CN117193047A - Industrial production simulation management system and method based on machine vision - Google Patents
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Abstract
The invention discloses an industrial production simulation management system and method based on machine vision, and belongs to the field of simulation management. The system comprises an acquisition module, a simulation module, a comparison module and a display terminal; the acquisition module is used for acquiring production data of any production area within a preset time range; the simulation module is used for inputting the production data of the acquisition module into a pre-established production simulation system to perform full-network hour-level production simulation and outputting the running state results of different systems in the production area; the comparison module is used for comparing different difference results obtained under different system running state results; and the display terminal monitors the production data of production and the running state results of the production simulation system and different systems. Meanwhile, the industrial production simulation management method based on machine vision is provided, and the visual industrial simulation application scene is met.
Description
Technical Field
The invention relates to the field of production simulation management, in particular to an industrial production simulation management system and method based on machine vision.
Background
Today, china is becoming one of the most active areas of world machine vision development, and the application range covers various industries of national economy such as industry, agriculture, medicine, military, aerospace, weather, astronomy, public security, traffic, safety, scientific research and the like. The important reason is that China has become a processing center of the global manufacturing industry, and high-demand part processing and corresponding advanced production lines thereof, so that a plurality of machine vision systems and application experiences with international advanced level are brought into China. Machine vision is a branch of the rapid development of artificial intelligence. In short, machine vision is to use a machine instead of a human eye to make measurements and decisions. The machine vision system converts the shot target into an image signal by a machine vision product, namely an image shooting device, and the image signal is divided into a CMOS (complementary metal oxide semiconductor) and a CCD (charge coupled device), and then the image signal is transmitted to a special image processing system to obtain the form information of the shot target, and the form information is converted into a digital signal according to the pixel distribution, the brightness, the color and other information; the image system performs various operations on these signals to extract characteristics of the object, and further controls the operation of the on-site device according to the result of the discrimination.
The production process modeling is to model a complete process from raw material preparation, production and finished product warehousing according to a certain process sequence. Although some enterprises implement informatization nowadays, due to the complexity of production lines and workshops, it is difficult to perform centralized management on multisource and multi-form data such as description information, production-related real-time and historical data, test and simulation data and the like, so as to realize the correspondence of information worlds and physical production lines or workshops, fusion and interaction, and realize the information modeling and the information-to-physical control of corresponding dry physics. The reduction of the rework rate is advantageous, but process modeling in terms of production technology is not very perfect today.
Therefore, there is a need for a machine vision-based industrial production simulation management system and method to solve the above problems.
Disclosure of Invention
The invention aims to provide an industrial production simulation management system and method based on machine vision, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
an industrial production simulation management system based on machine vision, the system comprising: the device comprises an acquisition module, a simulation module, an output feedback module and a display terminal;
the acquisition module is used for acquiring production data of any production area within a preset time range;
the simulation module is used for inputting the production data of the acquisition module into a pre-established production simulation system to perform full-network hour-level production simulation and outputting the running state results of different systems in the production area;
the output feedback module is used for carrying out visual processing on the data information obtained by analysis and sending the data information to the user terminal;
the display terminal monitors production data of production, production simulation systems and running state results of different systems;
the output end of the acquisition module is connected with the input end of the simulation module; the output end of the simulation module is connected with the input end of the output feedback module; the output end of the output feedback module is connected with the input end of the display terminal; the output end of the simulation module is connected with the input end of the display terminal.
According to the technical scheme, the acquisition module comprises a data acquisition unit, an image acquisition unit and a voice acquisition unit;
the data acquisition unit is used for acquiring historical information and production data of a product processing process and storing and recording the historical information and the production data;
the image acquisition unit is used for acquiring video streams in the production process and converting the video streams into digital images;
the voice acquisition unit acquires voice information of the production process in real time by using other voice acquisition equipment such as a pickup or a microphone.
According to the technical scheme, the simulation module comprises an input unit, a simulation unit and an analysis unit;
the input unit is used for inputting the production data into a pre-established production simulation system;
the simulation unit is used for carrying out full-network hour-level production simulation on the hourly operation state of the production area according to operation strategies corresponding to different preset schemes in the simulation process of the production simulation system to obtain simulation operation results of the different schemes in the regional power grid;
the analysis unit is used for analyzing simulation operation results of the different schemes in the production area according to an algorithm to obtain system operation state results of the different schemes in the area.
