CN115348293A - Intelligent control remote operation and maintenance method and platform for industrial internet equipment - Google Patents
Intelligent control remote operation and maintenance method and platform for industrial internet equipment Download PDFInfo
- Publication number
- CN115348293A CN115348293A CN202210997400.9A CN202210997400A CN115348293A CN 115348293 A CN115348293 A CN 115348293A CN 202210997400 A CN202210997400 A CN 202210997400A CN 115348293 A CN115348293 A CN 115348293A
- Authority
- CN
- China
- Prior art keywords
- industrial
- data
- product
- sensors
- sub
- 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
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
- G16Y40/00—IoT characterised by the purpose of the information processing
- G16Y40/30—Control
- G16Y40/35—Management of things, i.e. controlling in accordance with a policy or in order to achieve specified objectives
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
Abstract
The embodiment of the application provides an industrial internet device intelligent control remote operation and maintenance method and device, a computer readable medium and an electronic device. The intelligent control remote operation and maintenance method for the industrial internet equipment comprises the following steps: laying sensors in advance in an industrial production environment to acquire industrial data; the method comprises the steps that through a sub-network constructed by a plurality of sensors, industrial data collected by the sensors in the sub-network are obtained on the basis of a preset internet of things gateway; identifying data belonging to operation dimensions in the industrial data and determining the operation state of the industrial equipment; identifying data belonging to product dimensions in the industrial data and determining product performance of the industrial product; and controlling the industrial equipment based on the running state and the product performance. In the embodiment, the sub-network is constructed in the pre-laid sensors to acquire the industrial data, and the industrial data is analyzed through product dimensions and operation dimensions, so that the real-time control of the industrial equipment is realized, and the monitoring strength of the industrial equipment and the control efficiency of industrial production are improved.
Description
The application is a divisional application of an invention application with the application date of 2022, 13 th 06 th month, the Chinese application number of 202210659173.9 and the invention name of an industrial internet equipment intelligent control remote operation and maintenance platform and a method.
Technical Field
The application relates to the technical field of computers, in particular to an intelligent management and control remote operation and maintenance method and device for industrial internet equipment, a computer readable medium and electronic equipment.
Background
The main difference between intelligent control and traditional control is that traditional control methods must rely on models of controlled objects, and compared with traditional control, intelligent control systems have sufficient knowledge about human control strategies, controlled objects and the environment, and the ability to apply such knowledge. In practical applications, intelligent control is widely applied to the machinery manufacturing industry. However, in many scenes, full-automatic remote control and operation and maintenance of the equipment cannot be realized, and particularly under the conditions of large and complex industrial production environment, the actual production efficiency is greatly influenced.
Disclosure of Invention
The embodiment of the application provides an industrial internet device intelligent control remote operation and maintenance method, device, computer readable medium and electronic device, and further production monitoring efficiency can be improved to at least a certain extent.
Other features and advantages of the present application will be apparent from the following detailed description, or may be learned by practice of the application.
According to one aspect of the embodiment of the application, an intelligent management and control remote operation and maintenance method for industrial internet equipment is provided, and comprises the following steps: in an industrial production environment, sensors are distributed in advance; the sensor is used for acquiring industrial data; acquiring industrial data acquired by the sensors in the sub-network through the sub-network constructed by the sensors based on a preset internet of things gateway; identifying data belonging to operation dimensions in the industrial data, and determining the operation state of the industrial equipment based on the data of the operation dimensions; identifying data belonging to product dimensions in the industrial data, and determining the product performance of the industrial product based on the data of the product dimensions; and controlling the industrial equipment based on the running state and the product performance.
In some embodiments of the present application, based on the foregoing solution, the acquiring, by a sub-network constructed by a plurality of the sensors, industrial data collected by the sensors in the sub-network based on a preset internet of things gateway includes: constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors of the sub-network; acquiring industrial data acquired by sensors in the sub-network through a target sensor; and acquiring the industrial data from a target sensor based on a preset internet of things gateway.
In some embodiments of the present application, based on the foregoing solution, the constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors in the sub-network, includes: synchronizing machine information of the sensors in a network layer, wherein the machine information comprises stored data volume, data acquisition volume in unit time, machine position and machine type; identifying similar sensors belonging to the same machine type based on the machine type; for the similar sensors, constructing the sub-network among the similar sensors with the distances smaller than a set distance threshold; and selecting a target sensor from the sensors in the sub-network.
