Power plant material full-life-cycle intelligent management method and related equipment

Abstract

The invention relates to the technical field of production intelligent management and control of energy enterprises such as thermal power enterprises and nuclear power enterprises, and discloses a power plant material full-life-cycle intelligent management method and related equipment.The method comprises the steps that a power plant material full-life-cycle big data center is established, and the power plant material full-life-cycle big data center integrates multi-source data through a distributed architecture; unified data standard and interface specification are carried out on the distributed architecture integrated multi-source data; respectively carrying out life management, material management and equipment maintenance management on the unified data standard and interface specification to obtain dynamic multi-service data; dynamic multi-service data are butted and fused for data association analysis, material full-process penetration management, material full-life-cycle tracing, intelligent purchase decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and full-process visual monitoring. According to the invention, power plant material management is converted from passive response to active prediction and from segmented control to full-process collaboration, and the management efficiency is significantly improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent production and control of energy enterprises such as thermal power and nuclear power, in particular to a power plant material full life cycle intelligent management method and related equipment. BACKGROUND

[0002] As an important facility for energy production, the safe operation of the equipment and materials (such as boiler pipes, turbine blades, transformers, high-pressure valves, and motors) of a power plant is directly related to the stable power supply of the power grid. In the production process of a power plant, problems such as equipment aging and material degradation are widespread, which seriously affect production safety and economic benefits. At present, power plants are generally equipped with DCS systems (Distributed Control System), equipment maintenance management systems, and material management systems, but these systems are usually independently constructed by different suppliers, and there are serious data barriers. Taking a 1000MW coal-fired power plant as an example, its equipment operation monitoring data is stored in the DCS system, the material procurement parameters exist in the ERP system (Enterprise Resource Planning), and the maintenance records are scattered in the maintenance management system. There is a lack of effective data interaction mechanism between these systems, forming a clear data island phenomenon. This leads to the fact that the equipment state data and the material management data can only be manually compared and analyzed at the information center of the power plant, and the on-site operation and maintenance personnel cannot obtain real-time full life cycle health status information of the equipment, and the management decision-making is seriously lagging.

[0003] Specifically, the equipment warning information cannot be timely associated with the material inventory status, resulting in insufficient spare parts for rush repair; the material procurement decision lacks real-time operation data support of the equipment, causing inventory accumulation or shortage; and the remaining value of the retired material cannot be accurately assessed during disposal, resulting in resource waste.

[0004] This data fragmentation situation makes the material management of the power plant inefficient, and since the real-time linkage between the equipment state and the material management cannot be achieved, the risk of equipment sudden failure is increased, which constitutes a major hidden danger to the safe production of the power plant. SUMMARY

[0005] In order to overcome the defects of the prior art, the purpose of the present application is to provide a power plant material full life cycle intelligent management method and related equipment to solve the technical problem of how to realize the intelligent management of the full life cycle of the material of the power plant in the prior art.

[0006] The present application is achieved by the following technical solutions:

[0007] In a first aspect, the present application provides a power plant material full life cycle intelligent management method, comprising:

[0008] A power plant material life cycle big data center is created, which integrates multi-source data in a distributed architecture and performs unified data standards and interface specifications on the multi-source data integrated in the distributed architecture;

[0009] Life management, material management, and equipment maintenance management are performed on the unified data standards and interface specifications to obtain dynamic multi-business data;

[0010] The dynamic multi-business data is connected and fused for data correlation analysis, material full-process penetration management, material full-life cycle traceability, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization, and full-process visual monitoring.

[0011] Preferably, the multi-source data integrated in the distributed architecture includes power plant DCS system data, material management system data, and equipment maintenance management system data;

[0012] The DCS system data includes production equipment measurement point coding, production data, alarm data, and data update time of the overall control system of the power plant;

[0013] The material management system data includes basic identification data, technical specification data, supply chain process data, quality data, and inventory information;

[0014] The equipment maintenance management system data includes basic archive data, process execution data, and test detection data.

[0015] 4. Preferably, in the unified data standards and interface specifications for the multi-source data integrated in the distributed architecture, the unified data standards include data coding constructed according to the correspondence between unified equipment coding and material coding;

[0016] The data coding includes original equipment coding, equipment state, and equipment information;

[0017] The equipment state includes procurement, quality inspection, in-stock, installation and deployment, in-use, retired and ready for disposal, and disposed;

[0018] The equipment information includes batch, equipment type code, supplier, and purchase serial number;

[0019] The unified data standard interface specification includes protocol adaptation layer specification when collecting power plant DCS system data, material management system data, and equipment maintenance management system data, security interaction specification involving transmission security, identity authentication, data encryption, audit traceability, unified data time synchronization specification, and exception handling specification.

