CN113850453A - Material flow determining method, device and medium based on industrial neural network - Google Patents
Material flow determining method, device and medium based on industrial neural network Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device and a medium for determining a material flow based on an industrial neural network. Wherein, the method comprises the following steps: establishing a target flow structure chart of the material according to initial service data in the target flow and a preset flow chart model; the initial service data comprises original data of a bottom node, and the bottom node is connected with a root node through at least one layer of edge connection relation; and determining the process data of the root node in the target process structure chart according to the original data of the bottom node and a preset industrial neural network algorithm. By establishing a target process structure chart, the relationship among all nodes in the process is determined, and the data of the root node is determined according to the original data of the bottom node, so that the related conditions of the process can be conveniently and quickly looked up, the labor and the time are saved, and the determining efficiency of the processes such as material purchasing and distribution is improved.
Description
Technical Field
The embodiment of the invention relates to a computer technology, in particular to a material flow determining method, a device and a medium based on an industrial neural network.
Background
With the increase of the scale of the enterprise, each production or sales process becomes more and more complex, for example, the workload of purchasing and distributing materials by the enterprise gradually increases, and the efficiency of communication and collaboration among related departments is continuously reduced.
Under the prior art, enterprises usually store element information of the processes of purchasing, distribution and the like by adopting manual work, paper files and data lists, various information is stored at different positions, the flexibility is poor, the historical orders, the sequence of the elements, the association condition among the elements and the like cannot be consulted quickly, the process determining efficiency is low, and a large amount of manpower and time are wasted.
Disclosure of Invention
The embodiment of the invention provides a material flow determining method, a material flow determining device and a material flow determining medium based on an industrial neural network, and aims to improve the determining efficiency of the material flow.
In a first aspect, an embodiment of the present invention provides a method for determining a material flow based on an industrial neural network, where the method includes:
establishing a target flow structure chart of the material according to initial service data in the target flow and a preset flow chart model; the initial service data comprises original data of a bottom node, and the bottom node is connected with a root node through at least one layer of edge connection relation;
and determining the process data of the root node in the target process structure chart according to the original data of the bottom node and a preset industrial neural network algorithm.
In a second aspect, an embodiment of the present invention further provides an apparatus for determining a material flow based on an industrial neural network, where the apparatus includes:
the flow chart creating module is used for creating a target flow chart structure chart of the material according to initial service data in the target flow and a preset flow chart model; the initial service data comprises original data of a bottom node, and the bottom node is connected with a root node through at least one layer of edge connection relation;
and the flow data determining module is used for determining the flow data of the root node in the target flow structure chart according to the original data of the bottom layer node and a preset industrial neural network algorithm.
In a third aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for determining a material flow based on an industrial neural network according to any of the embodiments of the present invention.
The method and the device for calculating the root node flow data in the target flow acquire the initial service data of the target flow, create a target flow structure diagram according to a preset flow diagram model, and calculate the flow data of the root node in the target flow based on a preset industrial neural network algorithm. The problem of among the prior art, the manual work carries out flow data calculation to and look over the flow through the data list is solved, realized the automation and the intellectuality that flow confirms such as material purchase and delivery, look over the condition of target flow fast through the target flow structure chart, practice thrift manpower and time, improve each flow definite efficiency such as material purchase and delivery.
Drawings
Fig. 1 is a schematic flow chart of a material flow determination method based on an industrial neural network according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of a material purchasing business process structure in the first embodiment of the present invention;
fig. 3 is a schematic flow chart of a material flow determination method based on an industrial neural network according to a second embodiment of the present invention;
fig. 4 is a schematic flow chart of a material flow determination method based on an industrial neural network in a third embodiment of the present invention;
fig. 5 is a block diagram of a material flow determination apparatus based on an industrial neural network according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flow chart of a method for determining a material flow based on an industrial neural network according to an embodiment of the present invention, where the embodiment is applicable to determining a flow link, and the method can be executed by a device for determining a material flow based on an industrial neural network. As shown in fig. 1, the method specifically includes the following steps:
s110, creating a target flow structure diagram of the material according to initial service data in the target flow and a preset flow diagram model; the initial service data comprises original data of a bottom node, and the bottom node is connected with the root node through at least one layer of edge connection relation.
