A digital thermoplastic mold processing system, method and medium based on big data

Abstract

The present invention relates to artificial intelligence technology, and discloses a digital thermoplastic mold processing system, method and medium based on big data. The system comprises an information correlation analysis module, a directed graph conversion module, a virtual element configuration module, a supply chain augmented graph generation module, a target production path generation module and a production plan generation module. The dependency relationship of the supply chain data is obtained by performing correlation analysis on pre-acquired supply chain data, a directed graph is generated according to the dependency relationship and the supply chain data, virtual elements are configured on the directed graph according to a preset market demand trend, and then weights are updated to obtain a supply chain augmented graph, a target production path of a thermoplastic mold is generated by using the supply chain augmented graph, and a production plan is determined according to the target production path, so as to perform mold processing of the thermoplastic mold, optimize supply chain management by using big data technology, and improve the efficiency of digital thermoplastic mold processing.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a digital thermoplastic mold processing system, method and medium based on big data. Background Art

[0002] With the rapid development of the mold industry, digital technology has become an important means for enterprises to enhance their competitiveness. By using digital technology to improve product quality and production efficiency, enterprises can better meet market demand and enhance market competitiveness.

[0003] Digital thermoplastic mold processing has serious deficiencies in supply chain management. For example, traditional supply chain management may lack effective tools to integrate and analyze large amounts of data from different sources, resulting in information islands and an inability to fully grasp the supply chain status; it is impossible to accurately analyze the cost correlation of each link; decision makers often rely on experience rather than data-driven insights to make decisions, etc., which will affect the effective management of the supply chain and lead to low efficiency in digital thermoplastic mold processing. Summary of the invention

[0004] The present invention provides a digital thermoplastic mold processing system, method and medium based on big data, the main purpose of which is to solve the problem of low efficiency in digital thermoplastic mold processing.

[0005] To achieve the above-mentioned purpose, the present invention provides a digital thermoplastic mold processing system based on big data, characterized in that the system includes an information correlation analysis module, a directed graph conversion module, a virtual element configuration module, a supply chain augmented graph generation module, a target production path generation module and a production plan generation module, wherein:

[0006] The correlation analysis module is used to obtain the supply chain data of the thermoplastic mold, and perform correlation analysis on the supply chain data using a preset correlation analysis algorithm to obtain the dependency relationship of the supply chain data, wherein the preset correlation analysis algorithm is:

[0007]

[0008] Among them, ρ S (X,Y) is the correlation coefficient of the data characteristics of the supply chain data, d i is the level difference of the i-th data feature, n is the total number of the data features, X is the feature identifier of data feature X, and Y is the feature identifier of data feature Y;

[0009] The directed graph conversion module is used to perform directed graph conversion on the supply chain data according to the dependency relationship to obtain a directed graph of the supply chain data;

[0010] The virtual element configuration module is used to configure virtual elements of the directed graph according to a preset market demand trend;

[0011] The supply chain augmented graph generation module is used to update the weight of the directed graph after the virtual element configuration to obtain the supply chain augmented graph of the thermoplastic mold;

[0012] The target production path generation module is used to generate the target production path of the thermoplastic mold by using the supply chain augmented graph;

[0013] The production plan generating module is used to adjust the production plan of the thermoplastic mold according to the target production path, obtain the production plan of the thermoplastic mold, and perform mold processing of the thermoplastic mold according to the production plan.

[0014] Optionally, when the correlation analysis module performs correlation analysis on the supply chain data using a preset correlation analysis algorithm to obtain the dependency relationship of the supply chain data, the correlation analysis module includes:

[0015] Extracting features from the supply chain data to obtain data features of the supply chain data;

[0016] Calculating the correlation coefficient of the data feature using a preset correlation analysis algorithm;

[0017] A dependency relationship of the supply chain data is generated according to the correlation coefficient.

[0018] Optionally, when the directed graph conversion module performs directed graph conversion on the supply chain data according to the dependency relationship to obtain the directed graph of the supply chain data, the directed graph conversion module includes:

[0019] generating actual nodes of a directed graph to be constructed according to the supply chain data;

[0020] Generate edges of the directed graph according to the dependency relationship and the actual nodes;

[0021] A directed graph of the supply chain data is constructed using the edges and actual nodes.

[0022] Optionally, when the virtual element configuration module configures the virtual elements of the directed graph according to a preset market demand trend, the virtual element configuration module includes:

[0023] The directed graph is configured with virtual nodes according to a preset market demand trend, wherein the virtual nodes include: a virtual supplier node, a virtual production line node, a virtual warehousing node, a virtual transportation node and a virtual order node.