According to the technical scheme, the output feedback module comprises a data receiving unit, an information visualizing unit and a regulation and control correcting unit;
the data receiving unit is used for receiving the system running state result, converting the system running state result into a data type and then inputting the data type into the information visualizing unit;
the information visualization unit is used for carrying out visualization processing on the product production result by the acquired system running state result, and outputting and displaying the product production result to the user terminal;
the regulation and control correction unit is used for tracing errors in the production result of the product and correcting and regulating according to corresponding parts of preset production data.
According to the technical scheme, the display terminal comprises a display screen, an alarm and a wireless communication device; the display screen is used for displaying video images of production running states of products and system running state results; the alarm is used for entering an alarm mode when errors in monitoring production results of products; the wireless communication device is used for calling communication signals of staff.
An industrial production simulation management method based on machine vision, which comprises the following steps:
s1, acquiring production data of any production area in a preset time range;
s2, carrying out full-network hour-level production simulation through a production simulation system;
s3, outputting running state results of different systems in the production area;
s4, carrying out visual processing on the data information obtained by analysis;
s5, monitoring production data, production simulation systems and running state results of different systems.
According to the technical scheme, in step S2, a digitized virtual scene is constructed according to the production environment, the real production environment is mapped to the machine vision, the production process is verified and evaluated based on a virtual whole-network hour-level production environment, and the operation under different condition combinations is automatically completed through the digitized model of the product and the production workshop site, so that the automatic production process is realized.
According to the above technical solution, in step S3, historical data generated by production is collected by a data collecting unit, the data is stored in a database after being processed, characteristics of the processed data are extracted, a plurality of models of different types are trained by using standard parameters, performance evaluation is performed on each model, optimization of model parameters is performed, and the performance evaluation of the models specifically includes the following steps:
a1: randomly dividing the available data into two subsets, one production set { a } 1 ,a 2 ,a 3 ,a 4 Sum { b } of one monitoring set 1 ,b 2 ,b 3 ,b 4 }；
A2: the production data is input to the algorithm q= [ Q ] ij ] m×n Wherein Q represents the degree of association, Q ij Represented as a matrixI e {1,2,..m }, j e {1,2,..m, n }, m is the number of types in the operation production model, n is the number of working procedures in the operation production model, m and n are integers, the association degree between each step is evaluated, and the influence degree between each class of operation production is judged according to the evaluation result;
a3: predicting the monitoring set by using the model to obtain predicted data of the model on unknown data;
a4: and obtaining the accuracy of model monitoring by the error rate of the algorithm model on the monitoring set.
According to the technical scheme, in step S4, the received characteristic data is detected through the data receiving unit, model monitoring is carried out through a preset model, the possible abnormal types are determined based on the predicted trend, early warning is sent out, and a regulation strategy responding to an early warning event is triggered; wherein the input characteristic data comprises a time stamp, a sensor reading acquired at the time stamp and a production equipment number, and at time t, the production condition change trend of the product is predicted, and a smooth value S of the time t is obtained according to the following formula t And (3) performing calculation: s is S t ＝a*y t +(1-a)S t-1 Wherein a represents a smoothing constant, y t Representing the actual value of time t, S t-1 Representing the smoothed value of time t-1.
Compared with the prior art, the invention has the following beneficial effects:
1. the flexibility and the automation degree of the production are improved. Machine vision is often used to replace manual vision in some dangerous work environments unsuitable for manual work or where manual vision is difficult to meet requirements.
