WO2023240770A1 - 一种物联网设备集中控制管理系统 - Google Patents

一种物联网设备集中控制管理系统 Download PDF

Info

Publication number
WO2023240770A1
WO2023240770A1 PCT/CN2022/110419 CN2022110419W WO2023240770A1 WO 2023240770 A1 WO2023240770 A1 WO 2023240770A1 CN 2022110419 W CN2022110419 W CN 2022110419W WO 2023240770 A1 WO2023240770 A1 WO 2023240770A1
Authority
WO
WIPO (PCT)
Prior art keywords
production
mechanical
parameters
parameter
optimization
Prior art date
Application number
PCT/CN2022/110419
Other languages
English (en)
French (fr)
Inventor
柯海丰
张高燕
俞雪永
Original Assignee
浙大城市学院
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 浙大城市学院 filed Critical 浙大城市学院
Priority to JP2023509389A priority Critical patent/JP2024528350A/ja
Publication of WO2023240770A1 publication Critical patent/WO2023240770A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32252—Scheduling production, machining, job shop
    • 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
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to the field of Internet of Things control, specifically a centralized control and management system for Internet of Things equipment, a control device and a computer-readable storage medium.
  • the Internet of Things refers to the real-time collection of any objects or processes that need to be monitored, connected, and interacted with through various information sensors, radio frequency identification technology, global positioning systems, infrared sensors, laser scanners and other devices and technologies. Collect various required information such as sound, light, heat, electricity, mechanics, chemistry, biology, location, etc., and realize ubiquitous connection between things and things, things and people through various possible network access, and realize the ubiquitous connection between things and people. Intelligent perception, identification and management of processes, the Internet of Things is an information carrier based on the Internet, traditional telecommunications networks, etc., which allows all ordinary physical objects that can be independently addressed to form an interconnected network.
  • Equipment remote control is a combined application of IT system and automation control.
  • the electrical switch in the equipment is exposed to the signal point in the controller through the automatic control system, and then the switch signal of the equipment is controlled through the communication principle of the host computer, thereby achieving remote control.
  • Control makes it easy for managers to adjust the equipment according to the overall working condition parameters.
  • classification configuration is generally carried out through a preset system, which requires manual selection and adjustment.
  • the existing preset system has poor adjustment accuracy for production capacity, and often can only perform rough classification adjustments or direct stop operations to achieve the desired results.
  • the balance between semi-finished products and production capacity makes it difficult to make precise automated adjustments to operating results.
  • This application provides a centralized control and management system for Internet of Things equipment, which is used to solve the technical problems existing in existing Internet of Things control technology that rely on manual subjective operations and have poor adjustment effects.
  • this application provides a centralized control and management system for Internet of Things devices.
  • the first aspect of this application provides a centralized control and management system for Internet of Things equipment.
  • the system is applied to a centralized control equipment for Internet of Things equipment.
  • the system includes an Internet of Things control terminal and an intelligent control terminal.
  • the networked control terminal includes a control module.
  • the internal first collection unit collects multi-dimensional mechanical parameters of the production equipment to obtain a set of mechanical parameters.
  • the internal first control unit adjusts the production equipment according to the optimized set of mechanical parameters.
  • the intelligent control end includes a second collection unit for collecting multi-dimensional production parameters of the working environment and obtaining a production parameter set; a first processing unit for setting dynamic standards according to the production parameter set and mechanical parameter set; The first judgment unit is used to judge whether the current dynamic balance state meets the dynamic standard according to changes in the production parameters; the second processing unit is used to maintain the current dynamic balance state if the dynamic balance state meets the dynamic standard. If the dynamic equilibrium state does not meet the dynamic standard, the mechanical parameter set is optimized based on the dynamic standard.
  • a second aspect of this application provides a centralized control device for Internet of Things devices, including: a processor, the processor is coupled to a memory, and the memory is used to store a program. When the program is executed by the processor When the system is configured to perform the functions of the system described in the first aspect.
  • the third aspect of this application provides a remote intelligent unified control device based on the Internet of Things.
  • a computer program is stored on the storage medium. When the computer program is executed by the processor, the system as described in the first aspect is implemented. Function.
  • the embodiment of this application obtains a set of mechanical parameters by collecting multi-dimensional mechanical parameters of the Internet of Things control terminal, and then collects multi-dimensional production parameters of the working environment to obtain a set of production parameters, and sets dynamic standards based on the set of production parameters and the set of mechanical parameters.
  • the IoT control terminal performs production according to the mechanical parameter set, and determines whether the current dynamic balance state meets the dynamic standard based on the changes in the production parameter set. If the dynamic balance state meets the dynamic standard, the current mechanical parameter set will be maintained. If the dynamic balance state If the status does not meet the dynamic standards, the mechanical parameter set is optimized based on the dynamic standards, and the optimized mechanical parameter set is used to adjust the IoT control terminal to optimize the working status of the mechanical equipment;
  • the embodiment of this application collects multi-dimensional mechanical parameters and multi-dimensional production parameters to fully control the working status of the entire production line.
  • the assembly line equipment should not be stopped due to non-fault or maintenance reasons, combined with the accumulation of semi-finished products and labor in the automated part of the assembly line.
  • the remaining effective labor capacity of the link part on the day is fully analyzed, and a global optimization model is constructed to uniformly plan the maintenance nodes and maintenance redundancy time of the mechanical equipment, so that the production capacity of the automated assembly line part can be automatically adjusted at any time to meet the needs of the assembly line. Changes in labor capacity and preset production capacity.
  • Figure 1 is a system flow chart in a centralized control and management system for Internet of Things equipment provided by this application;
  • Figure 2 is a schematic flow chart of obtaining dynamic standards in a centralized control and management system for Internet of Things equipment provided by this application;
  • Figure 3 is a schematic flowchart of optimizing the set of mechanical parameters in a centralized control and management system for Internet of Things equipment provided by this application;
  • Figure 4 is a schematic structural diagram of an Internet of Things device centralized control device provided by this application.
  • Figure 5 is a schematic structural diagram of an Internet of Things equipment centralized control and management system provided by this application.
  • Figure 6 is a schematic structural diagram of an exemplary electronic device of the present application.
  • 100 Internet of Things control terminal; 110, control module; 111, first collection unit; 112, first control unit; 120, communication interface; 200, intelligent control terminal; 210, optimization module; 211, second collection Unit; 212, first processing unit; 213, first judgment unit; 214, second processing unit; 300, electronic device; 301, memory; 302, processor; 303, communication interface; 304, bus architecture.
  • This application provides a centralized control and management system for Internet of Things equipment, which is used to solve the technical problems existing in the existing Internet of Things control technology that rely on manual subjective operations and have poor adjustment effects.
  • the embodiment of this application obtains a set of mechanical parameters by collecting multi-dimensional mechanical parameters of the Internet of Things control terminal, and then collects multi-dimensional production parameters of the working environment to obtain a set of production parameters, and sets dynamic standards based on the set of production parameters and the set of mechanical parameters.
  • the IoT control terminal After the setting is completed, the IoT control terminal performs production according to the mechanical parameter set, and determines whether the current dynamic balance state meets the dynamic standard based on the changes in the production parameter set. If the dynamic balance state meets the dynamic standard, the current mechanical parameter set will be maintained. If the dynamic balance state If the status does not meet the dynamic standards, the mechanical parameter set is optimized based on the dynamic standards, and the optimized mechanical parameter set is used to adjust the IoT control terminal to optimize the working status of the mechanical equipment.
  • this application provides a centralized control and management system for Internet of Things equipment.
  • the system includes an Internet of Things control terminal 100 and an intelligent control terminal 200.
  • the system process includes:
  • S100 Collect multi-dimensional mechanical parameters of the Internet of Things control terminal 100 and obtain a set of mechanical parameters
  • FIG. 4 shows a possible structural diagram of the Internet of Things control terminal 100 in the embodiment of the present application.
  • the Internet of Things control terminal 100 can be an Internet of Things control terminal with any structure in the prior art, and can be a separate Internet of Things control terminal. , it can also be an Internet of Things control terminal installed in equipment such as assembly line transmission belts, production and processing, diversion and transportation adjustment, used to control the production efficiency of the mechanical automation part of the assembly line, so that the production capacity of the mechanical automation part of the assembly line matches the production capacity of the manual link. .
  • the Internet of Things control terminal 100 includes a communication interface 120 and a control module 110 .
  • the communication interface 120 is used to transmit the received signal to the Internet of Things control terminal 100, and transmit the parameters collected by the Internet of Things control terminal 100 to other matching communication interfaces 120 in the form of data signals.
  • the matching method may be a wireless data connection, a wired interface connection, or any other connection method.
  • the communication interface 120 will collect the remaining equipment maintenance time and current production time collected by the Internet of Things control terminal 100 connected to it.
  • the rate parameters are transmitted to the communication interface 120 connected to the optimization module 210, so that the optimization module 210 can optimize according to the global optimization model, and transmit the received optimized mechanical parameter set to the connected control module 110, thereby The equipment controlled by the control module 110 performs parameter adjustment.
  • the control module 110 is directly built into the device circuit or connected to the circuit through a data tape. After obtaining the device permission, it directly reads the mechanical parameters of the device from the device processor and transmits all the mechanical parameters to the communication interface 120. When the interface 120 receives the optimized mechanical parameter set, it overwrites the optimized mechanical parameter set into the equipment processor, and overwrites the original execution standard in the equipment processor, so that the equipment operates based on the optimized mechanical parameter set as the standard. .
  • the above-mentioned mechanical parameters include the current power-on status, current production efficiency and next maintenance interval of the equipment; therefore, the current power-on status, current production efficiency and next maintenance interval are periodically obtained through the control module 110 to form the mechanical parameters.
  • the effective production capacity and actual output of the machinery can be known, and then it can be analyzed whether the equipment production capacity under the control of the mechanical parameter collection can match the production capacity of the manual production link.
  • S200 Collect multi-dimensional production parameters of the working environment and obtain a production parameter set
  • the target environment is the part of the manual link in the current assembly line.
  • the target environment can be manual packaging, manual quality inspection, manual assembly and other environments.
  • the above-mentioned multi-dimensional production parameters mainly include the number of people on duty, the production capacity time curve of on-duty personnel, on-duty time and the current accumulation of semi-finished products at the work station.
  • on-the-job personnel will feel tired as the continuous working hours increase, which will lead to a decrease in concentration and the inability to maintain the previous work efficiency. Forcibly maintaining high efficiency will lead to an increase in the defective rate.
  • the work efficiency of the manual link should be adjusted according to the effective work efficiency of the personnel on the job.
  • this job is the manual quality inspection link of the assembly line, and the quality inspection products are small packaged daily necessities with defects that can be observed with the naked eye.