[0020] Preferably, the specific process of performing life management, material management, and equipment maintenance management on the unified data standards and interface specifications to obtain dynamic multi-business data is as follows:

[0021] The specific process of the life management is as follows:

[0022] A life management model is established, data is extracted according to key characteristics of the equipment, historical data records under different working conditions are established according to the data, equipment operation and failure samples are formed, and life management is carried out according to the equipment operation and failure samples, wherein the data extracted according to the key characteristics of the equipment includes the extraction of the pipe wall temperature of the boiler pipe, the steam pressure; the extraction of the bearing vibration of the steam turbine, the winding temperature and the load current of the transformer; the extraction of the pressure difference and the action frequency of the high-pressure valve;

[0023] The specific process of the material management is as follows:

[0024] A material closed-loop management model is established, and the whole process of intelligent management and control of materials from procurement to disposal is carried out by adopting the collaborative architecture of procurement chain, inventory chain, maintenance chain, life chain and regeneration chain in the establishment of the material closed-loop management model;

[0025] The specific process of the equipment maintenance management is as follows:

[0026] A dynamic correlation mechanism of equipment maintenance and material management is constructed, a closed-loop management system of data collection, analysis and decision, execution and feedback is formed combined with the material closed-loop management model, and dynamic multi-business data is obtained according to the closed-loop management system.

[0027] Preferably, the dynamic multi-business data docking fusion includes dynamically fusing multi-source data of equipment operation, material management and equipment maintenance, constructing an integrated management system of data-driven-intelligent decision-visualization, and performing data correlation analysis, material whole-process penetration management, material whole-life cycle traceability, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and whole-process visualization monitoring based on the integrated management system.

[0028] Further, the data correlation analysis includes mining the implicit relationship between equipment failure mode and spare part consumption, and using principal component analysis to process multi-dimensional operation data based on the implicit relationship, and identifying key factors affecting equipment life;

[0029] The process of the material whole-process penetration management includes establishing a material digital twin archive, and developing a penetrating query engine based on the material digital twin archive;

[0030] The material whole-life cycle traceability includes introducing blockchain technology to hash the key data, giving the material a unique two-dimensional code / RFID tag, and associating the whole-life cycle data;

[0031] The intelligent procurement decision support comprises constructing a spare part consumption prediction model, establishing a multi-dimensional evaluation system of suppliers based on the spare part consumption prediction model, and automatically triggering a procurement process and approval reminding;

[0032] The inventory dynamic optimization management comprises classifying spare parts according to value and frequency of use, and adjusting inventory thresholds in combination with real-time running states of equipment;

[0033] The intelligent decision algorithm optimization comprises training a model offline in a sandbox environment by using historical data, and automatically tuning parameters through actual running effect feedback;

[0034] The full-process visual monitoring comprises constructing a three-dimensional visual board based on digital twin technology, and displaying equipment health states and inventory distribution in real time, and supporting multi-terminal adaptation and multi-dimensional data display.

[0035] In a second aspect, the present application further provides a power plant material full-life cycle intelligent management system, comprising:

[0036] A data center creation module is configured to create a power plant material full-life cycle big data center, wherein the power plant material full-life cycle big data center adopts a distributed architecture to integrate multi-source data, and performs unified data standards and interface specifications on the distributed architecture to integrate multi-source data;

[0037] A data analysis processing module is configured to perform life management, material management and equipment maintenance management on the unified data standards and interface specifications to obtain dynamic multi-business data;

[0038] A data management module is configured to perform data correlation analysis, material full-process penetration management, material full-life cycle traceability, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and full-process visual monitoring on the dynamic multi-business data.

[0039] In a third aspect, the present application further provides a mobile terminal, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements steps of the power plant material full-life cycle intelligent management method as described above when executing the computer program.

[0040] In a fourth aspect, the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program implements steps of the power plant material full-life cycle intelligent management method as described above when executed by a processor.

[0041] In a fifth aspect, the present application further provides a computer program product, comprising computer instructions, wherein the computer instructions instruct a computing device to perform operations corresponding to the power plant material full-life cycle intelligent management method as described above.