[0024] Optionally, when the supply chain augmented graph generation module performs weight updating on the directed graph after the virtual element configuration to obtain the supply chain augmented graph of the thermoplastic mold, the module includes:

[0025] The weight of the directed graph after the virtual element configuration is updated using a preset weight update algorithm, wherein the preset weight update algorithm is:

[0026]

[0027] Among them, w′(v ri ) is the update weight of the ith actual node in the directed graph after the virtual element is configured, w(v ri ) is the initial weight of the ith actual node in the directed graph after the virtual element is configured, D ri is the node data of the ith actual node, w′(v vi ) is the update weight of the ith virtual node in the directed graph after the virtual element is configured, w(v vi ) is the initial weight of the ith virtual node in the directed graph after the virtual element is configured, D vi is the node data of the ith virtual node, w′(e k ) is the update weight of the kth edge in the directed graph after the virtual element is configured, w(e k ) is the initial weight of the kth edge in the directed graph after the virtual element is configured, D ek is the edge data of the kth edge, f(*) is the node weight update function of the actual node, g(*) is the node weight update function of the virtual node, and h(*) is the edge weight update function of the edge.

[0028] Optionally, when the target production path generation module generates the target production path of the thermoplastic mold by using the supply chain augmented graph, the target production path generation module includes:

[0029] Determine the starting node and the target node of the thermoplastic mold according to the supply chain augmented graph;

[0030] Generate a maximum flow path between the starting node and the target node;

[0031] The maximum flow path is determined as a target production path of the thermoplastic mold.

[0032] Optionally, the target production path generation module generates the maximum flow path between the start node and the target node by:

[0033] Initializing the node distance between the initial node and the target node to obtain the initial node distance between the initial node and the target node;

[0034] Performing a relaxation operation on the edges in the supply chain augmented graph, and updating the initial node distance according to the operation result of the relaxation operation to obtain a node update distance between the initial node and the target node;

[0035] Generating a residual network of the supply chain augmented graph according to the remaining capacities corresponding to the edges in the supply chain augmented graph;

[0036] generating an augmented path between the start node and the target node using the residual network;

[0037] The augmenting path is updated for flow, and the residual network is updated for network, until the number of updates of the augmenting path reaches a preset update threshold, and the updated augmenting path is determined to be the maximum flow path of the start node and the target node.

[0038] Optionally, when the production plan generation module adjusts the production plan of the thermoplastic mold according to the target production path to obtain the production plan of the thermoplastic mold, it includes:

[0039] Performing path parsing on the target production path to obtain path elements of the target production path;

[0040] The original production plan acquired in advance is adjusted according to the path elements to obtain a production plan for the thermoplastic mold.

[0041] In order to solve the above problems, the present invention also provides a digital thermoplastic mold processing method based on big data, the method comprising:

[0042] Acquire the supply chain data of the thermoplastic mold, and use a preset correlation analysis algorithm to perform correlation analysis on the supply chain data to obtain the dependency relationship of the supply chain data, wherein the preset correlation analysis algorithm is:

[0043]

[0044] Among them, ρ S (X,Y) is the correlation coefficient of the data characteristics of the supply chain data, d i is the level difference of the i-th data feature, n is the total number of the data features, X is the feature identifier of data feature X, and Y is the feature identifier of data feature Y;

[0045] Performing directed graph conversion on the supply chain data according to the dependency relationship to obtain a directed graph of the supply chain data;

[0046] Performing virtual element configuration on the directed graph according to a preset market demand trend;

[0047] updating the weights of the directed graph after the virtual element configuration to obtain an augmented graph of the supply chain of the thermoplastic mold;

[0048] generating a target production path for the thermoplastic mold using the supply chain augmented graph;

[0049] The production plan of the thermoplastic mold is adjusted according to the target production path to obtain a production plan of the thermoplastic mold, and mold processing of the thermoplastic mold is performed according to the production plan.

[0050] In order to solve the above problems, the present invention also provides a storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned digital thermoplastic mold processing method based on big data.

[0051] The present invention calculates the correlation coefficient of the supply chain data characteristics through a preset correlation analysis algorithm, thereby accurately identifying the dependency relationship between each link in the supply chain, which helps enterprises understand the mutual influence between different supply chain activities and provides a scientific basis for further data analysis and decision-making. The dependency relationship of the supply chain data is converted into a directed graph, and the directed graph is configured with virtual elements according to the market demand trend, which helps to simulate and predict the performance of the supply chain under different market conditions. The weight of the directed graph after the virtual element configuration is updated to obtain a supply chain augmented graph, which takes into account the changes in market demand and makes the supply chain model closer to the actual situation. The supply chain augmented graph is used to generate a target production path, and the original production plan is adjusted according to the target production path to obtain the final thermoplastic mold production plan, which integrates all relevant data and market demand. The selection is made. The entire process realizes the precise supply and allocation of raw materials, parts and equipment through precise data analysis and intelligent decision support, reduces inventory costs and production cycles, and improves processing efficiency. Therefore, the digital thermoplastic mold processing system, method and medium based on big data proposed in the present invention can improve the efficiency of digital thermoplastic mold processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A system architecture diagram of a digital thermoplastic mold processing system based on big data provided by an embodiment of the present invention;

[0053] Figure 2 A schematic flow chart of a digital thermoplastic mold processing method based on big data provided in one embodiment of the present invention.

[0054] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "the" and "the" used in the embodiments of the present invention are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0057] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0058] In addition, the step sequence in the following method embodiments is only an example and not a strict limitation.

