CN113327016A

CN113327016A - Block chain-based cosmetic production information indexing method and system and data center

Info

Publication number: CN113327016A
Application number: CN202110518856.8A
Authority: CN
Inventors: 成金梅
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-11-03
Filing date: 2020-11-03
Publication date: 2021-08-31
Also published as: CN113327017A; CN112270483A

Abstract

The embodiment of the application provides a block chain-based cosmetic production information indexing method, a system and a data center, the production simulation monitoring line with the difference of error data in the production simulation monitoring result is determined by comparing the production line switching behavior error data of the production monitoring configuration unit with the production line switching behavior database corresponding to the production monitoring configuration unit, then, according to the production simulation monitoring line with difference in error data, automatically updating the production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object, and indexing information of the production monitoring information of the target production monitoring cosmetic object accessed via the blockchain based on the updated production monitoring configuration information, therefore, the production monitoring effect of the production monitoring software platform for monitoring the cosmetic object in the target production is improved. In addition, the prior updating effect of the production monitoring software platform can not be influenced.

Description

Block chain-based cosmetic production information indexing method and system and data center

The application is a divisional application of Chinese application with the name of 'cosmetic production information monitoring method based on artificial intelligence and big data center' and is invented and created by application number 202011211860.1, with the application date of 2020, 11 and 03.

Technical Field

The application relates to the technical field of cosmetic production monitoring, in particular to a block chain-based cosmetic production information indexing method, system and data center.

Background

Along with the development of industrial internet and scientific technology, most of current cosmetic production is automatic production, and production monitoring software platforms usually involve some production behavior pushing processes.

Disclosure of Invention

In order to at least overcome the above disadvantages in the prior art, an object of the present application is to provide a method, a system, and a data center for indexing cosmetic production information based on a blockchain, where a raw material manufacturing collaborative operation distribution of a plurality of cosmetic production chains is determined based on a cosmetic raw material production map, the raw material manufacturing collaborative operation distribution is respectively input into a plurality of production decision push networks in a production decision push model, and each production decision push network performs at least one production line configuration partition matching to obtain at least one production line configuration partition. Wherein, each production decision push network has at least one time of production line configuration partition matching, which is carried out based on the associated big data production line partition, and the associated big data production line partition is associated to the production line configuration partitions extracted by other production decision push networks of the plurality of production decision push networks. Therefore, at least one exchange and fusion can be carried out between the production line configuration partitions extracted from different production decision pushing networks, and then the production line configuration partitions of different levels can be decided, so that the representation capability of production behavior pushing is improved by enriching the levels of the production line configuration partitions, and the pushing pertinence is better when the production line configuration partitions extracted based on the production decision pushing model obtain the pushing sequence result of the production monitoring information.

In a first aspect, the present application provides a cosmetic production information monitoring method based on artificial intelligence, which is applied to a big data center, where the big data center is in communication connection with a plurality of cosmetic production monitoring devices, and the method includes:

acquiring production monitoring configuration information corresponding to each production monitoring configuration unit of the cosmetic production monitoring equipment, and performing information indexing on the production monitoring information of a target production monitoring cosmetic object based on the production monitoring configuration information to acquire a corresponding production monitoring index segment;

acquiring a corresponding cosmetic raw material production map based on the production monitoring index fragment, and determining raw material manufacturing collaborative operation distribution of a plurality of cosmetic production chains based on the cosmetic raw material production map;

respectively inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in a production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition; wherein at least one production line configuration partition matching by the production decision-making push network is performed based on an associated big data production line partition associated to a production line configuration partition extracted by other production decision-making push networks of the plurality of production decision-making push networks, wherein the production decision-making push network is obtained based on artificial intelligence network and corresponding training sample training, and the training sample comprises a raw material manufacturing collaborative operation distribution sample and a corresponding production line configuration partition label;

and carrying out decision making on a plurality of production line configuration partitions output by the plurality of production decision pushing networks to obtain decision production line configuration partitions, obtaining a pushing sequence result of the cosmetic raw material production map under the production monitoring information based on the decision production line configuration partitions, and carrying out production behavior pushing based on the pushing sequence result of the cosmetic raw material production map under the production monitoring information.

In a possible implementation manner of the first aspect, the method further includes:

using one of the plurality of production decision push networks as a target production decision push network;

acquiring a first production line configuration partition extracted by the target production decision push network and a second production line configuration partition extracted by other production decision push networks except the target production decision push network in the plurality of production decision push networks;

when the cosmetic production chain of the second production line configuration partition does not match the cosmetic production chain of the first production line configuration partition, adding the state of the second production line configuration partition, wherein the cosmetic production chain of the second production line configuration partition after the state addition is the same as the cosmetic production chain of the first production line configuration partition;

performing production line configuration partition matching on the partition superposition result of the second production line configuration partition and the first production line configuration partition after the state addition through the target production decision push network;

the number of the second production line configuration subareas is at least two; the method further comprises the following steps:

when a second production line configuration partition of the cosmetic production chain which is not matched with the first production line configuration partition and a second production line configuration partition of the cosmetic production chain which is matched with the first production line configuration partition exist at the same time, state addition is carried out on the second production line configuration partition of the cosmetic production chain which is not matched with the first production line configuration partition, retrospective state addition is carried out on the second production line configuration partition of the cosmetic production chain which is matched with the first production line configuration partition, and the cosmetic production chain of the second production line configuration partition after state addition and the cosmetic production chain of the second production line configuration partition after retrospective state addition are all the same as the cosmetic production chain of the first production line configuration partition;

performing production line configuration partition matching on the second production line configuration partition after state addition, the second production line configuration partition after backtracking state addition and the partition superposition result of the first production line configuration partition through the target production decision push network;

wherein the method further comprises:

when the cosmetic production chain of the second production line configuration partition is matched with the cosmetic production chain of the first production line configuration partition, adding a backtracking state to the second production line configuration partition, wherein the added backtracking state of the cosmetic production chain of the second production line configuration partition is the same as that of the cosmetic production chain of the first production line configuration partition;

and performing production line configuration partition matching on the partition superposition result of the second production line configuration partition and the first production line configuration partition after the backtracking state is added through the target production decision pushing network.

In a possible implementation manner of the first aspect, the step of respectively inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in a production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition includes:

inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in the production decision push model respectively;

for one production decision push network, carrying out production line configuration partition matching on corresponding raw material manufacturing collaborative operation distribution through the production decision push network, obtaining a first production line configuration partition after the production line configuration partition matching, carrying out partition service matching on a second production line configuration partition extracted through other production decision push networks of the plurality of production decision push networks and the first production line configuration partition, and continuing to carry out production line configuration partition matching based on a partition service matching result so as to alternately carry out production line configuration partition matching and partition service matching.