  • the quality inspector uses his eyes and hands to quickly inspect them. As the working time goes by, the quality inspector's eyes will become tired, his concentration will decrease, and he will no longer be able to effectively inspect the assembly line products. If you continue to inspect, it is easy for defective products to be missed. At this time, slow down the speed of the assembly line so that the inspection speed of the assembly line matches the current working status of the quality inspector, thereby ensuring the effectiveness of the work.
  • the effective work efficiency of the work environment can be analyzed, which can then be used as the data basis for analyzing and judging the remaining effective production capacity.
  • Process S200 in the system provided by the embodiment of this application includes:
  • S210 Collect staff's arrival information and obtain the first production parameters
  • S220 Collect the single-cycle production capacity time curve information of the staff and obtain the first manual parameters
  • S230 Collect the single-cycle working hours information of workers and obtain the second manual parameters
  • S250 Collect the accumulation amount of semi-finished products at the station to obtain the third production parameter
  • S260 Use the first production parameter, the second production parameter and the third production parameter as the production parameter set.
  • the number of people present in the current production environment, the production capacity time curve of on-the-job personnel, on-the-job time and the current accumulation of semi-finished products at the work station are collected and detected as a set of production parameters in the current production environment.
  • the employee's single-cycle productivity can be obtained.
  • Time curve information and adjusted according to subsequent error outliers.
  • all personnel in this link can be regarded as a whole to measure the overall single-cycle production capacity time curve.
  • the above-mentioned first production parameter, second production parameter and third production parameter are regarded as a production parameter set in the current production environment, and the production parameter set can reflect the remaining effective production capacity in the production environment.
  • the embodiment of the present application collects multi-dimensional production parameters of the working environment.
  • the production parameter set can be used as the data basis for optimizing the pollution degree of the mechanical parameter set, and can improve the accuracy of adjusting and optimizing the mechanical parameter set.
  • the corresponding dynamic standard is set. The smaller the remaining effective production capacity is and the larger the accumulation amount of semi-finished products is, the lower the corresponding dynamic standard will be.
  • this dynamic standard is calculated based on the current remaining effective production capacity and semi-finished product accumulation in the working environment, and combined with a set of mechanical parameters as a reference, the obtained dynamic standard is more intelligent and can better fit the daily production goals and current production conditions. .
  • process S300 in the system provided by the embodiment of this application includes:
  • S320 Use the second production parameters to adjust the first dynamic balance efficiency to obtain the second dynamic balance efficiency
  • index values are selected for weighted calculation when adjusting the first dynamic balance efficiency, the second dynamic balance efficiency, and the third dynamic balance efficiency in different working environments.
  • Process S320 in the system provided by the embodiment of this application includes:
  • S321 Construct an effective production capacity attenuation model according to the first artificial parameter, where the effective production capacity attenuation model includes multiple time axis synchronized object models;
  • S322 Import the second artificial parameter into the effective production capacity attenuation model, dynamically adjust the weight coefficient of the first production parameter according to the adjustment formula, and obtain multiple remaining production capacity results after respective calculations;
  • the long-term goal can be roughly calculated as a reference for the long-term goal, and an effective capacity attenuation model can be constructed based on this. Since the average work efficiency does not It has the accuracy of precise calculation and is only suitable for rough calculation of long-term goals. Therefore, it is necessary to import the second artificial parameter as a weight coefficient into the effective production capacity attenuation model, and dynamically adjust the weight coefficient of the first production parameter according to the adjustment formula. .
  • the adjustment formula is:
  • I 2 is the remaining capacity result
  • X is the current time
  • b is the second manual parameter
  • y is the remaining working hours of the cycle
  • C 2 is the third mechanical parameter.
  • f(x) is the first manual parameter, which can be a constant depending on the working environment, or the error rate of the on-the-job personnel during their working period can be periodically counted, and the highest and lowest values can be removed. The remaining values are averaged, errors are distributed according to working hours, and the employee's single-cycle production capacity time curve information is obtained, and adjustments are made based on subsequent error outliers.
  • all personnel in this link can be regarded as a whole to measure the overall single-cycle production capacity time curve. As the value of
  • different indicator values are selected as absolute target values or weighted calculations. During each adjustment process, multiple indicator values can be set as absolute at the same time.
  • the target value may be calculated based on a combination of different production tasks.
  • the accumulation amount of semi-finished products should be used as the index value to adjust the dynamic standard, and the accumulation amount of semi-finished products should be used as the absolute target value, so that the remaining labor of the manual link part after the manual position is off work
  • the accumulation of semi-finished products is lower than the absolute target value.
  • the work efficiency is weighted to calculate, improve the work efficiency and then increase the production capacity, so that the production capacity of the manual link is enough to make the accumulation of semi-finished products as an indicator lower than the absolute target value.
  • each indicator can be weighted separately according to the production plan, or the default value can be used for default optimization.
  • Each indicator is related to each other, and adjusting the weight allocation can achieve different production capacity results.
  • the adjusted third dynamic balance efficiency is used as the dynamic standard.
  • the system provided by the embodiment of this application determines the index value by analyzing different production environments and job requirements, then sets absolute target values, performs weighted calculations according to different needs, and analyzes the working environment based on multi-dimensional production parameters, combining multi-dimensional
  • the analysis results of mechanical parameters can obtain personalized dynamic standards that meet the needs of the working environment, job positions, production goals, etc., and can analyze and judge more accurately whether the current mechanical production capacity matches the manual production capacity in a more targeted and accurate manner.
  • S400 The Internet of Things control terminal performs production according to the mechanical parameter set, and determines whether the current dynamic balance state meets the dynamic standard based on changes in the production parameter set;
  • the mechanical equipment is controlled for production.
  • the production parameter set changes synchronously.
  • the current set of mechanical parameters is imported into the global optimization model for result simulation to determine whether the mechanical production capacity results can match the manual link production capacity.
  • the impact is relatively low relative to the overall number of positions in the manual process. If there is a large overall impact, the indicator value can also be changed subjectively and Combined with weighted calculations for overall adjustment, the interval optimization of the current set of mechanical parameters can make the overall production situation closer to the desired situation in the subsequent period.
  • the current generation parameter set can meet the air purification needs in the target environment, and the air purification can be continued according to the generation parameter set.
  • step S500 in the method provided by the embodiment of this application includes:
  • S510 Construct a global optimization model, wherein the global optimization model includes a variety of mechanical parameter sets;
  • S520 Perform time-series progressive adjustments on the currently adopted set of mechanical parameters within the global optimization model, perform multiple optimization iterations, and generate multiple sets of optimized mechanical parameter sets within the global optimization model;
  • the global optimization model includes the feasible region for current optimization and related constraints to ensure that optimization is performed within the model.
  • the optimized mechanical parameter set in the global optimization model needs to meet the above preset conditions and satisfy other optimization conditions.
  • Step S510 in the method provided by the embodiment of this application includes:
  • S514 Construct and obtain the global optimization model according to the first optimization constraint condition, the second optimization constraint condition and the adjusted mechanical parameter set.
  • the first optimization constraint condition is set.
  • the optimization in the embodiment of the present application is for the Internet of Things control terminal 100
  • Optimization of the mechanical parameter set which includes first mechanical parameters, second equipment maintenance parameters and third mechanical parameters. Different first mechanical parameters, second equipment maintenance parameters and third mechanical parameters can form a variety of combinations, thereby forming multiple A collection of mechanical parameters.
  • the first optimization constraint After a set of multiple mechanical parameters is produced in a preset cycle, it is necessary to ensure that the third production parameter in the working environment reaches the preset condition, and to maximize the third mechanical parameter as much as possible, and then To maximize the overall production capacity while meeting the requirements, the first optimization constraint is also the most important constraint.
  • a second optimization constraint is set.
  • the second production parameter in the optimized mechanical parameter set obtained by optimization exceeds the maximum limit of the multi-dimensional production parameters, for example, artificial
  • the production capacity of automated machinery and equipment can be higher than the effective manual production capacity; in the manual quality inspection process, the production capacity of automated mechanical equipment must not be higher than the effective manual production capacity.
  • the production efficiency of the automation equipment cannot exceed the threshold set by the manufacturer. If the threshold is exceeded, the service life of the equipment will be damaged, and for the second equipment maintenance Parameter adjustment cannot exceed the threshold set by the manufacturer. If the threshold is exceeded, the automation equipment will accelerate aging due to untimely maintenance and affect its service life.
  • the performance parameter requirements include second equipment maintenance parameters and third mechanical parameters. Production work is carried out according to the requirements of the performance parameters, which can ensure that each module in the automation equipment will not be overloaded and ensure operation. life. Therefore, according to the performance parameter requirements, the current multiple optimized mechanical parameter sets are adjusted and the global optimization model is further set.
  • a global optimization model is constructed based on the first optimization constraint, the second optimization constraint and the adjusted mechanical parameter set.
  • the global optimization model includes multiple optimized mechanical parameter sets that meet the first optimization constraint conditions, the second optimization constraint conditions and performance parameter requirements.
  • the second equipment maintenance parameter and the third mechanical parameter in the mechanical parameter set are not constant during the operation cycle, but may change according to the time process. For example, during the initial period of use, the second equipment maintenance parameter The interval is longer and the third mechanical parameter value is higher. After long-term use, in order to ensure its working efficiency and service life, the second equipment maintenance parameter interval should be shortened, and the third mechanical parameter value will be reduced accordingly.
  • the embodiments of this application construct a global optimization model based on multi-dimensional constraints, and simultaneously meet the requirements of multiple other dimensions in the process of optimizing the mechanical parameter set, ensuring that the absolute target value is completed while minimizing the mechanical maintenance time. With the passage of time, the machinery can be maintained as quantitatively as possible and the overall production capacity can be maximized while meeting the above conditions.
  • optimization is performed within the global optimization model.
  • Step S530 in the method provided by the embodiment of this application includes:
  • the optimized mechanical parameter set is imported into the global optimization model, calculated based on the third mechanical parameter and remaining working hours, and then the second mechanical parameter is imported after calculation.
  • the timing adjustment of the first equipment maintenance parameters is performed. , and use the adjusted mechanical parameter set as the current optimization calculation result.