[0059] In fact, the server-side device deployed by the digital thermoplastic mold processing system based on big data may be composed of one or more devices. The above-mentioned digital thermoplastic mold processing system based on big data can be implemented as: business instance, virtual machine, hardware device. For example, the digital thermoplastic mold processing system based on big data can be implemented as a business instance deployed on one or more devices in the cloud node. In simple terms, the digital thermoplastic mold processing system based on big data can be understood as a software deployed on the cloud node, which is used to provide a digital thermoplastic mold processing system based on big data for each user terminal. Alternatively, the digital thermoplastic mold processing system based on big data can also be implemented as a virtual machine deployed on one or more devices in the cloud node. The virtual machine is installed with application software for managing each user terminal. Alternatively, the digital thermoplastic mold processing system based on big data can also be implemented as a server consisting of many hardware devices of the same or different types, and one or more hardware devices are set to provide a digital thermoplastic mold processing system based on big data for each user terminal.

[0060] In terms of implementation, the digital thermoplastic mold processing system based on big data and the user end are adapted to each other. That is, the digital thermoplastic mold processing system based on big data is an application installed on the cloud service platform, and the user end is a client that establishes a communication connection with the application; or the digital thermoplastic mold processing system based on big data is implemented as a website, and the user end is implemented as a web page; or the digital thermoplastic mold processing system based on big data is implemented as a cloud service platform, and the user end is implemented as a small program in an instant messaging application.

[0061] like Figure 1 , which is a system architecture diagram of a digital thermoplastic mold processing system based on big data provided by one embodiment of the present invention.

[0062] The digital thermoplastic mold processing system 100 based on big data of the present invention can be set in a cloud server. In terms of implementation, it can be used as one or more service devices, or it can be installed as an application on the cloud (such as a server of a mobile service operator, a server cluster, etc.), or it can be developed as a website. According to the functions implemented, the digital thermoplastic mold processing system 100 based on big data can include an information correlation analysis module 101, a directed graph conversion module 102, a virtual element configuration module 103, a supply chain augmented graph generation module 104, a target production path generation module 105 and a production plan generation module 106. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0063] In the embodiment of the present invention, in the digital thermoplastic mold processing system based on big data, each of the above modules can be independently implemented and called with other modules. The call here can be understood as a module that can connect to multiple modules of another type and provide corresponding services to the multiple modules connected to it. For example, the sharing evaluation module can call the same information acquisition module to obtain the information collected by the information acquisition module. Based on the above characteristics, in the digital thermoplastic mold processing system based on big data provided by the embodiment of the present invention, the scope of application of the digital thermoplastic mold processing system architecture based on big data can be adjusted by adding modules and directly calling them without modifying the program code, so as to achieve cluster-based horizontal expansion, so as to achieve the purpose of quickly and flexibly expanding the digital thermoplastic mold processing system based on big data. In actual applications, the above modules can be set in the same device or different devices, or they can be set in virtual devices, such as service instances in cloud servers.

[0064] In the following, in combination with specific embodiments, each component and specific workflow of the digital thermoplastic mold processing system based on big data are described respectively:

[0065] The correlation analysis module 101 is used to obtain supply chain data of thermoplastic molds, perform correlation analysis on the supply chain data, and obtain dependency relationships of the supply chain data.

[0066] In the embodiment of the present invention, when executing the acquisition of the supply chain data of the thermoplastic mold, the correlation analysis module 101 includes: supplier information, raw material information, production equipment information, processing procedure information and delivery time information, etc., wherein the supplier information includes the name, address, contact information, etc. of the supplier; the raw material information includes the type, specification, quantity, etc. of the required raw materials; the production equipment information includes the type, quantity, production capacity, etc. of the equipment used to process the mold; the processing procedure information includes the specific process, process requirements, processing time, etc. of the mold processing; the delivery time information includes the delivery time and transportation time of the supplier.

[0067] In the embodiment of the present invention, when the correlation analysis module 101 performs correlation analysis on the supply chain data using a preset correlation analysis algorithm to obtain the dependency relationship of the supply chain data, it includes:

[0068] Extracting features from the supply chain data to obtain data features of the supply chain data;

[0069] Calculating the correlation coefficient of the data feature using a preset correlation analysis algorithm;

[0070] A dependency relationship of the supply chain data is generated according to the correlation coefficient.

[0071] Specifically, in order to calculate the correlation of supply chain data, the supply chain data is firstly extracted to obtain the numerical representation of each data feature, and then the correlation coefficient of the data features of the supply chain data is calculated according to the preset correlation analysis algorithm. Finally, the dependency relationship between the supply chain data is judged according to the size of the correlation coefficient.

[0072] Specifically, numerical data features are extracted from the above supply chain data, for example, the types of raw materials are converted into quantifiable codes, and the delivery time is converted into numerical values.

[0073] Furthermore, the degree of dependence between the data features is determined based on the size of the calculated correlation coefficient. The closer the absolute value of the correlation coefficient is to 1, the stronger the dependence is; if it is close to 0, it means there is almost no linear correlation.

