[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

WO2021189620A1 - 基于情景感知的数字化车间电能管理研究方法 - Google Patents

基于情景感知的数字化车间电能管理研究方法 Download PDF

Info

Publication number
WO2021189620A1
WO2021189620A1 PCT/CN2020/090678 CN2020090678W WO2021189620A1 WO 2021189620 A1 WO2021189620 A1 WO 2021189620A1 CN 2020090678 W CN2020090678 W CN 2020090678W WO 2021189620 A1 WO2021189620 A1 WO 2021189620A1
Authority
WO
WIPO (PCT)
Prior art keywords
order
equipment
production
voting
information
Prior art date
Application number
PCT/CN2020/090678
Other languages
English (en)
French (fr)
Inventor
魏旻
李彩芹
王平
杨旭
Original Assignee
重庆邮电大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 重庆邮电大学 filed Critical 重庆邮电大学
Priority to KR1020217040189A priority Critical patent/KR20220017925A/ko
Publication of WO2021189620A1 publication Critical patent/WO2021189620A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R22/00—Arrangements for measuring time integral of electric power or current, e.g. electricity meters
    • G01R22/06—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods
    • G01R22/10—Arrangements for measuring time integral of electric power or current, e.g. electricity meters by electronic methods using digital techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0633—Workflow analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/103—Workflow collaboration or project management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0633—Lists, e.g. purchase orders, compilation or processing
    • G06Q30/0635—Processing of requisition or of purchase orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/82—Energy audits or management systems therefor

