Industrial internet equipment intelligent management and control remote operation and maintenance method and platform

Abstract

This application provides an embodiment of a method, apparatus, computer-readable medium, and electronic device for intelligent management and remote operation and maintenance of industrial internet devices. The method includes: pre-deploying sensors in an industrial production environment to collect industrial data; acquiring the industrial data collected by the sensors in a sub-network constructed from multiple sensors, based on a preset IoT gateway; identifying data belonging to the operational dimension of the industrial data and determining the operating status of the industrial equipment; identifying data belonging to the product dimension of the industrial data and determining the product performance of the industrial product; and controlling the industrial equipment based on the operating status and the product performance. In this embodiment, by constructing a sub-network from pre-deployed sensors to collect industrial data, and analyzing the industrial data through product and operational dimensions, real-time control of the industrial equipment is achieved, improving the monitoring capabilities of the industrial equipment and the management efficiency of industrial production.

Description

[0001] This application is a divisional application of the invention application filed on June 13, 2022, with Chinese application number 202210659173.9 and entitled "Intelligent Management and Remote Operation and Maintenance Platform and Method for Industrial Internet Equipment". Technical Field

[0002] This application relates to the field of computer technology, and more specifically, to a method, apparatus, computer-readable medium, and electronic device for intelligent management and remote operation and maintenance of industrial internet equipment. Background Technology

[0003] The main difference between intelligent control and traditional control lies in the fact that traditional control methods rely on a model of the controlled object. In contrast, intelligent control systems possess sufficient knowledge about human control strategies, the controlled object, and the environment, as well as the ability to apply this knowledge. In practical applications, intelligent control is widely used in the machinery manufacturing industry. However, there are still many scenarios where fully automated remote management and maintenance of equipment cannot be achieved, especially in large and complex industrial production environments, which significantly impacts actual production efficiency. Summary of the Invention

[0004] The embodiments of this application provide a method, apparatus, computer-readable medium, and electronic device for intelligent management and remote operation and maintenance of industrial Internet equipment, which can improve production monitoring efficiency to at least a certain extent.

[0005] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0006] According to one aspect of the embodiments of this application, a method for intelligent control and remote operation and maintenance of industrial Internet devices is provided, comprising: pre-deploying sensors in an industrial production environment; the sensors being used to collect industrial data; acquiring the industrial data collected by the sensors in a sub-network constructed by multiple sensors, based on a preset Internet of Things gateway; identifying data belonging to the operational dimension in the industrial data, and determining the operating status of the industrial equipment based on the data belonging to the operational dimension; identifying data belonging to the product dimension in the industrial data, and determining the product performance of the industrial product based on the data belonging to the product dimension; and controlling the industrial equipment based on the operating status and the product performance.

[0007] In some embodiments of this application, based on the foregoing scheme, the sub-network constructed through multiple sensors acquires industrial data collected by the sensors in the sub-network based on a preset IoT gateway, including: constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors in the sub-network; acquiring the industrial data collected by the sensors in the sub-network through the target sensor; and acquiring the industrial data from the target sensor based on the preset IoT gateway.

[0008] In some embodiments of this application, based on the foregoing scheme, the step of constructing a sub-network based on the machine information of the sensor and selecting a target sensor from the sensors in the sub-network includes: synchronizing the machine information of the sensor in the network layer, including the amount of data stored, the amount of data collected per unit time, the machine location, and the machine type; identifying similar sensors belonging to the same machine type based on the machine type; for similar sensors, constructing the sub-network among similar sensors whose distance is less than a set distance threshold; and selecting a target sensor from the sensors in the sub-network.

[0009] In some embodiments of this application, based on the foregoing scheme, the step of selecting a target sensor from the sensors in the sub-network includes: calculating the data processing efficiency of the sensor based on the amount of data stored in the sensors of the sub-network and the amount of data collected per unit time; calculating the average distance between each sensor in the sub-network and the other sensors based on the sensor's position; determining the target parameter based on the data processing efficiency and the average distance; and selecting the sensor corresponding to the maximum target parameter as the target sensor.