[0042] Compared with the prior art, the present application has the following beneficial technical effects:

[0043] The present application provides a power plant material full life cycle intelligent management method, which integrates DCS system, material management system, equipment maintenance management system data through distributed architecture, establishes unified standard and interface specification, realizes cross-system data real-time synchronization, and provides complete and accurate data foundation for full life cycle management. Based on the unified data platform, the management personnel can comprehensively master the related information such as equipment state, material circulation, maintenance record, etc., and avoid the decision-making blind area caused by segmented management. The equipment life management model is constructed, the key characteristic parameters are extracted, and the historical working condition data are combined to form a fault sample library. Through dynamic monitoring and model analysis, the equipment deterioration trend is early warned, and the non-planned shutdown caused by sudden failure is avoided. Based on the actual running state of the equipment, the maintenance plan is made, the problems of excessive maintenance or insufficient maintenance are reduced, and the service life of the equipment is prolonged. Through multi-modal data fusion and intelligent algorithm optimization, the present application realizes the change of power plant material management from passive response to active prediction, from segmented control to whole process cooperation, significantly improves the management efficiency and reduces the operation cost, at the same time, guarantees the safety production and sustainable development, has significant economic benefit and social benefit. BRIEF DESCRIPTION OF DRAWINGS

[0044] Figure 1 The present application provides a power plant material full life cycle intelligent management method, which integrates DCS system, material management system, equipment maintenance management system data through distributed architecture, establishes unified standard and interface specification, realizes cross-system data real-time synchronization, and provides complete and accurate data foundation for full life cycle management. Based on the unified data platform, the management personnel can comprehensively master the related information such as equipment state, material circulation, maintenance record, etc., and avoid the decision-making blind area caused by segmented management. The equipment life management model is constructed, the key characteristic parameters are extracted, and the historical working condition data are combined to form a fault sample library. Through dynamic monitoring and model analysis, the equipment deterioration trend is early warned, and the non-planned shutdown caused by sudden failure is avoided. Based on the actual running state of the equipment, the maintenance plan is made, the problems of excessive maintenance or insufficient maintenance are reduced, and the service life of the equipment is prolonged. Through multi-modal data fusion and intelligent algorithm optimization, the present application realizes the change of power plant material management from passive response to active prediction, from segmented control to whole process cooperation, significantly improves the management efficiency and reduces the operation cost, at the same time, guarantees the safety production and sustainable development, has significant economic benefit and social benefit.

[0045] Figure 2 The present application provides a power plant material full life cycle intelligent management method, which integrates DCS system, material management system, equipment maintenance management system data through distributed architecture, establishes unified standard and interface specification, realizes cross-system data real-time synchronization, and provides complete and accurate data foundation for full life cycle management. Based on the unified data platform, the management personnel can comprehensively master the related information such as equipment state, material circulation, maintenance record, etc., and avoid the decision-making blind area caused by segmented management. The equipment life management model is constructed, the key characteristic parameters are extracted, and the historical working condition data are combined to form a fault sample library. Through dynamic monitoring and model analysis, the equipment deterioration trend is early warned, and the non-planned shutdown caused by sudden failure is avoided. Based on the actual running state of the equipment, the maintenance plan is made, the problems of excessive maintenance or insufficient maintenance are reduced, and the service life of the equipment is prolonged. Through multi-modal data fusion and intelligent algorithm optimization, the present application realizes the change of power plant material management from passive response to active prediction, from segmented control to whole process cooperation, significantly improves the management efficiency and reduces the operation cost, at the same time, guarantees the safety production and sustainable development, has significant economic benefit and social benefit.

[0046] In the figure: 1, data center creation module; 2, data analysis processing module; 3, data management module. DETAILED DESCRIPTION

[0047] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the present application embodiment will be described clearly and completely in combination with the drawings in the present application embodiment. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by the ordinary skilled in the art without creative labor should belong to the scope of protection of the present application.

[0048] It is to be understood that the terms "first", "second", and the like, used in the description and the claims of the application as well as the above description of the drawings merely refer to structure that is different, and not necessarily to a specific order or sequence. It will be understood that the terms so used are interchangeable under appropriate circumstances. Also, the term "comprises", "comprising", "includes", "including" and the like, are used herein to mean either "consists" or "consisting of" and include the singular, plural and equivalents unless expressly stated otherwise. It is to be understood that such a transition term as "comprising", "comprising", "containing", "having" and the like, are not to be construed to limit the aforementioned description to a "closed" meaning; that is they are not to be construed to mean that the structure must consist of the elements explicitly named. Rather, "comprising" and the like are to be construed to mean "including, but not limited to".

[0049] The purpose of the present application is to provide a power plant material life cycle intelligent management method and related equipment to solve the technical problem of how to realize the power plant material life cycle intelligent management in the prior art.

