CN112215518B

CN112215518B - Cloud computing-combined cosmetic production chain scheduling method and artificial intelligence cloud platform

Info

Publication number: CN112215518B
Application number: CN202011150594.6A
Authority: CN
Inventors: 陈龙龙
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-10-24
Filing date: 2020-10-24
Publication date: 2022-01-11
Anticipated expiration: 2040-10-24
Also published as: CN112215518A; CN113034038A; CN113034039A

Abstract

The invention relates to a cosmetic production chain scheduling method combined with cloud computing and an artificial intelligence cloud platform. Firstly, the first device collaborative operation record is analyzed, so that a device scheduling sequence list corresponding to the first device scheduling path data is determined. And secondly, determining the dynamic production line characteristics of the first automatic production equipment according to the equipment scheduling sequence list, and further determining the target dynamic mapping characteristics. And finally, generating a production chain scheduling strategy based on the product list record and the target dynamic mapping characteristics. In this way, the production state of the first automated production equipment can be adjusted based on the production chain scheduling policy when the production chain scheduling signal triggered by the first automated production equipment is detected. Therefore, compatibility consideration of a plurality of production chains can be realized based on a production chain scheduling strategy, so that flexible switching of the first automatic production equipment between different production chains and different types of cosmetic production can be realized quickly and accurately, and efficient operation of the whole cosmetic production factory is ensured.

Description

Cloud computing-combined cosmetic production chain scheduling method and artificial intelligence cloud platform

Technical Field

The application relates to the technical field of cloud computing, artificial intelligence and cosmetic production, in particular to a cosmetic production chain scheduling method and an artificial intelligence cloud platform which are combined with cloud computing.

Background

With the development of society, the daily living standard of people is gradually improved, and the consumption upgrade becomes the mainstream, thereby promoting the rapid and vigorous development of the cosmetic industry. Nowadays, the development of internet technology and online electronic commerce makes the sale of cosmetics not limited by time and place. The explosion of cosmetic demands and the innovation of sales channels and sales modes provide new demands for the productivity of cosmetics. In the practical application process, how to improve the productivity of cosmetics based on an established factory is a technical problem to be solved urgently at the present stage.

Disclosure of Invention

The first aspect of the application discloses a cosmetic production chain scheduling method combined with cloud computing, which is applied to an artificial intelligence cloud platform, and comprises the following steps:

acquiring a first equipment cooperative operation record of first automatic production equipment in a first cosmetic production chain;

acquiring first equipment scheduling path data between the first equipment collaborative operation record and a product list record of cosmetic production in a cosmetic production factory; acquiring an equipment scheduling sequence list corresponding to the first equipment scheduling path data according to the switching time consumption of a preset production line;

performing feature extraction on the current production line configuration data of the first automatic production equipment according to an equipment scheduling sequence list corresponding to the first equipment scheduling path data to obtain the production line dynamic features of the first automatic production equipment;

mapping the production line dynamic features to product list records of the cosmetics production to obtain first dynamic mapping features, and taking the first dynamic mapping features as target dynamic mapping features;

and generating a production chain scheduling strategy of the first automatic production equipment according to the product list record of the cosmetic production and the target dynamic mapping characteristics, performing associated storage, and adjusting the production state of the first automatic production equipment based on the production chain scheduling strategy when detecting that the first automatic production equipment triggers a production chain scheduling signal.

Optionally, mapping the dynamic production line feature to a product list record of the cosmetic production, and obtaining a first dynamic mapping feature includes:

acquiring a mapping path label configured for the first automatic production equipment, wherein the mapping path label is used for indicating a feature distribution format and a feature record format of the production line dynamic feature mapping on a product list record of the cosmetic production;

and mapping the dynamic characteristic of the production line to a product list record of the cosmetic production according to the characteristic distribution format and the characteristic record format indicated by the mapping path label to obtain the first dynamic mapping characteristic.

Optionally, before obtaining the first device co-operation record of the first automated manufacturing device in the first cosmetic manufacturing chain, the method further comprises:

configuring a mapping path label for each of the first automated manufacturing equipment present within the first cosmetic manufacturing chain; the first automated production equipment is any one of a plurality of preset automated production equipment, the feature recording format indicated by the mapping path label is any one of a plurality of preset feature recording formats, and the feature distribution format indicated by the mapping path label is any one of a plurality of preset feature distribution formats.