In some embodiments of the present application, based on the foregoing solution, the selecting a target sensor from the sensors in the sub-network includes: calculating the data processing efficiency of the sensor based on the data quantity stored by the sensor of the sub-network and the data acquisition quantity in unit time; calculating a distance average of distances between each sensor and the rest of the sensors in the sub-network based on the positions of the sensors; determining a target parameter based on the data processing efficiency and the distance average; and selecting the sensor corresponding to the maximum target parameter as the target sensor.
In some embodiments of the present application, based on the foregoing solution, the acquiring, by the target sensor, the industrial data collected by the sensors in the sub-network includes: and collecting industrial data collected by the sensors in the sub-network through the target sensor, and merging the industrial data.
In some embodiments of the present application, based on the foregoing solution, the identifying data belonging to an operation dimension in the industrial data, and determining an operation state of the industrial device based on the data of the operation dimension includes: identifying a data type of the industrial data, and determining data belonging to an operation dimension based on the data type; the operation dimension comprises operation speed, unit production and operation time; determining state parameters of the industrial equipment based on the data of the operation dimension; and determining a busy green level representing the operation state of the industrial equipment based on the comparison result between the state parameter and the set threshold value.
In some embodiments of the present application, based on the foregoing solution, the identifying data belonging to a product dimension in the industrial data, and determining a product performance of the industrial product based on the data of the product dimension includes: identifying a data type of the industrial data, and determining data belonging to a product dimension based on the data type; the product dimension comprises product test data; determining product parameters of an industrial product based on the product test data; determining a performance level indicative of product performance of the industrial product based on a comparison between the product parameter and a set threshold.
According to an aspect of the embodiment of the application, an intelligent management and control remote operation and maintenance platform for industrial internet equipment is provided, which comprises:
the hardware unit is used for laying the sensors in advance in an industrial production environment; the sensor is used for collecting industrial data;
the data unit is used for acquiring industrial data acquired by the sensors in the sub-network through the sub-network constructed by the sensors based on a preset internet of things gateway;
the device unit is used for identifying data belonging to operation dimensions in the industrial data and determining the operation state of the industrial device based on the data of the operation dimensions;
the product unit is used for identifying data belonging to product dimensions in the industrial data and determining the product performance of the industrial product based on the data of the product dimensions;
and the control unit is used for controlling the industrial equipment based on the running state and the product performance.
In some embodiments of the present application, based on the foregoing solution, the acquiring, by a sub-network constructed by a plurality of the sensors, industrial data collected by the sensors in the sub-network based on a preset internet of things gateway includes: constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors of the sub-network; acquiring industrial data acquired by sensors in the sub-network through a target sensor; and acquiring the industrial data from a target sensor based on a preset internet of things gateway.
In some embodiments of the present application, based on the foregoing solution, the constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors in the sub-network, includes: synchronizing machine information of the sensor in a network layer, wherein the machine information comprises stored data volume, data acquisition volume in unit time, machine position and machine type; identifying similar sensors belonging to the same machine type based on the machine type; for the similar sensors, constructing the sub-network among the similar sensors with the distances smaller than a set distance threshold; and selecting a target sensor from the sensors in the sub-network.
In some embodiments of the present application, based on the foregoing solution, the selecting a target sensor from the sensors in the sub-network includes: calculating the data processing efficiency of the sensor based on the data quantity stored by the sensor of the sub-network and the data acquisition quantity in unit time; calculating a distance average of distances between each sensor and the rest of the sensors in the sub-network based on the positions of the sensors; determining a target parameter based on the data processing efficiency and the distance average; and selecting the sensor corresponding to the maximum target parameter as the target sensor.
In some embodiments of the present application, based on the foregoing solution, the acquiring, by the target sensor, the industrial data collected by the sensors in the sub-network includes: and collecting industrial data collected by the sensors in the sub-network through the target sensor, and merging the industrial data.
In some embodiments of the present application, based on the foregoing solution, the identifying data belonging to an operation dimension in the industrial data, and determining an operation state of the industrial device based on the data of the operation dimension includes: identifying a data type of the industrial data, and determining data belonging to an operation dimension based on the data type; the operation dimension comprises operation speed, unit production and operation time; determining state parameters of the industrial equipment based on the data of the operation dimension; and determining a busy green level representing the operation state of the industrial equipment based on the comparison result between the state parameter and the set threshold value.
