CN115718758A - Material purchasing and transporting optimization method and device, electronic equipment and storage medium - Google Patents
Material purchasing and transporting optimization method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the application provides a material purchasing and transporting optimization method and device, electronic equipment and a computer-readable storage medium. The method comprises the following steps: collecting service data of a plurality of service systems, wherein the service data is used for representing associated attribute data of material purchase and transportation; model construction processing is carried out on the business data based on a preset construction strategy to obtain a business data conceptual model; generalizing the business data conceptual model to obtain a material data relation entity model; and monitoring and optimizing the material data relation entity model. According to the scheme of the embodiment of the application, the purchasing and transporting steps of the materials can be simplified, so that the rapid development of enterprise logistics transportation is facilitated.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for optimizing material purchasing transportation, an electronic device, and a computer-readable storage medium.
Background
With the continuous development of social economy, the logistics transportation industry is rapidly developed, and the logistics transportation amount is continuously increased; an enterprise often needs to be based on a plurality of different systems in the aspect of purchasing and distributing materials, and the data inside the systems are not associated, and often need to repeatedly check and confirm the data from the systems, so that the purchasing and transporting of materials are complicated, and the rapid development of enterprise logistics transportation is not facilitated.
Content of application
The present application is directed to solving at least one of the problems in the prior art.
Therefore, the material purchasing and transporting optimization method is provided, the purchasing and transporting steps of the materials can be simplified, and rapid development of enterprise logistics transportation is facilitated.
The application also provides a device applying the material purchasing transportation optimization method.
The application also provides the electronic equipment applying the material purchasing transportation optimization method.
The application also provides a computer readable storage medium applying the material purchasing transportation optimization method.
According to the material procurement transportation optimization method of the embodiment of the first aspect of the application, the method comprises the following steps:
the method comprises the steps of collecting service data of a plurality of service systems, wherein the service data are used for representing associated attribute data of material purchasing and transportation;
model construction processing is carried out on the business data based on a preset construction strategy to obtain a business data conceptual model;
generalizing the business data conceptual model to obtain a material data relation entity model;
and monitoring and optimizing the material data relation entity model.
The material purchasing transportation optimization method provided by the embodiment of the application at least has the following beneficial effects: in the process of optimizing material purchasing transportation, firstly, collecting a plurality of service system data, wherein the service data is used for representing associated attribute data of material purchasing and transportation; then model construction processing is carried out on the business data based on a preset construction strategy to obtain a business data conceptual model; then, generalizing the business data conceptual model to obtain a material relation entity model; and finally, monitoring and optimizing the material data relation entity model to complete material purchasing and transportation optimization. Through the technical scheme, the purchasing and transporting steps of the materials can be simplified, so that the rapid development of enterprise logistics transportation is facilitated.
According to some embodiments of the present application, the collecting service data of a plurality of service systems includes:
acquiring database link information corresponding to a plurality of service systems;
reading metadata table information based on each database link information, wherein the metadata table information comprises table name information and field information;
matching processing is carried out on the basis of the table name information and the field information to obtain a matching result;
matching and aligning the links of the field information based on the matching result;
and performing data extraction processing on the field information after the link matching and alignment to obtain the service data.
According to some embodiments of the present application, the performing data extraction processing on the field information after the link matching and alignment to obtain the service data includes:
and performing data extraction processing on the field information after the link matching and alignment according to a preset time interval by using a preset timer to obtain the service data.
According to some embodiments of the present application, the business data includes product domain data, demand domain data, relationship domain data, and approval domain data, and the model building process is performed on the business data based on a preset building strategy to obtain a business data conceptual model, including:
performing data association analysis processing on the product domain data, the demand domain data, the relation domain data and the examination and approval domain data to obtain data association analysis information;
and constructing and obtaining the business data conceptual model based on the construction strategy and the data association analysis information.