The material process is a process related to materials in a product production process, the initial business data refers to original data preset in a target process and can include original data of bottom nodes, the original data of the bottom nodes refers to basic attribute data of each link in the target process, and the basic attribute data nodes are used as the bottom nodes. For example, the target process of the material may be a process of material purchasing and distribution, and the like, the links in the process may include a contract signing link, the attribute data of the contract signing link may be data of names, job numbers, posts and the like of the signing personnel, and the bottom layer nodes are name nodes, job number nodes and post nodes of the signing personnel. The attribute data may originate from a file, picture, or other relational database.
The flow chart model is obtained by presetting, the target flow can be sleeved in the preset flow chart model to obtain a target flow structure chart of material purchasing and distribution, and all nodes in the flow chart model can be connected by adopting edges. For example, a child node and a parent node make an edge connection, and the child node is a subordinate node of the parent node. The bottom layer node is a child node, the root node is a father node, and the bottom layer node and the root node are connected through at least one layer of edge connection relation, namely, nodes of other levels can exist between the bottom layer node and the root node. For example, if there is a first-level intermediate node between the bottom node and the root node, the intermediate node is a parent node of the bottom node, and the root node is a parent node of the intermediate node.
In this embodiment, optionally, the initial service data further includes a flow relationship between nodes in the target flow, where each node includes a bottom node and a root node; correspondingly, according to the initial service data in the target process and a preset process diagram model, a target process structure diagram of the material is created, and the method comprises the following steps: and according to the flow relation among all nodes in the target flow and a preset flow chart model, performing edge connection on all nodes in the target flow to complete the creation of the target flow chart of the material.
Specifically, the initial service data may include original data of a bottom node, or may include a flow relationship between nodes, where the flow relationship refers to an edge connection relationship between the nodes, and each node may include a bottom node, a root node, and an intermediate node. According to the flow relation among the nodes and a preset flow model diagram, applying the nodes of the target flow to the flow model diagram, performing edge connection among the nodes to obtain a target flow structure diagram, and storing original data of the bottom-layer nodes into the bottom-layer nodes. The process model diagram is obtained by presetting, and the process model diagram can be formed by obtaining related personnel information, equipment information, material information, process determination rule information and process environment information in each process of the material and associating according to the association relationship among the personnel information, the equipment information, the material information, the process determination rule information and the process environment information. The material is a material required by the production field, and the material information can be information such as the components, proportion, hardness and the like of the material. The method has the advantages that the operation steps of creating the target flow structure chart by the user are reduced, the correctness of the target flow structure chart is improved by acquiring the flow relation, the condition that the target flow structure chart is quickly looked up is realized, the labor and the time are saved, and the flow determining efficiency is improved.
Fig. 2 is a schematic diagram of a material purchasing business process. The root node 201 includes the steps of purchasing task issuing, contract signing, supplier delivery, delivery handover, inspection warehousing and payment settlement, and the root node 201 represents each link of the material purchasing business process. The intermediate node 202 is a child node of the root node 201, and has an edge connection relationship with the root node 201, for example, the intermediate node having an edge connection relationship with the procurement task includes process handling and personnel. The bottom node 203 is a child node of the middle node 202, has an edge connection relationship with the middle node 202, and may represent attribute data of the middle node 202, for example, attribute data of a person includes a name, a job number and a position.
And S120, determining the process data of the root node in the target process structure diagram according to the original data of the bottom layer node and a preset industrial neural network algorithm.
The industrial neural network is an industrial network form which is obtained based on the combination of graph database technology and algorithm and can be used for industrial design, production, manufacturing and other applications. The preset industrial neural network algorithm is used for traversing the target flow structure diagram and calculating the flow data of the root node in the target flow structure diagram, for example, the time of the flow end or the amount of money spent at the flow end can be calculated. After a user sends a calculation instruction of the process data, a preset industrial neural network algorithm obtains original data in bottom nodes, and the process data in the root nodes are obtained through calculation step by step according to the original data.
In this embodiment, optionally, determining the process data of the root node in the target process structure diagram according to the original data of the bottom node and a preset industrial neural network algorithm includes: determining the edge connection relation between the bottom node and the root node according to the target process structure chart; and determining the flow data of the root node based on a preset industrial neural network algorithm according to the original data of the bottom node and the edge connection relation between the bottom node and the root node.
