Full-chain supply process tracking method based on engineering materials in data

Abstract

The invention provides a full-chain supply process tracking method based on engineering materials of a data medium station, and the method comprises the steps: collecting engineering project ERP, ECP, ELP, EIP, e material supply chain work platform data, cross-professional information platform data and external ecological resource sharing through the data medium station, enabling the full-chain data to be accurately accessed to the data medium station, achieving the integration of upstream and downstream resources of a supply chain, and achieving the tracking of the whole-chain supply process of the engineering materials of the data medium station. According to the method, supply chain internal, cross-professional, supplier and ecosphere data information is communicated, a set of cross-professional and cross-platform data management cooperation mechanism is established, big data analysis is performed on the obtained information according to supply chain business needs, and accurate visualization of supply chain full-chain data is realized through display of an engineering project full-chain visual business board. And the supply chain is driven to be transformed from traditional to wisdom.

Description

Technical Field

[0001] The present invention belongs to the technical field of information platform for material supply chain, and specifically relates to a method for tracking the full-chain supply process of engineering materials based on a data middle platform. Background Art

[0002] Modern smart supply chain material management is an important research direction in the field of supply chain. Many power engineering companies have established a modern smart supply chain system with "5E" system (enterprise resource management system ERP, e-commerce platform ECP, electrical equipment smart Internet of Things platform EIP, power logistics service platform ELP, handheld application e-materials) and "supply chain operation center ESC" as the core, that is, "5E one center" integrated platform, to realize the digitalization and online development of the whole chain business. The deployment mode and business functions of the "5E one center" platform are as follows: Figure 1 As an intelligent operation system, ESC brings together 9 major professional business data of the entire supply chain as well as external and external professional data. It is the data center of the material domain, with a large amount of business data and many integrated systems. At the same time, there is frequent information interaction and data transmission between various business systems.

[0003] ESC relies on the material domain and other related professional data of the two-level data middle platform at the headquarters level and the provincial level to carry out operational analysis and decision-making, and then transmit the corresponding risk warning instructions and auxiliary decision-making strategies back to the business system. The data of the business system deployed at the first level is fully connected to the headquarters data middle platform, and the data of the business system deployed at the second level is connected to the provincial data middle platform. Figure 2 The diagram is a two-level data connection diagram. The provincial data is uploaded to the headquarters mainly through the CIM model. The secondary deployment business system transfers the data 1:1 to the provincial data center source layer, and then uploads it to the headquarters data center according to the CIM model interface standard. The headquarters ESC obtains data through the headquarters data center and carries out relevant application model design and analysis. The headquarters data is sent to the second-level provincial company by connecting the headquarters' first-level deployment business system data to the headquarters data center source layer 1:1. After splitting, data cleaning and other processing, it is sent to the provincial data center through the sharing layer. The provincial ESC obtains the first-level business system data from the provincial data center to carry out analysis and application.

[0004] At present, the system framework of "5E One Center" covers the entire supply chain business nodes. Each system has different forms such as primary and secondary deployment. The functions of the secondary deployment system are also different between different networks and provinces, which makes it difficult to connect and coordinate data between business systems. The data comes from multiple source systems and the data standards are not unified. As a data center, ESC has many integrated business systems and a large amount of data, and has not formed a mature data management system. Specifically, the information of each link of the engineering material supply is managed by the different levels and different professions of the material departments of the head office, provincial and municipal companies and subordinate county companies. Each profession only manages the progress of the materials it is responsible for, and there is no unified mastery of the stage node data in the entire procurement process. After the project unit declares the demand, it is also unable to grasp the subsequent bidding batches, contract signing, supply progress, distribution site and other information in the material procurement link, and lacks a more intuitive way to obtain it. The various levels of material departments are also unable to obtain timely and effective information on the project's preliminary planning and design, construction cycle, demand time, etc., and cannot manage the material supply differently according to the characteristics of various projects, resulting in the impact of material supply problems on the project implementation efficiency.

[0005] At present, the implementation of project construction, material procurement, and supply processes involves multiple management systems and long chains. Information between them is not obtained in a timely manner, and some system interfaces are not highly compatible and have limited transmission capacity. As a result, the supply data of each system is not connected throughout the entire process. It is urgent to use effective means to run through the entire chain of material supply to improve the efficiency of material supply. Summary of the invention

[0006] To this end, the present invention provides a method for tracking the entire supply chain of engineering materials based on a data center in order to solve the above-mentioned problems. The specific solution is as follows:

[0007] A method for tracking the whole supply chain of engineering materials based on a data platform, comprising the following steps:

[0008] S1: The data center collects data from engineering project ERP, ECP, ELP, EIP, and e-material supply chain operation platforms, uses the engineering project code in the data as the primary search key, and connects the entire chain data of the engineering project supply chain.