Optionally, the obtaining a first device co-operation record of the first automated manufacturing device in the first cosmetic manufacturing chain comprises:

when the first automatic production equipment exists in a second cosmetic production chain in a previous production line period of a current production line period, acquiring a second equipment cooperative operation record of the first automatic production equipment in the second cosmetic production chain, and determining the first equipment cooperative operation record according to the second equipment cooperative operation record and equipment operation loss data of the first automatic production equipment; the first automatic production equipment exists in the first cosmetic production chain in the current production line period, and the second cosmetic production chain is a cosmetic production chain in the multiple cosmetic production chains, wherein the first automatic production equipment exists in the previous production line period of the current production line period;

when the first automatic production equipment does not exist in the second cosmetic production chain in the previous production line period of the current production line period, acquiring a first equipment cooperative operation record configured for the first automatic production equipment in the first cosmetic production chain; the first cosmetic production chain comprises second automatic production equipment controlled by an artificial intelligence cloud platform, and production records corresponding to the second automatic production equipment are product list records of cosmetic production.

Optionally, the generating and storing a production chain scheduling policy of the first automated production equipment according to the product list record of the cosmetic production and the target dynamic mapping feature includes:

obtaining a product category label in the product list record, and determining a first category label list corresponding to the product category label; acquiring an equipment state updating track corresponding to a target dynamic mapping feature, and calculating the product production matching degree between the product category label and the equipment state updating track according to the first category label list;

if the product production matching degree between the product type label and the equipment state updating track is smaller than a preset product production matching degree threshold value, matching current equipment operation data corresponding to the first automatic production equipment with the first type label list to obtain a second type label list; splitting the current equipment operation data into operation interval data, taking the operation interval data as reference data and the product list record as a matching object, and matching the operation state and the product type to obtain a first matching result; screening the first pairing result according to the second category label list to obtain a second pairing result; determining first production chain associated data of the second pairing result and the operation interval data, and matching an associated identifier in the first production chain associated data with the current equipment operation data to obtain production chain scheduling direction data;

if the product production matching degree between the product type label and the equipment state updating track is larger than or equal to the product production matching degree threshold value, determining second production chain associated data of the first matching result and the operation interval data, and matching an associated identifier in the second production chain associated data with the current equipment operation data to obtain production chain scheduling pointing data;

sequentially selecting the current production chain to be scheduled according to time sequence from the production chain scheduling pointing data; determining a reference production chain to be scheduled from a production chain to be scheduled, wherein the time sequence in the production chain scheduling pointing data is positioned in front of the current production chain to be scheduled; acquiring first topology area distribution of connection topology of automatic production equipment in the reference production chain to be scheduled; performing compatibility identification of automatic production equipment on the current production chain to be scheduled according to identification path parameters between the first topological area distribution and a preset topological identification model to obtain an equipment compatibility result of the current production chain to be scheduled; performing equipment type grouping on the equipment compatibility result to obtain a second topological area distribution of the connection topology of the automatic production equipment; determining a scheduling priority queue from the equipment compatibility result to obtain queue description data of the scheduling priority queue; determining an equipment scheduling authority list corresponding to the first automatic production equipment from the queue description data;

acquiring authority attribute information of the equipment scheduling authority list, and determining a plurality of authority attribute characteristics corresponding to the authority attribute information; inputting each plurality of authority attribute features to each feature recognition submodel in an operating state in parallel; the plurality of authority attribute features are used for indicating corresponding feature identification submodels to generate first feature identification results corresponding to the plurality of authority attribute features, and the plurality of authority attribute features are also used for indicating corresponding feature identification submodels to respectively convert the plurality of authority attribute features into device scheduling sequences and device cooperation weight sequences, respectively extract first sequence elements from device scheduling of the device scheduling sequences, respectively extract second sequence elements from device cooperation weights of the device cooperation weight sequences, determine scheduling direction information according to the first sequence elements and determine scheduling time sequence information according to the second sequence elements; analyzing each scheduling direction information and the scheduling time sequence information to obtain a first characteristic identification result corresponding to the plurality of authority attribute characteristics; removing redundant feature recognition results in the first feature recognition results fed back by each feature recognition submodel, and generating second feature recognition results corresponding to the authority attribute information in a combined mode according to the feature recognition results left after the redundant feature recognition results are removed; and generating a production chain scheduling strategy of the first automatic production equipment based on the feature recognition result and performing associated storage.

Optionally, when it is detected that the first automated manufacturing device triggers a production chain scheduling signal, adjusting the production state of the first automated manufacturing device based on the production chain scheduling policy includes:

determining identifier pointing information of a plurality of production behavior update identifiers to be screened for identifying production scheduling requirements and identifier similarity among different production behavior update identifiers according to the acquired dynamic data set and static data set for recording the production behaviors of the first automatic production equipment; screening the plurality of production behavior updating identifications based on the identification pointing information of the plurality of production behavior updating identifications and the identification similarity among different production behavior updating identifications, so that the pointing description value of the identification pointing information of the screened production behavior updating identifications is larger than a set value, and the identification similarity among the screened production behavior updating identifications is smaller than the set similarity;