[0050] The present application will be described in further detail below with reference to the accompanying drawings:

[0051] Embodiment 1

[0052] Referring to Figure 1 In an embodiment of the present application, a power plant material life cycle intelligent management method is provided, comprising:

[0053] Step 1, creating a power plant material life cycle big data center, the power plant material life cycle big data center adopts a distributed architecture to integrate multi-source data, and performs unified data standards and interface specifications on the distributed architecture to integrate multi-source data;

[0054] Specifically, the distributed architecture integrates multi-source data, including power plant DCS system data, material management system data and equipment maintenance management system data;

[0055] The DCS system data includes production equipment measurement point coding, production data, alarm data and data update time of the overall control system of the power plant;

[0056] The material management system data includes basic identification data, technical specification data, supply chain process data, quality data and inventory information;

[0057] The basic identification data includes material unique code, KKS device correlation code and material classification label. The technical specification data includes material grade, design life and design limit value. The supply chain process data includes supplier information and logistics information. The quality data includes incoming inspection report. The cost data includes cost type, procurement cost, installation cost, maintenance cost and residual value estimation. The inventory information includes real-time storage location, warehousing time, quantity, batch, inventory status and storage status.

[0058] The equipment overhaul management system data includes basic archive data, process execution data, and test detection data.

[0059] The basic archive data includes equipment name, KKS code, repair procedure library, spare parts list; the process execution data includes work order, repair time, resource consumption (spare parts consumption list + labor cost), installation and repair location and position, safety measures, installation and repair evaluation; the test detection data includes equipment operation parameters.

[0060] Specifically, in the unified data standard and interface specification for integrating multi-source data in the distributed architecture, the unified data standard includes data coding constructed according to the correspondence between unified equipment coding and material coding;

[0061] The data coding includes original equipment coding, equipment state, and equipment information;

[0062] The equipment state includes procurement, quality inspection, in-stock, installation and deployment, in-use, decommissioning and disposal, and disposal;

[0063] The equipment information includes batch, equipment type code, supplier, and procurement serial number;

[0064] The unified data standard interface specification includes protocol adaptation layer specification when collecting power plant DCS system data, material management system data, and equipment overhaul management system data, security interaction specification involving transmission security, identity authentication, data encryption, audit traceability, unified data time synchronization specification, and exception handling specification.

[0065] Step 2, dynamic multi-business data is obtained by performing life management, material management, and equipment overhaul management on the unified data standard and interface specification, respectively;

[0066] Specifically, the specific process of the life management is as follows:

[0067] A life management model is established, in which data is extracted according to equipment key features, and historical data records under different working conditions are established according to the data, forming equipment operation and failure samples, and life management is performed according to the equipment operation and failure samples, wherein the data extracted according to the equipment key features include boiler pipe wall temperature, steam pressure; turbine bearing vibration, transformer winding temperature, and load current; high-pressure valve differential pressure and action frequency;

[0068] The specific process of the material management is as follows:

[0069] The material closed-loop management model is established, and in the establishment of the material closed-loop management model, a collaborative architecture of a procurement chain, an inventory chain, a maintenance chain, a life chain and a regeneration chain is adopted to intelligently control the whole process of the material from procurement to disposal;

[0070] The specific process of the equipment maintenance management is as follows:

[0071] The dynamic association mechanism of equipment maintenance and material management is constructed, a closed-loop management system of data collection, analysis and decision, execution and feedback is formed in combination with the material closed-loop management model, and dynamic multi-business data is obtained according to the closed-loop management system.

[0072] Step 3, the dynamic multi-business data is connected and fused to perform data correlation analysis, material whole-process penetration management, material whole-life cycle tracing, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and whole-process visual monitoring.

[0073] Specifically, the dynamic multi-business data connection and fusion includes fusing the multi-source data of equipment operation, material management and equipment maintenance through dynamic fusion, constructing an integrated management system of data driving-intelligent decision-visualization presentation, and performing data correlation analysis, material whole-process penetration management, material whole-life cycle tracing, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and whole-process visual monitoring based on the integrated management system.

[0074] The data correlation analysis includes mining the implicit relationship between equipment failure modes and spare part consumption, and using principal component analysis to reduce the dimension of multi-dimensional operation data based on the implicit relationship to identify the key factors affecting the service life of the equipment.

[0075] The process of the material whole-process penetration management includes establishing a material digital twin file and developing a penetrating query engine based on the material digital twin file.

