CN116991070A - Comprehensive performance intelligent management method and system for multi-equipment terminal based on Internet of things - Google Patents
Comprehensive performance intelligent management method and system for multi-equipment terminal based on Internet of things Download PDFInfo
- Publication number
- CN116991070A CN116991070A CN202311023116.2A CN202311023116A CN116991070A CN 116991070 A CN116991070 A CN 116991070A CN 202311023116 A CN202311023116 A CN 202311023116A CN 116991070 A CN116991070 A CN 116991070A
- Authority
- CN
- China
- Prior art keywords
- equipment
- optimal
- task
- parameters
- parameter
- Prior art date
- Legal status (The legal status 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 status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 34
- 239000011159 matrix material Substances 0.000 claims abstract description 35
- 238000000034 method Methods 0.000 claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims description 30
- 238000013461 design Methods 0.000 claims description 30
- 238000003491 array Methods 0.000 claims description 22
- 238000012216 screening Methods 0.000 claims description 14
- 238000012544 monitoring process Methods 0.000 claims description 13
- 230000004069 differentiation Effects 0.000 claims description 12
- 210000001061 forehead Anatomy 0.000 claims description 6
- 238000012935 Averaging Methods 0.000 claims description 3
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 description 11
- 238000005516 engineering process Methods 0.000 description 3
- 230000006855 networking Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a comprehensive performance intelligent management method and system of a multi-device terminal based on the Internet of things, which relate to the technical field of the Internet of things and comprise the following steps: grouping the types according to the types of the devices; determining optimal operating parameters for each device; generating an optimal operation parameter matrix corresponding to each equipment type set; acquiring overall task demand data; generating task requirements of each type of device; calculating the optimal operation setting parameters of each piece of equipment, and obtaining an optimal operation setting parameter matrix; generating control signals to devices in each device class set; and judging whether the equipment is in a normal running state or not. The invention has the advantages that: the method can effectively ensure that the equipment keeps an optimized running state when executing tasks, reduce the equipment failure rate, effectively prolong the running life of the equipment and ensure the overall running stability of the Internet of things system.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to an intelligent comprehensive performance management method and system for a multi-device terminal based on the Internet of things.
Background
The internet of things refers to collecting any object or process needing to be monitored, connected and interacted in real time through various devices and technologies such as various information sensors, radio frequency identification technologies, global positioning systems, infrared sensors and laser scanners, collecting various needed information such as sound, light, heat, electricity, mechanics, chemistry, biology and positions, and realizing ubiquitous connection of objects and people through various possible network access, and realizing intelligent sensing, identification and management of objects and processes. The internet of things is an information carrier based on the internet, a traditional telecommunication network and the like, and enables all common physical objects which can be independently addressed to form an interconnection network.
Along with the development of the internet of things technology, the concept of internet of things gradually steps into various fields, however, in the prior art, when an internet of things device group is accessed to an internet of things control task, an optimal control scheme is difficult to form according to the actual performance of each device, so that in the management process, the devices are difficult to maintain an optimal running state, and further the running stability of the internet of things networking device is poor.
Disclosure of Invention
In order to solve the technical problems, the technical scheme solves the problems that in the prior art, when an Internet of things control task is performed on a device group connected to the Internet of things, an optimal control scheme is difficult to form according to the actual performance of each device, so that in the management process, each device is difficult to maintain an optimal running state, and the running stability of the Internet of things networking device is poor.
In order to achieve the above purpose, the invention adopts the following technical scheme:
an intelligent management method for comprehensive performance of a multi-device terminal based on the Internet of things comprises the following steps:
acquiring all equipment information accessed to the Internet of things, and grouping the equipment types according to the equipment types to acquire a plurality of equipment type sets;
determining optimal operating parameters for each device based on the design parameters for each device;
generating an optimal operation parameter matrix corresponding to each equipment type set based on the equipment type set and the optimal operation parameters of each equipment;
acquiring overall task demand data;
generating task demands of each type of equipment based on the overall task demand data, and recording the task demands as equipment type task demands;
comprehensively calculating the optimal operation setting parameters of each device based on the device type task requirements and the optimal operation parameter matrix corresponding to each device type set, and acquiring the optimal operation setting parameter matrix corresponding to each device type set;
respectively generating control signals to the devices in each device type set according to the optimal operation setting parameter matrix corresponding to each device type set, and controlling each device to operate according to the optimal operation setting parameters corresponding to each device type set;
and monitoring the operation parameters of all the equipment in real time, judging whether the equipment is in a normal operation state based on the corresponding optimal operation setting parameters, if so, not responding, and if not, outputting an alarm signal to the management terminal.
