CN115310895B - Warehouse replenishment method and system based on big data platform - Google Patents
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Abstract
The invention provides a warehouse replenishment method and a warehouse replenishment system based on a big data platform, wherein the method comprises the following steps: step 1: acquiring a region of a warehouse for supplying goods; step 2: acquiring local market feedback information of the goods from a preset big data platform corresponding to the area, and simultaneously acquiring the residual stock quantity of the goods in the warehouse; step 3: determining a replenishment strategy of the good based on the local market feedback information and the remaining stock quantity; step 4: and correspondingly restocking the goods based on the restocking strategy. According to the warehouse goods supplementing method and system based on the big data platform, the market feedback condition of goods is determined based on the big data platform, the comprehensiveness of the determination of the market feedback condition is improved, the goods supplementing quantity is determined based on the market feedback condition and the residual stock quantity, the manual determination of the goods supplementing quantity is not needed, the problem that limitation exists in the manual determination of the goods supplementing quantity is avoided, and the accuracy of the determination of the goods supplementing quantity is improved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a warehouse goods supplementing method and system based on a big data platform.
Background
At present, when a manufacturer supplies goods for an agent, a merchant or the like in a certain area, a warehouse special for storing goods is arranged in the area. The manufacturer determines for the market feedback of local goods of goods such as goods replenishment volume of warehouse needs manual work, however, the manual work determines that replenishment volume exists the limitation, probably leads to the replenishment volume to confirm inaccurately, leads to goods backlog or goods replenishment in the warehouse not in time to lead to out of stock scheduling problem to produce.
Thus, a solution is needed.
Disclosure of Invention
The invention provides a warehouse goods supplementing method and system based on a big data platform, which are used for determining market feedback conditions of goods based on the big data platform, improving the comprehensiveness of the determination of the market feedback conditions, determining the goods supplementing quantity based on the market feedback conditions and the residual stock quantity, avoiding the problem that the limitation exists in the manual determination of the goods supplementing quantity, improving the accuracy of the goods supplementing quantity determination, and avoiding the problems of stock of goods in a warehouse or out-of-stock caused by untimely goods supplementing and the like.
The invention provides a warehouse goods supplementing method based on a big data platform, which comprises the following steps:
step 1: acquiring a region of a warehouse for supplying goods;
Step 2: acquiring local market feedback information of the goods from a preset big data platform corresponding to the area, and simultaneously acquiring the residual stock quantity of the goods in the warehouse;
step 3: determining the replenishment quantity of the good based on the local market feedback information and the remaining inventory;
step 4: and correspondingly replenishing the goods based on the replenishing quantity.
Preferably, step 3: determining a restocking strategy based on the local market feedback information and the remaining inventory, comprising:
determining a model based on a preset replenishment quantity;
determining a model based on the replenishment quantity, and formulating the replenishment quantity of the goods according to the feedback information of the local market and the residual stock quantity;
the replenishment quantity determining model is a neural network model which is trained and converged based on a large number of determining records for manually determining the replenishment quantity.
Preferably, step 4: based on the replenishment quantity, carrying out corresponding replenishment to the goods, including:
acquiring a preset replenishment point corresponding to an article;
determining at least one target transporter suitable for transporting the restocking volume of the goods based on the restocking volume;
the dispatch target transport vehicle goes to the goods supplementing point to transport goods with the goods supplementing quantity to the warehouse;
when the target transport vehicle arrives at the warehouse, unloading and guiding the target transport vehicle;
And after the unloading guide is finished, the goods supplementing of the goods is finished.
Preferably, determining at least one target transporter suitable for transporting the restocking amount of the goods based on the restocking amount, comprises:
acquiring the single piece capacity of the goods;
determining the cargo capacity of the goods based on the replenishment quantity and the single-piece capacity;
formulating a first transporter screening condition based on cargo capacity;
constructing an outsourcing transportation garage, and screening first transportation vehicle targets meeting the screening conditions of the first transportation vehicles from the outsourcing transportation vehicle state garage, wherein the first transportation vehicle targets comprise at least one first transportation vehicle;
acquiring a current first position of a first transport vehicle, and simultaneously acquiring a second position of a replenishment point;
planning a first shortest route of the first transportation vehicle to the restocking point based on the first location and the second location;
if the first route distance of the first shortest route is larger than or equal to a preset first distance threshold value, eliminating the corresponding first transport vehicle target;
after all the first transport vehicle targets to be removed are removed, carrying out dynamic freight inquiry on the first transport vehicles contained in the remaining second transport vehicle targets in the first transport vehicle targets;
accumulating and calculating the sum of freight returned by the first transport vehicle contained in the second transport vehicle target after the preset first time;
Selecting a first transport vehicle contained in the minimum freight and a corresponding second transport vehicle target as a target transport vehicle suitable for transporting the goods with the replenishing quantity;
wherein, the first transporter screening conditions include:
the cargo capacity can be independently loaded and the loading is full or nearly full;
or alternatively, the first and second heat exchangers may be,
the cargo capacity can be loaded by division of work and the loading is full and/or nearly full of division of work.
