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WO2021189620A1 - Procédé de recherche de gestion d'énergie électrique d'atelier numérique fondé sur la sensibilité au contexte - Google Patents

Procédé de recherche de gestion d'énergie électrique d'atelier numérique fondé sur la sensibilité au contexte Download PDF

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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
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order
equipment
production
voting
information
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PCT/CN2020/090678
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English (en)
Chinese (zh)
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魏旻
李彩芹
王平
杨旭
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重庆邮电大学
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Priority to KR1020217040189A priority Critical patent/KR20220017925A/ko
Publication of WO2021189620A1 publication Critical patent/WO2021189620A1/fr

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    • 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"

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Abstract

La présente invention concerne un procédé de recherche de gestion d'énergie électrique d'atelier numérique fondé sur la sensibilité au contexte appartenant au domaine de l'internet des objets. Le procédé comprend les étapes suivantes consistant : S1 : à établir une architecture de gestion d'énergie électrique d'atelier numérique fondée sur la sensibilité au contexte; S2 : à classer des commandes; S3 : à surveiller l'énergie électrique; S4 : à prendre une décision par vote; et S5 : à planifier la production. Selon la présente invention, des informations physiques et des informations de demande de production concernant une ligne de production sont acquises au moyen de la sensibilité au contexte, de telle sorte que l'état de fonctionnement ou le niveau d'énergie de fonctionnement d'un dispositif donné sur la ligne de production est décidé par un vote effectué par d'autres dispositifs sur la ligne de production en fonction des informations susmentionnées, ce qui permet de réduire la consommation d'énergie électrique de toute la chaîne de production de manière flexible, automatique et intelligente.
PCT/CN2020/090678 2020-03-25 2020-05-15 Procédé de recherche de gestion d'énergie électrique d'atelier numérique fondé sur la sensibilité au contexte WO2021189620A1 (fr)

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KR1020217040189A KR20220017925A (ko) 2020-03-25 2020-05-15 상황인식에 기반하는 디지털화 작업장 전력관리 연구방법

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