Definitions

  • the invention belongs to the field of the Internet of Things, and relates to a research method for electric energy management of a digital workshop based on situational perception.
  • the purpose of the present invention is to provide a situational awareness-based digital workshop electric energy management research method.
  • the present invention provides the following technical solutions:
  • the research method of electric energy management in digital workshop based on situational perception includes the following steps:
  • the power management architecture of the digital workshop based on context perception is:
  • the digital workshop uses network information technology to connect workshop personnel, workshop site equipment information and production information to implement production tasks; during the execution of production tasks, factory personnel pay attention to the status of production tasks, including whether the production tasks have been completed, whether the output is up to standard, and The operating status of the line equipment and the power consumption of the production line;
  • non-shutdown equipment NCE non-shutdown equipment
  • VPE variable power equipment
  • STSE short-term shutdown equipment
  • the production process information, customer order information, and equipment information are used as the input of the online voting system, and the equipment working status and equipment operating power level of the production line are flexibly scheduled through the voting mechanism, so as to automatically switch the energy-first production line model according to the results of the online voting. Consumption arrangements for production.
  • the S2 includes the following steps:
  • the top layer is the target layer, the purpose is to select an order from a batch of customer orders for production;
  • the middle layer is the standard layer, including various factors that affect the behavior of "select order", product delivery time, and products required by the order Quantity, delay penalties for delayed delivery of products, and the relationship between the customer and the manufacturing company;
  • the lowest level is the program level, which means that the existing customer orders are available for selection;
  • the factory personnel will number the n customer orders received according to the order of receipt time, namely ⁇ order 1 , order 2 , order 3 ,...order n ⁇ ;
  • each customer order at the scheme level has an effect on the criterion level’s “product delivery date”, “product quantity”, “delay penalty”, and “customer important”.
  • Judgment matrix A of criterion layer to target layer :
  • the above matrix represents the comparison of the relative importance of the overall target of "order selection", product delivery date, product quantity, delay penalty, and customer importance;
  • the above matrix respectively represents the comparison of the relative importance of n customer orders for "product delivery”, “product quantity”, “delay penalty”, and "customer importance”;
  • the S3 is specifically:
  • the equipment power information of the workshop production layer is uploaded to the EMS through the industrial network, so that the EMS can grasp the power consumption of the equipment in real time; the equipment power consumption information will be used as one of the inputs of the voting decision system to change the operation status of the production equipment of the workshop production layer.
  • the S4 is specifically:
  • the task of the voting decision system is to receive each voting information, and control the working status and power level of the equipment in the production workshop after system voting calculation;
  • the input of the voting system includes order information, production process information, and equipment information
  • EMS After EMS sorts all the orders, it obtains the order information, namely Order_Info ⁇ ⁇ urgent order, regular order, dispatchable order ⁇ ; the electricity consumed to produce the three types of orders is different, and the order demand is used as one of the indicators of the scheduling production line;
  • the production process information is the current margin Vm_now of the intermediate product in the buffer of the workshop production line, and Vm_now must not exceed the saturation limit capacity Vm;
  • the buffer sensing node NH detects the current margin of the intermediate product in the buffer through sensing technology, and uses the sensing result as voting information , To vote on the power level and operating status of the production line equipment;
  • the composite node NF uses the sensing technology to collect the information of the production equipment, which is the Equipment_Info, which includes the equipment type Equipment Type and the equipment power consumption Equipment Power , where the Equipment Type indicates that the type of the production equipment belongs to one of ⁇ NCE, VPE, STSE ⁇ , Equipment Power Indicates the overall energy consumption of the STSE equipment during a certain operation period; the composite node NF uses the sensing result as voting information to vote on the working status of the production line equipment and the equipment operating power;
  • the voting decision system divides the content of "Order_Info”, "Mid_Info”, and "Equipment_Info” voting information into limiting factors Order_Info and Equipment_qType and variable factors Vm_now, V(m+1)_now, Equipment_qPower, limiting factors affect the operating power of production equipment ⁇ Determine the number of variable factors, which affect the working status of production equipment;
  • variable factor generates a logical vote based on the actual collected data value, that is, casts "on”, “off”, and “uncertain” on the production equipment; it is stipulated that if the logical vote is "on”, the mathematical expression is "1”; if If the logical ticket is "off”, the mathematical expression is "0”; if the logical ticket is "uncertain”, the mathematical expression is "0.5”, that is, a "mapping table of variable factors and production equipment working status" is constructed;
  • the voting decision system integrates the limiting factors Order_Info, the mapping relationship between variable factors and the working status of the production equipment, and the logical voting results of different types of production equipment, and generates a "correspondence table of the working status and operating power level of the production equipment", where the working status includes "running” “State, "shutdown” state, the operating power is divided into “high” level, “medium” level, and "low” level.
  • the S5 is specifically:
  • EMS will obtain the voting results of the operating power level and the working status of the production line equipment in the workshop.
  • the production scheduling module will issue corresponding control instructions according to the voting results to complete the change of the working status of the production line equipment in the production workshop. Scheduling production tasks.
  • the beneficial effect of the present invention is that the present invention obtains physical information and production demand information related to the production line through context perception, so that the working status or operating power level of a certain equipment on the production line is determined by voting by other equipment on the production line based on the above information, and realizes one A flexible, automatic and intelligent way to reduce the power consumption of the entire production line.
  • Figure 1 shows the routine operation of the factory production line
  • Figure 2 shows the power management architecture of the digital workshop based on situational awareness
  • Figure 3 shows the hierarchical structure
  • Figure 4 is a voting sequence diagram
  • Figure 5 shows an example of voting decision.
  • Information and production demand information enable the working status or operating power level of a certain equipment on the production line to be voted on by other equipment on the production line based on the above information, realizing a flexible, automatic and intelligent way to reduce the power consumption of the entire production line.
  • the "physical information” mainly refers to the production process information and production equipment information
  • the "production demand information” refers to the order information received by the factory.
  • the factory implements the energy-priority production model through the above method, that is, when the order delivery is not very urgent, it automatically switches to the energy-priority production model. Therefore, orders can be classified into urgent orders, regular orders and schedulable orders by introducing different decision-making methods.
  • the power level, operation, shutdown and other actions of the production equipment are determined by the network node using the voting mechanism.
  • the input of the voting mechanism includes order information, production process information obtained by the sensing technology, and production equipment information.
  • the output of the voting mechanism is reasonable for the production equipment. Scheduling tasks to reduce the power consumption required for production.
  • the digital workshop uses network information technology to connect workshop personnel, workshop site equipment information, and production information to implement production tasks.
  • factory personnel pay attention to the status of production tasks, such as whether the production task has been completed, whether the output meets the standard, the operating status of the production line equipment, and the power consumption of the production line.
  • the existing production equipment is divided into 3 categories:
  • VPE Variable power equipment
  • STSE Short-term shutdown equipment
  • NCE equipment is defined as special equipment in the factory that cannot be powered off or as uninterrupted as possible. For example, some high-power equipment in the factory, because starting the equipment will consume a lot of power, so as far as possible not to shut down the equipment and do not schedule the operating status of such equipment.
  • VPE equipment has different operating power levels, so when different VPE power levels are set, the overall energy consumption of different working lines will be different.
  • the STSE device is a device with fixed power consumption that can be shut down for a short period of time.
  • the device can enter the shutdown state if it exceeds the set fixed power consumption during a certain period of operation.
  • each type of production equipment corresponds to a unique power consumption type ⁇ NCE, VPE, STSE ⁇ .
  • This solution proposes a digital workshop power management method based on situational awareness.
  • This method takes production process information, customer order information, and equipment information as the input of the network voting system, and flexibly schedules the working status of the equipment on the production line through a voting mechanism (operation/ Shutdown) and equipment operating power level, realize automatic switching of energy-priority production line models according to the results of online voting, and arrange production with low energy consumption.
  • Figure 2 shows the power management architecture of the digital workshop based on situational awareness.
  • EMS mainly includes order classification, power monitoring, voting decision and production scheduling.
  • This method divides the node types into buffer-aware nodes (NH) and composite nodes (NF). Nodes complete different tasks according to different types. The tasks are shown in Table 2.