[0074] In detail, the preset correlation analysis algorithm is:

[0075]

[0076] Among them, ρ S(X,Y) is the correlation coefficient of the data characteristics of the supply chain data, d i is the level difference of the i-th data feature, n is the total number of the data features, X is the feature identifier of data feature X, and Y is the feature identifier of data feature Y.

[0077] In detail, the core of correlation analysis is to use a preset correlation analysis algorithm to evaluate the correlation between supply chain data features.

[0078] Furthermore, according to the calculated correlation coefficient ρ S (X, Y), the dependency between data features can be determined. The value range of the correlation coefficient is from -1 to +1, where +1 indicates a completely positive correlation, -1 indicates a completely negative correlation, and 0 indicates no linear correlation. Finally, based on the correlation coefficients between all data features, a dependency graph of the entire supply chain data can be obtained, which reflects the degree of dependency or correlation between different data features and helps to understand the association between the data features.

[0079] In detail, the rank difference refers to the difference between the values ​​of the corresponding data feature at different time points or under different conditions, which involves the operation of sorting and grading the data.

[0080] The directed graph conversion module 102 is used to perform directed graph conversion on the supply chain data according to the dependency relationship to obtain a directed graph of the supply chain data.

[0081] In the embodiment of the present invention, when the directed graph conversion module 102 performs directed graph conversion on the supply chain data according to the dependency relationship to obtain the directed graph of the supply chain data, the directed graph conversion module 102 includes:

[0082] generating actual nodes of a directed graph to be constructed according to the supply chain data;

[0083] Generate edges of the directed graph according to the dependency relationship and the actual nodes;

[0084] A directed graph of the supply chain data is constructed using the edges and actual nodes.

[0085] In detail, the actual nodes in the directed graph represent different components in the supply chain, such as suppliers, raw materials, production equipment, processing procedures, etc. Each node has a unique identifier (ID) and some attributes, such as node type, name, quantity, specifications, etc.

[0086] In detail, the edges in the directed graph represent the dependency relationships in the supply chain data. The edge connects two nodes and indicates the direction from one node to another. Each edge has a weight, which indicates the degree of dependency between the two nodes, etc.

[0087] Furthermore, the generation of the edges of the directed graph based on the dependency relationship and the actual nodes can be performed according to the following steps: for each actual node in the supply chain data, a corresponding directed graph node is constructed, and its unique identifier (ID) and other attributes are recorded; based on the dependency relationship in the supply chain data, the predecessor node of each actual node (i.e., the node on which the actual node depends) is determined. These predecessor nodes are the nodes in the directed graph that are connected to the current node. Based on the dependency relationship, a directed edge pointing to its predecessor node is created for each node. These edges represent the dependency relationship in the supply chain.

[0088] Furthermore, all nodes and edges are combined together to construct a complete directed graph to represent the dependency relationship of the supply chain data.

[0089] The virtual element configuration module 103 is used to configure virtual elements of the directed graph according to a preset market demand trend.

[0090] In the embodiment of the present invention, when the virtual element configuration module 103 configures the virtual elements of the directed graph according to the preset market demand trend, it includes:

[0091] The directed graph is configured with virtual nodes according to a preset market demand trend, wherein the virtual nodes include: a virtual supplier node, a virtual production line node, a virtual warehousing node, a virtual transportation node and a virtual order node.

[0092] Furthermore, according to market demand trends, virtual supplier nodes can be added to represent possible new suppliers or potential partners, and these nodes can be configured based on market research or cooperation intentions.

[0093] Furthermore, based on market demand trends, virtual production line nodes can be added to represent the expansion of production capacity or the introduction of new production lines. These nodes can predict future production needs and be configured according to capacity planning.

[0094] Furthermore, according to market demand trends, virtual storage nodes can be added to represent newly added storage facilities or adjusted storage capacity, and these nodes can be configured according to inventory management and logistics needs.

[0095] Furthermore, according to market demand trends, virtual transportation nodes can be added to represent new transportation modes or adjust transportation routes. These nodes can be configured according to logistics optimization and delivery requirements.

[0096] Furthermore, according to market demand trends, virtual order nodes can be added to represent future order volumes and demand changes, and these nodes can be configured based on sales forecasts and market trends.

[0097] In summary, by adding virtual elements to the directed graph, different market demand scenarios can be simulated and the adaptability and resource allocation of the supply chain can be evaluated, which helps to formulate reasonable supply chain strategies, prepare in advance, meet market demand and maintain competitiveness.

[0098] In detail, virtual nodes are added to the directed graph to explore more possibilities to meet market demands, representing potential solutions such as backup production lines, emergency procurement channels, etc. These added elements expand the structure of the original graph, allowing it to cover a wider decision space.

[0099] Furthermore, adding virtual nodes can better describe the topology and dependencies of the supply chain, but virtual nodes do not represent actual supply chain components, they are only used to better represent specific characteristics or optimization requirements of the supply chain.