[0010] In some embodiments of this application, based on the foregoing scheme, the step of acquiring industrial data collected by sensors in the sub-network through the target sensor includes: collecting industrial data collected by sensors in the sub-network through the target sensor, and merging the industrial data.

[0011] In some embodiments of this application, based on the foregoing scheme, identifying data belonging to the operational dimension in industrial data and determining the operational status of industrial equipment based on the data of the operational dimension includes: identifying the data type of the industrial data, and determining data belonging to the operational dimension based on the data type; the operational dimension includes operating speed, unit output, and operating time; determining the status parameters of the industrial equipment based on the data of the operational dimension; and determining the busy / green level representing the operational status of the industrial equipment based on the comparison result between the status parameters and a set threshold.

[0012] In some embodiments of this application, based on the foregoing scheme, identifying data belonging to the product dimension in industrial data and determining the product performance of industrial products based on the product dimension data includes: identifying the data type of the industrial data, determining data belonging to the product dimension based on the data type; the product dimension includes product test data; determining product parameters of industrial products based on the product test data; and determining a performance level representing the product performance of industrial products based on the comparison result between the product parameters and a set threshold.

[0013] According to one aspect of the embodiments of this application, an intelligent management and remote operation and maintenance platform for industrial internet devices is provided, comprising:

[0014] A hardware unit for pre-deploying sensors in an industrial production environment; the sensors are used to collect industrial data.

[0015] A data unit is used to acquire industrial data collected by sensors in a sub-network constructed through multiple sensors, based on a preset IoT gateway.

[0016] The equipment unit is used to identify data belonging to the operational dimension in industrial data, and to determine the operating status of industrial equipment based on the data in the operational dimension.

[0017] Product unit, used to identify data belonging to the product dimension in industrial data, and to determine the product performance of industrial products based on the data of the product dimension;

[0018] A control unit is used to control industrial equipment based on the operating status and the product performance.

[0019] In some embodiments of this application, based on the foregoing scheme, the sub-network constructed through multiple sensors acquires industrial data collected by the sensors in the sub-network based on a preset IoT gateway, including: constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors in the sub-network; acquiring the industrial data collected by the sensors in the sub-network through the target sensor; and acquiring the industrial data from the target sensor based on the preset IoT gateway.

[0020] In some embodiments of this application, based on the foregoing scheme, the step of constructing a sub-network based on the machine information of the sensor and selecting a target sensor from the sensors in the sub-network includes: synchronizing the machine information of the sensor in the network layer, including the amount of data stored, the amount of data collected per unit time, the machine location, and the machine type; identifying similar sensors belonging to the same machine type based on the machine type; for similar sensors, constructing the sub-network among similar sensors whose distance is less than a set distance threshold; and selecting a target sensor from the sensors in the sub-network.

[0021] In some embodiments of this application, based on the foregoing scheme, the step of selecting a target sensor from the sensors in the sub-network includes: calculating the data processing efficiency of the sensor based on the amount of data stored in the sensors of the sub-network and the amount of data collected per unit time; calculating the average distance between each sensor in the sub-network and the other sensors based on the sensor's position; determining the target parameter based on the data processing efficiency and the average distance; and selecting the sensor corresponding to the maximum target parameter as the target sensor.

[0022] In some embodiments of this application, based on the foregoing scheme, the step of acquiring industrial data collected by sensors in the sub-network through the target sensor includes: collecting industrial data collected by sensors in the sub-network through the target sensor, and merging the industrial data.

[0023] In some embodiments of this application, based on the foregoing scheme, identifying data belonging to the operational dimension in industrial data and determining the operational status of industrial equipment based on the data of the operational dimension includes: identifying the data type of the industrial data, and determining data belonging to the operational dimension based on the data type; the operational dimension includes operating speed, unit output, and operating time; determining the status parameters of the industrial equipment based on the data of the operational dimension; and determining the busy / green level representing the operational status of the industrial equipment based on the comparison result between the status parameters and a set threshold.