Preferably, constructing an outsourced transportation garage includes:
acquiring a carrier vehicle screening condition set corresponding to a plurality of preset expert nodes;
acquiring transport vehicle information of a plurality of preset outsourced second transport vehicles;
determining, based on the transporter information, a degree of compliance of the second transporter to the transporter screening condition set;
acquiring a preset coincidence-score library corresponding to expert weights of expert nodes;
determining a score based on the conformity and the corresponding conformity-score library and associating with a corresponding second transporter;
accumulating and calculating the score sum of the scores associated with the second transport vehicles;
if the score sum is greater than or equal to a preset score sum threshold value, pairing the corresponding second transport vehicle and the corresponding transport vehicle information to obtain a pairing item;
integrating all the pairing items and warehousing to obtain an outsourcing transportation garage, so as to complete construction;
The coincidence degree is the ratio of the coincidence number of the second transport vehicle screening conditions in the transport vehicle information coincidence transport vehicle screening condition set to the total number of the second transport vehicle screening conditions in the transport vehicle screening condition set.
Preferably, when the target carrier arrives at the warehouse, the unloading guide is performed on the target carrier, including:
determining a guide trolley which is currently free and has the shortest second route distance to a second shortest route at an entrance of the warehouse based on a preset guide trolley distribution diagram;
controlling the guiding trolley to go to the entrance based on the second shortest route guiding the trolley to go to the entrance;
when the guiding trolley reaches the entrance, controlling the guiding trolley to collect an entrance image of the entrance;
determining a third position and window orientation of the cab of the target transporter based on the portal image;
constructing a first vector based on window orientation;
dynamically acquiring a fourth position and a display direction of a prompt screen of the guiding trolley;
constructing a second vector based on the display direction;
controlling the guiding trolley to carry out cruising adjustment in a first local range corresponding to the orientation of the window in a preset first range around the third position until the linear distance between the third position and the fourth position is smaller than or equal to a preset second distance threshold value and a first included angle between the first vector and the second vector is within the preset first included angle range, and controlling the guiding trolley to stop cruising adjustment;
The control prompt screen displays preset unloading guide prompt information;
controlling the guiding trolley to dynamically acquire a first cab image at a third position;
determining a first face orientation of a first face of at least one occupant within the cab based on the first cab image;
constructing a third vector based on the first face orientation;
if the duration that the second included angle between the third vector and the second vector falls within the preset second included angle range reaches the preset second time, acquiring a fifth position of the goods in a storage area in the warehouse;
acquiring a current sixth position of the guiding trolley;
planning a third shortest route for guiding the trolley to the storage area based on the fifth position and the sixth position;
controlling a guiding trolley to guide the target transport vehicle to the storage area based on the third shortest route;
when the guiding trolley reaches the storage area, acquiring a first occupied area of the target transport vehicle, and simultaneously acquiring a second occupied area of the unloading area in the storage area;
acquiring an image of a discharging area of the discharging area through image acquisition equipment corresponding to the discharging area in the storage area;
determining the complexity of temporary goods placement in the unloading area based on the unloading area image;
if the ratio of the first occupied area to the second occupied area is greater than or equal to a preset duty ratio threshold value and/or the complexity is greater than or equal to a preset complexity threshold value, controlling the guiding trolley to assist drivers and passengers to stop the target transport vehicle in the unloading area;
Otherwise, the guiding trolley is controlled to leave and wait for executing the next unloading guiding task.
Preferably, the control and guidance cart assists the driver in stopping the target transport cart into the unloading area, comprising:
determining whether the target transport vehicle is reversing in the unloading area based on the current unloading area image;
if yes, determining a current seventh position of a cab of the target transport vehicle based on the current unloading area image;
controlling the guiding trolley to go to the side of the seventh position;
when the guiding trolley reaches the side of the seventh position, controlling the guiding trolley to dynamically acquire a second cab image of the seventh position;
determining a second face orientation of a second face of the driver in the cab and a first mirror orientation of a left mirror and a second mirror orientation of a right mirror outside the cab based on the second cab image;
determining a first driver blind zone based on the second face orientation, the first mirror orientation, and the second mirror orientation;
determining the tail direction of the tail of the target transport vehicle and the left side direction and the right side direction of the vehicle body based on the current unloading area image;
taking a second local range corresponding to the direction of the vehicle tail in a second range preset at the periphery of the vehicle tail as a second blind area;
Determining area images corresponding to the first blind area and the second blind area based on the current discharge area image;
determining whether a scratch of the target transport vehicle is about to occur in the unloading area based on the area image;
if yes, reminding a driver of impending scratch through a prompting screen of the guiding trolley;
wherein determining the driver first blind zone based on the second face orientation, the first mirror orientation, and the second mirror orientation comprises:
constructing a fourth vector based on the second face orientation;
constructing a fifth vector based on the first specular orientation;
constructing a sixth vector based on the second specular orientation;
if a third included angle between the second surface orientation and the first mirror orientation falls within a preset third included angle range, taking a third local range corresponding to the right orientation in a third preset range on the right side of the body of the target transport vehicle as a first blind area;
and if the fourth included angle between the second surface orientation and the second mirror orientation falls within the third included angle range, taking a fourth local range corresponding to the left orientation in the third left range of the body of the target transport vehicle as the first blind area.
The invention provides a warehouse replenishment system based on a big data platform, which comprises:
The first acquisition module is used for acquiring the area of the warehouse for supplying goods;
the second acquisition module is used for acquiring local market feedback information of the goods from a preset big data platform corresponding to the area where the goods are located, and acquiring the residual stock quantity of the goods in the warehouse;
the determining module is used for determining the goods supplementing quantity of the goods based on the local market feedback information and the residual stock quantity;
and the goods supplementing module is used for correspondingly supplementing goods to the goods based on the goods supplementing quantity.