aiming at the real-time production behavior record of the first automatic production equipment at any time period, judging whether the real-time production behavior record of the first automatic production equipment simultaneously triggers the production scheduling requirement and a production chain scheduling signal according to an updating evaluation coefficient of the real-time production behavior record of the first automatic production equipment at any time period under each production behavior updating identifier in the screened production behavior updating identifiers; on the premise that the real-time production behavior record of the first automatic production equipment is judged to simultaneously trigger the production scheduling requirement and the production chain scheduling signal, determining the current production state parameter of the first automatic production equipment, and determining the target production state parameter of each strategy event corresponding to the production chain scheduling strategy;

generating a state adjustment index list corresponding to the current production state parameter and a state compatible index list corresponding to the target production state parameter, and determining a plurality of list units with different adjustment delay time values respectively included in the state adjustment index list and the state compatible index list; extracting the production chain type characteristics of the current production state parameters in any list unit of the state adjustment index list, and determining the list unit with the minimum adjustment delay time value in the state compatible index list as a target list unit; mapping the production chain type characteristics to the target list unit according to the time sequence difference information between the production scheduling requirements and the production chain scheduling signals so as to obtain scheduling type characteristics in the target list unit; generating a production state evaluation weight between the current production state parameter and the target production state parameter based on the production chain class characteristics and the scheduling class characteristics;

obtaining production line compatibility characteristics in the target list unit by taking the scheduling category characteristics as reference characteristics, mapping the production line compatibility characteristics to the list unit where the production chain category characteristics are located according to weight tracing information corresponding to the production state evaluation weight, obtaining global compatibility characteristics corresponding to the production line compatibility characteristics in the list unit where the production chain category characteristics are located, and determining a production chain adjustment index corresponding to the global compatibility characteristics; and adjusting the production state of the first automatic production equipment according to the strategy event corresponding to the maximum production chain adjustment index.

Optionally, performing feature extraction on the current production line configuration data of the first automated production equipment according to the equipment scheduling order list corresponding to the first equipment scheduling path data to obtain a production line dynamic feature of the first automated production equipment, where the feature extraction includes:

determining the device identification corresponding to the scheduling object in the device scheduling sequence list;

determining feature extraction dimension information corresponding to current production line configuration data based on the plurality of equipment identifiers;

and according to the feature extraction dimension information, performing feature extraction on the current production line configuration data to obtain the production line dynamic features of the first automatic production equipment.

The second aspect of the application discloses an artificial intelligence cloud platform, which comprises a cosmetic production chain scheduling device, wherein a functional module in the device realizes the method of the first aspect during operation.

The third aspect of the application discloses an artificial intelligence cloud platform, which comprises a processing engine, a network module and a memory; the processing engine and the memory communicate via the network module, and the processing engine reads the computer program from the memory and runs it to perform the method of the first aspect.

A fourth aspect of the present application is a computer-readable signal medium having stored thereon a computer program which, when executed, implements the method of the first aspect.

Compared with the prior art, the cloud computing-combined cosmetic production chain scheduling method and the artificial intelligence cloud platform provided by the embodiment of the invention have the following technical effects: firstly, a first device collaborative operation record of a first automatic production device in a first cosmetic production chain is analyzed, so that corresponding first device scheduling path data and a device scheduling sequence list corresponding to the first device scheduling path data are determined. And secondly, determining the dynamic production line characteristics of the first automatic production equipment according to the equipment scheduling sequence list, and further determining the target dynamic mapping characteristics. And finally, generating a production chain scheduling strategy based on the product list record and the target dynamic mapping characteristics. By the design, the production state of the first automatic production equipment can be adjusted based on the production chain scheduling strategy when the first automatic production equipment is detected to trigger the production chain scheduling signal. Therefore, compatibility consideration of a plurality of production chains can be realized based on a production chain scheduling strategy, so that flexible switching of the first automatic production equipment between different production chains and different types of cosmetic production can be realized quickly and accurately, and efficient operation of the whole cosmetic production factory is ensured.

In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which reference numerals represent similar mechanisms throughout the various views of the drawings.

FIG. 1 is a block diagram illustrating an exemplary cloud-computing-integrated cosmetic production chain scheduling system, according to some embodiments of the invention.

FIG. 2 is a schematic diagram illustrating the hardware and software components of an exemplary artificial intelligence cloud platform, according to some embodiments of the invention.

FIG. 3 is a flowchart illustrating an exemplary cloud-computing-integrated cosmetic production chain scheduling method and/or process, according to some embodiments of the invention.

Fig. 4 is a block diagram illustrating an exemplary cloud-computing-integrated cosmetic production chain scheduling apparatus, according to some embodiments of the invention.