  • the production line shown in Figure 2 includes raw materials, production equipment, buffers, intermediate products and final products in the buffers. According to the actual situation of different factories, the type of production line equipment configured, the power level of the equipment, the number of equipment, the number of buffers, the maximum capacity of the buffer, and the limited saturation capacity are therefore different.
  • the production line elements are shown in Table 3.
  • EMS needs to classify all orders, and by introducing methods such as analytic hierarchy process, customer orders can be ranked and classified by importance.
  • This method uses the analytic hierarchy process to determine the order processing sequence, and arranges the order production sequence as follows: orders with high importance> orders with high importance> orders with low importance. Since orders of different importance levels will affect the power consumption of the production line, the EMS uses the classified order information as one of the inputs of the voting decision system to schedule the production line.
  • the analytic hierarchy process is used to obtain the comprehensive weight of each order.
  • the weight indicates the importance of the order and the weight value is in the interval (0,1). The larger the value, the higher the importance of the order.
  • Analytic Hierarchy Process decomposes factors related to decision-making into target level, criterion level, and plan level. The steps are shown in Figure 3.
  • the uppermost layer is the target layer.
  • This layer serves as a "decision-making behavior” whose purpose is to select an order from a batch of customer orders for production;
  • the middle layer is the criterion layer, which includes various factors that affect the behavior of "select order". For example, the delivery time of the product, the number of products required by the order, the penalties for delays in the delivery of the product, the relationship between the customer and the manufacturing company; the lowest level is the program level, which means that the existing customer orders are available for selection.
  • the factory personnel number the n customer orders received according to the order of receipt time, namely ⁇ order 1 , order 2 , order 3 , ... order n ⁇ .
  • each customer order at the scheme level has an effect on the criterion level’s "product delivery”, “product quantity”, “delay penalty”, and "customer important”. Therefore, a ratio scale of 1-9 is introduced. The scale is shown in Table 4.
  • Judgment matrix A of the criterion layer to the target layer :
  • the above matrix represents the relative importance comparison of the overall goal of "order selection", product delivery date, product quantity, delay penalty, and customer importance.
  • the above matrices respectively represent the comparison of the relative importance of n customer orders for "product delivery”, “product quantity”, “delay penalty”, and "customer importance”.
  • Consistency inspection indicators are as follows:
  • Consistency judgment requirements It is generally considered that when CI ⁇ 0.1 and CR ⁇ 0.1, the consistency of the judgment matrix is acceptable, otherwise the pairwise comparison is performed again.
  • the equipment power information of the production layer of the workshop is uploaded to the EMS through the industrial network, so that the EMS can grasp the power consumption of the equipment in real time.
  • Equipment power consumption information will be used as one of the inputs of the voting decision system to change the operating status of production equipment at the production level of the workshop.
  • the task of the voting decision system is to receive various voting information, and control the working status (running/shutdown) and power level of the equipment in the production workshop after system voting calculation.
  • the input of the voting system includes order information, production process information, and equipment information.
  • EMS After EMS sorts all the orders, it obtains the order information, namely Order_Info ⁇ expedited order, regular order, schedulable order ⁇ .
  • the electricity consumed to produce the three types of orders is different, so the order demand can be used as one of the indicators for scheduling the production line.
  • the production process information is the current margin (Vm_now) of the intermediate product in the workshop production line buffer.
  • the buffer sensing node NH detects the current margin of intermediate products in the buffer by sensing technology, and uses the sensing result as voting information to vote on the power level of the production line equipment and the equipment operating state.
  • the composite node NF uses the sensing technology to collect the information of the production equipment as Equipment_Info, which includes the equipment type (Equipment Type ) and the equipment power consumption (Equipment Power ).
  • Equipment_Info which includes the equipment type (Equipment Type ) and the equipment power consumption (Equipment Power ).
  • Equipment Information element table is shown in Table 9.
  • the composite node NF uses the sensing result as voting information to vote on the working status of the production line equipment and the operating power of the equipment.
  • Integrating voting information such as "Order_Info”, “Mid_Info”, and “Equipment_Info"
  • the voting decision system will output the operating power level and working status of the production line equipment.
  • EMS will obtain the voting results of the operating power level and the working status of the production line equipment in the workshop.
  • the production scheduling module will issue corresponding control instructions according to the voting results to complete the change of the working status of the production line equipment in the production workshop. Scheduling production tasks.
  • EMS sorts orders. Factory personnel enter the content of the judgment matrix in the EMS according to Table 4. The EMS completes the classification of each order type in this process link, calculates which order to give priority to production and obtains Order_Info.
  • the industrial network is started to detect and control VPE equipment, STSE equipment, and nodes on the buffer zone to start working.
  • EMS as the initiator of voting, initiates a vote on the NF of the voted object, and takes Order_Info as one of the inputs of the voting decision system
  • NH as the voting party, will vote on the caste NF.
  • NH Mid_Info acquisition is completed within a time interval T mid_Info and transmitting Mid_Info to EMS as decision makers. Since there may be multiple buffers, there may be multiple NH nodes as voting parties to vote on NF;
  • NF as the voting party, will vote for itself as the caste at the same time.
  • the NF obtains the Equipment_Info and sends the Equipment_Info to the EMS within the time interval T equipment . All the above information is the voting information for the NF and is sent to the voting decision system in the EMS.
  • the two buffers affect the working state or power of the production equipment between the two buffers, namely Vm_now, V(m+1)_now.
  • variable factors are limiting factors
  • Vm_now, V(m+1)_now and Equipment_q Power are variable factors.
  • the specific value of the variable factor changes with time, so the variable factor affects the working state of the production equipment, and the working state of the production equipment is voted on.
  • the mapping table between variable factors and the working status of production equipment is as follows:
  • Vm_now and V(m+1)_now generate logical votes according to the actual capacity value
  • Equipment_q Power generates logical votes according to the actual collected electric energy value, that is, casts "on", "off”, and "uncertain” on the production equipment. It is stipulated that if the logical ticket is "on”, the mathematical expression is “1”; if the logical vote is “off”, the mathematical expression is "0”; if the logical vote is "uncertain", the mathematical expression is "0.5".
  • Vm_now V(m+1)_now
  • V(m+1)_now variable factors
  • logical vote weights are all set to 0.5.
  • the logical voting table for VPE equipment is as follows:
  • VPE Logic Voting
  • the weight value is: the logical vote weight of Vm _now and V(m+1) _now are both 0.3, and the logical vote weight of Equipment_q Power is 0.4.
  • the logical voting table for STSE equipment is as follows:
  • the limiting factor Order_Info affects the operating power of the production equipment
  • the limiting factor Equipment_q Type determines the number of variable factors
  • the variable factors Vm_now, V(m+1)_now and Equipment_q Power affect the working status of the production equipment.
  • the working status includes "running" status and "shutdown” status. Operating power is divided into “high” level, “medium” level, and “low” level.
  • the corresponding table of the working status and operating power level of the production equipment is as follows:
  • the EMS issues scheduling instructions to the NF node according to the output of the previous step, and the NF node controls the working status of the equipment and the operating power level of the equipment according to the instructions.
  • factory personnel will number these 5 customer orders according to the order of receipt time, namely ⁇ order 1 , order 2 , order 3 , order 4 , order 5 ⁇ .
  • Factory personnel enter the content of the judgment matrix in the EMS according to Table 4, and the EMS completes the classification of each order type in this process link.
  • the factory personnel input the judgment matrix A, B 1 , B 2 , B 3 , B 4 .
  • EMS obtains the eigenvectors, eigenvalues and consistency test indicators of each matrix through calculation.
  • the comprehensive weight of order 1 is 0.2368
  • the comprehensive weight of order 2 is 0.2592
  • the comprehensive weight of order 3 is 0.2448
  • the comprehensive weight of order 4 is 0.1568
  • the comprehensive weight of order 5 is 0.085.
  • Order_Info "expedited order”.
  • the voting decision example diagram is shown in Figure 5.
  • the thick pink arrow indicates the logical voting process.
  • the node NF_1 bound to the device VPE_1 is voted first.
  • the elements involved in the voting decision process are shown in Table 17.
  • EMS as the vote initiator, initiates a vote on the voted object NF_1, and takes Order_Info as one of the inputs of the voting decision system.
  • Order_Info "Rush Order”
  • NH_1 as the voting party, votes on the voted object NF_1.
  • NH_1 completed within T mid_Info Mid_Info acquired at a time interval and transmitting Mid_Info to EMS as decision makers.
  • NH_2 acts as the voting party to vote for the voted object NF_1.
  • NH_2 completes acquiring Mid_Info and sending Mid_Info to the EMS as the decision maker within the time interval T mid_Info.
  • NF_1 acts as a voting party to vote for itself as the caste at the same time.
  • NF_1 completes obtaining Equipment_Info and sending Equipment_Info to the EMS within the time interval T equipment.
  • the above 3 pieces of information are the voting information for NF_1 sent to the voting decision system in the EMS.
  • the voting decision system performs voting calculation. According to the mapping table of variable factors and production equipment working status in Table 11, it can be known that:
  • VPE runs with a power of 3KW.
  • NF_1 controls the working status of VPE_1 to be "running” and the operating power level of the equipment to "3KW” according to the instructions; or NF_1 controls the working state of VPE_1 to be " Operation” and the operating power level of the equipment is "1KW"