Preferably, the determining module performs the following operations:
determining a model based on a preset replenishment quantity;
determining a model based on the replenishment quantity, and formulating the replenishment quantity of the goods according to the feedback information of the local market and the residual stock quantity;
the replenishment quantity determining model is a neural network model which is trained and converged based on a large number of determining records for manually determining the replenishment quantity.
Preferably, the restocking module performs the following operations:
acquiring a preset replenishment point corresponding to an article;
determining at least one target transporter suitable for transporting the restocking volume of the goods based on the restocking volume;
the dispatch target transport vehicle goes to the goods supplementing point to transport goods with the goods supplementing quantity to the warehouse;
when the target transport vehicle arrives at the warehouse, unloading and guiding the target transport vehicle;
And after the unloading guide is finished, the goods supplementing of the goods is finished.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a warehouse restocking method based on a big data platform in an embodiment of the invention;
fig. 2 is a schematic diagram of a warehouse replenishment system based on a big data platform according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The invention provides a warehouse replenishment method based on a big data platform, which is shown in figure 1 and comprises the following steps:
step 1: acquiring a region of a warehouse for supplying goods;
step 2: acquiring local market feedback information of the goods from a preset big data platform corresponding to the area, and simultaneously acquiring the residual stock quantity of the goods in the warehouse;
step 3: determining the replenishment quantity of the good based on the local market feedback information and the remaining inventory;
step 4: and correspondingly replenishing the goods based on the replenishing quantity.
The working principle and the beneficial effects of the technical scheme are as follows:
the big data platform is responsible for collecting feedback information of the commodity market in the area where the warehouse is located; the local market feedback information may be, for example: goods evaluation, agent daily sales, merchant daily sales, etc. Determining a replenishment quantity of the good based on the local market feedback information and a remaining inventory quantity of the good in the warehouse; for example: the feedback of the goods market is excellent, the stock in the warehouse is insufficient, and the goods supplementing quantity is greatly increased. Based on the replenishment quantity, carrying out corresponding replenishment on the goods; for example: and notifying the manufacturer to send the goods with the corresponding goods quantity to the warehouse. Based on big data platform, confirm the market feedback condition of goods, promoted market feedback condition and confirmed the comprehensiveness, based on market feedback condition and remaining stock volume, confirm the replenishment volume, need not the manual work and confirm the replenishment volume, avoided the manual work to confirm the problem that the replenishment volume exists the limitation, promoted the accurate nature that the replenishment volume was confirmed, also avoided goods backlog or goods replenishment not in time to lead to out of stock scheduling problem to produce in the warehouse.
The invention provides a warehouse replenishment method based on a big data platform, which comprises the following steps of: determining a restocking strategy based on the local market feedback information and the remaining inventory, comprising:
determining a model based on a preset replenishment quantity;
determining a model based on the replenishment quantity, and formulating the replenishment quantity of the goods according to the feedback information of the local market and the residual stock quantity;
the replenishment quantity determining model is a neural network model which is trained and converged based on a large number of determining records for manually determining the replenishment quantity.
The working principle and the beneficial effects of the technical scheme are as follows:
and a goods replenishment quantity determining model is introduced, and the goods replenishment quantity is formulated according to the local market feedback information and the residual stock quantity based on the goods replenishment quantity determining model, so that the intellectualization and the accuracy of the goods replenishment quantity determination are improved.
The invention provides a warehouse replenishment method based on a big data platform, which comprises the following steps of: based on the replenishment quantity, carrying out corresponding replenishment to the goods, including:
acquiring a preset replenishment point corresponding to an article;
determining at least one target transporter suitable for transporting the restocking volume of the goods based on the restocking volume;
the dispatch target transport vehicle goes to the goods supplementing point to transport goods with the goods supplementing quantity to the warehouse;
When the target transport vehicle arrives at the warehouse, unloading and guiding the target transport vehicle;
and after the unloading guide is finished, the goods supplementing of the goods is finished.
The working principle and the beneficial effects of the technical scheme are as follows:
the preset replenishment point is the place of the manufacturer. Determining a target transport vehicle suitable for transporting the goods of the replenishment quantity based on the replenishment quantity; for example: the capacity of the target transport vehicle is larger than the cargo supplementing capacity. The dispatch target transportation is carried to the goods delivery point and the goods of the goods delivery quantity are transported to the warehouse. When the target transport vehicle arrives at the warehouse, it is guided for unloading. The manual arrangement of the transport vehicle and the unloading guide of the transport vehicle are not needed, and the labor cost is reduced.
The invention provides a warehouse replenishment method based on a big data platform, which determines at least one target transport vehicle suitable for transporting the replenishment amount of goods based on the replenishment amount, and comprises the following steps:
acquiring the single piece capacity of the goods;
determining the cargo capacity of the goods based on the replenishment quantity and the single-piece capacity;
formulating a first transporter screening condition based on cargo capacity;
constructing an outsourcing transportation garage, and screening first transportation vehicle targets meeting the screening conditions of the first transportation vehicles from the outsourcing transportation vehicle state garage, wherein the first transportation vehicle targets comprise at least one first transportation vehicle;
Acquiring a current first position of a first transport vehicle, and simultaneously acquiring a second position of a replenishment point;
planning a first shortest route of the first transportation vehicle to the restocking point based on the first location and the second location;
if the first route distance of the first shortest route is larger than or equal to a preset first distance threshold value, eliminating the corresponding first transport vehicle target;
after all the first transport vehicle targets to be removed are removed, carrying out dynamic freight inquiry on the first transport vehicles contained in the remaining second transport vehicle targets in the first transport vehicle targets;
accumulating and calculating the sum of freight returned by the first transport vehicle contained in the second transport vehicle target after the preset first time;
selecting a first transport vehicle contained in the minimum freight and a corresponding second transport vehicle target as a target transport vehicle suitable for transporting the goods with the replenishing quantity;
wherein, the first transporter screening conditions include:
the cargo capacity can be independently loaded and the loading is full or nearly full;
or alternatively, the first and second heat exchangers may be,
the cargo capacity can be loaded by division of work and the loading is full and/or nearly full of division of work.
