IoT equipment centralized control management system

Abstract

The present invention discloses a centralized control management system for IoT devices, in which the IoT control terminal: Includes: a control module, the first collection unit inside collects multi-dimensional machine parameters of the production equipment; The first internal control unit obtains the optimal machine parameters after optimization. A set of machine parameters obtained by a control module that adjusts the production equipment based on the meter set The intelligent control terminal transmits the optimized machine performance to the intelligent control terminal. A signal module for transmitting a set of parameters to a control module, an intelligent control The terminal collects multi-dimensional production parameters of the working environment and performs a first calculation to obtain a set of production parameters. 2. Setting dynamic criteria based on collection unit, production parameter set and machine parameter set The first processing unit is configured to:

Description

[Technical field]

[0001] The present invention relates to the field of IoT control, specifically to an IoT equipment centralized control management system, a control device and a computer-readable storage medium. [Background technology]

[0002] The Internet of Things (IoT) is a combination of various information sensors, radio frequency identification technology, and Through various devices and technologies such as global positioning systems, infrared sensors, and laser scanners, It collects all the objects and processes that need to be monitored, connected, and interacted with in real time. It refers to collecting various necessary information such as sound, light, heat, electricity, mechanics, chemistry, biology, and position. Through various possible network connections, ubiquitous connectivity between objects and between objects and people is achieved. Realize intelligent perception, identification, and management of goods and processes. IoT is an information medium based on the Internet and traditional telecommunications networks. This allows all normally-physical objects that can be addressed independently to be part of an interconnected network. A c can be formed.

[0003] Equipment remote control is a combined application of IT systems and automation control. Expose the signal points in the controller through the self-control system. And the host communication This principle allows the switching signal of the equipment to be controlled, and even remote control can be realized.

[0004] It is convenient for the manager to adjust the equipment based on the overall operating status parameters. In general, classification and placement are based on a pre-assembly system, and manual selection and adjustment is not required. In addition, the existing pre-assembly system has poor production capacity adjustment accuracy. To achieve balance, coarse classification adjustments or direct stopping operations are often the only options available. Accurate automatic adjustment of row results is difficult. Summary of the Invention

[0005] This application relies on artificial subjective manipulation in existing IoT control technology, which has poor adjustment effects. Provides an IoT equipment centralized control management system to solve technical problems. In view of the above problems, the present application provides an IoT equipment centralized control and management system.

[0006] A first aspect of the present application provides an IoT equipment centralized control management system, the system comprising: Applied to centralized control device, the system includes IoT control terminal and intelligent control terminal The IoT control terminal includes a control module, and the first collection unit therein collects the multiple data of the production equipment. The original machine parameters are collected to obtain a set of machine parameters, and the first internal control unit The machine obtained by the control module adjusts the production equipment based on the optimized machine parameter set. The parameter set is transmitted to the intelligent control terminal, and the parameter set is transmitted to the intelligent control terminal. A signal module for transmitting the optimized machine parameter set to the control module, The agent control terminal collects multi-dimensional production parameters of the working environment and outputs the production parameter set. A second collection unit for obtaining a set of production parameters and a set of machine parameters. The first processing unit for setting the objective criteria and the current a first judgment unit for judging whether the dynamic equilibrium state satisfies a dynamic criterion; If the state satisfies the dynamic criterion, the current set of machine parameters is maintained and the dynamic equilibrium state is dynamically If the criteria are not met, a second step is performed to optimize the machine parameter set based on the dynamic criteria. and a processing unit.

[0007] A second aspect of the present application is a processor for storing a program, and a connection between the processor and a memory. In this case, when the program is executed by the processor, the system is provided with a first system configuration. A memory for executing a function such as a system, a memory for storing a program, We provide IoT equipment centralized control devices, including:

[0008] A third aspect of the present application provides an IoT-based remote intelligent integrated control device, comprising: A computer program is stored in the body and executed by a processor. When executed, it achieves the functionality of the system of the first embodiment.

[0009] One or more technical scenarios provided herein include at least the following technical scenarios: Having an effect or advantage. The embodiment of the present application collects multi-dimensional machine parameters of a network control terminal of things, and The parameter set is obtained, and multi-dimensional production parameters of the working environment are collected, and the production parameters A set is obtained, and a dynamic criterion is calculated based on the production parameter set and the machine parameter set. After the installation is completed, the network control terminal of the object is produced based on the machine parameter set. Then, based on the change in the production parameter set, the current dynamic equilibrium state is set to the dynamic criterion. If the dynamic equilibrium state meets the dynamic criteria, the current machine The parameter set is maintained, and if the dynamic equilibrium state does not satisfy the dynamic criteria, the dynamic criteria are used to determine the dynamic equilibrium state. Based on this, the machine parameter set is optimized, and the optimized machine parameter set is used to perform IoT Adjust the control terminal, thereby optimizing the operating conditions of the machinery and equipment. The embodiments of the present application collect multi-dimensional machine parameters and multi-dimensional production parameters to , control the working status of the entire production line, and respond to production line equipment failures or maintenance Accumulation of already produced semi-finished products in the automated part of the production line, which should not be stopped due to the cause of Analyze the current situation and the available labor capacity of the artificial linkages and optimize the whole A unified model is constructed to calculate the maintenance nodes and maintenance redundancy times of the machinery and equipment. By planning the production capacity of the automated pipeline section at any time, the production capacity of the automated pipeline section can be automatically adjusted. It is possible to change the artificial energy of the pipeline and the preset production capacity change situation. can be satisfied. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a system flow diagram of the IoT equipment centralized control management system provided by the present application. [Diagram 2] 1 is a flowchart for acquiring dynamic references in an IoT equipment centralized control management system provided by the present application. [Diagram 3] Flowchart for optimizing machine parameter sets in the IoT equipment centralized control management system provided by this application [Figure 4] 1 is a schematic configuration diagram of an IoT equipment centralized control device provided by the present application. [Diagram 5] FIG. 1 is a configuration diagram of an IoT equipment centralized control management system provided by the present application. [Figure 6] FIG. 1 is a block diagram of an exemplary electronic device according to the present application.