Specifically, the edge connection relationship between the nodes in the graph is determined by obtaining the target flow structure diagram, and the path from the bottom node to the root node is obtained according to the edge connection relationship between the bottom node and the root node. And acquiring original data in the bottom layer node by a preset industrial neural network algorithm, and calculating from the bottom layer node to the root node according to the acquired path to acquire the flow data of the root node. For example, the time period for each root node in the preset flow to complete is 1 day, three root nodes are provided in total, the original data of the bottom node is the time when the first root node starts, and is 7 months and 1 day, and the time for predicting the flow to end is 7 months and 4 days. The method has the advantages that the flow data of the root node can be quickly obtained through the target flow structure diagram of the material and the industrial neural network algorithm, the problem that in the prior art, the flow data is obtained by manually calculating or checking the flow data table is solved, the calculation efficiency of the flow data is improved, and the method is favorable for checking flows such as purchasing and distribution of the material.
In this embodiment, optionally, determining the flow data of the root node based on a preset industrial neural network algorithm according to the original data of the bottom node and the edge connection relationship between the bottom node and the root node includes: traversing the edge connection relation between the bottom node and the root node in the target flow structure chart by adopting a preset industrial neural network algorithm; determining the shortest path from the bottom node to the root node according to the traversal result; and transmitting the original data of the bottom node to the root node according to the shortest path and a preset industrial neural network algorithm, and determining the flow data of the root node.
Specifically, a preset industrial neural network algorithm is adopted to traverse the target flow structure diagram, and the edge connection relation among all nodes in the diagram is traversed to obtain a path from a bottom node to a root node. And comparing the lengths of all the paths from the bottom layer nodes to the root node, and determining the shortest path from the bottom layer nodes to the root node so as to calculate the flow data according to the shortest path. For example, there are two root nodes, node one and node two, respectively, and the shortest paths from the bottom node to node one and node two, respectively, can be obtained, that is, two shortest paths are obtained. The method comprises the steps of obtaining flow data of a first node and a second node according to a shortest path, wherein the flow data is the end time of the first node and the second node, presetting the interval time between the two links of the first node and the second node, and obtaining the finish time of the whole flow according to the preset interval time and the end time of the first node and the second node.
According to a preset industrial neural network algorithm, acquiring original data of bottom nodes in the shortest path, processing and calculating the original data, and transmitting the data in the calculation process layer by layer from bottom to top to the top node of a target process, namely a root node. The user can calculate the flow data at any time, and can set the constraint conditions for automatic calculation. The constraint conditions of each node can be stored in an external function, and the external function is called to execute the related function by executing the industrial neural network algorithm. And when the constraint condition is met, automatically calculating the next node. For example, the target process is a purchase business process, and the constraint condition is that if raw data of the bottom node is received, a preset industrial neural network algorithm is operated. The calculation is carried out from the first root node of 'purchasing task delivery', and when the 'supplier delivery' is carried out, the operation is suspended because the supplier has not delivered goods and has no original data of the root node. And when the delivery data of the supplier is received, the calculation is continued when the constraint condition is met. The method has the advantages that the shortest path from the bottom node to the root node is found by traversing the target process structure diagram, calculation is carried out according to the shortest path strength, and calculation time is saved. Through the layer-by-layer transmission of the data, the omission of the nodes is avoided, and the calculation precision and the calculation efficiency of the flow data are improved.
According to the technical scheme of the embodiment, initial service data of the target process is obtained, a target process structure diagram is created according to a preset process diagram model, and process data of a root node in the target process are calculated based on a preset industrial neural network algorithm. The problems that in the prior art, flow data calculation is carried out manually and the flow is checked through a data list are solved, the constraint of a traditional relational database is eliminated, the performance is greatly improved for large-scale deep traversal, and rapid analysis based on massive relational data can be realized. The situation of the target flow is quickly looked up through the target flow structure diagram, manpower and time are saved, and the efficiency of determining the material flow is improved.