In one possible implementation manner of the first aspect, the step of determining a raw material manufacturing collaborative operation distribution of a plurality of cosmetic production chains based on the cosmetic raw material production map includes:

performing index matching of the cosmetic production chain on the cosmetic raw material production map to obtain raw material manufacturing collaborative operation distribution of a plurality of different cosmetic production chains;

the step of inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in a production decision push model respectively, and obtaining at least one production line configuration partition by performing at least one production line configuration partition matching through the production decision push networks comprises:

inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in the production decision push model respectively; the raw material manufacturing collaborative operation distribution is in one-to-one correspondence with one of the plurality of production decision push networks respectively;

performing at least one production line configuration partition matching on the corresponding raw material manufacturing collaborative operation distribution through the production decision push network;

and the extracted cosmetic production chain of the production line configuration partition is consistent with the cosmetic production chain of the raw material manufacturing cooperative operation distribution corresponding to the production decision pushing network.

In one possible implementation manner of the first aspect, the raw material manufacturing co-operation distribution includes at least a first raw material manufacturing co-operation distribution, a second raw material manufacturing co-operation distribution, and a third raw material manufacturing co-operation distribution, the production decision push model comprises a first production decision push network, a second production decision push network and a third production decision push network, wherein the first, second and third feedstock manufacturing co-operating distributions correspond to feedstock manufacturing co-operating distributions of a production quality control point, a material supply point and a material output point, respectively, the first production decision push network, the second production decision push network and the third production decision push network respectively correspond to production decision push networks of a production quality control point, a material supply point and a material output point;

the step of respectively inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in a production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition includes:

inputting the first raw material manufacturing collaborative operation distribution into a first production decision pushing network to carry out first decision layer production line configuration partition matching, so as to obtain a production line configuration partition extracted by the first production decision pushing network on a first decision layer;

making a decision on a production line configuration partition extracted by the first production decision push network on a first decision layer and the second raw material manufacturing cooperative operation distribution to obtain a decision push information node corresponding to the second production decision push network on a second decision layer;

acquiring a production line configuration partition extracted by the first production decision push network on a first decision layer, and using the production line configuration partition as a decision push information node corresponding to the first production decision push network on a second decision layer;

carrying out first decision-making layer production line configuration partition matching on decision-making push information nodes corresponding to a first decision-making layer of the first production decision-making push network through the first production decision-making push network to obtain a production line configuration partition extracted for the first time at the second decision-making layer by the first production decision-making push network;

carrying out first decision-making layer production line configuration partition matching on decision-making push information nodes corresponding to a second decision-making layer of the second production decision-making push network through the second production decision-making push network to obtain a production line configuration partition extracted for the first time at the second decision-making layer by the second production decision-making push network;

transmitting the production line configuration partition firstly extracted by the first production decision push network at a second decision layer to the second production decision push network, and transmitting the production line configuration partition firstly extracted by the second production decision push network at the second decision layer to the first production decision push network;

the production line configuration partition extracted by the first production decision push network at a second decision layer for the first time and the production line configuration partition transmitted by the second production decision push network are decided through the first production decision push network, and production line configuration partition matching is carried out on partition superposition results;

the production line configuration partition extracted by the second production decision push network at the second decision layer for the first time and the production line configuration partition transmitted by the first production decision push network are decided through the second production decision push network, and production line configuration partition matching is carried out on partition superposition results;

carrying out decision-making on a production line configuration partition extracted by the first production decision-making push network on a second decision-making layer, a production line configuration partition extracted by the second production decision-making push network on the second decision-making layer and the third material manufacturing cooperative operation distribution to obtain a decision-making push information node corresponding to the third production decision-making push network on a third decision-making layer;

and matching the production line configuration subareas of the third decision layer through the first production decision push network based on the production line configuration subarea extracted by the first production decision push network at the second decision layer, matching the production line configuration subareas of the third decision layer through the second production decision push network based on the production line configuration subarea extracted by the second production decision push network at the second decision layer, and matching the production line configuration subareas of the third decision layer through the third production decision push network on the decision push information node corresponding to the third production decision push network at the third decision layer.

In a possible implementation manner of the first aspect, the step of obtaining a pushing sequence result of the cosmetic raw material production map under the production monitoring information based on the decision-making production line configuration partition includes:

determining a pushing model corresponding to the production monitoring information;

inputting the decision production line configuration subareas into the pushing model, and obtaining a pushing sequence result of the cosmetic raw material production map under the production monitoring information through the pushing model;

the step of configuring the production decision push model and the push model comprises:

acquiring a decision production line configuration partition sample, the production decision push model and the push model, wherein a push label of the decision production line configuration partition sample is used for representing a labeling result of the decision production line configuration partition sample under the production monitoring information;

configuring a subarea sample based on the decision production line, and determining raw material manufacturing collaborative operation distribution samples of a plurality of cosmetic production chains;

respectively inputting the raw material manufacturing collaborative operation distribution samples into a plurality of production decision push networks in the production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one decision production line configuration partition; wherein at least one production line configuration partition matching by the production decision-making push network is performed based on an associated big data production line partition associated to a decision-making production line configuration partition extracted by another production decision-making push network of the plurality of production decision-making push networks;

making a decision on a plurality of production line configuration partitions output by the plurality of production decision pushing networks to obtain a target decision production line configuration partition;

inputting the target decision-making production line configuration partition into the push model, and obtaining a decision result of the decision-making production line configuration partition sample under the production monitoring information through the push model;

configuring the production decision push model and the push model based on the decision result and the push tag.

In a possible implementation manner of the first aspect, the step of obtaining a corresponding cosmetic raw material production map based on the production monitoring index segment includes:

acquiring a corresponding cosmetic raw material production map corresponding to the production monitoring index fragment from a preset production line partition set;

the step of inputting the decision production line configuration partition into the pushing model and obtaining the pushing sequence result of the cosmetic raw material production map under the production monitoring information through the pushing model comprises the following steps:

and inputting the configuration partition of the target decision production line into the push model, and carrying out push information indexing on the configuration partition of the target decision production line through the push model to obtain a push sequence result of the configuration partition of the target decision production line.

In a possible implementation manner of the first aspect, the step of making a decision on multiple production line configuration partitions output by the multiple production decision pushing networks to obtain a decision-making production line configuration partition includes:

obtaining a plurality of production line configuration partitions output by the plurality of production decision pushing networks, and determining a target production line configuration partition of a global cosmetic production chain in the plurality of production line configuration partitions;

deciding other production line configuration subareas except the target production line configuration subarea in the plurality of production line configuration subareas, wherein the decided cosmetic production chain of the other production line configuration subareas is the same as the cosmetic production chain of the target production line configuration subarea;

and listing the other production line configuration subareas and the target production line configuration subarea after decision making to obtain the decision production line configuration subarea.