[0076] The material digital twin file is established from the whole-process data chain of procurement order (supplier information, contract number) to storage inspection (quality inspection report) to inventory turnover (use order association) to installation and use (corresponding equipment KKS code) to disposal (residual value evaluation report); and the penetrating query engine is developed to support the reverse tracing of associated information through any link data (such as inputting the material code, the running state of all equipment replaced by the spare part, maintenance effect evaluation and disposal destination can be viewed). The traditional material management “segmented recording” problem is solved, the whole-link transparency from “procurement source” to “disposal end” is realized, and the fine control demand of the power plant for key equipment materials is met.

[0077] The material whole-life cycle tracing includes introducing the block chain technology to hash the key data, giving the material a unique two-dimensional code / RFID tag and associating the whole-life cycle data.

[0078] Among them, the blockchain technology is introduced to chain the hash values of key data such as material procurement contracts, quality inspection reports and maintenance replacement records, to ensure that the data is tamper-proof and traceable; a traceability identification system is built: a unique two-dimensional code / radio frequency identification (RFID) is given to each material, and its full life cycle data (such as scanning the spare parts two-dimensional code to view: installation location change record, previous detection data, remaining life prediction value) is associated. Ensure the recall of material quality problems and compliance audit.

[0079] The intelligent procurement decision support includes building a spare parts consumption prediction model, establishing a multi-dimensional evaluation system of suppliers based on the spare parts consumption prediction model, and automatically triggering the procurement process and approval reminder;

[0080] Among them, demand prediction: build a spare parts consumption prediction model, input equipment operation data, historical consumption data, maintenance plan, output future 3 months spare parts demand quantity; supplier evaluation: establish a multi-dimensional evaluation system (delivery period, quality pass rate, price competitiveness, after-sales service response speed), dynamically generate supplier optimization ranking, and automatically recommend the most cost-effective procurement scheme. Decision automation: when the inventory is lower than the safety threshold, the system triggers the automatic procurement process, and at the same time, the relevant personnel are reminded by SMS or mobile application software for approval, reducing the decision delay caused by manual intervention.

[0081] The dynamic optimization management of inventory includes classifying spare parts according to value and frequency of use, and adjusting the inventory threshold according to the real-time running state of the equipment;

[0082] Among them, classification method combined with intelligent algorithm: spare parts are classified according to value and frequency of use (Class A: high-value low-frequency spare parts; Class B: medium-value medium-frequency spare parts; Class C: low-value high-frequency spare parts), Class A uses safety inventory + predictive procurement, Class B uses regular inventory management + regular replenishment, and Class C uses just-in-time procurement + minimum inventory mode; and the inventory threshold is adjusted according to the real-time running state of the equipment.

[0083] The intelligent decision algorithm optimization includes training the model offline in a sandbox environment using historical data, and automatically tuning the parameters through actual running effect feedback;

[0084] Among them, an algorithm training sandbox environment is established: historical data (such as 3 years of equipment failure and spare parts consumption data) are used to train the models of procurement prediction, life management, etc. offline, and the parameters are automatically tuned through actual running effect feedback (such as prediction error rate); support users to define decision rules, realize intelligent decision-making driven by "rules + data" through machine learning, and adapt to the individualized management needs of different power plants.

[0085] The whole-process visual monitoring includes constructing a three-dimensional visual board based on digital twin technology, displaying the equipment health status and inventory distribution in real time, and supporting multi-terminal adaptation and multi-dimensional data display.

[0086] Among them, the three-dimensional visual board: based on digital twin technology, a 3D model of power plant equipment and materials is constructed, and the equipment health status (green: normal, yellow: early warning, red: failure) and inventory distribution (different colors mark inventory levels) are displayed in real time; multi-terminal adaptation: supporting PC terminal large screen monitoring, mobile terminal APP real-time early warning (such as sending WeChat notifications when spare parts inventory is lower than the safety threshold); data display dimensions: equipment dimension (remaining life prediction value, key component health degree), material dimension (inventory turnover rate, spare parts complete rate), management dimension (procurement cycle compliance rate, retired material recycling rate).

[0087] In this embodiment, distributed storage technology is used to build a storage architecture of the big data center to meet the efficient storage needs of multi-source data. A unified data interface specification is developed to realize data docking with the DCS system, the material management system, and the equipment maintenance management system. A data cleaning, conversion, and loading process is established to preprocess the collected multi-source data, ensuring data consistency and accuracy. The data of the big data center is backed up and maintained regularly to ensure data security and stable operation of the system.

[0088] In this embodiment, through the communication interface with the DCS system, the running parameters of key equipment such as boiler pipes, steam turbines, transformers, and high-pressure valves are collected in real time at a set frequency, such as temperature, pressure, vibration, and current data. Machine learning algorithms such as neural networks and random forests are used to construct equipment life management models based on historical operation data and design parameters of the equipment. Real-time analysis is performed on the collected data, and the current state of the equipment is evaluated based on the equipment life management model to predict the remaining life of the equipment, and generate corresponding decision suggestions such as equipment maintenance plans and spare parts procurement plans based on the prediction results.