[0011] [Explanation of symbols] 100 IoT control terminals 110 Control Module 111 Collection Unit 1 112 First control unit 120 Communication Interface 200 Intelligent Control Terminal 210 Optimization Module 211 Collection Unit No. 2 212 First Processing Unit 213 First Judging Unit 214 Second Processing Unit 300 Electronic equipment 301 Memory 302 Processor 303 Communication Interface 304 Bus Architecture DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Specific embodiments This application relies on artificial subjective manipulation in existing IoT control technology, which has poor adjustment effects. Provides an IoT equipment centralized control management system to solve technical problems. The embodiment of the present application collects multi-dimensional machine parameters of a network control terminal of things, and The parameter set is obtained, and multi-dimensional production parameters of the working environment are collected, and the production parameters A set is obtained, and a dynamic criterion is calculated based on the production parameter set and the machine parameter set. After the installation is completed, the network control terminal of the object is produced based on the machine parameter set. Then, based on the change in the production parameter set, the current dynamic equilibrium state is set to the dynamic criterion. If the dynamic equilibrium state meets the dynamic criteria, the current machine The parameter set is maintained, and if the dynamic equilibrium state does not satisfy the dynamic criteria, the dynamic criteria are used to determine the dynamic equilibrium state. The machine parameter set is optimized based on the above, and the IoT control terminal is controlled using the optimized machine parameter set. The output of the machine is adjusted to optimize the operating conditions of the machine. After describing the basic principle of the present application, the following will explain the technical solution in the present application clearly and completely with reference to the accompanying drawings. However, obviously, the described embodiments are not all the embodiments of the present application, and the present application These are just some of the embodiments.

[0013] Example 1 As shown in FIG. 1, the present application relates to an IoT control terminal 100 and an intelligent control terminal 200. We provide an IoT equipment centralized control management system including the following. The system flow is as follows: S100: Collect multi-dimensional machine parameters of the IoT control terminal 100 and generate a machine parameter set. to obtain. FIG. 4 shows a schematic diagram of a possible configuration of the mono network control terminal 100 in an embodiment of the present application. The mono network control terminal 100 is a mono network control terminal having any configuration in the prior art. It may be a network control terminal, or a single mono network control terminal, Mono network control terminal installed in equipment such as in-transmission band, production processing, distribution and transport regulation The end can be the pipeline machine automation part of the production efficiency control, pipeline machine It is used to match the production capacity of the automated part with the production capacity of the manual part. In the embodiment of the present application, as shown in FIG. 4, the IoT control terminal 100 includes a communication interface 120 and the control module 110. Here, the communication interface 120 receives The collected signal is transmitted to the IoT control terminal 100, and the IoT control terminal 100 transmits the collected parameters to the and for transmitting to another matching communication interface 120 in a data signaling manner. The matching method between the communication interfaces 120 may be a wireless data connection; It may be a wired interface connection or any other connection method. Between the communication interface 120, the equipment maintenance information collected by the connected IoT control terminal 100 is The remaining maintenance time and the current production rate parameters are connected to the optimization module 210. The global optimization module 210 then transfers the global optimization result to the communication interface 120. The optimized machine parameter set is then optimized based on the optimized model, and the optimized machine parameter set is then connected to the control module. The control module 110 then transmits the parameters to the device that it controls. Meter adjustment is performed. The control module 110 is either directly integrated into the equipment circuit or connected to the circuit via a data band. After obtaining the equipment permit, the equipment's mechanical parameters can be read directly from the equipment processor and the All machine parameters are transferred to the communication interface 120, and the communication interface 120 When receiving the optimized machine parameter set from the equipment processor, the optimized machine parameter set is sent to the equipment processor. This writes the original execution criteria in the equipment processor and the equipment optimizes the machine performance. It operates based on a set of parameters. Optionally, the above mentioned machine parameters can be used to monitor the current power-on status of the equipment, the current production efficiency and The next maintenance interval is included. Therefore, the current Obtain the power-on status, current production efficiency and next maintenance interval, and set machine parameters You can configure the machine parameters to know the effective production capacity and the actual production volume of the machine. Analyze whether the production capacity of the equipment under the control of the data set can match the production capacity of the artificial production stage. It is possible. S200: Collect multi-dimensional production parameters of the working environment to obtain a production parameter set. In an embodiment of the present application, the target environment is an artificial part of the current pipeline, For example, the target environment may be manual assembly, manual quality inspection, manual assembly, etc. It can be in any environment. In the embodiment of the present application, the above-mentioned multi-dimensional production parameters are mainly the number of guards, the life of the guards, This includes the production capacity time curve, watch time and the current stock of semi-finished products in the workshop. During the manual work period, workers feel fatigued as the continuous work hours increase, and their concentration also decreases. This leads to the inability to maintain the previous work efficiency, forcing the company to maintain high efficiency. This leads to an increase in the defective rate, and in order to ensure the product quality rate, the work efficiency of the manual stage is This work should be adjusted based on the effective work efficiency of the pipeline. As part of the inspection, the quality inspection products are small packaged daily necessities that can be observed with the naked eye, and the small packaged When everyday items pass through the pipeline, quality inspectors must keep both eyes on the product and move their hands quickly. As the work day progresses, the quality inspectors' eyes become fatigued and their concentration decreases. It is no longer possible to continuously inspect valid pipeline products, and defective products are more likely to leak out. In this case, the speed of the pipeline is reduced, and the inspection speed of the pipeline is adjusted to the current speed of the inspector. This will ensure the effectiveness of the work by conforming to the working conditions of the customer. Objective: To analyze the multi-dimensional production parameters of the work environment to improve the effective work efficiency of the work environment. This can be used as a basis for further analysis and judgment of remaining effective production capacity. can be done.