[0100] The supply chain augmented graph generating module 104 is used to update the weight of the directed graph after the virtual element configuration to obtain the supply chain augmented graph of the thermoplastic mold.

[0101] In the embodiment of the present invention, when the supply chain augmented graph generation module 104 performs weight updating on the directed graph after the virtual element configuration to obtain the supply chain augmented graph of the thermoplastic mold, it includes:

[0102] The weight of the directed graph after the virtual element configuration is updated using a preset weight update algorithm, wherein the preset weight update algorithm is:

[0103]

[0104] Among them, w′(v ri ) is the update weight of the ith actual node in the directed graph after the virtual element is configured, w(v ri ) is the initial weight of the ith actual node in the directed graph after the virtual element is configured, D ri is the node data of the ith actual node, w′(v vi ) is the update weight of the ith virtual node in the directed graph after the virtual element is configured, w(v vi ) is the initial weight of the ith virtual node in the directed graph after the virtual element is configured, D vi is the node data of the ith virtual node, w′(e k ) is the update weight of the kth edge in the directed graph after the virtual element is configured, w(e k ) is the initial weight of the kth edge in the directed graph after the virtual element is configured, D ekis the edge data of the kth edge, f(*) is the node weight update function of the actual node, g(*) is the node weight update function of the virtual node, and h(*) is the edge weight update function of the edge.

[0105] In detail, using a preset weight update algorithm to update the weights of the directed graph after the virtual elements are configured includes: updating the node weights of the actual nodes in the directed graph after the virtual elements are configured, updating the node weights of the virtual nodes in the directed graph after the virtual elements are configured, and updating the edge weights of the edges in the directed graph after the virtual elements are configured.

[0106] Furthermore, updating the node weights of actual nodes in the directed graph after the virtual element configuration refers to updating the weights of actual supplier nodes, production line nodes, warehousing nodes and transportation nodes, and updating the weight of each node according to actual conditions, such as the supply speed of supplier nodes, the production capacity of production line nodes, the storage capacity and cargo turnover rate of warehousing nodes, the transportation capacity of transportation nodes, etc. These updates can be based on historical data, actual tests or real-time monitoring.

[0107] Furthermore, updating the node weights of the virtual nodes in the directed graph after the virtual elements are configured refers to updating the weights of the virtual nodes according to market demand trends and forecasts, for example: the supply capacity of the virtual supplier node, the production capacity of the virtual production line node, the storage capacity and cargo turnover rate of the virtual warehousing node, the transportation capacity of the virtual transportation node, etc. These weight updates can be based on market analysis, competition situation, industry trends, etc.

[0108] In detail, through this preset weight update algorithm, flexible weight adjustments can be made according to the characteristics of actual nodes and virtual nodes and the attributes of edges, so as to more accurately reflect the changes in the supply chain. This weight update method can help generate an augmented graph of the supply chain for thermoplastic molds, so that the weights of nodes and edges in the graph can better reflect the actual situation, providing more accurate data support for subsequent supply chain optimization and decision-making.

[0109] The target production path generation module 105 is used to generate the target production path of the thermoplastic mold by using the supply chain augmented graph.

[0110] In the embodiment of the present invention, when the target production path generation module 105 generates the target production path of the thermoplastic mold by using the supply chain augmented graph, the target production path generation module 105 includes:

[0111] Determine the starting node and the target node of the thermoplastic mold according to the supply chain augmented graph;

[0112] Generate a maximum flow path between the starting node and the target node;

[0113] The maximum flow path is determined as a target production path of the thermoplastic mold.

[0114] In detail, in the supply chain augmented graph, the start node and target node of the thermoplastic mold need to be clearly defined first. For example, the start node can be the raw material supplier node or the production line node, while the target node is the order node of the thermoplastic mold.

[0115] Furthermore, the maximum flow path from the start node to the target node is found in the supply chain augmented graph. This path is the target production path of the thermoplastic mold. The nodes and selected edges passed by the maximum flow path are determined. These nodes and edges constitute the target production path of the thermoplastic mold.

[0116] In detail, determining the start node and the target node of the thermoplastic mold according to the supply chain augmented graph is to determine where to start producing the thermoplastic mold and where to deliver it in the entire supply chain.

[0117] Furthermore, the maximum flow path between the start node and the target node is generated to find the most efficient path for resource flow in the supply chain so as to maximize the utilization of resources in the production process.

[0118] Finally, determining the maximum flow path as the target production path for the thermoplastic mold is to ensure that the thermoplastic mold can be produced in the most efficient way throughout the supply chain and delivered to the target node.

[0119] In detail, the target production path generation module 105 generates the maximum flow path between the start node and the target node by:

[0120] Initializing the node distance between the initial node and the target node to obtain the initial node distance between the initial node and the target node;

[0121] Performing a relaxation operation on the edges in the supply chain augmented graph, and updating the initial node distance according to the operation result of the relaxation operation to obtain a node update distance between the initial node and the target node;

[0122] Generating a residual network of the supply chain augmented graph according to the remaining capacities corresponding to the edges in the supply chain augmented graph;

[0123] generating an augmented path between the start node and the target node using the residual network;

[0124] The augmenting path is updated for flow, and the residual network is updated for network, until the number of updates of the augmenting path reaches a preset update threshold, and the updated augmenting path is determined to be the maximum flow path of the start node and the target node.