In a possible implementation manner of the first aspect, the step of obtaining production monitoring configuration information corresponding to each production monitoring configuration unit of the cosmetic production monitoring device, and performing information indexing on production monitoring information of a target production monitoring cosmetic object based on the production monitoring configuration information to obtain a corresponding production monitoring index segment includes:

the production simulation monitoring of the target production monitoring cosmetic object is carried out in each production simulation environment by calling the production monitoring software platform of the target production monitoring cosmetic object which is updated in advance by the production monitoring software platform, so as to obtain a production simulation monitoring result;

comparing the production line switching behavior error data of the production monitoring configuration unit in the production simulation monitoring result with production line switching behavior comparison data in a production line switching behavior database corresponding to the production monitoring configuration unit;

according to the comparison result, determining a production simulation monitoring line with difference of error data in the production simulation monitoring result, and acquiring production line result parameters of the production simulation monitoring line with difference of error data in each production simulation monitoring link;

and updating production monitoring configuration information corresponding to the production monitoring configuration unit in a production monitoring software platform of the target production monitoring cosmetic object according to production line result parameters of the production simulation monitoring line with the difference in the error data in each production simulation monitoring link, and performing information indexing on the production monitoring information of the target production monitoring cosmetic object accessed through the block chain based on the updated production monitoring configuration information to obtain a corresponding production monitoring index segment.

In a second aspect, an embodiment of the present application further provides an artificial intelligence-based cosmetic production information monitoring apparatus, which is applied to a big data center, where the big data center is in communication connection with a plurality of cosmetic production monitoring devices, and the apparatus includes:

the acquisition indexing module is used for acquiring production monitoring configuration information corresponding to each production monitoring configuration unit of the cosmetic production monitoring equipment, and indexing the production monitoring information of the target production monitoring cosmetic object based on the production monitoring configuration information to acquire a corresponding production monitoring index segment;

the determining module is used for acquiring a corresponding cosmetic raw material production map based on the production monitoring index fragment and determining raw material manufacturing collaborative operation distribution of a plurality of cosmetic production chains based on the cosmetic raw material production map;

the matching module is used for respectively inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in a production decision push model, and carrying out at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition; wherein at least one production line configuration partition matching by the production decision-making push network is performed based on an associated big data production line partition associated to a production line configuration partition extracted by other production decision-making push networks of the plurality of production decision-making push networks, wherein the production decision-making push network is obtained based on artificial intelligence network and corresponding training sample training, and the training sample comprises a raw material manufacturing collaborative operation distribution sample and a corresponding production line configuration partition label;

the pushing module is used for making decisions on a plurality of production line configuration partitions output by the plurality of production decision pushing networks to obtain decision production line configuration partitions, obtaining a pushing sequence result of the cosmetic raw material production map under the production monitoring information based on the decision production line configuration partitions, and pushing production behaviors based on the pushing sequence result of the cosmetic raw material production map under the production monitoring information.

In a third aspect, an embodiment of the present application further provides an artificial intelligence-based cosmetic production information monitoring system, where the artificial intelligence-based cosmetic production information monitoring system includes a big data center and multiple cosmetic production monitoring devices in communication connection with the big data center;

the big data center is used for:

In a fourth aspect, an embodiment of the present application further provides a big data center, where the big data center includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be communicatively connected to at least one cosmetic production monitoring device, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium to perform the method for monitoring the artificial intelligence-based cosmetic production information in the first aspect or any one of the possible implementations of the first aspect.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the computer executes the method for monitoring information on production of cosmetics based on artificial intelligence in the first aspect or any one of the possible implementations of the first aspect.

Based on any one of the aspects, the method includes determining raw material manufacturing collaborative operation distribution of a plurality of cosmetic production chains based on a cosmetic raw material production map, inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in a production decision push model respectively, and performing at least one production line configuration partition matching on each production decision push network to obtain at least one production line configuration partition. Wherein, each production decision push network has at least one time of production line configuration partition matching, which is carried out based on the associated big data production line partition, and the associated big data production line partition is associated to the production line configuration partitions extracted by other production decision push networks of the plurality of production decision push networks. Therefore, at least one exchange and fusion can be carried out between the production line configuration partitions extracted from different production decision pushing networks, and then the production line configuration partitions of different levels can be decided, so that the representation capability of production behavior pushing is improved by enriching the levels of the production line configuration partitions, and the pushing pertinence is better when the production line configuration partitions extracted based on the production decision pushing model obtain the pushing sequence result of the production monitoring information.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application 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.

Fig. 1 is a schematic view of an application scenario of an artificial intelligence-based cosmetic production information monitoring system according to an embodiment of the present application;

FIG. 2 is a schematic flow chart of a method for monitoring information on production of cosmetics based on artificial intelligence according to an embodiment of the present application;

FIG. 3 is a schematic diagram of functional modules of an artificial intelligence-based cosmetic production information monitoring device according to an embodiment of the present application;

fig. 4 is a schematic block diagram of structural components of a big data center for implementing the artificial intelligence-based cosmetic production information monitoring method according to the embodiment of the present application.

Detailed Description

The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.

Fig. 1 is an interactive schematic diagram of an artificial intelligence-based cosmetic production information monitoring system 10 according to an embodiment of the present application. The artificial intelligence based cosmetic production information monitoring system 10 may include a big data center 100 and a cosmetic production monitoring device 200 communicatively connected to the big data center 100. The artificial intelligence based cosmetic production information monitoring system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the artificial intelligence based cosmetic production information monitoring system 10 may also include only a portion of the components shown in fig. 1 or may also include other components.

In this embodiment, the big data center 100 and the cosmetic production monitoring device 200 in the artificial intelligence based cosmetic production information monitoring system 10 can cooperatively perform the artificial intelligence based cosmetic production information monitoring method described in the following method embodiments, and the detailed description of the following method embodiments can be referred to for the implementation steps of the big data center 100 and the cosmetic production monitoring device 200.

In order to solve the technical problems in the background art, fig. 2 is a schematic flow chart of an artificial intelligence based cosmetic production information monitoring method according to an embodiment of the present application, which can be executed by the big data center 100 shown in fig. 1, and the artificial intelligence based cosmetic production information monitoring method is described in detail below.

Step S110, obtaining production monitoring configuration information corresponding to each production monitoring configuration unit of the cosmetic production monitoring device, and performing information indexing on the production monitoring information of the target production monitoring cosmetic object based on the production monitoring configuration information to obtain a corresponding production monitoring index segment.

In this embodiment, the production monitoring configuration information may be used to represent a parameter instruction for making an information index decision in the production monitoring process, the target production monitoring cosmetic object may refer to an identified cosmetic business of the production monitoring cosmetic object data, and the production monitoring information may refer to a production monitoring configuration parameter related to the initiated production monitoring request. The production monitoring index segment may be an index segment which is obtained from a pre-configured index database for the production monitoring information of the target production monitoring cosmetic object and is matched with the parameter instruction of each information index decision, and specifically may include a production monitoring behavior obtained by indexing, a production monitoring cosmetic business label, a production monitoring team, a production monitoring marketing date, and the like.