2. In the mass industrial production process, the quality and the efficiency of the product are low and the precision is not high by using the manual visual inspection, and the production efficiency and the automation degree of production can be greatly improved by using the machine visual inspection method. And the machine vision is easy to realize information integration, and is a basic technology for realizing computer integrated manufacturing.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a machine vision-based industrial production simulation management system;
FIG. 2 is a schematic diagram of steps of a machine vision-based industrial production simulation management method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: an industrial production simulation management system based on machine vision, referring to fig. 1, the system comprises: the device comprises an acquisition module, a simulation module, an output feedback module and a display terminal;
the acquisition module is used for acquiring production data of any production area within a preset time range;
the simulation module is used for inputting the production data of the acquisition module into a pre-established production simulation system to perform full-network hour-level production simulation and outputting the running state results of different systems in the production area;
the output feedback module is used for carrying out visual processing on the data information obtained by analysis and sending the data information to the user terminal;
the display terminal monitors production data of production, production simulation systems and running state results of different systems;
the output end of the acquisition module is connected with the input end of the simulation module; the output end of the simulation module is connected with the input end of the output feedback module; the output end of the output feedback module is connected with the input end of the display terminal; the output end of the simulation module is connected with the input end of the display terminal.
The acquisition module comprises a data acquisition unit, an image acquisition unit and a voice acquisition unit;
the data acquisition unit is used for acquiring historical information and production data of a product processing process and storing and recording the historical information and the production data;
the image acquisition unit is used for acquiring video streams in the production process and converting the video streams into digital images;
the voice acquisition unit acquires voice information of the production process in real time by using the pickup.
The simulation module comprises an input unit, a simulation unit and an analysis unit;
the input unit is used for inputting the production data into a pre-established production simulation system;
the simulation unit is used for carrying out full-network hour-level production simulation on the hourly operation state of the production area according to operation strategies corresponding to different preset schemes in the simulation process of the production simulation system to obtain simulation operation results of the different schemes in the regional power grid;
the analysis unit is used for analyzing simulation operation results of the different schemes in the production area according to an algorithm to obtain system operation state results of the different schemes in the area.
The output feedback module comprises a data receiving unit, an information visualizing unit and a regulation and control correcting unit;
the data receiving unit is used for receiving the system running state result, converting the system running state result into a data type and then inputting the data type into the information visualizing unit;
the information visualization unit is used for carrying out visualization processing on the product production result by the acquired system running state result, and outputting and displaying the product production result to the user terminal;
the regulation and control correction unit is used for tracing errors in the production result of the product and correcting and regulating according to corresponding parts of preset production data.
The display terminal comprises a display screen, an alarm and a wireless communication device; the display screen is used for displaying video images of production running states of products and system running state results; the alarm is used for entering an alarm mode when errors in monitoring production results of products; the wireless communication device is used for calling communication signals of staff.
An industrial production simulation management method based on machine vision, which comprises the following steps:
s1, acquiring production data of any production area in a preset time range;
s2, carrying out full-network hour-level production simulation through a production simulation system;
s3, outputting running state results of different systems in the production area;
s4, carrying out visual processing on the data information obtained by analysis;
s5, monitoring production data, production simulation systems and running state results of different systems.
In step S2, a digitized virtual scene is constructed according to the production environment, the real production environment is mapped into the machine vision, the production process is verified and evaluated based on a virtual whole-network hour-level production environment, and the operation under different condition combinations is automatically completed through the digitized model of the product and the production workshop site, so that the automated production process is realized.
In step S3, historical data generated by production is collected by a data collection unit, the data is stored in a database after being processed, characteristics of the processed data are extracted, a plurality of models of different types are trained by using standard parameters, performance evaluation is performed on each model, model parameters are optimized, and the performance evaluation of the models specifically comprises the following steps:
a1: randomly dividing the available data into two subsets, one production set { a } 1 ,a 2 ,a 3 ,a 4 Sum { b } of one monitoring set 1 ,b 2 ,b 3 ,b 4 }；
A2: production ofData is input to algorithm q= [ Q ] ij ] m×n Wherein Q represents the degree of association, Q ij Expressed as the elements of row i, column j in the matrix, i e {1,2,..m }, j e {1,2,., n }, m is the number of types in the job production model, n is the number of working procedures in the operation production model, m and n are integers, the association degree between each step is evaluated, and the influence degree between each class of operation production is judged according to the evaluation result;
a3: predicting the monitoring set by using the model to obtain predicted data of the model on unknown data;
a4: and obtaining the accuracy of model monitoring by the error rate of the algorithm model on the monitoring set.