[0024] In some embodiments of this application, based on the foregoing scheme, identifying data belonging to the product dimension in industrial data and determining the product performance of industrial products based on the product dimension data includes: identifying the data type of the industrial data, determining data belonging to the product dimension based on the data type; the product dimension includes product test data; determining product parameters of industrial products based on the product test data; and determining a performance level representing the product performance of industrial products based on the comparison result between the product parameters and a set threshold.

[0025] According to one aspect of the embodiments of this application, a computer-readable medium is provided having a computer program stored thereon, which, when executed by a processor, implements the intelligent management and remote operation and maintenance method for industrial internet devices as described in the above embodiments.

[0026] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors enable the one or more processors to implement the intelligent management and remote operation and maintenance method for industrial Internet devices as described in the above embodiments.

[0027] According to one aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the industrial internet device intelligent management and remote operation and maintenance method provided in the various optional implementations described above.

[0028] In some embodiments of this application, the technical solutions involve pre-deploying sensors to collect industrial data in an industrial production environment; acquiring the industrial data collected by the sensors in a sub-network constructed from multiple sensors, based on a preset IoT gateway; identifying data belonging to the operational dimension within the industrial data, and determining the operating status of the industrial equipment based on the operational dimension data; identifying data belonging to the product dimension within the industrial data, and determining the product performance of the industrial product based on the product dimension data; and controlling the industrial equipment based on the operating status and the product performance. In this embodiment, industrial data is collected by constructing a sub-network from pre-deployed sensors, and then the industrial data is analyzed through product and operational dimensions to enable real-time control of the industrial equipment, thereby improving the monitoring capabilities of the industrial equipment and the management efficiency of industrial production.

[0029] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0030] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0031] Figure 1 A flowchart illustrating an embodiment of an industrial internet device intelligent control and remote operation and maintenance method according to this application is shown in the schematic diagram.

[0032] Figure 2 A flowchart illustrating the acquisition of industrial data according to one embodiment of this application is shown schematically;

[0033] Figure 3 This illustration schematically shows a remote operation and maintenance platform for intelligent management and control of industrial internet devices according to an embodiment of this application;

[0034] Figure 4A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown. Detailed Implementation

[0035] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0036] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0037] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0038] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0039] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0040] Figure 1 A flowchart illustrating a method for intelligent control and remote operation and maintenance of industrial internet devices according to an embodiment of this application is shown. (Refer to...) Figure 1 As shown, the intelligent control and remote operation and maintenance method for industrial internet devices includes at least steps S110 to S150, which are detailed below:

[0041] In step S110, sensors are pre-deployed in the industrial production environment; the sensors are used to collect industrial data.

[0042] In one embodiment of this application, sensors are pre-deployed in an industrial production environment to collect industrial data. The sensors in this embodiment can be of various types, capable of collecting operational data from industrial equipment or product data from industrial products.

[0043] In step S120, the industrial data collected by the sensors in the sub-network constructed by the multiple sensors is acquired based on a preset Internet of Things gateway.

[0044] In one embodiment of this application, a sub-network is constructed among multiple sensors to collect sensor data through the sub-network, and industrial data collected by the sensors in the sub-network is acquired based on a preset IoT gateway.

[0045] In one embodiment of this application, such as Figure 2 As shown, a sub-network constructed through multiple sensors acquires industrial data collected by the sensors in the sub-network based on a preset IoT gateway, including steps S210 to S230:

[0046] S210, Based on the machine information of the sensor, construct a sub-network and select a target sensor from the sensors in the sub-network;

[0047] S220, acquire industrial data collected by sensors in the sub-network through the target sensor;

[0048] S230, based on a preset IoT gateway, acquires the industrial data from the target sensor.

