first The assistance of an idle unmanned forklift. For the first The travel speed of an idle, unmanned forklift. The preset delay constant;

[0153] The formula for calculating multiple assistance cost values ​​is as follows:

[0154] ;

[0155] ;

[0156] in, For the first The value of an idle unmanned forklift. For the first Current battery level of an idle automated forklift. For the first Functional compatibility of idle unmanned forklifts For the first The risk level of an idle unmanned forklift. , , and This is the preset cost coefficient.

[0157] Specifically, in another preferred embodiment provided by the present invention, the assisted analysis selection module 103 specifically includes:

[0158] An idle unmanned forklift selection unit is used to select multiple idle unmanned forklifts from the multiple online unmanned forklifts;

[0159] The idle monitoring data selection unit is used to select idle monitoring data corresponding to multiple idle unmanned forklifts from multiple basic monitoring data;

[0160] The assisting influence data extraction unit is used to identify multiple idle monitoring data and extract multiple assisting influence data.

[0161] The assisted route determination unit is used to plan fuzzy assistance routes for multiple idle unmanned forklifts based on multiple assistance impact data, and determine the corresponding assistance routes;

[0162] An arrival time calculation unit is used to calculate the arrival time of multiple idle unmanned forklifts based on multiple assisted routes and multiple assisted impact data.

[0163] An assistance cost calculation unit is used to calculate the assistance cost of multiple idle unmanned forklifts based on multiple arrival times and multiple assistance impact data.

[0164] The assisting forklift selection unit is configured to compare the assistance values and select the assisting forklift from the idle forklifts.

[0165] Further, the intelligent scheduling management system of the forklift further comprises:

[0166] The scheduling assistance control module 104 is configured to schedule and acquire the assistance detection data transmitted by the assisting forklift, perform assistance analysis, generate assistance action instructions, and control the assisting forklift.

[0167] In the embodiment of the present application, the scheduling assistance control module 104 plans an assistance detection position on the side of the goods of the goods-carrying forklift, and then plans a scheduling assistance route of the assisting forklift according to the assistance detection position. The assisting forklift is controlled to automatically travel to the assistance detection position according to the scheduling assistance route. Then, the assisting forklift is controlled to detect and photograph the goods of the goods-carrying forklift and transmit the feedback. The assistance detection data transmitted by the assisting forklift is acquired. The assistance detection data is analyzed to plan an assistance point, an assistance angle, and an assistance force. Corresponding assistance action instructions are generated. The assisting forklift is controlled by the assistance action instructions. The assisting forklift is controlled to perform assistance intervention on the assistance point of the goods of the goods-carrying forklift according to the assistance angle and the assistance force.

[0168] The above-described embodiments only express several embodiments of the present application, and the description is relatively specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be noted that, for ordinary skilled persons in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application. Therefore, the scope of protection of the patent of the present application should be subject to the appended claims.

Claims

1. An intelligent scheduling and management method for unmanned forklifts, characterized in that, The method specifically includes the following steps: Perform basic operation monitoring on multiple online unmanned forklifts, obtain multiple basic monitoring data, select a delivery unmanned forklift, and obtain the delivery monitoring data of the delivery unmanned forklift; The cargo monitoring data is identified, an instability confidence value is calculated, and it is determined whether the cargo is in an unstable and dangerous state. The cargo monitoring data is identified, and unstable identification data, including cargo tilting, cargo offset, and cargo shaking, are periodically extracted. Based on this unstable identification data, an instability evaluation value is calculated, and an instability confidence value is calculated based on the evaluation value. By comparing the instability confidence value with a preset confidence threshold, it is determined whether the cargo is in an unstable and dangerous state. Specifically, the formula for calculating the instability evaluation value is as follows: ； ； in, This is an unstable evaluation value. The angle at which the goods are tilted. This is the preset maximum allowable tilt angle. This represents the distance the cargo has shifted. This is the preset maximum allowable offset distance. The amplitude of cargo shaking. The preset maximum allowable jitter amplitude, , and These are the preset evaluation coefficients; The formula for calculating the unstable confidence value is: ； in, Unstable confidence values, The preset confidence midpoint, This is the preset adjustment coefficient; When in an unstable and dangerous state, multiple basic monitoring data are analyzed to calculate multiple assistance costs, and an assistance unmanned forklift is selected from multiple online unmanned forklifts. The system schedules and acquires the assistance detection data transmitted by the assisted unmanned forklift, performs assistance analysis, generates assistance action commands, and performs assistance control on the assisted unmanned forklift. By analyzing the assisted detection data, the assisted points, assisted angles, and assisted forces are planned, and corresponding assisted action commands are generated. The assisted action commands are used to control the assisted unmanned forklift, so that the assisted unmanned forklift can make assisted interventions to stabilize the goods at the assisted points of the unmanned forklift according to the assisted angle and assisted force.

