CN110378503B - Method and device for predicting production capacity of multi-layer shuttle shelf - Google Patents

Method and device for predicting production capacity of multi-layer shuttle shelf Download PDF

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CN110378503B
CN110378503B CN201811390998.5A CN201811390998A CN110378503B CN 110378503 B CN110378503 B CN 110378503B CN 201811390998 A CN201811390998 A CN 201811390998A CN 110378503 B CN110378503 B CN 110378503B
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魏豫
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Beijing Jingdong Qianshi Technology Co Ltd
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Abstract

The invention discloses a method and a device for predicting the production capacity of a multilayer shuttle shelf, and relates to the field of warehouse logistics. One embodiment of the method comprises: according to the historical wave number data, determining the average production efficiency of the work station in the historical wave number time period, the average production efficiency of the current work station and the average production efficiency of the current hoister; predicting the average work station production efficiency, the work station production capacity and the elevator production capacity of the next wave time period of the current wave; and taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period. The implementation mode comprehensively considers the historical production efficiency and the current production efficiency, so that the technical problem of low prediction precision of the production capacity caused by over dependence on the high peak value of the sorting capacity in the historical data in the prior art is solved, and the aim of more accurate prediction of the production capacity is achieved.

Description

Method and device for predicting production capacity of multi-layer shuttle shelf
Technical Field
The invention relates to the field of warehouse logistics, in particular to a method and a device for predicting the production capacity of a multilayer shuttle shelf.
Background
The multilayer shuttle goods shelf uses an automatic shuttle car and a lifting machine to carry articles from storage positions of the multilayer goods shelf to a picking workstation (hereinafter referred to as a workstation for short), the workstation is used for warehouse-out picking production and can be divided into a manual picking workstation, a robot picking workstation and a robot picking abnormal workstation. The multi-level shuttle rack production capacity refers to the handling capacity and the picking capacity. In the existing capacity calculation, the high peak value of the picking capacity of the robot area is counted every day according to historical data, namely the number of picks of each workstation per minute, and then the current production capacity is obtained by multiplying the current wave time remaining time by the number of workstations. Briefly, the method of calculating throughput in the prior art is: the current throughput is the number of pickers per workstation per minute (high peak in the picking capacity of the robot zone) and the current number of workstations in the wave.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
1. since the historical data is fixed, the high peak is an ideal state, and the historical peak can not be achieved in reality.
2. The abnormal condition of the current relevant equipment cannot be predicted, so that the prediction accuracy of the production capacity is not high.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for predicting throughput of a multi-level shuttle rack, which can solve the problem of low prediction accuracy of throughput due to the fact that the method and the apparatus depend too much on a high peak value of the picking capacity in historical data and cannot predict an abnormal condition of a current relevant device.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of predicting productivity of a multi-level shuttle rack, including:
according to the historical wave number data, determining the average production efficiency of the work station in the historical wave number time period, the average production efficiency of the current work station and the average production efficiency of the current hoister;
predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the workstation at the current time and the average production efficiency of the workstation in the historical wave time period;
predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the current elevator average production efficiency, the next wave time duration, the workstation number and the elevator number;
and taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period.
Optionally, predicting the average production efficiency of the workstation in the next wave time period of the current wave according to the average production efficiency of the workstation in the current wave time period and the average production efficiency of the workstation in the historical wave time period includes:
according to the current time period, determining the average production efficiency of the workstations in the historical time period which is the same as the current time period and the average production efficiency of the workstations in the historical time period which is the same as the next time period of the current time;
calculating the workstation average production efficiency q (t +1) of the next wave time period of the current wave by adopting a formula q (t +1) ═ (ql (t)/q' (t)) -ql (t + 1);
wherein ql (t +1) represents the workstation average production efficiency of the same historical wave time period as the next wave time period of the current wave, ql (t) represents the workstation average production efficiency of the same historical wave time period as the time period, q' (t) represents the current workstation average production efficiency, t represents the time period of the current wave, and t +1 represents the next wave time period of the current wave.
Optionally, predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the average workstation production efficiency of the next wave time period, the average current elevator production efficiency, the time length of the next wave time, the number of workstations, and the number of elevators, includes:
taking the product of the average production efficiency of the workstations in the next wave time period, the duration of the next wave time and the number of the workstations as the production capacity of the workstations in the next wave time period;
and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity of the hoister in the next wave time period.
Optionally, after predicting the average production efficiency of the workstation in the next turn time period of the current turn according to the average production efficiency of the workstation in the current turn and the average production efficiency of the workstation in the historical turn time period, the method further includes:
determining the average time length Tx of each shuttle and/or elevator in a fault state according to historical wave number data;
acquiring the number m of shuttle vehicles in a fault state at present and the number n of elevators in the fault state at present;
determining that the shuttle vehicle use ratio p1 in the normal state is 1-m/the total number of shuttle vehicles, and the elevator use ratio p2 in the normal state is 1-n/the total number of elevators;
predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the average production efficiency of the current elevator, the time length of the next wave time, the number of workstations and the number of elevators, and the method comprises the following steps:
calculating the workstation throughput Wq for the next waveform time period using the formula Wq (t +1) Tx Ng p1 p2+ q (t +1) Tb Tx Ng;
wherein Ng represents the number of workstations, and Tb represents the time length of the next wave;
and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity Cq of the hoister in the time period of the next wave.