Detailed Description

After research and analysis on how to improve the productivity of cosmetics based on a built factory, the inventor finds that the productivity of cosmetics can be improved based on the built factory by realizing the scheduling and switching of production chains. Therefore, the technical scheme provided by the embodiment of the invention can realize compatibility consideration of a plurality of production chains based on a production chain scheduling strategy, thereby quickly and accurately realizing flexible switching of first automatic production equipment between different production chains and different types of cosmetic production and ensuring efficient operation of the whole cosmetic production factory.

In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.

In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, systems, compositions, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the invention.

These and other features, functions, methods of execution, and combination of functions and elements of related elements in the structure and economies of manufacture disclosed in the present application may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this application. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale.

Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.

Fig. 1 is a block diagram illustrating an exemplary cloud-computing-integrated cosmetic production chain scheduling system 300, which may include an artificial intelligence cloud platform 100 and an automated production facility 200, according to some embodiments of the invention.

In some embodiments, as shown in FIG. 2, artificial intelligence cloud platform 100 can include a processing engine 110, a network module 120, and a memory 130, processing engine 110 and memory 130 communicating through network module 120.

Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.

Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network 120 may include wired or wireless network access points, such as base stations and/or network access points.

The Memory 130 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.

It is to be understood that the configuration shown in FIG. 2 is merely illustrative and that artificial intelligence cloud platform 100 may include more or fewer components than shown in FIG. 2 or have a different configuration than shown in FIG. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

Fig. 3 is a flowchart illustrating an exemplary cloud-computing-based cosmetic production chain scheduling method and/or process, which is applied to the artificial intelligence cloud platform 100 in fig. 1 and may specifically include the contents described in the following steps S310 to S340.

Step S310, acquiring a first equipment cooperative operation record of the first automatic production equipment in the first cosmetic production chain.

For example, the first cosmetic production chain is a cosmetic production chain in which the first automated production apparatus is located among a plurality of cosmetic production chains included in a cosmetic production plant, and the first automated production apparatus is an automated production apparatus in the cosmetic production plant. The first equipment cooperative operation record represents cooperative operation records of the first automatic production equipment and other automatic production equipment.

Step S320, acquiring first equipment scheduling path data between the first equipment collaborative operation record and a product list record of cosmetic production in a cosmetic production factory; and acquiring an equipment scheduling sequence list corresponding to the first equipment scheduling path data according to the switching time consumption of a preset production line.

For example, the preset production line switching time is used for indicating a corresponding relationship between a matching relationship between a production state record of the first automatic production equipment in a cosmetic production chain and a product list record of the cosmetic production and an equipment scheduling sequence list. The first device scheduling path data is used to indicate scheduling between different automation production devices. The device scheduling order list is used to characterize scheduling priorities between different automation production devices.

Step S330, performing feature extraction on the current production line configuration data of the first automatic production equipment according to an equipment scheduling sequence list corresponding to the first equipment scheduling path data to obtain the production line dynamic features of the first automatic production equipment; and mapping the production line dynamic characteristics to product list records of the cosmetics production to obtain first dynamic mapping characteristics, and taking the first dynamic mapping characteristics as target dynamic mapping characteristics.

For example, the first set of associated features of the line dynamics features is associated with a matching relationship between the first device co-operation record and the inventory record of the cosmetic product production. The second associated feature set of the target dynamic mapping feature is related to a matching relationship between the first device collaborative operation record and the product list record of the cosmetic production, and the third associated feature set of the target dynamic mapping feature is different from the second dynamic mapping feature obtained by mapping the current production line configuration data to the product list record of the cosmetic production. The production line dynamic characteristics are used for representing characteristics corresponding to operation parameters which can be switched and correspond to the first automatic production equipment.

Step S340, generating a production chain scheduling policy of the first automated production device according to the product list record of the cosmetic production and the target dynamic mapping feature, performing association storage, and when detecting that a production chain scheduling signal is triggered by the first automated production device, adjusting the production state of the first automated production device based on the production chain scheduling policy.

For example, the production chain scheduling policy is used to trigger the production chain scheduling signal at the first automated production equipment to indicate the adjustment of the production state of the first automated production equipment, so as to quickly and accurately realize the flexible switching of the first automated production equipment between different production chains and different kinds of cosmetic production.

In this embodiment, by executing the steps S310 to S340, first, a first device co-operation record of a first automatic production device in a first cosmetic production chain is analyzed, so as to determine corresponding first device scheduling path data and a device scheduling order list corresponding to the first device scheduling path data. And secondly, determining the dynamic production line characteristics of the first automatic production equipment according to the equipment scheduling sequence list, and further determining the target dynamic mapping characteristics. And finally, generating a production chain scheduling strategy based on the product list record and the target dynamic mapping characteristics. By the design, the production state of the first automatic production equipment can be adjusted based on the production chain scheduling strategy when the first automatic production equipment is detected to trigger the production chain scheduling signal. Therefore, compatibility consideration of a plurality of production chains can be realized based on a production chain scheduling strategy, so that flexible switching of the first automatic production equipment between different production chains and different types of cosmetic production can be realized quickly and accurately, and efficient operation of the whole cosmetic production factory is ensured.