[0014] The flow S200 in the system provided by the embodiment of the present invention includes: S210: Collect work information of workers and obtain first production parameters; S220: Collect information on the single-cycle production capacity time curve of the worker and obtain a first artificial parameter. R, S230: Collecting single-cycle work time information of workers and obtaining a second artificial parameter; S240: A second production parameter is calculated based on the first artificial parameter and the second artificial parameter. to obtain S250: Collect the semi-finished product stack amount of the station and obtain a third production parameter; S260: The first production parameter, the second production parameter, and the third production parameter are set as production parameters. The meter set is as follows. Specifically, the number of furloughed employees in the current production environment, the production capacity time curve of furloughed employees, Collect and measure the time and amount of semi-finished products in the station and the current production environment. Let the production parameter set be: For example, the error rate of the worker during his / her tenure is periodically collected and the highest and lowest values ​​are removed. The remaining values ​​are averaged, and the errors are distributed based on working hours, and then the errors are calculated based on the worker's working hours. Obtain the information of the production capacity time curve, and adjust it from the group point according to the subsequent error, and the artificial pipe If part of the line employs multi-person collaborative operations, all the people at this stage are considered as one whole. Thus, an entire single cycle capacity time curve measurement can be made. The above first, second and third production parameters are The set of production parameters in the production environment is defined as the surplus effective production capacity in the production environment. The embodiment of the present application optimizes the contamination level of the machine parameter set. By collecting multi-dimensional production parameters of the working environment, which can serve as a data base for This can improve the accuracy of adjusting the optimized machine parameter set.

[0015] S300: Set dynamic criteria based on the production parameter set and the machine parameter set . Based on the surplus effective production capacity of the working environment reflected in this production parameter set and the amount of semi-finished products piled up, Based on this, a corresponding dynamic standard is set, and the smaller this excess effective production capacity is, the more it is reflected. The greater the product pile volume, the corresponding dynamic criteria will be correspondingly lower. In the production stage, the number of man-hours per day is limited, and the amount of work that can be done within that time is also limited. However, since the machinery can be produced without interruption, the semi-finished products can be produced during the manual work hours. The amount of product pile-up should be reduced as much as possible. This dynamic criterion is calculated based on the current available spare capacity of the work environment and the amount of semi-finished products stacked. ,Since the machine parameter set is combined as a reference, the ,obtained dynamic criteria are smarter. This allows for a better adaptation to daily production targets and current production conditions.

[0016] As shown in FIG. 2, the flow S300 in the system provided by the embodiment of the present application teeth, S310: A first dynamic equilibrium efficiency is calculated based on the first production parameter and the third machine parameter. obtain, S320: Adjust the first dynamic equilibrium efficiency using the second production parameter, and set the second dynamic equilibrium efficiency to acquire, S330: Adjust the second dynamic equilibrium efficiency using the third production parameter, and set the third dynamic equilibrium efficiency to acquire, S340: Obtaining a machine adjustment node based on a first machine parameter and a second machine parameter. R, S350: Adjust the third dynamic balance efficiency using a machine adjustment node; S360: The adjusted third dynamic equilibrium efficiency is taken as the dynamic standard. Specifically, different working environments, workplaces, and work efficiency have different impacts on production. Different products have different storage requirements, e.g. bagged bread. In the food processing industry, the artificial stage is often present in the stages of quality inspection and packaging. The quality inspection stage requires high concentration of work, and any mistake in the work will have a big impact on the whole product. This allows for lower dynamic standards, further reducing the intensity of the work and ensuring the effectiveness of the work. However, the degree of concentration is low, and the error rate of work has little impact on the product, and it is necessary to It can improve the dynamic standard and improve work efficiency, but some semi-finished products are piled up for a long time. For workplaces where this is not appropriate, the semi-finished products must be promptly disposed of and the semi-finished products must not be altered, contaminated or expired. To avoid this, the dynamic standard should be adjusted using the amount of accumulated semi-finished products as an index value. Therefore, in different working environments, the first dynamic balance efficiency, the second dynamic balance efficiency, and the third dynamic balance efficiency are When adjusting, different index values ​​are selected and weighted calculations are performed. The flow S320 in the system provided by the embodiment of the present invention includes the following steps: S321: Construct an effective production capacity decay model based on the first artificial parameter, and The force decay model includes multiple time-synchronized target models, S322: A second artificial parameter is introduced into the effective production capacity decay model, and the second 1 Dynamically adjust the weight coefficients of production parameters and calculate multiple excess production capacities Get results. S323: The obtained multiple excess production capacity results are consolidated to obtain a second dynamic equilibrium efficiency. Specifically, the first artificial parameter is combined with the average work efficiency, and then the long-term goal is roughly calculated. This can be used as a reference for long-term goals and as a basis for building an effective production capacity decay model. Average work efficiency does not have the precision to calculate accurately and can only be used as a rough estimate of long-term goals. Therefore, the second artificial parameter is introduced into the effective production capacity decay model as a weighting factor. Then, the weight coefficient of the first production parameter is dynamically adjusted based on the adjustment formula. do. The adjustment formula is as follows: TIFF0007618126000001.tif62147. Specifically, f(x) is the first artificial parameter, which varies depending on the working environment. Specifically, it may be a constant, or it may be a periodic statistical function of the worker's tenure error rate. It calculates the number of values, removes the highest and lowest values, and then takes the average of the remaining values ​​to eliminate mistakes. The worker's single-cycle production capacity time curve information is obtained based on the worker's work hours, and As part of this artificial pipeline, multiple people collaborate to adjust the image based on the subsequent miss-points. When adopting this operation, all the personnel in this loop are regarded as a whole, and the entire single-cycle production It is possible to measure the capacity time curve, and the specific numerical values ​​change as the value changes, allowing detailed This is important for the accuracy of the final results. The current requirements for the work quality of the above-mentioned work environment and the work efficiency of that work environment Based on the demand, different indicator values ​​can be selected as absolute targets or weighted calculations to facilitate adjustments. You can set multiple indicators as absolute targets at the same time, or organize them according to different production tasks. It is possible to perform combined weighting calculations. For example, in some workplaces where semi-finished products should not be piled up for long periods of time, The dynamic standard is adjusted using the index value, the amount of semi-finished product piled up is set as the absolute target value, and the artificial stage The amount of remaining semi-finished products after leaving some of the labor-intensive work sites must be lower than the absolute target value. As an example, we will use weighted calculations to improve work efficiency and further increase production capacity. Therefore, the amount of semi-finished products, which is an indicator of the production capacity of artificial parts, is lower than the absolute target value. is sufficient. Optionally, each indicator can be weighted based on the production plan. You can also use the default values ​​to perform default optimization, and there is a correlation between each indicator. ,By adjusting the weight distribution, different production capacity results can be ,achieved. The third dynamic equilibrium efficiency after adjustment is the dynamic standard. By analyzing the production environment and workplace requirements, the index value is determined, and the absolute target value is , and perform weighting calculations based on different demands, and then perform multi-dimensional production parameter calculations. Based on the analysis, the working environment is analyzed, and the analysis results of multi-dimensional machine parameters are combined to create personalized dynamics. It can obtain the standard of condition, meet the needs of various aspects such as workplace and production targets, and can be used with the current machinery It is now possible to more precisely and accurately analyze and judge whether production capacity matches artificial production capacity. Cut.

