CN110673564B - Remote analysis method for engineering machinery based on Internet of things - Google Patents
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses a remote analysis method for engineering machinery based on the Internet of things, and relates to the technical field of remote analysis; gather engineering mechanical equipment information and engineering mechanical equipment operating state data and environmental data, state data includes engine temperature data and vibration data, and environmental data includes outdoor temperature data, and each data when transmitting the information and the data of gathering to computing platform analysis engineering mechanical equipment operating through the thing networking: when the outdoor temperature data is in a certain range, dividing the engine temperature data into corresponding sections, calculating the correlation between the vibration data corresponding to each section of the engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method, establishing a remote monitoring model according to the correlation, and performing remote analysis on the engineering mechanical equipment.
Description
Technical Field
The invention discloses a remote analysis method for engineering machinery based on the Internet of things, and relates to the technical field of remote analysis.
Background
Under the regulation and control of macro economic policies, the country increases the investment on infrastructure construction. The application of the engineering machinery equipment is more and more extensive, but from the perspective of engineering machinery equipment manufacturers and equipment users, the management of the engineering machinery equipment is more and more difficult, and becomes a management blind area; in addition, the number of the devices is continuously increased, competition is continuously upgraded, the service of each engineering mechanical device manufacturer is forced to be continuously upgraded, and it is not practical to perform each field analysis test on each engineering mechanical device so as to realize accurate control of the engineering mechanical device.
The invention discloses a method for remotely analyzing engineering machinery based on the Internet of things, which is characterized in that equipment vibration and environment data are acquired from engineering machinery equipment and are transmitted to a background through the Internet of things technology, comprehensive indexes such as vibration waveforms, equipment and environment are comprehensively analyzed, the regular relation between vibration changes, environmental factors and equipment use states is found by utilizing big data artificial intelligence operation, the operation condition of the engineering machinery equipment is remotely acquired, and further the equipment use condition is remotely monitored on the premise of not modifying the equipment to guide equipment production.
Disclosure of Invention
The invention provides a remote analysis method for engineering machinery based on the Internet of things, provides a scientific and effective new thought and means for equipment use supervision, equipment scheduling and equipment maintenance in the engineering construction process, can effectively supervise the equipment use in an engineering construction site, realizes intelligent monitoring, optimizes equipment scheduling and improves the equipment utilization rate.
The specific scheme provided by the invention is as follows:
a remote analysis method for engineering machinery based on the Internet of things collects information of engineering machinery equipment and state data and environment data of the engineering machinery equipment during operation, wherein the state data comprises engine temperature data and vibration 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 operation of the engineering mechanical equipment: when the outdoor temperature data is in a certain range, dividing the engine temperature data into corresponding sections, calculating the correlation between the vibration data corresponding to each section of the engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method,
and establishing a remote monitoring model according to the correlation, and carrying out remote analysis on the engineering mechanical equipment.
According to the method, when the outdoor temperature data is less than or equal to minus 10 ℃, the engine temperature data is divided into 10 sections, and the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 engine temperature data into 8 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 engine temperature data into 12 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃ and less than 50 ℃, dividing the engine temperature data into 15 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than or equal to 50 ℃, dividing the engine temperature data into 8 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 the information of the engineering mechanical equipment 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.
A remote analysis system for engineering machinery based on the Internet of things comprises an acquisition module and a computing platform,
the acquisition module acquires the information of the engineering mechanical equipment, and the state data and the environment data of the engineering mechanical equipment during operation, wherein the state data comprises engine temperature data and vibration data, the environment data comprises outdoor temperature data,
the computing platform transmits the acquired information and data to a background analysis engineering mechanical device of the computing platform through the Internet of things to analyze the data during operation: when the outdoor temperature data is in a certain range, dividing the engine temperature data into corresponding sections, calculating the correlation between the vibration data corresponding to each section of the engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method,
and establishing a remote monitoring model according to the correlation, and carrying out remote analysis on the engineering mechanical equipment.
When the outdoor temperature data is less than or equal to minus 10 ℃, the computing platform divides the engine temperature data into 10 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 10 ℃, the calculation platform divides the engine temperature data into 8 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 ℃, the calculation platform divides the engine temperature data into 12 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃ and less than 50 ℃, the calculation platform divides the engine temperature data into 15 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than or equal to 50 ℃, the calculation platform divides the engine temperature data into 8 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by 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 engineering mechanical 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 method of the invention collects information of the engineering mechanical equipment, and status data and environmental data of the engineering mechanical equipment during operation, wherein the status data comprises engine temperature data and vibration data, the environmental data comprises outdoor temperature data, and the collected information and data are transmitted to a computing platform through the Internet of things to analyze the data during the operation of the engineering mechanical equipment: when the outdoor temperature data is in a certain range, dividing the engine temperature data into corresponding sections, calculating the correlation between the vibration data corresponding to each section of the engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method, establishing a remote monitoring model according to the correlation, and performing remote analysis on the engineering mechanical equipment;
compared with the prior art, the method has the advantages that the equipment vibration and environment data are obtained on the engineering mechanical equipment, the data are transmitted to the computing platform through the internet of things technology, the vibration waveform, the equipment, the environment and other comprehensive indexes are comprehensively analyzed, the regular relation between the vibration change, the environment factors and the equipment use state is found through big data artificial intelligence operation, the operation condition of the engineering mechanical equipment is remotely obtained, the equipment use condition is remotely monitored on the premise that the equipment is not modified, and the equipment production is guided.