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Strategic Management (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • Tourism & Hospitality (AREA)
  • Development Economics (AREA)
  • Educational Administration (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • Accounting & Taxation (AREA)
  • Finance (AREA)
  • Data Mining & Analysis (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Power Engineering (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本发明涉及一种基于情景感知的数字化车间电能管理研究方法，属于物联网领域。该方法包括以下步骤：S1：建立基于情景感知的数字化车间电能管理架构；S2：订单分类；S3：电能监测；S4：投票决策；S5：生产调度。本发明通过情景感知获取与生产线相关的物理信息、生产需求信息，使生产线上某台设备的工作状态或运行功率等级由生产线其他设备根据上述信息进行投票决定，实现一种灵活的、自动的，智能的方式降低整条生产线的电能消耗。

Description

基于情景感知的数字化车间电能管理研究方法 技术领域
本发明属于物联网领域，涉及基于情景感知的数字化车间电能管理研究方法。
背景技术
随着工业4.0的演进，传统工业生产环境所面临的“生产方式落后”、“管理效率低下”、“产能过剩或不足”、“工厂电能消耗无法管控”等问题愈加突出。然而由于近年来物联网(Internet of Things,IoT)的蓬勃发展，信息科技研究学者着力将IoT应用于工业生产环境，提出了工业物联网(Industrial Internet of Things,IIoT)以解决工业生产相关问题。一方面，由于国家正在推进工业的绿色健康发展理念，高耗能企业为了避免被市场所淘汰，企业降低电能消耗以此降低生产成本成为了保持市场竞争力的重要保障。因此，控制企业生产所耗费的电能、考虑灵活调度生产任务也变得越来越重要。
目前，在工业生产的电能消耗方面，大多数工业生产企业存在着缺乏电能管理机制的问题。首先工厂大多只关注设备电能情况的监视、生产情况的监视，但未结合监视数据考虑如何改变工厂设备状态来实现降低电能消耗；其次由于车间的电能管理大多依靠人力经验，缺乏科学的节能指标，因此急需适用于工业生产的电能管理机制。
发明内容
有鉴于此，本发明的目的在于提供一种基于情景感知的数字化车间电能管理研究方法。
为达到上述目的，本发明提供如下技术方案：
基于情景感知的数字化车间电能管理研究方法，该方法包括以下步骤：
S1：建立基于情景感知的数字化车间电能管理架构；
S2：订单分类；
S3：电能监测；
S4：投票决策；
S5：生产调度。
可选的，所述基于情景感知的数字化车间电能管理架构为：
数字化车间利用网络信息技术将车间人员、车间现场设备信息和生产信息相连接，实施生产任务；在生产任务执行过程中，工厂人员关注生产任务状态，包括生产任务是否已完成、产量是否达标、产线设备运行状态和产线电能消耗；
定义三种生产设备：不可关闭设备NCE、可变功率设备VPE、短期关闭设备STSE，基于上述三种生产设备，搭建基于情景感知的数字化车间电能管理架构，架构内容包括能源管理系统EMS、工业网络，以及由各产线元素组成的车间生产线；
将生产过程信息、客户订单信息、设备信息作为网络投票系统的输入，经过投票机制灵活调度生产线上的设备工作状态和设备运行功率等级，实现根据网络投票结果自动切换能源优先的生产线模型，以低能耗安排生产。
可选的，所述S2包括以下步骤：
S21：建立层次结构
最上层为目标层，目的是从一批客户订单中选择一个订单进行生产；中间层为准则层，包括影响“选择订单”这一行为的各种因素，产品的交期时间、订单要求的产品数量、产品延期交货将受到的延期惩罚、客户与制造企业的关系；最下层为方案层，即表明现有可供选择的客户订单；
工厂人员将接收到的n个客户订单根据收单时间的先后顺序对客户订单进行编号，即{order 1，order 2，order 3，…order n}；
S22：构造对比矩阵
为确定准则层的各因素对目标层的“订单选择”行为的影响权重、方案层的各客户订单分别对准则层的“产品交期”、“产品数量”、“延期惩罚”、“客户重要性”的影响权重，引入1～9比率标度；
准则层对目标层的判断矩阵A：
Figure PCTCN2020090678-appb-000001
Figure PCTCN2020090678-appb-000002
以上矩阵表示对于“订单选择”总目标，产品交期、产品数量、延期惩罚，客户重要性各因素的相对重要性比较；
方案层对准则层的判断矩阵共有4个:B 1、B 2、B 3、B 4
Figure PCTCN2020090678-appb-000003
Figure PCTCN2020090678-appb-000004
以上矩阵分别表示对于“产品交期”、“产品数量”、“延期惩罚”、“客户重要性”，n个客户订单的相对重要性比较；
S23：计算各矩阵特征向量、特征值及一致性检验指标
根据求根法计算特征向量；
计算B 1、B 2、B 3、B 4的特征向量并且判断一致性要求；
根据上述计算后，准则层“产品交期”、“产品数量”、“延期惩罚”，“客户重要性”对目标层“订单选择”的影响权重为W＝(w 1，...w 4) T；
方案层{order 1，order 2，order 3，…order n}对“产品交期”的影响权重为:W 1＝(α 1，…α n) T；
方案层{order 1，order 2，order 3，…order n}对“产品数量”的影响权重为:W 2＝(β 1，…β n) T；
方案层{order 1，order 2，order 3，…order n}对“延期惩罚”的影响权重为:W 3＝(γ 1，…γ n) T；
方案层{order 1，order 2，order 3，…order n}对“客户重要性”的影响权重为:W 4＝(δ 1，…δ n) T；
S24：层次总排序及决策
计算最下层方案层对最上层目标层的权重向量，所求即为各个客户订单的权重大小，以此结果做出订单选择；综合权重计算公式如下：
order n的综合权重为：w′ n＝α n*w 1+β n*w 2+γ n*w 3+δ n*w 4；
S25：将上述订单的w′ i进行排序，w′ i越大的订单排序在前，综合权重值相同的订单根据接收订单的时间先后顺序决定订单排序；
对n个客户订单的综合权重大小进行排序，并对排序后的n个客户订单进行分类。
可选的，所述S3具体为：
车间生产层的设备电能信息通过工业网络上传至EMS，实现EMS能够实时掌握设备的电能消耗情况；设备电能消耗信息将作为投票决策系统的输入之一，改变车间生产层的生产 设备运行状态。
可选的，所述S4具体为：
投票决策系统的任务是接收各个投票信息，经过系统投票计算后对生产车间的设备工作状态、设备功率等级进行控制；
(1)投票系统的输入包括订单信息、生产过程信息、设备信息；
订单信息Order_Info：
EMS对所有订单进行订单分类后，获得订单信息，即Order_Info∈{加急订单，常规订单，可调度订单}；生产三种订单所消耗的电量不同，订单需求作为调度生产线的指标之一；
生产过程信Mid_Info：
生产过程信息即为车间生产线缓冲区中间产品的当前余量Vm_now，且Vm_now不得超过饱和限定容量Vm；缓冲区感知节点NH通过感知技术检测缓冲区中间产品的当前余量，将感知结果作为投票信息，对生产线设备的功率等级和设备运行状态进行投票；
设备信息Equipment_Info：
复合节点NF利用感知技术采集生产设备的信息即为Equipment_Info，该信息包括设备类型Equipment Type和设备电能消耗Equipment Power，其中Equipment Type表明生产设备的类型属于{NCE，VPE，STSE}之一，Equipment Power表明STSE设备在某运行期间的总体能耗；复合节点NF将感知结果作为投票信息，对生产线设备的工作状态和设备运行功率进行投票；
(2)投票系统的输出：
投票决策系统将“Order_Info”、“Mid_Info”、“Equipment_Info”投票信息的内容划分为限定因素Order_Info和Equipment_qType和可变因素Vm_now，V(m+1)_now，Equipment_qPower，限定因素影响生产设备的运行功率、决定可变因素的数量，可变因素影响生产设备的工作状态；
构造“可变因素与生产设备工作状态映射关系”：
可变因素根据实际采集的数据数值产生逻辑投票，即对生产设备投以“开”、“关”，“不确定”；规定若逻辑票为“开”，则数学表达为“1”；若逻辑票为“关”，则数学表达为“0”；若逻辑票为“不确定”，则数学表达为“0.5”，即构造“可变因素与生产设备工作状态映射表”；
计算针对“不同类型生产设备的逻辑投票结果”：
根据限定因素Equipment_qType设定可变因素数量，并对可变因素设计权重值，综合各可变因素与生产设备工作状态映射关系和权重，计算“不同类型生产设备的逻辑投票结果”；
生成“生产设备的工作状态、运行功率等级的对应表”：
投票决策系统综合限定因素Order_Info、可变因素与生产设备工作状态映射关系、不同类型生产设备的逻辑投票结果，生成“生产设备的工作状态、运行功率等级的对应表”，其中工作状态包括“运行”状态，“关机”状态，运行功率被分为“高”等级，“中”等级，“低”等级。
可选的，所述S5具体为：
EMS经过投票决策系统将获得车间生产线设备的运行功率等级投票结果和工作状态投票结果，生产调度模块将根据投票结果下发相应的控制指令，完成对生产车间生产线设备的工作状态改变，以此对生产任务进行调度。
本发明的有益效果在于：本发明通过情景感知获取与生产线相关的物理信息、生产需求信息，使生产线上某台设备的工作状态或运行功率等级由生产线其他设备根据上述信息进行投票决定，实现一种灵活的、自动的，智能的方式降低整条生产线的电能消耗。
本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述，并且在某种程度上，基于对下文的考察研究对本领域技术人员而言将是显而易见的，或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。
附图说明
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作优选的详细描述，其中：
图1为工厂生产线常规运作；
图2为基于情景感知的数字化车间电能管理架构；
图3为层次结构；
图4为投票时序图；
图5为投票决策实例。
具体实施方式
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭露的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。需要说明的是，以下实施例中所提供的图示仅以示意方式说明本发明的基本构想，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。