[0017] S400: The IoT control terminal performs production based on the machine parameter set, and the production parameter Based on the change in the population, it is determined whether the current dynamic equilibrium state satisfies the dynamic criterion. Based on the currently subjectively set index value and the previously set machine parameter set, The machine is controlled to produce the product. At this time, the machine and the manual operation are performed simultaneously. The production parameter set varies synchronously, and after a subjectively set inspection interval, the current The machine parameter set is introduced into the global optimization model and the results are simulated. Determine whether the machine capacity results match the manpower capacity. During the artificial production stage, unexpected situations may occur that may lead to improvements or decreases in individual efficiency. However, the situation where the impact occurs is low for the total number of man-made stages. If physical effects occur, subjectively change the index value and combine the weighting calculation to obtain an overall Adjustments can also be made so that the spacing is optimized for the current set of machine parameters. Therefore, the production status of the entire production in the subsequent time can be brought closer to the desired status. In the present embodiment, when performing total production, the above-mentioned production parameters are used instead of performing actual production. Based on the data set and the machine parameter set, a mathematical model was constructed and the results of the overall production were It can be simulated, which is convenient for estimating the overall production capacity and saving time.

[0018] S500: If the dynamic equilibrium state satisfies the dynamic criteria, maintain the current machine parameter set; If the dynamic equilibrium state does not satisfy the dynamic criterion, the machine parameter set is adjusted based on the dynamic criterion. Optimize. Air purification using the current set of generated parameters meets the criteria of the purification test standard mentioned above. For example, the current set of generated parameters can meet the air purification demand in the target environment. Once explained, air purification can continue according to the generated parameter set. Air purification using the current set of emission parameters does not meet the criteria of the purification test standard mentioned above. If not, the current set of generating parameters cannot meet the air purification demand in the target environment, It is explained that the generation parameter set needs to be optimized. As shown in FIG. 3, step S500 in the method provided by the embodiment of the present application includes: , S510: Construct a global optimization model, where the global optimization model includes multiple The set of machine parameters is S520: For the set of machine parameters currently employed in the global optimization model By incrementally adjusting the timing, multiple optimization iterations are performed, and multiple optimizations are performed within the global optimization model. Generate several sets of optimized machine parameters; S530: Determine whether the optimized machine parameter set meets the pre-set conditions, and If it matches, the optimized machine parameter set is the option optimization result, and if it does not match, iterative optimization is performed. Continue to develop S540: Until the number of option optimization results that satisfy the pre-set optimization condition reaches the pre-set value Then, we perform multiple iterative optimization. S650: Compare multiple sets of option optimization results to obtain an optimized machine parameter set. A global optimization model contains the feasible domain over which the optimization is currently taking place and the The associated constraints ensure that optimization occurs within the model. The optimized machine parameter set in the global optimization model satisfies the above pre-set conditions. , and other optimization conditions must be satisfied. Step S510 in the method provided by the embodiment of the present invention includes: S511: Set a first optimization constraint based on the dynamic criteria; S512: Setting a second optimization constraint condition based on a third production parameter; S513: Adjusting the multiple machine parameter sets according to the second production parameters; S514: The first optimization constraint, the second optimization constraint, and the adjusted machine parameter set Based on this, an acquisition global optimization model is constructed.