Preferably, the determining the optimal operation parameter of each device based on the design parameter of each device specifically includes:
determining an average lifetime of each device based on historical operational data of each device;
determining design rated operating parameters of each device;
based on the loss rate of each device in unit time and the designed rated operation parameters of each device, establishing an optimal parameter attenuation model, wherein the optimal parameter attenuation model takes the service life of the device as output and takes the optimal operation parameters of the device as output;
acquiring the actual service life of equipment, inputting the actual service life of the equipment into an optimal parameter attenuation model, and acquiring the optimal operation parameters of the equipment;
the expression of the optimal parameter attenuation model is as follows:
wherein M is 0 Is the most of the equipmentOptimal operating parameters, M Forehead (forehead) For the design rated operation parameter of the equipment, gamma is the ratio between the design rejection minimum operation parameter value of the equipment and the design rated operation parameter of the equipment, T is the design rejection limit of the equipment, T is the actual service life of the equipment, x is an intermediate variable, alpha 1 、α 2 、α 3 、α 4 Are model parameters.
Preferably, the generating the task demands of each kind of equipment based on the overall task demand data specifically includes;
determining subtasks of the equipment in the internet of things based on the equipment type;
determining the subtask quantity of each device under the unit overall task quantity, and recording the subtask quantity as the unit subtask quantity;
calculating the total subtask amount of each type of equipment according to a subtask amount calculation formula based on the whole task demand amount and the unit subtask amount, and recording the total subtask amount as the task demand of the equipment;
the subtask amount calculation formula is as follows:
wherein r is zw R is the total amount of subtasks 0 In units of subtask quantity, R z R is the unit overall task quantity 0 Is the overall task demand.
Preferably, the comprehensively calculating the optimal operation setting parameter of each device based on the device type task requirement and the optimal operation parameter matrix corresponding to each device type set specifically includes:
determining the optimal task output quantity of the equipment based on the optimal operation parameters of the equipment, and combining the optimal task output quantities of all the equipment into an optimal task output quantity array of the equipment;
generating a plurality of equipment task allocation quantity arrays based on task demands corresponding to equipment types and optimal task output quantities of equipment;
calculating a differentiation index between each equipment task allocation amount array and the optimal task output amount array of the equipment according to a difference amount calculation formula based on the equipment task allocation amount array and the optimal task output amount array of the equipment;
screening out the equipment task allocation amount array with the minimum difference index as an optimal equipment task allocation amount array;
calculating optimal operation setting parameters of each device based on the task output quantity of each device in the task allocation quantity array of the type optimal device, and forming an optimal operation setting parameter matrix from all the optimal operation setting parameters;
wherein, the difference amount calculation formula is:
wherein C is y For the differentiation index, n is the total number of elements in the equipment type set, w 0i Task allocation amount, w, for ith device in device task allocation amount array 1i The optimal task output of the ith device in the optimal task output array.
Preferably, the generating a plurality of device task allocation amount arrays based on the task requirements corresponding to the device types and the optimal task output amounts of the devices specifically includes:
establishing task allocation conditions based on task demands corresponding to the device types and the optimal task output quantity of the device;
generating a plurality of equipment task allocation quantity arrays based on the task allocation conditions;
the task allocation conditional expression is as follows:
wherein w is 0i Task allocation amount, w, for ith device in device task allocation amount array 1i R is the optimal task output of the ith device in the optimal task output array zw ' is the total amount of subtasks corresponding to the device class.
Preferably, the monitoring the operation parameters of all the devices in real time, and judging whether the devices are in a normal operation state based on the corresponding optimal operation setting parameters specifically includes:
obtaining the maximum error rate of the running parameters of the equipment design;
acquiring historical operating data of the device, and calculating a standard operating error rate of the device based on the historical operating data of the device;
judging whether the standard operation error rate of the equipment is larger than the maximum error rate, if so, the equipment is in a low stable operation state, outputting an alarm signal to the management terminal, and if not, the equipment is in a high stable operation state;
determining, for a device in a high steady state operating condition, a standard operating parameter interval for the device based on its standard operating error rate and an optimal operating setting parameter for the device;
judging whether the real-time operation parameters of the equipment are in the standard operation parameter interval of the equipment, if so, the equipment is in a normal operation state, and if not, the equipment is in an abnormal operation state.
Preferably, the acquiring the historical operation data of the device, calculating the standard operation error rate of the device based on the historical operation data of the device specifically includes:
calculating and obtaining average operation deviation rate of the equipment in a plurality of equipment operation periods based on historical operation data of the equipment;
based on Grubbs criterion, screening out the credible values in the average operation deviation rate of the equipment in the operation periods of the plurality of equipment;
averaging the trusted values in the average operation deviation rate of the equipment in the operation period of all the equipment to obtain an average value as a standard operation error rate of the equipment;
wherein the Grubbs criterion has the expression:
in the formula, v j For the jth average run bias rate，S is the standard deviation of the average running deviation rate of the equipment in the running period of all the equipment, bpn is the Grubbs critical value, and the Grubbs critical value is determined by looking up a Grubbs table;
if the expression of the Grubbs criterion is satisfied, v j If the expression of Grubbs criterion is not satisfied, v j Is a trusted value.