  • the current optimization calculation result can be used as an optional optimization result, and the optimized mechanical parameter set corresponding to the current optimization calculation result is added to the optional optimization result. .
  • the second equipment maintenance parameter has the smallest change with the initial value among the various optional optimization results; the maximum and minimum values of the third mechanical parameter among the various optional optimization results are compared; the various optional optimization results are compared.
  • the adjustment value of the first artificial parameter in the optional optimization result is selected based on the subjectively selected index value and weighted weight, thereby obtaining the optimal set of mechanical parameters.
  • the embodiments of this application build a global optimization model based on multi-dimensional constraints, and simultaneously meet the requirements of multiple other dimensions during the process of optimizing the mechanical parameter set, and can quickly obtain better optional optimizations among multiple optional optimization results.
  • the results are selected based on the subjectively selected index values and weights to achieve the technical effect of optimizing the optimal set of mechanical parameters that obtains the global optimum.
  • S700 Use the optimized set of optimal mechanical parameters to adjust the Internet of Things control terminal.
  • the first mechanical parameters, the second equipment maintenance parameters and the third mechanical parameters of the optimal mechanical parameter set obtained after optimization are used, and the automation equipment is controlled through the Internet of Things control terminal 100 for production and maintenance.
  • the embodiment of this application obtains a mechanical parameter set by collecting multi-dimensional mechanical parameters of the Internet of Things control terminal, and then collects multi-dimensional production parameters of the working environment to obtain a production parameter set, and based on the production parameter set and the mechanical parameter set , set the dynamic standard.
  • the IoT control terminal performs production according to the mechanical parameter set, and determines whether the current dynamic balance state meets the dynamic standard based on changes in the production parameter set. If the dynamic balance state meets the dynamic standard, the current mechanical parameters are maintained. If the dynamic equilibrium state does not meet the dynamic standard, the mechanical parameter set is optimized based on the dynamic standard, and the optimized mechanical parameter set is used to adjust the IoT control terminal to optimize the working status of the mechanical equipment.
  • composition structure includes:
  • the first collection unit 111 is used to collect multi-dimensional mechanical parameters of production equipment and obtain a set of mechanical parameters
  • the first control unit 112 is used to adjust the production equipment according to the optimized set of optimal mechanical parameters
  • the second collection unit 211 is used to collect multi-dimensional production parameters of the working environment and obtain a production parameter set;
  • the first processing unit 212 is used to set dynamic standards according to the production parameter set and the mechanical parameter set;
  • the first judgment unit 213 is used to judge whether the current dynamic balance state meets the dynamic standard according to changes in the production parameters
  • the second processing unit 214 is configured to maintain the current set of mechanical parameters if the dynamic balance state meets the dynamic standard; if the dynamic balance state does not meet the dynamic standard, based on the dynamic standard, The set of mechanical parameters is optimized.
  • system also includes:
  • the third collection unit is used to collect the start and stop status of the production equipment and obtain the first mechanical parameters
  • the fourth collection unit is used to collect the working time of the equipment after the latest maintenance and obtain the first equipment maintenance parameters
  • the fifth collection unit is used to collect the working time of the equipment after the latest maintenance and obtain the first equipment maintenance parameters
  • the sixth collection unit is used to collect the maintenance cycle intervals of the equipment and obtain the second equipment maintenance parameters
  • a third processing unit configured to use the first equipment maintenance parameters and the second equipment maintenance parameters as second mechanical parameters
  • the seventh collection unit is used to collect the production efficiency information of the equipment and obtain the third mechanical parameters
  • the fourth processing unit is used to use the first mechanical parameter, the second mechanical parameter and the third mechanical parameter as a mechanical parameter set.
  • system also includes:
  • the eighth collection unit is used to collect staff arrival information and obtain the first production parameters
  • the ninth collection unit is used to collect the single-cycle production capacity time curve information of the staff and obtain the first manual parameters
  • the tenth collection unit is used to collect the single-cycle working hours information of workers and obtain the second manual parameters
  • the fifth processing unit is used to use the first manual parameter and the second manual parameter as the second production parameter
  • the eleventh collection unit is used to collect the accumulation amount of semi-finished products at the work station to obtain the third production parameter
  • a sixth processing unit is configured to use the first production parameter, the second production parameter and the third production parameter as the production parameter set.
  • system also includes:
  • the sixth processing unit is used to obtain the first dynamic balance efficiency according to the first production parameters and the third mechanical parameters;
  • a seventh processing unit used to adjust the first dynamic balance efficiency using the second production parameters to obtain the second dynamic balance efficiency
  • the eighth processing unit is used to adjust the second dynamic balance efficiency using the third production parameter to obtain the third dynamic balance efficiency
  • a ninth processing unit configured to obtain a mechanical adjustment node according to the first mechanical parameter and the second mechanical parameter
  • the tenth processing unit is used to adjust the third dynamic balance efficiency using the mechanical adjustment node
  • An eleventh processing unit configured to use the adjusted third dynamic balance efficiency as the dynamic standard.
  • a first construction unit configured to construct an effective production capacity attenuation model according to the first artificial parameter, wherein the effective production capacity attenuation model includes multiple object models with synchronized timelines;
  • a twelfth processing unit configured to import the second artificial parameter into the effective production capacity attenuation model, dynamically adjust the weight coefficient of the first production parameter according to the adjustment formula, and obtain multiple remaining production capacity results after respective calculations;
  • a thirteenth processing unit configured to combine and calculate multiple obtained remaining production capacity results to obtain the second dynamic balance efficiency
  • I 2 is the remaining capacity result
  • f (x) is the first manual parameter
  • X is the current time
  • b is the second manual parameter
  • y is the remaining working hours of the cycle
  • C 2 is the third mechanical parameter.
  • system also includes:
  • the fourteenth processing unit is used to make time-series progressive adjustments to the currently adopted set of mechanical parameters within the global optimization model, perform multiple optimization iterations, and generate multiple sets of optimized mechanical parameter sets within the global optimization model;
  • the first judgment unit is used to judge whether the optimized mechanical parameter set meets the preset conditions. If it meets the preset conditions, the optimized mechanical parameter set will be used as an optional optimization result. If it does not meet, iterative optimization will continue;
  • the second judgment unit is used to perform multiple iterative optimizations until the number of optional optimization results that meet the preset optimization conditions reaches the preset value;
  • the fifteenth processing unit is used to compare multiple sets of optional optimization results to obtain the optimal set of mechanical parameters.
  • system also includes:
  • a sixteenth processing unit configured to set the first optimization constraint according to the dynamic standard
  • a seventeenth processing unit configured to set second optimization constraints according to the third production parameter
  • the eighteenth processing unit is used to adjust the plurality of mechanical parameter sets according to the second production parameters.
  • the present application also provides a computer-readable storage medium, with a computer program stored on the storage medium, and the computer program is processed When the processor is executed, the method in Embodiment 1 is implemented.
  • this application also provides a system for adjusting the generation parameters of nano water ions, including: a processor, the processor is coupled to a memory, The memory is used to store a program. When the program is executed by the processor, the system can perform the steps of the method described in Embodiment 1.
  • the electronic device 300 includes: a processor 302, a communication interface 303, and a memory 301.
  • the electronic device 300 may also include a bus architecture 304.
  • the communication interface 303, the processor 302 and the memory 301 can be connected to each other through a bus architecture 304;
  • the bus architecture 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (extended industry Standard architecture). , referred to as EISA) bus, etc.
  • the bus architecture 304 can be divided into an address bus, a data bus, a control bus, etc. For ease of presentation, only one thick line is used in Figure 6, but it does not mean that there is only one bus or one type of bus.
  • the processor 302 may be a CPU, a microprocessor, an ASIC, or one or more integrated circuits used to control the execution of the program of the present application.
  • Communication interface 303 uses any device such as a transceiver to communicate with other devices or communication networks, such as Ethernet, wireless access network (RAN), wireless local area networks (WLAN), Cable access network, etc.
  • RAN wireless access network
  • WLAN wireless local area networks
  • Cable access network etc.
  • the memory 301 can be ROM or other types of static storage devices that can store static information and instructions, RAM or other types of dynamic storage devices that can store information and instructions, or it can be electrically erasable programmable read-only memory (electrically erasable programmable). read-only memory (EEPROM), compact disc (compact disc-only memory, CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures that can be accessed by a computer, without limitation.
  • the memory may exist independently and be connected to the processor through the bus architecture 304. Memory can also be integrated with the processor.
  • the memory 301 is used to store computer execution instructions for executing the solution of the present application, and the processor 302 controls the execution.
  • the processor 302 is configured to execute computer execution instructions stored in the memory 301, thereby implementing a method for adjusting the generation parameters of nanowater ions provided in the above embodiments of the present application.
  • At least one of a, b, or c can mean: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, c can be single or Multiple.
  • the computer program product includes one or more computer instructions.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.
  • the computer refers to
  • Instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions may be transmitted from a website, computer, server, or data center via a wired (for example, coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) means to transmit to another website, computer, server or data center.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center integrated with one or more available media.
  • the available media may be magnetic media (eg, floppy disk, hard disk, tape), optical media (eg, DVD), or semiconductor media (eg, Solid State Disk (SSD)), etc.
  • the various illustrative logic units and circuits described in this application may be implemented by a general purpose processor, a digital signal processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic, discrete hardware components, or any combination of the foregoing designed to implement or operate the functions described.
  • the general-purpose processor may be a microprocessor.
  • the general-purpose processor may also be any conventional processor, controller, microcontroller or state machine.
  • a processor may also be implemented as a combination of computing devices, such as a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration. accomplish.
  • the steps of the method or algorithm described in this application may be directly embedded in hardware, a software unit executed by a processor, or a combination of the two.
  • the software unit may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM or any other form of storage medium in the art.
  • the storage medium can be connected to the processor, so that the processor can read information from the storage medium and can store and write information to the storage medium.
  • the storage medium can also be integrated into the processor.
  • the processor and the storage medium can be installed in the ASIC, and the ASIC can be installed in the terminal.
  • the processor and the storage medium may also be provided in different components in the terminal.
  • These computer program instructions may also be loaded onto a computer or other programmable data processing device, causing a series of operating steps to be performed on the computer or other programmable device to produce computer-implemented processing, thereby executing on the computer or other programmable device.
  • Instructions provide steps for implementing the functions specified in a process or processes of a flowchart diagram and/or a block or blocks of a block diagram.