According to some embodiments of the present application, the generalizing the business data conceptual model to obtain a material data relationship entity model includes:
performing conversion processing on the data object and the data attribute of the business data conceptual model to obtain a first physical entity table corresponding to the data object and a first physical entity field corresponding to the data attribute;
processing and converting the first physical entity table and the first physical entity field to obtain a second physical entity table corresponding to the first physical entity table and a second physical entity field corresponding to the first physical entity field;
generating a plurality of translation load subtasks based on the second physical entity table and the second physical entity field;
according to preset service logic, carrying out tandem connection processing on a plurality of conversion loading sub-tasks;
and executing the plurality of conversion subtasks after the serial connection processing to obtain the material data relation entity model.
According to some embodiments of the present application, the performing monitoring optimization processing on the material data relationship entity model includes:
performing operation processing on the material data relation entity model to obtain operation parameters;
and carrying out optimization adjustment processing on the material data relation entity model according to the operation parameters.
According to some embodiments of the application, the operational parameters include an index monitoring parameter, an event monitoring parameter, and a log monitoring parameter.
The material purchasing transportation optimizing device according to the embodiment of the second aspect of the application comprises:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for collecting service data of a plurality of service systems, and the service data is used for representing associated attribute data of material purchasing and transportation;
the second processing module is used for carrying out model construction processing on the business data based on a preset construction strategy to obtain a business data conceptual model;
the third processing module is used for carrying out generalization processing on the business data conceptual model to obtain a material data relation entity model;
and the fourth processing module is used for monitoring and optimizing the material data relationship entity model.
An electronic device according to an embodiment of the third aspect of the present application includes:
the system comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the material purchasing transportation optimization method.
A computer-readable storage medium according to an embodiment of the fourth aspect of the present application, stores computer-executable instructions, which when executed by a control processor, implement the material procurement shipping optimization method as described above.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
The accompanying drawings are included to provide a further understanding of the disclosed embodiments and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the example serve to explain the principles of the disclosure and not to limit the disclosure.
FIG. 1 is a flow chart of a method for optimizing procurement of materials for transportation according to an embodiment of the present application;
fig. 2 is a specific flowchart of the method for optimizing material procurement transportation according to an embodiment of the present application;
FIG. 3 is a detailed flow chart of data extraction of the method for optimizing material procurement transportation according to an embodiment of the present application;
FIG. 4 is a flowchart of a method for optimizing procurement of materials transportation for building a business data conceptual model according to an embodiment of the disclosure;
FIG. 5 is a flowchart illustrating a method for optimizing procurement of materials and transportation according to an embodiment of the present application for generalizing a business data conceptual model;
fig. 6 is a flowchart of a monitoring optimization process performed on a material data relationship entity model by a material purchasing transportation optimization method according to an embodiment of the present application;
FIG. 7 is a database configuration diagram provided by one embodiment of the present application;
FIG. 8 is a diagram illustrating a field configuration of a gallery table according to an embodiment of the present application
FIG. 9 is a conceptual model of business data provided by one embodiment of the present application;
FIG. 10 is a diagram of a material data relational entity model provided by an embodiment of the application;
FIG. 11 is a block diagram of an apparatus architecture for a material procurement shipping optimization apparatus according to an embodiment of the present application;
fig. 12 is a schematic configuration diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
In the description of the present application, the meaning of a plurality is one or more, the meaning of a plurality is two or more, and larger, smaller, larger, etc. are understood as excluding the present number, and larger, smaller, inner, etc. are understood as including the present number. If there is a description of first and second for the purpose of distinguishing technical features only, this is not to be understood as indicating or implying a relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of technical features indicated.
In the description of the present application, unless otherwise specifically limited, terms such as set, installed, connected and the like should be understood broadly, and those skilled in the art can reasonably determine the specific meanings of the above terms in the present application in combination with the specific contents of the technical solutions.
The application provides a material purchasing transportation optimization method, a material purchasing transportation optimization device, electronic equipment and a computer-readable storage medium, wherein the method comprises the following steps: in the process of optimizing material purchasing and transportation, firstly, collecting a plurality of business system data, wherein the business data is used for representing associated attribute data of material purchasing and transportation; then model construction processing is carried out on the business data based on a preset construction strategy to obtain a business data conceptual model; then, generalizing the business data conceptual model to obtain a material relation entity model; and finally, monitoring and optimizing the material data relation entity model to complete material purchasing and transportation optimization. Through above-mentioned technical scheme, can simplify the purchase transportation step of material to be favorable to the rapid development of enterprise's logistics transportation.