The working principle and the beneficial effects of the technical scheme are as follows:
introducing an outsourcing transportation garage, wherein a large amount of vehicle information of logistics transportation vehicles and the like are stored in the outsourcing transportation garage; the vehicle information may be, for example: capacity, etc. Firstly, the first route distance of the first shortest route of the transport vehicle from the manufacturer is required to be ensured not to be larger, the timeliness of transporting goods by the transport vehicle is improved, and the transport cost is also reduced. In addition, the freight rate price is required to be carried out, the transportation cost is further reduced, when the freight rate price is carried out, the dynamic freight rate inquiry is carried out, and when the transport vehicle returns to transport, the freight rate price is carried out; dynamic freight inquiry is, for example: and sending the position of the replenishment point and the position of the warehouse to a driver of the transport vehicle at regular time, and waiting for the reply of the driver. In addition, a first carrier vehicle screening condition is set, when one carrier vehicle can carry goods, the carrier vehicle is required to be fully loaded or nearly fully loaded, the carrier vehicle utilization rate is ensured, and when a plurality of carrier vehicles carry goods separately, each carrier vehicle is required to be fully loaded and/or nearly fully loaded, and the carrier vehicle utilization rate is also ensured; Near full load may be, for example: the difference between the stacking amount of the goods on the transport vehicle and the containing amount of the transport vehicle is smaller than a certain value. In addition, the formula for cumulatively calculating the freight sum of freight is:
for sum of freight, L i For the ith freight, n is the total number of freight.
The invention provides a warehouse replenishment method based on a big data platform, which constructs an outsourcing transportation garage and comprises the following steps:
acquiring a carrier vehicle screening condition set corresponding to a plurality of preset expert nodes;
acquiring transport vehicle information of a plurality of preset outsourced second transport vehicles;
determining, based on the transporter information, a degree of compliance of the second transporter to the transporter screening condition set;
acquiring a preset coincidence-score library corresponding to expert weights of expert nodes;
determining a score based on the conformity and the corresponding conformity-score library and associating with a corresponding second transporter;
accumulating and calculating the score sum of the scores associated with the second transport vehicles;
if the score sum is greater than or equal to a preset score sum threshold value, pairing the corresponding second transport vehicle and the corresponding transport vehicle information to obtain a pairing item;
integrating all the pairing items and warehousing to obtain an outsourcing transportation garage, so as to complete construction;
the coincidence degree is the ratio of the coincidence number of the second transport vehicle screening conditions in the transport vehicle information coincidence transport vehicle screening condition set to the total number of the second transport vehicle screening conditions in the transport vehicle screening condition set.
The working principle and the beneficial effects of the technical scheme are as follows:
expert nodes are introduced, and the expert nodes are staff with abundant experience on transport vehicle schedulingExpert nodes correspond to a carrier selection condition set in which there are a plurality of carrier selection conditions, such as: the driving age of the driver is more than or equal to 4.5 years, the service time of the vehicle is less than or equal to 12 years, and the like. Acquiring transport vehicle information of a second transport vehicle of a plurality of preset outsources, for example: driver driving age and vehicle in-service duration, etc. Determining, based on the transporter information, a degree of compliance of the second transporter with the transporter screening condition set, the compliance being, for example: the number of the carrier vehicle screening conditions in the carrier vehicle screening condition set is 4, and the total number of the carrier vehicle screening conditions in the carrier vehicle screening condition set is 5, the coincidence degree is 4/5=0.8. The greater the expert weight of the expert node, the more abundant the experience of the staff in scheduling the transport vehicle is explained. And introducing a coincidence-score library corresponding to the expert weight, wherein the higher the expert weight is, the same coincidence degree has higher score. And determining a score corresponding to the coincidence degree based on the coincidence degree-score library, accumulating the score sum, and if the score sum is greater than or equal to a preset score sum threshold value, indicating that the corresponding second transport vehicle meets the condition, and pairing with the transport vehicle information for warehousing. The second transport vehicle can be put in storage through screening, so that the construction quality of the outsourcing transport garage is improved, and the transport efficiency of commodity logistics transport is indirectly improved. In addition, the formula of the score sum of the accumulated scores is: 
Gamma is the sum of the scores, alpha J The J-th score associated with the second transporter, D the total number of scores associated with the second transporter.