In some embodiments of the present application, based on the foregoing solution, the identifying data belonging to product dimensions in the industrial data, and determining the product performance of the industrial product based on the data of the product dimensions includes: identifying a data type of the industrial data, and determining data belonging to a product dimension based on the data type; the product dimension comprises product test data; determining product parameters of an industrial product based on the product test data; determining a performance level indicative of product performance of the industrial product based on a comparison between the product parameter and a set threshold.
According to an aspect of the embodiments of the present application, there is provided a computer readable medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for intelligently managing and remotely operating and maintaining an industrial internet device as described in the above embodiments.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are enabled to realize the intelligent management and remote operation and maintenance method for the industrial internet equipment in the embodiment.
According to an aspect of an embodiment of the present application, there is provided a computer program product or a computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, so that the computer device executes the method for intelligently managing and remotely maintaining the industrial internet device provided in the above various optional implementation modes.
In the technical scheme provided by some embodiments of the application, in an industrial production environment, sensors are arranged in advance to acquire industrial data; acquiring industrial data acquired by sensors in a sub-network through the sub-network constructed by the sensors based on a preset internet of things gateway; identifying data belonging to operation dimensions in the industrial data, and determining the operation state of the industrial equipment based on the data of the operation dimensions; identifying data belonging to product dimensions in the industrial data, and determining the product performance of the industrial product based on the data of the product dimensions; and controlling the industrial equipment based on the running state and the product performance. In the embodiment, the sub-networks are constructed in the sensors which are distributed in advance to collect the industrial data, and then the industrial data are analyzed through product dimensions and operation dimensions so as to control the industrial equipment in real time, so that the monitoring strength of the industrial equipment is improved, and the management and control efficiency of industrial production is also improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 schematically shows a flowchart of an intelligent management and control remote operation and maintenance method for an industrial internet device according to an embodiment of the present application;
FIG. 2 schematically illustrates a flow diagram for obtaining industrial data according to an embodiment of the present application;
fig. 3 schematically illustrates a schematic diagram of an industrial internet device intelligent management and control remote operation and maintenance platform according to an embodiment of the present application;
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use in implementing the electronic device of an embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the application.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The implementation details of the technical solution of the embodiment of the present application are set forth in detail below:
fig. 1 shows a flowchart of an industrial internet device intelligent management and control remote operation and maintenance method according to an embodiment of the present application. Referring to fig. 1, the method for intelligently managing and controlling remote operation and maintenance of industrial internet equipment at least includes steps S110 to S150, which are described in detail as follows:
in step S110, in an industrial production environment, sensors are laid out in advance; the sensor is used for collecting industrial data.
In one embodiment of the present application, sensors are pre-deployed in an industrial production environment for collecting industrial data. The sensors in this embodiment may be various types of sensors, and may collect operation data of industrial equipment, and may also collect product data of industrial products.
In step S120, industrial data collected by the sensors in the sub-network is obtained through the sub-network constructed by the plurality of sensors based on a preset internet of things gateway.
In one embodiment of the application, a sub-network is constructed among a plurality of sensors, so that the data of the sensors are collected through the sub-network, and the industrial data collected by the sensors in the sub-network is collected based on a preset internet of things gateway.
In an embodiment of the present application, as shown in fig. 2, the acquiring, by a sub-network constructed by a plurality of the sensors, industrial data collected by the sensors in the sub-network based on a preset internet of things gateway includes S210 to S230:
s210, constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors of the sub-network;
s220, acquiring industrial data acquired by the sensors in the sub-network through a target sensor;
and S230, acquiring the industrial data from the target sensor based on a preset internet of things gateway.
Constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors of the sub-network, wherein the method comprises the following steps:
synchronizing machine information of the sensor in a network layer, wherein the machine information comprises stored data volume, data acquisition volume in unit time, machine position and machine type;
identifying similar sensors belonging to the same machine type based on the machine type;
for the similar sensors, constructing the sub-network among the similar sensors with the distance smaller than a set distance threshold;
and selecting a target sensor from the sensors in the sub-network.