[0009] S2: The data center collects data from cross-disciplinary information platforms such as project development, infrastructure, marketing, equipment, regulation, and finance, connects the front-end and back-end data ports of the project supply chain, and enables cross-disciplinary information collaborative interaction.

[0010] S3: The data center integrates external information and shares resources with the outside world, builds data standard interfaces, and introduces external third-party data information into the supply chain management and control system.

[0011] S4: Through steps S1-S3, upstream and downstream and internal and external information of the project supply chain business are connected and collected, and big data analysis is performed on the obtained information according to the needs of the supply chain business. Through the display of the visual business dashboard of the entire chain of the project, a panoramic visualization of the supply chain business information is achieved.

[0012] Specifically, in step S4, the big data analysis includes data demand analysis and design, data collection and access, data preprocessing and data analysis.

[0013] Furthermore, the data demand analysis and design include business analysis, demand combing and business design. The demand combing is to conduct demand research based on the material management business work. The demand research is to conduct an overall combing of the business data standard tables for material demand planning, procurement management, contract management, warehouse management, quality supervision management and material master data management, conduct differential demand analysis, and confirm the business content that needs to be implemented. Through demand research, data requirements, data quality, data security and evaluation mechanisms are clarified, data quality is managed during the construction of each standard table, and data management behavior is standardized.

[0014] Furthermore, the data collection and access is to trace the source of some tables of the source system of the supply chain; the base table of the source system is connected to the source layer of the data middle platform in a 1:1 manner, and the data is converted and stored in the shared layer. After review, a result table is created in the analysis layer, and the ESC system displays the front-end interface based on the result table of the analysis layer.

[0015] According to business needs, data needs are sorted, data sources are traced, data access is verified, and data applications are issued, and data preparation is performed based on the required data table and data dictionary. The data middle platform source layer provides source system data access function, unifies internal and external data, and realizes 1:1 replication of source business system data.

[0016] Furthermore, the data preparation work includes data exploration, data information verification, unified field names, sorting out authoritative and valid fields, and sorting out business rules.

[0017] Furthermore, the data preprocessing is to design data access strategies according to data sources, and to perform data integration, cleaning and desensitization based on business rules of each supply chain management node and data field validity.

[0018] The data middle platform sharing layer realizes data storage, standard conversion and standard integration, extracts, cleans, converts and integrates the source layer data, realizes the unified collection of business detail data, and forms enterprise-level shared data.

[0019] Furthermore, the data analysis includes hypothesis testing, difference analysis, correlation analysis, cluster analysis, principal component analysis and correspondence analysis.

[0020] Specifically, in step S4, the visualized business dashboard is constructed in the full-chain management and control area of ​​the engineering project of the supply chain operation platform. According to the dimensions of year, voltage level, project unit, single project and sub-project, the full-chain business scale, execution progress, operation status and performance evaluation of the engineering project from early planning to waste disposal can be viewed in real time, so as to realize a panoramic overview of the full-chain business of the engineering project supply chain.

[0021] Beneficial effects:

[0022] 1. The present invention integrates upstream and downstream resources of the supply chain, establishes a cross-professional and cross-platform data management collaboration mechanism, accurately connects the data of the entire chain to the data middle platform, and realizes the precise visualization of the data of the entire supply chain through digital technology, driving the transformation of the supply chain from traditional to intelligent.

[0023] 2. The present invention effectively improves the material support capability and provides high-quality and efficient material services, which is of great significance for promoting the smooth construction of engineering projects and ensuring safe and reliable operation.

[0024] 3. This invention solves the pain points of insufficient decision-making support, insufficient overall coordination, limited risk perception, and the need to improve the rapid response mechanism under the traditional material management model. It further improves the management system and mechanism, strengthens cross-professional collaboration, consolidates the management foundation, and creates a new model of full supply chain management and control with "business visibility, smart decision-making, and agile response." BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to make the contents of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings.