Optionally, after taking the minimum value of the workstation throughput and the elevator throughput of the next turn time period as the predicted value of the multilayer shuttle shelf throughput of the next turn time period, the method further comprises:
taking the product of the average production efficiency of the current workstation, the current time surplus time, the number of workstations, the usage proportion of the shuttle vehicles in the normal state and the usage proportion of the elevators in the normal state as the production capacity W of the workstation in the current time surplus time period0q；
Taking the product of the average production efficiency of the current hoister, the current time-of-wave remaining duration, the quantity of the hoists and the average selected number of the bins as the production capacity C of the hoister in the current time-of-wave remaining period0q；
The W is0q and C0And q is the minimum value of the current time period, and the production capacity prediction value of the multilayer shuttle shelf is used as the minimum value of the q.
Optionally, taking the minimum value of the workstation throughput and the elevator throughput of the next time period as the predicted throughput of the multi-deck shuttle shelf of the next time period, including:
determining an actual value of the production capacity of the multi-layer shuttle shelf in the historical wave time period according to the historical wave data;
acquiring a production capacity predicted value of the multilayer shuttle shelf in the historical wave time period, and determining the ratio of the production capacity predicted value of the multilayer shuttle shelf in the historical wave time period to the actual value of the production capacity of the multilayer shuttle shelf in the historical wave time period as the difference percentage of the production capacity of the multilayer shuttle shelf in the historical wave time period;
according to the difference percentage, determining a quartile P in the difference percentage in all historical waves;
the predicted multi-level shuttle shelf capacity value Q for the next wave time period P Min (Wq, Cq), where Min (Wq, Cq) represents the minimum of the workstation capacity Wq for the next wave time period and the elevator capacity Cq for the next wave time period.
Optionally, determining the current average production efficiency of the workstation according to the historical wave number data comprises:
the historical wave data comprises the production quantity Sg of the workstations and the duration T' g of the workstations in the unbound order state in a set time period from the current moment to the previous moment;
the production efficiency of the workstation is Sg/(Tg-T' g), and Tg represents the time length of a set time period from the current time to the previous time;
and determining the average production efficiency of the current workstation according to the production efficiency of all workstations and the number of the workstations.
Optionally, determining the average production efficiency of the current elevator according to the historical wave data includes:
the historical wave time data comprises the number Sj of lifting boxes of the lifting machine and the working time Tj of the lifting machine in a set time period from the current time to the previous time;
the production efficiency of the hoister is Sj/Tj;
and determining the average production efficiency of the current hoister according to the production efficiency of all hoisters and the number of hoisters.
To achieve the above object, according to another aspect of an embodiment of the present invention, there is provided an apparatus for predicting shuttle rack productivity, including:
an efficiency determination module to: according to the historical wave number data, determining the average production efficiency of the work station in the historical wave number time period, the average production efficiency of the current work station and the average production efficiency of the current hoister;
an efficiency prediction module to: predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the workstation at the current time and the average production efficiency of the workstation in the historical wave time period;
a capacity quasi-prediction module for: predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the current elevator average production efficiency, the next wave time duration, the workstation number and the elevator number;
a predictor determination module to: and taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period.
Optionally, the efficiency prediction module is further configured to:
according to the current time period, determining the average production efficiency of the workstations in the historical time period which is the same as the current time period and the average production efficiency of the workstations in the historical time period which is the same as the next time period of the current time;
calculating the workstation average production efficiency q (t +1) of the next wave time period of the current wave by adopting a formula q (t +1) ═ (ql (t)/q' (t)) -ql (t + 1);
wherein ql (t +1) represents the workstation average production efficiency of the same historical wave time period as the next wave time period of the current wave, ql (t) represents the workstation average production efficiency of the same historical wave time period as the time period, q' (t) represents the current workstation average production efficiency, t represents the time period of the current wave, and t +1 represents the next wave time period of the current wave.
Optionally, the capacity quasi-forecasting module is further configured to:
taking the product of the average production efficiency of the workstations, the duration of the next wave time and the number of the workstations as the production capacity of the workstations in the next wave time period;
and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity of the hoister in the next wave time period.
Optionally, the apparatus further comprises a fault statistics module configured to:
determining the average time length Tx of each shuttle and/or elevator in a fault state according to historical wave number data;
acquiring the number m of shuttle vehicles in a fault state at present and the number n of elevators in the fault state at present;
determining that the shuttle vehicle use ratio p1 in the normal state is 1-m/the total number of shuttle vehicles, and the elevator use ratio p2 in the normal state is 1-n/the total number of elevators;
the capacity quasi-forecasting module is further used for:
calculating the workstation throughput Wq for the next waveform time period using the formula Wq (t +1) Tx Ng p1 p2+ q (t +1) Tb Tx Ng;
wherein Ng represents the number of workstations, and Tb represents the time length of the next wave;
and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity Cq of the hoister in the time period of the next wave.
Optionally, the predictor determination module is further configured to:
taking the product of the average production efficiency of the current workstation, the current time surplus time, the number of workstations, the usage proportion of the shuttle vehicles in the normal state and the usage proportion of the elevators in the normal state as the production capacity W of the workstation in the current time surplus time period0q；
Taking the product of the average production efficiency of the current hoister, the current time-of-wave remaining duration, the quantity of the hoists and the average selected number of the bins as the production capacity C of the hoister in the current time-of-wave remaining period0q；
The W is0q and C0And q is the minimum value of the current time period, and the production capacity prediction value of the multilayer shuttle shelf is used as the minimum value of the q.