Specifically, in this embodiment, machine information of each sensor is synchronized in a network layer, so that each sensor can acquire information of other sensors to perform free networking. The machine information in the present embodiment includes the amount of data stored by the sensor, the data collection amount per unit time, the machine position, and the machine type, and the like.
Then identifying the same type of sensor belonging to the same machine type based on the machine type; for the similar sensors, the sub-networks are constructed by freely networking among the similar sensors with the distances smaller than the set distance threshold value, so that the similar sensors belonging to the same type are in the same sub-network, and the data acquisition and transmission are facilitated.
After a sub-network is constructed, selecting a target sensor from the sensors of the sub-network, comprising the following steps:
calculating the data processing efficiency of the sensor based on the data quantity stored by the sensor of the sub-network and the data acquisition quantity in unit time;
calculating a distance average of distances between each sensor and the rest of the sensors in the sub-network based on the positions of the sensors;
determining a target parameter based on the data processing efficiency and the distance average;
and selecting the sensor corresponding to the maximum target parameter as the target sensor.
Specifically, in an embodiment of the present application, calculating the data processing efficiency dsta _ fec of the sensor based on the data amount dsta _ vol stored by the sensor of the sub-network and the data acquisition amount dsta _ col in unit time is as follows:
Dta_fec＝1-α·Dta_col·Dta_vol/Dta_max；
where Dta _ max represents the maximum storage amount of each sensor, and in this embodiment, the maximum storage amounts of all sensors are the same by default, and α represents a preset storage factor. In the present embodiment, the storage ratio and the data acquisition density of the sensor are considered to evaluate the degree of the busy or green state of the sensor, and the higher the two values are, the more busy the sensor is, that is, the lower the efficiency of data processing in the future is.
Based on the location of the sensors, an average of the distances between each sensor and the remaining sensors in the sub-network is determined by calculating the linear distance between the two points. And then determining the target parameter of the sensor i to be Par _ sen (i) based on the data processing efficiency Dta _ fec and the distance average value Dit:
wherein β represents a predetermined parameter factor, and p is a randomly generated random number. And after the target parameters are obtained through calculation, selecting the sensor corresponding to the maximum target parameters as the target sensor. According to the scheme, the data processing efficiency and the distance average value are considered in the determination of the target parameters, so that the comprehensiveness and objectivity of the target sensor evaluation are improved.
In addition, in this embodiment, the sensors may be randomly selected from the sub-network as the target sensors, or the sensors may be sequentially and alternately used as the target sensors in sequence based on the machine identifiers of the sensors.
After a sub-network is constructed and a target sensor is selected, industrial data collected by sensors in the sub-network are collected through the target sensor, and the industrial data are combined, so that the repetition rate of the industrial data is reduced, and data redundancy is reduced. And then, based on a preset internet of things gateway, sending the collected data to a management server through a target sensor. By the method, the efficiency of data transmission is improved, and the energy consumption in the data transmission process is reduced.
In step S130, data belonging to an operation dimension in the industrial data is identified, and an operation state of the industrial device is determined based on the data of the operation dimension.
In an embodiment of the application, after the upper computer or the management server obtains the industrial data, data belonging to the operation dimension are screened out from the industrial data, so that the operation state of the industrial equipment is evaluated based on the data of the operation dimension.
In one embodiment of the present application, identifying data belonging to an operation dimension in industrial data, and determining an operation state of an industrial device based on the data of the operation dimension includes:
identifying a data type of the industrial data, and determining data belonging to an operation dimension based on the data type; the operation dimension comprises operation speed, unit production and operation time;
determining a state parameter of the industrial equipment based on the data of the operation dimension;
and determining a busy green level representing the operation state of the industrial equipment based on the comparison result between the state parameter and the set threshold value.
In identifying the data type of the industrial data, the determination may be based on the type of sensor that generated the industrial data. And screening out data corresponding to the running speed run _ spe, the unit yield opt _ uni and the running time run _ tim as running dimension data.
Determining the state parameter par _ que of the industrial device based on the data of the operation dimension may be:
par_que＝γ 1 ·run_spe+γ 2 ·opt_uni+γ 3 ·run_tim
wherein, γ 1 、γ 2 、γ 3 Representing a preset parameter factor. And after the state parameter is obtained through calculation, comparing the state parameter with a set threshold value, determining the busy green level to which the state parameter belongs, and representing the operation state of the industrial equipment through the busy green level. Wherein, the higher the busy green level, the heavier the work task load of the industrial equipment is represented.