And S120, acquiring a corresponding cosmetic raw material production map based on the production monitoring index fragment, and determining the raw material manufacturing cooperative operation distribution of a plurality of cosmetic production chains based on the cosmetic raw material production map.

In this embodiment, the cosmetic raw material production map may include production distribution conditions of cosmetic raw materials on each cosmetic production line, the cosmetic production chain may refer to that each cosmetic production line constitutes an operation relationship set, the raw material manufacturing coordinated operation distribution may refer to a distribution condition of a coordinated operation relationship of each cosmetic production line in a production process of the cosmetic raw materials, and the coordinated operation relationship may refer to an input/output control sequence in the production process.

Step S130, respectively inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in the production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition.

In this embodiment, the at least one production line configuration partition matching performed by the production decision push network is performed based on the associated big data production line partition, and the associated big data production line partition is associated with the production line configuration partitions extracted by the other production decision push networks of the plurality of production decision push networks. The production decision push network is obtained based on an artificial intelligence network and corresponding training samples in training, and the training samples comprise raw material manufacturing collaborative operation distribution samples and corresponding production line configuration partition labels.

Step S140, making a decision on a plurality of production line configuration partitions output by a plurality of production decision pushing networks to obtain decision production line configuration partitions, obtaining pushing sequence results of the cosmetic raw material production maps under the production monitoring information based on the decision production line configuration partitions, and pushing production behaviors based on the pushing sequence results of the cosmetic raw material production maps under the production monitoring information.

In this embodiment, the pushing sequence result of the cosmetic raw material production map under the production monitoring information may be used to represent a pushing instruction operation set of the cosmetic raw material production map in a subsequent production behavior pushing process, that is, a pushing control instruction for controlling a flow direction relationship of a pushed data node in the production behavior pushing process, so as to execute the computer program according to an execution sequence of the pushing control instructions, thereby pushing the production behavior.

Based on the above steps, in this embodiment, a raw material manufacturing collaborative operation distribution of a plurality of cosmetic production chains is determined based on a cosmetic raw material production map, the raw material manufacturing collaborative operation distribution is respectively input into a plurality of production decision push networks in a production decision push model, each production decision push network performs at least one production line configuration partition matching to obtain at least one production line configuration partition, and at least one production line configuration partition matching is performed based on an associated big data production line partition, which is associated with a production line configuration partition extracted by other production decision push networks of the plurality of production decision push networks, wherein the production decision push network is obtained based on artificial intelligence network training and corresponding training samples, which include raw material manufacturing collaborative operation distribution samples and corresponding production line configuration partition labels, therefore, at least one exchange and fusion can be carried out between the production line configuration partitions extracted from different production decision push networks, and then the production line configuration partitions of different levels can be decided, so that the representation capability of production behavior push is improved by enriching the levels of the production line configuration partitions, and the push pertinence is better.

In a possible implementation manner, on the basis of the above scheme, in this embodiment, one of the multiple production decision push networks may further be used as a target production decision push network, and then a first production line configuration partition extracted by the target production decision push network and a second production line configuration partition extracted by another production decision push network in the multiple production decision push networks except the target production decision push network are obtained.

In this way, when the cosmetic production chain of the second production line configuration partition does not match the cosmetic production chain of the first production line configuration partition, the state addition is performed on the second production line configuration partition, and the cosmetic production chain of the second production line configuration partition after the state addition is the same as the cosmetic production chain of the first production line configuration partition. Therefore, the production line configuration partition matching can be performed on the partition overlapping results of the second production line configuration partition and the first production line configuration partition after the state addition through the target production decision pushing network.

And the number of the second production line configuration subareas is at least two.

On the basis, when a second production line configuration partition of the cosmetic production chain, of which the cosmetic production chain is not matched with the first production line configuration partition, and a second production line configuration partition of the cosmetic production chain, of which the cosmetic production chain is matched with the first production line configuration partition, exist at the same time, state addition is performed on the second production line configuration partition of the cosmetic production chain, of which the cosmetic production chain is not matched with the first production line configuration partition, retrospective state addition is performed on the second production line configuration partition of the cosmetic production chain, of which the cosmetic production chain is matched with the first production line configuration partition, and the cosmetic production chain of the second production line configuration partition, of which the state is added, and the cosmetic production chain of which the retrospective state is added, are all the same as the cosmetic production chain of the first production line configuration partition.

In this way, the production line configuration partition matching can be performed on the second production line configuration partition after the state addition, the second production line configuration partition after the backtracking state addition and the partition superposition result of the first production line configuration partition through the target production decision push network.

For another example, in addition to the above, when the cosmetic production chain of the second production line configuration partition matches the cosmetic production chain of the first production line configuration partition, the retrospective state addition is performed on the second production line configuration partition, and the cosmetic production chain of the second production line configuration partition after the retrospective state addition is the same as the cosmetic production chain of the first production line configuration partition. Therefore, the production line configuration partition matching can be performed on the partition overlapping results of the second production line configuration partition and the first production line configuration partition after the backtracking state is added through the target production decision pushing network.

In a possible implementation manner, for step S130, in the process of inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in the production decision push model respectively, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition, the following exemplary sub-steps can be implemented, which are described in detail below.

And a substep S131 of inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in the production decision push model, respectively.

And a substep S132 of performing production line configuration partition matching on one of the production decision push networks according to the corresponding raw material manufacturing collaborative operation distribution through the production decision push network, obtaining a first production line configuration partition through production line configuration partition matching, performing partition service matching on a second production line configuration partition extracted through other production decision push networks of the plurality of production decision push networks and the first production line configuration partition, and continuing to perform production line configuration partition matching based on a partition service matching result so as to alternately perform production line configuration partition matching and partition service matching.

Still further, in step S120, in the process of determining the raw material manufacturing collaborative operation distribution of the plurality of cosmetic production chains based on the cosmetic raw material production spectrum, index matching of the cosmetic production chains may be performed on the cosmetic raw material production spectrum to obtain the raw material manufacturing collaborative operation distribution of the plurality of different cosmetic production chains.

For example, the push information nodes of the cosmetic raw material production map for each cosmetic production chain may be indexed and matched, and all the push information nodes are spliced according to the association relationship between the push information nodes, so that the raw material manufacturing collaborative operation distribution of a plurality of different cosmetic production chains is obtained.

Thus, in another parallel possible implementation manner, for step S130, in the process of respectively inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in the production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition, the following exemplary sub-steps can be implemented, which are described in detail below.

And a substep S133 of inputting the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in the production decision push model, respectively.

Wherein the raw material manufacturing collaborative operation distribution respectively corresponds to one of the plurality of production decision push networks one to one.

And a substep S134 of performing at least one production line configuration partition matching on the corresponding raw material manufacturing collaborative operation distribution through a production decision pushing network. Wherein, the extracted cosmetic production chain of the production line configuration subarea is consistent with the cosmetic production chain of the raw material manufacturing collaborative operation distribution corresponding to the production decision pushing network.