[0026] Figure 1 It is the deployment mode and business function diagram of the "5E One Center" platform;

[0027] Figure 2 This is a schematic diagram of the data connection between the two-level data platforms;

[0028] Figure 3 This is a flow chart of the method for tracking the full-chain supply process of engineering materials based on the data middle platform of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. The principles and features of the present invention are described below in combination with the accompanying drawings. It should be noted that the embodiments in this application and the features in the embodiments can be combined with each other without conflict. The embodiments are only used to explain the present invention and are not used to limit the scope of the present invention.

[0030] A method for tracking the entire supply chain of engineering materials based on a data middle platform. By integrating diversified data information, it connects data information within the supply chain, across disciplines, suppliers, and the ecosystem, and achieves real-time control of node status information in the entire chain of materials. Relying on the platform's expandable and open connection functions, it flexibly opens up data connections, realizes the interconnection of internal and external data resources, gives play to the aggregation effect of data resources, and generates added value such as shared applications. Figure 3 This is a flow chart of the method for tracking the full-chain supply process of engineering materials based on the data center platform of the present invention, which is specifically implemented in the following way:

[0031] S1. Information exchange within the supply chain. Through the data center, data from supply chain operation platforms such as ERP, ECP, ELP, EIP, and e-materials are collected, and basic data from various business links such as planning, procurement, contracts, supply, and quality in the supply chain are integrated. The project code is used as the primary search key to connect the data of the entire supply chain of the project, break down the professional data barriers within the supply chain, and deepen the data and business linkage between internal professionals.

[0032] S2. Inter-departmental information exchange. Through the data center, data from cross-professional information platforms such as development, infrastructure, marketing, equipment, regulation, and finance are collected, the front-end and back-end data ports of the supply chain are connected, and a cross-professional information collaborative interaction mechanism is established to enhance the business interoperability, data availability, and resource sharing of each business node in the supply chain with other professions, and realize the digital linkage of supply chain management with company development planning, engineering project construction, power grid equipment operation, and financial operation control.

[0033] S3. Sharing of external ecological resources. Carry out information integration and resource sharing with suppliers, design units, quality inspection units, third-party logistics and other supply chain partners, as well as external service platforms such as public services and administrative supervision, build data standard interfaces and smooth information communication channels, introduce external third-party data information into the supply chain management and control system, and promote the efficient operation of the entire process of material planning, procurement, production, logistics, and testing to generate benefits.

[0034] S4. Through steps S1-S3, the upstream and downstream as well as internal and external information of the supply chain business are connected, and big data analysis is performed on the obtained information according to the needs of the supply chain business. Through the display of the visual business dashboard of the entire chain of the engineering project, a panoramic visualization of the supply chain business information is achieved.

[0035] Combined with performance indicators and process monitoring architecture, the needs of supply chain monitoring and management of engineering projects are comprehensively considered, and the analysis dimensions of the entire business process are sorted out to form a "point + line + surface" full-dimensional analysis system. Specifically, a visual business dashboard for the entire chain of engineering projects is developed in the full-chain management and control area of ​​the supply chain operation platform. The data of the entire chain of engineering projects is connected in a way that integrates business flow, physical flow, and capital flow. According to the dimensions of year, voltage level, project unit, single project, sub-project, etc., the full-chain business scale, execution progress, operation status and performance evaluation of the engineering project from preliminary planning to waste disposal can be viewed in real time, achieving a panoramic overview of the full-chain business of the engineering project supply chain.

[0036] Big data analysis can extract the most effective information from massive amounts of data and play a key role in the supply chain of enterprises. It mainly includes the following technologies:

[0037] Data collection, quickly and extensively collects distributed data, and quickly imports data from other platform data sources into the tool, cleans, converts, and integrates the data, so that it is formed in the tool's database or data mart, providing a basis for relationship analysis and processing and data mining.

[0038] Data access can be associated with a database, allowing users to store original data during use and quickly collect and use it, such as cloud storage and distributed file storage.