[0049] Based on the machine information from the sensors, a sub-network is constructed, and a target sensor is selected from the sensors in the sub-network, including:

[0050] Synchronize the machine information of the sensors in the network layer, including the amount of data stored, the amount of data collected per unit time, the machine location, and the machine type;

[0051] Based on the machine type, identify similar sensors belonging to the same machine type;

[0052] For the aforementioned type of sensors, a sub-network is constructed among similar sensors whose distance is less than a set distance threshold;

[0053] Select the target sensor from the sensors in the sub-network.

[0054] Specifically, in this embodiment, the machine information of each sensor is synchronized in the network layer, so that each sensor can acquire information from the other sensors and form a flexible network. The machine information in this embodiment includes the amount of data stored by the sensor, the amount of data collected per unit time, the machine location, and the machine type, etc.

[0055] Then, based on the machine type, similar sensors belonging to the same machine type are identified; for the similar sensors, the sub-network is constructed by freely networking among similar sensors whose distance is less than a set distance threshold, so that similar sensors of the same type are all in the same sub-network, which facilitates data acquisition and transmission.

[0056] After constructing the subnetwork, the target sensor is selected from the sensors in the subnetwork, including the following steps:

[0057] The data processing efficiency of the sensor is calculated based on the amount of data stored by the sensor in the sub-network and the amount of data collected per unit time.

[0058] Based on the sensor's location, calculate the average distance between each sensor in the sub-network and the other sensors;

[0059] Based on the data processing efficiency and the average distance, the target parameters are determined;

[0060] The sensor corresponding to the maximum target parameter is selected as the target sensor.

[0061] Specifically, in one embodiment of this application, the data processing efficiency Dta_fec of the sensor is calculated based on the amount of data stored by the sensor in the sub-network, Dta_vol, and the amount of data collected per unit time, Dta_col:

[0062] Dta_fec=1-α·Dta_col·Dta_vol / Dta_max;

[0063] Where Dta_max represents the maximum storage capacity of each sensor. In this embodiment, it is assumed that the maximum storage capacity of all sensors is the same. α represents the preset storage factor. In this embodiment, the sensor storage ratio and data acquisition density are considered in the evaluation of sensor busyness. The higher both are, the busier the sensor is, meaning the lower the efficiency of future data processing.

[0064] Based on the sensor's location, the average distance between each sensor in the sub-network and the other sensors is determined by calculating the straight-line distance between two points. Then, based on the data processing efficiency Dta_fec and the average distance Dit, the target parameter of sensor i is determined as Par_sen(i).

[0065]

[0066] Where β represents a preset parameter factor, and p is a randomly generated number. After calculating the target parameters, the sensor corresponding to the maximum target parameter is selected as the target sensor. The above scheme takes data processing efficiency and average distance into account in the determination of target parameters, improving the comprehensiveness and objectivity of target sensor evaluation.

[0067] In addition, in this embodiment, sensors can be randomly selected from the sub-network as target sensors, or they can be selected as target sensors in turn according to their machine identifiers.

[0068] After constructing the subnetwork and selecting target sensors, industrial data collected by sensors within the subnetwork is gathered through the target sensors. This industrial data is then merged to reduce redundancy and duplication. Subsequently, based on a pre-defined IoT gateway, the collected data is transmitted to the management server via the target sensors. This approach improves data transmission efficiency and reduces energy consumption during data transmission.

[0069] In step S130, data belonging to the operational dimension in the industrial data is identified, and the operational status of the industrial equipment is determined based on the data in the operational dimension.

[0070] In one embodiment of this application, after the host computer or management server obtains industrial data, it filters out data belonging to the operation dimension from the industrial data, and evaluates the operation status of industrial equipment based on the data of the operation dimension.

[0071] In one embodiment of this application, identifying data belonging to the operational dimension within industrial data and determining the operational status of industrial equipment based on the data in the operational dimension includes:

[0072] Identify the data type of the industrial data, and determine the data belonging to the operational dimension based on the data type; the operational dimension includes operating speed, unit output, and operating time.