In step S4, the received feature data is detected by the data receiving unit, model monitoring is performed by a preset model, the possible abnormal type is determined based on the predicted trend, an early warning is sent out, and a regulation strategy for responding to the early warning event is triggered; wherein the input characteristic data comprises a time stamp, a sensor reading acquired at the time stamp and a production equipment number, and at time t, the production condition change trend of the product is predicted, and a smooth value S of the time t is obtained according to the following formula t And (3) performing calculation: s is S t ＝a*y t +(1-a)S t-1 Wherein a represents a smoothing constant, y t Representing the actual value of time t, S t-1 Representing the smoothed value of time t-1.
Example 2: referring to fig. 2, a machine vision-based industrial production simulation management method includes the steps of:
s1, acquiring production data of any production area in a preset time range;
s2, carrying out full-network hour-level production simulation through a production simulation system;
the method comprises the steps of constructing a digitalized virtual scene according to a production environment, mapping a real production environment into machine vision, verifying and evaluating a production process based on a virtual whole-network hour-level production environment, automatically completing operation under different condition combinations through a digitalized model of a product and a production workshop site, and realizing an automatic production process.
S3, outputting running state results of different systems in the production area;
the method comprises the steps of collecting historical data generated by production through a data collecting unit, processing the data, storing the data into a database, extracting characteristics of the processed data, training a plurality of models of different types by using standard parameters, evaluating the performance of each model, optimizing the parameters of the model, and performing performance evaluation of the model, wherein the method specifically comprises the following steps:
a1: randomly dividing the available data into two subsets, one production set { a } 1 ,a 2 ,a 3 ,a 4 Sum { b } of one monitoring set 1 ,b 2 ,b 3 ,b 4 }；
A2: the production data is input to the algorithm q= [ Q ] ij ] m×n Wherein Q represents the degree of association, Q ij Expressed as the elements of row i, column j in the matrix, i e {1,2,..m }, j e {1,2,., n }, m is the number of types in the job production model, n is the number of working procedures in the operation production model, m and n are integers, the association degree between each step is evaluated, and the influence degree between each class of operation production is judged according to the evaluation result;
a3: predicting the monitoring set by using the model to obtain predicted data of the model on unknown data;
a4: and obtaining the accuracy of model monitoring by the error rate of the algorithm model on the monitoring set.
S4, carrying out visual processing on the data information obtained by analysis;
detecting the received characteristic data through a data receiving unit, performing model monitoring through a preset model, determining the possible abnormal type based on the predicted trend, sending out early warning, and triggering a regulation strategy responding to the early warning event; wherein the input characteristic data comprises a time stamp, a sensor reading acquired at the time stamp and a production equipment number, and at time t, the production condition change trend of the product is predicted, and a smooth value S of the time t is obtained according to the following formula t And (3) performing calculation: s is S t ＝a*y t +(1-a)S t-1 Wherein a represents a smoothing constant, y t Representing the actual value of time t, S t-1 Representing the smoothed value of time t-1.
S5, monitoring production data, production simulation systems and running state results of different systems.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An industrial production simulation management system based on machine vision is characterized in that: the system comprises: the device comprises an acquisition module, a simulation module, an output feedback module and a display terminal;
the acquisition module is used for acquiring production data of any production area within a preset time range;
the simulation module is used for inputting the production data of the acquisition module into a pre-established production simulation system to carry out production simulation and outputting the running state results of different systems in the production area;
the output feedback module is used for carrying out visual processing on the data information obtained by analysis and sending the data information to the user terminal;
the display terminal monitors production data of production, production simulation systems and running state results of different systems;
the output end of the acquisition module is connected with the input end of the simulation module; the output end of the simulation module is connected with the input end of the output feedback module; the output end of the output feedback module is connected with the input end of the display terminal; the output end of the simulation module is connected with the input end of the display terminal.
2. The machine vision-based industrial production simulation management system of claim 1, wherein: the acquisition module comprises a data acquisition unit, an image acquisition unit and a voice acquisition unit;
the data acquisition unit is used for acquiring historical information and production data of a product processing process and storing and recording the historical information and the production data;
the image acquisition unit is used for acquiring video streams in the production process through machine vision and converting the video streams into digital images;
the voice acquisition unit acquires voice information in the production process in real time.