[0039] Among them, when accessing data to a distributed database based on the data middle station, in order to solve the problem of unbalanced storage among multiple databases, the following technical solution is adopted: the data operation type is determined by the scheduling node of the data middle station. If the operation is to add or modify, it is determined based on the operation data identifier whether the data object of the operation is stored on the current database node. If so, a data operation memory space is opened on the current database node. If not, a database node is randomly selected from several database nodes of the distributed database based on a neural network, and a data operation memory space is opened on the selected database node. That is, the method of determining the storage location of the updated data object is solved by using artificial intelligence, thereby ensuring that data migration is completed when the data object is updated according to the status of each database node, replacing the balancing algorithm and data migration algorithm in the prior art, greatly reducing the burden on the scheduling node, and improving the stability of the system. Furthermore, since the data operation memory space is opened up, the data update operation is performed therein, and the data is inserted into the database node after the update is completed, ensuring that the data object update fails during the update process due to downtime, power failure, etc., resulting in inconsistent data objects.

[0040] In another preferred embodiment, the operation of opening up a data operation memory space on a randomly selected database node includes: if there is only one optional database node, opening up a data operation memory space on the database node; if there are more than one database nodes, randomly selecting a database node to open up a data operation memory space on it. In the present invention, in order to minimize the burden on the scheduling node, when there are more than one optional database node, randomly selecting a database node to open up a data operation memory space on it greatly improves the computational load of the scheduling node. This premise is based on the fact that the states of all database nodes are basically consistent, that is, the state parameters of all database nodes in the present invention are basically consistent. In another preferred embodiment, the data operation memory space is also locked. If the operation type is addition, the operation data identifier and the data object are combined into a data record in the data operation memory space, and the data record is inserted into the database table in the current database node. If there is no current database node connected to the operation, the data record is inserted into the database table in a database node selected from a plurality of database nodes based on the neural network. If the operation type is modification and there is a current database node connected to the operation, the original data object is read from the current database node into the data operation memory space based on the operation data identifier, the original data object is modified in the data operation memory space using the data object, and the modified data object and the operation data identifier are combined into a data record in the data operation memory space, and the data record is used to replace the original data record of the updated database table.

[0041] Data processing can use some computational methods or statistical methods to process data, including statistics, induction, classification, etc., so that users can deeply understand the deep value of the data.

[0042] Statistical analysis is a core function of big data analysis. For example, hypothesis testing can help users analyze the cause of a certain data phenomenon; difference analysis can compare the huge differences shown in different times and regions; correlation analysis can analyze the correlation relationship through the growth and reduction of data changes. In addition, cluster analysis, principal component analysis and correspondence analysis are all commonly used techniques.

[0043] In the big data analysis application of the present invention, the following methods are specifically involved:

[0044] 1. Data demand analysis and design. Including business analysis, demand sorting and business design, demand surveys are carried out according to the key tasks and daily work of material management business management. The scope of demand surveys includes material demand planning, procurement management, contract management (including bid results, contract signing, performance, settlement), warehouse management, quality supervision management, material master data management, etc., to sort out the business data standard tables as a whole, conduct difference demand analysis, and confirm the final business content. Through demand surveys, data requirements, data quality, data security, and evaluation mechanisms are clarified, and data quality management work is carried out in all aspects during the construction of various standard tables to standardize data management behaviors.

[0045] 2. Data collection and access. Trace some tables of the source system of the supply chain; the bottom table of the source system is connected to the source layer of the data middle platform in a 1:1 manner, and the data is converted and stored in the shared layer. After review, the result table is created in the analysis layer, and the ESC system displays the front-end interface based on the result table of the analysis layer. According to the final business needs, data requirements are sorted, data sources are traced, data access is verified, and data applications are issued. In addition, data exploration, data information verification, unified field names, sorting of authoritative and valid fields, sorting of business rules and other data preparation work are carried out based on the data dictionary of the demand data table. The source layer of the data middle platform provides the data access function of the source system, which unifies internal and external data and realizes 1:1 replication of source business system data.

[0046] 3. Data preprocessing. Design data access strategies based on data sources, and perform data integration, cleaning, desensitization and other data processing based on the business rules of each node in the supply chain management and the validity of data fields. The data middle platform sharing layer realizes data storage, standard conversion, and integration, extracts, cleans, converts, integrates the data of the source layer, and realizes the unified collection of business detail data to form enterprise-level shared data. In combination with business application requirements, refer to the data structure of the source business system, and standardize the existing data through data cleaning. At the same time, control business processes such as data application and data approval to control the standardization of new data and improve quality issues such as incomplete data, redundancy, and errors. Through data architecture design, data storage and standardized data services, seamless integration of data sharing processes is achieved, breaking down data usage barriers between information systems, between professions, and between unit levels, realizing standardized data storage, and efficiently meeting the simple and easy-to-use needs of data sharing of headquarters, provincial companies, and municipal companies.