其中，附图仅用于示例性说明，表示的仅是示意图，而非实物图，不能理解为对本发明 的限制；为了更好地说明本发明的实施例，附图某些部件会有省略、放大或缩小，并不代表实际产品的尺寸；对本领域技术人员来说，附图中某些公知结构及其说明可能省略是可以理解的。
本发明实施例的附图中相同或相似的标号对应相同或相似的部件；在本发明的描述中，需要理解的是，若有术语“上”、“下”、“左”、“右”、“前”、“后”等指示的方位或位置关系为基于附图所示的方位或位置关系，仅是为了便于描述本发明和简化描述，而不是指示或暗示所指的装置或元件必须具有特定的方位、以特定的方位构造和操作，因此附图中描述位置关系的用语仅用于示例性说明，不能理解为对本发明的限制，对于本领域的普通技术人员而言，可以根据具体情况理解上述术语的具体含义。
考虑可以通过直接控制工厂中生产设备的工作状态(运行/关机)和运行功率等级，避免生产设备长时间以不必要的高功率持续运行，实现降低生产设备的电能消耗，本方案提出一种面向数字化车间的电能管理方法：以车间生产线设备作为研究对象，提出生产线上某台生产设备的工作状态或运行功率受生产线上其他生产设备的工作状态所影响，即通过情景感知获取与生产线相关的物理信息、生产需求信息，使生产线上某台设备的工作状态或运行功率等级由生产线其他设备根据上述信息进行投票决定，实现一种灵活的、自动的，智能的方式降低整条生产线的电能消耗。其中“物理信息”主要指生产过程信息和生产设备信息，“生产需求信息”指工厂所接收的订单信息。将订单信息与生产线能耗相关联，工厂通过上述方法实现能耗优先生产模型，即当订单交付不是很急迫的情况下，自动切换到能源优先生产模型。因此可通过引入不同的决策方法实现将订单分类为加急订单、常规订单和可调度订单。
生产设备的功率等级、运行、关机等动作由网络节点利用投票机制决定，投票机制的输入包括订单信息、感知技术所获取的生产过程信息和生产设备信息，通过投票机制的输出完成对生产设备合理的调度任务，实现降低生产所需的电能消耗。
3.1基于情景感知的数字化车间电能管理架构
数字化车间利用网络信息技术将车间人员、车间现场设备信息、生产信息等相连接，实施生产任务。在生产任务执行过程中，工厂人员关注生产任务状态，如生产任务是否已完成、产量是否达标、产线设备运行状态、产线电能消耗等。
本方法中将现有生产设备分为3类：
表1生产设备类型
设备名称 描述
NCE(Non-closing equipment) 不可关闭设备
VPE(Variable power equipment) 可变功率设备
STSE(Short-term shutdown equipment) 短期关闭设备
NCE设备被定义为工厂中不能断电或者尽量不断电的特殊设备。如工厂中的某些大功率设备，由于启动该设备会消耗大量的电力，因此尽可能不关闭该设备并且不对此类设备的运行状态进行调度。
VPE设备具有不同的运行功率等级，因此当设置了不同VPE功率等级后，不同的工作线的总体能耗会不同。
STSE设备是一种功耗固定的可短期关闭设备，该设备在某段运行期间内超过设定的固定功耗可进入关机状态。
工厂生产线常规运作如下：
如图1所示，每种生产设备对应一个唯一的耗电类型{NCE，VPE，STSE}。原料一旦被投入产线，则3种类型的生产设备都将启动并且以最高功率持续运行，直到原料被消耗完、产品制造完成。工厂常规生产线工作模式由于欠缺对生产线设备功率等级和工作状态的灵活调度，导致生产线电能消耗高。
本方案提出一种基于情景感知的数字化车间电能管理的方法，该方法将生产过程信息、客户订单信息、设备信息作为网络投票系统的输入，经过投票机制灵活调度生产线上的设备工作状态(运行/关机)和设备运行功率等级，实现根据网络投票结果自动切换能源优先的生产线模型，以低能耗安排生产。基于情景感知的数字化车间电能管理架构如图2所示。
(1)能源管理系统EMS
EMS作为MES的协作者，其功能主要包括了订单分类、电能监测、投票决策和生产调度。
(2)网关
负责形成和配置网络。
(3)路由
负责转发数据信息。
(4)节点
本方法将节点类型分为缓冲区感知节点(NH)和复合节点(NF)。节点根据不同类型完成不同任务，任务如表2所示。
表2节点类型
Figure PCTCN2020090678-appb-000005
Figure PCTCN2020090678-appb-000006
(5)车间生产线
图2所示的生产线包括生产原料、生产设备、缓冲区、缓冲区的中间产品及最终产品。根据不同工厂的实际情况，所配置的生产线设备类型、设备的功率等级、设备的数量、缓冲区数量，缓冲区最大容量以及限定饱和容量也会因此不同。产线元素如表3所示。
表3产线元素表
Figure PCTCN2020090678-appb-000007
3.2EMS功能模块
3.2.1订单分类
工厂人员接收到一批客户订单时，由于各个订单的重要性程度不同，因此工厂人员需要决定优先生产哪一个订单。EMS需要将所有订单进行订单分类，通过引入层次分析法等方法可将客户订单进行重要性排序和分类。本方法采用层次分析法决定订单处理顺序，安排订单生产顺序为：重要性程度高的订单>重要性程度较高的订单>重要性程度低的订单。由于不同重要性程度的订单会影响生产线电能消耗，因此EMS将分类后的订单信息作为投票决策系统的输入之一对生产线进行调度。
影响订单需求分类的因素较多，在本方案中选取产品交期、产品数量、延期惩罚，客户重要性四个因素。采用层次分析法获得各个订单的综合权重，该权重表示订单的重要程度且权重值位于区间(0,1)，数值越大则订单的重要程度越高。
层次分析法作为一种决策方法，把和决策有关的因素分解成目标层、准则层、方案层。步骤如图3所示。
1.建立层次结构
最上层为目标层，该层作为“决策行为”，其目的是从一批客户订单中选择一个订单进行生产；中间层为准则层，包括了影响“选择订单”这一行为的各种因素，如产品的交期时间、 订单要求的产品数量、产品延期交货将受到的延期惩罚、客户与制造企业的关系；最下层为方案层，即表明现有可供选择的客户订单。
工厂人员将接收到的n个客户订单根据收单时间的先后顺序对客户订单进行编号，即{order 1，order 2，order 3，…order n}。
2.构造对比矩阵
为了确定准则层的各因素对目标层的“订单选择”行为的影响权重、方案层的各客户订单分别对准则层的“产品交期”、“产品数量”、“延期惩罚”、“客户重要性”的影响权重，因此引入了1～9比率标度。标度如表4所示。
表4标度描述
标度 含义
1 因素i相比于因素j，两个因素同样重要
3 因素i比因素j稍微重要
5 因素i比因素j明显重要
7 因素i比因素j强烈重要
9 因素i比因素j极端重要
2,4,6,8 上述两相邻判断的中值
倒数 因素i比因素j的值为a ij，则因素j比因素i的值为a ji＝1/a ij
准则层对目标层的判断矩阵A:
Figure PCTCN2020090678-appb-000008
Figure PCTCN2020090678-appb-000009
以上矩阵表示对于“订单选择”总目标，产品交期、产品数量、延期惩罚，客户重要性各因素的相对重要性比较。
方案层对准则层的判断矩阵共有4个:B 1、B 2、B 3、B 4
Figure PCTCN2020090678-appb-000010
Figure PCTCN2020090678-appb-000011
以上矩阵分别表示对于“产品交期”、“产品数量”、“延期惩罚”、“客户重要性”，n个客户订单的相对重要性比较。
3.计算各矩阵特征向量、特征值及一致性检验指标
根据求根法计算特征向量(以下以计算矩阵A为例)：
1)计算矩阵A每行元素乘积的4次方根，
Figure PCTCN2020090678-appb-000012
2)归一化
Figure PCTCN2020090678-appb-000013
Figure PCTCN2020090678-appb-000014
W＝(w 1，...w 4) T即为A的特征向量近似值。最大特征值：
Figure PCTCN2020090678-appb-000015
3)一致性检验指标如下：
Figure PCTCN2020090678-appb-000016
计算
Figure PCTCN2020090678-appb-000017
RI的值查表如表5所示：
表5 RI值对应表
阶数 3 4 5 6 7 8 9 10 11 12 13 14
RI 0.58 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58
一致性判断要求：一般认为CI<0.1、CR<0.1时，判断矩阵的一致性可以接受，否则重新进行两两比较。
4)重复上述步骤计算B 1、B 2、B 3、B 4的特征向量并且判断一致性要求。
根据上述计算后，准则层“产品交期”、“产品数量”、“延期惩罚”，“客户重要性”对目标层“订单选择”的影响权重为W＝(w 1，...w 4) T；
方案层{order 1，order 2，order 3，…order n}对“产品交期”的影响权重为:W 1＝(α 1，…α n) T；
方案层{order 1，order 2，order 3，…order n}对“产品数量”的影响权重为:W 2＝(β 1，…β n) T；
方案层{order 1，order 2，order 3，…order n}对“延期惩罚”的影响权重为:W 3＝(γ 1，…γ n) T；
方案层{order 1，order 2，order 3，…order n}对“客户重要性”的影响权重为:W 4＝(δ 1，…δ n) T。
4.层次总排序及决策
计算最下层方案层对最上层目标层的权重向量，所求即为各个客户订单的权重大小，以此结果做出订单选择。综合权重计算公式如下：
order n的综合权重为：w′ n＝α n*w 1+β n*w 2+γ n*w 3+δ n*w 4。
5.将上述订单的w′ i进行排序，w′ i越大的订单排序在前，综合权重值相同的订单根据接收订单的时间先后顺序决定订单排序。
对n个客户订单的综合权重大小进行排序，并对排序后的n个客户订单进行分类。订单分类表如表6所示，订单信息表如表7所示。
表6订单分类表
Figure PCTCN2020090678-appb-000018
注：当n＝1时，该订单为加急订单；当n＝2时，两个订单同为加急订单，且优先安排订单综合权重更高者进行生产。
表7订单信息表
Figure PCTCN2020090678-appb-000019
3.2.2电能监测
车间生产层的设备电能信息通过工业网络上传至EMS，实现EMS能够实时掌握设备的电能消耗情况。设备电能消耗信息将作为投票决策系统的输入之一，改变车间生产层的生产设备运行状态。
3.2.3投票决策系统
投票决策系统的任务是接收各个投票信息，经过系统投票计算后对生产车间的设备工作 状态(运行/关机)、设备功率等级进行控制。
(1)投票系统的输入包括订单信息、生产过程信息、设备信息。
·订单信息(Order_Info)
EMS对所有订单进行订单分类后，获得订单信息，即Order_Info∈{加急订单，常规订单，可调度订单}。生产三种订单所消耗的电量不同，因此订单需求可作为调度生产线的指标之一。
·生产过程信息(Mid_Info)
生产过程信息即为车间生产线缓冲区中间产品的当前余量(Vm_now)。缓冲区感知节点NH通过感知技术检测缓冲区中间产品的当前余量，将感知结果作为投票信息，对生产线设备的功率等级和设备运行状态进行投票。
表8缓冲区信息表
Figure PCTCN2020090678-appb-000020
·设备信息(Equipment_Info)
复合节点NF利用感知技术采集生产设备的信息即为Equipment_Info，该信息包括设备类型(Equipment Type)和设备电能消耗(Equipment Power)，设备信息元素表如表9所示。复合节点NF将感知结果作为投票信息，对生产线设备的工作状态和设备运行功率进行投票。