Optionally, the predictor determination module is further configured to:
determining an actual value of the production capacity of the multi-layer shuttle shelf in the historical wave time period according to the historical wave data;
acquiring a production capacity predicted value of the multilayer shuttle shelf in the historical wave time period, and determining the ratio of the production capacity predicted value of the multilayer shuttle shelf in the historical wave time period to the actual value of the production capacity of the multilayer shuttle shelf in the historical wave time period as the difference percentage of the production capacity of the multilayer shuttle shelf in the historical wave time period;
according to the difference percentage, determining a quartile P in the difference percentage in all historical waves;
the predicted multi-level shuttle shelf capacity value Q for the next wave time period P Min (Wq, Cq), where Min (Wq, Cq) represents the minimum of the workstation capacity Wq for the next wave time period and the elevator capacity Cq for the next wave time period.
Optionally, the efficiency determination module is further configured to:
the historical wave data comprises the production quantity Sg of the working station and the duration T' g of the working station in an unbound order state in a set time period from the current moment to the previous moment;
the production efficiency of the workstation is Sg/(Tg-T' g), and Tg represents the time length of a set time period from the current time to the previous time;
and determining the average production efficiency of the current workstation according to the production efficiency of all workstations and the number of the workstations.
Optionally, the efficiency determination module is further configured to:
the historical wave time data comprises the number Sj of lifting boxes of the lifting machine and the working time Tj of the lifting machine in a set time period from the current time to the previous time;
the production efficiency of the hoister is Sj/Tj;
and determining the average production efficiency of the current hoister according to the production efficiency of all hoisters and the number of hoisters.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided an electronic apparatus including: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for predicting multi-level shuttle rack production capacity provided by embodiments of the present invention.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided a computer readable medium having stored thereon a computer program, which when executed by a processor, implements the method for predicting multi-level shuttle rack production capacity provided by the embodiments of the present invention.
One embodiment of the above invention has the following advantages or benefits: the historical production efficiency and the current production efficiency are comprehensively considered, so that the technical problem that the prediction precision of the production capacity is not high due to the fact that the prediction precision of the selection capacity in historical data is excessively depended on the high peak value of the selection capacity in the historical data in the prior art is solved, and the predicted production capacity result of the multi-layer shuttle shelf is more accurate.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of the basic flow of a method of predicting the capacity of a multi-level shuttle rack according to an embodiment of the invention;
FIG. 2 is a schematic representation of the end of production time for each wave in accordance with an embodiment of the present invention;
FIG. 3 is a graph comparing predicted values and actual values of historical multiples according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of the basic modules of an apparatus for predicting the production capacity of a multi-level shuttle rack according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
FIG. 1 is a schematic diagram of the basic flow of a method of predicting the production capacity of a multi-level shuttle rack according to an embodiment of the invention. As shown in fig. 1, an embodiment of the present invention provides a method for predicting the productivity of a multi-layer shuttle shelf, including:
s101, determining the average production efficiency of the work station, the average production efficiency of the current work station and the average production efficiency of the current hoister in a historical wave time period according to historical wave data;
s102, predicting the average production efficiency of the work station in the next wave time period of the current wave time according to the average production efficiency of the work station in the current work station and the average production efficiency of the work station in the historical wave time period;
s103, predicting the production capacity of the workstation and the production capacity of the hoister in the next wave time period based on the average production efficiency of the workstation in the next wave time period, the average production efficiency of the current hoister, the time length of the next wave time, the number of workstations and the number of hoisters;
and S104, taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period.
The order is a mode for improving the picking operation efficiency, and different orders are combined into one order according to a certain standard to guide one-time picking. A wave has a wave time period and an end of production time, and the end of production times for all orders belonging to the wave are consistent. FIG. 2 is a schematic representation of the production end times for each wave, as shown in FIG. 2, for 7 waves a day, which is a 7 wave production end time in the figure, according to an embodiment of the present invention.
The embodiment of the invention comprehensively considers the historical production efficiency and the current production efficiency, thereby overcoming the technical problem of low prediction precision of the production capacity caused by over dependence on the high peak value of the picking capacity in the historical data in the prior art, and further achieving more accurate result of the predicted production capacity of the multi-layer shuttle shelf.
In step S101 of the embodiment of the present invention, determining the average production efficiency of the workstation in the historical wave time period according to the historical wave data includes: counting the time length of the working station in the work load saturation state and the number of the picking pieces of the working station in the work load saturation state in each historical wave, and then calculating the picking efficiency of each working station in the work load saturation state according to the data; since a multi-layer shuttle shelf is produced by binding with a plurality of workstations, the average production efficiency of the workstations in each historical wave time period is calculated by combining the number of the workstations, for example: the average production efficiency of the workstations in the historical wave time period 06:00-11:00 is ql (06:00-11:00) which is 3.4, and the average production efficiency of the workstations in the historical wave time period 11:00-12:00 is ql (11:00-12:00) which is 3.0. The work load saturation state means that the workstation is completely in a busy stage within a period of time, the workstation is not in a busy state when being idle due to too small order quantity, and the work efficiency of the workstation calculated during the period of time in the work load saturation state is more accurate.