The embodiments of the present application will be further explained with reference to the drawings.
As shown in fig. 1, fig. 1 is a flowchart of a material procurement transportation optimization method according to an embodiment of the present application. The method includes but is not limited to step S100, step S200, step S300 and step S400:
step S100, collecting service data of a plurality of service systems, wherein the service data is used for representing associated attribute data of material purchase and transportation;
s200, performing model construction processing on the business data based on a preset construction strategy to obtain a business data conceptual model;
step S300, generalizing the business data conceptual model to obtain a material data relation entity model;
and S400, monitoring and optimizing the material data relation entity model.
It should be noted that, in the process of optimizing material purchasing transportation, a plurality of service system data are collected first, wherein the service data are used for representing associated attribute data of material purchasing and transportation; then model construction processing is carried out on the business data based on a preset construction strategy to obtain a business data conceptual model; then, generalizing the business data conceptual model to obtain a material relation entity model; and finally, monitoring and optimizing the material data relation entity model to complete material purchasing and transportation optimization. Through above-mentioned technical scheme, can simplify the purchase transportation step of material to be favorable to the rapid development of enterprise's logistics transportation.
It should be noted that the business system may be an Enterprise Resource Planning (ERP) system, an Office Automation (OA) approval system, a supplier system, a web portal, and the like; the business data can be classified and unified according to four categories of products, requirements, relationships and approvals. In the field of purchasing and transportation, the product field is mainly purchasing products, and is divided into two categories, namely high-value material products and low-value material products according to the purchasing value of the products. The high-value or low-value material has the following properties: transportation information, material numbers, inventory information, production plans, and approval information. The material serial numbers are integrated with different systems such as a material relation system, an enterprise ERP system and the like to form uniform material serial numbers. The material number is used as a main key, and other attributes are identified by depending on the main key. The requirements are various requirements plans, production plans and the like which are specified by a factory for producing related products. The demand data field integrates data of multiple planning systems such as APS production scheduling plans, ERP plans and the like, and transverse planning information of various planning data pull-through with unified planning numbers as main keys is formed. The plan field and the product field are linked by associating the product number with a production plan number of a corresponding product. The inventory area is related information of the factory multi-level warehousing inventory. The method can be divided into related attribute information of safety stock, end-of-term stock, in-transit materials and the like of a factory. The inventory field and the product field are linked by corresponding inventory location information of the product. The relationship domain mainly classifies information for relationships among various entities. The integrated product distribution information and production information can be recorded according to two categories of distribution relation and production relation. The relation domain and the product domain are linked through product number, distribution and production code. The approval domain collects various approval process record information such as: and checking and approving related information of materials, plans and stocks. The data recorded in different systems such as the planning field, material integration, inventory checking and the like can be integrated through the unified approval serial number information. The examination and approval domain is linked with information in different fields such as products, inventory, requirements, relations and the like through the unified examination and approval serial numbers.
It can be understood that, in the process of obtaining the service data conceptual model by performing model construction processing on the service data based on the preset construction strategy, the preset construction strategy may be a 3NF paradigm theoretical strategy, and the heart model is used for performing the model construction processing.
It is worth noting that the material data relation entity model is monitored and optimized, so that the material data relation entity model can be continuously optimized and adjusted, and better material purchasing and transporting processing is achieved.
In addition, in an embodiment, as shown in fig. 2, the step S100 may include, but is not limited to, the steps S110 to S150.
Step S110, acquiring database link information corresponding to a plurality of service systems;
step S120, metadata table information is read based on each database link information, wherein the metadata table information comprises table name information and field information;
step S130, matching processing is carried out based on the table name information and the field information to obtain a matching result;
step S140, based on the matching result, the links of the field information are matched and aligned;
and step S150, performing data extraction processing on the field information after the link matching alignment to obtain service data.
It should be noted that, in the process of collecting service data of a plurality of service systems, first, database link information corresponding to the plurality of service systems is obtained; reading metadata table information based on each database link information, wherein the metadata table information comprises table name information and field information; matching processing is carried out based on the table name information and the field information to obtain a matching result; then, based on the matching result, the links of the field information are matched and aligned; and finally, performing data extraction processing on the field information after the link matching alignment to obtain service data.