[0089] In this embodiment, data such as material procurement, inventory, and use is obtained from the material management system, the material coding system is associated with the KKS coding system one-to-one, and a material information mapping table is established. Based on the associated coding system, a material closed-loop management model is constructed, and operations research and supply chain management theory are used to optimize the management of the four stages of material procurement, inventory, maintenance, and retirement. For example, in the procurement stage, based on the predicted spare parts demand of the equipment life management model, combined with the inventory status and supplier information, an optimal procurement plan is developed; in the inventory management stage, inventory optimization algorithms are used to achieve dynamic inventory optimization.

[0090] In this embodiment, data interaction is carried out with the equipment maintenance management system to obtain work tickets, spare part consumption records, installation points, maintenance effect evaluation, test detection data and other information. These data are associated and analyzed with the material life cycle management data, for example, by analyzing the spare part consumption records and the equipment operation data, the equipment maintenance effect is evaluated, and the maintenance strategy is optimized; according to the work ticket and the installation point information, precise management of the equipment maintenance process is realized. A maintenance knowledge base is established, and the experience and data in the maintenance process are summarized and induced to provide support for subsequent equipment maintenance and decision-making.

[0091] In this embodiment, data mining and data analysis techniques are used to carry out associated analysis on the fused multi-service data, and to mine the potential relationships and rules between the data. Based on the associated analysis results, material full-process penetration management is realized, that is, the whole process information from the material procurement source to the retirement disposal is traceable and queryable; the blockchain technology is used to guarantee the authenticity and non-tamperability of the material life cycle traceability. An intelligent procurement decision support model is constructed, which combines the equipment operation state, inventory condition, quality and other factors, and uses decision tree, analytic hierarchy process and other algorithms to provide scientific decision suggestions for material procurement. Visualization technology is used to realize full-process visual monitoring, and the running state and key indicators of each link of material management are displayed in the form of intuitive charts, so that the management personnel can master the material life cycle management situation in real time and make timely decision adjustment.

[0092] In summary, the power plant material life cycle intelligent management method provided in this embodiment breaks through the data barriers between the DCS system, the material management system and the equipment maintenance management system by creating a big data center to integrate multi-source data, realizes real-time synchronization and sharing of data, and provides comprehensive and accurate data support for material life cycle management.

[0093] The established equipment life management model, material closed-loop management model and closed-loop decision chain realize accurate evaluation and life prediction of the equipment state, guarantee intelligent real-time linkage of material procurement, inventory, maintenance and retirement stages, and effectively solve the problems of insufficient spare parts preparation, inventory accumulation or shortage, inaccurate retirement material value evaluation and the like.

[0094] The realization of data association analysis, material full-process penetration management and other functions improves the efficiency of power plant material management, reduces the risk of equipment sudden failure, guarantees the safety production of power plants, and has significant economic and social benefits.

[0095] Embodiment 2

[0096] According to Figure 2 As shown in the figure, the embodiment also provides a power plant material life cycle intelligent management system, which comprises:

[0097] The data center creation module 1 is used to create a power plant material full life cycle big data center, and the power plant material full life cycle big data center integrates multi-source data in a distributed architecture and performs unified data standards and interface specifications on the multi-source data integrated in the distributed architecture.

[0098] The data analysis processing module 2 is used to obtain dynamic multi-business data by performing life management, material management and equipment maintenance management on the unified data standards and interface specifications.

[0099] The data management module 3 is used to perform data correlation analysis, material full-process penetration management, material full life cycle traceability, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and full-process visual monitoring on the dynamic multi-business data.

[0100] Embodiment 3

[0101] The application further provides a mobile terminal comprising a memory, a processor and a computer program, such as a power plant material full life cycle intelligent management program, stored in the memory and executable on the processor.

[0102] The processor implements the steps of the power plant material full life cycle intelligent management method when executing the computer program, for example:

[0103] The power plant material full life cycle big data center is created, and the power plant material full life cycle big data center integrates multi-source data in a distributed architecture and performs unified data standards and interface specifications on the multi-source data integrated in the distributed architecture.

[0104] The dynamic multi-business data is obtained by performing life management, material management and equipment maintenance management on the unified data standards and interface specifications.

[0105] The dynamic multi-business data is fused to perform data correlation analysis, material full-process penetration management, material full life cycle traceability, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and full-process visual monitoring.