[0125] In detail, the performing node distance initialization on the initial node and the target node to obtain the initial node distance between the initial node and the target node means setting the distance from the target node to the initial node to infinity, except that the distance from the initial node to itself is 0.

[0126] Furthermore, the relaxation operation is performed on the edges in the supply chain augmented graph, and the initial node distance is updated according to the operation result of the relaxation operation to obtain the node update distance between the initial node and the target node, which means that each edge in the graph is subjected to |V|-1 relaxation operations, where |V| is the number of vertices in the graph. In each relaxation operation, it is checked whether the path length from the initial node to the target node can be improved through the current edge. If so, the shortest path estimate of the target node is updated. That is, the relaxation operation is to gradually optimize the node distance on the path to ensure that the optimal path is found.

[0127] Furthermore, the residual network that generates the supply chain augmented graph according to the remaining capacity corresponding to the edges in the supply chain augmented graph is usually a new network constructed on the basis of the supply chain augmented graph to assist in solving the maximum flow problem. In the residual network, the weight of each edge represents the resource flow that can still pass through the edge. By continuously adjusting and updating the residual network, the solution to the maximum flow can be gradually found.

[0128] In detail, the nodes in the supply chain augmented graph represent the source and destination of resources, the edges represent the flow paths of resources, and the capacity on the edges represents the maximum resource flow that can pass through the path. When resources flow from the initial node to the target node, we hope to find a path from the initial node to the target node so that the resource flow on this path is maximized and each edge on this path has residual capacity.

[0129] In detail, the flow update is performed on the augmenting path, and the network update is performed on the residual network until the number of updates of the augmenting path reaches a preset update threshold, and determining that the updated augmenting path is the maximum flow path between the starting node and the target node refers to increasing the flow along the found augmenting path and updating the residual network. Specifically, the flow value of each edge on the path is increased by one unit, and the flow value of the reverse edge is updated to decrease by one unit until the augmenting path is not found. At this time, the maximum flow has been reached.

[0130] The production plan generating module 106 is used to adjust the production plan of the thermoplastic mold according to the target production path, obtain the production plan of the thermoplastic mold, and perform mold processing of the thermoplastic mold according to the production plan.

[0131] In the embodiment of the present invention, when the production plan generation module 106 adjusts the production plan of the thermoplastic mold according to the target production path to obtain the production plan of the thermoplastic mold, it includes:

[0132] Performing path parsing on the target production path to obtain path elements of the target production path;

[0133] The original production plan acquired in advance is adjusted according to the path elements to obtain a production plan for the thermoplastic mold.

[0134] In detail, the target production path is analyzed and interpreted to determine the path elements therein, wherein the path elements may be key nodes, production stages, logistics points, etc. The result of the path parsing is a series of key information about the target production path.

[0135] Furthermore, after obtaining the path elements of the target production path, it is necessary to compare and analyze them with the original production plan obtained in advance. Then, based on the information in the path elements, related plans such as the production schedule, production quantity, and logistics arrangements can be adjusted to ensure that the production plan is more in line with the actual situation.

[0136] In detail, after completing the adjustment of the original production plan, the final production plan for the thermoplastic mold can be obtained. This plan takes into account the information of the target production path, making the production plan more in line with actual needs and able to achieve the expected goals.

[0137] Reference Figure 2 FIG. 1 is a flow chart of a digital thermoplastic mold processing method based on big data provided by an embodiment of the present invention. In this embodiment, the digital thermoplastic mold processing method based on big data includes:

[0138] S1. Obtain supply chain data of thermoplastic molds, and use a preset correlation analysis algorithm to perform correlation analysis on the supply chain data to obtain dependency relationships of the supply chain data, wherein the preset correlation analysis algorithm is:

[0139]

[0140] Among them, ρ S (X,Y) is the correlation coefficient of the data characteristics of the supply chain data, d iis the level difference of the i-th data feature, n is the total number of the data features, X is the feature identifier of data feature X, and Y is the feature identifier of data feature Y;

[0141] S2. Convert the supply chain data into a directed graph according to the dependency relationship to obtain a directed graph of the supply chain data;

[0142] S3, configuring virtual elements of the directed graph according to a preset market demand trend;

[0143] S4, updating the weight of the directed graph after the virtual element configuration to obtain an augmented graph of the supply chain of the thermoplastic mold;

[0144] S5. Generate a target production path for the thermoplastic mold using the supply chain augmented graph;

[0145] S6. Adjust the production plan of the thermoplastic mold according to the target production path to obtain a production plan of the thermoplastic mold, and perform mold processing of the thermoplastic mold according to the production plan.