The invention provides a warehouse goods supplementing method based on a big data platform, which is used for unloading and guiding a target transport vehicle when the target transport vehicle arrives at a warehouse, and comprises the following steps:
determining a guide trolley which is currently free and has the shortest second route distance to a second shortest route at an entrance of the warehouse based on a preset guide trolley distribution diagram;
controlling the guiding trolley to go to the entrance based on the second shortest route guiding the trolley to go to the entrance;
when the guiding trolley reaches the entrance, controlling the guiding trolley to collect an entrance image of the entrance;
determining a third position and window orientation of the cab of the target transporter based on the portal image;
constructing a first vector based on window orientation;
dynamically acquiring a fourth position and a display direction of a prompt screen of the guiding trolley;
constructing a second vector based on the display direction;
controlling the guiding trolley to carry out cruising adjustment in a first local range corresponding to the orientation of the window in a preset first range around the third position until the linear distance between the third position and the fourth position is smaller than or equal to a preset second distance threshold value and a first included angle between the first vector and the second vector is within the preset first included angle range, and controlling the guiding trolley to stop cruising adjustment;
The control prompt screen displays preset unloading guide prompt information;
controlling the guiding trolley to dynamically acquire a first cab image at a third position;
determining a first face orientation of a first face of at least one occupant within the cab based on the first cab image;
constructing a third vector based on the first face orientation;
if the duration that the second included angle between the third vector and the second vector falls within the preset second included angle range reaches the preset second time, acquiring a fifth position of the goods in a storage area in the warehouse;
acquiring a current sixth position of the guiding trolley;
planning a third shortest route for guiding the trolley to the storage area based on the fifth position and the sixth position;
controlling a guiding trolley to guide the target transport vehicle to the storage area based on the third shortest route;
when the guiding trolley reaches the storage area, acquiring a first occupied area of the target transport vehicle, and simultaneously acquiring a second occupied area of the unloading area in the storage area;
acquiring an image of a discharging area of the discharging area through image acquisition equipment corresponding to the discharging area in the storage area;
determining the complexity of temporary goods placement in the unloading area based on the unloading area image;
if the ratio of the first occupied area to the second occupied area is greater than or equal to a preset duty ratio threshold value and/or the complexity is greater than or equal to a preset complexity threshold value, controlling the guiding trolley to assist drivers and passengers to stop the target transport vehicle in the unloading area;
Otherwise, the guiding trolley is controlled to leave and wait for executing the next unloading guiding task.
The working principle and the beneficial effects of the technical scheme are as follows:
a preset guiding trolley distribution diagram is introduced, and the real-time position of each guiding trolley in the warehouse is marked on the guiding trolley distribution diagram. When the target transport vehicle arrives at the warehouse, the target transport vehicle waits for guiding and unloading at the warehouse entrance, and the guiding trolley with the shortest second route distance of the second shortest route to the entrance is scheduled to go to the entrance, so that the guiding trolley scheduling rationality is improved.
When the lead trolley arrives at the entrance, the driver of the target transport vehicle needs to be prompted to follow the lead trolley to the unloading area. Acquiring an entrance image by the guiding trolley, and determining a third position and a window orientation of the cab based on the entrance image; the window orientation is the orientation of the respective windshields on the cab. Based on the window orientation, a first vector is constructed, the spatial direction of which coincides with the window orientation. Acquiring a fourth position and a display direction of a prompt screen of the guiding trolley; the display direction is the direction perpendicular to the prompt screen. Based on the display direction, a second vector is constructed, the spatial direction of which coincides with the display direction. Controlling the guiding trolley to carry out cruising adjustment in a first local range corresponding to the orientation of the window in a first range preset around the third position; the preset first range is, for example: within 4.5 meters, the first partial range is the range in the first range near each windshield on the cab, and the cruising adjustment is used for controlling the guiding trolley to randomly move forward and forward directions. When the linear distance between the third position and the fourth position is smaller than a preset second distance threshold value and a first included angle between the first vector and the second vector is within a preset first included angle range, indicating that a driver in the cab is visible to the prompt screen; the preset second distance threshold may be, for example: 2.8 meters, and the preset first included angle range can be 90-180 degrees. At this time, the control prompt screen displays preset unloading guiding prompt information, for example: "please follow me to the unloading zone-! ".
In addition, after the prompt is completed, it is necessary to confirm whether the driver has checked the discharge guide prompt information. Similarly, a third vector is constructed based on the first face orientation of the first face of the driver, and when the duration that the second included angle between the third vector and the second vector falls within the preset second included angle range reaches the preset second time, the driver is informed that the unloading guide prompt information is checked; the preset second included angle may be in the range of 70 to 180 °, and the preset second time may be 5 seconds. Make the driver and passenger know to follow the guide dolly, realize with the butt joint of driver and passenger, reduce the human cost of artifical butt joint, simultaneously, also more intelligent.
Then, the guiding trolley is controlled to guide the target transport vehicle to the storage area based on the three shortest routes.
When the target transport vehicle arrives at the storage area, parking assistance is needed due to the fact that the vehicles of the target transport vehicle are large and/or the complexity of temporary goods placement in the target transport vehicle is high, humanization is improved, goods loss caused by scratch during parking is avoided, otherwise, the guiding trolley is controlled to leave, flexible control is achieved according to actual needs, and utilization of parking auxiliary resources is reduced; in general, when goods are unloaded, the goods need to be temporarily placed in a parking area of a vehicle, waiting for manual handling or stacking, and the like, and complex determination can be realized based on an image recognition technology.