The comprehensive performance intelligent management system of the multi-device terminal based on the Internet of things is used for realizing the comprehensive performance intelligent management method of the multi-device terminal based on the Internet of things, and comprises the following steps:
the optimal parameter determining module is used for generating an optimal operation parameter matrix corresponding to each equipment type set;
the operation parameter determining module is electrically connected with the optimal parameter determining module and is used for obtaining an optimal operation setting parameter matrix corresponding to each equipment type set;
the control module is electrically connected with the operation parameter determining module and is used for respectively generating control signals to the devices in each device type set according to the optimal operation setting parameter matrix corresponding to each device type set and controlling each device to operate according to the optimal operation setting parameters corresponding to each device type set;
the monitoring module is electrically connected with the operation parameter determining module and is used for monitoring the operation parameters of all the equipment in real time and judging whether the equipment is in a normal operation state or not based on the corresponding optimal operation setting parameters.
Optionally, the optimal parameter determining module is integrated with:
the device type classification unit is used for acquiring all device information accessed to the Internet of things, grouping the types according to the device types and acquiring a plurality of device type sets;
and an optimal parameter calculation unit for determining an optimal operation parameter of each device based on the design parameter of each device.
Optionally, the operation parameter determining module is integrated with:
a subtask amount calculation unit for calculating a total amount of subtasks for each kind of device;
the task allocation unit is used for establishing task allocation conditions based on task demands corresponding to equipment types and optimal task output quantities of the equipment and generating a plurality of equipment task allocation quantity arrays based on the task allocation conditions;
the difference calculation unit is used for calculating a differentiation index between each equipment task allocation amount array and the optimal task output quantity array of the equipment;
the screening unit is used for screening out the equipment task allocation amount array with the minimum differential index as an optimal equipment task allocation amount array;
the operation parameter calculation unit is used for calculating the optimal operation setting parameters of each device based on the task output quantity of each device in the task allocation quantity array of the optimal device, and forming an optimal operation setting parameter matrix from all the optimal operation setting parameters.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an intelligent comprehensive performance management scheme of a multi-device terminal based on the Internet of things, which is used for calculating the current optimal operation parameters of devices by combining the rated operation parameters in the process of device operation and the loss in the process of device operation, and intelligently distributing the task quantity to each device by combining the current optimal operation parameters of the devices on the basis of the overall tasks of the Internet of things system.
Drawings
FIG. 1 is a flow chart of a comprehensive performance intelligent management method of a multi-device terminal based on the Internet of things, which is provided by the invention;
FIG. 2 is a flow chart of a method of determining optimal operating parameters for each device in accordance with the present invention;
FIG. 3 is a flow chart of a method of generating task demands for each type of device in the present invention;
FIG. 4 is a flow chart of a method of calculating optimal operational settings for each device in accordance with the present invention;
FIG. 5 is a flow chart of a method of generating a plurality of device task allocation arrays in accordance with the present invention;
FIG. 6 is a flow chart of a method for determining whether a device is in a normal operation state according to the present invention;
FIG. 7 is a flow chart of a method of a standard run error rate of a computing device in accordance with the present invention;
fig. 8 is a block diagram of an intelligent management system for comprehensive performance of a multi-device terminal based on the internet of things.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art.
Referring to fig. 1, an intelligent management method for comprehensive performance of a multi-device terminal based on internet of things includes:
acquiring all equipment information accessed to the Internet of things, and grouping the equipment types according to the equipment types to acquire a plurality of equipment type sets;
determining optimal operating parameters for each device based on the design parameters for each device;
generating an optimal operation parameter matrix corresponding to each equipment type set based on the equipment type set and the optimal operation parameters of each equipment;
acquiring overall task demand data;
generating task demands of each type of equipment based on the overall task demand data, and recording the task demands as equipment type task demands;
comprehensively calculating the optimal operation setting parameters of each device based on the device type task requirements and the optimal operation parameter matrix corresponding to each device type set, and acquiring the optimal operation setting parameter matrix corresponding to each device type set;
respectively generating control signals to the devices in each device type set according to the optimal operation setting parameter matrix corresponding to each device type set, and controlling each device to operate according to the optimal operation setting parameters corresponding to each device type set;
and monitoring the operation parameters of all the equipment in real time, judging whether the equipment is in a normal operation state based on the corresponding optimal operation setting parameters, if so, not responding, and if not, outputting an alarm signal to the management terminal.
According to the scheme, the current optimal operation parameters of the equipment are calculated by combining the rated operation parameters of the equipment design and the loss in the operation process of the equipment, and then the intelligent task amount distribution is carried out on each piece of equipment based on the overall task of the Internet of things system and the current optimal operation parameters of the equipment, so that the optimal operation state of the equipment can be effectively ensured to be maintained when the task is executed.