[0019] Specifically, according to the above pre-aggregation condition, this pre-aggregation condition is based on the difference in the index value. This is a subjectively set absolute target value, and sets the first optimization constraint condition. The optimization is performed on the first machine parameter, the second equipment maintenance parameter, and the third machine parameter. Meter, different first machine parameters, second equipment maintenance parameters and third machine parameters , multiple combinations can be constructed, and multiple sets of machine parameters can be constructed. Under this first optimization constraint, the various machine parameter sets are preset. After the production of the selected cycle, the third production parameter in the working environment reaches the preset condition. To ensure that the third machine parameters are maximized as much as possible, and the overall production The first optimization constraint is also the most important, since we need to maximize production capacity when demand is reached. This is an important constraint. Based on the multi-dimensional production parameters of the working environment, a second optimization constraint is set, and this second optimization constraint is The second production parameter in the optimized machine parameter set obtained by optimization under the optimization constraints is The parameters exceed the maximum limits of multi-dimensional production parameters, e.g., in the artificial assembly stage, automation The production capacity of the machinery and equipment is higher than the artificial effective production capacity. At the stage of artificial quality inspection, automated machinery The production capacity of a facility must not be higher than its effective artificial production capacity. After satisfying the first and second optimization constraints above, the two constraints A set of multiple optimized machine parameters that satisfy the conditions is obtained. Furthermore, according to the performance parameters of the IoT control terminal 100 and the synchronous automation device, The production capacity efficiency of the equipment cannot exceed the threshold set by the manufacturer, and if it exceeds the threshold, The life of the equipment is compromised, and the adjustment of the second equipment maintenance parameters is beyond the threshold set by the manufacturer. If the threshold is exceeded, the automation equipment will be delayed in maintenance and will age. Increases speed and affects lifespan. In the embodiment of the present application, the performance parameter requirements include a second equipment maintenance parameter and a first 3 Machine parameters are included, and production work is carried out according to the requirements of these performance parameters, and this automatic Each module in the automation equipment will not be overloaded and its operating life can be guaranteed. According to the requirements of this performance parameter, the current various optimization machine parameter sets are adjusted. , and further set up a global optimization model. In this way, the first optimization constraint, the second optimization constraint, and the adjusted set of mechanical parameters are Based on this, we build a global optimization model. This global optimization model includes the first An optimizer that satisfies the requirements of the optimization constraints, the second optimization constraint, and the performance parameters. It contains multiple sets of machine parameters. Here, the second equipment maintenance parameter and the third machine parameter in the machine parameter set is not constant within the operating cycle, but may vary over time. During the initial start of the maintenance, the interval between the second equipment maintenance parameters is long, and at the same time, the third The parameters are high, and after long-term use, the efficiency and service life of the product are guaranteed. Therefore, the interval of the second equipment maintenance parameters must be short. The mechanical parameters will decrease accordingly. The embodiment of the present application is to construct a global optimization model based on multi-dimensional constraints. Therefore, in the process of optimizing the machine parameter set, other multi-dimensional requirements can be met at the same time, and the absolute If you can guarantee that the target value is achieved, you can track the progress of machine maintenance time. The size of the machine should be as small as possible, and the machine should be maintained as quantitatively as possible. Overall production capacity can be maximized when multiple conditions are met. Once the global optimization model is built, we perform optimization within the global optimization model. First, we optimize the currently adopted set of machine parameters in a global optimization model. The timing is then adjusted incrementally based on the machine parameters. The optimization is repeated until the data set satisfies all the pre-set conditions mentioned above and becomes the current solution of the optimization. Do iterations. Then, the number of option optimization results that satisfy the pre-set optimization conditions is calculated until it reaches the pre-set value. , and perform multiple iterative optimization based on the current solution. Step S530 in the method provided by the embodiment of the present invention includes: S531: Optimize the machine parameter set by introducing it into the global optimization model and calculating it accordingly Obtain the optimization calculation results, S532: Determine whether the third production parameter change amount satisfies a preset result. , S533: If the pre-set result is satisfied, the current optimization machine parameter set is updated to the option Enter the optimization result, and if not satisfied, continue iterative optimization. Specifically, the optimized machine parameter set is introduced into the global optimization model, and the third machine parameter The second machine parameter is introduced after the calculation to calculate the performance parameter. Adjust the timing of the first equipment maintenance parameters according to the requirements of the parameters. The adjusted machine parameter set is the current optimization calculation result. Determine whether the current optimization result satisfies all pre-set conditions, and if so, If the current optimization result is set as the optional optimization result, the current optimization result is The optimization machine parameter set corresponding to the optimization result can be added to the option optimization result. Indicates that: If not satisfied, this indicates that the current optimization calculation result is invalid and The timing is adjusted again to generate new optimized calculation results, which satisfy the pre-set conditions. The decision is made again until the number of the plus option optimization results reaches the preset value. Here, among the multiple option optimization results, the second setting A multiple option test was performed to compare the preparatory maintenance parameters with the initial values ​​to ensure that the deviation was minimal. Multiple options for comparing the maximum and minimum values ​​of the third machine parameter in the optimization results Compare the adjusted values ​​of the first artificial parameters in the optimization results with the subjectively selected indicator values. Weighting: A selection based on the weighting is made to obtain an optimized set of machine parameters. The embodiment of the present application constructs a global optimization model based on multidimensional constraints, and In the process of optimizing the machine parameter set, other multi-dimensional requirements are simultaneously met, and various options are available. It is possible to quickly obtain relatively good option optimization results among the option optimization results, and Based on subjectively selected indicator values ​​and weighting weights, the optimization The technical effect is achieved by obtaining the optimal machine parameter set through the BARB optimization.