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FIG. 1 is a schematic flow diagram of the process of the present invention.
Detailed Description
The invention provides a remote analysis method for engineering machinery based on the Internet of things, which is used for collecting engineering machinery equipment information, and state data and environment data of the engineering machinery equipment during operation, wherein the state data comprises engine temperature data and vibration 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 operation of the engineering mechanical equipment: when the outdoor temperature data is in a certain range, dividing the engine temperature data into corresponding sections, calculating the correlation between the vibration data corresponding to each section of the engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method,
and establishing a remote monitoring model according to the correlation, and carrying out remote analysis on the engineering mechanical equipment.
Meanwhile, a system for carrying out remote analysis on engineering machinery based on the Internet of things, which corresponds to the method, is provided, and comprises an acquisition module and a computing platform,
the acquisition module acquires the information of the engineering mechanical equipment, and the state data and the environment data of the engineering mechanical equipment during operation, wherein the state data comprises engine temperature data and vibration data, the environment data comprises outdoor temperature data,
the computing platform transmits the acquired information and data to a background analysis engineering mechanical device of the computing platform through the Internet of things to analyze the data during operation: when the outdoor temperature data is in a certain range, dividing the engine temperature data into corresponding sections, calculating the correlation between the vibration data corresponding to each section of the engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method,
and establishing a remote monitoring model according to the correlation, and carrying out remote analysis on the engineering mechanical 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 analysis on the engineering machinery based on the Internet of things, and the specific process is as follows:
collecting engineering mechanical equipment information, and state data and environment data of the engineering mechanical equipment during operation, wherein the state data comprises engine temperature data and vibration data, the vibration data mainly forms vibration monitoring waveform, namely vibration waveform data, through a vibration sensing device,
the environmental data includes outdoor temperature data and,
the equipment information comprises an equipment manufacturer and an equipment model, vibration and temperature conditions of different mechanical equipment manufacturers are different, a model can be independently established, the equipment model is mainly the engine equipment model, and a modeling basis can be used as an inquiry identifier and corresponds to a correlation analysis result; for example, aiming at the engineering mechanical equipment of the equipment manufacturer DC1 with the model number M1, the correlation between the engine temperature Ti and the vibration waveform W and the use state S of the engineering mechanical equipment is analyzed by combining the outdoor weather temperature T0 to obtain a correlation index K,
the information and the data collected are transmitted to a computing platform through the Internet of things to analyze data of engineering mechanical 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 engine temperature data T1 into 10 sections, and calculating a correlation index K1 of a vibration waveform W1 corresponding to each section of engine temperature data and the operation state S1 of the engineering mechanical equipment in the outdoor temperature data T0 range by a big data operation method;
when the outdoor temperature data T0 is larger than minus 10 ℃ and smaller than or equal to 10 ℃, dividing the engine temperature data T2 into 8 sections, and calculating a correlation index K2 of a vibration waveform W2 corresponding to each section of engine temperature data in the outdoor temperature data range T0 and the operation state S2 of the engineering mechanical equipment by a big data operation method;
when the outdoor temperature data T0 is larger than 10 ℃ and smaller than or equal to 30 ℃, dividing the engine temperature data T3 into 12 sections, and calculating a correlation index K3 of a vibration waveform W3 corresponding to each section of engine temperature data and the operation state S3 of the engineering mechanical equipment in the outdoor temperature data T0 range by a big data operation method;
when the outdoor temperature data T0 is larger than 30 ℃ and smaller than 50 ℃, dividing the engine temperature data T4 into 15 sections, and calculating a correlation index K4 of a vibration waveform W4 corresponding to each section of engine temperature data and the operation state S4 of the engineering mechanical equipment in the outdoor temperature data T0 range by a big data operation method;
when the outdoor temperature data T0 is more than or equal to 50 ℃, the engine temperature data T5 is divided into 8 sections, the correlation index K5 of the vibration waveform W5 corresponding to each section of engine temperature data in the outdoor temperature data T0 range and the operation state S5 of the engineering mechanical equipment 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 monitoring model according to a correlation coefficient of correlation, carrying out remote analysis on engineering mechanical equipment, correcting an algorithm model according to the temperature condition T0, combining the temperature T of an engine and a vibration waveform W and the actual using state of the 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 analysis on the engineering machinery based on the Internet of things, and the specific process is as follows:
the acquisition module acquires information of the engineering mechanical equipment, and state data and environmental data of the engineering mechanical equipment during operation, wherein the state data comprises engine temperature data and vibration data, the vibration data mainly forms a vibration monitoring waveform, namely vibration waveform data, through a vibration sensing device,
the environmental data includes outdoor temperature data and,
the equipment information comprises an equipment manufacturer and an equipment model, vibration and temperature conditions of different mechanical equipment manufacturers are different, a model can be independently established, the equipment model is mainly the engine equipment model, and a modeling basis can be used as an inquiry identifier and corresponds to a correlation analysis result; for example, aiming at the engineering mechanical equipment of the equipment manufacturer DC1 with the model number M1, the correlation between the engine temperature Ti and the vibration waveform W and the use state S of the engineering mechanical equipment is analyzed by combining the outdoor weather temperature T0 to obtain a correlation index K,