In a parallel possible implementation manner, the raw material manufacturing collaborative operation distribution at least includes a first raw material manufacturing collaborative operation distribution, a second raw material manufacturing collaborative operation distribution and a third raw material manufacturing collaborative operation distribution, the production decision push model includes a first production decision push network, a second production decision push network and a third production decision push network, the first material manufacturing collaborative operation distribution, the second material manufacturing collaborative operation distribution and the third material manufacturing collaborative operation distribution respectively correspond to material manufacturing collaborative operation distributions of a production quality control point, a material supply point and a material output point, and the first production decision push network, the second production decision push network and the third production decision push network respectively correspond to production decision push networks of the production quality control point, the material supply point and the material output point.

On this basis, still referring to step S130, in the process of respectively inputting the raw material manufacturing collaborative operation distribution into the plurality of production decision push networks in the production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition, the following exemplary sub-steps can be implemented, which are described in detail below.

And a substep S135, inputting the first raw material manufacturing collaborative operation distribution into a first production decision pushing network to perform first decision layer production line configuration partition matching, so as to obtain a production line configuration partition extracted by the first production decision pushing network at a first decision layer.

And a substep S136 of making a decision on the production line configuration partition extracted by the first production decision-making push network at the first decision-making layer and the second raw material manufacturing cooperative operation distribution to obtain a decision-making push information node corresponding to the second production decision-making push network at the second decision-making layer.

In substep S137, the production line configuration partition extracted by the first production decision pushing network at the first decision layer is obtained and used as a decision pushing information node corresponding to the first production decision pushing network at the second decision layer.

And a substep S138, performing first production line configuration partition matching of the second decision layer on the decision push information node corresponding to the second decision layer of the first production decision push network through the first production decision push network to obtain a production line configuration partition extracted for the first time in the second decision layer by the first production decision push network.

And in the substep S1391, performing, by using the second production decision push network, second decision layer primary production line configuration partition matching on a decision push information node of the second production decision push network corresponding to the second decision layer to obtain a production line configuration partition extracted by the second production decision push network at the second decision layer for the first time.

And a substep S1392 of transferring the production line configuration partition first extracted by the first production decision push network at the second decision layer to the second production decision push network, and transferring the production line configuration partition first extracted by the second production decision push network at the second decision layer to the first production decision push network.

And a substep S1393, deciding the production line configuration partition firstly extracted by the first production decision pushing network at the second decision layer and the production line configuration partition transmitted by the second production decision pushing network through the first production decision pushing network, and performing production line configuration partition matching on the partition superposition result.

And a substep S1394, deciding the production line configuration subarea extracted by the second production decision pushing network at the second decision layer for the first time in the second production decision layer and the production line configuration subarea transmitted by the first production decision pushing network through the second production decision pushing network, and matching the subarea overlapping results with the production line configuration subareas.

And in the substep S1395, a decision is made on the production line configuration partition extracted by the first production decision push network at the second decision layer, the production line configuration partition extracted by the second production decision push network at the second decision layer and the third raw material manufacturing cooperative operation distribution, so as to obtain a decision push information node corresponding to the third production decision push network at the third decision layer.

And a substep S1396 of performing, by the first production decision push network, third decision layer production line configuration partition matching in the production line configuration partition extracted in the second decision layer based on the first production decision push network, performing, by the second production decision push network, third decision layer production line configuration partition matching in the production line configuration partition extracted in the second decision layer based on the second production decision push network, and performing, by the third production decision push network, third decision layer production line configuration partition matching in the decision push information node corresponding to the third production decision layer of the third production decision push network.

Further, in a possible implementation manner, regarding step S140, in the process of obtaining the pushing sequence result of the production map of the cosmetic raw material under the production monitoring information based on the decision line configuration partition, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S141 of determining a push model corresponding to the production monitoring information.

And the substep S142, inputting the configuration of the decision production line into a pushing model in a partition mode, and obtaining a pushing sequence result of the cosmetic raw material production map under the production monitoring information through the pushing model.

The production decision push model and the configuration mode of the push model can be realized through the following exemplary embodiments:

(1) and acquiring a decision production line configuration subarea sample, a production decision pushing model and a pushing model, wherein a pushing label of the decision production line configuration subarea sample is used for expressing a labeling result of the decision production line configuration subarea sample under production monitoring information.

(2) And configuring a subarea sample based on a decision production line, and determining the raw material manufacturing collaborative operation distribution samples of a plurality of cosmetic production chains.

(3) And respectively inputting the raw material manufacturing collaborative operation distribution samples into a plurality of production decision push networks in the production decision push model, and performing at least one production line configuration partition matching through the production decision push networks to obtain at least one decision production line configuration partition.

The at least one production line configuration partition matching through the production decision push network is performed based on the associated big data production line partition, and the associated big data production line partition is associated with the decision production line configuration partition extracted by other production decision push networks of the plurality of production decision push networks.

(4) And making a decision on a plurality of production line configuration partitions output by a plurality of production decision pushing networks to obtain a target decision production line configuration partition.

(5) And inputting the configuration subareas of the target decision production line into a push model, and obtaining a decision result of the configuration subarea samples of the decision production line under the production monitoring information through the push model.

(6) And configuring a production decision pushing model and a pushing model based on the decision result and the pushing label.

Further, for step S120, in the process of obtaining the corresponding cosmetic raw material production map based on the production monitoring index segment, the corresponding cosmetic raw material production map corresponding to the production monitoring index segment may be obtained from a preset production line partition set.

On the basis, in the above steps, in the process of inputting the decision-making production line configuration partition into the push model, and obtaining the push sequence result of the cosmetic raw material production map under the production monitoring information through the push model, the target decision-making production line configuration partition can be input into the push model, and the push information index is performed on the target decision-making production line configuration partition through the push model, so as to obtain the push sequence result of the target decision-making production line configuration partition.

Further, in a possible implementation manner, for step S140, in the process of making a decision for multiple production line configuration partitions output by multiple production decision pushing networks to obtain a decision production line configuration partition, the following exemplary sub-steps may be implemented, which are described in detail below.

And a substep S143, obtaining a plurality of production line configuration partitions output by the plurality of production decision pushing networks, and determining a target production line configuration partition of the global cosmetic production chain in the plurality of production line configuration partitions.

And a substep S144, deciding other production line configuration subareas except the target production line configuration subarea in the plurality of production line configuration subareas, wherein the cosmetic production chain of the decided other production line configuration subareas is the same as the cosmetic production chain of the target production line configuration subarea.

And a substep S145, listing other production line configuration subareas and target production line configuration subareas after decision making to obtain a decision production line configuration subarea.

Further, in a possible implementation manner, for step S110, in the process of obtaining production monitoring configuration information corresponding to each production monitoring configuration unit of the cosmetic production monitoring device, and performing information indexing on the production monitoring information of the target production monitoring cosmetic object based on the production monitoring configuration information to obtain a corresponding production monitoring index segment, the following exemplary sub-steps can be implemented, which are described in detail as follows.