Referring to fig. 2, determining optimal operating parameters for each device based on the design parameters for each device specifically includes:
determining an average lifetime of each device based on historical operational data of each device;
determining design rated operating parameters of each device;
based on the loss rate of each device in unit time and the designed rated operation parameters of each device, an optimal parameter attenuation model is established, the optimal parameter attenuation model takes the service life of the device as output, and the optimal operation parameters of the device as output;
acquiring the actual service life of equipment, inputting the actual service life of the equipment into an optimal parameter attenuation model, and acquiring the optimal operation parameters of the equipment;
the expression of the optimal parameter attenuation model is as follows:
wherein M is 0 For the optimal operating parameters of the plant, M Forehead (forehead) For the design rated operation parameter of the equipment, gamma is the ratio between the design rejection minimum operation parameter value of the equipment and the design rated operation parameter of the equipment, T is the design rejection limit of the equipment, T is the actual service life of the equipment, x is an intermediate variable, alpha 1 、α 2 、α 3 、α 4 Are model parameters.
It can be understood that each component is lost in the running process of the equipment, so that the loss power in the running process of the equipment is increased, and each running parameter of the equipment is further reduced;
it will be appreciated that the optimum operating parameter of the plant is 80% of the rated operating parameter of the plant, the loss of which increases progressively as the service life of the plant increases, the rated operating parameter designing the rated operating parameter decreasing progressively closer to the design rejection minimum operating parameter value of the plant.
Referring to fig. 3, generating task demands of each kind of devices based on the overall task demand data specifically includes;
determining subtasks of the equipment in the internet of things based on the equipment type;
determining the subtask quantity of each device under the unit overall task quantity, and recording the subtask quantity as the unit subtask quantity;
calculating the total subtask amount of each type of equipment according to a subtask amount calculation formula based on the whole task demand amount and the unit subtask amount, and recording the total subtask amount as the task demand of the equipment;
the subtask amount calculation formula is as follows:
wherein r is zw R is the total amount of subtasks 0 In units of subtask quantity, R z R is the unit overall task quantity 0 Is the overall task demand.
Referring to fig. 4, the comprehensive calculation of the optimal operation setting parameters of each device based on the device type task requirement and the optimal operation parameter matrix corresponding to each device type set specifically includes:
determining the optimal task output quantity of the equipment based on the optimal operation parameters of the equipment, and combining the optimal task output quantities of all the equipment into an optimal task output quantity array of the equipment;
generating a plurality of equipment task allocation quantity arrays based on task demands corresponding to equipment types and optimal task output quantities of equipment;
calculating a differentiation index between each equipment task allocation amount array and the optimal task output amount array of the equipment according to a difference amount calculation formula based on the equipment task allocation amount array and the optimal task output amount array of the equipment;
screening out the equipment task allocation amount array with the minimum difference index as an optimal equipment task allocation amount array;
calculating optimal operation setting parameters of each device based on the task output quantity of each device in the task allocation quantity array of the type optimal device, and forming an optimal operation setting parameter matrix from all the optimal operation setting parameters;
wherein, the difference amount calculation formula is:
wherein C is y For the differentiation index, n is the total number of elements in the equipment type set, w 0i Task allocation amount, w, for ith device in device task allocation amount array 1i The optimal task output of the ith device in the optimal task output array.
Referring to fig. 5, generating a plurality of device task allocation amount arrays based on task requirements corresponding to device types and optimal task output amounts of devices specifically includes:
establishing task allocation conditions based on task demands corresponding to the device types and the optimal task output quantity of the device;
generating a plurality of equipment task allocation quantity arrays based on the task allocation conditions;
the task allocation conditional expression is as follows:
wherein w is 0i Task allocation amount, w, for ith device in device task allocation amount array 1i R is the optimal task output of the ith device in the optimal task output array zw ' is the total amount of subtasks corresponding to the device class.
In the scheme, firstly, a task allocation condition is established based on that the task quantity of all the devices is smaller than the optimal task output quantity of the devices and the total task output quantity of all the devices is guaranteed to be equal to the total subtask quantity of the device type, a plurality of device task allocation quantity arrays are generated based on the task allocation condition, then the multidimensional vector distance between the device task allocation quantity arrays and the optimal task output quantity arrays is used as a differentiation index between the device task allocation quantity arrays and the optimal task output quantity arrays of the devices, the optimal device task allocation quantity arrays of the devices can be obtained by screening out the device task allocation quantity arrays with the minimum differentiation index, and the optimal operation setting parameters of the devices can be obtained by calculating the optimal operation setting parameters of the devices by the optimal device task allocation quantity arrays.