[0073] Based on the data from the aforementioned operational dimensions, the status parameters of the industrial equipment are determined;

[0074] Based on the comparison between the state parameters and the set threshold, a busy / green level representing the operating status of the industrial equipment is determined.

[0075] When identifying the data type of industrial data, it can be determined based on the type of sensor that generates the industrial data. Then, data corresponding to operating speed (run_spe), unit output (opt_uni), and running time (run_tim) are selected as the data for the operating dimension.

[0076] Based on operational dimension data, the state parameter par_que of industrial equipment can be determined as follows:

[0077] par_que=γ1·run_spe+γ2·opt_uni+γ3·run_tim

[0078] Here, γ1, γ2, and γ3 represent preset parameter factors. After calculating the state parameters, the state parameters are compared with the set thresholds to determine the busy / green level of the state parameters. The busy / green level represents the operating status of the industrial equipment. The higher the busy / green level, the heavier the workload of the industrial equipment.

[0079] In step S140, data belonging to the product dimension in the industrial data is identified, and the product performance of the industrial product is determined based on the data of the product dimension.

[0080] In one embodiment of this application, after the host computer or management server obtains industrial data, it filters out data belonging to the product dimension from the industrial data, and evaluates the product performance based on the product dimension data.

[0081] In one embodiment of this application, identifying product-dimensional data within industrial data and determining the product performance of industrial products based on the product-dimensional data includes:

[0082] Identify the data type of the industrial data, and determine the data belonging to the product dimension based on the data type; the product dimension includes product test data.

[0083] Based on the product test data, determine the product parameters of the industrial product;

[0084] Based on the comparison between the product parameters and the set threshold, a performance level representing the product performance of the industrial product is determined.

[0085] When identifying the data type of industrial data, it can be determined based on the type of sensor that generated the industrial data. Then, product test data (dta_tes) is filtered out. Based on this product test data, the product parameter (pru_que) of the industrial product is determined as follows:

[0086] pru_que=η·dta_tes

[0087] Here, η represents a preset parameter factor. After calculating the product parameters, the product parameters are compared with the set thresholds to determine the performance level to which the product parameters belong. The performance level represents the operating status of the industrial product. A higher performance level indicates better quality of the industrial product.

[0088] In step S150, the industrial equipment is controlled based on the operating status and the product performance.

[0089] In one embodiment of this application, after determining the operating status of the industrial equipment and the performance of the industrial products produced by the equipment, the industrial equipment is controlled based on the above information. For example, when the operating status is a level three busy / green state, the industrial equipment is paused and placed in standby mode. If the product performance level is low or more than a certain number of industrial products are in a faulty state, the industrial equipment is stopped and fault detection is performed. This method improves the efficiency and reliability of the industrial equipment operation.

[0090] In some embodiments of this application, the technical solutions involve pre-deploying sensors to collect industrial data in an industrial production environment; acquiring the industrial data collected by the sensors in a sub-network constructed from multiple sensors, based on a preset IoT gateway; identifying data belonging to the operational dimension within the industrial data, and determining the operating status of the industrial equipment based on the operational dimension data; identifying data belonging to the product dimension within the industrial data, and determining the product performance of the industrial product based on the product dimension data; and controlling the industrial equipment based on the operating status and the product performance. In this embodiment, industrial data is collected by constructing a sub-network from pre-deployed sensors, and then the industrial data is analyzed through product and operational dimensions to enable real-time control of the industrial equipment, thereby improving the monitoring capabilities of the industrial equipment and the management efficiency of industrial production.

[0091] The following describes an embodiment of the apparatus described in this application, which can be used to execute the intelligent remote operation and maintenance method for industrial internet devices described in the above embodiments of this application. It is understood that the apparatus can be a computer program (including program code) running on a computer device, for example, the apparatus is an application software; the apparatus can be used to execute the corresponding steps in the method provided in the embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the intelligent remote operation and maintenance method for industrial internet devices described above in this application.