And the substep S111, calling the production monitoring software platform of the target production monitoring cosmetic object after the production monitoring software platform is updated in advance, and performing production simulation monitoring on the target production monitoring cosmetic object in each production simulation environment to obtain a production simulation monitoring result.

And a substep S112, comparing the production line switching behavior error data of the production monitoring configuration unit in the production simulation monitoring result with the production line switching behavior comparison data in the production line switching behavior database corresponding to the production monitoring configuration unit.

And a substep S113, determining a production simulation monitoring line with difference of error data in the production simulation monitoring result according to the comparison result, and obtaining production line result parameters of the production simulation monitoring line with difference of error data in each production simulation monitoring link.

And a substep S114 of updating production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object according to production line result parameters of the production simulation monitoring line with difference in error data in each production simulation monitoring link, and indexing the production monitoring information of the target production monitoring cosmetic object accessed via the block chain based on the updated production monitoring configuration information.

For example, in the embodiment, in the step S111, in the process of calling the production monitoring software platform of the target production monitoring cosmetic object subjected to the production monitoring software platform update in advance, performing production simulation monitoring on the target production monitoring cosmetic object in each production simulation environment to obtain the production simulation monitoring result, the production simulation monitoring process may be, for example, performing production simulation monitoring on the target production monitoring cosmetic object in each production simulation environment in a targeted manner for each production simulation monitoring item through the production monitoring software platform, so as to obtain the production line switching behavior error data of each production monitoring configuration unit in the production simulation monitoring result. Each production simulation monitoring item corresponds to one production line switching behavior error data, the production simulation monitoring items can refer to simulation monitoring items in different production simulation environments, the corresponding production line switching behavior error data can refer to simulation index parameters generated under the simulation monitoring items, and in addition, the production monitoring configuration unit can refer to a production monitoring configuration script for completing an updating process.

For example, in the present embodiment, in step S112, in the process of comparing the line switching behavior error data of the production monitoring configuration unit in the production simulation monitoring result with the line switching behavior comparison data in the line switching behavior database corresponding to the production monitoring configuration unit, the parameter change or parameter difference existing in the line switching behavior error data and the line switching behavior comparison data may be specifically compared, so that the production simulation monitoring line with the error data difference in the subsequent production simulation monitoring result may be determined.

For example, in the present embodiment, the line result parameters may refer to configuration parameters used for controlling the entire production monitoring process during the production simulation monitoring process, and these configuration parameters are usually related to the production simulation monitoring project. Therefore, according to the production line result parameters of the production simulation monitoring lines with difference in error data in each production simulation monitoring link, the production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object is updated, and the production monitoring information of the target production monitoring cosmetic object accessed through the block chain is indexed based on the updated production monitoring configuration information, in the specific updating process, the production line result parameters can be configured in each production simulation monitoring item, the specific project configuration process can be executed according to the scheme in the prior art, for example, for the production line switching item, the project configuration can be performed through the production line switching configuration category corresponding to the production line switching item, and the manual configuration can be performed by developers, the project configuration can also be automatically carried out through a preset automation script.

Based on the above design, in this embodiment, the production line switching behavior error data of the production monitoring configuration unit is compared with the production line switching behavior database corresponding to the production monitoring configuration unit, so as to determine a production simulation monitoring line with a difference in error data in a production simulation monitoring result, and then according to the production simulation monitoring line with a difference in error data, the production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object is automatically updated, and the production monitoring information of the target production monitoring cosmetic object accessed via the block chain is indexed based on the updated production monitoring configuration information, so that the production monitoring effect of the production monitoring software platform of the target production monitoring cosmetic object is improved, the human involvement is reduced, and the human resources are saved. In addition, the production monitoring configuration information is only updated in the whole process, and the prior updating effect of the production monitoring software platform is not influenced.

For example, in a possible implementation manner, for step S113, in the process of determining a production simulation monitoring line with a difference in error data in the production simulation monitoring result according to the comparison result, a plurality of implementation rules may be selected to determine, for example, at least one or any combination of the following may be included:

the first embodiment: determining a new production simulation monitoring line in the production simulation monitoring results according to the comparison result of the production line switching behavior error data of the production monitoring configuration unit and the production line switching behavior database, wherein the new production simulation monitoring line may include: and the number of error data elements which do not appear in the production line switching behavior database but appear in the production line switching behavior error data matches the number of first set error data elements, and the attribute Bayesian posterior probability reaches the production simulation monitoring line with the proportion matching set proportion of the first preset Bayesian posterior probability when the error data appear in the production line switching behavior error data each time.

The second embodiment: and determining a failed production simulation monitoring line in the production monitoring configuration unit according to the comparison result of the production line switching behavior error data of the production monitoring configuration unit and the production line switching behavior database. The failure production simulation monitoring line comprises: and a production simulation monitoring line in which the number of consecutive error data elements that appear in the line-switching behavior database but do not appear in the line-switching behavior error data matches the number of second set error data elements.

Third embodiment: and determining a speed switching production simulation monitoring line with abnormal speed switching in the production monitoring configuration unit according to the comparison result of the production line switching behavior error data of the production monitoring configuration unit and the production line switching behavior database. The fast switching production simulation monitoring line includes: and the production simulation monitoring line is determined according to the production line switching behavior database, wherein the event time sequence tracking simulation monitoring node information is inconsistent with the event time sequence tracking simulation monitoring node information in the production line switching behavior error data.

Further, for example, in a possible implementation manner, for step S113, in the process of obtaining the production line result parameters of the production simulation monitoring links in which the production simulation monitoring lines with the difference in error data exist, the following exemplary sub-steps can be implemented.

And a substep S1131, performing information matching on error data time sequence tracking information corresponding to the error data of each production line switching behavior and a time sequence tracking object of the production simulation monitoring line.

In the sub-step S1132, the error data timing tracking information whose matching degree reaches the set matching degree is added to the target reference simulation monitoring library.

And a substep S1133 of determining, for each production simulation monitoring link, each error data time sequence tracking information in the time sequence tracking simulation monitoring subsection interval of the production simulation monitoring link in the target reference simulation monitoring library.

And a substep S1134 of determining production line result parameters of the production simulation monitoring line in the production simulation monitoring link according to the Bayesian posterior probability of the attribute of the production simulation monitoring line in each determined error data time sequence tracking information and the difference between each error data time sequence tracking information and the production simulation monitoring link.

Illustratively, for example, in the sub-step S1131, for each successive error data timing tracking information, a corresponding timing tracking object simulation component of the error data timing tracking information in the timing tracking object is determined, and then a matching degree of the error data timing tracking information is determined according to a difference between the error data timing tracking information and the corresponding timing tracking object simulation component.