The invention provides a warehouse replenishment method based on a big data platform, which controls and guides a trolley to assist drivers to stop a target transport vehicle into a unloading area, and comprises the following steps:
determining whether the target transport vehicle is reversing in the unloading area based on the current unloading area image;
if yes, determining a current seventh position of a cab of the target transport vehicle based on the current unloading area image;
controlling the guiding trolley to go to the side of the seventh position;
when the guiding trolley reaches the side of the seventh position, controlling the guiding trolley to dynamically acquire a second cab image of the seventh position;
determining a second face orientation of a second face of the driver in the cab and a first mirror orientation of a left mirror and a second mirror orientation of a right mirror outside the cab based on the second cab image;
determining a first driver blind zone based on the second face orientation, the first mirror orientation, and the second mirror orientation;
determining the tail direction of the tail of the target transport vehicle and the left side direction and the right side direction of the vehicle body based on the current unloading area image;
taking a second local range corresponding to the direction of the vehicle tail in a second range preset at the periphery of the vehicle tail as a second blind area;
Determining area images corresponding to the first blind area and the second blind area based on the current discharge area image;
determining whether a scratch of the target transport vehicle is about to occur in the unloading area based on the area image;
if yes, reminding a driver of impending scratch through a prompting screen of the guiding trolley;
wherein determining the driver first blind zone based on the second face orientation, the first mirror orientation, and the second mirror orientation comprises:
constructing a fourth vector based on the second face orientation;
constructing a fifth vector based on the first specular orientation;
constructing a sixth vector based on the second specular orientation;
if a third included angle between the second surface orientation and the first mirror orientation falls within a preset third included angle range, taking a third local range corresponding to the right orientation in a third preset range on the right side of the body of the target transport vehicle as a first blind area;
and if the fourth included angle between the second surface orientation and the second mirror orientation falls within the third included angle range, taking a fourth local range corresponding to the left orientation in the third left range of the body of the target transport vehicle as the first blind area.
The working principle and the beneficial effects of the technical scheme are as follows:
When auxiliary parking is carried out on the target transport vehicle, firstly, determining whether the target transport vehicle is in reverse or not based on the unloading area image; for example: and determining the moving direction of the headstock based on the continuous multi-frame images, if the moving direction is forward, reversing the headstock, otherwise reversing the headstock. If the car is backing, controlling the guiding trolley to go to the side of the cab, and waiting for reminding the driver in the cab to scratch. Similarly, a fourth vector, a fifth vector, and a sixth vector are constructed based on the second face orientation, the first mirror orientation, and the second mirror orientation, respectively, to determine a first driver blind zone, such as: when the driver looks at the right rearview mirror, the left side of the vehicle body is the blind area, the first blind area is flexibly determined, and the comprehensiveness and humanization of parking assistance are improved. Meanwhile, the tail part of the truck is a blind area of a driver and is used as a second blind area. Determining whether a scratch of the target transporter is imminent based on the area images in the unloading area image corresponding to the first blind area and the second blind area, for example: the vehicle is about to hit an article or the like at a certain position. If yes, the prompt screen of the guiding trolley is controlled to remind the driver of the impending scratch, and parking assistance is achieved. In addition, the preset third included angle may be in the range of 100 to 180 °.
The invention provides a warehouse replenishment system based on a big data platform, as shown in fig. 2, comprising:
a first acquiring module 1 for acquiring a location area of a warehouse for supplying goods;
the second acquisition module 2 is used for acquiring local market feedback information of the goods from a preset big data platform corresponding to the area where the goods are located, and acquiring the residual stock quantity of the goods in the warehouse;
a determining module 3, configured to determine a replenishment quantity of the good based on the local market feedback information and the remaining inventory quantity;
and the replenishment module 4 is used for correspondingly replenishing the goods based on the replenishment quantity.
The invention provides a warehouse replenishment system based on a big data platform, wherein a determination module 3 executes the following operations:
determining a model based on a preset replenishment quantity;
determining a model based on the replenishment quantity, and formulating the replenishment quantity of the goods according to the feedback information of the local market and the residual stock quantity;
the replenishment quantity determining model is a neural network model which is trained and converged based on a large number of determining records for manually determining the replenishment quantity.
The invention provides a warehouse replenishment system based on a big data platform, wherein a replenishment module 4 performs the following operations:
acquiring a preset replenishment point corresponding to an article;
Determining at least one target transporter suitable for transporting the restocking volume of the goods based on the restocking volume;
the dispatch target transport vehicle goes to the goods supplementing point to transport goods with the goods supplementing quantity to the warehouse;
when the target transport vehicle arrives at the warehouse, unloading and guiding the target transport vehicle;
and after the unloading guide is finished, the goods supplementing of the goods is finished.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. The warehouse replenishment method based on the big data platform is characterized by comprising the following steps:
step 1: acquiring a region of a warehouse for supplying goods;
step 2: acquiring local market feedback information of the goods from a preset big data platform corresponding to the area, and simultaneously acquiring the residual stock quantity of the goods in the warehouse;
step 3: determining a restocking quantity of the good based on the local market feedback information and the remaining inventory quantity;
step 4: based on the replenishment quantity, carrying out corresponding replenishment on the goods;
The step 4: based on the replenishment quantity, performing corresponding replenishment of the article, including:
acquiring a preset replenishment point corresponding to the goods;
determining at least one target transporter for transporting the good of the replenishment quantity based on the replenishment quantity;
dispatching the target transporter to transport the goods of the replenishment quantity to the warehouse to the replenishment point;
when the target transport vehicle arrives at the warehouse, unloading and guiding the target transport vehicle;