2. The intelligent scheduling and management method for unmanned forklifts according to claim 1, characterized in that, The process of performing basic operational monitoring on multiple online unmanned forklifts, acquiring multiple basic monitoring data, selecting a delivery unmanned forklift, and acquiring its delivery monitoring data specifically includes the following steps: Acquire multiple online status information to identify multiple online unmanned forklifts; Basic operational monitoring was performed on multiple online unmanned forklifts to obtain multiple basic monitoring data. The system identifies the goods being transported based on multiple sets of basic monitoring data, and selects a goods transport unmanned forklift from among multiple online unmanned forklifts. Obtain the cargo transportation monitoring data of the unmanned forklift.

3. The intelligent scheduling and management method for unmanned forklifts according to claim 1, characterized in that, The process of assisting in the analysis of multiple basic monitoring data, calculating multiple assistance costs, and selecting an assisting unmanned forklift from multiple online unmanned forklifts specifically includes the following steps: Select multiple idle automated forklifts from the multiple online automated forklifts; Select idle monitoring data corresponding to multiple idle unmanned forklifts from multiple basic monitoring data; Multiple idle monitoring data are identified, and multiple contributing data are extracted. Based on multiple assistance impact data, fuzzy assistance routes for multiple idle unmanned forklifts are planned, and the corresponding assistance distances are determined; Based on the multiple assisted routes and the multiple assisted impact data, calculate the arrival time of the multiple idle unmanned forklifts; Calculate the assistance cost of multiple idle unmanned forklifts based on multiple arrival times and multiple assistance impact data; The multiple assistance costs are compared, and an assistance unmanned forklift is selected from the multiple idle unmanned forklifts.

4. The intelligent scheduling and management method for unmanned forklifts according to claim 3, characterized in that, The formulas for calculating the arrival times are as follows: ； in, Representing the An idle unmanned forklift For the first The arrival time of an idle unmanned forklift. For the first The assistance of an idle unmanned forklift. For the first The travel speed of an idle, unmanned forklift. The preset delay constant; The formulas for calculating the multiple assistance cost values ​​are as follows: ； ； in, For the first The value of an idle unmanned forklift. For the first Current battery level of an idle automated forklift. For the first Functional compatibility of idle unmanned forklifts For the first The risk level of an idle unmanned forklift. , , and This is the preset cost coefficient.

5. The intelligent scheduling and management method for unmanned forklifts according to claim 1, characterized in that, The process of scheduling and acquiring the assistance detection data transmitted by the assisted unmanned forklift, performing assistance analysis, generating assistance action commands, and assisting in the control of the assisted unmanned forklift specifically includes the following steps: Planning and assisting in the detection location; Based on the detected location, plan the dispatching and assistance route of the unmanned forklift, and perform dispatching and control of the unmanned forklift. Acquire the assisted detection data transmitted by the unmanned forklift; The assisted detection data is analyzed to generate assisted action commands; The assisted unmanned forklift is controlled in accordance with the assisted action instructions.