For another example, in the sub-step S1131, a plurality of error data timing trace information sequences may be combined into an error data timing trace unit sequence, a plurality of reference timing trace partitions may be determined according to the initial error data timing trace information in the error data timing trace information sequences and the timing trace parameters in the line switching behavior error data to form a reference timing trace partition set, and then the matching degree of each error data timing trace information in the error data timing trace unit sequence may be determined according to the difference between the error data timing trace unit sequence and the reference timing trace partition set.

Further, for example, in a possible implementation manner, for step S114, in the process of updating the production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object according to the production line result parameters of the production simulation monitoring line with difference in error data in each production simulation monitoring link, and performing information indexing on the production monitoring information of the target production monitoring cosmetic object accessed via the block chain based on the updated production monitoring configuration information, the following exemplary sub-steps may be implemented.

And a substep S1141 of determining a compatibility evaluation index of the production simulation monitoring line according to the production line result parameters of the production simulation monitoring line with the difference of the error data in each production simulation monitoring link.

And a substep S1142 of updating the production line result parameters of the production simulation monitoring lines with the difference in error data in each production simulation monitoring link to the production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object if the compatibility evaluation index of the production simulation monitoring lines with the difference in error data meets a preset condition, and performing information indexing on the production monitoring information of the target production monitoring cosmetic object accessed via the block chain based on the updated production monitoring configuration information.

The specific updating process is illustrated in the foregoing description, and is not described herein again.

Further, in some possible examples, the preset condition may include one or any combination of the following:

1) the distribution set degree of the time sequence tracking simulation monitoring distribution of the production simulation monitoring line is matched with the set distribution set degree.

2) The time sequence tracking simulation monitoring of the production simulation monitoring line is distributed at the adjacent time sequence tracking simulation monitoring nodes.

3) The coverage range of the signal of the production simulation monitoring line is within a set range interval.

4) And the key production line result parameters of the production simulation monitoring line are matched with the preset production line result parameters.

It is understood that, in the actual implementation process, any combination of the above preset conditions can be used for implementation, and the implementation is not limited specifically.

Fig. 3 is a schematic diagram of functional modules of the artificial intelligence based cosmetic production information monitoring device 300 according to an embodiment of the present disclosure, and in this embodiment, the artificial intelligence based cosmetic production information monitoring device 300 may be divided into the functional modules according to the method embodiment executed by the big data center 100, that is, the following functional modules corresponding to the artificial intelligence based cosmetic production information monitoring device 300 may be used to execute the method embodiments executed by the big data center 100. The artificial intelligence based cosmetic production information monitoring device 300 may include an obtaining index module 310, a determining module 320, a matching module 330, and a pushing module 340, and the functions of the functional modules of the artificial intelligence based cosmetic production information monitoring device 300 are described in detail below.

The obtaining and indexing module 310 is configured to obtain production monitoring configuration information corresponding to each production monitoring configuration unit of the cosmetic production monitoring device, and perform information indexing on the production monitoring information of the target production monitoring cosmetic object based on the production monitoring configuration information to obtain a corresponding production monitoring index segment. The index obtaining module 310 may be configured to perform the step S110, and the detailed implementation of the index obtaining module 310 may refer to the detailed description of the step S110.

A determining module 320, configured to obtain a corresponding cosmetic raw material production map based on the production monitoring index segment, and determine a raw material manufacturing collaborative operation distribution of a plurality of cosmetic production chains based on the cosmetic raw material production map. The determining module 320 may be configured to perform the step S120, and the detailed implementation of the determining module 320 may refer to the detailed description of the step S120.

A matching module 330, configured to input the raw material manufacturing collaborative operation distribution into a plurality of production decision push networks in a production decision push model, respectively, and perform at least one production line configuration partition matching through the production decision push networks to obtain at least one production line configuration partition; the production line configuration partition matching performed at least once through the production decision push network is performed based on an associated big data production line partition, the associated big data production line partition is associated to a production line configuration partition extracted by other production decision push networks of the plurality of production decision push networks, wherein the production decision push network is obtained based on artificial intelligence networks and corresponding training samples, and the training samples comprise raw material manufacturing collaborative operation distribution samples and corresponding production line configuration partition labels. The matching module 330 may be configured to perform the step S130, and the detailed implementation of the matching module 330 may refer to the detailed description of the step S130.

The pushing module 340 is configured to make a decision on a plurality of production line configuration partitions output by the plurality of production decision pushing networks to obtain a decision production line configuration partition, obtain a pushing sequence result of the cosmetic raw material production map under the production monitoring information based on the decision production line configuration partition, and push a production behavior based on the pushing sequence result of the cosmetic raw material production map under the production monitoring information. The pushing module 340 may be configured to perform the step S140, and the detailed implementation manner of the pushing module 340 may refer to the detailed description of the step S140.

It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules may all be implemented in software invoked by a processing element. Or may be implemented entirely in hardware. And part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the index obtaining module 310 may be a separate processing element, or may be integrated into a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and a processing element of the apparatus calls and executes the functions of the index obtaining module 310. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.

Fig. 4 shows a hardware structure diagram of a big data center 100 for implementing the artificial intelligence-based cosmetic production information monitoring method, provided by the embodiment of the present disclosure, and as shown in fig. 4, the big data center 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a transceiver 140.

In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120 (for example, the obtaining index module 310, the determining module 320, the matching module 330, and the pushing module 340 included in the artificial intelligence based cosmetic production information monitoring apparatus 300 shown in fig. 3), so that the processor 110 may execute the artificial intelligence based cosmetic production information monitoring method according to the above method embodiment, where the processor 110, the machine-readable storage medium 120, and the transceiver 140 are connected via the bus 130, and the processor 110 may be configured to control the transceiving action of the transceiver 140, so as to perform data transceiving with the aforementioned cosmetic production monitoring apparatus 200.

For a specific implementation process of the processor 110, reference may be made to the above-mentioned various method embodiments executed by the big data center 100, which implement principles and technical effects similar to each other, and details of this embodiment are not described herein again.

In the embodiment shown in fig. 4, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The machine-readable storage medium 120 may comprise high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory.

The bus 130 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus 130 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

In addition, the embodiment of the application also provides a readable storage medium, wherein the readable storage medium stores computer-executable instructions, and when a processor executes the computer-executable instructions, the method for monitoring the cosmetic production information based on the artificial intelligence is realized.

The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

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

Also, the description uses specific words to describe embodiments of the description. Reference 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 specification. 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 places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.

The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.