In step S140, data belonging to a product dimension in the industrial data is identified, and a product performance of the industrial product is determined based on the data of the product dimension.
In one embodiment of the application, after the upper computer or the management server obtains the industrial data, data belonging to product dimensions are screened out from the industrial data, so that the product performance is evaluated based on the data of the product dimensions.
In one embodiment of the present application, identifying data belonging to product dimensions in industrial data, and determining product performance of an industrial product based on the data of the product dimensions, includes:
identifying a data type of the industrial data, and determining data belonging to a product dimension based on the data type; the product dimension comprises product test data;
determining product parameters of an industrial product based on the product test data;
determining a performance level indicative of product performance of the industrial product based on a comparison between the product parameter and a set threshold.
In identifying the data type of the industrial data, the determination may be based on the type of sensor that generated the industrial data. Then screening out product test data dta _ tes from the product test data, and determining product parameters pru _ que of the industrial product as follows based on the product test data:
pru_que＝η·dta_tes
where η represents a predetermined parameter factor. After the product parameters are obtained through calculation, the product parameters are compared with a set threshold value, the performance grade to which the product parameters belong is determined, and the running state of the industrial product is represented through the performance grade. Wherein a higher performance grade indicates a better quality of the industrial product.
In step S150, the industrial equipment is controlled based on the operating state and the product performance.
In one embodiment of the application, after the operation state of the industrial equipment and the product performance of the industrial product produced by the industrial equipment are determined, the industrial equipment is controlled based on the condition. For example, when the operation state is a three-level busy-green state, the suspended industrial equipment is in a standby state, and when the state of the product performance level is lower or the state of the industrial products exceeding a certain number is a fault, the operation of the industrial equipment is stopped, and fault detection is performed. The efficiency and the reliability of the operation of the industrial equipment are improved through the method.
In the technical scheme provided by some embodiments of the application, in an industrial production environment, sensors are arranged in advance to acquire industrial data; acquiring industrial data acquired by the sensors in the sub-network through the sub-network constructed by the sensors based on a preset internet of things gateway; identifying data belonging to operation dimensions in the industrial data, and determining the operation state of the industrial equipment based on the data of the operation dimensions; identifying data belonging to product dimensions in the industrial data, and determining the product performance of the industrial product based on the data of the product dimensions; and controlling the industrial equipment based on the running state and the product performance. In the embodiment, the sub-networks are constructed in the sensors which are distributed in advance to collect the industrial data, and then the industrial data are analyzed through product dimensions and operation dimensions so as to control the industrial equipment in real time, so that the monitoring strength of the industrial equipment is improved, and the management and control efficiency of industrial production is also improved.
The following introduces an embodiment of the apparatus of the present application, which may be used to execute the intelligent management and control remote operation and maintenance method for the industrial internet device in the foregoing embodiment of the present application. It will be appreciated that the apparatus may be a computer program (comprising program code) running on a computer device, for example an application software; the apparatus may be configured to perform corresponding steps in the methods provided in the embodiments of the present application. For details not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the method for intelligently managing and controlling remote operation and maintenance of the industrial internet device described above.
Fig. 3 shows a block diagram of an industrial internet device intelligent management remote operation and maintenance platform according to an embodiment of the present application.
Referring to fig. 3, an industrial internet device intelligent management and control remote operation and maintenance platform 300 according to an embodiment of the present application includes:
a hardware unit 310, which is used for laying sensors in advance in an industrial production environment; the sensor is used for acquiring industrial data;
the data unit 320 is configured to acquire industrial data acquired by the sensors in the sub-network through the sub-network constructed by the plurality of sensors based on a preset internet of things gateway;
the equipment unit 330 is configured to identify data belonging to an operation dimension in the industrial data, and determine an operation state of the industrial equipment based on the data of the operation dimension;
the product unit 340 is configured to identify data belonging to a product dimension in the industrial data, and determine a product performance of the industrial product based on the data of the product dimension;
a control unit 350, configured to control the industrial equipment based on the operation state and the product performance.