[0020] S700: Adjust the IoT control terminal using the optimized machine parameter set. The first machine parameter of the optimized machine parameter set obtained after optimization, the second equipment maintainer The third machine parameter is adopted, and the automation setting is performed through the IoT control terminal 100. It controls the equipment for production and maintenance. From the above, the embodiment of the present application collects multi-dimensional mechanical parameters of the network control terminal of the object Then, the machine parameter set is obtained, and the multi-dimensional production parameters of the working environment are collected, and the production A parameter set is obtained, and based on the production parameter set and the machine parameter set, After the dynamic reference is set, the network control terminal of the object collects the machine parameters. Based on the change in the production parameter set, the current dynamic equilibrium is Determine whether the state meets the dynamic criteria, and determine whether the dynamic equilibrium state meets the dynamic criteria. If so, the current set of machine parameters is maintained and the dynamic equilibrium state is updated if it satisfies the dynamic criteria. For example, a set of machine parameters is optimized based on a dynamic criterion, and the optimized set of machine parameters is used to The IoT control terminal is then adjusted based on the information provided by the system, thereby optimizing the operating status of the machinery and equipment.

[0021] Example 2 As shown in FIG. 4 and FIG. 5, the mono network equipment centralized control system in the above-mentioned embodiment Based on an inventive idea similar to that of a logical system, the present application provides a configuration structure thereof, Includes: The first collection unit is used to collect multi-dimensional machine parameters of production equipment and obtain a machine parameter set. Knit 111, A first control unit for adjusting a production device based on an optimized machine parameter set. 112, It is used to collect multi-dimensional production parameters of the working environment and obtain a production parameter set. A second collection unit 211, For setting dynamic criteria based on a production parameter set and a machine parameter set a first processing unit 212 which is Based on the changes in production parameters, determine whether the current dynamic equilibrium state meets the dynamic criteria. a first determination unit 213 for determining whether If the dynamic equilibrium state satisfies the dynamic criteria, the current set of machine parameters is maintained and the dynamic equilibrium state is If the state of the machine does not satisfy the dynamic criterion, a set of machine parameters is optimized based on the dynamic criterion. and a second processing unit 214 used for In addition, the system a third collecting unit for collecting start / stop status of the production equipment and obtaining the first machine parameters; Collect the equipment operating hours after the latest maintenance and obtain the first equipment maintenance parameters. The fourth collection unit to obtain Collect the equipment operating hours after the latest maintenance and obtain the first equipment maintenance parameters. The fifth collection unit to obtain Collect equipment maintenance cycle intervals and obtain second equipment maintenance parameters 6th collection unit for The first equipment maintenance parameters and the second equipment maintenance parameters are set as the second machine parameters. a third processing unit for converting the a seventh collecting unit for collecting production capacity efficiency information of the equipment and obtaining a third machine parameter; The first machine parameter, the second machine parameter, and the third machine parameter are grouped into a machine parameter set. The fourth processing unit is configured to: In addition, the system an eighth collecting unit for collecting worker arrival information and obtaining the first production parameters; The ninth collection step is used to collect the worker's single-cycle production capacity time curve information and obtain the first artificial parameter. Group unit, The 10th collection is for collecting the worker's single-cycle work time information and obtaining the second artificial parameter. unit, a fifth process for determining the first artificial parameter and the second artificial parameter as a second production parameter; unit, Used to collect the semi-finished product stack amount of the station and obtain the third production parameter 11th collection unit, The first production parameter, the second production parameter, and the third production parameter are referred to as a production parameter set. and a sixth processing unit. In addition, the system to obtain a first dynamic equilibrium efficiency based on the first production parameter and the third machine parameter. A sixth processing unit; and adjusting the first dynamic equilibrium efficiency using the second production parameter to obtain a second dynamic equilibrium efficiency. A seventh processing unit; Using a third production parameter to adjust the second dynamic equilibrium efficiency and obtain a third dynamic equilibrium efficiency. An eighth processing unit; A ninth machine adjustment node based on the first machine parameter and the second machine parameter. Processing unit, a tenth processing unit for adjusting the third dynamic balance efficiency using a mechanical adjustment node; and an eleventh processing unit for taking the adjusted third dynamic equilibrium efficiency as a dynamic reference. A first construction unit for constructing an effective production capacity decay model based on the first artificial parameters. The available production capacity decay model includes multiple time-synchronized object models. A twelfth processing unit introduces a second artificial parameter into the effective production capacity decay model and adjusts the Dynamically adjust the weight coefficient of the first production parameter based on the formula, and then calculate multiple The excess capacity result is obtained. The 13th processing unit: The multiple excess production capacity results obtained are integrated and calculated to obtain the second dynamic equilibrium. Gain efficiency. where the adjustment formula is: TIFF0007618126000002.tif62147. In addition, the system A time series analysis is performed for the set of machine parameters currently employed in the global optimization model. Perform incremental adjustments, perform multiple optimization iterations, and optimize multiple sets within a global optimization model. a fourteenth processing unit used for generating a machine parameter set; The purpose is to determine whether the optimized machine parameter set meets the pre-set conditions. If they match, the optimized machine parameter set is the option optimization result. If they do not match, A first decision unit that continues the iterative optimization; Multiple times until the number of option optimization results that meet the pre-set optimization conditions reaches the pre-set value. a second decision unit for performing iterative optimization; 15th method to compare multiple sets of option optimization results and obtain the optimized machine parameter set a processing unit for In addition, the system a sixteenth processing unit for setting a first optimization constraint based on the dynamic criterion; A seventeenth processing unit for setting a second optimization constraint based on a third production parameter. tt, and adjusting a plurality of sets of machine parameters based on the second production parameters. an 18th processing unit.