Computer program code required for the operation of various portions of this specification may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, VisualBasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and 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 in which the elements and sequences are processed, the use of alphanumeric characters, or the use of other designations in this specification is not intended to limit the order of the processes and methods in this specification, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to 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 devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, 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 one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Finally, it should be understood that the examples in this specification are only intended to illustrate the principles of the examples in this specification. Other variations are also possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. A cosmetic production information indexing method based on a blockchain is applied to a big data center which is in communication connection with a plurality of cosmetic production monitoring devices, and the method comprises the following steps:

the production simulation monitoring method comprises the steps of calling a production monitoring software platform of a target production monitoring cosmetic object subjected to production monitoring software platform updating in advance, carrying out production simulation monitoring on the target production monitoring cosmetic object in each production simulation environment to obtain a production simulation monitoring result, wherein the production simulation monitoring process is that the production simulation monitoring is carried out on the target production monitoring cosmetic object in each production simulation environment by the production monitoring software platform in a targeted manner aiming at each production simulation monitoring item, so that production line switching behavior error data of each production monitoring configuration unit in the production simulation monitoring result can be obtained; each production simulation monitoring item corresponds to one production line switching behavior error data, the production simulation monitoring items refer to simulation monitoring items under different production simulation environments, the corresponding production line switching behavior error data can refer to simulation index parameters generated under the simulation monitoring items, and in addition, the production monitoring configuration unit can refer to a production monitoring configuration script for completing an updating process;

comparing the production line switching behavior error data of the production monitoring configuration unit in the production simulation monitoring result with the production line switching behavior comparison data in the production line switching behavior database corresponding to the production monitoring configuration unit to determine a production simulation monitoring line with error data difference in the subsequent production simulation monitoring result;

2. The method for indexing cosmetics production information based on block chain according to claim 1, wherein the step of determining a production simulation monitoring line having a difference in error data in the production simulation monitoring result according to the comparison result includes at least one or any combination of the following:

determining a new production simulation monitoring line in the production simulation monitoring result according to the comparison result of the production line switching behavior error data of the production monitoring configuration unit and the production line switching behavior database, wherein the new production simulation monitoring line comprises: the number of error data elements which do not appear in the production line switching behavior database but appear in the production line switching behavior error data matches the number of first set error data elements, and the attribute Bayesian posterior probability reaches the production simulation monitoring line of the proportion matching set proportion of the first preset Bayesian posterior probability when appearing in the production line switching behavior error data at each time;

determining a failed production simulation monitoring line in the production monitoring configuration unit according to the comparison result of the production line switching behavior error data of the production monitoring configuration unit and the production line switching behavior database; the failure production simulation monitoring line comprises: a production simulation monitoring line in which the number of continuous error data elements appearing in the production line switching behavior database but not appearing in the production line switching behavior error data matches the number of second set error data elements;

determining a speed switching production simulation monitoring line in which abnormal speed switching occurs in the production monitoring configuration unit according to a comparison result of production line switching behavior error data of the production monitoring configuration unit and a production line switching behavior database; the fast switching production simulation monitoring line includes: and the production simulation monitoring line is determined according to the production line switching behavior database, wherein the event time sequence tracking simulation monitoring node information is inconsistent with the event time sequence tracking simulation monitoring node information in the production line switching behavior error data.

3. The block chain-based cosmetic production information indexing method according to claim 1, wherein the step of obtaining the production line result parameters of the production simulation monitoring lines with differences in error data at each production simulation monitoring link comprises:

carrying out information matching on error data time sequence tracking information corresponding to the error data of the switching behaviors of each production line and a time sequence tracking object of a production simulation monitoring line;

adding error data time sequence tracking information with the matching degree reaching the set matching degree to a target reference simulation monitoring library;

for each production simulation monitoring link, determining each error data time sequence tracking information in the time sequence tracking simulation monitoring subsection interval of the production simulation monitoring link in a target reference simulation monitoring base;

and determining production line result parameters of the production simulation monitoring line in the production simulation monitoring link according to the determined attribute Bayesian posterior probability of the production simulation monitoring line on each error data time sequence tracking information and the difference between each error data time sequence tracking information and the production simulation monitoring link.

4. The block chain-based cosmetic production information indexing method according to claim 3, wherein the step of performing information matching of error data timing trace information corresponding to the error data for each line switching action with a timing trace object of a production simulation monitoring line comprises:

and for each continuous error data time sequence tracking information, determining a corresponding time sequence tracking object simulation component of the error data time sequence tracking information in a time sequence tracking object, and then determining the matching degree of the error data time sequence tracking information according to the difference between the error data time sequence tracking information and the corresponding time sequence tracking object simulation component.

5. The block chain-based cosmetic production information indexing method according to claim 3, wherein the step of performing information matching of error data timing trace information corresponding to the error data for each line switching action with a timing trace object of a production simulation monitoring line comprises:

the method comprises the steps of enabling a plurality of continuous error data time sequence tracking information to form an error data time sequence tracking unit sequence, determining a plurality of reference time sequence tracking partitions according to initial error data time sequence tracking information in the error data time sequence tracking information and time sequence tracking parameters in production line switching behavior error data to form a reference time sequence tracking partition set, and then determining the matching degree of each error data time sequence tracking information in the error data time sequence tracking unit sequence according to the difference between the error data time sequence tracking unit sequence and the reference time sequence tracking partition set.

6. The block chain-based cosmetic production information indexing method according to claim 1, wherein the step of updating production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object according to the production line result parameters of the production simulation monitoring lines with differences in error data in each production simulation monitoring link, and performing information indexing on the production monitoring information of the target production monitoring cosmetic object accessed via the block chain based on the updated production monitoring configuration information comprises:

determining the compatibility evaluation index of the production simulation monitoring line according to the production line result parameters of the production simulation monitoring line with difference in error data in each production simulation monitoring link;

and if the compatibility evaluation index of the production simulation monitoring lines with the difference in error data meets a preset condition, updating the production line result parameters of the production simulation monitoring lines with the difference in error data in each production simulation monitoring link to the production monitoring configuration information corresponding to the production monitoring configuration unit in the production monitoring software platform of the target production monitoring cosmetic object, and performing information indexing on the production monitoring information of the target production monitoring cosmetic object accessed through the block chain based on the updated production monitoring configuration information.

7. The block chain-based cosmetic production information indexing method according to claim 6, wherein the preset condition includes one or any combination of:

the distribution set degree of the time sequence tracking simulation monitoring distribution of the production simulation monitoring line is matched with the set distribution set degree;

the time sequence tracking simulation monitoring of the production simulation monitoring line is distributed at adjacent time sequence tracking simulation monitoring nodes;

the coverage range of the signal of the production simulation monitoring line is in a set range interval;

and the key production line result parameters of the production simulation monitoring line are matched with the preset production line result parameters.

8. The block chain-based cosmetic production information indexing method according to any one of claims 1 to 7, further comprising:

9. The artificial intelligence-based cosmetic production information monitoring system is characterized by comprising a big data center and a plurality of cosmetic production monitoring devices in communication connection with the big data center;

the big data center is used for:

10. A big data center, comprising a processor, a machine-readable storage medium, and a network interface, wherein the machine-readable storage medium, the network interface, and the processor are connected via a bus system, the network interface is configured to communicatively connect with at least one cosmetic production monitoring device, the machine-readable storage medium is configured to store a program, instructions, or code, and the processor is configured to execute the program, instructions, or code in the machine-readable storage medium to perform the block chain based cosmetic production information indexing method of any one of claims 1-8.