CN117193273B - Positioning and tracing system and method for digital energy air compression station - Google Patents
Positioning and tracing system and method for digital energy air compression station Download PDFInfo
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Abstract
The invention relates to a positioning and tracing system and a positioning and tracing method for a digital energy air compression station, and belongs to the technical field of air compression station safety monitoring. The method comprises the steps of obtaining an air compression station field signal, obtaining deviation and deviation increment of main pipe pressure of air compression station equipment from the air compression station field signal, and obtaining control information through fuzzy calculation; converting the control information into instantaneous reliability, establishing a reliability prediction model, and inputting the control information into the reliability prediction model to obtain machine running state information; positioning information is obtained from the field signals of the air compression station, and a three-dimensional visual scene of the air compression station is obtained through digital modeling of the positioning information; the machine running state information is subjected to fault analysis to obtain fault warning information, a mapping relation is established between the fault warning information and positioning information, and detailed information of fault equipment is obtained through focusing in a three-dimensional visual scene. Through three-dimensional visual positioning tracing, the inquiry and positioning of fault equipment are realized, and the running performance of the air compression station equipment is dynamically reflected.
Description
Technical Field
The invention belongs to the technical field of safety monitoring of air compression stations, and particularly relates to a positioning and tracing system and method of a digital energy air compression station.
Background
The air compression station provides stable air supply pressure requirements for industries such as coal mine production, medicine, textile, electric power and the like. The safety, stability and automation degree of the work of the air compression station play a decisive role in the normal production and economic benefit of enterprises, so that the safety protection of equipment in the air compression station and the real-time monitoring of main technological process parameters are necessary. The control of the pressure of the main air pipe of the whole monitoring system is most important, and the sufficient and stable air supply pressure can ensure the smooth operation of air terminal equipment and improve the production efficiency. There are still the following areas to be improved:
because the working conditions of a plurality of production lines in the industrial field are different, the pipe fittings to be treated are different in size, the gas consumption is different, and the requirements of gas supply equipment, environmental change and production technology on the real-time property of gas supply pressure are not considered at the same time.
The state information of the running process of the air compression station equipment can not reflect the running performance, the precision and the dynamic characteristics of the equipment in real time.
(3) The method lacks the visualized fine control capability, and can not intuitively and completely browse and review various information in the operation management of the air compression station.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a positioning and tracing method and a system for a digital energy air compression station, and the aim of the invention can be realized by the following technical scheme:
s1: acquiring an air compression station field signal, acquiring deviation and deviation increment of main pipe pressure of air compression station equipment from the air compression station field signal, performing discrete quantization on the deviation and the deviation increment to obtain a discrete quantized value, presetting a fuzzy variable area and a control rule, determining membership degree of the discrete quantized value according to the fuzzy variable area, converting the discrete quantized value into a fuzzy vector through the membership degree and the control rule, and performing definition processing on the fuzzy vector to obtain control information;
s2: periodically sampling the control information to obtain sample data, extracting state characteristics of the sample data, presetting state characteristic indexes of the sample data in a sampling period, obtaining instantaneous reliability of the control information according to the state characteristics, establishing a reliability prediction model through the instantaneous reliability, and inputting the control information into the reliability prediction model to obtain machine running state information;
s3: positioning information is obtained from the air compression station field signals, and a three-dimensional visual scene of the air compression station is obtained through digital modeling of the positioning information;
s4: and obtaining fault warning information from the machine running state information through fault analysis, establishing a mapping relation between the fault warning information and the positioning information, and obtaining detailed information of fault equipment through focusing in the three-dimensional visual scene.
Specifically, the step S1 includes the steps of:
s101: presetting a basic domain, and calculating to obtain a deviation quantized value and a deviation increment quantized value according to the basic domain, wherein a calculation formula is as follows:
，
，
wherein,ein order for the deviation to be a function of,ecin order for the deviation to be incremental,VHas the upper limit value of the basic domain of discussion,VLas the lower limit value of the basic domain of discussion,f(e) In order to quantify the value of the deviation,f(ec) Quantifying the value for the delta deviation;
s102: calculating the deviation quantized value and the deviation increment quantized value through the control rule and the membership degree to obtain a fuzzy vector, wherein the calculation formula is as follows:
，
wherein,Kin order to blur the vector of the image,Mefor the degree of membership of the deviation,Mecmembership for the delta deviation;
s103: and obtaining a clear vector from the fuzzy vector through a sharpening process, and obtaining control information from the clear vector through scale transformation.