Landscapes

  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Manufacturing & Machinery (AREA)
  • Quality & Reliability (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • General Factory Administration (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本发明公开了一种物联网设备集中控制管理系统，其中所述物联网控制终端包括：控制模块，内部的第一采集单元对生产设备的多维度机械参数进行采集，获得机械参数集合，内部的第一控制单元根据优化后的所述最优机械参数集合对生产设备进行调整；信号模块，用于将所述控制模块获得的所述机械参数集合传输至所述智能调控端，并将所述智能调控端传输的优化后的所述机械参数集合传输至所述控制模块；所述智能调控端包括：第二采集单元，用于采集工作环境的多维度生产参数，获得生产参数集合；第一处理单元，用于根据所述生产参数集合及机械参数集合，设置动态标准。

Description

一种物联网设备集中控制管理系统 技术领域
本发明涉及物联网控制领域，具体为一种物联网设备集中控制管理系统、控制设备及计算机可读存储介质。
背景技术
物联网，简称IoT，是指通过各种信息传感器、射频识别技术、全球定位系统、红外感应器、激光扫描器等各种装置与技术，实时采集任何需要监控、连接、互动的物体或过程，采集其声、光、热、电、力学、化学、生物、位置等各种需要的信息，通过各类可能的网络接入，实现物与物、物与人的泛在连接，实现对物品和过程的智能化感知、识别和管理，物联网是一个基于互联网、传统电信网等的信息承载体，它让所有能够被独立寻址的普通物理对象形成互联互通的网络。
设备远程控制，是IT系统与自动化控制的结合应用，通过自控系统将设备中的电气开关暴露到控制器中的信号点位，然后通过上位机通信原理对设备的开关信号进行控制，进而实现远程控制，方便管理人员根据整体工况参数对设备进行调节。
现有技术中一般通过预设系统进行分类配置，需要配合人工进行选择调解，且现有预设系统对产能的调节精度较差，往往只能进行粗略的分类调节或直接进行停止操作，以达到半成品和产能之间的平衡，难以对运行结果进行精确的自动化调节。
发明内容
本申请提供了一种物联网设备集中控制管理系统，用于针对解决现有物联网控制技术中存在的依赖人工主观操作、调节效果差的技术问题。
鉴于上述问题，本申请提供了一种物联网设备集中控制管理系统。
本申请的第一个方面，提供了一种物联网设备集中控制管理系统，所述系统应用于一种物联网设备集中控制设备，所述系统包括物联网控制终端及智能调控端，所述物联网控制终端包括，控制模块，内部的第一采集单元对生产设备的多维度机械参数进行采集，获得机械参数集合，内部的第一控制单元根据优化后的所述机械参数集合对生产设备进行调整；信号模块，用于将所述控制模块获得的所述机械参数集合传输至所述智能调控端，并将所述智能调控端传输的优化后的所述机械参数集合传输至所述控制模块；所述智能调控端包括，第二采集单元，用于采集工作环境的多维度生产参数，获得生产参数集合；第一处理单元，用于根据所述生产参数集合及机械参数集合，设置动态标准；第一判断单元，用于根据所述生产参数的变化，判断当前动态平衡状态是否满足所述动态标准；第二处理单元，用于若所述动态平衡状态满足所述动态标准，则维持当前所述机械参数集合，若所述动态平衡状态不满足所述动态标准，则基于所述动态标准，对所述机械参数集合进行优化。
本申请的第二个方面，提供了一种物联网设备集中控制设备，包括：处理器，所述处理器与存储器耦合，所述存储器用于存储程序，当所述程序被所述处理器执行时，使系统以执行如第一方面所述系统的功能。
本申请的第三个方面，提供了一种基于物联网的远程智能统一控制设备，所述存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如第一方面所述系统的功能。
本申请中提供的一个或多个技术方案，至少具有如下技术效果或优点：
本申请实施例通过采集物联网控制终端的多维度机械参数，获得机械参数集合，再采集工作环境的多维度生产参数，获得生产参数集合，并根据生产参数集合及机械参数集合，设置动态标准，设置完毕后物联网控制终端根据机械参数集合进行生产，并根据生产参数集合的变化，判断当前动态平衡状态是否满足动态标准，若动态平衡状态满足动态标准，则维持当前机械参数集合，若动态平衡状态不满足动态标准，则基于动态标准，对机械参数集合进行优化，并采用优化后的机械参数集合对物联网控制终端进行调整，从 而对机械设备的工作状态进行优化；
本申请实施例通过采集多维度机械参数及多维度生产参数，对整体生产线的工作状况进行全部把控，由于流水线设备非故障或维护原因不宜停止，结合流水线自动化部分已生产半成品的堆积情况及人工环节部分的当日剩余有效劳动能力进行全部分析，并构建全局优化模型，将机械设备的维护节点及维护冗余时间进行统一规划，从而使自动化流水线部分的产能能够在任意时刻自动调节，并满足流水线人工能变化及预设产能变化的情况。
上述说明仅是本申请技术方案的概述，为了能够更清楚了解本申请的技术手段，而可依照说明书的内容予以实施，并且为了让本申请的上述和其它目的、特征和优点能够更明显易懂，以下特举本申请的具体实施方式。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本申请提供的一种物联网设备集中控制管理系统中的系统流程图；
图2为本申请提供的一种物联网设备集中控制管理系统中获得动态标准的流程示意图；
图3为本申请提供的一种物联网设备集中控制管理系统中优化机械参数集合的流程示意图；
图4为本申请提供的一种物联网设备集中控制设备的结构示意图；
图5为本申请提供的一种物联网设备集中控制管理系统的结构示意图；
图6为本申请示例性电子设备的结构示意图。
图中：100、物联网控制终端；110、控制模块；111、第一采集单元；112、第一控制单元；120、通信接口；200、智能调控端；210、优化模块；211、 第二采集单元；212、第一处理单元；213、第一判断单元；214、第二处理单元；300、电子设备；301、存储器；302、处理器；303、通信接口；304、总线架构。
具体实施方式
本申请通过提供了一种物联网设备集中控制管理系统，用于针对解决现有物联网控制技术中存在的依赖人工主观操作、调节效果差的技术问题。
针对上述技术问题，本申请提供的技术方案总体思路如下：
本申请实施例通过采集物联网控制终端的多维度机械参数，获得机械参数集合，再采集工作环境的多维度生产参数，获得生产参数集合，并根据生产参数集合及机械参数集合，设置动态标准，设置完毕后物联网控制终端根据机械参数集合进行生产，并根据生产参数集合的变化，判断当前动态平衡状态是否满足动态标准，若动态平衡状态满足动态标准，则维持当前机械参数集合，若动态平衡状态不满足动态标准，则基于动态标准，对机械参数集合进行优化，并采用优化后的机械参数集合对物联网控制终端进行调整，从而对机械设备的工作状态进行优化。
在介绍了本申请基本原理后，下面，将参考附图对本申请中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅是本申请的一部分实施例，而不是本申请的全部实施例，应理解，本申请不受这里描述的示例实施例的限制。基于本申请的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。另外还需要说明的是，为了便于描述，附图中仅示出了与本申请相关的部分而非全部。
实施例一
如图1所示，本申请提供了一种物联网设备集中控制管理系统，所述系统包括物联网控制终端100及智能调控端200，所述系统流程包括：
S100：采集所述物联网控制终端100的多维度机械参数，获得机械参数集合；
图4示出了本申请实施例中物联网控制终端100一种可能的结构示意图， 该物联网控制终端100可为现有技术中任意结构的物联网控制终端，可为单独的物联网控制终端，也可为设置于流水线传输带、生产加工、分流及输送调节等设备中的物联网控制终端，用于控制流水线机械自动化部分的生产效率，使流水线机械自动化部分的产能与人工环节的产能匹配。
本申请实施例中，如图4所示，该物联网控制终端100包括通信接口120及控制模块110。其中通信接口120用于将接收到的信号传输至物联网控制终端100，并将物联网控制终端100采集到的参数以数据信号方式传输至其他相匹配的通信接口120，通信接口120之间的匹配方式可以是无线数据连接，也可是有线接口连接或其他任意连接方式，示例性地，该通信接口120之间将与之相连的物联网控制终端100所采集到的设备维护剩余时间及目前生产速率参数传输至与优化模块210所连的通信接口120，进而使优化模块210可以根据全局优化模型进行优化，并将接收到的优化后的机械参数集合传输至相连的控制模块110，从而对该控制模块110所控制的设备进行参数调节。