[0146] The present invention calculates the correlation coefficient of the supply chain data characteristics through a preset correlation analysis algorithm, thereby accurately identifying the dependency relationship between each link in the supply chain, which helps enterprises understand the mutual influence between different supply chain activities and provides a scientific basis for further data analysis and decision-making. The dependency relationship of the supply chain data is converted into a directed graph, and the directed graph is configured with virtual elements according to the market demand trend, which helps to simulate and predict the performance of the supply chain under different market conditions. The weight of the directed graph after the virtual element configuration is updated to obtain a supply chain augmented graph, which takes into account the changes in market demand and makes the supply chain model closer to the actual situation. The supply chain augmented graph is used to generate a target production path, and the original production plan is adjusted according to the target production path to obtain the final thermoplastic mold production plan, which integrates all relevant data and market demand. The whole process realizes the precise supply and allocation of raw materials, parts and equipment through precise data analysis and intelligent decision support, reduces inventory costs and production cycles, and improves processing efficiency. Therefore, the digital thermoplastic mold processing method based on big data proposed in the present invention can improve the efficiency of digital thermoplastic mold processing.

[0147] The present invention further provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can achieve:

[0148] Acquire the supply chain data of the thermoplastic mold, and use a preset correlation analysis algorithm to perform correlation analysis on the supply chain data to obtain the dependency relationship of the supply chain data, wherein the preset correlation analysis algorithm is:

[0149]

[0150] Among them, ρ S (X,Y) is the correlation coefficient of the data characteristics of the supply chain data, d i is the level difference of the i-th data feature, n is the total number of the data features, X is the feature identifier of data feature X, and Y is the feature identifier of data feature Y;

[0151] Performing directed graph conversion on the supply chain data according to the dependency relationship to obtain a directed graph of the supply chain data;

[0152] Performing virtual element configuration on the directed graph according to a preset market demand trend;

[0153] updating the weights of the directed graph after the virtual element configuration to obtain an augmented graph of the supply chain of the thermoplastic mold;

[0154] generating a target production path for the thermoplastic mold using the supply chain augmented graph;

[0155] The production plan of the thermoplastic mold is adjusted according to the target production path to obtain a production plan of the thermoplastic mold, and mold processing of the thermoplastic mold is performed according to the production plan.

[0156] In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0157] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0159] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0160] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A digital thermoplastic mold processing system based on big data, characterized in that: The system includes an information correlation analysis module, a directed graph conversion module, a virtual element configuration module, a supply chain augmented graph generation module, a target production path generation module and a production plan generation module, wherein: The correlation analysis module is used to obtain the supply chain data of the thermoplastic mold, and perform correlation analysis on the supply chain data using a preset correlation analysis algorithm to obtain the dependency relationship of the supply chain data, wherein the preset correlation analysis algorithm is: Among them, ρ S (X,Y) is the correlation coefficient of the data characteristics of the supply chain data, d i is the level difference of the i-th data feature, n is the total number of the data features, X is the feature identifier of data feature X, and Y is the feature identifier of data feature Y; The directed graph conversion module is used to perform directed graph conversion on the supply chain data according to the dependency relationship to obtain a directed graph of the supply chain data; The virtual element configuration module is used to configure virtual elements of the directed graph according to a preset market demand trend; wherein, when the virtual element configuration module performs virtual element configuration of the directed graph according to the preset market demand trend, it includes: configuring virtual nodes of the directed graph according to the preset market demand trend, wherein the virtual nodes include: virtual supplier nodes, virtual production line nodes, virtual warehousing nodes, virtual transportation nodes and virtual order nodes; The supply chain augmented graph generation module is used to perform weight update on the directed graph after the virtual elements are configured to obtain the supply chain augmented graph of the thermoplastic mold; wherein, when the supply chain augmented graph generation module performs weight update on the directed graph after the virtual elements are configured to obtain the supply chain augmented graph of the thermoplastic mold, the module includes: performing weight update on the directed graph after the virtual elements are configured using a preset weight update algorithm, wherein the preset weight update algorithm is: Among them, w′(v ri ) is the update weight of the ith actual node in the directed graph after the virtual element is configured, w(v ri ) is the initial weight of the ith actual node in the directed graph after the virtual element is configured, D ri is the node data of the ith actual node, w′(v vi ) is the update weight of the ith virtual node in the directed graph after the virtual element is configured, w(v vi ) is the initial weight of the ith virtual node in the directed graph after the virtual element is configured, D vi is the node data of the ith virtual node, w′(e k ) is the update weight of the kth edge in the directed graph after the virtual element is configured, w(e k ) is the initial weight of the kth edge in the directed graph after the virtual element is configured, is the edge data of the kth edge, f(*) is the node weight update function of the actual node, g(*) is the node weight update function of the virtual node, and h(*) is the edge weight update function of the edge; The target production path generation module is used to generate the target production path of the thermoplastic mold by using the supply chain augmented graph; wherein, when the target production path generation module generates the target production path of the thermoplastic mold by using the supply chain augmented graph, the module includes: determining the starting node and the target node of the thermoplastic mold according to the supply chain augmented graph; generating the maximum flow path of the starting node and the target node; and determining the maximum flow path as the target production path of the thermoplastic mold; The production plan generating module is used to adjust the production plan of the thermoplastic mold according to the target production path, obtain the production plan of the thermoplastic mold, and perform mold processing of the thermoplastic mold according to the production plan.