[0092] Figure 3 A block diagram of an industrial internet device intelligent management and remote operation and maintenance platform according to an embodiment of this application is shown.

[0093] Reference Figure 3 As shown, an industrial internet device intelligent management and remote operation and maintenance platform 300 according to an embodiment of this application includes:

[0094] Hardware unit 310 is used to pre-deploy sensors in an industrial production environment; the sensors are used to collect industrial data.

[0095] Data unit 320 is used to acquire industrial data collected by sensors in a sub-network constructed through multiple sensors, based on a preset Internet of Things gateway.

[0096] Equipment unit 330 is used to identify data belonging to the operational dimension in industrial data, and to determine the operational status of industrial equipment based on the data in the operational dimension;

[0097] Product unit 340 is used to identify data belonging to the product dimension in industrial data, and to determine the product performance of industrial products based on the data of the product dimension.

[0098] Control unit 350 is used to control industrial equipment based on the operating status and the product performance.

[0099] In some embodiments of this application, based on the foregoing scheme, the sub-network constructed through multiple sensors acquires industrial data collected by the sensors in the sub-network based on a preset IoT gateway, including: constructing a sub-network based on the machine information of the sensors, and selecting a target sensor from the sensors in the sub-network; acquiring the industrial data collected by the sensors in the sub-network through the target sensor; and acquiring the industrial data from the target sensor based on the preset IoT gateway.

[0100] In some embodiments of this application, based on the foregoing scheme, the step of constructing a sub-network based on the machine information of the sensor and selecting a target sensor from the sensors in the sub-network includes: synchronizing the machine information of the sensor in the network layer, including the amount of data stored, the amount of data collected per unit time, the machine location, and the machine type; identifying similar sensors belonging to the same machine type based on the machine type; for similar sensors, constructing the sub-network among similar sensors whose distance is less than a set distance threshold; and selecting a target sensor from the sensors in the sub-network.

[0101] In some embodiments of this application, based on the foregoing scheme, the step of selecting a target sensor from the sensors in the sub-network includes: calculating the data processing efficiency of the sensor based on the amount of data stored in the sensors of the sub-network and the amount of data collected per unit time; calculating the average distance between each sensor in the sub-network and the other sensors based on the sensor's position; determining the target parameter based on the data processing efficiency and the average distance; and selecting the sensor corresponding to the maximum target parameter as the target sensor.

[0102] In some embodiments of this application, based on the foregoing scheme, the step of acquiring industrial data collected by sensors in the sub-network through the target sensor includes: collecting industrial data collected by sensors in the sub-network through the target sensor, and merging the industrial data.

[0103] In some embodiments of this application, based on the foregoing scheme, identifying data belonging to the operational dimension in industrial data and determining the operational status of industrial equipment based on the data of the operational dimension includes: identifying the data type of the industrial data, and determining data belonging to the operational dimension based on the data type; the operational dimension includes operating speed, unit output, and operating time; determining the status parameters of the industrial equipment based on the data of the operational dimension; and determining the busy / green level representing the operational status of the industrial equipment based on the comparison result between the status parameters and a set threshold.

[0104] In some embodiments of this application, based on the foregoing scheme, identifying data belonging to the product dimension in industrial data and determining the product performance of industrial products based on the product dimension data includes: identifying the data type of the industrial data, determining data belonging to the product dimension based on the data type; the product dimension includes product test data; determining product parameters of industrial products based on the product test data; and determining a performance level representing the product performance of industrial products based on the comparison result between the product parameters and a set threshold.

[0105] In some embodiments of this application, the technical solutions involve pre-deploying sensors to collect industrial data in an industrial production environment; acquiring the industrial data collected by the sensors in a sub-network constructed from multiple sensors, based on a preset IoT gateway; identifying data belonging to the operational dimension within the industrial data, and determining the operating status of the industrial equipment based on the operational dimension data; identifying data belonging to the product dimension within the industrial data, and determining the product performance of the industrial product based on the product dimension data; and controlling the industrial equipment based on the operating status and the product performance. In this embodiment, industrial data is collected by constructing a sub-network from pre-deployed sensors, and then the industrial data is analyzed through product and operational dimensions to enable real-time control of the industrial equipment, thereby improving the monitoring capabilities of the industrial equipment and the management efficiency of industrial production.