控制模块110直接内置于设备电路中或通过数据带进行电路连接，在获取设备许可后，从设备处理器中直接读取设备的机械参数，并将机械参数全部传输至通信接口120，当从通信接口120接收到优化后的机械参数集合时，将优化后的机械参数集合覆盖至设备处理器中，并覆盖设备处理器中原先的执行标准，使设备以优化后的机械参数集合为标准进行工作。
可选的，上述的机械参数包括设备的当前开机状态、当前生产效率及下一次维护间隔；因此通过控制模块110周期性获取当前的当前开机状态、当前生产效率及下一次维护间隔，组成机械参数集合，能够获知机械的有效产能和实际产量，进而分析该机械参数集合调控下的设备产能是否能够与人工生产环节的产能相匹配。
S200：采集工作环境的多维度生产参数，获得生产参数集合；
本申请实施例中，目标环境即为当前流水线中人工环节的部分，示例性地，目标环境可为手工分装、人工质检、手工组装等环境。
本申请实施例中，上述的多维度生产参数主要包括到岗人数、在岗人员 的产能时间曲线、在岗时间及工位当前半成品的堆积量。具体的，在岗人员在人工作业期间会随着连续工作时长的增加而产生疲劳感，进而导致专注力下降，从而无法维持之前的工作效率，强行维持高效率会导致次品率的提升，为了保证产品的良品率，人工环节的工作效率应当根据在岗人员的有效工作效率进行调节，示例性的，该工作岗位为流水线人工质检环节，质检产品为肉眼可观察出瑕疵的小包装日用品，当小包装日用品从流水线上经过时，质检员通过双眼配合双手翻动进行快速检视，随着工作时间的推移，质检员的双眼会出现疲劳，专注度下降，无法再有效的对流水线产品进行持续检视，容易出现瑕疵品漏过的情况，此时将流水线的速度放缓，使流水线的检视速度与质检员当前的工作状况相匹配，从而保证工作的有效性。
通过分析目工作环境的多维度生产参数，能够分析工作环境的有效工作效率，进而作为分析判断剩余有效产能的数据基础。
本申请实施例提供的系统中的流程S200包括：
S210：采集工作人员的到岗信息，获得第一生产参数；
S220：采集工作人员的单周期产能时间曲线信息，获得第一人工参数；
S230：采集工作人员的单周期工时信息，获得第二人工参数；
S240：根据第一人工参数及第二人工参数，获得第二生产参数；
S250：采集工位的半成品堆积量，获得第三生产参数；
S260：将所述第一生产参数、第二生产参数及第三生产参数作为所述生产参数集合。
具体地，采集检测当前生产环境中到岗人数、在岗人员的产能时间曲线、在岗时间及工位当前半成品的堆积量，作为当前生产环境内的生产参数集合。
示例性地，通过对该在岗人员在岗期间的失误率进行周期性的统计，去掉最高和最低值，并对其余值取均值，将失误根据工作时间进行分布，进而得出该员工的单周期产能时间曲线信息，并根据后续的失误离群点进行调节，当该人工流水线环节采用多人共同操作时，可以将该环节所有人员视为一个整体进行整体的单周期产能时间曲线测定。
将上述的第一生产参数、第二生产参数和第三生产参数作为当前生产环境内的生产参数集合，该生产参数集合能够反映生产环境内的剩余有效产能。本申请实施例通过采集工作环境的多维度生产参数，该生产参数集合可作为优化机械参数集污染程度越合的数据基础，能够提升调节优化机械参数集合的准确性。
S300：根据所述生产参数集合及机械参数集合，设置动态标准；
根据该生产参数集合反映的工作环境的剩余有效产能及半成品堆积量，设置相应的动态标准，该剩余有效产能反映越小且半成品堆积量越大，则该相应的动态标准则相应的越低。
在生产环节中，人工每天工作时间有限，且工作时间内的工作量也有限，而机械设备可以不间断生产，因此，应当在人工的工作时间时，尽可能的减少半成品堆积量。
由于该动态标准根据工作环境当前剩余有效产能及半成品堆积量计算，并结合了机械参数集合作为参考，因此，获得的动态标准更加智能，可以更好的契合每日的生产目标及当前的生产情况。
如图2所示，本申请实施例提供的系统中的流程S300包括：
S310：根据所述第一生产参数及第三机械参数,获得第一动态平衡效率；
S320：采用所述第二生产参数对所述第一动态平衡效率进行调整，获得第二动态平衡效率；
S330：采用所述第三生产参数对第二动态平衡效率进行调整，获得第三动态平衡效率；
S340：根据所述第一机械参数和第二机械参数，获得机械调整节点；
S350：采用所述机械调整节点对第三动态平衡效率进行调整；
S360：将调整后的所述第三动态平衡效率作为所述动态标准。
具体地，根据不同的工作环境，工作岗位、工作效率的不同对于生产的影响能力不同，以及不同产品对于堆积存放的要求不同，示例性地，对于袋装面包等食品加工行业，人工环节多存在于质检或分装等环节，对于质检环 节，工作的专注度要求较高，工作的失误对于整体产品影响较大，因此需要将动态标准调低，进而降低工作强度，保证工作的有效性，而分装对专注度较低，工作的失误率对产品的影响较小，可以适当提高动态标准，提高工作效率，而对于部分不宜长时间堆积半成品的岗位，为了快速处理半成品，避免半成品变质、污染或失效，应当以半成品堆积量为指标值去进行动态标准的调节。
因此，根据在不同的工作环境内，对第一动态平衡效率、第二动态平衡效率、第三动态平衡效率进行调整时选择不同的指标值进行加权计算。
本申请实施例提供的系统中的流程S320包括：
S321：根据第一人工参数构建有效产能衰减模型，其中所述有效产能衰减模型包括多个时间轴同步的对象模型；
S322：将第二人工参数导入至所述有效产能衰减模型中，根据调整公式对所述第一生产参数的权重系数进行动态调整，分别计算后得到多个剩余产能结果；
S323：将得到的多个所述剩余产能结果进行合并计算，获得第二动态平衡效率。
具体地，在将第一人工参数与平均工作效率进行结合后，可以对长期的目标进行粗略的计算，作为长期目标的参考，并以此为基础构建有效产能衰减模型，由于平均工作效率并不具备精确计算的精度，仅适用于长期目标的粗略计算，因此需要将第二人工参数作为权重系数导入至有效产能衰减模型中，并根据调整公式对所述第一生产参数的权重系数进行动态调整。
该调整公式为：
Figure PCTCN2022110419-appb-000001
其中，I 2为剩余产能结果，X为当前时间，b为第二人工参数，y为周期剩余工时，C 2为第三机械参数。
具体地，f(x)为第一人工参数，其根据工作环境的不同，具体可为一常数，也可通过对该在岗人员在岗期间的失误率进行周期性的统计，去掉最高和最低值，并对其余值取均值，将失误根据工作时间进行分布，进而得出该员工的单周期产能时间曲线信息，并根据后续的失误离群点进行调节，当该人工流水线环节采用多人共同操作时，可以将该环节所有人员视为一个整体进行整体的单周期产能时间曲线测定，随着X值的推移，具体数值会发生改变，进行细致的计算，对于最终结果的准确度具有重要意义。
根据当前上述工作环境对工作质量的要求，以及该工作环境对工作效率的需求度，选择不同的指标值作为绝对目标值或加权计算，在每次调整过程中可以同时设置多个指标值为绝对目标值或根据不同的生产任务进行组合加权计算。
示例性地，对于部分不宜长时间堆积半成品的岗位，应当以半成品堆积量为指标值去进行动态标准的调节，将半成品堆积量作为绝对目标值，进而使人工环节部分在人工岗位下班后的剩余半成品堆积量低于绝对目标值。其中，示例性地，将工作效率进行加权计算，提高工作效率进而提高产能，从而使人工环节的产能足以使作为指标的半成品堆积量低于绝对目标值。
可选的，每个指标可以根据生产计划分别进行权重分配，也可以使用默认值进行默认优化，每个指标之间相互存在关联，调整权重分配可以实现不同的产能结果。
将调整后的上述第三动态平衡效率作为所述动态标准。本申请实施例提供的系统通过分析不同生产环境及岗位要求进行指标值的确定，进而设置绝对目标值，根据不同的需求进行加权计算，并根据多维度生产参数对工作环境进行分析，结合多维度机械参数的分析结果，能够获得个性化的动态标准，符合工作环境、工作岗位及生产目标等多个方面的需求，能够更加针对性和准确地分析判断当前的机械产能是否达与人工产能匹配。
S400：所述物联网控制终端根据所述机械参数集合进行生产，根据所述生产参数集合的变化，判断当前动态平衡状态是否满足所述动态标准；
基于前述的当前主观设置的指标值及之前设置的机械参数集合，控制机械设备进行生产，此时随着机械设备与人工操作环节的同时工作，生产参数集合同步发生变动，在经过主观设置的检查间隔时长后，将当前机械参数集合导入至全局优化模型中进行结果模拟，判断机械的产能结果是否能与人工环节产能匹配。