Referring to fig. 6, monitoring operation parameters of all devices in real time, and judging whether the devices are in a normal operation state based on the corresponding optimal operation setting parameters specifically includes:
obtaining the maximum error rate of the running parameters of the equipment design;
acquiring historical operating data of the device, and calculating a standard operating error rate of the device based on the historical operating data of the device;
judging whether the standard operation error rate of the equipment is larger than the maximum error rate, if so, the equipment is in a low stable operation state, outputting an alarm signal to the management terminal, and if not, the equipment is in a high stable operation state;
determining, for a device in a high steady state operating condition, a standard operating parameter interval for the device based on its standard operating error rate and an optimal operating setting parameter for the device;
judging whether the real-time operation parameters of the equipment are in the standard operation parameter interval of the equipment, if so, the equipment is in a normal operation state, and if not, the equipment is in an abnormal operation state.
Referring to fig. 7, acquiring device historical operation data, calculating a standard operation error rate of a device based on the device historical operation data specifically includes:
calculating and obtaining average operation deviation rate of the equipment in a plurality of equipment operation periods based on historical operation data of the equipment;
based on Grubbs criterion, screening out the credible values in the average operation deviation rate of the equipment in the operation periods of the plurality of equipment;
averaging the trusted values in the average operation deviation rate of the equipment in the operation period of all the equipment to obtain an average value as a standard operation error rate of the equipment;
wherein the expression of Grubbs criteria is:
in the formula, v j For the jth average operating bias rate,for the average value of the average operating deviation rate of the equipment in the operating period of all the equipment, s is the operating period of all the equipmentThe standard deviation of the average operating deviation rate of the internal equipment is bpn, which is a Grubbs critical value, the Grubbs critical value is determined by checking a Grubbs table, the Grubbs critical value is determined by the determined credibility level and the total number of the average operating deviation rate of the equipment in the operating period of the equipment, the credibility level is in the range of 0.01-0.1, in some embodiments, the credibility level is 0.05, and the corresponding Grubbs critical value can be determined by checking the Grubbs table through the determined credibility level and the total number of the average operating deviation rate;
if the expression of the Grubbs criterion is satisfied, v j If the expression of Grubbs criterion is not satisfied, v j Is a trusted value.
It can be understood that in the operation process of the device, a certain error value exists between the actual parameter and the set parameter, in the scheme, the standard operation error rate of the device is calculated by analyzing the operation state data of the device according to the glaubes criterion, and then whether the device is in a normal=operation state is judged based on the standard operation error rate of the device, so that the operation state of the device can be effectively and comprehensively monitored, the abnormality of the device in the internet of things system can be timely found, the timely overhaul of the device is further realized, and the operation stability of the internet of things system is ensured.
Further, referring to fig. 8, based on the same inventive concept as the above-mentioned intelligent management method for comprehensive performance of multi-device terminals based on the internet of things, the present disclosure further provides an intelligent management system for comprehensive performance of multi-device terminals based on the internet of things, which includes:
the optimal parameter determining module is used for generating an optimal operation parameter matrix corresponding to each equipment type set;
the operation parameter determining module is electrically connected with the optimal parameter determining module and is used for obtaining an optimal operation setting parameter matrix corresponding to each equipment type set;
the control module is electrically connected with the operation parameter determining module and is used for respectively generating control signals to the devices in each device type set according to the optimal operation setting parameter matrix corresponding to each device type set and controlling each device to operate according to the optimal operation setting parameters corresponding to each device type set;
the monitoring module is electrically connected with the operation parameter determining module and is used for monitoring the operation parameters of all the equipment in real time and judging whether the equipment is in a normal operation state or not based on the corresponding optimal operation setting parameters.
The optimal parameter determining module is internally integrated with:
the device type classification unit is used for acquiring all device information accessed to the Internet of things, grouping the types according to the device types, and acquiring a plurality of device type sets;
and an optimal parameter calculation unit for determining an optimal operation parameter of each device based on the design parameter of each device.
The operation parameter determining module is internally integrated with:
a subtask amount calculation unit for calculating the total amount of subtasks of each type of equipment;
the task allocation unit is used for establishing task allocation conditions based on task demands corresponding to the equipment types and optimal task output quantities of the equipment and generating a plurality of equipment task allocation quantity arrays based on the task allocation conditions;
the difference calculation unit is used for calculating a differentiation index between each equipment task allocation quantity array and the optimal task output quantity array of the equipment;
the screening unit is used for screening out the equipment task allocation amount array with the minimum differentiation index as an optimal equipment task allocation amount array;
the operation parameter calculation unit is used for calculating the optimal operation setting parameters of each device based on the task output quantity of each device in the task allocation quantity array of the type optimal device, and forming an optimal operation setting parameter matrix from all the optimal operation setting parameters.