In a specific embodiment, as shown in fig. 7, for example, the required business data may be extracted from the business system data layer into the ODS layer (open data source leave l) of the material data model generalization tool product by using an automated batch extraction data tool in the prior art to form mirror image data of the original business system; the tool for automatically extracting the service system data in batches can complete the link configuration of the database through a visual configuration interface; automatically reading metadata table information through the link of a database, forming a matching relation through the table field names and the matching rules of the table names, and completing the automatic link matching alignment from table to table and from field to field; and then, completing the alignment of the field level through a configured graphical interface, and automatically running related data extraction synchronization operation at fixed points through a timer after completing the link alignment of the fields.
It is worth noting that when a corresponding database is selected, field metadata information of the related database is automatically read, name matching is conducted according to related data metadata such as index names and table names, matching results are displayed through a configured graphical interface, a matching tool enables data field information of a source system to correspond to a target field through matching rules such as field names and appearance sequences, corresponding fields and field types are designated through the configured graphical interface, and the fact that the field importing process is abnormal is guaranteed. Each webpage front-end graphical interface corresponds to one or more system rear-end base tables, and information displayed by the front-end graphical interface is from table data corresponding to a database processed by a program; as shown in fig. 8, the left data source field is the metadata of the service source system in the source database, and the right write table field is the table and field corresponding to the ODS layer of the material data model generalization tool product; forming a corresponding matching relationship through the field matching rule as shown in fig. 8, for example, according to the rule relationship such as the labeling relationship of the metadata; the used rules are all preset rules of the model, and after a corresponding matching relation is formed, the data are extracted from the service system to the ODS layer of the material data model generalization tool product. And automatically extracting the data of the service system in batches into an ODS data layer of a material data model product in the purchasing and transporting field to form a source pasting data layer of the material data model product.
In addition, in an embodiment, as shown in fig. 3, the step S150 may include, but is not limited to, the step S151.
And step S151, performing data extraction processing on the field information after link matching and alignment according to a preset time interval through a preset timer to obtain service data.
It should be noted that, the timer performs data extraction processing on the field information after the link matching and alignment according to a preset time interval to obtain service data, and performs synchronous extraction processing on related data, so as to prepare for subsequent model construction.
In addition, in an embodiment, as shown in fig. 4, the business data includes product domain data, requirement domain data, relationship domain data and approval domain data, and the step S200 may include, but is not limited to, step S210 and step S220.
Step S210, performing data association analysis processing on the product domain data, the demand domain data, the relation domain data and the approval domain data to obtain data association analysis information;
step S220, a business data conceptual model is constructed and obtained based on the construction strategy and the data association analysis information.
It should be noted that the service data may include product domain data, requirement domain data, relationship domain data, and approval domain data; performing data association analysis processing on the product domain data, the demand domain data, the relation domain data and the approval domain data to obtain data association analysis information; and then, based on the construction strategy and the data association analysis information, a business data conceptual model can be constructed.
Illustratively, the business data conceptual model may be as shown in FIG. 9; the business data can be classified and unified according to four categories of products, requirements, relationships and approval. In the field of purchasing and transportation, the product field is mainly purchasing products, and is divided into two categories, namely high-value material products and low-value material products according to the purchasing value of the products. The high-value or low-value material has the following properties: transportation information, material numbers, inventory information, production plans and approval information. The material serial numbers are integrated with different systems such as a material relation system, an enterprise ERP system and the like to form uniform material serial numbers. The material number is used as a main key, and other attributes are identified by depending on the main key. The requirements are various demand plans, production plans, and the like that are specified by a factory to produce a relevant product. The demand data field integrates data of multiple planning systems such as APS production scheduling plans, ERP plans and the like, and transverse planning information of various planning data pull-through with unified planning numbers as main keys is formed. The plan field and the product field are linked by associating the product number with a production plan number of a corresponding product. The inventory area is related information of the factory multi-level warehousing inventory. The method can be divided into related attribute information of safety stock, end-of-term stock, in-transit materials and the like of a factory. The inventory field and the product field are linked by corresponding inventory location information of the product. The relationship domain mainly classifies information for relationships among various entities. The integrated product distribution information and production information can be recorded according to two categories of distribution relation and production relation. The relation domain and the product domain are linked through product number, distribution and production code. The approval domain collects various approval process record information such as: and checking and approving related information of materials, plans and stocks. The data recorded in different systems such as the planning field, material integration, inventory checking and the like can be integrated through the unified examination and approval number information. The examination and approval domain is linked with information in different fields such as products, inventory, requirements, relations and the like through the unified examination and approval number.