In step S101 of the embodiment of the present invention, determining the average production efficiency of the current workstation according to the historical wave number data includes: the historical wave data comprises the production quantity Sg of the working station and the duration T' g of the working station in an unbound order state in a set time period from the current moment to the previous moment; the production efficiency of the workstation is Sg/(Tg-T' g), and Tg represents the time length of a set time period from the current time to the previous time; and determining the average production efficiency of the current workstation according to the production efficiency of all workstations and the number of the workstations. Preferably, the set time period from the current time to the previous time may refer to a time period from the current time to the previous hour, for example: the current time is 11: 20, the time period from the current time to the previous hour is 10: 20-11: 20; a cross-wave order (i.e., a different order) is also possible here, just to obtain the throughput over the last period of time. Inquiring the production condition of each workstation in the time period, and obtaining the time length Tg-T' g of the workstation in the saturated working state, wherein the obtained production quantity is possibly unsaturated production capacity, and the time length of unbound orders of the workstation is discarded; or the starting time and the finishing time of each order bound by the workstation can be found from the historical wave data, and the remaining time is the time period in which no order is bound.
In step S101 of the embodiment of the present invention, determining the average production efficiency of the current elevator according to the historical frequency data includes: the historical wave time data comprises the number Sj of lifting boxes of the lifting machine and the working time Tj of the lifting machine in a set time period from the current time to the previous time; the production efficiency of the hoister is Sj/Tj; since one multi-layer shuttle shelf is produced by binding with a plurality of elevators, the average production efficiency of the current elevator is determined according to the production efficiency of all the elevators and the number of the elevators. Preferably, the set time period from the current time to the previous time may also refer to a time period from the current time to the previous hour.
In step S102 of the embodiment of the present invention, predicting the average production efficiency of the workstation in the next turn time period of the current turn according to the average production efficiency of the workstation in the current turn and the average production efficiency of the workstation in the historical turn time period includes: according to the current time period, determining the average production efficiency of the workstations in the historical time period which is the same as the current time period and the average production efficiency of the workstations in the historical time period which is the same as the next time period of the current time; calculating the workstation average production efficiency q (t +1) of the next wave time period of the current wave by adopting a formula q (t +1) ═ (ql (t)/q' (t)) -ql (t + 1); wherein ql (t +1) represents the workstation average production efficiency of the same historical wave time period as the next wave time period of the current wave, ql (t) represents the workstation average production efficiency of the same historical wave time period as the time period, q' (t) represents the current workstation average production efficiency, t represents the time period of the current wave, and t +1 represents the next wave time period of the current wave. According to the comparison between the historical ql (t) and the current q '(t), if the current production efficiency q' (t) is higher than the historical efficiency ql (t) at the same time, the production efficiency of the next future wave is predicted to be higher than the historical efficiency. For example: the current time is 15:00:00 belonging to wave numbers 12:05-16:00 and the next wave time period is 16:00-17:00, so the workstation average production efficiency q (16:00-17:00) is predicted for the next wave time period 16:00-17:00 of the current wave number (ql (12:05-16: 00)/q' (15:00:00)) ql (16:00-17: 00).
In the embodiment of the present invention, in consideration of the hardware operating condition, the occurrence of problems such as equipment failure that may occur in the production process, the time of failure, and the number of failed equipment all affect the prediction of the next-wave production capacity, so after the step S102 of predicting the station average production efficiency of the next-wave time period of the current wave according to the station average production efficiency of the current station average production efficiency and the station average production efficiency of the historical wave time period, the method further includes: determining the average time length Tx of each shuttle and/or elevator in a fault state according to historical wave number data; acquiring the number m of shuttle vehicles in a fault state at present and the number n of elevators in the fault state at present; and determining that the shuttle vehicle usage proportion p1 in the normal state is 1-m/the total number of the shuttle vehicles, and the elevator usage proportion p2 in the normal state is 1-n/the total number of the elevators.
Based on the above embodiment, in consideration of the number of devices with failure problems and the time length of the devices in the failure state, in step S103 of the embodiment of the present invention, the predicting the workstation throughput and the elevator throughput in the next wave time period based on the average workstation throughput in the next wave time period, the average current elevator throughput, the time length in the next wave time period, the number of workstations, and the number of elevators includes: calculating the workstation throughput Wq for the next waveform time period using the formula Wq (t +1) Tx Ng p1 p2+ q (t +1) Tb Tx Ng; wherein Ng represents the number of workstations, and Tb represents the time length of the next wave; and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected part quantity of the bins as the production capacity Cq of the hoister in the time period of the next wave. The average selection quantity of the bins refers to the average article quantity in the bins when one bin is output by the elevator; the types of the storehouses are different, the average sorting quantity of the material boxes of different storehouses is greatly different, but the articles in the same storehouse are of the same type, and the average sorting quantity of the material boxes of different elevators in the same storehouse is basically the same. Therefore, the embodiment of the invention can more accurately predict the future production capacity by considering the hardware running condition and part of abnormal conditions.
In step S103 of the embodiment of the present invention, predicting the workstation throughput and the elevator throughput in the next cycle time period based on the average workstation production efficiency in the next cycle time period, the average production efficiency of the current elevator, the next cycle time duration, the number of workstations, and the number of elevators, includes: taking the product of the average production efficiency of the workstations, the duration of the next wave time and the number of the workstations as the production capacity of the workstations in the next wave time period; and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected piece quantity of the material boxes as the production capacity of the hoister in the time period of the next wave. Can also be expressed as: the workstation throughput of the next wave time period Wq q (t +1) duration of the next wave time is workstation number; and the elevator production capacity Cq of the next wave time period is the average production efficiency ct of the current elevator, the time length of the next wave time is the number of elevators and the average selected number of the bins.