[0022] Example 3 Based on the same inventive idea as the nano water ion generation parameter adjustment method in the above-mentioned embodiment, Based on this, the present application also relates to a computer-readable storage medium having a computer program stored thereon. The computer program, when executed by a processor, We will realize such a method.

[0023] Exemplary Electronic Devices Hereinafter, the electronic device of the present application will be described with reference to FIG. Based on the same concept as the nano-water ion generation parameter adjustment method in the above embodiment, Based on this, the present application also provides a processor for storing a program, a processor and a memory connected thereto. When the program is executed by a processor, the program causes the system to execute the steps of the method of embodiment 1. A nano-water ion generation parameter adjustment system including a memory for executing the steps is provided. Provide. The electronic device 300 includes a processor 302, a communication interface 303, and a memory 301. Optionally, the electronic equipment 300 may also include a bus architecture 304. Here, the communication interface 303, the processor 302, and the memory 301 are connected to a bus The devices can be interconnected via a bus architecture 304, which includes , Peripheral component interconnect target (peripheral component PCI bus or extended industry standard structure eINDUSTRYSTANDARCHITECTURE (EISA) bus The bus architecture 304 may include an address bus, a data bus, a control bus, etc. For ease of display, only one thick line is shown in Fig. 6. Although it is displayed, it does not show only one bus or one type of bus. The processor 302 may be a CPU, a microprocessor, an ASIC, or a program of the present application. There may be one or more integrated circuits for controlling the execution. The communication interface 303 is an Ethernet, a radio access network (RAD accessnetwork (RAN), wireless local area network (wirele sslocalareanetworks, WLAN), wired access network, etc. , using equipment such as transceivers to communicate with other equipment and communication networks. Memory 301 may be a ROM or other type of memory capable of storing static information and instructions. A static memory facility, RAM or other type of dynamic memory capable of storing information and instructions. Memory facility, electrically erasable programmable read-only memory (electrica llyerasableProgrammableread-onlymemory,E EPROM), read-only disk (compactdiscread-onlyme memory, CD-ROM, or other disk storage, disk storage devices (compressed data) Discs, laser discs, discs, digital general-purpose discs, Blu-ray discs, etc. (including any software or other software programs), disk storage media or other magnetic storage devices, or forms of instructions or data structures A computer program that carries or stores a desired program code having the The memory may be any medium, including but not limited to any other medium that can accommodate a bus. The memory may be a separate entity connected to the processor via the architecture 304. can also be integrated with the processor. Here, the memory 301 stores computer-executable instructions for carrying out the present invention, and the processor The processor 302 controls the execution of the memory 301. , and is used to execute computer-executable instructions stored in the The method for adjusting the generation parameters of nano-water ions provided by the above-described embodiments is realized. In the above embodiments, software, hardware, firmware, or any of them may be used. The software may be implemented in whole or in part by any combination of the above. When implemented using, implemented in whole or in part in the form of a computer program product The computer program product may include one or more computer instructions. When you load computer program instructions into a computer and execute them, The process or function described herein is produced, or in part, in accordance with the principles of this application. A computer, special-purpose computer, computer network, or other programmable Equipment may be computer-based. The instructions may be stored on a computer-readable storage medium or may be The information may be transmitted from the storage medium to another computer-readable storage medium, for example to a computer The instructions may come from a website site, computer, server, or data center. Wired (e.g., axial cable, fiber optic, digital subscriber line (DSL)), or wireless ( For example, infrared, radio, microwave, etc.) to another website, computer The computer-readable storage medium is transmitted by the It may be any available medium that the data can access, or it may consist of one or more available media. The system may include integrated servers, data centers, and other data storage devices. Possible media include magnetic media (e.g., floppy disk, hard disk, magnetic tape) , optical media (e.g., DVD), or solid-state media (e.g., SSD (Solid State Drive) eDisk), etc. The various exemplary logic units and circuits described herein may include general purpose processors, Digital signal processors, application specific integrated circuits (ASICs), field programmable FPGAs, or other programmable logic devices, discrete gates chip or transistor logic, discrete hardware components, or any of the above. The combination of designs can implement or perform the described functions. The processor may be a microprocessor, or optionally a general purpose processor such as any conventional It may be a processor, controller, microcontroller, or state machine. A processor may also be a digital signal processor, a microprocessor, or a multi-microprocessor. Processor, one or more microprocessors and a digital signal processor core or any other similar configuration of a combination of computing devices. It is possible. The steps of the methods or algorithms described herein may be implemented using hardware, a processor, directly incorporates software units executed by The software unit can be implemented using RAM memory, flash memory, and ROM memory. EEPROM memory, EPROM memory, register, hard disk, removable Stored on disk, CD-ROM, or any other form of storage medium known in the art. For example, the storage medium may be a and can be coupled to the processor so as to be able to write information to the storage medium. Optionally, the storage medium may be integrated into the processor. The processor and the storage medium may be located in an ASIC, and the ASIC may be located in a terminal. In the alternative, the processor and the storage medium may be implemented as separate components within a device. These computer program instructions may also be provided in a computer or other programmable data processing device, A computer-implemented process that executes a series of operational steps on another programmable device. can be generated and therefore executed on a computer or other programmable device. The instructions to be executed may be represented by one or more of the flows and / or block diagrams in the flowcharts. Provides steps to implement the functionality specified in a block or blocks do.