[0047] 4. Data analysis. Data analysis is a core function of big data analysis. For example, hypothesis testing can help users analyze the cause of a certain data phenomenon. Difference analysis can compare the huge differences shown in different times and regions. Correlation analysis can analyze the correlation relationship through the growth and reduction of data. In addition, cluster analysis, principal component analysis and correspondence analysis are all commonly used techniques.

[0048] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0049] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention. It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of the present invention. In practical applications, those skilled in the art can select part or all of them according to actual needs to achieve the purpose of the solution of this embodiment, and there is no limitation here.

Claims

1. A method for tracking the whole supply chain of engineering materials based on a data platform, comprising the following steps: S1: The data center collects data from the engineering project ERP, ECP, ELP, EIP, and e-material supply chain operation platform, uses the engineering project code in the data as the search primary key, and connects the entire supply chain data of the engineering project; S2: The data center gathers data from cross-disciplinary information platforms for project development, infrastructure, marketing, equipment, regulation, and finance, connects the front-end and back-end data ports of the project supply chain, and enables cross-disciplinary information collaboration and interaction; S3: The data center integrates external information and shares resources with the outside world, builds data standard interfaces, and introduces external third-party data information into the supply chain management and control system; S4: Through steps S1-S3, upstream and downstream and internal and external information of the project supply chain business are connected and collected, and big data analysis is performed on the obtained information according to the needs of the supply chain business. Through the display of the visual business dashboard of the entire chain of the project, a panoramic visualization of the supply chain business information is achieved.

2. According to the method for tracking the full-chain supply process of engineering materials based on the data middle platform in claim 1, in step S4, the big data analysis includes data demand analysis and design, data collection and access, data preprocessing and data analysis.

3. According to the full-chain supply process tracking method for engineering materials based on the data middle platform in claim 2, the data demand analysis and design include business analysis, demand combing and business design; the demand combing is to conduct demand investigation according to the material management business work, and the demand investigation is to conduct an overall combing of the business data standard tables for material demand planning, procurement management, contract management, warehouse management, quality supervision management and material master data management, conduct differential demand analysis, and confirm the business content that needs to be implemented; through demand investigation, data requirements, data quality, data security and evaluation mechanism are clarified, data quality is managed during the construction of each standard table, and data management behavior is standardized.

4. According to the method for tracking the whole-chain supply process of engineering materials based on the data middle platform in claim 2, the data collection access is to trace the source of some tables of the source end system of the supply chain; the bottom table of the source end system is connected to the source layer of the data middle platform in a 1:1 manner, and the data is converted and stored in the sharing layer. After review, a result table is created in the analysis layer, and the ESC system displays the front-end interface based on the result table of the analysis layer; Sorting out data requirements, tracing data sources, verifying data access and issuing data applications according to business needs, and preparing data based on required data tables and data dictionaries; The data middle platform source layer provides source system data access function, unifies internal and external data, and realizes 1:1 replication of source business system data.

5. According to the method for tracking the full-chain supply process of engineering materials based on the data middle platform in claim 4, the data preparation work includes data exploration, data information verification, unified field names, sorting out authoritative and valid fields, and sorting out business rules.

6. According to the method for tracking the whole supply chain of engineering materials based on the data middle platform in claim 2, the data preprocessing is to design the data access strategy according to the data source, and to integrate, clean and desensitize the data based on the business rules of each node of the supply chain management and the validity of the data field; The data middle platform sharing layer realizes data storage, standard conversion and standard integration, extracts, cleans, converts and integrates the source layer data, realizes the unified collection of business detail data, and forms enterprise-level shared data.

7. According to the method for tracking the full-chain supply process of engineering materials based on the data middle platform in claim 2, the data analysis includes hypothesis testing, difference analysis, correlation analysis, cluster analysis, principal component analysis and correspondence analysis.

8. According to the method for tracking the full-chain supply process of engineering materials based on a data middle platform according to claim 1, in the step S4, the visual business dashboard is constructed in the full-chain management and control area of ​​the engineering project of the supply chain operation platform, and the full-chain business scale, execution progress, operation status and performance evaluation of the engineering project from preliminary planning to waste disposal can be viewed in real time according to the annual, voltage level, project unit, single project and sub-project dimensions, so as to realize a panoramic overview of the full-chain business of the engineering project supply chain.

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