FIG. 3 is a graph comparing predicted values and actual values of historical multiples, according to an embodiment of the invention. In step S104 of the embodiment of the present invention, taking the minimum value of the workstation throughput and the elevator throughput in the next time period as the predicted value of the throughput of the multi-layer shuttle shelf in the next time period, includes: as shown in fig. 3, determining an actual value of the production capacity of the multi-layer shuttle shelf in the historical wave time period according to the historical wave data; acquiring a production capacity predicted value of the multilayer shuttle shelf in the historical wave time period, and determining the ratio of the production capacity predicted value of the multilayer shuttle shelf in the historical wave time period to the actual value of the production capacity of the multilayer shuttle shelf in the historical wave time period as the difference percentage of the production capacity of the multilayer shuttle shelf in the historical wave time period; and determining a quartile P in the difference percentage in all historical waves according to the difference percentage. For example: for example, the time of one wave of the morning, the current forecast is 1000, and the actual production capacity is 800, which indicates that the forecast is higher, the difference percentage is 80%, and the difference percentage of each historical wave is calculated: 80%, 60%, 70%, 80%, and 80% is the upper quartile. The predicted multi-level shuttle shelf capacity value Q for the next wave time period P Min (Wq, Cq), where Min (Wq, Cq) represents the minimum of the workstation capacity Wq for the next wave time period and the elevator capacity Cq for the next wave time period. In all the historical waves traversed, the workstation has idle time in the historical wave time, and the fact that the workload is not saturated (the order quantity is insufficient) is indicated. If no idle time exists, the wave is saturated; preferably, a statistically saturated historical wave number is selected here. According to the embodiment of the invention, the predicted production capacity of the multi-layer shuttle shelf is checked and adjusted according to the predicted value and the actual value of the historical wave number, so that the prediction accuracy of the production capacity is higher.
In step S104 of the embodiment of the present invention, after the minimum value of the workstation throughput and the elevator throughput in the next time period is used as the predicted value of the throughput of the multi-layer shuttle shelf in the next time period, the method further includes: taking the product of the average production efficiency of the current workstation, the current time surplus time, the number of workstations, the usage proportion of the shuttle vehicles in the normal state and the usage proportion of the elevators in the normal state as the production capacity W of the workstation in the current time surplus time period0q; taking the product of the average production efficiency of the current hoister, the current time-of-wave remaining duration, the quantity of the hoists and the average selected number of the bins as the production capacity C of the hoister in the current time-of-wave remaining period0q; the W is0q and C0And q is the minimum value of the current time period, and the production capacity prediction value of the multilayer shuttle shelf is used as the minimum value of the q. The embodiment of the invention can predict the production capacity prediction value of the multilayer shuttle shelf in the remaining time period in the current time in real time, and provides favorable reference for smooth warehousing production process.
In the embodiment of the invention, a manual regulation scheme is added. The number of the workstations is dynamically adjusted by field workers and is changed in real time. The capacity ratio is a whole day adjustment button, and the percentage can be adjusted in abnormal or emergency situations. The prediction of the system always has errors or some artificial factors cause inaccuracy, and the whole scheme is more applicable by operating according to the artificial experience.
Fig. 4 is a schematic diagram of the basic modules of the apparatus for predicting the production capacity of a multi-level shuttle rack according to an embodiment of the present invention. As shown in fig. 4, an embodiment of the present invention provides an apparatus 400 for predicting shuttle shelf throughput, comprising:
an efficiency determination module 401 configured to: according to the historical wave number data, determining the average production efficiency of the work station in the historical wave number time period, the average production efficiency of the current work station and the average production efficiency of the current hoister;
an efficiency prediction module 402 to: predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the workstation at the current time and the average production efficiency of the workstation in the historical wave time period;
a capacity quasi-prediction module 403 for: predicting the workstation production capacity and the elevator production capacity of the next time period based on the workstation average production efficiency of the next time period, the average production efficiency of the current elevator, the time length of the next time period, the workstation number and the elevator number;
a predictor determination module 404 to: and taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period.
In this embodiment of the present invention, the efficiency determining module 401 is further configured to: the historical wave data comprises the production quantity Sg of the workstations and the duration T' g of the workstations in the unbound order state in a set time period from the current moment to the previous moment; the production efficiency of the workstation is Sg/(Tg-T' g), and Tg represents the time length of a set time period from the current time to the previous time; and determining the average production efficiency of the current workstation according to the production efficiency of all workstations and the number of the workstations.
In this embodiment of the present invention, the efficiency determining module 401 is further configured to: the historical wave time data comprises the number Sj of lifting boxes of the lifting machine and the working time Tj of the lifting machine in a set time period from the current time to the previous time; the production efficiency of the hoister is Sj/Tj; and determining the average production efficiency of the current hoister according to the production efficiency of all hoisters and the number of hoisters.