[0106] Alternatively, the processor implements the functions of the modules in the above system when executing the computer program, for example:

[0107] The data center creation module 1 is used to create a power plant material full life cycle big data center, and the power plant material full life cycle big data center integrates multi-source data in a distributed architecture and performs unified data standards and interface specifications on the multi-source data integrated in the distributed architecture.

[0108] The data analysis processing module 2 is used for obtaining dynamic multi-service data by respectively performing life management, material management and equipment maintenance management on the unified data standard and interface specification.

[0109] The data management module 3 is used for performing data correlation analysis, material whole-process penetration management, material whole-life cycle tracing, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and whole-process visual monitoring by interfacing and fusing the dynamic multi-service data.

[0110] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program in the mobile terminal.

[0111] For example, the computer program can be divided into a data center creation module 1, a data analysis processing module 2 and a data management module 3.

[0112] The specific functions of each module are as follows:

[0113] The data center creation module 1 is used for creating a power plant material whole-life cycle big data center, and the power plant material whole-life cycle big data center integrates multi-source data by using a distributed architecture and performs unified data standard and interface specification on the multi-source data integrated by the distributed architecture.

[0114] The data analysis processing module 2 is used for obtaining dynamic multi-service data by respectively performing life management, material management and equipment maintenance management on the unified data standard and interface specification.

[0115] The data management module 3 is used for performing data correlation analysis, material whole-process penetration management, material whole-life cycle tracing, intelligent procurement decision support, inventory dynamic optimization management, intelligent decision algorithm optimization and whole-process visual monitoring by interfacing and fusing the dynamic multi-service data.

[0116] The mobile terminal can be a desktop computer, a notebook computer, a palm computer and a cloud server and the like computing devices. The mobile terminal can include, but is not limited to, a processor and a memory.

[0117] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The processor is a control center of the mobile terminal, which can connect all parts of the mobile terminal through various interfaces and lines.

[0118] The memory can be used to store the computer programs and / or modules, and the processor can realize various functions of the mobile terminal by running or executing the computer programs and / or modules stored in the memory, and calling data stored in the memory.

[0119] The memory can mainly include a program storage area and a data storage area. The program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc. The data storage area can store data created according to the use of the mobile terminal (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a nonvolatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state memory device.

[0120] Embodiment 4

[0121] The application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps of the power plant material life cycle intelligent management method.

[0122] If the modules / units integrated in the mobile terminal are realized in the form of software function units and sold or used as independent products, the modules / units can be stored in a computer readable storage medium.

[0123] Based on such understanding, the present application implements all or part of the processes in the above method, and can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned aggregated reinforcement learning resource scheduling method can be implemented. The computer program includes computer program codes, which can be in the form of source code, object code, executable files or some intermediate forms, etc.

[0124] The computer readable medium can include any entity or device, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. that can carry the computer program code.

[0125] It should be noted that the content contained in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0126] Embodiment 5

[0127] A computer program product includes computer instructions instructing a computing device to perform operations corresponding to the power plant material life cycle intelligent management method described above.

[0128] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application and not to limit them, although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that: the specific embodiments of the present application can still be modified or replaced equivalently without departing from the spirit and scope of the present application, any modification or equivalent replacement thereof should be covered within the protection scope of the claims of the present application.

Claims

1. A method for intelligent management of power plant materials throughout their entire lifecycle, characterized in that, include: A big data center for the entire lifecycle of power plant materials is created. The big data center adopts a distributed architecture to integrate multi-source data and uses unified data standards and interface specifications for the multi-source data integrated by the distributed architecture. Dynamic multi-service data is obtained by performing lifespan management, material management, and equipment maintenance management on unified data standards and interface specifications; It integrates dynamic multi-business data for data correlation analysis, full-process material management, full-lifecycle material traceability, intelligent procurement decision support, dynamic inventory optimization management, intelligent decision algorithm optimization, and full-process visual monitoring.

2. The intelligent management method for the entire life cycle of power plant materials according to claim 1, characterized in that, The distributed architecture integrates multi-source data, including power plant DCS system data, material management system data, and equipment maintenance management system data. The DCS system data includes the production equipment measurement point codes, production data, alarm data, and data update time of the power plant's overall control system. The data in the materials management system includes basic identification data, technical specification data, supply chain process data, quality data, and inventory information; The equipment maintenance management system data includes basic file data, process execution data, and test and inspection data.