Specifically, the step S2 includes the steps of:
s201: converting the control information into instantaneous reliability, wherein the calculation formula is as follows:
，
wherein,tfor the time of day count to be counted,ifor the counting of the number of samples,kas a total number of samples,for time sampling, ++>For instantaneous reliability, ++>For state parameter->Is a state characteristic index;
s202: obtaining a training sample set by intercepting instantaneous reliability data in a certain time period, and predicting by adopting a root mean square error evaluation model to obtain model parameters, wherein the model parameters comprise kernel function parameters, insensitive loss functions and penalty factors;
s203: obtaining a reliability prediction model through multi-objective optimization of the model parameters;
s204: and inputting the control information into the reliability prediction model to obtain machine running state information.
Specifically, the step S3 includes the steps of:
s301: acquiring positioning information from the air compression station field signal through a sensor, and storing the positioning information as a geometric description file;
s302: analyzing the geometric description file into a program class instance through an analyzer;
s303: constructing a simple model and a source model by gridding modeling on the program class instance, and obtaining a complex model by Boolean operation on the simple model and the source model, wherein the calculation formula is as follows:
，
wherein,Ain order to make the model simple,Bas a source model of the object to be processed,Cis a complex model;
s304: and establishing a three-dimensional visual scene of the air compression station through the complex model by the positioning information.
Specifically, the step S4 includes:
s401: trend analysis is carried out on the machine running state information to obtain a machine state change trend, and a calculation formula is as follows:
，
wherein,for the trend of machine state change, +.>For the length of the time period,A1 is the initial discharge frequency amplitude of the switching station,A2 is the switch station warp->The amplitude of the discharge frequency after the time,Fthe discharge frequency amplitude value is the fault state of the switching station;
s402: performing fault analysis on the machine state change trend to obtain fault warning information;
s403: constructing a mapping relation between the machine running state information and the complex model to obtain a model structure table, and associating the fault warning information with the complex model through the model structure table to obtain fault equipment position information;
s404: and focusing a corresponding complex model in the three-dimensional visual scene through the fault equipment position information to obtain detailed information of the fault equipment.
Preferably, the positioning and tracing system of the digital energy air compression station comprises an air compression station basic data acquisition module, a prediction module, a positioning and tracing module and a fault analysis module; the air compression station basic data acquisition module acquires an air compression station field signal, acquires deviation and deviation increment of main pipe pressure of air compression station equipment from the air compression station field signal, and obtains control information through fuzzy calculation; the prediction module is used for converting the control information into instantaneous reliability, establishing a reliability prediction model through the instantaneous reliability, and inputting the control information into the reliability prediction model to obtain machine running state information; the positioning and tracing module is used for acquiring positioning information from the air compression station field signal and obtaining a three-dimensional visual scene of the air compression station through digital modeling of the positioning information; the fault analysis module is used for obtaining fault warning information from the machine running state information through fault analysis, establishing a mapping relation between the fault warning information and the positioning information, and obtaining detailed information of fault equipment through focusing in the three-dimensional visual scene.
Further, the prediction module comprises data flow management, historical data state analysis, runtime state analysis and prediction state analysis.
The beneficial effects of the invention are as follows:
(1) By setting fuzzy calculation, the nonlinear variation of the air main pipe pressure is quantified by means of fuzzy mathematics, and the deviation increment are simulated and controlled by a preset control rule, so that the dynamic response of the system is improved.
(2) By taking the control information as sample data, a reliability prediction model is established, and the model parameters are optimized by combining an intelligent algorithm, so that more accurate prediction of the instantaneous reliability of the air compression station equipment is realized, the running performance of the air compression station equipment is dynamically reflected, and the reliability of the air compression station equipment is accurately judged.
(3) The three-dimensional visual model is established, the spatial position of the three-dimensional model is transformed by combining the spatial information of the air compression station, and the information visual fine management and fault location tracing of the air compression station are realized through the mapping of the equipment control information of the air compression station and the three-dimensional model.
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The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
Fig. 1 is a schematic flow chart of a positioning and tracing method of a digital energy air compression station.
Description of the embodiments
In order to further describe the technical means and effects adopted by the invention for achieving the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects according to the invention with reference to the attached drawings and the preferred embodiment.