the computing platform transmits the collected information and data to background analysis engineering mechanical equipment through the Internet of things to analyze the data during operation, and can select an outdoor weather temperature interval needing analysis 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 engine temperature data T1 into 10 sections, and calculating a correlation index K1 of a vibration waveform W1 corresponding to each section of engine temperature data and the operation state S1 of the engineering mechanical equipment in the outdoor temperature data T0 range by a big data operation method;
when the outdoor temperature data T0 is larger than minus 10 ℃ and smaller than or equal to 10 ℃, dividing the engine temperature data T2 into 8 sections, and calculating a correlation index K2 of a vibration waveform W2 corresponding to each section of engine temperature data in the outdoor temperature data range T0 and the operation state S2 of the engineering mechanical equipment by a big data operation method;
when the outdoor temperature data T0 is larger than 10 ℃ and smaller than or equal to 30 ℃, dividing the engine temperature data T3 into 12 sections, and calculating a correlation index K3 of a vibration waveform W3 corresponding to each section of engine temperature data and the operation state S3 of the engineering mechanical equipment in the outdoor temperature data T0 range by a big data operation method;
when the outdoor temperature data T0 is larger than 30 ℃ and smaller than 50 ℃, dividing the engine temperature data T4 into 15 sections, and calculating a correlation index K4 of a vibration waveform W4 corresponding to each section of engine temperature data and the operation state S4 of the engineering mechanical equipment in the outdoor temperature data T0 range by a big data operation method;
when the outdoor temperature data T0 is more than or equal to 50 ℃, the engine temperature data T5 is divided into 8 sections, the correlation index K5 of the vibration waveform W5 corresponding to each section of engine temperature data in the outdoor temperature data T0 range and the operation state S5 of the engineering mechanical equipment 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 monitoring model according to a correlation coefficient of correlation, carrying out remote analysis on engineering mechanical equipment, correcting an algorithm model according to the temperature condition T0, combining the temperature T of an engine and a vibration waveform W and the actual using state of the 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 (6)
1. A remote analysis method for engineering machinery based on the Internet of things is characterized in that engineering machinery equipment information, state data and environment data of the engineering machinery equipment during operation are collected, the state data comprise engine temperature data and vibration data, the environment 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 operation of the engineering mechanical equipment: when the outdoor temperature data is less than or equal to minus 10 ℃, dividing the engine temperature data into 10 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 10 ℃, dividing the engine temperature data into 8 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 engine temperature data into 12 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃ and less than 50 ℃, dividing the engine temperature data into 15 sections, and calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than or equal to 50 ℃, dividing the engine temperature data into 8 sections, calculating the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method,
and establishing a remote monitoring model according to the correlation, and carrying out remote analysis on the engineering mechanical equipment.
2. The method of claim 1, wherein the correlation analysis is performed using a nonlinear artificial intelligence prediction algorithm in big data arithmetic.
3. The method as claimed in claim 2, wherein the information on the construction machinery equipment includes information on equipment manufacturer and information on equipment model, and the information on the equipment model is used as a query identifier corresponding to the result of the correlation analysis.
4. A remote analysis system for engineering machinery based on the Internet of things is characterized by comprising an acquisition module and a computing platform,
the acquisition module acquires the information of the engineering mechanical equipment, and the state data and the environment data of the engineering mechanical equipment during operation, wherein the state data comprises engine temperature data and vibration data, the environment data comprises outdoor temperature data,
the computing platform transmits the acquired information and data to a background analysis engineering mechanical device of the computing platform through the Internet of things to analyze the data during operation: when the outdoor temperature data is less than or equal to minus 10 ℃, the calculation platform divides the engine temperature data into 10 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 10 ℃, the calculation platform divides the engine temperature data into 8 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering 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 ℃, the calculation platform divides the engine temperature data into 12 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than 30 ℃ and less than 50 ℃, the calculation platform divides the engine temperature data into 15 sections, and calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method;
and/or when the outdoor temperature data is more than or equal to 50 ℃, the calculation platform divides the engine temperature data into 8 sections, calculates the correlation between the vibration data corresponding to each section of engine temperature data in the outdoor temperature data range and the operation state of the engineering mechanical equipment by a big data operation method,
and establishing a remote monitoring model according to the correlation, and carrying out remote analysis on the engineering mechanical equipment.
5. The system of claim 4, wherein the computing platform performs the correlation analysis using a nonlinear artificial intelligence prediction algorithm in big data arithmetic.
6. The system as claimed in claim 5, wherein the information of the engineering machinery equipment collected by the collecting module includes information of equipment manufacturer and information of equipment model, the information of equipment model is used as a query mark, and corresponds to the result of the correlation analysis.
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