In some embodiments of the present application, based on the foregoing solution, the acquiring, by a sub-network constructed by a plurality of the sensors, industrial data collected by the sensors in the sub-network based on a preset internet of things gateway includes: constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors of the sub-network; acquiring industrial data acquired by sensors in the sub-network through a target sensor; and acquiring the industrial data from a target sensor based on a preset internet of things gateway.
In some embodiments of the present application, based on the foregoing solution, the constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors in the sub-network, includes: synchronizing machine information of the sensors in a network layer, wherein the machine information comprises stored data volume, data acquisition volume in unit time, machine position and machine type; identifying similar sensors belonging to the same machine type based on the machine type; for the similar sensors, constructing the sub-network among the similar sensors with the distances smaller than a set distance threshold; and selecting a target sensor from the sensors in the sub-network.
In some embodiments of the present application, based on the foregoing solution, the selecting a target sensor from the sensors in the sub-network includes: calculating the data processing efficiency of the sensor based on the data quantity stored by the sensor of the sub-network and the data acquisition quantity in unit time; calculating a distance average of distances between each sensor and the rest of the sensors in the sub-network based on the positions of the sensors; determining a target parameter based on the data processing efficiency and the distance average; and selecting the sensor corresponding to the maximum target parameter as the target sensor.
In some embodiments of the present application, based on the foregoing solution, the acquiring, by a target sensor, industrial data collected by sensors in the sub-network includes: and collecting industrial data collected by the sensors in the sub-network through the target sensor, and merging the industrial data.
In some embodiments of the present application, based on the foregoing solution, the identifying data belonging to an operation dimension in the industrial data, and determining an operation state of the industrial device based on the data of the operation dimension includes: identifying a data type of the industrial data, and determining data belonging to an operation dimension based on the data type; the operation dimension comprises operation speed, unit production and operation time; determining state parameters of the industrial equipment based on the data of the operation dimension; and determining a busy green level representing the operation state of the industrial equipment based on the comparison result between the state parameter and the set threshold value.
In some embodiments of the present application, based on the foregoing solution, the identifying data belonging to product dimensions in the industrial data, and determining the product performance of the industrial product based on the data of the product dimensions includes: identifying a data type of the industrial data, and determining data belonging to a product dimension based on the data type; the product dimension comprises product test data; determining product parameters of an industrial product based on the product test data; determining a performance level indicative of product performance of the industrial product based on a comparison between the product parameter and a set threshold.
In the technical scheme provided by some embodiments of the application, in an industrial production environment, sensors are arranged in advance to acquire industrial data; acquiring industrial data acquired by the sensors in the sub-network through the sub-network constructed by the sensors based on a preset internet of things gateway; identifying data belonging to operation dimensions in the industrial data, and determining the operation state of the industrial equipment based on the data of the operation dimensions; identifying data belonging to product dimensions in the industrial data, and determining the product performance of the industrial product based on the data of the product dimensions; and controlling the industrial equipment based on the running state and the product performance. In the embodiment, the sub-networks are constructed in the sensors which are distributed in advance to collect the industrial data, and then the industrial data are analyzed through product dimensions and operation dimensions so as to control the industrial equipment in real time, so that the monitoring strength of the industrial equipment is improved, and the management and control efficiency of industrial production is also improved.
FIG. 4 illustrates a schematic structural diagram of a computer system suitable for use to implement the electronic device of the embodiments of the subject application.
It should be noted that the computer system 400 of the electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the application scope of the embodiments of the present application.
As shown in fig. 4, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, according to a program stored in a Read-Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for system operation are also stored. The CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An Input/Output (I/O) interface 405 is also connected to the bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a Display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. A driver 410 is also connected to the I/O interface 405 as needed. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 as necessary, so that a computer program read out therefrom is mounted into the storage section 408 as necessary.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409 and/or installed from the removable medium 411. The computer program executes various functions defined in the system of the present application when executed by a Central Processing Unit (CPU) 401.
It should be noted that the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM), a flash Memory, an optical fiber, a portable Compact Disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer-readable signal medium may include a propagated data signal with a computer program embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present application may be implemented by software, or may be implemented by hardware, and the described units may also be disposed in a processor. Wherein the names of the elements do not in some way constitute a limitation on the elements themselves.