In some examples, mapping the dynamic production line characteristics onto the inventory record of the cosmetic product production described in step S330 to obtain a first dynamic mapping characteristic may include the following: acquiring a mapping path label configured for the first automatic production equipment, wherein the mapping path label is used for indicating a feature distribution format and a feature record format of the production line dynamic feature mapping on a product list record of the cosmetic production; and mapping the dynamic characteristic of the production line to a product list record of the cosmetic production according to the characteristic distribution format and the characteristic record format indicated by the mapping path label to obtain the first dynamic mapping characteristic. In this way, the mapping of the dynamic features of the production line can be realized according to the feature distribution format and the feature record format indicated by the mapping path label, so that the integrity of the first dynamic mapping feature is ensured.

In an optional manner, before the content described in step S310, the following content may also be included: configuring a mapping path label for each of the first automated manufacturing equipment present within the first cosmetic manufacturing chain; the first automated production equipment is any one of a plurality of preset automated production equipment, the feature recording format indicated by the mapping path label is any one of a plurality of preset feature recording formats, and the feature distribution format indicated by the mapping path label is any one of a plurality of preset feature distribution formats. Therefore, by configuring the mapping path labels in advance, the overall configuration of the cosmetic production factory can be realized, and the normal and safe operation of the cosmetic production factory before and after the scheduling of the production chain is ensured.

In some examples, the obtaining of the first device co-operation record of the first automated manufacturing device in the first cosmetic manufacturing chain described in step S310 may be further implemented as described in steps S311 and S312 below.

Step S311, when the first automatic production equipment exists in a second cosmetic production chain in a previous production line period of the current production line period, acquiring a second equipment cooperative operation record of the first automatic production equipment in the second cosmetic production chain, and determining the first equipment cooperative operation record according to the second equipment cooperative operation record and the equipment operation loss data of the first automatic production equipment.

For example, the first automated production equipment exists in the first cosmetic production chain in the current production line cycle, and the second cosmetic production chain is a cosmetic production chain of a plurality of cosmetic production chains in which the first automated production equipment exists in a previous production line cycle of the current production line cycle.

Step S312, when the first automatic production device does not exist in the second cosmetic production chain in the previous production line period of the current production line period, obtaining the first device collaborative operation record configured for the first automatic production device in the first cosmetic production chain.

For example, a second automatic production device controlled by an artificial intelligence cloud platform is included in the first cosmetic production chain, and a production record corresponding to the second automatic production device is a product list record of the cosmetic production.

It can be understood that, by executing the above step S311 and step S312, the acquisition of the first device collaborative operation record can be realized through two situations, so that the adaptability of the first device collaborative operation record between different cosmetic production chains is ensured, and further, the accurate acquisition of the first device collaborative operation record is realized.

In practical application, the inventor finds that when a production chain scheduling strategy is generated, compatibility and global stability between different cosmetic production chains and different automatic production equipment need to be considered, and further production faults occurring before and after scheduling of the production chain are avoided. To achieve this, the step S340 of generating and storing a production chain scheduling policy of the first automated production equipment according to the inventory record of the cosmetics production and the target dynamic mapping characteristics may further include the following steps S341 to S345.

Step S341, obtaining the product category label in the product list record, and determining a first category label list corresponding to the product category label; and acquiring an equipment state updating track corresponding to the target dynamic mapping characteristics, and calculating the product production matching degree between the product category label and the equipment state updating track according to the first category label list.

Step S342, if the product production matching degree between the product category label and the equipment state updating track is smaller than a preset product production matching degree threshold, matching the current equipment operation data corresponding to the first automated production equipment with the first category label list to obtain a second category label list; splitting the current equipment operation data into operation interval data, taking the operation interval data as reference data and the product list record as a matching object, and matching the operation state and the product type to obtain a first matching result; screening the first pairing result according to the second category label list to obtain a second pairing result; and determining first production chain associated data of the second pairing result and the operation interval data, and matching an associated identifier in the first production chain associated data with the current equipment operation data to obtain production chain scheduling direction data.

Step S343, if the product production matching degree between the product type label and the equipment state update trajectory is greater than or equal to the product production matching degree threshold, determining second production chain associated data of the first pairing result and the operation interval data, and matching the association identifier in the second production chain associated data with the current equipment operation data to obtain production chain scheduling direction data.