虽然人工生产环节中可能出现意外情况导致个体效率提高或降低，但相对于总体的人工环节岗位基数，出现影响的情况较低，若出现较大整体性影响时，也可通过主观更改指标值并结合加权计算来进行整体调节，因此，间隔性对当前机械参数集合的优化，可使后续时间内整体生产的生产情况向期望情况靠近。
本申请实施例中，在进行整体生产时，可不进行实际上的生产，而是根据前述的生产参数集合、机械参数集合构建数学模型，模拟进行整体生产的结果，方便对整体产能的预估并节省时间。
S500：若所述动态平衡状态满足所述动态标准，则维持当前所述机械参数集合，若所述动态平衡状态不满足所述动态标准，则基于所述动态标准，对所述机械参数集合进行优化；
若采用当前发生参数集合进行的空气净化满足了上述的净化核验标准的标准，则说明当前的发生参数集合能够满足目标环境内的空气净化需求，则可按照该发生参数集合持续进行空气净化。
若采用当前发生参数集合进行的空气净化未满足上述的净化核验标准的标准，则说明当前的发生参数集合无法满足目标环境内的空气净化需求，需要对发生参数集合进行优化。
如图3所示，本申请实施例提供的方法中的步骤S500包括：
S510：构建全局优化模型，其中，所述全局优化模型内包括多种机械参数集合；
S520：在所述全局优化模型内对当前采用的机械参数集合进行时序递进调整，多次优化迭代，在所述全局优化模型内生成多组优化机械参数集合；
S530：判断所述优化机械参数集合是否符合预设条件，若符合，则将所述优化机械参数集合作为可选优化结果，若不符合，则将继续进行迭代优化；
S540：进行多次迭代优化，直到满足预设优化条件的所述可选优化结果数量达到预设值；
S650：对多组所述可选优化结果进行比对，获得最优机械参数集合。
全局优化模型包括当前进行优化的可行域，以及相关的约束条件，保证优化在该模型内进行。在本申请实施例中，全局优化模型内的优化机械参数集合需满足上述的预设条件，并满足其他的优化条件。
本申请实施例提供的方法中的步骤S510包括：
S511：根据所述动态标准，设置第一优化约束条件；
S512：根据所述第三生产参数，设置第二优化约束条件；
S513：根据所述第二生产参数，对所述多种机械参数集合进行调整；
S514：根据所述第一优化约束条件、第二优化约束条件和调整后的所述机械参数集合，构建获得所述全局优化模型。
具体而言，根据上述的预设条件，该预设条件为根据指标值不同而主观设置的绝对目标值，设置第一优化约束条件，本申请实施例中的优化是对物联网控制终端100的机械参数集合的优化，其包括第一机械参数、第二设备维护参数和第三机械参数，不同的第一机械参数、第二设备维护参数和第三机械参数可组成多种组合，进而组成多种机械参数集合。在该第一优化约束条件下，多种机械参数集合在进行预设周期的生产后，需要保证工作环境内的第三生产参数达到预设条件，且尽可能的最大化第三机械参数，进而使整体生产在达到要求的情况下最大化产能，因此，第一优化约束条件也为最主要的约束条件。
根据工作环境的多维度生产参数，设置第二优化约束条件，在该第二优化约束条件下，优化获得的优化机械参数集合内的第二生产参数超过多维度生产参数的最大限度，例如，人工分装环节中，自动化机械设备产能可高于人工有效产能；人工质检环节中，自动化机械设备产能不得高于人工有效产 能。
在设置满足了上述的第一优化约束条件和第二优化约束条件之后，得到多种满足该两个约束条件的优化机械参数集合。
进一步地，根据与物联网控制终端100同步自动化设备的性能参数，该自动化设备的产能效率不能超过厂家设置的阈值，若超过阈值，则会导致设备的使用寿命受损，且对于第二设备维护参数调整不能超过厂家设置的阈值，若超过阈值，则该自动化设备会因保养维护不及时而加快老化速度，影响其使用寿命。
本申请实施例中，该性能参数要求内包括第二设备维护参数和第三机械参数，按照该性能参数的要求进行生产工作，能够保证该自动化设备内的各模块不会超负荷运行，保证运行寿命。因此，按照该性能参数要求，对当前的多种优化机械参数集合进行调整，进一步设置全局优化模型。
如此，根据第一优化约束条件、第二优化约束条件和调整后的机械参数集合，构建获得全局优化模型。在该全局优化模型，包括多个满足第一优化约束条件、第二优化约束条件以及性能参数要求的优化机械参数集合。
其中，机械参数集合内的第二设备维护参数和第三机械参数在运行周期内并非是恒定的，而可能是根据时间进程变化的，例如，在刚开始使用的期间，其第二设备维护参数间隔较长，同时第三机械参数数值较高，在经过长期的使用后，为了保证其工作效率及使用寿命，第二设备维护参数间隔应当缩短，同时第三机械参数数值会相应的降低。
本申请实施例通过基于多维度的约束条件，构建全局优化模型，在优化机械参数集合的过程中同时满足其他多个维度的要求，能够保证完成绝对目标值的情况下，尽量减小机械维护时间的推移，使机械能够尽可能的定量维护，且在满足上述多种条件的情况下使整体产能最大化。
在构建完成全局优化模型后，在该全局优化模型内进行优化。首先，在全局优化模型内选择当前采用的机械参数集合作为优化基础，在此基础上进行时序递进调整，多次优化迭代，直至该机械参数集合满足所有上述预设条 件，并作为优化的当前解。
然后，基于当前解进行多次迭代优化，直到满足预设优化条件的所述可选优化结果数量达到预设值。
本申请实施例提供的方法中的步骤S530包括：
S531：将所述优化机械参数集合导入至所述全局优化模型中进行计算，获得相对应的优化计算结果；
S532：判断所述第三生产参数变化量是否满足预设结果；
S533：若满足预设结果，则将当前所述优化机械参数集合输入至所述可选优化结果，若不满足，则继续进行迭代优化。
具体地，将优化机械参数集合导入全局优化模型中，根据第三机械参数及剩余工时进行计算，计算后再导入第二机械参数，在性能参数的要求下，对第一设备维护参数进行时序调整，并将调整后的机械参数集合作为当前优化计算结果。
判断该当前优化计算结果是否满足所有的预设条件，若满足，则说明当前优化计算结果可以作为可选优化结果，并将当前优化计算结果所对应的优化机械参数集合加入至可选优化结果中。
若不满足，这说明当前优化计算结果无效，并在此优化计算结果上再次进行时序调整，产生新的优化计算结果，并再次进行判断，直到满足预设条件的可选优化结果数量达到预设值。
其中，在多种可选优化结果中，对比多种可选优化结果中第二设备维护参数与初始值变动最小；对比多种可选优化结果中第三机械参数最大及最小值；对比多种可选优化结果中第一人工参数的调整值，根据主观选择的指标值及加权权重进行选择，从而获得最优机械参数集合。
本申请实施例基于多维度的约束条件，构建全局优化模型，在优化机械参数集合的过程中同时满足其他多个维度的要求，能够快速在多种可选优化结果中获得较优的可选优化结果，并根据主观选择的指标值及加权权重进行选择，达到优化获得全局最优的最优机械参数集合的技术效果。
S700：采用优化后的所述最优机械参数集合对所述物联网控制终端进行调整。
采用优化后获得的最优机械参数集合的第一机械参数、第二设备维护参数和第三机械参数，并通过物联网控制终端100控制自动化设备进行生产及维护。
综上所述，本申请实施例通过采集物联网控制终端的多维度机械参数，获得机械参数集合，再采集工作环境的多维度生产参数，获得生产参数集合，并根据生产参数集合及机械参数集合，设置动态标准，设置完毕后物联网控制终端根据机械参数集合进行生产，并根据生产参数集合的变化，判断当前动态平衡状态是否满足动态标准，若动态平衡状态满足动态标准，则维持当前机械参数集合，若动态平衡状态不满足动态标准，则基于动态标准，对机械参数集合进行优化，并采用优化后的机械参数集合对物联网控制终端进行调整，从而对机械设备的工作状态进行优化。
实施例二
基于与前述实施例中一种物联网设备集中控制管理系统相同的发明构思，如图4和图5所示，本申请提供了其组成结构，其中，所述组成结构包括：
第一采集单元111，用于对生产设备的多维度机械参数进行采集，获得机械参数集合；
第一控制单元112，用于根据优化后的所述最优机械参数集合对生产设备进行调整；
第二采集单元211，用于采集工作环境的多维度生产参数，获得生产参数集合；
第一处理单元212，用于根据所述生产参数集合及机械参数集合，设置动态标准；
第一判断单元213，用于根据所述生产参数的变化，判断当前动态平衡状态是否满足所述动态标准；
第二处理单元214，用于若所述动态平衡状态满足所述动态标准，则维持 当前所述机械参数集合，若所述动态平衡状态不满足所述动态标准，则基于所述动态标准，对所述机械参数集合进行优化。
进一步地，所述系统还包括：
第三采集单元，用于采集生产设备的启停状态，获得第一机械参数；
第四采集单元，用于采集设备在最近一次维护后的工作时间，获得第一设备维护参数；
第五采集单元，用于采集设备在最近一次维护后的工作时间，获得第一设备维护参数；
第六采集单元，用于采集设备的维护周期间隔，获得第二设备维护参数；
第三处理单元，用于将第一设备维护参数及第二设备维护参数作为第二机械参数；