表9设备信息元素表
Figure PCTCN2020090678-appb-000021
(2)投票系统的输出：
综合“Order_Info”、“Mid_Info”、“Equipment_Info”等投票信息，投票决策系统将输出生产线设备的运行功率等级和工作状态。
3.2.4生产调度
EMS经过投票决策系统将获得车间生产线设备的运行功率等级投票结果和工作状态投票结果，生产调度模块将根据投票结果下发相应的控制指令，完成对生产车间生产线设备的工作状态改变，以此对生产任务进行调度。
3.3通用流程
具体流程如下：
1.搭建基于情景感知的数字化车间电能管理架构。根据工厂实际的生产设备类型、设备 数量、缓冲区数量等配置生产线；根据工厂实际生产线情况搭建工业网络。
2.预设投票决策配置。工厂人员在EMS中输入工厂实际的缓冲区限定饱和容量Vm、VPE设备的多个功率等级，STSE设备的最大固定功耗MaxPower。
3.EMS进行订单分类。工厂人员根据表4在EMS中输入判断矩阵内容，EMS在本流程环节完成各个订单类型的分类工作，计算出优先生产哪一个订单并获得Order_Info。
4.工厂人员根据上一步骤的结果执行订单生产，启动生产线。
5.工业网络启动，用于检测和控制VPE设备、STSE设备、缓冲区上的节点开始工作。
6.根据生产线生产流程顺序，某台生产设备所绑定的NF节点被进行投票，投票决策流程所涉及元素如表10所示。
表10投票决策流程元素
Figure PCTCN2020090678-appb-000022
(1)EMS作为投票发起者，对被投对象NF发起投票，将Order_Info作为投票决策系统的输入之一；
(2)NH作为投票方，对被投对象NF进行投票。NH在时间间隔T mid_Info内完成获取Mid_Info以及向作为决策者的EMS发送Mid_Info。由于可能存在多个缓冲区，因此可能存在多个NH节点作为投票方对NF进行投票；
(3)随后NF作为投票方，对同时作为被投对象的自身进行投票。NF在时间间隔T equipment内完成获取Equipment_Info以及向EMS发送Equipment_Info，以上所有信息即为对NF的投票信息而发送到EMS中的投票决策系统。
7.进入投票决策系统。
(1)EMS中的投票决策系统收到输入：
{Order_Info，Equipment_q Type，Equipment_q Power，Vm_now，V(m+1)_now}
注：根据图3中缓冲区和生产设备的位置关系，则由两个缓冲区影响两个缓冲区之间的生产设备工作状态或功率，即Vm_now，V(m+1)_now。
(2)投票计算：
a.在(1)中，所有输入信息被分为限定因素和可变因素，其中Order_Info和Equipment_q Type为限定因素，Vm_now、V(m+1)_now和Equipment_q Power为可变因素。可变因素的具体数值随时间变化而发生变化，因此可变因素影响生产设备的工作状态，对生产设备的工作状态进行投票。可变因素与生产设备工作状态映射表如下：
表11可变因素与生产设备工作状态映射表
Figure PCTCN2020090678-appb-000023
Vm_now和V(m+1)_now根据实际容量数值产生逻辑投票，Equipment_q Power根据实际采集的电能数值产生逻辑投票，即对生产设备投以“开”、“关”，“不确定”。规定若逻辑票为“开”，则数学表达为“1”；若逻辑票为“关”，则数学表达为“0”；若逻辑票为“不确定”，则数学表达为“0.5”。
b.针对VPE设备，存在2个可变因素，即Vm_now、V(m+1)_now，且逻辑票权重均被设定为0.5。针对VPE设备的逻辑投票表如下所示：
表12逻辑投票(VPE)
Figure PCTCN2020090678-appb-000024
Figure PCTCN2020090678-appb-000025
若0≤逻辑投票加权输出结果＜0.5，则“VPE设备进入关机状态”；若0.5≤逻辑投票加权输出结果≤1，则“VPE设备进入运行状态并根据Order_Info设置功率等级”。
针对STSE设备，存在3个可变因素，即Vm_now、V(m+1)_now，Equipment_q Power，设Vm _now的和V(m+1) _now的逻辑票权重均为x，Equipment_q Power的逻辑票权重为y，逻辑票权重满足以下公式：
Figure PCTCN2020090678-appb-000026
根据上式取权重值为：Vm _now的和V(m+1) _now的逻辑票权重均为0.3，Equipment_q Power的逻辑票权重为0.4。针对STSE设备的逻辑投票表如下所示：
表13逻辑投票(STSE)
Figure PCTCN2020090678-appb-000027
Figure PCTCN2020090678-appb-000028
若0≤逻辑投票加权输出结果≤0.55，则“STSE设备进入关机状态”；
若0.55＜逻辑投票加权输出结果≤1，则“STSE设备进入运行状态”。
限定因素Order_Info影响生产设备的运行功率，限定因素Equipment_q Type决定可变因素的数量，可变因素Vm_now、V(m+1)_now和Equipment_q Power影响生产设备的工作状态。其中工作状态包括“运行”状态，“关机”状态。运行功率被分为“高”等级，“中”等级，“低”等级。生产设备的工作状态、运行功率等级的对应表如下所示：
表14设备控制对应表
Figure PCTCN2020090678-appb-000029
Figure PCTCN2020090678-appb-000030
Figure PCTCN2020090678-appb-000031
(3)输出：EMS经过投票决策系统后输出设备的工作状态和设备的运行功率等级。
8.EMS根据上一步骤的输出下发调度指令到NF节点，NF节点根据指令控制设备的工作状态和设备的运行功率等级。
9.按照生产线流程顺序，依次对生产线上所有设备重复步骤6、7，8完成对生产线上每一个生产设备的工作状态和功率等级的调度。
投票时序图如图4所示。
3.4应用举例
假设某工厂的某条生产线组成元素有NCE设备1台，VPE设备2台，STSE设备2台， 缓冲区4个，生产原料及最终产品若干。使用本文所提出的基于情景感知的数字化车间电能管理研究方法对上述生产线进行调度。
1.搭建基于情景感知的数字化车间电能管理架构。
2.预设投票决策配置。在EMS中输入工厂实际的缓冲区限定饱和容量Vm、VPE设备的多个功率等级，设备限制最大能耗MaxPower。现假设每个缓冲区最大容量为2立方米，限定饱和容量为1.7立方米；VPE设备有3个运行功率等级，高功率为一级3KW，中等功率为二级1KW，低等功率为三级0.5KW；STSE设备最大限制能耗0.5KW.h。即Vm＝1.7m 3，VPE∈{3KW，1KW，0.5KW}，MaxPower＝0.5KW.h。
3.EMS进行订单分类并排产
假设某工厂收到5个客户订单，工厂人员将这5个客户订单根据收单时间的先后顺序对客户订单进行编号，即{order 1，order 2，order 3，order 4，order 5}。工厂人员根据表4在EMS中输入判断矩阵内容，EMS在本流程环节完成各个订单类型的分类工作。
(1)EMS计算各个订单综合权重
工厂人员输入判断矩阵A，B 1、B 2、B 3、B 4。
Figure PCTCN2020090678-appb-000032
Figure PCTCN2020090678-appb-000033
Figure PCTCN2020090678-appb-000034
EMS通过计算得到各矩阵特征向量、特征值及一致性检验指标。
表15矩阵计算结果
Figure PCTCN2020090678-appb-000035
Figure PCTCN2020090678-appb-000036
EMS通过计算得到各订单的综合权重
order 1的综合权重为0.2368，order 2的综合权重为0.2592，order 3的综合权重为0.2448，order 4的综合权重为0.1568，order 5的综合权重为0.085。
(2)EMS根据各个订单综合权重进行订单分类
表16订单信息表
Figure PCTCN2020090678-appb-000037
EMS根据综合权重大小对上述5各订单进行生产排序：
order 2，order 3，order 1，order 4，order 5。即EMS优先排产order 2。即Order_Info＝“加急订单”。
4.工厂人员执行订单生产，启动生产线。
5.随即启动工业网络，用于检测和控制VPE设备、STSE设备、缓冲区上的节点开始工作。
投票决策实例图如图5所示。其中粉色粗箭头表示逻辑投票过程。
根据生产线流程顺序，先对设备VPE_1所绑定的节点NF_1进行投票，投票决策流程所涉及元素如表格17所示。
表17对于NF_1节点的投票决策流程元素
Figure PCTCN2020090678-appb-000038
Figure PCTCN2020090678-appb-000039
(1)EMS作为投票发起者，对被投对象NF_1发起投票，将Order_Info作为投票决策系统的输入之一。经过通用流程中的(3)计算后，得到Order_Info＝“加急订单”；
(2)NH_1作为投票方，对被投对象NF_1进行投票。NH_1在时间间隔T mid_Info内完成获取Mid_Info以及向作为决策者的EMS发送Mid_Info。设NH_1所采集到的中间产品余量为V1_now＝1m 3。；
(3)随后NH_2作为投票方，对被投对象NF_1进行投票。NH_2在时间间隔T mid_Info内完成获取Mid_Info以及向作为决策者的EMS发送Mid_Info。设NH_2所采集到的中间产品余量为V2_now＝1.2m 3。；
(4)随后NF_1作为投票方，对同时作为被投对象的自身进行投票。NF_1在时间间隔T equipment内完成获取Equipment_Info以及向EMS发送Equipment_Info。NF_1采集Equipment_1 Type＝VPE，NF_1采集Equipment_1 Power＝0.55KW.h。
以上3条信息即为对NF_1的投票信息而发送到EMS中的投票决策系统。
4.进入投票决策系统
输入：
{Order_Info＝“加急订单”，Equipment_1 Type＝VPE，Equipment_1 Power＝0.55KW.h，V1_now＝1.3m 3，V2_now＝0.7m 3}
投票决策系统进行投票计算，根据表11可变因素与生产设备工作状态映射表可知：
Figure PCTCN2020090678-appb-000040
根据表14设备控制对应表可知：
Figure PCTCN2020090678-appb-000041
Figure PCTCN2020090678-appb-000042
输出：VPE以一级功率3KW运行。
5.EMS根据上一步骤的结果下发调度指令到NF_1，NF_1根据指令控制VPE_1的工作状态为“运行”、设备的运行功率等级为“3KW”；或者NF_1根据指令控制VPE_1的工作状态为“运行”和设备的运行功率等级为“1KW”
6.重复步骤3、4、5，根据生产线流程顺序依次对生产设备所绑定的NF节点进行循环投票决策。
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本技术方案的宗旨和范围，其均应涵盖在本发明的权利要求范围当中。