Step S344, sequentially selecting the current production chain to be scheduled from the production chain scheduling pointing data according to a time sequence; determining a reference production chain to be scheduled from a production chain to be scheduled, wherein the time sequence in the production chain scheduling pointing data is positioned in front of the current production chain to be scheduled; acquiring first topology area distribution of connection topology of automatic production equipment in the reference production chain to be scheduled; performing compatibility identification of automatic production equipment on the current production chain to be scheduled according to identification path parameters between the first topological area distribution and a preset topological identification model to obtain an equipment compatibility result of the current production chain to be scheduled; performing equipment type grouping on the equipment compatibility result to obtain a second topological area distribution of the connection topology of the automatic production equipment; determining a scheduling priority queue from the equipment compatibility result to obtain queue description data of the scheduling priority queue; and determining a device scheduling authority list corresponding to the first automatic production device from the queue description data.

Step S345, obtaining authority attribute information of the equipment scheduling authority list, and determining a plurality of authority attribute characteristics corresponding to the authority attribute information; inputting each plurality of authority attribute features to each feature recognition submodel in an operating state in parallel; the plurality of authority attribute features are used for indicating corresponding feature identification submodels to generate first feature identification results corresponding to the plurality of authority attribute features, and the plurality of authority attribute features are also used for indicating corresponding feature identification submodels to respectively convert the plurality of authority attribute features into device scheduling sequences and device cooperation weight sequences, respectively extract first sequence elements from device scheduling of the device scheduling sequences, respectively extract second sequence elements from device cooperation weights of the device cooperation weight sequences, determine scheduling direction information according to the first sequence elements and determine scheduling time sequence information according to the second sequence elements; analyzing each scheduling direction information and the scheduling time sequence information to obtain a first characteristic identification result corresponding to the plurality of authority attribute characteristics; removing redundant feature recognition results in the first feature recognition results fed back by each feature recognition submodel, and generating second feature recognition results corresponding to the authority attribute information in a combined mode according to the feature recognition results left after the redundant feature recognition results are removed; and generating a production chain scheduling strategy of the first automatic production equipment based on the feature recognition result and performing associated storage.

It can be understood that, by executing the above steps S341 to S345, the acquisition of the production chain scheduling direction data is first implemented according to the size relationship between the product production matching degree between the product category label and the device state update track and the preset product production matching degree threshold, so that the reliability of the production chain scheduling direction data can be ensured. And secondly, determining an equipment scheduling authority list corresponding to the first automatic production equipment based on the production chain scheduling pointing data. And finally, generating a production chain scheduling strategy of the first automatic production equipment based on the equipment scheduling authority list. By the design, compatibility and overall stability between different cosmetic production chains and different automatic production equipment can be considered, and further production faults occurring before and after scheduling of the production chains are avoided.

Further, based on the content described in the above steps S341 to S345, when the triggering of the production chain scheduling signal by the first automated production equipment is detected, the adjusting of the production state of the first automated production equipment based on the production chain scheduling policy described in the step S340 may further include the content described in the following steps S3461 to S3464.

Step S3461, determining identification pointing information of a plurality of production behavior update identifications to be screened for identifying production scheduling requirements and identification similarity among different production behavior update identifications according to the acquired dynamic data set and static data set for recording the production behaviors of the first automatic production equipment; and screening the plurality of production behavior updating identifications based on the determined identification pointing information of the plurality of production behavior updating identifications and the identification similarity among different production behavior updating identifications, so that the pointing description value of the identification pointing information of the screened production behavior updating identifications is larger than a set value, and the identification similarity among the screened production behavior updating identifications is smaller than the set similarity.

Step S3462, aiming at the real-time production behavior record of the first automatic production equipment in any time period, judging whether the real-time production behavior record of the first automatic production equipment simultaneously triggers the production scheduling requirement and a production chain scheduling signal according to an updating evaluation coefficient of the real-time production behavior record of the first automatic production equipment in any time period under each production behavior updating mark in the screened production behavior updating marks; and on the premise of judging that the real-time production behavior record of the first automatic production equipment simultaneously triggers the production scheduling requirement and the production chain scheduling signal, determining the current production state parameter of the first automatic production equipment, and determining the target production state parameter of each strategy event corresponding to the production chain scheduling strategy.

Step S3463, generating a state adjustment index list corresponding to the current production state parameter and a state compatible index list corresponding to the target production state parameter, and determining a plurality of list units with different adjustment delay length values respectively included in the state adjustment index list and the state compatible index list; extracting the production chain type characteristics of the current production state parameters in any list unit of the state adjustment index list, and determining the list unit with the minimum adjustment delay time value in the state compatible index list as a target list unit; mapping the production chain type characteristics to the target list unit according to the time sequence difference information between the production scheduling requirements and the production chain scheduling signals so as to obtain scheduling type characteristics in the target list unit; and generating a production state evaluation weight between the current production state parameter and the target production state parameter based on the production chain class characteristics and the scheduling class characteristics.