In addition, in an embodiment, as shown in fig. 5, the step S300 may include, but is not limited to, the step S310 and the step S350.
Step S310, carrying out conversion processing on a data object and a data attribute of the business data conceptual model to obtain a first physical entity table corresponding to the data object and a first physical entity field corresponding to the data attribute;
step S320, processing and converting the first physical entity table and the first physical entity field to obtain a second physical entity table corresponding to the first physical entity table and a second physical entity field corresponding to the first physical entity field;
step S330, generating a plurality of conversion loading subtasks based on the second physical entity table and the second physical entity field;
step S340, a plurality of conversion loading sub-tasks are connected in series according to preset service logic;
and step S350, executing the plurality of conversion subtasks after the serial connection processing to obtain a material data relationship entity model.
It should be noted that, first, a data object and a data attribute of a service data conceptual model are subjected to conversion processing to obtain a first physical entity table corresponding to the data object and a first physical entity field corresponding to the data attribute; then, processing and converting the first physical entity table and the first physical entity field to obtain a second physical entity table corresponding to the first physical entity table and a second physical entity field corresponding to the first physical entity field; then generating a plurality of conversion loading subtasks based on the second physical entity table and the second physical entity field; then, according to preset service logic, a plurality of conversion loading sub-tasks are connected in series; and finally, executing and processing the plurality of conversion subtasks after the serial connection processing to obtain a material data relationship entity model.
Illustratively, the material data model in the field of procurement and transportation can be constructed by the business data conceptual model, the process of generalizing the conceptual model shown by the material data conceptual model in the field of procurement and transportation into the corresponding physical entity model is referred to as generalization of the model, the process can be summarized as data objects and attributes thereof designed according to the conceptual model and converted into a physical entity table and fields thereof, and a base table field level graphical configuration tool is used for processing ODS data layer data of a data model product into the data entity table and fields required by a material data relational entity (PTDW) model. Generating corresponding ETL operation through a base table field level graphical configuration tool; ETL operation is serially connected according to a business logic sequence, and ODS layer data corresponding to data produced by a business system corresponding to the fields of production, planning, purchasing, selling and the like of a factory are loaded into a material data relation entity model according to rules (according to PTDW model index rules). By linking ODS layer data with the streaming input element and the streaming output element, and by means of dragging and combining the data computing elements, visual processing flow design of the streaming data is quickly realized, and custom input, cleaning, conversion, extraction, calculation and output of the streaming data are completed according to the index definition and relevant requirements of the PTDW, so that various ETL operations are formed. Different business systems are linked through related ETL operation and a field level graphical configuration tool of a base table provided by the scheme, such as: an ERP system, an APS advanced scheduling system and the like are constructed to form a unified, complete and universal data domain model (PTDW).
Specifically, the material data entity relationship model in the field of purchasing transportation takes a business data conceptual model as a theoretical basis to construct a database physical table field entity relationship model which can be realized and established in a database range. The model takes a material information table as a main entity table object. A non-redundant heart model conforming to the 3NF paradigm is built around the material. Taking material inventory information as an example around relevant data objects and indexes of the model, taking a material information table and the material inventory index out of material end inventory index data in ERP by using a base table field level graphical configuration tool, inputting the material end inventory index data into an ODS layer of a material data model generalization tool product, then summarizing and processing the inventory data of the ODS by using a material data model generalization tool according to inventory information indexes in a material data conceptual model in a purchase and transportation field, and then storing the summarized inventory data into a stone _ info field of a meta l _ info table in a material data relational entity (PTDW) model; similarly, other fields of the material information table are processed by using a material data model generalization tool according to index rules in the data conceptual model, and are stored in the table and the fields corresponding to the material data relational entity (PTDW) model, so that a material data relational entity (PTDW) model entity relation is formed, and finally a material data entity relational model in the purchasing and transporting field is formed in various databases (such as Mysq l/Orac and the like).