Claims (6)

  1. 基于情景感知的数字化车间电能管理研究方法，其特征在于：该方法包括以下步骤：
    S1：建立基于情景感知的数字化车间电能管理架构；
    S2：订单分类；
    S3：电能监测；
    S4：投票决策；
    S5：生产调度。
  2. 根据权利要求1所述的基于情景感知的数字化车间电能管理研究方法，其特征在于：所述基于情景感知的数字化车间电能管理架构为：
    数字化车间利用网络信息技术将车间人员、车间现场设备信息和生产信息相连接，实施生产任务；在生产任务执行过程中，工厂人员关注生产任务状态，包括生产任务是否已完成、产量是否达标、产线设备运行状态和产线电能消耗；
    定义三种生产设备：不可关闭设备NCE、可变功率设备VPE、短期关闭设备STSE，基于上述三种生产设备，搭建基于情景感知的数字化车间电能管理架构，架构内容包括能源管理系统EMS、工业网络，以及由各产线元素组成的车间生产线；
    将生产过程信息、客户订单信息、设备信息作为网络投票系统的输入，经过投票机制灵活调度生产线上的设备工作状态和设备运行功率等级，实现根据网络投票结果自动切换能源优先的生产线模型，以低能耗安排生产。
  3. 根据权利要求1所述的基于情景感知的数字化车间电能管理研究方法，其特征在于：所述S2包括以下步骤：
    S21：建立层次结构
    最上层为目标层，目的是从一批客户订单中选择一个订单进行生产；中间层为准则层，包括影响“选择订单”这一行为的各种因素，产品的交期时间、订单要求的产品数量、产品延期交货将受到的延期惩罚、客户与制造企业的关系；最下层为方案层，即表明现有可供选择的客户订单；
    工厂人员将接收到的n个客户订单根据收单时间的先后顺序对客户订单进行编号，即{order 1，order 2，order 3，…order n}；
    S22：构造对比矩阵
    为确定准则层的各因素对目标层的“订单选择”行为的影响权重、方案层的各客户订单分别对准则层的“产品交期”、“产品数量”、“延期惩罚”、“客户重要性”的影响权重，引入1～9比率标度；
    准则层对目标层的判断矩阵A：
    Figure PCTCN2020090678-appb-100001
    以上矩阵表示对于“订单选择”总目标，产品交期、产品数量、延期惩罚，客户重要性各因素的相对重要性比较；
    方案层对准则层的判断矩阵共有4个:B 1、B 2、B 3、B 4
    Figure PCTCN2020090678-appb-100002
    Figure PCTCN2020090678-appb-100003
    以上矩阵分别表示对于“产品交期”、“产品数量”、“延期惩罚”、“客户重要性”，n个客户订单的相对重要性比较；
    S23：计算各矩阵特征向量、特征值及一致性检验指标
    根据求根法计算特征向量；
    计算B 1、B 2、B 3、B 4的特征向量并且判断一致性要求；
    根据上述计算后，准则层“产品交期”、“产品数量”、“延期惩罚”，“客户重要性”对目标层“订单选择”的影响权重为W＝(w 1，...w 4) T；
    方案层{order 1，order 2，order 3，…order n}对“产品交期”的影响权重为:W 1＝(α 1，…α n) T；
    方案层{order 1，order 2，order 3，…order n}对“产品数量”的影响权重为:W 2＝(β 1，…β n) T；
    方案层{order 1，order 2，order 3，…order n}对“延期惩罚”的影响权重为:W 3＝(γ 1，…γ n) T；
    方案层{order 1，order 2，order 3，…order n}对“客户重要性”的影响权重为:W 4＝(δ 1，…δ n) T；
    S24：层次总排序及决策
    计算最下层方案层对最上层目标层的权重向量，所求即为各个客户订单的权重大小，以此结果做出订单选择；综合权重计算公式如下：
    order n的综合权重为：w′ n＝α n*w 1+β n*w 2+γ n*w 3+δ n*w 4；
    S25：将上述订单的w′ i进行排序，w′ i越大的订单排序在前，综合权重值相同的订单根据接收订单的时间先后顺序决定订单排序；
    对n个客户订单的综合权重大小进行排序，并对排序后的n个客户订单进行分类。
  4. 根据权利要求1所述的基于情景感知的数字化车间电能管理研究方法，其特征在于：所述S3具体为：
    车间生产层的设备电能信息通过工业网络上传至EMS，实现EMS能够实时掌握设备的电能消耗情况；设备电能消耗信息将作为投票决策系统的输入之一，改变车间生产层的生产设备运行状态。
  5. 根据权利要求1所述的基于情景感知的数字化车间电能管理研究方法，其特征在于：所述S4具体为：
    投票决策系统的任务是接收各个投票信息，经过系统投票计算后对生产车间的设备工作状态、设备功率等级进行控制；
    (1)投票系统的输入包括订单信息、生产过程信息、设备信息；
    订单信息Order_Info：
    EMS对所有订单进行订单分类后，获得订单信息，即Order_Info∈{加急订单，常规订单，可调度订单}；生产三种订单所消耗的电量不同，订单需求作为调度生产线的指标之一；
    生产过程信Mid_Info：
    生产过程信息即为车间生产线缓冲区中间产品的当前余量Vm_now，且Vm_now不得超过饱和限定容量Vm；缓冲区感知节点NH通过感知技术检测缓冲区中间产品的当前余量，将感知结果作为投票信息，对生产线设备的功率等级和设备运行状态进行投票；
    设备信息Equipment_Infc：
    复合节点NF利用感知技术采集生产设备的信息即为Equipment_Infc，该信息包括设备类型Equipment Type和设备电能消耗Equipment Power，其中Equipment Type表明生产设备的类型属于{NCE，VPE，STSE}之一，Equipment Power表明STSE设备在某运行期间的总体能耗；复合节点NF将感知结果作为投票信息，对生产线设备的工作状态和设备运行功率进行投票；
    (2)投票系统的输出：
    投票决策系统将“Order_INfo”、“Mid_Info”、“Equipment_Info”投票信息的内容划分为限定因素Order_Info和Equipment_qType和可变因素Vm_now，V(m+1)_now，Equipment_qPower，限定因素影响生产设备的运行功率、决定可变因素的数量，可变因素影响生产设备的工作状态；
    构造“可变因素与生产设备工作状态映射关系”：
    可变因素根据实际采集的数据数值产生逻辑投票，即对生产设备投以“开”、“关”，“不确定”；规定若逻辑票为“开”，则数学表达为“1”；若逻辑票为“关”，则数学表达为“0”；若逻辑票为“不确定”，则数学表达为“0.5”，即构造“可变因素与生产设备工作状态映射表”；
    计算针对“不同类型生产设备的逻辑投票结果”：
    根据限定因素Equipment_qType设定可变因素数量，并对可变因素设计权重值，综合各可变因素与生产设备工作状态映射关系和权重，计算“不同类型生产设备的逻辑投票结果”；
    生成“生产设备的工作状态、运行功率等级的对应表”：
    投票决策系统综合限定因素Order_Info、可变因素与生产设备工作状态映射关系、不同类型生产设备的逻辑投票结果，生成“生产设备的工作状态、运行功率等级的对应表”，其中工作状态包括“运行”状态，“关机”状态，运行功率被分为“高”等级，“中”等级，“低”等级。
  6. 根据权利要求1所述的基于情景感知的数字化车间电能管理研究方法，其特征在于：所述S5具体为：
    EMS经过投票决策系统将获得车间生产线设备的运行功率等级投票结果和工作状态投票结果，生产调度模块将根据投票结果下发相应的控制指令，完成对生产车间生产线设备的工作状态改变，以此对生产任务进行调度。
PCT/CN2020/090678 2020-03-25 2020-05-15 基于情景感知的数字化车间电能管理研究方法 WO2021189620A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
KR1020217040189A KR20220017925A (ko) 2020-03-25 2020-05-15 상황인식에 기반하는 디지털화 작업장 전력관리 연구방법