The using process of the intelligent management system for the comprehensive performance of the multi-device terminal based on the Internet of things is as follows:
step one: the device type classification unit acquires all device information accessed to the Internet of things, and performs type grouping according to the device types to acquire a plurality of device type sets;
step two: the optimal parameter calculation unit is used for determining the optimal operation parameters of each device based on the design parameters of each device, and generating an optimal operation parameter matrix corresponding to each device type set based on the device type set and the optimal operation parameters of each device;
step three: the operation parameter determining module obtains the whole task demand data;
step four: determining subtasks of the equipment in the internet of things based on the equipment type; determining the subtask quantity of each device under the unit overall task quantity, and recording the subtask quantity as the unit subtask quantity; calculating the total subtask amount of each type of equipment according to a subtask amount calculation formula based on the whole task demand amount and the unit subtask amount, and recording the total subtask amount as the task demand of the equipment;
step five: the task allocation unit determines the optimal task output quantity of the equipment based on the optimal operation parameters of the equipment, combines the optimal task output quantities of all the equipment into an optimal task output quantity array of the equipment, establishes task allocation conditions based on task demands corresponding to equipment types and the optimal task output quantity of the equipment, and generates a plurality of equipment task allocation quantity arrays based on the task allocation conditions;
step six: the difference calculation unit calculates a difference index between the task allocation quantity array of each device and the optimal task output quantity array of the device;
step seven: the screening unit screens out the equipment task allocation amount array with the minimum difference index as an optimal equipment task allocation amount array;
step eight: the operation acceptance number calculation unit calculates the optimal operation setting parameters of each device based on the task output quantity of each device in the task allocation quantity array of the type optimal device, and forms an optimal operation setting parameter matrix from all the optimal operation setting parameters;
step nine: the control module respectively generates control signals to the devices in each device type set according to the optimal operation setting parameter matrix corresponding to each device type set, and controls each device to operate according to the optimal operation setting parameters corresponding to each device type set;
step ten: the monitoring module monitors the operation parameters of all the devices in real time and judges whether the devices are in a normal operation state or not based on the corresponding optimal operation setting parameters.
In summary, the invention has the advantages that: the method can effectively ensure that the equipment keeps an optimized running state when executing tasks, reduce the equipment failure rate, effectively prolong the running life of the equipment and ensure the overall running stability of the Internet of things system.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made therein without departing from the spirit and scope of the invention, which is defined by the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. The comprehensive performance intelligent management method of the multi-device terminal based on the Internet of things is characterized by comprising the following steps of:
acquiring all equipment information accessed to the Internet of things, and grouping the equipment types according to the equipment types to acquire a plurality of equipment type sets;
determining optimal operating parameters for each device based on the design parameters for each device;
generating an optimal operation parameter matrix corresponding to each equipment type set based on the equipment type set and the optimal operation parameters of each equipment;
acquiring overall task demand data;
generating task demands of each type of equipment based on the overall task demand data, and recording the task demands as equipment type task demands;
comprehensively calculating the optimal operation setting parameters of each device based on the device type task requirements and the optimal operation parameter matrix corresponding to each device type set, and acquiring the optimal operation setting parameter matrix corresponding to each device type set;
respectively generating control signals to the devices in each device type set according to the optimal operation setting parameter matrix corresponding to each device type set, and controlling each device to operate according to the optimal operation setting parameters corresponding to each device type set;
and monitoring the operation parameters of all the equipment in real time, judging whether the equipment is in a normal operation state based on the corresponding optimal operation setting parameters, if so, not responding, and if not, outputting an alarm signal to the management terminal.
2. The intelligent management method for comprehensive performance of a multi-device terminal based on the internet of things according to claim 1, wherein the determining the optimal operation parameters of each device based on the design parameters of each device specifically comprises:
determining an average lifetime of each device based on historical operational data of each device;
determining design rated operating parameters of each device;
based on the loss rate of each device in unit time and the designed rated operation parameters of each device, establishing an optimal parameter attenuation model, wherein the optimal parameter attenuation model takes the service life of the device as output and takes the optimal operation parameters of the device as output;
acquiring the actual service life of equipment, inputting the actual service life of the equipment into an optimal parameter attenuation model, and acquiring the optimal operation parameters of the equipment;
the expression of the optimal parameter attenuation model is as follows:
wherein M is 0 For the optimal operating parameters of the plant, M Forehead (forehead) For the design rated operation parameter of the equipment, gamma is the ratio between the design rejection minimum operation parameter value of the equipment and the design rated operation parameter of the equipment, T is the design rejection limit of the equipment, T is the actual service life of the equipment, x is an intermediate variable, alpha 1 、α 2 、α 3 、α 4 Are model parameters.
3. The comprehensive performance intelligent management method of a multi-device terminal based on the internet of things according to claim 2, wherein the generating task demands of each kind of devices based on the overall task demand data specifically comprises;
determining subtasks of the equipment in the internet of things based on the equipment type;
determining the subtask quantity of each device under the unit overall task quantity, and recording the subtask quantity as the unit subtask quantity;
calculating the total subtask amount of each type of equipment according to a subtask amount calculation formula based on the whole task demand amount and the unit subtask amount, and recording the total subtask amount as the task demand of the equipment;
the subtask amount calculation formula is as follows:
wherein r is zw R is the total amount of subtasks 0 In units of subtask quantity, R z R is the unit overall task quantity 0 Is the overall task demand.