3. The intelligent management method for the entire life cycle of power plant materials according to claim 1, characterized in that, In the unified data standard and interface specification for integrating multi-source data in a distributed architecture, the unified data standard includes constructing data codes based on the correspondence between unified equipment codes and material codes. The data encoding includes the original device code, device status, and device information; The equipment status includes procurement, quality inspection, in stock, installation and deployment, in use, decommissioned and awaiting disposal, and disposed of; Equipment information includes batch number, equipment type code, supplier, and purchase serial number; The unified data standard interface specification includes protocol adaptation layer specifications for collecting data from power plant DCS systems, material management systems, and equipment maintenance management systems; security interaction specifications involving transmission security, identity authentication, data encryption, and audit traceability; unified data time synchronization specifications; and anomaly handling specifications.

4. The intelligent management method for the entire life cycle of power plant materials according to claim 1, characterized in that, The specific process of obtaining dynamic multi-service data by performing lifespan management, material management, and equipment maintenance management on unified data standards and interface specifications is as follows: The specific process of lifespan management is as follows: A life management model is established, in which data is extracted based on key equipment characteristics, and historical data records under different operating conditions are created to form equipment operation and failure samples. Life management is then carried out based on these samples. The data extracted based on key equipment characteristics includes: boiler tube wall temperature and steam pressure; turbine bearing vibration; transformer winding temperature and load current; and high-pressure valve differential pressure and number of actuations. The specific process of materials management is as follows: Establish a closed-loop management model for materials, and adopt a collaborative architecture of procurement chain, inventory chain, maintenance chain, life chain and recycling chain to carry out intelligent control of the entire process of materials from procurement to decommissioning and disposal. The specific process of equipment maintenance and management is as follows: A dynamic linkage mechanism between equipment maintenance and materials management is established. Combined with a closed-loop materials management model, a closed-loop management system is formed that includes data collection, analysis, decision-making, and execution feedback. Dynamic multi-business data is obtained based on the closed-loop management system.

5. The intelligent management method for the entire life cycle of power plant materials according to claim 1, characterized in that, The dynamic multi-business data integration includes dynamically integrating multi-source data from equipment operation, material management, and equipment maintenance to build an integrated management system driven by data, intelligent decision-making, and visualization. Based on this integrated management system, data correlation analysis, full-process material management, material lifecycle traceability, intelligent procurement decision support, dynamic inventory optimization management, intelligent decision algorithm optimization, and full-process visualization monitoring are performed.

6. The intelligent management method for the entire life cycle of power plant materials according to claim 5, characterized in that, The data correlation analysis includes mining the implicit relationship between equipment failure modes and spare parts consumption, and using principal component analysis to reduce the dimensionality of multi-dimensional operating data based on the implicit relationship to identify key factors affecting equipment lifespan. The process of full-process transparent management of materials includes establishing a digital twin archive of materials and developing a transparent query engine based on the digital twin archive of materials. The material lifecycle traceability includes introducing blockchain technology to hash key data and put it on the chain, assigning a unique QR code / RFID tag to the material, and linking it with the full lifecycle data. The intelligent procurement decision support includes building a spare parts consumption prediction model, establishing a multi-dimensional supplier evaluation system based on the spare parts consumption prediction model, and automatically triggering procurement processes and approval reminders. The dynamic inventory optimization management includes classifying spare parts by value and usage frequency, and adjusting inventory thresholds based on the real-time operating status of equipment. The intelligent decision-making algorithm optimization includes offline training of the model in a sandbox environment using historical data, and automatic parameter tuning based on feedback from actual operating results. The full-process visual monitoring includes building a 3D visual dashboard based on digital twin technology, which displays the health status of equipment and inventory distribution in real time, and supports multi-terminal adaptation and multi-dimensional data display.

7. A power plant materials lifecycle intelligent management system, characterized in that, include: The data center creation module is used to create a big data center for the entire life cycle of power plant materials. The big data center for the entire life cycle of power plant materials adopts a distributed architecture to integrate multi-source data and uses a unified data standard and interface specification for the integrated multi-source data in the distributed architecture. The data analysis and processing module is used to perform lifespan management, material management, and equipment maintenance management on unified data standards and interface specifications to obtain dynamic multi-business data. The data management module is used to integrate dynamic multi-business data for data correlation analysis, full-process material management, full lifecycle material traceability, intelligent procurement decision support, dynamic inventory optimization management, intelligent decision algorithm optimization, and full-process visual monitoring.

8. A mobile terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent management method for the entire life cycle of power plant materials as described in any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent management method for the entire life cycle of power plant materials as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computing device to perform the operations corresponding to the intelligent management method for the entire life cycle of power plant materials as described in any one of claims 1-6.

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