3. The machine vision-based industrial production simulation management system of claim 1, wherein: the simulation module comprises an input unit, a simulation unit and an analysis unit;
the input unit is used for inputting the production data into a pre-established production simulation system;
the simulation unit is used for carrying out full-network hour-level production simulation on the hourly operation state of the production area according to operation strategies corresponding to different preset schemes in the simulation process of the production simulation system to obtain simulation operation results of the different schemes in the regional power grid;
the analysis unit is used for analyzing simulation operation results of the different schemes in the production area according to an algorithm to obtain system operation state results of the different schemes in the area.
4. The machine vision-based industrial production simulation management system of claim 1, wherein: the output feedback module comprises a data receiving unit, an information visualizing unit and a regulation and control correcting unit;
the data receiving unit is used for receiving the system running state result, converting the system running state result into a data type and then inputting the data type into the information visualizing unit;
the information visualization unit is used for carrying out visualization processing on the product production result by the acquired system running state result, and outputting and displaying the product production result to the user terminal;
the regulation and control correction unit is used for tracing errors in the production result of the product and correcting and regulating according to corresponding parts of preset production data.
5. The machine vision-based industrial production simulation management system of claim 1, wherein: the display terminal comprises a display screen, an alarm and a wireless communication device; the display screen is used for displaying video images of production running states of products and system running state results; the alarm is used for entering an alarm mode when errors in monitoring production results of products; the wireless communication device is used for calling communication signals of staff.
6. The industrial production simulation management method based on machine vision is characterized by comprising the following steps of: the method comprises the following steps:
s1, acquiring production data of any production area in a preset time range;
s2, carrying out full-network hour-level production simulation through a production simulation system;
s3, outputting running state results of different systems in the production area;
s4, carrying out visual processing on the data information obtained by analysis;
s5, monitoring production data, production simulation systems and running state results of different systems.
7. The machine vision-based industrial production simulation management method as claimed in claim 6, wherein: in step S2, a digitized virtual scene is constructed according to the production environment, the real production environment is mapped into the machine vision, the production process is verified and evaluated based on a virtual whole-network hour-level production environment, and the operation under different condition combinations is automatically completed through the digitized model of the product and the production workshop site, so that the automated production process is realized.
8. The machine vision-based industrial production simulation management method as claimed in claim 6, wherein: in step S3, historical data generated by production is collected by a data collection unit, the data is stored in a database after being processed, characteristics of the processed data are extracted, a plurality of models of different types are trained by using standard parameters, performance evaluation is performed on each model, model parameters are optimized, and the performance evaluation of the models specifically comprises the following steps:
a1: randomly dividing the available data into two subsets, one production set { a } 1 ,a 2 ,a 3 ,a 4 Sum { b } of one monitoring set 1 ,b 2 ,b 3 ,b 4 }；
A2: production data input to an algorithmWherein Q represents the degree of association, Q ij Represented as the element of the ith row and jth column in the matrix, i e {1, 2..m }, j e {1, 2..,
n, m is the number of types in the operation production model, n is the number of working procedures in the operation production model, m and n are integers, the relevance between each step is evaluated, and the influence degree between each class of operation production is judged according to the evaluation result;
a3: predicting the monitoring set by using the model to obtain predicted data of the model on unknown data;
a4: and obtaining the accuracy of model monitoring by the error rate of the algorithm model on the monitoring set.
9. The machine vision-based industrial production simulation management method as claimed in claim 6, wherein: in step S4, the received feature data is detected by the data receiving unit, model monitoring is performed by a preset model, the possible abnormal type is determined based on the predicted trend, an early warning is sent out, and a regulation strategy for responding to the early warning event is triggered; wherein the input characteristic data comprises a time stamp, a sensor reading acquired at the time stamp and a production equipment number, and at time t, the production condition change trend of the product is predicted, and a smooth value S of the time t is obtained according to the following formula t And (3) performing calculation: s is S t ＝a*y t +(1-a)S t-1 Wherein a represents a smoothing constant, y t Representing the actual value of time t, S t-1 Representing the smoothed value of time t-1.
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