According to an aspect of the application, a computer program product or computer program is provided, comprising computer instructions, the computer instructions being stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method provided in the various alternative implementations described above.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the electronic device described in the above embodiments; or may be separate and not incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by an electronic device, cause the electronic device to implement the method described in the above embodiments.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (8)
1. The utility model provides a long-range fortune dimension method of industry internet equipment intelligence management and control which characterized in that includes:
in an industrial production environment, sensors are distributed in advance; the sensor is used for acquiring industrial data;
acquiring industrial data acquired by sensors in a sub-network through the sub-network constructed by the sensors based on a preset internet of things gateway;
identifying data belonging to operation dimensions in the industrial data, and determining the operation state of the industrial equipment based on the data of the operation dimensions;
identifying data belonging to product dimensions in the industrial data, and determining the product performance of the industrial product based on the data of the product dimensions;
and controlling the industrial equipment based on the running state and the product performance.
2. The method of claim 1, wherein the acquiring of the industrial data collected by the sensors in the sub-network based on the preset internet of things gateway through the sub-network constructed by a plurality of the sensors comprises:
constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors of the sub-network;
acquiring industrial data acquired by sensors in the sub-network through a target sensor;
and acquiring the industrial data from the target sensor based on a preset internet of things gateway.
3. The method of claim 2, wherein acquiring the industrial data collected by the sensors in the sub-network via the target sensor comprises:
and collecting industrial data collected by the sensors in the sub-network through the target sensor, and merging the industrial data.
4. The method of claim 1, wherein identifying data of the industrial data that belongs to an operational dimension and determining an operational state of the industrial equipment based on the data of the operational dimension comprises:
identifying a data type of the industrial data, and determining data belonging to an operation dimension based on the data type; the operation dimension comprises operation speed, unit production and operation time;
determining state parameters of the industrial equipment based on the data of the operation dimension;
and determining a busy green level representing the operation state of the industrial equipment based on the comparison result between the state parameter and the set threshold value.
5. The method of claim 1, wherein identifying data in the industrial data pertaining to product dimensions and determining product performance of the industrial product based on the data for the product dimensions comprises:
identifying a data type of the industrial data, and determining data belonging to a product dimension based on the data type; the product dimension comprises product test data;
determining product parameters of an industrial product based on the product test data;
determining a performance level indicative of product performance of the industrial product based on a comparison between the product parameter and a set threshold.
6. The utility model provides a long-range fortune dimension platform of industry internet equipment intelligence management and control which characterized in that includes:
the hardware unit is used for laying the sensors in advance in an industrial production environment; the sensor is used for collecting industrial data;
the data unit is used for acquiring industrial data acquired by the sensors in the sub-network through the sub-network constructed by the sensors based on a preset internet of things gateway;
the device unit is used for identifying data belonging to operation dimensions in the industrial data and determining the operation state of the industrial device based on the data of the operation dimensions;
the product unit is used for identifying data belonging to product dimensions in the industrial data and determining the product performance of the industrial product based on the data of the product dimensions;
and the control unit is used for controlling the industrial equipment based on the running state and the product performance.
7. A computer-readable medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method for intelligently managing and remotely maintaining an industrial internet device according to any one of claims 1 to 5.
8. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the intelligent management remote operation and maintenance method for industrial internet devices as claimed in any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210997400.9A CN115348293A (en) | 2022-06-13 | 2022-06-13 | Intelligent control remote operation and maintenance method and platform for industrial internet equipment |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210659173.9A CN114760341B (en) | 2022-06-13 | 2022-06-13 | Intelligent control remote operation and maintenance platform and method for industrial internet equipment |
| CN202210997400.9A CN115348293A (en) | 2022-06-13 | 2022-06-13 | Intelligent control remote operation and maintenance method and platform for industrial internet equipment |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210659173.9A Division CN114760341B (en) | 2022-06-13 | 2022-06-13 | Intelligent control remote operation and maintenance platform and method for industrial internet equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115348293A true CN115348293A (en) | 2022-11-15 |
Family
ID=82337192
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210997400.9A Pending CN115348293A (en) | 2022-06-13 | 2022-06-13 | Intelligent control remote operation and maintenance method and platform for industrial internet equipment |
| CN202210659173.9A Active CN114760341B (en) | 2022-06-13 | 2022-06-13 | Intelligent control remote operation and maintenance platform and method for industrial internet equipment |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210659173.9A Active CN114760341B (en) | 2022-06-13 | 2022-06-13 | Intelligent control remote operation and maintenance platform and method for industrial internet equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (2) | CN115348293A (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115801845B (en) * | 2023-02-07 | 2023-09-05 | 深圳市玄羽科技有限公司 | Industrial Internet data acquisition method and related equipment based on edge calculation |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8041772B2 (en) * | 2005-09-07 | 2011-10-18 | International Business Machines Corporation | Autonomic sensor network ecosystem |
| US20170351226A1 (en) * | 2016-06-01 | 2017-12-07 | Rockwell Automation Technologies, Inc. | Industrial machine diagnosis and maintenance using a cloud platform |
| CN112099449A (en) * | 2020-09-10 | 2020-12-18 | 南京柏瑞斯特智能科技有限公司 | Equipment data acquisition system based on industrial Internet of things cloud platform |
| CN113468022B (en) * | 2021-07-01 | 2024-02-09 | 丁鹤 | Automatic operation and maintenance method for centralized monitoring of products |
| CN113805548B (en) * | 2021-09-18 | 2023-04-14 | 深圳市玄羽科技有限公司 | Machining intelligent control system, machining intelligent control method and computer readable medium |
| CN113569117B (en) * | 2021-09-27 | 2022-01-04 | 深圳市玄羽科技有限公司 | Cloud platform system and method based on industrial internet big data service |
| CN113885408A (en) * | 2021-11-03 | 2022-01-04 | 江西省智能产业技术创新研究院 | Remote operation and maintenance system |
-
2022
- 2022-06-13 CN CN202210997400.9A patent/CN115348293A/en active Pending
- 2022-06-13 CN CN202210659173.9A patent/CN114760341B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN114760341A (en) | 2022-07-15 |
| CN114760341B (en) | 2022-09-30 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN115122155B (en) | Machine tool remote diagnosis method and system based on industrial internet big data | |
| CN113569117B (en) | Cloud platform system and method based on industrial internet big data service | |
| CN109743356B (en) | Industrial internet data acquisition method and device, readable storage medium and terminal | |
| CN114666224A (en) | Dynamic allocation method, device, equipment and storage medium for business resource capacity | |
| CN114757587B (en) | Product quality control system and method based on big data | |
| CN114429250B (en) | Fault prediction method and device applied to industrial Internet and electronic equipment | |
| CN116862081B (en) | Operation and maintenance method and system for pollution treatment equipment | |
| CN117078048B (en) | Digital twinning-based intelligent city resource management method and system | |
| CN114760341B (en) | Intelligent control remote operation and maintenance platform and method for industrial internet equipment | |
| CN114977176A (en) | Power load decomposition method, device, equipment and storage medium | |
| CN114491171A (en) | Data processing method, system, medium and electronic device based on industrial Internet of things | |
| CN115809818A (en) | Multidimensional diagnosis and evaluation method and device for auxiliary equipment of pumped storage power station | |
| CN115935285A (en) | Multi-element time series anomaly detection method and system based on mask map neural network model | |
| CN114821985A (en) | Industrial early warning system and method based on artificial intelligence | |
| CN117092309B (en) | Community water supply quality monitoring method and system based on big data | |
| CN111625727B (en) | Information processing method, device and storage medium for social relationship data | |
| CN117291311A (en) | Power consumption prediction method and device, electronic equipment and storage medium | |
| CN115128986A (en) | Industrial internet yield real-time monitoring system and method based on SaaS (software as a service) level | |
| CN115879041A (en) | Method and system for evaluating health state of production line equipment and computer equipment | |
| CN112560882A (en) | Data output system anomaly detection method, device, equipment and readable storage medium | |
| CN115602245A (en) | Method, device and equipment for screening fluorescence map and storage medium | |
| CN117407158A (en) | Cluster capacity expansion method and device, electronic equipment and storage medium | |
| CN117907706A (en) | Smart grid fault judging method and device, electronic equipment and storage medium | |
| CN116962462A (en) | Multi-agent cooperative data transmission analysis device and system | |
| CN116450445A (en) | API information processing and LSTM model training method and device, equipment and medium |
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 |