6. An intelligent scheduling and management system for unmanned forklifts, characterized in that, The system includes a basic operation monitoring module, an instability hazard assessment module, an auxiliary analysis and selection module, and a scheduling and auxiliary control module, wherein: The basic operation monitoring module is used to perform basic operation monitoring on multiple online unmanned forklifts, acquire multiple basic monitoring data, select a delivery unmanned forklift, and acquire the delivery monitoring data of the delivery unmanned forklift. The instability risk assessment module is used to identify the cargo monitoring data, calculate the instability confidence value, and determine whether it is in an unstable risk state. The instability hazard assessment module identifies cargo monitoring data and periodically extracts instability identification data, including cargo tilting, cargo offset, and cargo shaking. Based on this data, it calculates an instability evaluation value and, based on that, an instability confidence value. By comparing this confidence value with a preset confidence threshold, it determines whether the cargo is in an unstable hazard state. Specifically, the formula for calculating the instability evaluation value is as follows: ； ； in, This is an unstable evaluation value. The angle at which the goods are tilted. This is the preset maximum allowable tilt angle. This represents the distance the cargo has shifted. This is the preset maximum allowable offset distance. The amplitude of cargo shaking. The preset maximum allowable jitter amplitude, , and These are the preset evaluation coefficients; The formula for calculating the unstable confidence value is: ； in, Unstable confidence values, The preset confidence midpoint, This is the preset adjustment coefficient; The assistance analysis and selection module is used to perform assistance analysis on multiple basic monitoring data when the situation is unstable and dangerous, calculate multiple assistance costs, and select an assistance unmanned forklift from multiple online unmanned forklifts. The scheduling and assistance control module is used to schedule and acquire the assistance detection data transmitted by the assisted unmanned forklift, perform assistance analysis, generate assistance action commands, and perform assistance control on the assisted unmanned forklift. The scheduling and assistance control module analyzes the assistance detection data, plans the assistance points, assistance angles and assistance forces, generates corresponding assistance action commands, and uses these commands to control the assisted unmanned forklift. This allows the assisted unmanned forklift to intervene by adjusting the goods at the assistance points of the unmanned forklift according to the assistance angle and assistance force.

7. The intelligent scheduling and management system for unmanned forklifts according to claim 6, characterized in that, The instability risk assessment module specifically includes: An unstable identification data extraction unit is used to identify the freight monitoring data and periodically extract unstable identification data. An evaluation value calculation unit is used to calculate an instability evaluation value based on the instability identification data. A confidence value calculation unit is used to calculate an instability confidence value based on the instability evaluation value; An unstable danger state determination unit is used to compare the instability confidence value with a preset confidence threshold to determine whether the state is unstable or dangerous.

8. The intelligent scheduling and management system for unmanned forklifts according to claim 6, characterized in that, The assisted analysis selection module specifically includes: An idle unmanned forklift selection unit is used to select multiple idle unmanned forklifts from the multiple online unmanned forklifts; The idle monitoring data selection unit is used to select idle monitoring data corresponding to multiple idle unmanned forklifts from multiple basic monitoring data; An assisting influence data extraction unit is used to identify multiple idle monitoring data and extract multiple assisting influence data. The assisted route determination unit is used to plan fuzzy assistance routes for multiple idle unmanned forklifts based on multiple assistance impact data, and determine the corresponding assistance routes; An arrival time calculation unit is used to calculate the arrival time of multiple idle unmanned forklifts based on multiple assisted routes and multiple assisted impact data. An assistance cost calculation unit is used to calculate the assistance cost of multiple idle unmanned forklifts based on multiple arrival times and multiple assistance impact data. An assisting unmanned forklift selection unit is used to compare multiple assistance costs and select an assisting unmanned forklift from multiple idle unmanned forklifts.

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