[0106] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0107] It should be noted that, Figure 4 The computer system 400 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0108] like Figure 4As shown, the computer system 400 includes a Central Processing Unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 402 or programs loaded from Storage Unit 408 into Random Access Memory (RAM) 403, such as performing the methods described in the above embodiments. The RAM 403 also stores various programs and data required for system operation. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An Input / Output (I / O) interface 405 is also connected to the bus 404.

[0109] The following components are connected to I / O interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to I / O interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 410 as needed so that computer programs read from it can be installed into storage section 408 as needed.

[0110] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs various functions defined in the system of this application.

[0111] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0112] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0113] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0114] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0115] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0116] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0117] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0118] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0119] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for intelligent management and control of industrial internet equipment and remote operation, characterized in that, The application relates to an industrial data processing method and device. In an industrial production environment, sensors are pre-deployed; The sensors are used to collect industrial data; A sub-network formed by a plurality of the sensors acquires industrial data collected by the sensors in the sub-network based on a preset Internet of Things gateway; Data belonging to a running dimension in the industrial data is identified, and a running state of an industrial device is determined based on the data belonging to the running dimension; Data belonging to a product dimension in the industrial data is identified, and product performance of an industrial product is determined based on the data belonging to the product dimension; The industrial device is controlled based on the running state and the product performance; The sub-network formed by a plurality of the sensors acquires industrial data collected by the sensors in the sub-network based on a preset Internet of Things gateway, including: constructing a sub-network based on machine information of the sensors, and selecting a target sensor from the sensors in the sub-network; acquiring the industrial data collected by the sensors in the sub-network through the target sensor; and acquiring the industrial data from the target sensor based on the preset Internet of Things gateway; The sub-network is constructed based on the machine information of the sensors, and the target sensor is selected from the sensors in the sub-network, including: synchronizing the machine information of the sensors in a network layer, wherein the machine information includes a stored data amount, a data collection amount per unit time, a machine position and a machine type; identifying same-type sensors belonging to the same machine type based on the machine type; constructing the sub-network between same-type sensors with a distance less than a set distance threshold for the same-type sensors; and selecting the target sensor from the sensors in the sub-network; The target sensor is selected from the sensors in the sub-network, including: calculating a data processing efficiency of the sensors based on the stored data amount and the data collection amount per unit time of the sensors in the sub-network; calculating a distance average value 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 value; and selecting a sensor corresponding to the maximum target parameter as the target sensor; Wherein, the amount of data stored by the sensors of the sub-network The amount of data acquisition per unit time The data processing efficiency of the sensors Is: ； Based on data processing efficiency and distance average value , the target parameter of sensor i is determined as : ； wherein, represents the maximum storage amount of each sensor, represents a preset storage factor, represents a preset parameter factor, and p is a random number randomly generated; The data belonging to the running dimension in the industrial data is identified, and the running state of the industrial device is determined based on the data belonging to the running dimension, including: The data type of the industrial data is identified, and data belonging to the running dimension is determined based on the data type; the running dimension includes a running speed, a unit output and a running time; A state parameter of the industrial device is determined based on the data of the running dimension; A busy-green level representing the running state of the industrial device is determined based on a comparison result between the state parameter and a set threshold; Among them, in identifying the data type of industrial data, it is determined based on the type of sensor generating the industrial data; screening out the running speed , unit output and running time corresponding data as data of running dimension; Correspondingly, the state parameter of the industrial device is determined based on the data of the running dimension, specifically: ； wherein denotes a preset parameter factor.