4. The intelligent management method for comprehensive performance of a multi-device terminal based on the internet of things according to claim 3, wherein the comprehensive calculation of the optimal operation setting parameters of each device based on the device type task requirements and the optimal operation parameter matrix corresponding to each device type set specifically comprises:
determining the optimal task output quantity of the equipment based on the optimal operation parameters of the equipment, and combining the optimal task output quantities of all the equipment into an optimal task output quantity array of the equipment;
generating a plurality of equipment task allocation quantity arrays based on task demands corresponding to equipment types and optimal task output quantities of equipment;
calculating a differentiation index between each equipment task allocation amount array and the optimal task output amount array of the equipment according to a difference amount calculation formula based on the equipment task allocation amount array and the optimal task output amount array of the equipment;
screening out the equipment task allocation amount array with the minimum difference index as an optimal equipment task allocation amount array;
calculating optimal operation setting parameters of each device based on the task output quantity of each device in the task allocation quantity array of the type optimal device, and forming an optimal operation setting parameter matrix from all the optimal operation setting parameters;
wherein, the difference amount calculation formula is:
wherein C is y For the differentiation index, n is the total number of elements in the equipment type set, w 0i Task allocation amount, w, for ith device in device task allocation amount array 1i The optimal task output of the ith device in the optimal task output array.
5. The method for intelligently managing comprehensive performance of multiple device terminals based on the internet of things according to claim 4, wherein the generating a plurality of device task allocation amount arrays based on the task demands corresponding to the device types and the optimal task output amounts of the devices specifically comprises:
establishing task allocation conditions based on task demands corresponding to the device types and the optimal task output quantity of the device;
generating a plurality of equipment task allocation quantity arrays based on the task allocation conditions;
the task allocation conditional expression is as follows:
wherein w is 0i Task allocation amount, w, for ith device in device task allocation amount array 1i R is the optimal task output of the ith device in the optimal task output array zw ' is the total amount of subtasks corresponding to the device class.
6. The intelligent management method for comprehensive performance of a multi-device terminal based on the internet of things according to claim 5, wherein the monitoring the operation parameters of all devices in real time and judging whether the devices are in a normal operation state based on the corresponding optimal operation setting parameters specifically comprises:
obtaining the maximum error rate of the running parameters of the equipment design;
acquiring historical operating data of the device, and calculating a standard operating error rate of the device based on the historical operating data of the device;
judging whether the standard operation error rate of the equipment is larger than the maximum error rate, if so, the equipment is in a low stable operation state, outputting an alarm signal to the management terminal, and if not, the equipment is in a high stable operation state;
determining, for a device in a high steady state operating condition, a standard operating parameter interval for the device based on its standard operating error rate and an optimal operating setting parameter for the device;
judging whether the real-time operation parameters of the equipment are in the standard operation parameter interval of the equipment, if so, the equipment is in a normal operation state, and if not, the equipment is in an abnormal operation state.
7. The intelligent management method for comprehensive performance of multiple device terminals based on internet of things according to claim 6, wherein the obtaining the historical operating data of the device, calculating the standard operating error rate of the device based on the historical operating data of the device specifically comprises:
calculating and obtaining average operation deviation rate of the equipment in a plurality of equipment operation periods based on historical operation data of the equipment;
based on Grubbs criterion, screening out the credible values in the average operation deviation rate of the equipment in the operation periods of the plurality of equipment;
averaging the trusted values in the average operation deviation rate of the equipment in the operation period of all the equipment to obtain an average value as a standard operation error rate of the equipment;
wherein the Grubbs criterion has the expression:
in the formula, v j For the jth average operating bias rate,s is the standard deviation of the average running deviation rate of the equipment in the running period of all the equipment, bpn is the Grubbs critical value, and the Grubbs critical value is determined by looking up a Grubbs table;
if the expression of the Grubbs criterion is satisfied, v j If the expression of Grubbs criterion is not satisfied, v j Is a trusted value.
8. An intelligent management system for comprehensive performance of a multi-device terminal based on the internet of things, configured to implement the intelligent management method for comprehensive performance of a multi-device terminal based on the internet of things as set forth in any one of claims 1 to 7, and the intelligent management system is characterized by comprising:
the optimal parameter determining module is used for generating an optimal operation parameter matrix corresponding to each equipment type set;
the operation parameter determining module is electrically connected with the optimal parameter determining module and is used for obtaining an optimal operation setting parameter matrix corresponding to each equipment type set;
the control module is electrically connected with the operation parameter determining module and is used for respectively generating control signals to the devices in each device type set according to the optimal operation setting parameter matrix corresponding to each device type set and controlling each device to operate according to the optimal operation setting parameters corresponding to each device type set;
the monitoring module is electrically connected with the operation parameter determining module and is used for monitoring the operation parameters of all the equipment in real time and judging whether the equipment is in a normal operation state or not based on the corresponding optimal operation setting parameters.