Step S3464, obtaining production line compatibility characteristics in the target list unit by taking the scheduling category characteristics as reference characteristics, mapping the production line compatibility characteristics to the list unit where the production chain category characteristics are located according to weight tracing information corresponding to the production state evaluation weight, obtaining global compatibility characteristics corresponding to the production line compatibility characteristics in the list unit where the production chain category characteristics are located, and determining a production chain adjustment index corresponding to the global compatibility characteristics; and adjusting the production state of the first automatic production equipment according to the strategy event corresponding to the maximum production chain adjustment index.

By implementing the above steps S3461 to S3464, on the premise that the real-time production behavior record of the first automated production equipment simultaneously triggers the production scheduling requirement and the production chain scheduling signal, the current production state parameter of the first automated production equipment can be determined, and the target production state parameter of each policy event corresponding to the production chain scheduling policy is determined, so that the reliability of detection on the production chain scheduling signal of the first automated production equipment can be ensured, and further, the production state evaluation weight between the current production state parameter and the target production state parameter can be accurately generated in real time. It can be understood that the production state adjustment can be rapidly realized through the content, and the influence of the production chain scheduling on the running state of the automatic production equipment can be reduced. Therefore, compatibility consideration of a plurality of production chains can be realized based on a production chain scheduling strategy, so that flexible switching of the first automatic production equipment between different production chains and different types of cosmetic production can be realized quickly and accurately, efficient operation of the whole cosmetic production plant is ensured, and the productivity of cosmetics is further improved.

Further, the performing, according to the device scheduling order list corresponding to the first device scheduling path data, feature extraction on the current production line configuration data of the first automatic production device in step S330 to obtain the production line dynamic feature of the first automatic production device includes: determining the device identification corresponding to the scheduling object in the device scheduling sequence list; determining feature extraction dimension information corresponding to current production line configuration data based on the plurality of equipment identifiers; and according to the feature extraction dimension information, performing feature extraction on the current production line configuration data to obtain the production line dynamic features of the first automatic production equipment.

In an alternative embodiment, the step of obtaining the first device scheduling path data between the first device co-operation record and the inventory record of the cosmetic product production in the cosmetic production plant described in step S320 may further include the following steps a-d.

Step a, determining a record node to be identified between the first equipment collaborative operation record and the product list record; and controlling the record node to be identified to perform iterative update in the product list record, wherein at least one path attribute record for pairing the record node to be identified exists in the product list record.

Step b, when detecting that the product list record has a first path attribute record in the iterative updating process of the record node to be identified, detecting whether the first path attribute record has a first path priority and a first path value; the first path priority sum value is recorded on the first path attribute record when the record node to be identified is not paired with the first path attribute record last time, and the first path priority sum value is a weighted sum of path priorities of path attribute records possessed by the record node to be identified when the record node to be identified is not paired last time.

Step c, when the first path attribute record does not have the first path priority and the first path priority value, detecting whether the first path attribute record is a path attribute record which is iteratively associated with the record node to be identified; when the first path attribute record is determined to be the path attribute record which is in iterative association with the record node to be identified, the record node to be identified is paired with the first path attribute record, and the second path priority and the value of the record node to be identified are updated according to the first path priority of the first path attribute record.

Step d, when the first path attribute record has the first path priority and value, detecting whether the first path priority and value are the same as the second path priority and value of the record node to be identified, wherein the second path priority and value is the weighted sum of the path priorities of the path attribute records currently possessed by the record node to be identified; when the priority and the value of the first path are different from the priority and the value of the second path of the recording node to be identified, returning to a trigger time period in the process of updating the priority and the value of the first path to the priority and the value of the second path; acquiring the first path priority and a second path priority with changed values according to the trigger time interval; detecting whether the first path attribute record meets the matching condition according to the first path priority and the second path priority; when the first path attribute record meets the matching condition, matching the first path attribute record, and updating the second path priority and the value according to the first path priority; generating the first device dispatch path data based on the paired path attribute records.

It will be appreciated that by performing steps a-d as described above, the integrity and timeliness of the first device dispatch path data can be ensured by iterative updating.

In an alternative embodiment, the acquiring of the device scheduling order list corresponding to the first device scheduling path data according to the preset production line switching time described in step S320 may further include the following steps: determining a scheduling time consumption distribution list corresponding to the first equipment scheduling path data according to preset production line switching time consumption, and determining the current scheduling time consumption of each piece of automatic production equipment from the scheduling time consumption distribution list; and generating the device scheduling sequence list based on the current scheduling consumed time. In this way, the sequential continuity of the device scheduling order list can be ensured.