Example two
Fig. 3 is a schematic flow chart of a material flow determination method based on an industrial neural network according to a second embodiment of the present invention, and the present embodiment is further optimized based on the above embodiments. As shown in fig. 3, the method specifically includes the following steps:
s310, creating a target flow structure diagram of the material according to initial service data in the target flow and a preset flow diagram model; the initial service data comprises original data of a bottom node, and the bottom node is connected with the root node through at least one layer of edge connection relation.
S320, determining the process data of the root node in the target process structure diagram according to the original data of the bottom layer node and a preset industrial neural network algorithm.
S330, responding to a node data updating instruction of a user, and updating data of a target bottom layer node in the target flow structure diagram.
The method comprises the steps of determining a target node of node data to be updated according to a node data updating instruction sent by a user, acquiring the latest node data input by the user, and storing the latest node data into the target node. For example, a root node for contracting exists in the target flow structure diagram, and after the contracting is performed, data such as time, personnel and the like for contracting are stored in the related bottom nodes.
In this embodiment, optionally, updating the data of the target bottom node in the target flow structure diagram in response to the node data update instruction of the user includes: acquiring the latest data of a target bottom node in a target flow structure chart; and replacing the original data of the target bottom node with the latest data in response to the node data updating instruction.
Specifically, a target node is determined in response to a node data update instruction sent by a user, wherein the target node is a bottom node, data of the bottom node is changed, and other nodes having an edge connection relationship with the bottom node are updated accordingly. And acquiring the latest data input by the user, and replacing the original data in the target bottom node with the latest data. Interfaces corresponding to the nodes in the target process structure diagram of the industrial neural network can be connected with the database, so that the data of the nodes can be acquired through the database. For example, in the process of determining the flow, the data of the node may need to be updated in real time, so that the interface corresponding to the node may be connected to the database to obtain the node data from the database in time. Flow data assembly rules can be constructed and stored in a database, so that the nodes can be conveniently called. The method has the advantages that the user only needs to update the data of the bottom node, the problem that the data of the bottom node is still original data and cannot be updated in real time after the user changes the data of the middle node or the root node is solved, and the accuracy of the data in the target flow structure chart is improved.
S340, determining the latest process data of the root node in the target process structure diagram according to the latest data of the target bottom node and a preset industrial neural network algorithm.
After the update data of the target bottom node is determined, traversing the target flow structure diagram by adopting a preset industrial neural network algorithm, and determining nodes which have edge connection relation with the target bottom node, wherein the nodes can be intermediate nodes and root nodes. According to the industrial neural network algorithm, data are transmitted from the bottom node to the upper layer by layer, the data of the nodes which are in edge connection relation with the bottom node are updated until the process data in the root node are updated, and the latest process data of the root node are obtained.
The method and the device for calculating the root node flow data in the target flow are characterized in that initial service data of the target flow are obtained, a target flow structure diagram of the material is created according to a preset flow diagram model, and the flow data of the root node in the target flow are calculated based on a preset industrial neural network algorithm. When the process data is updated, the process data of the root node can be automatically calculated only by changing the data of the bottom node. The problem of among the prior art, the manual work carries out flow data calculation to and look over the flow through the data list is solved, realized looking up the condition of target flow fast through the target flow structure chart, practice thrift manpower and time, improve the efficiency that the flow is confirmed, avoid each node data update untimely in the target flow, improve the efficiency and the precision of data update.
EXAMPLE III
Fig. 4 is a schematic flow chart of a material flow determination method based on an industrial neural network according to a third embodiment of the present invention, and the present embodiment is further optimized based on the above embodiments. As shown in fig. 4, the method specifically includes the following steps:
s410, creating a target flow structure diagram of the material according to initial service data in the target flow and a preset flow diagram model; the initial service data comprises original data of a bottom node, and the bottom node is connected with the root node through at least one layer of edge connection relation.
And S420, determining the process data of the root node in the target process structure diagram according to the original data of the bottom layer node and a preset industrial neural network algorithm.
And S430, responding to a flow data query instruction of a user, and determining a target query node.
The user sends a flow data query instruction, and determines a target query node according to the flow data query instruction, wherein the target query node can be a bottom node, an intermediate node or a root node. For example, the user may select a target query node on the visual interface of the target flow structure diagram and click on the query instruction.
S440, according to the target process structure diagram, determining adjacent nodes which have edge connection relation with the target query node.