As shown in fig. 10, the material data entity relationship model integration process of the procurement transportation field can be as follows: the method is characterized in that materials, examination and approval, inventory and planning data in ERP, MDM, MES, WMS, PTS and other systems are synchronized to an ODS data layer of a material transportation relation entity relation model through a base table field level graphical configuration tool, and stable source pasting data (data consistent with business data) for isolating business system changes are obtained through timed ETL operation of the step. And then, according to the conceptual model of the material data in the purchasing and transporting field, constructing a material data entity relation model (PTDW) in the purchasing and transporting field by using a material data model generalization tool.
In addition, in an embodiment, as shown in fig. 6, the step S400 may include, but is not limited to, step S410 and step S420.
Step S410, operating the material data relation entity model to obtain operation parameters;
and step S420, carrying out optimization adjustment processing on the material data relation entity model according to the operation parameters.
It should be noted that, after the material data relationship solid model is constructed, the model may be inspected, the material data relationship solid model is firstly operated to obtain operation parameters, and then the material data relationship solid model is optimized and adjusted according to the operation parameters.
In some embodiments of the present application, the operational parameters include an index monitoring parameter, an event monitoring parameter, and a log monitoring parameter. The index monitoring parameters can be obtained through the following modes: the operation indexes of the acquisition flow and the streaming flow can be monitored. The acquisition indexes comprise acquisition record number, acquisition file number, acquisition byte number, acquisition error record number, acquisition error file number and acquisition speed. The streaming indexes comprise streaming recording number, streaming input speed, streaming processing speed and the like; the event monitoring parameters may be obtained by: the event output list of the flow is viewed. The event list comprises event time, event type, host, event level and event description. The event grades include normal, general, important and serious; the log monitoring parameters may be obtained by: and displaying the operation log record related to the flow during the process operation so as to monitor operation and maintenance of the flow and check and position problems.
By the technical scheme, the relevant data field data of the purchasing transportation field is obtained, and the target model is constructed based on the historical data. By acquiring field data of various factories such as production, planning, purchasing and selling and implementing the construction method according to the purchasing and transporting field material data model provided by the scheme, a purchasing data market can be quickly formed, and the requirements of enterprises for purchasing analysis and material transporting optimization are quickly met at low cost.
Further, as shown in fig. 11, an embodiment of the present application further provides a material procurement transportation optimization device 10, which includes:
the first processing module 100 is configured to collect service data of a plurality of service systems, where the service data is used to represent associated attribute data of material purchasing and transportation;
the second processing module 200 is configured to perform model building processing on the service data based on a preset building strategy to obtain a service data conceptual model;
the third processing module 300 is configured to perform generalization on the business data conceptual model to obtain a material data relationship entity model;
and the fourth processing module 400 is configured to perform monitoring optimization processing on the material data relationship entity model.
The specific implementation of the material purchasing transportation optimizing device 10 is substantially the same as the specific implementation of the material purchasing transportation optimizing method, and is not described herein again.
Further, as shown in fig. 12, an embodiment of the present application also provides an electronic device 700, including: the memory 720, the processor 710, and a computer program stored on the memory 720 and operable on the processor 710, when the processor 710 executes the computer program, implement the material procurement transportation optimization method in the above embodiments, for example, execute the above-described method steps S100 to S400 in fig. 1, method steps S110 to S150 in fig. 2, method step S151 in fig. 3, method steps S210 to S220 in fig. 4, method steps S310 to S350 in fig. 5, and method steps S410 to S420 in fig. 6.