2. The method of claim 1, wherein, The industrial data collected by the sensors in the sub-network is collected through the target sensor, and the industrial data is merged and processed. The data belonging to the product dimension in the industrial data is identified, and the product performance of the industrial product is determined based on the data belonging to the product dimension, including:

3. The method of claim 1, wherein, ​ Identify the data type of the industrial data, determine the data belonging to the product dimension based on the data type; the product dimension includes product test data; Determine the product parameters of the industrial product based on the product test data; Determine the performance level of the product performance of the industrial product based on the comparison result between the product parameters and the set threshold.

4. An industrial internet device intelligent management and control remote operation and maintenance platform, characterized in that, Comprise: Hardware unit, for pre-arranging sensors in industrial production environment; The sensor is used for collecting industrial data; Data unit, for obtaining the industrial data collected by the sensors in the sub-network constructed by a plurality of sensors based on the preset Internet of Things gateway; Equipment unit, for identifying the data belonging to the running dimension in the industrial data, and determining the running state of the industrial equipment based on the data of the running dimension; Product unit, for identifying the data belonging to the product dimension in the industrial data, and determining the product performance of the industrial product based on the data of the product dimension; Control unit, for controlling the industrial equipment based on the running state and the product performance; Wherein, through the sub-network constructed by a plurality of sensors, based on the preset Internet of Things gateway, the industrial data collected by the sensors in the sub-network, including: based on the machine information of the sensor, constructing the sub-network, and selecting the target sensor from the sensors of the sub-network; through the target sensor, the industrial data collected by the sensors in the sub-network is obtained; based on the preset Internet of Things gateway, the industrial data is obtained from the target sensor; Wherein, based on the machine information of the sensor, the sub-network is constructed, and the target sensor is selected from the sensors of the sub-network, including: synchronizing the machine information of the sensor in the network layer, which includes the stored data amount, the data acquisition amount per unit time, the machine position and the machine type; based on the machine type, the same type of sensors belonging to the same machine type are identified; for the same type of sensors, the sub-network is constructed between the same type of sensors with a distance less than the set distance threshold; select the target sensor from the sensors of the sub-network; Wherein, selecting the target sensor from the sensors of the sub-network, including: calculating the data processing efficiency of the sensor based on the data amount stored by the sensor and the data acquisition amount per unit time; based on the position of the sensor, the distance average value of the distance between each sensor and the rest of the sensors in the sub-network is calculated; based on the data processing efficiency and the distance average value, determine the target parameter; select the sensor corresponding to the maximum target parameter as the target sensor; Wherein, the data amount stored by the sensor of the sub-network The data acquisition amount per unit time The data processing efficiency of the sensor Is: ； Based on data processing efficiency and distance average value , the target parameter of sensor i is determined as : ； wherein, represents a maximum storage amount of each sensor, represents a preset storage factor, represents a preset parameter factor, and p is a random number randomly generated. Wherein, the identification of the data belonging to the running dimension in the industrial data, and the determination of the running state of the industrial equipment based on the data of the running dimension, including: Identify the data type of the industrial data, determine the data belonging to the running dimension based on the data type; the running dimension includes running speed, unit output and running time; Determine the state parameters of the industrial equipment based on the data of the running dimension; Determine the busy green level representing the running state of the industrial equipment based on the comparison result between the state parameters and the set threshold. Among them, in identifying the data type of industrial data, it is determined based on the type of sensor generating the industrial data; screening out the running speed , unit output and running time corresponding data as data of running dimension; Correspondingly, based on the data of the operation dimension, a state parameter of the industrial equipment is determined, specifically: ； wherein denotes a preset parameter factor.

5. A computer readable medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to realize the intelligent management and control remote operation and maintenance method of the industrial internet equipment in any one of claims 1 to 3.

6. An electronic device, comprising: Comprise: One or more processors; Storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, so that the one or more processors realize the intelligent management and control remote operation and maintenance method of the industrial internet equipment in any one of claims 1 to 3.

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