Fig. 4 is a block diagram illustrating an exemplary cloud-computing-integrated cosmetic production chain scheduling device 140 according to some embodiments of the present invention, where the cloud-computing-integrated cosmetic production chain scheduling device 140 is applied to the artificial intelligence cloud platform 100 in fig. 1, and the cloud-computing-integrated cosmetic production chain scheduling device 140 includes:

the operation record obtaining module 141 is configured to obtain a first device collaborative operation record of a first automatic production device in a first cosmetic production chain;

a scheduling sequence obtaining module 142, configured to obtain first device scheduling path data between the first device collaborative operation record and a product list record of cosmetic production in a cosmetic production factory; acquiring an equipment scheduling sequence list corresponding to the first equipment scheduling path data according to the switching time consumption of a preset production line;

the dynamic feature mapping module 143 is configured to perform feature extraction on the current production line configuration data of the first automated production equipment according to the equipment scheduling order list corresponding to the first equipment scheduling path data, so as to obtain a production line dynamic feature of the first automated production equipment; mapping the production line dynamic features to product list records of the cosmetics production to obtain first dynamic mapping features, and taking the first dynamic mapping features as target dynamic mapping features;

and the production state adjusting module 144 is configured to generate a production chain scheduling policy of the first automated production equipment according to the product list record of the cosmetic production and the target dynamic mapping feature, perform associated storage, and adjust the production state of the first automated production equipment based on the production chain scheduling policy when detecting that the first automated production equipment triggers a production chain scheduling signal.

It should be understood that the corresponding description of the above-described embodiment of the apparatus refers to the description of the embodiment of the method shown in fig. 3.

Based on the same inventive concept, a cosmetic production chain scheduling system combined with cloud computing is also provided, and further description is provided as follows.

A cosmetic production chain scheduling system combined with cloud computing comprises an artificial intelligence cloud platform and automatic production equipment which are communicated with each other; wherein the artificial intelligence cloud platform is configured to:

performing feature extraction on the current production line configuration data of the first automatic production equipment according to an equipment scheduling sequence list corresponding to the first equipment scheduling path data to obtain the production line dynamic features of the first automatic production equipment; mapping the production line dynamic features to product list records of the cosmetics production to obtain first dynamic mapping features, and taking the first dynamic mapping features as target dynamic mapping features;

It should be understood that the corresponding description of the above system embodiment refers to the description of the method embodiment shown in fig. 3.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.

Also, this application uses specific terminology to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of at least one embodiment of the present application may be combined as appropriate.

In addition, those skilled in the art will recognize that the various aspects of the application may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of procedures, machines, articles, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.

A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may 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 on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the execution of aspects of the present application may be written in any combination of one or more programming languages, including object oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby, and Groovy, or other programming languages. The programming code may execute entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).

Additionally, the order of the process elements and sequences described herein, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods unless otherwise indicated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware means, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

It should also be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Claims

1. A cosmetic production chain scheduling method combined with cloud computing is applied to an artificial intelligence cloud platform, and comprises the following steps:

acquiring first equipment scheduling path data between the first equipment collaborative operation record and a product list record of cosmetic production in a cosmetic production factory; acquiring an equipment scheduling sequence list corresponding to the first equipment scheduling path data according to the switching time consumption of a preset production line; wherein the first device scheduling path data is to indicate scheduling between different automated production devices;

2. The method of claim 1, wherein mapping the line dynamic features onto a product inventory record for the cosmetic product, resulting in a first dynamically mapped feature comprises:

3. The method of claim 1, wherein prior to obtaining the first device co-operational record of the first automated manufacturing device in the first cosmetic manufacturing chain, the method further comprises:

4. The method of claim 1, wherein obtaining a first device co-operation record of a first automated manufacturing device in a first cosmetic manufacturing chain comprises:

5. The method according to any one of claims 1-4, wherein generating and storing in association a production chain scheduling policy of the first automated production facility based on the inventory record of the cosmetic production and the target dynamic mapping feature comprises:

6. The method of claim 5, wherein adjusting the production status of the first automated production equipment based on the production chain scheduling policy upon detecting that the first automated production equipment triggers a production chain scheduling signal comprises:

7. The method of claim 1, wherein performing feature extraction on the current production line configuration data of the first automated production equipment according to the equipment scheduling order list corresponding to the first equipment scheduling path data to obtain the production line dynamic feature of the first automated production equipment comprises:

8. An artificial intelligence cloud platform, comprising a cosmetic production chain scheduling device, wherein a functional module in the device implements the method of any one of claims 1 to 7 when the functional module is run.

9. An artificial intelligence cloud platform is characterized by comprising a processing engine, a network module and a memory; the processing engine and the memory communicate through the network module, the processing engine reading a computer program from the memory and operating to perform the method of any of claims 1-7.

10. A computer-readable signal medium, on which a computer program is stored which, when executed, implements the method of any one of claims 1-7.