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010219118.9A CN111476466B (zh) 2020-03-25 2020-03-25 基于情景感知的数字化车间电能管理研究方法
CN202010219118.9 2020-03-25

Publications (1)

Publication Number Publication Date
WO2021189620A1 true WO2021189620A1 (zh) 2021-09-30

Family

ID=71747789

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/090678 WO2021189620A1 (zh) 2020-03-25 2020-05-15 基于情景感知的数字化车间电能管理研究方法

Country Status (3)

Country Link
KR (1) KR20220017925A (zh)
CN (1) CN111476466B (zh)
WO (1) WO2021189620A1 (zh)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116578049A (zh) * 2023-05-16 2023-08-11 安徽企服工业技术有限公司 基于工业互联网的数字化改造方法
CN117590790A (zh) * 2024-01-17 2024-02-23 青岛创新奇智科技集团股份有限公司 一种基于工业大模型的智能生产线监控方法及系统
CN117647962A (zh) * 2024-01-29 2024-03-05 山东国泰民安玻璃科技有限公司 一种注射剂瓶的生产控制方法、设备及介质
CN117973722A (zh) * 2022-12-30 2024-05-03 周慧琴 用于生产加工的订单处理方法及存储介质

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113158401B (zh) * 2020-12-20 2022-10-04 河海大学常州校区 一种多粒度smt产线能耗建模方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102650880A (zh) * 2012-04-24 2012-08-29 佛山科学技术学院 智能柔性制造系统
CN107832946A (zh) * 2017-11-07 2018-03-23 中山大学 一种用于生产车间的电能监控与管理方法及系统
WO2019063617A1 (en) * 2017-09-26 2019-04-04 Université Du Luxembourg IMPROVED CALCULATION DEVICE
CN109598416A (zh) * 2018-11-13 2019-04-09 中国航天系统科学与工程研究院 一种复材车间的动态排产系统及排产方法
CN109902954A (zh) * 2019-02-27 2019-06-18 浙江工业大学 一种基于工业大数据的柔性作业车间动态调度方法

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR200424506Y1 (ko) * 2006-05-22 2006-08-28 주식회사 노비타 온수세정기의 변좌커버를 이용한 절전장치
CN101303749A (zh) * 2008-06-19 2008-11-12 上海交通大学 面向客户需求的车间作业调度方法
CN106376023A (zh) * 2016-10-10 2017-02-01 西北大学 一种基于情景感知的数据下载方法及其系统
CN106529787A (zh) * 2016-11-02 2017-03-22 南京国电南自轨道交通工程有限公司 一种基于人脸检测技术的地铁能源管理系统
CN110069627A (zh) * 2017-11-20 2019-07-30 中国移动通信集团上海有限公司 短文本的分类方法、装置、电子设备和存储介质
CN110490438B (zh) * 2019-08-01 2022-05-31 浙江大学 一种工业流水线车间数字孪生能力的简化策略推荐方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102650880A (zh) * 2012-04-24 2012-08-29 佛山科学技术学院 智能柔性制造系统
WO2019063617A1 (en) * 2017-09-26 2019-04-04 Université Du Luxembourg IMPROVED CALCULATION DEVICE
CN107832946A (zh) * 2017-11-07 2018-03-23 中山大学 一种用于生产车间的电能监控与管理方法及系统
CN109598416A (zh) * 2018-11-13 2019-04-09 中国航天系统科学与工程研究院 一种复材车间的动态排产系统及排产方法
CN109902954A (zh) * 2019-02-27 2019-06-18 浙江工业大学 一种基于工业大数据的柔性作业车间动态调度方法

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117973722A (zh) * 2022-12-30 2024-05-03 周慧琴 用于生产加工的订单处理方法及存储介质
CN116578049A (zh) * 2023-05-16 2023-08-11 安徽企服工业技术有限公司 基于工业互联网的数字化改造方法
CN116578049B (zh) * 2023-05-16 2023-10-31 安徽企服工业技术有限公司 基于工业互联网的数字化改造方法
CN117590790A (zh) * 2024-01-17 2024-02-23 青岛创新奇智科技集团股份有限公司 一种基于工业大模型的智能生产线监控方法及系统
CN117590790B (zh) * 2024-01-17 2024-04-12 青岛创新奇智科技集团股份有限公司 一种基于工业大模型的智能生产线监控方法及系统
CN117647962A (zh) * 2024-01-29 2024-03-05 山东国泰民安玻璃科技有限公司 一种注射剂瓶的生产控制方法、设备及介质
CN117647962B (zh) * 2024-01-29 2024-04-12 山东国泰民安玻璃科技有限公司 一种注射剂瓶的生产控制方法、设备及介质

Also Published As

Publication number Publication date
CN111476466B (zh) 2022-06-03
CN111476466A (zh) 2020-07-31
KR20220017925A (ko) 2022-02-14

Similar Documents

Publication Publication Date Title
WO2021189620A1 (zh) 基于情景感知的数字化车间电能管理研究方法
Ongsakul et al. Unit commitment by enhanced adaptive Lagrangian relaxation
Trivedi et al. Enhanced multiobjective evolutionary algorithm based on decomposition for solving the unit commitment problem
WO2021114661A1 (zh) 一种基于边云协同的工厂电能管控系统及方法
CN104049600B (zh) 用与自动化系统相关的能量信息更新置信值的系统及方法
CN102622667B (zh) 一种基于多产品多生产线生产模式下的均衡排产方法
Mingran Measurement and spatial statistical analysis of green science and technology innovation efficiency among Chinese Provinces
CN109272145A (zh) 基于非线性整数规划的卷纸分切和排产优化方法及系统
TWI767525B (zh) 基於強化學習的再生能源配置方法及裝置
Shao et al. A multi-neighborhood-based multi-objective memetic algorithm for the energy-efficient distributed flexible flow shop scheduling problem
Jin et al. Optimal policy characterization enhanced proximal policy optimization for multitask scheduling in cloud computing
CN115549109A (zh) 海量柔性负荷快速聚合控制方法及装置
CN112736918A (zh) 微电网中源储荷协调的两级优化调度方法、装置及设备
Liang Production Logistics Management of Industrial Enterprises Based on Wavelet Neural Network.
CN113935856A (zh) 电能的控制方法及其装置、处理器、计算机可读存储介质
CN207976876U (zh) 多核心传感器数据处理芯片
Xie et al. A Two‐Workshop Collaborative, Integrated Scheduling Algorithm considering the Prescheduling of the Root‐Subtree Processes
Xu et al. Actor-critic with transformer for cloud computing resource three stage job scheduling
CN105933702A (zh) 一种基于任务敏感的功耗控制方法
Wang et al. A Portrait-Based Method for Constructing Multi-Time Scale Demand Response Resource Pools
Li et al. A Two-Stage Packetized Energy Trading and Management Framework for Virtual Power Plants
CN109818352A (zh) 一种基于近似动态规划算法的配电网应急电源车调度方法
Wan et al. Research on resource allocation management of industrial supply chain based on blockchain
CN112184486B (zh) 一种电力用户成本管理方法
Niu Multi Skill Human Resource Group Intelligent Scheduling Method Based on Reinforcement Learning

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20926451

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20926451

Country of ref document: EP

Kind code of ref document: A1