After the target query node is determined, traversing all nodes in the target flow structure chart according to a preset query algorithm, and determining adjacent nodes which have edge connection relation with the target query node.
S450, storing the process data of the target query node and the adjacent nodes into a data list for displaying.
The method comprises the steps of obtaining flow data in a target query node and adjacent nodes, and displaying and storing the obtained flow data in a data list mode. For example, if the destination query node is a supplier shipping node, the neighboring node is an equipment node, and the equipment node is a child node of the supplier shipping node, the process data in the supplier shipping node and the process data in the equipment node are listed in the table, and the fields may be the node name and the process data. The node name is supplier delivery, and the corresponding flow data can be delivery time; the node name is equipment, and the corresponding flow data is equipment model and the like.
The method and the device for calculating the root node flow data in the target flow are characterized in that initial service data of the target flow are obtained, a target flow structure diagram is created according to a preset flow diagram model, and the flow data of the root node in the target flow are calculated based on a preset industrial neural network algorithm. The process data of the target query node and the adjacent nodes can be obtained by determining the target query node. The problem of among the prior art, the manual work carries out flow data calculation to and look over flow data through the data list is solved, realized looking up the condition of target flow fast through the target flow structure chart, avoid data or process node's omission, practice thrift manpower and time, improve the efficiency and the data query efficiency that the material flow is confirmed.
Example four
Fig. 5 is a block diagram of a material flow determining apparatus based on an industrial neural network according to a fourth embodiment of the present invention, which is capable of executing a material flow determining method based on an industrial neural network according to any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the executing method. As shown in fig. 5, the apparatus specifically includes:
a flow chart creating module 501, configured to create a target flow chart structure diagram of a material according to initial service data in a target flow and a preset flow chart model; the initial service data comprises original data of a bottom node, and the bottom node is connected with a root node through at least one layer of edge connection relation;
the flow data determining module 502 is configured to determine flow data of a root node in the target flow structure diagram according to the original data of the bottom node and a preset industrial neural network algorithm.
Optionally, the initial service data further includes a process relationship between nodes in the target process, where each node includes a bottom-layer node and a root node;
accordingly, the flowchart creating module 501 is specifically configured to:
and according to the flow relation among all nodes in the target flow and a preset flow chart model, performing edge connection on all nodes in the target flow to complete the creation of the target flow chart of the material.
Optionally, the flow data determining module 502 includes:
the relation determining unit is used for determining the edge connection relation between the bottom node and the root node according to the target process structure chart;
and the data determining unit is used for determining the flow data of the root node based on a preset industrial neural network algorithm according to the original data of the bottom node and the edge connection relation between the bottom node and the root node.
Optionally, the relationship determining unit is specifically configured to:
traversing the edge connection relation between the bottom node and the root node in the target flow structure chart by adopting a preset industrial neural network algorithm;
determining the shortest path from the bottom node to the root node according to the traversal result;
and transmitting the original data of the bottom node to the root node according to the shortest path and a preset industrial neural network algorithm, and determining the flow data of the root node.
Optionally, the apparatus further comprises:
the target node updating module is used for responding to a node data updating instruction of a user and updating the data of the target bottom layer node in the target flow structure chart after determining the flow data of the root node in the target flow structure chart according to the original data of the bottom layer node and a preset industrial neural network algorithm;
and the root node updating module is used for determining the latest process data of the root node in the target process structure chart according to the latest data of the target bottom layer node and a preset industrial neural network algorithm.
Optionally, the target node updating module is specifically configured to:
acquiring the latest data of a target bottom node in a target flow structure chart;
and replacing the original data of the target bottom node with the latest data in response to the node data updating instruction.
Optionally, the apparatus further comprises:
the target node determining module is used for responding to a flow data query instruction of a user and determining a target query node;
the adjacent node determining module is used for determining adjacent nodes which have edge connection relation with the target inquiry node according to the target flow structure chart;
and the data display module is used for storing the process data of the target query node and the adjacent nodes into a data list for display.