In this embodiment of the present invention, the efficiency prediction module 402 is further configured to: according to the current time period, determining the average production efficiency of the workstations in the historical time period which is the same as the current time period and the average production efficiency of the workstations in the historical time period which is the same as the next time period of the current time; calculating the workstation average production efficiency q (t +1) of the next wave time period of the current wave by adopting a formula q (t +1) ═ (ql (t)/q' (t)) -ql (t + 1); wherein ql (t +1) represents the workstation average production efficiency of the same historical wave time period as the next wave time period of the current wave, ql (t) represents the workstation average production efficiency of the same historical wave time period as the time period, q' (t) represents the current workstation average production efficiency, t represents the time period of the current wave, and t +1 represents the next wave time period of the current wave.
In this embodiment of the present invention, the capacity quasi-forecasting module 403 is further configured to: taking the product of the average production efficiency of the workstations in the next wave time period, the duration of the next wave time and the number of the workstations as the production capacity of the workstations in the next wave time period; and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity of the hoister in the next wave time period.
In an embodiment of the present invention, the apparatus further includes a fault statistics module, configured to: determining the average time length Tx of each shuttle and/or elevator in a fault state according to historical wave number data; acquiring the number m of shuttle vehicles in a fault state at present and the number n of elevators in the fault state at present; and determining that the shuttle vehicle usage proportion p1 in the normal state is 1-m/the total number of the shuttle vehicles, and the elevator usage proportion p2 in the normal state is 1-n/the total number of the elevators.
The capacity quasi-prediction module 403 is further configured to: calculating the workstation throughput Wq for the next waveform time period using the formula Wq (t +1) Tx Ng p1 p2+ q (t +1) Tb Tx Ng; wherein Ng represents the number of workstations, and Tb represents the time length of the next wave; and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity Cq of the hoister in the time period of the next wave. Can also be expressed as: the workstation throughput Wq ═ q (t +1) × Tx workstation count ═ p1 × p2+ ql (t +1) × (duration-Tx of next wave) × workstation count for the next wave time period; and the elevator production capacity Cq of the next wave time period is the average production efficiency ct of the current elevator, the time length of the next wave time is the number of elevators and the average selected number of the bins.
In this embodiment of the present invention, the predicted value determining module 404 is further configured to: determining an actual value of the production capacity of the multi-layer shuttle shelf in the historical wave time period according to the historical wave data; acquiring a production capacity predicted value of the multilayer shuttle shelf in the historical wave time period, and determining the ratio of the production capacity predicted value of the multilayer shuttle shelf in the historical wave time period to the actual value of the production capacity of the multilayer shuttle shelf in the historical wave time period as the difference percentage of the production capacity of the multilayer shuttle shelf in the historical wave time period; according to the difference percentage, determining a quartile P in the difference percentage in all historical waves; the predicted multi-level shuttle shelf capacity value Q for the next wave time period P Min (Wq, Cq), where Min (Wq, Cq) represents the minimum of the workstation capacity Wq for the next wave time period and the elevator capacity Cq for the next wave time period.
In this embodiment of the present invention, the predicted value determining module 404 is further configured to: taking the product of the average production efficiency of the current workstation, the current time surplus time, the number of workstations, the usage proportion of the shuttle vehicles in the normal state and the usage proportion of the elevators in the normal state as the production capacity W of the workstation in the current time surplus time period0q; taking the product of the average production efficiency of the current hoister, the current time-of-wave remaining duration, the quantity of the hoists and the average selected number of the bins as the production capacity C of the hoister in the current time-of-wave remaining period0q; the W is0q and C0And q is the minimum value of the current time period, and the production capacity prediction value of the multilayer shuttle shelf is used as the minimum value of the q.
FIG. 5 illustrates an exemplary system architecture 500 for a method or apparatus for predicting multi-level shuttle shelf throughput to which embodiments of the present invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have various communication client applications installed thereon, such as a shopping application, a web browser application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server that provides various services, such as a background management server that supports shopping websites browsed by users using the terminal devices 501, 502, 503. The background management server can analyze and process the received data such as the product information inquiry request and feed back the processing result to the terminal equipment.
It should be noted that the method for predicting the productivity of the multi-level shuttle shelf provided by the embodiment of the present invention is generally executed by the server 505, and accordingly, the device for predicting the productivity of the multi-level shuttle shelf is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Embodiments of the invention also provide an electronic device and a computer-readable medium.
The electronic device of the embodiment of the invention comprises: one or more processors; a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the method for predicting multi-level shuttle rack production capacity provided by embodiments of the present invention.
A computer-readable medium of an embodiment of the invention has stored thereon a computer program which, when executed by a processor, implements the method of predicting production capacity of a multi-level shuttle rack provided by an embodiment of the invention.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present invention, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor, comprising: the device comprises an efficiency determining module, an efficiency predicting module, an energy production quasi-predicting module and a predicted value determining module. Where the names of these modules do not in some cases constitute a limitation of the module itself, for example, the efficiency determination module may also be described as a "module for determining the average production efficiency".
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: according to the historical wave number data, determining the average production efficiency of the work station in the historical wave number time period, the average production efficiency of the current work station and the average production efficiency of the current hoister; predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the workstation at the current time and the average production efficiency of the workstation in the historical wave time period; predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the current elevator average production efficiency, the next wave time duration, the workstation number and the elevator number; and taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period.