after the unloading guide is finished, finishing the replenishment of the goods;
when the target transport vehicle arrives at the warehouse, unloading and guiding the target transport vehicle, including:
determining a guide trolley which is currently free and has the shortest second route distance to a second shortest route at an entrance of the warehouse based on a preset guide trolley distribution diagram;
controlling the guiding trolley to go to the entrance based on a second shortest route of the guiding trolley to go to the entrance;
when the guiding trolley reaches the entrance, controlling the guiding trolley to collect an entrance image of the entrance;
determining a third position and window orientation of a cab of the target transporter based on the portal image;
Constructing a first vector based on the window orientation;
dynamically acquiring a fourth position and a display direction of a prompt screen of the guide trolley;
constructing a second vector based on the display direction;
controlling the guiding trolley to carry out cruising adjustment in a first local range which is preset around the third position and corresponds to the direction of the window until the linear distance between the third position and the fourth position is smaller than or equal to a preset second distance threshold value and the first included angle between the first vector and the second vector is within a preset first included angle range, and controlling the guiding trolley to stop cruising adjustment;
controlling the prompting screen to display preset unloading guiding prompting information;
controlling the guiding trolley to dynamically acquire a first cab image of the third position;
determining a first face orientation of a first face of at least one occupant within the cab based on the first cab image;
constructing a third vector based on the first face orientation;
if the duration that the second included angle between the third vector and the second vector falls within the preset second included angle range reaches the preset second time, acquiring a fifth position of the goods in a storage area in the warehouse;
Acquiring a current sixth position of the guide trolley;
planning a third shortest route of the lead trolley to the storage area based on the fifth location and the sixth location;
controlling the guiding trolley to guide the target transport vehicle to the storage area based on the third shortest route;
when the guiding trolley reaches the storage area, acquiring a first occupied area of the target transport trolley, and simultaneously acquiring a second occupied area of a discharging area in the storage area;
acquiring an image of a cargo unloading area of the cargo unloading area through an image acquisition device corresponding to the cargo unloading area in the storage area;
determining the complexity of temporary goods placement in the unloading area based on the unloading area image;
if the ratio of the first occupied area to the second occupied area is greater than or equal to a preset duty ratio threshold value and/or the complexity is greater than or equal to a preset complexity threshold value, controlling the guiding trolley to assist the driver and passengers to stop the target transport vehicle in the unloading area;
otherwise, the guiding trolley is controlled to leave and wait for executing the next unloading guiding task.
2. A warehouse restocking method based on big data platform as claimed in claim 1, wherein the step 3: determining a restocking strategy based on the local market feedback information and the remaining inventory, comprising:
Determining a model based on a preset replenishment quantity;
based on the replenishment quantity determining model, formulating the replenishment quantity of the goods according to the local market feedback information and the residual inventory;
the replenishment quantity determining model is a neural network model which is trained and converged based on a large number of determining records for manually determining the replenishment quantity.
3. A warehouse restocking method based on a big data platform as claimed in claim 1, wherein the determining at least one target transporter for transporting the item of the restocking volume based on the restocking volume comprises:
acquiring a single piece capacity of the item;
determining a cargo capacity of the item based on the replenishment quantity and the single piece capacity;
formulating a first transporter screening condition based on the cargo capacity;
constructing an outsourcing transportation garage, and screening a first transportation vehicle target meeting the screening conditions of the first transportation vehicle from the outsourcing transportation vehicle state garage, wherein the first transportation vehicle target comprises at least one first transportation vehicle;
acquiring a current first position of the first transport vehicle, and simultaneously acquiring a second position of the replenishment point;
planning a first shortest route for the first transportation vehicle to the restocking point based on the first location and the second location;
If the first route distance of the first shortest route is larger than or equal to a preset first distance threshold value, eliminating the corresponding first transport vehicle target;
after all the first transport vehicle targets to be removed are removed, carrying out dynamic freight inquiry on the first transport vehicles contained in the remaining second transport vehicle targets in the first transport vehicle targets;
accumulating and calculating the sum of freight returned by the first transport vehicle and contained by the second transport vehicle target after the preset first time;
selecting the first transport vehicle contained in the minimum transport cost and the corresponding second transport vehicle target as a target transport vehicle suitable for transporting the goods with the replenishment quantity;
wherein, the first transporter screening conditions include:
the cargo capacity can be loaded alone and is full or nearly full when loaded;
or alternatively, the first and second heat exchangers may be,
the cargo capacity can be loaded by division of work and the loading is at division of work full load and/or near full load.
4. A warehouse restocking method based on a big data platform as claimed in claim 3, wherein the constructing the outsourced transportation garage comprises:
acquiring a carrier vehicle screening condition set corresponding to a plurality of preset expert nodes;
Acquiring transport vehicle information of a plurality of preset outsourced second transport vehicles;
determining, based on the transporter information, a compliance of the second transporter with the transporter screening condition set;
acquiring a preset coincidence-score library corresponding to expert weights of the expert nodes;
determining a score based on the compliance and the corresponding compliance-score library and associating with the corresponding second transporter;
cumulatively calculating a sum of the scores associated with the second transporter;
if the score sum is greater than or equal to a preset score sum threshold, pairing the corresponding second transport vehicle and the corresponding transport vehicle information to obtain a pairing item;
integrating the pairing items and warehousing to obtain an outsourced transportation garage, so as to complete construction;
the coincidence degree is the ratio of the coincidence number of the second transport vehicle screening conditions in the transport vehicle screening condition set to the total number of the second transport vehicle screening conditions in the transport vehicle screening condition set.