The method and the device for calculating the root node flow data in the target flow are characterized in that initial service data of the target flow are obtained, a target flow structure diagram is created according to a preset flow diagram model, and the flow data of the root node in the target flow are calculated based on a preset industrial neural network algorithm. The problem of among the prior art, the manual work carries out flow data calculation to and look over the flow through the data list is solved, realized looking up the condition of target flow fast through the target flow structure chart, practice thrift manpower and time, improve the efficiency that the flow is confirmed.
EXAMPLE five
The fifth embodiment of the present invention further provides a storage medium containing computer executable instructions, where the storage medium stores a computer program, and when the program is executed by a processor, the method for determining a flow based on an industrial neural network according to the fifth embodiment of the present invention includes:
establishing a target flow structure chart of the material according to initial service data in the target flow and a preset flow chart model; the initial service data comprises original data of a bottom node, and the bottom node is connected with a root node through at least one layer of edge connection relation;
and determining the process data of the root node in the target process structure chart according to the original data of the bottom node and a preset industrial neural network algorithm.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. 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 computer device. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including 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 using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (9)
1. A material flow determining method based on an industrial neural network is characterized by comprising the following steps:
establishing a target flow structure chart of the material according to initial service data in the target flow and a preset flow chart model; the initial service data comprises original data of a bottom node, and the bottom node is connected with a root node through at least one layer of edge connection relation;
and determining the process data of the root node in the target process structure chart according to the original data of the bottom node and a preset industrial neural network algorithm.
2. The method of claim 1, wherein the initial business data further comprises a process relationship between nodes in a target process, wherein the nodes comprise an underlying node and a root node;
correspondingly, according to the initial service data in the target process and a preset process diagram model, a target process structure diagram of the material is created, and the method comprises the following steps:
and according to the flow relation among all nodes in the target flow and a preset flow chart model, performing edge connection on all nodes in the target flow to complete the creation of the target flow chart of the material.
3. The method of claim 1, wherein determining the process data of the root node in the target process structure diagram according to the raw data of the bottom node and a preset industrial neural network algorithm comprises:
determining the edge connection relation between the bottom layer node and the root node according to the target process structure diagram;
and determining the flow data of the root node based on a preset industrial neural network algorithm according to the original data of the bottom node and the edge connection relation between the bottom node and the root node.
4. The method according to claim 3, wherein determining the flow data of the root node based on a preset industrial neural network algorithm according to the raw data of the bottom node and the edge connection relationship between the bottom node and the root node comprises:
traversing the edge connection relation between the bottom layer node and the root node in the target process structure chart by adopting a preset industrial neural network algorithm;
determining the shortest path from the bottom layer node to the root node according to the traversal result;
and transmitting the original data of the bottom layer node to the root node according to the shortest path and a preset industrial neural network algorithm, and determining the flow data of the root node.
5. The method of claim 1, after determining the process data of the root node in the target process structure diagram according to the raw data of the bottom node and a preset industrial neural network algorithm, further comprising:
responding to a node data updating instruction of a user, and updating data of a target bottom layer node in the target flow structure chart;
and determining the latest process data of the root node in the target process structure chart according to the latest data of the target bottom layer node and a preset industrial neural network algorithm.
6. The method of claim 5, wherein updating the data of the target underlying node in the target flow structure diagram in response to the node data update instruction of the user comprises:
acquiring the latest data of a target bottom node in the target flow structure chart;
and in response to a node data updating instruction, replacing the original data of the target bottom layer node with the latest data.
7. The method of claim 1, after determining the process data of the root node in the target process structure diagram according to the updated node data and a preset industrial neural network algorithm, further comprising:
responding to a flow data query instruction of a user, and determining a target query node;
according to the target flow structure diagram, determining adjacent nodes which have edge connection relation with the target query node;
and storing the process data of the target query node and the adjacent nodes into a data list for displaying.
8. A material flow determining device based on an industrial neural network is characterized by comprising:
the flow chart creating module is used for creating a target flow chart structure chart of the material according to initial service data in the target flow and a preset flow chart model; the initial service data comprises original data of a bottom node, and the bottom node is connected with a root node through at least one layer of edge connection relation;
and the flow data determining module is used for determining the flow data of the root node in the target flow structure chart according to the original data of the bottom layer node and a preset industrial neural network algorithm.
9. A storage medium containing computer-executable instructions for performing the industrial neural network-based material flow determination method of any one of claims 1-7 when executed by a computer processor.
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