9. The intelligent management system for comprehensive performance of multiple device terminals based on internet of things according to claim 8, wherein the optimal parameter determining module is integrated with:
the device type classification unit is used for acquiring all device information accessed to the Internet of things, grouping the types according to the device types and acquiring a plurality of device type sets;
and an optimal parameter calculation unit for determining an optimal operation parameter of each device based on the design parameter of each device.
10. The intelligent management system for comprehensive performance of multiple device terminals based on the internet of things according to claim 9, wherein the operation parameter determining module is internally integrated with:
a subtask amount calculation unit for calculating a total amount of subtasks for each kind of device;
the task allocation unit is used for establishing task allocation conditions based on task demands corresponding to equipment types and optimal task output quantities of the equipment and generating a plurality of equipment task allocation quantity arrays based on the task allocation conditions;
the difference calculation unit is used for calculating a differentiation index between each equipment task allocation amount array and the optimal task output quantity array of the equipment;
the screening unit is used for screening out the equipment task allocation amount array with the minimum differential index as an optimal equipment task allocation amount array;
the operation parameter calculation unit is used for calculating the optimal operation setting parameters of each device based on the task output quantity of each device in the task allocation quantity array of the optimal device, and forming an optimal operation setting parameter matrix from all the optimal operation setting parameters.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311023116.2A CN116991070A (en) | 2023-08-15 | 2023-08-15 | Comprehensive performance intelligent management method and system for multi-equipment terminal based on Internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311023116.2A CN116991070A (en) | 2023-08-15 | 2023-08-15 | Comprehensive performance intelligent management method and system for multi-equipment terminal based on Internet of things |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116991070A true CN116991070A (en) | 2023-11-03 |
Family
ID=88526548
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311023116.2A Pending CN116991070A (en) | 2023-08-15 | 2023-08-15 | Comprehensive performance intelligent management method and system for multi-equipment terminal based on Internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116991070A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117323684A (en) * | 2023-12-01 | 2024-01-02 | 唐山瑞达实业股份有限公司 | Rectifying tower comprehensive temperature control method and system based on temperature wave characteristics |
-
2023
- 2023-08-15 CN CN202311023116.2A patent/CN116991070A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117323684A (en) * | 2023-12-01 | 2024-01-02 | 唐山瑞达实业股份有限公司 | Rectifying tower comprehensive temperature control method and system based on temperature wave characteristics |
| CN117323684B (en) * | 2023-12-01 | 2024-01-30 | 唐山瑞达实业股份有限公司 | Rectifying tower comprehensive temperature control method and system based on temperature wave characteristics |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116991070A (en) | Comprehensive performance intelligent management method and system for multi-equipment terminal based on Internet of things | |
| CN114298863A (en) | Data acquisition method and system of intelligent meter reading terminal | |
| CN107241440B (en) | Method for determining energy-saving strategy of cluster | |
| CN116545954B (en) | Communication gateway data transmission method and system based on internet of things | |
| CN114742487A (en) | Production task management and control method and system based on industrial Internet of things | |
| CN114500339A (en) | Node bandwidth monitoring method and device, electronic equipment and storage medium | |
| CN116244081A (en) | Multi-core calculation integrated accelerator network topology structure control system | |
| CN116320831A (en) | Intelligent park security system based on edge calculation | |
| CN114901057A (en) | Multi-point energy consumption detection and dynamic regulation system in data center machine room | |
| CN113806094A (en) | Cloud platform resource dynamic scheduling method based on deep learning | |
| CN111327492B (en) | Full link monitoring processing method | |
| CN117221069A (en) | Management method and device based on micro-server architecture | |
| CN116557322A (en) | Fan control method and device | |
| CN111915211A (en) | Power resource scheduling method and device and electronic equipment | |
| CN106127396A (en) | A kind of method of intelligent grid medium cloud scheduler task | |
| CN113129168B (en) | Line loss determination method and device for power distribution area and terminal equipment | |
| CN111628901B (en) | Index anomaly detection method and related device | |
| CN115935201A (en) | Monitoring method and device for key emergency equipment GPS and electric quantity data acquisition | |
| CN115766376A (en) | Data analysis processing method based on data analysis of Internet of things equipment | |
| KR102296021B1 (en) | System and Method of DR Potential Estimation Based on Load Characteristics Classification | |
| CN107193706A (en) | A kind of method and apparatus for determining target computer room information technology information technoloy equipment | |
| CN110912957B (en) | Multi-service cluster design method for realizing real-time monitoring of production equipment state | |
| US20190317463A1 (en) | Adaptive energy storage operating system for multiple economic services | |
| CN115995853B (en) | AC/DC power distribution system process management method and system based on power control | |
| CN117650628B (en) | Energy efficiency management system based on self-adaptive preconditioning scene |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |