CN110687881A - Remote intelligent analysis method for electromechanical equipment based on Internet of things - Google Patents
Remote intelligent analysis method for electromechanical equipment based on Internet of things Download PDFInfo
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Abstract
The invention discloses an electric mechanical equipment remote intelligent analysis method based on the Internet of things, and relates to the technical field of engineering remote analysis; gather electromechanical device information and electromechanical device operation state data and environmental data, state data includes electromechanical device temperature data, power consumption data and electromechanical device work load data, and environmental data includes outdoor temperature data, transmits each data when the information and the data of gathering pass through the thing networking and analyze electromechanical device operation to computing platform: when the outdoor temperature data is in a certain range, dividing the temperature data of the electric mechanical equipment into corresponding sections, calculating the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method, establishing a remote analysis model according to the correlation, and performing remote analysis on the electric mechanical equipment.
Description
Technical Field
The invention discloses an electric mechanical equipment remote intelligent analysis method based on the Internet of things, and relates to the technical field of engineering remote analysis.
Background
Under the regulation and control of macro economic policies, the country increases the investment on infrastructure construction. The application of electromechanical devices is becoming more and more widespread, but from the viewpoint of electromechanical device manufacturers and device users, the management of the electromechanical devices is becoming more and more difficult, becoming a management blind area; in addition, the increasing number of devices and the increasing competition upgrade force the service of each electromechanical device manufacturer to upgrade continuously, but it is not practical to perform each field analysis test of each electromechanical device to realize the precise control of the electromechanical device.
The invention discloses an electric mechanical equipment remote intelligent analysis method based on the Internet of things, which is characterized in that the electric quantity use and environment data of the electric mechanical equipment are acquired on the electric mechanical equipment and are transmitted to a background through the Internet of things technology, comprehensive indexes such as the electric quantity use, the equipment, the environment and the like are comprehensively analyzed, the regular relation between the electric quantity use, the environment factors and the equipment workload is found through big data artificial intelligent operation, the remote acquisition of the electric mechanical equipment workload is realized, and further, the equipment workload is remotely monitored and acquired on the premise of not modifying the equipment, and the production is guided.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides the remote intelligent analysis method for the electromechanical equipment based on the Internet of things, which realizes intelligent monitoring, optimizes equipment scheduling and improves the equipment utilization rate.
The specific scheme provided by the invention is as follows:
an electric mechanical equipment remote intelligent analysis method based on the Internet of things collects electric mechanical equipment information, state data and environment data of the electric mechanical equipment during operation, the state data comprises electric mechanical equipment temperature data, power consumption data and electric mechanical equipment workload data, the environment data comprises outdoor temperature data,
transmitting the collected information and data to a computing platform through the Internet of things to analyze all data during the operation of the electromechanical equipment: when the outdoor temperature data is in a certain range, dividing the temperature data of the electric mechanical equipment into corresponding sections, calculating the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method,
and establishing a remote analysis model according to the correlation, and carrying out remote analysis on the electromechanical equipment.
According to the method, when outdoor temperature data is less than or equal to minus 10 ℃, the temperature data of the electric mechanical equipment is divided into 10 sections, and the correlation between power consumption data corresponding to the temperature data of each section of electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is calculated through a big data operation method;
and/or when the outdoor temperature data is more than-10 ℃ and less than or equal to 10 ℃, dividing the temperature data of the electric mechanical equipment into 8 sections, and calculating the correlation between the power consumption data corresponding to each section of the temperature data of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 10 ℃ and less than or equal to 30 ℃, dividing the temperature data of the electric mechanical equipment into 12 sections, and calculating the correlation between the power consumption data corresponding to each section of the temperature data of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃, dividing the temperature data of the electric mechanical equipment into 15 sections, and calculating the correlation between the power consumption data corresponding to the temperature data of each section of electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method.
In the method, a nonlinear artificial intelligence prediction algorithm in a big data operation method is utilized for correlation analysis.
The method comprises the steps that electromechanical equipment information comprises equipment manufacturer information and equipment model information, the equipment model information is used as a query identifier, and a correlation analysis result corresponds to the equipment model information.
An electric mechanical equipment remote intelligent analysis system based on the Internet of things comprises an acquisition module and a computing platform,
the acquisition module acquires electromechanical equipment information, electromechanical equipment running state data and environmental data, wherein the state data comprises electromechanical equipment temperature data, power consumption data and electromechanical equipment workload data, the environmental data comprises outdoor temperature data,
the computing platform transmits the acquired information and data to a background of the computing platform through the Internet of things to analyze the data when the electromechanical equipment runs: when the outdoor temperature data is in a certain range, dividing the temperature data of the electric mechanical equipment into corresponding sections, calculating the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method,
and establishing a remote analysis model according to the correlation, and carrying out remote analysis on the electromechanical equipment.
In the system, when the outdoor temperature data is less than or equal to minus 10 ℃, the computing platform divides the temperature data of the electric mechanical equipment into 10 sections, and the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is computed by a big data operation method;
and/or when the outdoor temperature data is more than-10 ℃ and less than or equal to 10 ℃, the computing platform divides the temperature data of the electric mechanical equipment into 8 sections, and the correlation between the power consumption data corresponding to the temperature data of each section of electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is computed through a big data operation method;
and/or when the outdoor temperature data is more than 10 ℃ and less than or equal to 30 ℃, the computing platform divides the temperature data of the electric mechanical equipment into 12 sections, and the correlation between the power consumption data corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is computed through a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃, the computing platform divides the temperature data of the electric mechanical equipment into 15 sections, and the correlation between the power consumption data corresponding to the temperature data of each section of electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is computed through a big data operation method.
The computing platform in the system utilizes a nonlinear artificial intelligence prediction algorithm in a big data operation method to carry out correlation analysis.
The electromechanical equipment information acquired by the acquisition module in the system comprises equipment manufacturer information and equipment model information, wherein the equipment model information is used as a query identifier and corresponds to a correlation analysis result.
The invention has the advantages that:
the invention provides an electric mechanical equipment remote intelligent analysis method based on the Internet of things, which is characterized in that the electric quantity use and environment data of equipment are acquired on the electric mechanical equipment, the electric quantity use and environment data are transmitted to a background through the Internet of things technology, the comprehensive indexes of the electric quantity use, the equipment, the environment and the like are comprehensively analyzed, the regular relation between the electric quantity use, the environment factors and the equipment workload is found through big data artificial intelligent operation, the situation of remotely acquiring the electric mechanical equipment workload is realized, the equipment workload is remotely monitored and acquired on the premise of not modifying the equipment, the production is guided, and compared with the existing personnel field supervision, the cost is saved by more than 60%.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The invention provides an electromechanical equipment remote intelligent analysis method based on the Internet of things, which is used for collecting electromechanical equipment information, electromechanical equipment running state data and environment data, wherein the state data comprises electromechanical equipment temperature data, power consumption data and electromechanical equipment workload data, the environment data comprises outdoor temperature data,
transmitting the collected information and data to a computing platform through the Internet of things to analyze all data during the operation of the electromechanical equipment: when the outdoor temperature data is in a certain range, dividing the temperature data of the electric mechanical equipment into corresponding sections, calculating the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method,
and establishing a remote analysis model according to the correlation, and carrying out remote analysis on the electromechanical equipment.
Meanwhile, the system for remotely and intelligently analyzing the electromechanical equipment based on the Internet of things, which corresponds to the method, comprises an acquisition module and a computing platform,
the acquisition module acquires electromechanical equipment information, electromechanical equipment running state data and environmental data, wherein the state data comprises electromechanical equipment temperature data, power consumption data and electromechanical equipment workload data, the environmental data comprises outdoor temperature data,
the computing platform transmits the acquired information and data to a background of the computing platform through the Internet of things to analyze the data when the electromechanical equipment runs: when the outdoor temperature data is in a certain range, dividing the temperature data of the electric mechanical equipment into corresponding sections, calculating the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method,
and establishing a remote analysis model according to the correlation, and carrying out remote analysis on the electromechanical equipment.
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
The method is used for carrying out remote intelligent analysis on the electromechanical equipment based on the Internet of things, and comprises the following specific processes:
collecting the information of the electric mechanical equipment, the running state data of the electric mechanical equipment and the environment data, wherein the state data comprises the temperature data of the electric mechanical equipment, the power consumption data and the workload data of the electric mechanical equipment, the environment data comprises the outdoor temperature data,
the temperature data of the electric mechanical equipment is the temperature data generated when the equipment is used, the electricity consumption data is the electricity consumption and electricity consumption duration data formed by the electric equipment,
the electromechanical equipment information comprises equipment manufacturer information and equipment model information, energy consumption conditions of different electromechanical equipment manufacturers are different, models can be independently established, the equipment model information is used as query identification, and a correlation analysis result is obtained, for example, for electromechanical equipment of which the model is M1 of an equipment manufacturer EC1, the correlation between electromechanical equipment temperature data Ti and power consumption data W and electromechanical equipment workload S is analyzed by combining outdoor weather temperature T0 to obtain a correlation index K, for example, the electromechanical equipment workload can refer to electric pumping equipment, 25 square waters are pumped when the workload is 1 hour,
the information and the data collected are transmitted to a computing platform through the Internet of things to analyze data of the electromechanical equipment during operation, the outdoor weather temperature interval needing to be analyzed can be selected according to the outdoor weather at that time, and all the intervals are taken as examples:
when the outdoor temperature data T0 is less than or equal to minus 10 ℃, dividing the electromechanical device temperature data T1 into 10 sections, and calculating a correlation index K1 of power consumption data W1 and electromechanical device workload S1 corresponding to each section of electromechanical device temperature data T1 in the outdoor temperature data range T0 by a big data operation method;
when the outdoor temperature data T0 is greater than-10 ℃ and less than or equal to 10 ℃, dividing the temperature data T2 of the electric mechanical equipment into 8 sections, and calculating the related index K2 of the electric quantity data W2 and the electric mechanical equipment workload S2 corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data T0 range through a big data operation method;
when the outdoor temperature data T0 is larger than 10 ℃ and smaller than or equal to 30 ℃, dividing the temperature data T3 of the electric mechanical equipment into 12 sections, and calculating the related index K3 of the power consumption data W3 and the electric mechanical equipment workload S3 corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data T0 range through a big data operation method;
when the outdoor temperature data T0 is larger than 30 ℃, the temperature data T4 of the electric mechanical equipment is divided into 15 sections, and the correlation index K4 of the electric quantity data W4 and the electric mechanical equipment workload S4 corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data T0 range is calculated through a big data operation method.
Performing correlation analysis by utilizing a nonlinear artificial intelligence prediction algorithm in a big data operation method: the method comprises the steps of calculating mass data by adopting a hyperbolic function Y as a + b (1/X), confirming a correlation index K, establishing a remote analysis model according to a correlation coefficient of correlation, carrying out remote analysis on the engineering mechanical equipment, correcting an algorithm model according to a temperature condition T0 and combining temperature data T of the electric mechanical equipment, power consumption data W and workload S of the electric mechanical equipment to obtain the optimal correlation index K, so that the model is optimized, and remote analysis is better carried out.
The system disclosed by the invention is used for carrying out remote intelligent analysis on the electromechanical equipment based on the Internet of things, and the specific process is as follows:
the acquisition module acquires electromechanical equipment information, electromechanical equipment running state data and environmental data, wherein the state data comprises electromechanical equipment temperature data, power consumption data and electromechanical equipment workload data, the environmental data comprises outdoor temperature data,
the temperature data of the electric mechanical equipment is the temperature data generated when the equipment is used, the electricity consumption data is the electricity consumption and electricity consumption duration data formed by the electric equipment,
the electromechanical equipment information comprises equipment manufacturer information and equipment model information, energy consumption conditions of different electromechanical equipment manufacturers are different, models can be independently established, the equipment model information is used as query identification, and a correlation analysis result is obtained, for example, for electromechanical equipment of which the model is M1 of an equipment manufacturer EC1, the correlation between electromechanical equipment temperature data Ti and power consumption data W and electromechanical equipment workload S is analyzed by combining outdoor weather temperature T0 to obtain a correlation index K, for example, the electromechanical equipment workload can refer to electric pumping equipment, 25 square waters are pumped when the workload is 1 hour,
the computing platform transmits the collected information and data to the computing platform through the internet of things to analyze the data of the electromechanical equipment during operation, and can select an outdoor weather temperature interval needing to be analyzed according to the outdoor weather at that time, taking all the intervals as an example:
when the outdoor temperature data T0 is less than or equal to minus 10 ℃, dividing the electromechanical device temperature data T1 into 10 sections, and calculating a correlation index K1 of power consumption data W1 and electromechanical device workload S1 corresponding to each section of electromechanical device temperature data T1 in the outdoor temperature data range T0 by a big data operation method;
when the outdoor temperature data T0 is greater than-10 ℃ and less than or equal to 10 ℃, dividing the temperature data T2 of the electric mechanical equipment into 8 sections, and calculating the related index K2 of the electric quantity data W2 and the electric mechanical equipment workload S2 corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data T0 range through a big data operation method;
when the outdoor temperature data T0 is larger than 10 ℃ and smaller than or equal to 30 ℃, dividing the temperature data T3 of the electric mechanical equipment into 12 sections, and calculating the related index K3 of the power consumption data W3 and the electric mechanical equipment workload S3 corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data T0 range through a big data operation method;
when the outdoor temperature data T0 is larger than 30 ℃, the temperature data T4 of the electric mechanical equipment is divided into 15 sections, and the correlation index K4 of the electric quantity data W4 and the electric mechanical equipment workload S4 corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data T0 range is calculated through a big data operation method.
The computing platform performs correlation analysis by utilizing a nonlinear artificial intelligence prediction algorithm in a big data operation method: the method comprises the steps of calculating mass data by adopting a hyperbolic function Y as a + b (1/X), confirming a correlation index K, establishing a remote analysis model according to a correlation coefficient of correlation, carrying out remote analysis on the engineering mechanical equipment, correcting an algorithm model according to a temperature condition T0 and combining temperature data T of the electric mechanical equipment, power consumption data W and workload S of the electric mechanical equipment to obtain the optimal correlation index K, so that the model is optimized, and remote analysis is better carried out.
The above-mentioned embodiments are merely preferred embodiments for fully illustrating the present invention, and the scope of the present invention is not limited thereto. The equivalent substitution or change made by the technical personnel in the technical field on the basis of the invention is all within the protection scope of the invention. The protection scope of the invention is subject to the claims.
Claims (8)
1. An Internet of things-based remote intelligent analysis method for electromechanical equipment is characterized in that electromechanical equipment information, electromechanical equipment running state data and environmental data are collected, the state data comprise electromechanical equipment temperature data, power consumption data and electromechanical equipment workload data, the environmental data comprise outdoor temperature data,
transmitting the collected information and data to a computing platform through the Internet of things to analyze all data during the operation of the electromechanical equipment: when the outdoor temperature data is in a certain range, dividing the temperature data of the electric mechanical equipment into corresponding sections, calculating the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method,
and establishing a remote analysis model according to the correlation, and carrying out remote analysis on the electromechanical equipment.
2. The method as claimed in claim 1, wherein when the outdoor temperature data is less than or equal to-10 ℃, the temperature data of the electromechanical device is divided into 10 sections, and the correlation between the power consumption data corresponding to each section of the temperature data of the electromechanical device in the outdoor temperature data range and the workload of the electromechanical device is calculated by a big data operation method;
and/or when the outdoor temperature data is more than-10 ℃ and less than or equal to 10 ℃, dividing the temperature data of the electric mechanical equipment into 8 sections, and calculating the correlation between the power consumption data corresponding to each section of the temperature data of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 10 ℃ and less than or equal to 30 ℃, dividing the temperature data of the electric mechanical equipment into 12 sections, and calculating the correlation between the power consumption data corresponding to each section of the temperature data of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃, dividing the temperature data of the electric mechanical equipment into 15 sections, and calculating the correlation between the power consumption data corresponding to the temperature data of each section of electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method.
3. The method of claim 1 or 2, wherein the correlation analysis is performed by using a nonlinear artificial intelligence prediction algorithm in a big data operation method.
4. The method of claim 3, wherein the electromechanical device information includes device manufacturer information and device model information, the device model information being used as a query identifier corresponding to the correlation analysis result.
5. A remote intelligent analysis system of electromechanical equipment based on the Internet of things is characterized by comprising an acquisition module and a computing platform,
the acquisition module acquires electromechanical equipment information, electromechanical equipment running state data and environmental data, wherein the state data comprises electromechanical equipment temperature data, power consumption data and electromechanical equipment workload data, the environmental data comprises outdoor temperature data,
the computing platform transmits the acquired information and data to a background of the computing platform through the Internet of things to analyze the data when the electromechanical equipment runs: when the outdoor temperature data is in a certain range, dividing the temperature data of the electric mechanical equipment into corresponding sections, calculating the correlation between the power consumption data corresponding to the temperature data of each section of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment by a big data operation method,
and establishing a remote analysis model according to the correlation, and carrying out remote analysis on the electromechanical equipment.
6. The system as claimed in claim 5, wherein when the outdoor temperature data is less than or equal to-10 ℃, the computing platform divides the temperature data of the electromechanical device into 10 sections, and calculates the correlation between the power consumption data corresponding to the temperature data of each section of the electromechanical device in the outdoor temperature data range and the workload of the electromechanical device by a big data operation method;
and/or when the outdoor temperature data is more than-10 ℃ and less than or equal to 10 ℃, the computing platform divides the temperature data of the electric mechanical equipment into 8 sections, and the correlation between the power consumption data corresponding to the temperature data of each section of electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is computed through a big data operation method;
and/or when the outdoor temperature data is more than 10 ℃ and less than or equal to 30 ℃, the computing platform divides the temperature data of the electric mechanical equipment into 12 sections, and the correlation between the power consumption data corresponding to each section of temperature data of the electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is computed through a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃, the computing platform divides the temperature data of the electric mechanical equipment into 15 sections, and the correlation between the power consumption data corresponding to the temperature data of each section of electric mechanical equipment in the outdoor temperature data range and the workload of the electric mechanical equipment is computed through a big data operation method.
7. The system of claim 5 or 6, wherein the computing platform performs the correlation analysis by using a nonlinear artificial intelligence prediction algorithm in a big data operation method.
8. The system of claim 7, wherein the electromechanical device information collected by the collection module includes device manufacturer information and device model information, the device model information being used as a query identifier corresponding to the correlation analysis result.
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