Furthermore, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions, which are executed by a processor or a controller, for example, by a processor in the above-mentioned apparatus embodiment, and can enable the above-mentioned processor to execute the material procurement transportation optimization method in the above-mentioned embodiment, for example, execute the above-mentioned method steps S100 to S400 in fig. 1, method steps S110 to S150 in fig. 2, method step S151 in fig. 3, method steps S210 to S220 in fig. 4, method steps S310 to S350 in fig. 5, and method steps S410 to S420 in fig. 6.
One of ordinary skill in the art will appreciate that all or some of the steps, systems, and methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as is well known to those skilled in the art.
While the preferred embodiments of the present invention have been described, the present invention is not limited to the above embodiments, and those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and such equivalent modifications or substitutions are included in the scope of the present invention defined by the claims.
Claims (10)
1. A material procurement transportation optimization method is characterized by comprising the following steps:
the method comprises the steps of collecting service data of a plurality of service systems, wherein the service data are used for representing associated attribute data of material purchasing and transportation;
model construction processing is carried out on the business data based on a preset construction strategy to obtain a business data conceptual model;
generalizing the business data conceptual model to obtain a material data relation entity model;
and monitoring and optimizing the material data relation entity model.
2. The method for optimizing material procurement transportation according to claim 1, wherein the step of collecting the business data of a plurality of business systems comprises:
acquiring database link information corresponding to a plurality of service systems;
reading metadata table information based on each database link information, wherein the metadata table information comprises table name information and field information;
matching processing is carried out on the basis of the table name information and the field information to obtain a matching result;
aligning the link matching of the field information based on the matching result;
and performing data extraction processing on the field information after the link matching and alignment to obtain the service data.
3. The method for optimizing material procurement and transportation according to claim 2, wherein the step of extracting and processing the field information after the link matching and alignment to obtain the business data comprises:
and performing data extraction processing on the field information after the link matching alignment according to a preset time interval through a preset timer to obtain the service data.
4. The material procurement transportation optimization method of claim 1, wherein the business data comprises product domain data, demand domain data, relationship domain data and approval domain data, and the model construction processing is performed on the business data based on a preset construction strategy to obtain a business data conceptual model, comprising:
performing data association analysis processing on the product domain data, the demand domain data, the relation domain data and the examination and approval domain data to obtain data association analysis information;
and constructing the business data conceptual model based on the construction strategy and the data association analysis information.
5. The method of claim 1, wherein the step of generalizing the business data conceptual model to obtain a material data relational entity model comprises:
converting the data object and the data attribute of the business data conceptual model to obtain a first physical entity table corresponding to the data object and a first physical entity field corresponding to the data attribute;
processing and converting the first physical entity table and the first physical entity field to obtain a second physical entity table corresponding to the first physical entity table and a second physical entity field corresponding to the first physical entity field;
generating a plurality of translation load subtasks based on the second physical entity table and the second physical entity field;
according to preset service logic, carrying out tandem connection processing on a plurality of conversion loading sub-tasks;
and executing and processing the plurality of conversion subtasks after the serial connection processing to obtain the material data relationship entity model.
6. The method for optimizing material procurement transportation according to claim 1, wherein the monitoring and optimizing the material data relational entity model comprises:
performing operation processing on the material data relation entity model to obtain operation parameters;
and carrying out optimization adjustment processing on the material data relation entity model according to the operation parameters.
7. The material procurement shipping optimization method of claim 1 wherein, the operational parameters include an index monitoring parameter, an event monitoring parameter, and a log monitoring parameter.
8. A material procurement transportation optimizing apparatus, comprising:
the system comprises a first processing module, a second processing module and a third processing module, wherein the first processing module is used for collecting service data of a plurality of service systems, and the service data is used for representing associated attribute data of material purchasing and transportation;
the second processing module is used for carrying out model construction processing on the business data based on a preset construction strategy to obtain a business data conceptual model;
the third processing module is used for carrying out generalization processing on the business data conceptual model to obtain a material data relation entity model;
and the fourth processing module is used for monitoring and optimizing the material data relationship entity model.
9. An electronic device, comprising:
memory, processor and computer program stored on the memory and executable on the processor, the processor implementing the method for optimizing material procurement and transportation according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing computer-executable instructions, which when executed by a control processor implement the material procurement shipping optimization method of any one of claims 1-7.
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