According to the method for predicting the production capacity of the multi-layer shuttle shelf, the historical production efficiency and the current production efficiency are comprehensively considered, so that the technical problem that the prediction precision of the production capacity is not high due to the fact that the method too depends on the high peak value of the picking capacity in the historical data in the prior art is solved, and the predicted production capacity of the multi-layer shuttle shelf is more accurate.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (16)

1. A method of predicting multi-level shuttle shelf productivity, comprising:
according to the historical wave number data, determining the average production efficiency of the work station in the historical wave number time period, the average production efficiency of the current work station and the average production efficiency of the current hoister;
predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the workstation at the current time and the average production efficiency of the workstation in the historical wave time period;
predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the current elevator average production efficiency, the next wave time duration, the workstation number and the elevator number;
taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period;
predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the workstation at the current time and the average production efficiency of the workstation in the historical wave time period, wherein the predicting comprises the following steps: according to the current time period, determining the average production efficiency of the workstations in the historical time period which is the same as the current time period and the average production efficiency of the workstations in the historical time period which is the same as the next time period of the current time; predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the current workstation, the average production efficiency of the workstation in the historical wave time period which is the same as the current wave time period and the average production efficiency of the workstation in the historical wave time period which is the same as the next wave time period of the current wave time;
predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the average production efficiency of the current elevator, the time length of the next wave time, the number of workstations and the number of elevators, and the method comprises the following steps: taking the product of the average production efficiency of the workstations, the duration of the next wave time and the number of the workstations as the production capacity of the workstations in the next wave time period; and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity of the hoister in the next wave time period.
2. The method of claim 1,
calculating the workstation average production efficiency q (t +1) of the next wave time period of the current wave by adopting a formula q (t +1) ═ (ql (t)/q' (t)) -ql (t + 1);
wherein ql (t +1) represents the workstation average production efficiency of the same historical wave time period as the next wave time period of the current wave, ql (t) represents the workstation average production efficiency of the same historical wave time period as the time period, q' (t) represents the current workstation average production efficiency, t represents the time period of the current wave, and t +1 represents the next wave time period of the current wave.
3. The method of claim 2, wherein after predicting an average production efficiency for a workstation for a next session of the current session based on the average production efficiency for the current workstation and the average production efficiency for the workstation for the historical sessions, the method further comprises:
determining the average time length Tx of the shuttle car and/or the elevator in a fault state every time according to historical wave data;
acquiring the number m of shuttle vehicles in a fault state at present and the number n of elevators in the fault state at present;
determining that the shuttle vehicle use ratio p1 in the normal state is 1-m/the total number of shuttle vehicles, and the elevator use ratio p2 in the normal state is 1-n/the total number of elevators;
predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the average production efficiency of the current elevator, the time length of the next wave time, the number of workstations and the number of elevators, and the method comprises the following steps:
calculating the workstation throughput Wq for the next waveform time period using the formula Wq (t +1) Tx Ng p1 p2+ q (t +1) Tb Tx Ng;
wherein Ng represents the number of workstations, and Tb represents the time length of the next wave;
and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity Cq of the hoister in the time period of the next wave.
4. The method of claim 3, wherein after taking the minimum of the workstation throughput and the elevator throughput for the next sub-time period as the predicted multi-level shuttle shelf throughput for the next sub-time period, the method further comprises:
taking the product of the average production efficiency of the current workstation, the current time surplus time, the number of workstations, the usage proportion of the shuttle vehicles in the normal state and the usage proportion of the elevators in the normal state as the production capacity W of the workstation in the current time surplus time period0q；
Taking the product of the average production efficiency of the current hoister, the current wave-time residual duration, the quantity of the hoists and the average selected piece quantity of the bins as the production capacity C of the hoister in the current wave-time residual period0q；
The W is0q and C0And q is the minimum value of the current time period, and the production capacity prediction value of the multilayer shuttle shelf is used as the minimum value of the q.
5. The method of claim 1, wherein using the minimum of the workstation throughput and the elevator throughput for the next recurring time period as the predicted multilayer shuttle shelf throughput for the next recurring time period comprises:
determining an actual value of the production capacity of the multi-layer shuttle shelf in the historical wave time period according to the historical wave data;
acquiring a production capacity predicted value of the multilayer shuttle shelf in the historical wave time period, and determining the ratio of the production capacity predicted value of the multilayer shuttle shelf in the historical wave time period to the actual value of the production capacity of the multilayer shuttle shelf in the historical wave time period as the difference percentage of the production capacity of the multilayer shuttle shelf in the historical wave time period;
according to the difference percentage, determining a quartile P in the difference percentage in all historical waves;
the predicted multi-level shuttle shelf capacity value Q for the next wave time period P Min (Wq, Cq), where Min (Wq, Cq) represents the minimum of the workstation capacity Wq for the next wave time period and the elevator capacity Cq for the next wave time period.
6. The method of claim 1, wherein determining the current workstation average production efficiency from the historical wave number data comprises:
the historical wave data comprises the production quantity Sg of the workstations and the duration T' g of the workstations in the unbound order state in a set time period from the current moment to the previous moment;
the production efficiency of the workstation is Sg/(Tg-T' g), and Tg represents the time length of a set time period from the current time to the previous time;
and determining the average production efficiency of the current workstation according to the production efficiency of all workstations and the number of the workstations.
7. The method of claim 1, wherein determining the average production efficiency of the current elevator based on the historical frequency data comprises:
the historical wave time data comprises the number Sj of lifting boxes of the lifting machine and the time length Tj of the lifting machine in the working state in a set time period from the current moment to the previous moment;
the production efficiency of the hoister is Sj/Tj;
and determining the average production efficiency of the current hoister according to the production efficiency of all hoisters and the number of hoisters.
8. An apparatus for predicting shuttle rack throughput, comprising:
an efficiency determination module to: according to the historical wave number data, determining the average production efficiency of the work station in the historical wave number time period, the average production efficiency of the current work station and the average production efficiency of the current hoister;
an efficiency prediction module to: predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the workstation at the current time and the average production efficiency of the workstation in the historical wave time period;
a capacity quasi-prediction module for: predicting the workstation production capacity and the elevator production capacity of the next wave time period based on the workstation average production efficiency of the next wave time period, the current elevator average production efficiency, the next wave time duration, the workstation number and the elevator number;
a predictor determination module to: taking the minimum value of the workstation production capacity and the elevator production capacity in the next time period as the predicted value of the multilayer shuttle shelf production capacity in the next time period;
wherein the efficiency prediction module is further configured to: according to the current time period, determining the average production efficiency of the workstations in the historical time period which is the same as the current time period and the average production efficiency of the workstations in the historical time period which is the same as the next time period of the current time; predicting the average production efficiency of the workstation in the next wave time period of the current wave time according to the average production efficiency of the current workstation, the average production efficiency of the workstation in the historical wave time period which is the same as the current wave time period and the average production efficiency of the workstation in the historical wave time period which is the same as the next wave time period of the current wave time;
the capacity quasi-prediction module is further used for: taking the product of the average production efficiency of the workstations, the duration of the next wave time and the number of the workstations as the production capacity of the workstations in the next wave time period; and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity of the hoister in the next wave time period.
9. The apparatus of claim 8, wherein the efficiency prediction module is further configured to:
calculating the workstation average production efficiency q (t +1) of the next wave time period of the current wave by adopting a formula q (t +1) ═ (ql (t)/q' (t)) -ql (t + 1);
wherein ql (t +1) represents the workstation average production efficiency of the same historical wave time period as the next wave time period of the current wave, ql (t) represents the workstation average production efficiency of the same historical wave time period as the time period, q' (t) represents the current workstation average production efficiency, t represents the time period of the current wave, and t +1 represents the next wave time period of the current wave.
10. The apparatus of claim 9, further comprising a fault statistics module to:
determining the average time length Tx of the shuttle car and/or the elevator in a fault state every time according to historical wave data;
acquiring the number m of shuttle vehicles in a fault state at present and the number n of elevators in the fault state at present;
determining that the shuttle vehicle use ratio p1 in the normal state is 1-m/the total number of shuttle vehicles, and the elevator use ratio p2 in the normal state is 1-n/the total number of elevators;
the capacity quasi-prediction module is further used for:
calculating the workstation throughput Wq for the next waveform time period using the formula Wq (t +1) Tx Ng p1 p2+ q (t +1) Tb Tx Ng;
wherein Ng represents the number of workstations, and Tb represents the time length of the next wave;
and taking the product of the average production efficiency of the current hoister, the duration of the next wave, the quantity of the hoists and the average selected number of the bins as the production capacity Cq of the hoister in the time period of the next wave.
11. The apparatus of claim 10, wherein the predictor determination module is further configured to:
taking the product of the average production efficiency of the current workstation, the current time surplus time, the number of workstations, the usage proportion of the shuttle vehicles in the normal state and the usage proportion of the elevators in the normal state as the production capacity W of the workstation in the current time surplus time period0q；
Leveling the current elevatorThe product of the average production efficiency, the current time remaining, the number of the elevators and the average selected number of the bins is used as the production capacity C of the elevator in the current time remaining period0q；
The W is0q and C0And q is the minimum value of the current time period, and the production capacity prediction value of the multilayer shuttle shelf is used as the minimum value of the q.
12. The apparatus of claim 8, wherein the predictor determination module is further configured to:
determining an actual value of the production capacity of the multi-layer shuttle shelf in the historical wave time period according to the historical wave data;
acquiring a production capacity predicted value of the multilayer shuttle shelf in the historical wave time period, and determining the ratio of the production capacity predicted value of the multilayer shuttle shelf in the historical wave time period to the actual value of the production capacity of the multilayer shuttle shelf in the historical wave time period as the difference percentage of the production capacity of the multilayer shuttle shelf in the historical wave time period;
according to the difference percentage, determining a quartile P in the difference percentage in all historical waves;
the predicted multi-level shuttle shelf capacity value Q for the next wave time period P Min (Wq, Cq), where Min (Wq, Cq) represents the minimum of the workstation capacity Wq for the next wave time period and the elevator capacity Cq for the next wave time period.
13. The apparatus of claim 8, wherein the efficiency determination module is further configured to:
the historical wave data comprises the production quantity Sg of the workstations and the duration T' g of the workstations in the unbound order state in a set time period from the current moment to the previous moment;
the production efficiency of the workstation is Sg/(Tg-T' g), and Tg represents the time length of a set time period from the current time to the previous time;
and determining the average production efficiency of the current workstation according to the production efficiency of all workstations and the number of the workstations.
14. The apparatus of claim 8, wherein the efficiency determination module is further configured to:
the historical wave time data comprises the number Sj of lifting boxes of the lifting machine and the working time Tj of the lifting machine in a set time period from the current time to the previous time;
the production efficiency of the hoister is Sj/Tj;
and determining the average production efficiency of the current hoister according to the production efficiency of all hoisters and the number of hoisters.
15. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
16. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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