第七采集单元，用于采集设备的产能效率信息，获得第三机械参数；
第四处理单元，用于将第一机械参数、第二机械参数及第三机械参数作为机械参数集合。
进一步地，所述系统还包括：
第八采集单元，用于采集工作人员的到岗信息，获得第一生产参数；
第九采集单元，用于采集工作人员的单周期产能时间曲线信息，获得第一人工参数；
第十采集单元，用于采集工作人员的单周期工时信息，获得第二人工参数；
第五处理单元，用于将第一人工参数及第二人工参数作为第二生产参数；
第十一采集单元，用于采集工位的半成品堆积量，获得第三生产参数；
第六处理单元，用于将所述第一生产参数、第二生产参数及第三生产参数作为所述生产参数集合。
进一步地，所述系统还包括：
第六处理单元，用于根据所述第一生产参数及第三机械参数,获得第一动 态平衡效率；
第七处理单元，用于采用所述第二生产参数对所述第一动态平衡效率进行调整，获得第二动态平衡效率；
第八处理单元，用于采用所述第三生产参数对第二动态平衡效率进行调整，获得第三动态平衡效率；
第九处理单元，用于根据所述第一机械参数和第二机械参数，获得机械调整节点；
第十处理单元，用于采用所述机械调整节点对第三动态平衡效率进行调整；
第十一处理单元，用于将调整后的所述第三动态平衡效率作为所述动态标准。
第一构建单元，用于根据第一人工参数构建有效产能衰减模型，其中所述有效产能衰减模型包括多个时间轴同步的对象模型；
第十二处理单元，用于将第二人工参数导入至所述有效产能衰减模型中，根据调整公式对所述第一生产参数的权重系数进行动态调整，分别计算后得到多个剩余产能结果；
第十三处理单元，用于将得到的多个所述剩余产能结果进行合并计算，获得第二动态平衡效率；
其中，所述调整公式为：
Figure PCTCN2022110419-appb-000002
其中，I 2为剩余产能结果，f(x)为第一人工参数，X为当前时间，b为第二人工参数，y为周期剩余工时，C 2为第三机械参数。
进一步地，所述系统还包括：
第十四处理单元，用于在所述全局优化模型内对当前采用的机械参数集 合进行时序递进调整，多次优化迭代，在所述全局优化模型内生成多组优化机械参数集合；
第一判断单元，用于判断所述优化机械参数集合是否符合预设条件，若符合，则将所述优化机械参数集合作为可选优化结果，若不符合，则将继续进行迭代优化；
第二判断单元，用于进行多次迭代优化，直到满足预设优化条件的所述可选优化结果数量达到预设值；
第十五处理单元，用于对多组所述可选优化结果进行比对，获得最优机械参数集合。
进一步地，所述系统还包括：
第十六处理单元，用于根据所述动态标准，设置第一优化约束条件；
第十七处理单元，用于根据所述第三生产参数，设置第二优化约束条件；
第十八处理单元，用于根据所述第二生产参数，对所述多种机械参数集合进行调整。
实施例三
基于与前述实施例中一种纳米水离子的发生参数调节方法相同的发明构思，本申请还提供了一种计算机可读存储介质，所述存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如实施例一内的方法。
示例性电子设备
下面参考图6来描述本申请的电子设备，
基于与前述实施例中一种纳米水离子的发生参数调节方法相同的发明构思，本申请还提供了一种纳米水离子的发生参数调节系统，包括：处理器，所述处理器与存储器耦合，所述存储器用于存储程序，当所述程序被所述处理器执行时，使得系统以执行实施例一所述方法的步骤。
该电子设备300包括：处理器302、通信接口303、存储器301。可选的，电子设备300还可以包括总线架构304。其中，通信接口303、处理器302以 及存储器301可以通过总线架构304相互连接；总线架构304可以是外设部件互连标(peripheral component interconnect，简称PCI)总线或扩展工业标准结构(extended industry Standard architecture，简称EISA)总线等。所述总线架构304可以分为地址总线、数据总线、控制总线等。为便于表示，图6中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
处理器302可以是一个CPU，微处理器，ASIC，或一个或多个用于控制本申请方案程序执行的集成电路。
通信接口303，使用任何收发器一类的装置，用于与其他设备或通信网络通信，如以太网，无线接入网(radio access network，RAN),无线局域网(wireless local area networks，WLAN)，有线接入网等。
存储器301可以是ROM或可存储静态信息和指令的其他类型的静态存储设备，RAM或者可存储信息和指令的其他类型的动态存储设备，也可以是电可擦可编程只读存储器(electrically erasable Programmable read-only memory，EEPROM)、只读光盘(compact discread-only memory，CD-ROM)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质或者其他磁存储设备、或者能够用于携带或存储具有指令或数据结构形式的期望的程序代码并能够由计算机存取的任何其他介质，但不限于此。存储器可以是独立存在，通过总线架构304与处理器相连接。存储器也可以和处理器集成在一起。
其中，存储器301用于存储执行本申请方案的计算机执行指令，并由处理器302来控制执行。处理器302用于执行存储器301中存储的计算机执行指令，从而实现本申请上述实施例提供的一种纳米水离子的发生参数调节方法。
本领域普通技术人员可以理解：本申请中涉及的第一、第二等各种数字编号仅为描述方便进行的区分，并不用来限制本申请的范围，也不表示先后顺序。“和/或”，描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情 况。字符“/”一般表示前后关联对象是一种“或”的关系。“至少一个”是指一个或者多个。至少两个是指两个或者多个。“至少一个”、“任意一个”或其类似表达，是指的这些项中的任意组合，包括单项(个)或复数项(个)的任意组合。例如，a,b,或c中的至少一项(个、种)，可以表示：a,b,c,a-b,a-c,b-c,或a-b-c，其中a,b,c可以是单个，也可以是多个。
在上述实施例中，可以全部或部分地通过软件、硬件、固件或者其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指
令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线(DSL))或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包括一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质，(例如，软盘、硬盘、磁带)、光介质(例如，DVD)、或者半导体介质(例如固态硬盘(Solid State Disk，SSD))等。
本申请中所描述的各种说明性的逻辑单元和电路可以通过通用处理器，数字信号处理器，专用集成电路(ASIC)，现场可编程门阵列(FPGA)或其它可编程逻辑装置，离散门或晶体管逻辑，离散硬件部件，或上述任何组合的设计来实现或操作所描述的功能。通用处理器可以为微处理器，可选地，该通用处理器也可以为任何传统的处理器、控制器、微控制器或状态机。处理器也可以通过计算装置的组合来实现，例如数字信号处理器和微处理器，多个微处理器，一个或多个微处理器联合一个数字信号处理器核，或任何其它类 似的配置来实现。
本申请中所描述的方法或算法的步骤可以直接嵌入硬件、处理器执行的软件单元、或者这两者的结合。软件单元可以存储于RAM存储器、闪存、ROM存储器、EPROM存储器、EEPROM存储器、寄存器、硬盘、可移动磁盘、CD-ROM或本领域中其它任意形式的存储媒介中。示例性地，存储媒介可以与处理器连接，以使得处理器可以从存储媒介中读取信息，并可以向存储媒介存写信息。可选地，存储媒介还可以集成到处理器中。处理器和存储媒介可以设置于ASIC中，ASIC可以设置于终端中。可选地，处理器和存储媒介也可以设置于终端中的不同的部件中。这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
尽管结合具体特征及其实施例对本申请进行了描述，显而易见的，在不脱离本申请的精神和范围的情况下，可对其进行各种修改和组合。相应地，本说明书和附图仅仅是本申请的示例性说明，且视为已覆盖本申请范围内的任意和所有修改、变化、组合或等同物。显然，本领域的技术人员可以对本申请进行各种改动和变型而不脱离本申请的范围。这样，倘若本申请的这些修改和变型属于本申请及其等同技术的范围之内，则本申请意图包括这些改动和变型在内。