5. A warehouse restocking method based on a big data platform as claimed in claim 1, wherein the controlling the lead cart to assist the rider in parking the target transporter into the unloading zone includes:
Determining whether the target transport vehicle is backing in the unloading area based on the current unloading area image;
if yes, determining a current seventh position of the cab of the target transport vehicle based on the current unloading area image;
controlling the guiding trolley to go beside the seventh position;
when the guiding trolley reaches the side of the seventh position, controlling the guiding trolley to dynamically acquire a second cab image of the seventh position;
determining a second face orientation of a second face of the driver in the cab and a first mirror orientation of the left mirror and a second mirror orientation of the right mirror outside the cab based on the second cab image;
determining the driver first blind zone based on the second face orientation, a first mirror orientation, and the second mirror orientation;
determining the tail direction of the tail of the target transport vehicle and the left side direction and the right side direction of the vehicle body based on the current unloading area image;
taking a second local range corresponding to the tail direction in a second range preset by the periphery of the tail as a second blind area;
determining area images corresponding to the first blind area and the second blind area based on the current discharge area image;
Determining whether a scratch of the target transport vehicle is about to occur in the unloading area based on the area image;
if yes, reminding the driver of the impending scratch through a prompt screen of the guiding trolley;
wherein determining the driver first blind zone based on the second face orientation, first mirror orientation, and the second mirror orientation comprises:
constructing a fourth vector based on the second face orientation;
constructing a fifth vector based on the first mirror orientation;
constructing a sixth vector based on the second mirror orientation;
if a third included angle between the second surface orientation and the first mirror orientation is within a preset third included angle range, taking a third local range corresponding to the right orientation in a third preset range on the right side of the body of the target transport vehicle as a first blind area region;
and if the third included angle between the second surface orientation and the second mirror orientation is within the third included angle range, taking a fourth local range corresponding to the left orientation in the third range on the left side of the body of the target transport vehicle as a first blind area.
6. Warehouse restocking system based on big data platform, characterized by comprising:
The first acquisition module is used for acquiring the area of the warehouse for supplying goods;
the second acquisition module is used for acquiring local market feedback information of the goods from a preset big data platform corresponding to the area where the goods are located, and acquiring the residual inventory of the goods in the warehouse;
the determining module is used for determining the replenishment quantity of the goods based on the local market feedback information and the residual stock quantity;
the replenishment module is used for correspondingly replenishing the goods based on the replenishment quantity;
the restocking module performs the following operations:
acquiring a preset replenishment point corresponding to the goods;
determining at least one target transporter for transporting the good of the replenishment quantity based on the replenishment quantity;
dispatching the target transporter to transport the goods of the replenishment quantity to the warehouse to the replenishment point;
when the target transport vehicle arrives at the warehouse, unloading and guiding the target transport vehicle;
after the unloading guide is finished, finishing the replenishment of the goods;
and the replenishment module is used for guiding the unloading of the target transport vehicle when the target transport vehicle arrives at the warehouse, and comprises the following components:
Determining a guide trolley which is currently free and has the shortest second route distance to a second shortest route at an entrance of the warehouse based on a preset guide trolley distribution diagram;
controlling the guiding trolley to go to the entrance based on a second shortest route of the guiding trolley to go to the entrance;
when the guiding trolley reaches the entrance, controlling the guiding trolley to collect an entrance image of the entrance;
determining a third position and window orientation of a cab of the target transporter based on the portal image;
constructing a first vector based on the window orientation;
dynamically acquiring a fourth position and a display direction of a prompt screen of the guide trolley;
constructing a second vector based on the display direction;
controlling the guiding trolley to carry out cruising adjustment in a first local range which is preset around the third position and corresponds to the direction of the window until the linear distance between the third position and the fourth position is smaller than or equal to a preset second distance threshold value and the first included angle between the first vector and the second vector is within a preset first included angle range, and controlling the guiding trolley to stop cruising adjustment;
Controlling the prompting screen to display preset unloading guiding prompting information;
controlling the guiding trolley to dynamically acquire a first cab image of the third position;
determining a first face orientation of a first face of at least one occupant within the cab based on the first cab image;
constructing a third vector based on the first face orientation;
if the duration that the second included angle between the third vector and the second vector falls within the preset second included angle range reaches the preset second time, acquiring a fifth position of the goods in a storage area in the warehouse;
acquiring a current sixth position of the guide trolley;
planning a third shortest route of the lead trolley to the storage area based on the fifth location and the sixth location;
controlling the guiding trolley to guide the target transport vehicle to the storage area based on the third shortest route;
when the guiding trolley reaches the storage area, acquiring a first occupied area of the target transport trolley, and simultaneously acquiring a second occupied area of a discharging area in the storage area;
acquiring an image of a cargo unloading area of the cargo unloading area through an image acquisition device corresponding to the cargo unloading area in the storage area;
Determining the complexity of temporary goods placement in the unloading area based on the unloading area image;
if the ratio of the first occupied area to the second occupied area is greater than or equal to a preset duty ratio threshold value and/or the complexity is greater than or equal to a preset complexity threshold value, controlling the guiding trolley to assist the driver and passengers to stop the target transport vehicle in the unloading area;
otherwise, the guiding trolley is controlled to leave and wait for executing the next unloading guiding task.
7. A warehouse restocking system as claimed in claim 6, wherein the determination module performs the following operations:
determining a model based on a preset replenishment quantity;
based on the replenishment quantity determining model, formulating the replenishment quantity of the goods according to the local market feedback information and the residual inventory;
the replenishment quantity determining model is a neural network model which is trained and converged based on a large number of determining records for manually determining the replenishment quantity.
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