2. The digital thermoplastic mold processing system based on big data according to claim 1, characterized in that: When the correlation analysis module performs correlation analysis on the supply chain data using a preset correlation analysis algorithm to obtain the dependency relationship of the supply chain data, it includes: Extracting features from the supply chain data to obtain data features of the supply chain data; Calculating the correlation coefficient of the data feature using a preset correlation analysis algorithm; A dependency relationship of the supply chain data is generated according to the correlation coefficient.

3. The digital thermoplastic mold processing system based on big data according to claim 1, characterized in that: When the directed graph conversion module performs directed graph conversion on the supply chain data according to the dependency relationship to obtain the directed graph of the supply chain data, the directed graph conversion module includes: generating actual nodes of a directed graph to be constructed according to the supply chain data; Generate edges of the directed graph according to the dependency relationship and the actual nodes; A directed graph of the supply chain data is constructed using the edges and actual nodes.

4. The digital thermoplastic mold processing system based on big data according to claim 1, characterized in that: The target production path generation module generates the maximum flow path between the start node and the target node, including: Initializing the node distance between the starting node and the target node to obtain the starting node distance between the starting node and the target node; Performing a relaxation operation on the edges in the supply chain augmented graph, and updating the distance of the starting node according to the operation result of the relaxation operation to obtain a node update distance between the starting node and the target node; Generating a residual network of the supply chain augmented graph according to the remaining capacities corresponding to the edges in the supply chain augmented graph; generating an augmented path between the start node and the target node using the residual network; The augmenting path is updated for flow, and the residual network is updated for network, until the number of updates of the augmenting path reaches a preset update threshold, and the updated augmenting path is determined to be the maximum flow path of the start node and the target node.

5. The digital thermoplastic mold processing system based on big data according to any one of claims 1 to 4, characterized in that: When the production plan generation module adjusts the production plan of the thermoplastic mold according to the target production path to obtain the production plan of the thermoplastic mold, it includes: Performing path parsing on the target production path to obtain path elements of the target production path; The original production plan acquired in advance is adjusted according to the path elements to obtain a production plan for the thermoplastic mold.

6. A digital thermoplastic mold processing method based on big data, characterized in that: The method comprises: Acquire the supply chain data of the thermoplastic mold, and use a preset correlation analysis algorithm to perform correlation analysis on the supply chain data to obtain the dependency relationship of the supply chain data, wherein the preset correlation analysis algorithm is: Among them, ρ S (X,Y) is the correlation coefficient of the data characteristics of the supply chain data, d i is the level difference of the i-th data feature, n is the total number of the data features, X is the feature identifier of data feature X, and Y is the feature identifier of data feature Y; Performing directed graph conversion on the supply chain data according to the dependency relationship to obtain a directed graph of the supply chain data; The directed graph is configured with virtual elements according to a preset market demand trend; wherein the virtual elements of the directed graph are configured according to a preset market demand trend, including: configuring virtual nodes of the directed graph according to a preset market demand trend, wherein the virtual nodes include: a virtual supplier node, a virtual production line node, a virtual warehousing node, a virtual transportation node, and a virtual order node; The weight of the directed graph after the virtual element configuration is updated to obtain the supply chain augmented graph of the thermoplastic mold; wherein the weight of the directed graph after the virtual element configuration is updated to obtain the supply chain augmented graph of the thermoplastic mold includes: using a preset weight update algorithm to update the weight of the directed graph after the virtual element configuration, wherein the preset weight update algorithm is: Among them, w′(v ri ) is the update weight of the ith actual node in the directed graph after the virtual element is configured, w(v ri ) is the initial weight of the ith actual node in the directed graph after the virtual element is configured, D ri is the node data of the ith actual node, w′(v vi ) is the update weight of the ith virtual node in the directed graph after the virtual element is configured, w(v vi ) is the initial weight of the ith virtual node in the directed graph after the virtual element is configured, D vi is the node data of the ith virtual node, w′(e k ) is the update weight of the kth edge in the directed graph after the virtual element is configured, w(e k ) is the initial weight of the kth edge in the directed graph after the virtual element is configured, is the edge data of the kth edge, f(*) is the node weight update function of the actual node, g(*) is the node weight update function of the virtual node, and h(*) is the edge weight update function of the edge; The target production path of the thermoplastic mold is generated by using the supply chain augmented graph; wherein the target production path of the thermoplastic mold is generated by using the supply chain augmented graph, including: determining a starting node and a target node of the thermoplastic mold according to the supply chain augmented graph; generating a maximum flow path of the starting node and the target node; and determining the maximum flow path as the target production path of the thermoplastic mold; The production plan of the thermoplastic mold is adjusted according to the target production path to obtain a production plan of the thermoplastic mold, and mold processing of the thermoplastic mold is performed according to the production plan.

7. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the digital thermoplastic mold processing method based on big data as described in claim 6 is implemented.

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