Claims (10)

  1. 一种物联网设备集中控制管理系统，包括物联网控制终端及智能调控端，其特征在于，所述物联网控制终端包括：
    控制模块，内部的第一采集单元对生产设备的多维度机械参数进行采集，获得机械参数集合，内部的第一控制单元根据优化后的所述最优机械参数集合对生产设备进行调整；
    信号模块，用于将所述控制模块获得的所述机械参数集合传输至所述智能调控端，并将所述智能调控端传输的优化后的所述机械参数集合传输至所述控制模块；
    所述智能调控端包括：
    第二采集单元，用于采集工作环境的多维度生产参数，获得生产参数集合；
    第一处理单元，用于根据所述生产参数集合及机械参数集合，设置动态标准；
    第一判断单元，用于根据所述生产参数的变化，判断当前动态平衡状态是否满足所述动态标准；
    第二处理单元，用于若所述动态平衡状态满足所述动态标准，则维持当前所述机械参数集合，若所述动态平衡状态不满足所述动态标准，则基于所述动态标准，对所述机械参数集合进行优化。
  2. 根据权利要求1所述的一种物联网设备集中控制管理系统，其特征在于，所述第一采集单元对生产设备的多维度机械参数进行采集，包括：
    采集生产设备的启停状态，获得第一机械参数；
    采集设备在最近一次维护后的工作时间，获得第一设备维护参数；
    采集设备的维护周期间隔，获得第二设备维护参数；
    根据第一设备维护参数及第二设备维护参数，获得第二机械参数；
    采集设备的产能效率信息，获得第三机械参数；
    将所述第一机械参数、第二机械参数及第三机械参数作为所述机械参数集合。
  3. 根据权利要求1所述的一种物联网设备集中控制管理系统，其特征在于，所述采集工作环境的多维度生产参数，包括：
    采集工作人员的到岗信息，获得第一生产参数；
    采集工作人员的单周期产能时间曲线信息，获得第一人工参数；
    采集工作人员的单周期工时信息，获得第二人工参数；
    根据第一人工参数及第二人工参数，获得第二生产参数；
    采集工位的半成品堆积量，获得第三生产参数；
    将所述第一生产参数、第二生产参数及第三生产参数作为所述生产参数集合。
  4. 根据权利要求1所述的一种物联网设备集中控制管理系统，其特征在于，所述根据所述生产参数集合及机械参数集合，设置动态标准，包括：
    根据所述第一生产参数及第三机械参数,获得第一动态平衡效率；
    采用所述第二生产参数对所述第一动态平衡效率进行调整，获得第二动态平衡效率；
    采用所述第三生产参数对第二动态平衡效率进行调整，获得第三动态平衡效率；
    根据所述第一机械参数和第二机械参数，获得机械调整节点；
    采用所述机械调整节点对第三动态平衡效率进行调整；
    将调整后的所述第三动态平衡效率作为所述动态标准。
  5. 根据权利要求4所述的一种物联网设备集中控制管理系统，其特征在于，所述采用所述第二生产参数对所述第一动态平衡效率进行调整，获得第二动态平衡效率，包括：
    根据第一人工参数构建有效产能衰减模型，其中所述有效产能衰减模型 包括多个时间轴同步的对象模型；
    将第二人工参数导入至所述有效产能衰减模型中，根据调整公式对所述第一生产参数的权重系数进行动态调整，分别计算后得到多个剩余产能结果；
    将得到的多个所述剩余产能结果进行合并计算，获得第二动态平衡效率；
    其中，所述调整公式为：
    Figure PCTCN2022110419-appb-100001
    其中，I 2为剩余产能结果，f(x)为第一人工参数，X为当前时间，b为第二人工参数，y为周期剩余工时，C 2为第三机械参数。
  6. 根据权利要求1所述的一种物联网设备集中控制管理系统，其特征在于，所述基于所述动态标准，对所述机械参数集合进行优化，包括：
    构建全局优化模型，其中，所述全局优化模型内包括多种机械参数集合；
    在所述全局优化模型内对当前采用的机械参数集合进行时序递进调整，多次优化迭代，在所述全局优化模型内生成多组优化机械参数集合；
    判断所述优化机械参数集合是否符合预设条件，若符合，则将所述优化机械参数集合作为可选优化结果，若不符合，则将继续进行迭代优化；
    进行多次迭代优化，直到满足预设优化条件的所述可选优化结果数量达到预设值；
    对多组所述可选优化结果进行比对，获得最优机械参数集合。
  7. 根据权利要求6所述的一种物联网设备集中控制管理系统，其特征在于，所述构建全局优化模型，包括：
    根据所述动态标准，设置第一优化约束条件；
    根据所述第三生产参数，设置第二优化约束条件；
    根据所述第二生产参数，对所述多种机械参数集合进行调整；
    根据所述第一优化约束条件、第二优化约束条件和调整后的所述机械参 数集合，构建获得所述全局优化模型。
  8. 根据权利要求6所述的一种物联网设备集中控制管理系统，其特征在于，所述判断所述优化机械参数集合是否符合预设条件，包括：
    将所述优化机械参数集合导入至所述全局优化模型中进行计算，获得相对应的优化计算结果，其中，所述优化计算结果包含将所述优化机械参数集合作为运行参数下的机械调整节点偏移值及第三生产参数变化量；
    判断所述第三生产参数变化量是否满足预设结果，若满足预设结果，则将当前所述优化机械参数集合输入至所述可选优化结果，若不满足，则继续进行迭代优化。
  9. 根据权利要求1所述的一种物联网设备集中控制设备，其特征在于，包括：处理器，所述处理器与存储器耦合，所述存储器用于存储程序，当所述程序被所述处理器执行时，使系统以执行如权利要求1至8任一项所述系统的步骤。
  10. 根据权利要求1所述的一种计算机可读存储介质，其特征在于：所述存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至8任一项所述系统的步骤。
PCT/CN2022/110419 2022-06-17 2022-08-05 一种物联网设备集中控制管理系统 WO2023240770A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2023509389A JP2024528350A (ja) 2022-06-17 2022-08-05 ＩｏＴ設備集中制御管理システム

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210688144.5 2022-06-17
CN202210688144.5A CN115016415A (zh) 2022-06-17 2022-06-17 一种物联网设备集中控制管理系统

Publications (1)

Publication Number Publication Date
WO2023240770A1 true WO2023240770A1 (zh) 2023-12-21

Family

ID=83075960

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/110419 WO2023240770A1 (zh) 2022-06-17 2022-08-05 一种物联网设备集中控制管理系统

Country Status (3)

Country Link
JP (1) JP2024528350A (zh)
CN (1) CN115016415A (zh)
WO (1) WO2023240770A1 (zh)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111737827A (zh) * 2020-07-29 2020-10-02 上海上飞飞机装备制造有限公司 立体集群式飞机壁板自动化装配生产线及其规划控制方法
CN113419585A (zh) * 2021-07-22 2021-09-21 江阴市欧莱特彩印有限公司 印刷车间温湿度动态调整生产工艺参数的方法、系统以及设备
US20210349447A1 (en) * 2018-09-18 2021-11-11 Abb Schweiz Ag A method of controlling data transfer in a manufacturing plant and a system thereof
CN114488988A (zh) * 2022-04-14 2022-05-13 成都秦川物联网科技股份有限公司 用于生产线平衡率调控的工业物联网及控制方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210349447A1 (en) * 2018-09-18 2021-11-11 Abb Schweiz Ag A method of controlling data transfer in a manufacturing plant and a system thereof
CN111737827A (zh) * 2020-07-29 2020-10-02 上海上飞飞机装备制造有限公司 立体集群式飞机壁板自动化装配生产线及其规划控制方法
CN113419585A (zh) * 2021-07-22 2021-09-21 江阴市欧莱特彩印有限公司 印刷车间温湿度动态调整生产工艺参数的方法、系统以及设备
CN114488988A (zh) * 2022-04-14 2022-05-13 成都秦川物联网科技股份有限公司 用于生产线平衡率调控的工业物联网及控制方法

Also Published As

Publication number Publication date
CN115016415A (zh) 2022-09-06
JP2024528350A (ja) 2024-07-30

Similar Documents

Publication Publication Date Title
JP5962190B2 (ja) 短期電力負荷を予測する方法及び装置
CN114298863A (zh) 一种智能抄表终端的数据采集方法及系统
CN117176560B (zh) 一种基于物联网的监测设备监管系统及其方法
WO2021007845A1 (zh) 一种氧化铝生产指标的云-边协同预报系统及方法
CN114037673B (zh) 一种基于机器视觉的硬件衔接接口监测方法及系统
CN114155072A (zh) 基于大数据分析的财务预测模型构建方法及系统
WO2020252666A1 (zh) 工业物联网的边缘计算设备、方法和计算机可读存储介质
WO2021102838A1 (zh) 确定采集频率的方法、装置、计算设备和存储介质
CN118211943B (zh) 一种注塑产品生产管理方法及系统
CN117450635A (zh) 一种基于大数据分析的新风系统控制方法
CN117687884A (zh) 一种电网调度自动化主站运维操作票智能优化方法及系统
CN117391591B (zh) 一种基于数字孪生的智能仓储优化系统
WO2023240770A1 (zh) 一种物联网设备集中控制管理系统
CN113559630A (zh) 一种用于除尘设备的自动清灰方法及系统
CN113225994A (zh) 一种面向数据中心的智能空调控制方法
CN116167604A (zh) 一种基于生产全流程跟踪的电力产品生产智能管理系统
Lee et al. Development of edge-based deep learning prediction model for defect prediction in manufacturing process
CN114544068A (zh) 一种基于电子单元的管线监测方法及系统
CN117474710B (zh) 中空玻璃全过程信息化管理系统
CN115086190B (zh) 数据处理方法及装置、计算机可存储介质
CN117092977B (zh) 工业数字孪生的数据空间构建方法、系统、设备及介质
CN115307482B (zh) 一种换热器清洗策略的确定方法和装置
CN118300101B (zh) 用电负荷预测方法、设备及存储介质
TW201915845A (zh) 基於機台多級別穩態生產速率之派工方法及系統
TWI826087B (zh) 派工系統和派工方法

Legal Events

Date Code Title Description
ENP Entry into the national phase

Ref document number: 2023509389

Country of ref document: JP

Kind code of ref document: A

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

Ref document number: 22946443

Country of ref document: EP

Kind code of ref document: A1