Referring to fig. 1, a positioning and tracing method for a digital energy air compression station includes the following steps:
s1: acquiring an air compression station field signal, acquiring deviation and deviation increment of main pipe pressure of air compression station equipment from the air compression station field signal, performing discrete quantization on the deviation and the deviation increment to obtain a discrete quantized value, presetting a fuzzy variable area and a control rule, determining membership degree of the discrete quantized value according to the fuzzy variable area, converting the discrete quantized value into a fuzzy vector through the membership degree and the control rule, and performing definition processing on the fuzzy vector to obtain control information;
s2: periodically sampling the control information to obtain sample data, extracting state characteristics of the sample data, presetting state characteristic indexes of the sample data in a sampling period, obtaining instantaneous reliability of the control information according to the state characteristics, establishing a reliability prediction model through the instantaneous reliability, and inputting the control information into the reliability prediction model to obtain machine running state information;
s3: positioning information is obtained from the air compression station field signals, and a three-dimensional visual scene of the air compression station is obtained through digital modeling of the positioning information;
s4: and obtaining fault warning information from the machine running state information through fault analysis, establishing a mapping relation between the fault warning information and the positioning information, and obtaining detailed information of fault equipment through focusing in the three-dimensional visual scene.
In this embodiment, specifically, the step S1 includes the following steps:
s101: presetting a basic domain, and calculating to obtain a deviation quantized value and a deviation increment quantized value according to the basic domain, wherein a calculation formula is as follows:
，
，
wherein,ein order for the deviation to be a function of,ecin order for the deviation to be incremental,VHas the upper limit value of the basic domain of discussion,VLas the lower limit value of the basic domain of discussion,f(e) In order to quantify the value of the deviation,f(ec) Quantifying the value for the delta deviation;
s102: calculating the deviation quantized value and the deviation increment quantized value through the control rule and the membership degree to obtain a fuzzy vector, wherein the calculation formula is as follows:
，
wherein,Kin order to blur the vector of the image,Mefor the degree of membership of the deviation,Mecmembership for the delta deviation;
s103: and obtaining a clear vector from the fuzzy vector through a sharpening process, and obtaining control information from the clear vector through scale transformation.
Determining basic domains of pressure deviation according to field control experience of staff on actual system
Is [ -0.2,0.2], the basic argument of the deviation delta is [ -0.02,0.02]. The fuzzy rule for adjusting the parameters of the traditional controller is obtained by defining membership functions of language values of the language variables for fuzzy input variables, selecting triangular membership functions for the language variables during system modeling, and combining analysis and actual operation experience of technicians according to self-setting requirements of the controlled system on three parameters Kp, ki and Kd. Fuzzy reasoning is carried out by adopting a Martini type reasoning algorithm, and a single value which can represent the set relatively most is taken as a control quantity from fuzzy sets obtained by reasoning by using a selected area gravity center method.
By the technical scheme, the working conditions and environmental changes of all devices in the air compression station are comprehensively considered, and the robustness of the system is improved.
Specifically, the step S2 includes the steps of:
s201: converting the control information into instantaneous reliability, wherein the calculation formula is as follows:
，
wherein,tfor the time of day count to be counted,ifor the counting of the number of samples,kas a total number of samples,for time sampling, ++>For instantaneous reliability, ++>For state parameter->Is a state characteristic index;
s202: obtaining a training sample set by intercepting instantaneous reliability data in a certain time period, and predicting by adopting a root mean square error evaluation model to obtain model parameters, wherein the model parameters comprise kernel function parameters, insensitive loss functions and penalty factors;
s203: obtaining a reliability prediction model through multi-objective optimization of the model parameters;
s204: and inputting the control information into the reliability prediction model to obtain machine running state information.
The input of the determination model is the flow in the current time interval of the system and the flow of the previous 9 time intervals, and the output is the instantaneous reliability of the next time interval of the system. Substituting the collected flow data into an instantaneous reliability formula to calculate the instantaneous reliability of 6 hydraulic systems corresponding to different moments, intercepting the instantaneous reliability data of the hydraulic systems in a certain time interval, taking the sample data of 5 systems as training samples, taking the sample data of another system as test samples, and adopting a root mean square error evaluation model to predict the effect.
By the technical scheme, more accurate prediction of the instantaneous reliability of the air compression station equipment is realized, dynamic reflection of the running performance of the air compression station equipment is realized, and the reliability of the air compression station equipment is accurately judged.
Specifically, the step S3 includes the steps of:
s301: acquiring positioning information from the air compression station field signal through a sensor, and storing the positioning information as a geometric description file;
s302: analyzing the geometric description file into a program class instance through an analyzer;
s303: constructing a simple model and a source model by gridding modeling on the program class instance, and obtaining a complex model by Boolean operation on the simple model and the source model, wherein the calculation formula is as follows:
，
wherein,Ain order to make the model simple,Bas a source model of the object to be processed,Cis a complex model;
s304: and establishing a three-dimensional visual scene of the air compression station through the complex model by the positioning information.