Claims

1. Including an IoT control terminal and an intelligent control terminal, The IoT control terminal is The internal first collection unit collects multi-dimensional machine parameters of the production equipment, and the machine parameters The first control unit inside obtains the optimized machine parameter set. A control module for adjusting the production equipment; The machine parameter set obtained by the control module is transferred to the intelligent control terminal. In order to achieve this, the intelligent control terminal transmits the optimized machine parameter set to the control module. a signal module for transmitting the signal to the Intelligent control terminals are It is used to collect multi-dimensional production parameters of the working environment and obtain a production parameter set. a second collecting unit for collecting the first and second A first method for setting dynamic criteria based on a production parameter set and a machine parameter set. A processing unit; Setting dynamic criteria based on the production parameter set and the machine parameter set 、 Obtaining a first dynamic equilibrium efficiency based on the first production parameter and the third machine parameter. And, The second production parameter is used to adjust the first dynamic equilibrium efficiency to obtain a second dynamic equilibrium efficiency. And, The third production parameter is used to adjust the second dynamic equilibrium efficiency to obtain a third dynamic equilibrium efficiency. And, Obtaining a machine adjustment node based on the first machine parameter and the second machine parameter. and, Adjusting a third dynamic balance efficiency using a machine adjustment node; and and determining the adjusted third dynamic equilibrium efficiency as a dynamic reference; The second production parameter is used to adjust the first dynamic equilibrium efficiency to obtain a second dynamic equilibrium efficiency. What is that? An effective production capacity decay model is constructed based on the first artificial parameter, and an effective production capacity decay model is constructed based on the first artificial parameter. the model includes multiple time-synchronized object models; The second artificial parameter is introduced into the effective production capacity decay model, and the first production capacity decay model is adjusted based on the adjustment formula. Dynamically adjust the weighting coefficients of the parameters and calculate multiple excess capacity results after each calculation. To get and The results of the multiple excess production capacities obtained are linked and calculated to obtain a second dynamic equilibrium efficiency. and The adjustment formula is 、 Based on the changes in production parameters, determine whether the current dynamic equilibrium state meets the dynamic criteria. a first judgment unit for judging whether If the dynamic equilibrium state satisfies the dynamic criterion, maintain the current machine parameter set and dynamically equilibrate If the state does not meet the dynamic criteria, optimize the machine parameter set based on the dynamic criteria. and a second processing unit for performing the same. Optimizing the machine parameter set based on the dynamic criteria includes: A global optimization model is constructed, which includes multiple machine parameters. The set includes The timing is applied to the set of machine parameters currently employed in the global optimization model. By gradually adjusting the weighting, multiple optimization iterations are performed, and multiple sets of weighting are included in the global optimization model. generating an optimized machine parameter set; Determine whether the optimized machine parameter set meets the pre-set conditions, and if so, The optimized machine parameter set is the optional optimization result, and if there is no match, the iterative optimization is continued. To continue, Until the number of option optimization results that satisfy the pre-set optimization conditions reaches the pre-set value. performing multiple iterative optimizations; Comparing the multiple sets of option optimization results to obtain an optimized machine parameter set; Including, Building a global optimization model is establishing a first optimization constraint based on the dynamic criterion; establishing a second optimization constraint based on a third production parameter; and Adjusting the set of various machine parameters based on the second production parameters; Based on the first optimization constraint, the second optimization constraint, and the adjusted machine parameter set, building a global optimization model; Determining whether the optimized machine parameter set satisfies the pre-set condition includes: The optimized machine parameter set is imported into the global optimization model and calculated accordingly. and obtaining an optimization calculation result for the optimized machine parameter set. The machine adjustment node off set value and the third production parameter change amount are set as the operation parameters. Including, Determine whether the third production parameter change amount satisfies the pre-set result, and If it is satisfied, input the current optimized machine parameter set into the option Optimization result; if not, continuing with the iterative optimization; Collecting multi-dimensional production parameters of the working environment is Collecting work information of workers and acquiring first production parameters; Collecting information on the single-cycle production capacity time curve of the worker and obtaining a first artificial parameter; 、 Collecting single-cycle work time information of the worker to obtain a second artificial parameter; Obtaining a second production parameter based on the first artificial parameter and the second artificial parameter. And, Collecting the amount of semi-finished products accumulated in the station to obtain a third production parameter; The first production parameter, the second production parameter, and the third production parameter are included in a production parameter collection. and IoT equipment centralized control management system.

2. The first collection unit collects multi-dimensional machine parameters of the production device, Collecting start and stop statuses of the production equipment and acquiring first machine parameters; Collect the work time after the latest equipment maintenance to obtain the first equipment maintenance parameters To do, Collecting maintenance cycle intervals of the equipment to obtain second equipment maintenance parameters. And, Based on the first equipment maintenance parameter and the second equipment maintenance parameter, Obtaining machine parameters; Collecting production capacity efficiency information of the equipment to obtain a third machine parameter; The first machine parameter, the second machine parameter, and the third machine parameter are grouped into a machine parameter set. and The IoT facility centralized control management system according to claim 1.

3. A processor is included. The processor is coupled to the memory. The memory is used to store programs and the programs are executed by the processor. When the above-mentioned is received, the system is caused to execute the steps of the system according to any one of claims 1 and 2. The IoT equipment centralized control device according to claim 1.

4. The storage medium stores a computer program, and the computer program When executed by a processor, the steps of the system according to any one of claims 1 to 2 are realized. The computer-readable storage medium of claim 1 .

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