The model file is read line by line, then is divided by equal sign, is divided into key value pairs and is stored in the JSON file, the model file is analyzed into an instance of a class which can be identified by a program language by adopting a tinyxml library, and then is stored in the JSON; each model file is stored in a memory as an independent JSON instance, and the value corresponding to the key of the calling file in the JSON is replaced by the JSON instance corresponding to the value according to the original structure from the SON corresponding to the entry project. The model analysis step generates a JSON containing all models and level information in the three-dimensional data, and the mapping from the model file to the JSON instance is completed.
Specifically, the step S4 includes:
s401: trend analysis is carried out on the machine running state information to obtain a machine state change trend, and a calculation formula is as follows:
，
wherein,for the trend of machine state change, +.>For the length of the time period,A1 is the initial discharge frequency amplitude of the switching station,A2 is the switch station warp->The amplitude of the discharge frequency after the time,Fthe discharge frequency amplitude value is the fault state of the switching station;
s402: performing fault analysis on the machine state change trend to obtain fault warning information;
s403: constructing a mapping relation between the machine running state information and the complex model to obtain a model structure table, and associating the fault warning information with the complex model through the model structure table to obtain fault equipment position information;
s404: and focusing a corresponding complex model in the three-dimensional visual scene through the fault equipment position information to obtain detailed information of the fault equipment.
And the loading and rendering speed of the three-dimensional model is improved, an LOD multi-detail level technology is introduced, and the three-dimensional model is organized according to a tree structure, and is similar to a two-dimensional tile map. When a three-dimensional space is divided into a plurality of subspaces, each subspace is stored as a child node of the control, geometric information of a space range is stored in the node, and geometric information of the space range is stored in the node. The nodes are tile, all the nodes form tile set, and the tile set is stored in a file in a JSON format; attributes such as boundingVolume, geometry error, content and the like are mainly stored in tileset; a series of models with different precision are generated by a surface subdivision method, and the models are organized from a root node to a leaf node in a tile set according to the sequence from low precision to high precision. When a user views the model in a visual interface, intersecting the bounding box stored in the bounding volume attribute with the view cone, screening out a model intersecting the bounding box with the view cone, and then recursively judging the model meeting the precision requirement in the model intersecting the bounding box with the view cone through a screen space error SSE to render. By the technical scheme, information visual fine management and fault location traceability of the air compression station are realized.
Preferably, the positioning and tracing system of the digital energy air compression station comprises an air compression station basic data acquisition module, a prediction module, a positioning and tracing module and a fault analysis module; the air compression station basic data acquisition module acquires an air compression station field signal, acquires deviation and deviation increment of main pipe pressure of air compression station equipment from the air compression station field signal, and obtains control information through fuzzy calculation; the prediction module is used for converting the control information into instantaneous reliability, establishing a reliability prediction model through the instantaneous reliability, and inputting the control information into the reliability prediction model to obtain machine running state information; the positioning and tracing module is used for acquiring positioning information from the air compression station field signal and obtaining a three-dimensional visual scene of the air compression station through digital modeling of the positioning information; the fault analysis module is used for obtaining fault warning information from the machine running state information through fault analysis, establishing a mapping relation between the fault warning information and the positioning information, and obtaining detailed information of fault equipment through focusing in the three-dimensional visual scene.
Further, the prediction module comprises data flow management, historical data state analysis, runtime state analysis and prediction state analysis.
The system uses a framework design plug-in type development method, follows the development ideas of high cohesion and low coupling, and the function modules are mutually independent, and the influence on the modification of a certain function module only generates on the inside of the function module, and has no influence on the outside of the function module. The framework program adopts an object-oriented design method, the PnP design concept adopts an MVC mode (model-view-controller mode) based on the framework program design, and the model is internally composed of data and functional modules, so that functions and input and output modes are mutually independent. The view portion presents a graphical interface to the user through the data support provided by the model. The user interface can be easily changed by the air compression station system of the application framework design through the mutual separation of the model view and the controller.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing. Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The present invention is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present invention.
Claims (5)
1. The positioning and tracing method of the digital energy air compression station is characterized by comprising the following steps of:
s1: acquiring an air compression station field signal, acquiring deviation and deviation increment of main pipe pressure of air compression station equipment from the air compression station field signal, presetting a basic domain, calculating according to the basic domain to obtain a deviation quantized value and a deviation increment quantized value, wherein the calculation formula is as follows:
，
，
wherein,ein order for the deviation to be a function of,ecin order for the deviation to be incremental,VHas the upper limit value of the basic domain of discussion,VLas the lower limit value of the basic domain of discussion,f(e) In order to quantify the value of the deviation,f(ec) Quantifying the value for the delta deviation;
calculating the deviation quantized value and the deviation increment quantized value through the control rule and the membership degree to obtain a fuzzy vector, wherein the calculation formula is as follows:
，
wherein,Kin order to blur the vector of the image,Mefor the degree of membership of the deviation,Mecmembership for the delta deviation;
the fuzzy vector is subjected to sharpening processing to obtain a sharp vector, and the sharp vector is subjected to scale transformation to obtain control information;
s2: converting the control information into instantaneous reliability, wherein the calculation formula is as follows:
，
wherein,tfor the time of day count to be counted,ifor the counting of the number of samples,kas a total number of samples,for time sampling, ++>For instantaneous reliability, ++>For state parameter->Is a state characteristic index;
obtaining a training sample set by intercepting instantaneous reliability data in a certain time period, and predicting by adopting a root mean square error evaluation model to obtain model parameters, wherein the model parameters comprise kernel function parameters, insensitive loss functions and penalty factors;
obtaining a reliability prediction model through multi-objective optimization of the model parameters;
inputting the control information into the reliability prediction model to obtain machine running state information;
s3: positioning information is obtained from the air compression station field signals, and a three-dimensional visual scene of the air compression station is obtained through digital modeling of the positioning information;
s4: and obtaining fault warning information from the machine running state information through fault analysis, establishing a mapping relation between the fault warning information and the positioning information, and obtaining detailed information of fault equipment through focusing in the three-dimensional visual scene.
2. The method for locating and tracing a digital energy air compression station according to claim 1, wherein the step S3 comprises the steps of:
s301: acquiring positioning information from the air compression station field signal through a sensor, and storing the positioning information as a geometric description file;
s302: analyzing the geometric description file into a program class instance through an analyzer;
s303: constructing a simple model and a source model by gridding modeling on the program class instance, and obtaining a complex model by Boolean operation on the simple model and the source model, wherein the calculation formula is as follows:
，
wherein,Ain order to make the model simple,Bas a source model of the object to be processed,Cis a complex model;
s304: and establishing a three-dimensional visual scene of the air compression station through the complex model by the positioning information.
3. The method for locating and tracing a digital energy air compression station according to claim 2, wherein the step S4 comprises:
s401: trend analysis is carried out on the machine running state information to obtain a machine state change trend, and a calculation formula is as follows:
，
wherein,for the trend of machine state change, +.>For the length of the time period,A1 is the initial discharge frequency amplitude of the switching station,A2 is the switch station warp->The amplitude of the discharge frequency after the time,Fthe discharge frequency amplitude value is the fault state of the switching station;
s402: performing fault analysis on the machine state change trend to obtain fault warning information;
s403: constructing a mapping relation between the machine running state information and the complex model to obtain a model structure table, and associating the fault warning information with the complex model through the model structure table to obtain fault equipment position information;
s404: and focusing a corresponding complex model in the three-dimensional visual scene through the fault equipment position information to obtain detailed information of the fault equipment.
4. A positioning and tracing system of a digital energy air compression station, which is characterized by operating by the method as claimed in any one of claims 1-3, comprising an air compression station basic data acquisition module, a prediction module, a positioning and tracing module and a fault analysis module; the air compression station basic data acquisition module acquires an air compression station field signal, acquires deviation and deviation increment of main pipe pressure of air compression station equipment from the air compression station field signal, and obtains control information through fuzzy calculation; the prediction module is used for converting the control information into instantaneous reliability, establishing a reliability prediction model through the instantaneous reliability, and inputting the control information into the reliability prediction model to obtain machine running state information; the positioning and tracing module is used for acquiring positioning information from the air compression station field signal and obtaining a three-dimensional visual scene of the air compression station through digital modeling of the positioning information; the fault analysis module is used for obtaining fault warning information from the machine running state information through fault analysis, establishing a mapping relation between the fault warning information and the positioning information, and obtaining detailed information of fault equipment through focusing in the three-dimensional visual scene.
5. The location traceability system of a digital energy air compression station of claim 4, wherein said prediction module comprises data flow management, historical data state analysis, runtime state analysis, and prediction state analysis.
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