CN113835374B - Real-time monitoring method and system for intelligent manufacturing workshop - Google Patents
Real-time monitoring method and system for intelligent manufacturing workshop Download PDFInfo
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- CN113835374B CN113835374B CN202111083201.9A CN202111083201A CN113835374B CN 113835374 B CN113835374 B CN 113835374B CN 202111083201 A CN202111083201 A CN 202111083201A CN 113835374 B CN113835374 B CN 113835374B
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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
- G05B19/00—Programme-control systems
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- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
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Abstract
The application is suitable for the technical field of computers, and particularly relates to a real-time monitoring method and a real-time monitoring system for an intelligent manufacturing workshop, wherein the method comprises the following steps: acquiring monitoring video information and infrared monitoring information, wherein the monitoring video information is continuously acquired video information, and the infrared monitoring information is an intermittently acquired infrared image; analyzing the environment visibility according to the monitoring video information to obtain a visibility analysis result; analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain a temperature analysis result; and judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result. According to the real-time monitoring method for the intelligent manufacturing workshop, the collected information is analyzed in real time, so that whether the visibility abnormality and the temperature abnormality exist in the current workshop or not can be judged in real time, whether the workshop has dangerous situations or not can be judged according to the abnormal conditions, the dangerous situations are timely corresponding, and the problem that the timeliness of manual analysis is poor is solved.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to a real-time monitoring method and system for an intelligent manufacturing workshop.
Background
Intelligent manufacturing is a man-machine integrated intelligent system consisting of intelligent machines and human experts, which can conduct intelligent activities such as analysis, reasoning, judgment, conception, decision-making, etc. during manufacturing. Through the cooperation of the human and the intelligent machine, the brain work of human expert in the manufacturing process is enlarged, extended and partially replaced. It extends the concept of manufacturing automation to flexibility, intelligence and high integration.
In current workshops, monitoring is widely used. The monitoring in the workshop can be used for monitoring the factory environment in real time so as to ensure the safety of the working environment, and the operation condition of the workshop can be mastered in real time by utilizing the monitoring.
However, in the existing workshops, the monitoring is mainly used for collecting pictures, and the content can be judged only by manually analyzing the pictures in the later period, so that the manual judgment has hysteresis, and the timeliness of identifying the picture content is affected.
Disclosure of Invention
The embodiment of the application aims to provide a real-time monitoring method for an intelligent manufacturing workshop, and aims to solve the problem in the third part of the background technology.
The embodiment of the application is realized in such a way that the real-time monitoring method of the intelligent manufacturing workshop comprises the following steps:
acquiring monitoring video information and infrared monitoring information, wherein the monitoring video information is continuously acquired video information, and the infrared monitoring information is an intermittently acquired infrared image;
analyzing the environment visibility according to the monitoring video information to obtain a visibility analysis result;
analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain a temperature analysis result;
and judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result.
Preferably, the step of analyzing the environmental visibility according to the monitoring video information to obtain a visibility analysis result specifically includes:
dividing the monitoring video information according to a preset time step, and randomly extracting a preset number of picture frames from the monitoring video information;
identifying target identification points in each group of picture frames to obtain an identification result;
and counting the number of target recognition points which can be successfully recognized and included in the recognition result, calculating the successful recognition rate, and generating a visibility analysis result.
Preferably, the step of analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain a temperature analysis result specifically includes:
reading each infrared image in the infrared monitoring information;
judging the infrared image according to a preset standard image, and judging whether a region with the temperature exceeding a preset temperature value exists in the infrared image;
marking a region exceeding a preset temperature value, and generating a temperature analysis result.
Preferably, the step of judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result specifically includes:
inquiring a preset visibility data comparison table according to the visibility analysis result to obtain a first inquiry result;
inquiring a preset temperature data comparison table according to the temperature analysis result to obtain a second inquiry result;
judging whether the threshold value is reached at the same time according to the first query result and the second query result, and if so, sending out alarm information.
Preferably, the monitoring video information and the infrared monitoring information both comprise collected time information.
Preferably, the method further comprises judging whether the highest threshold is reached according to the first query result and/or the second query result, and if yes, sending out alarm information.
Preferably, the temperature analysis result marks a region exceeding a preset temperature value.
Another object of an embodiment of the present application is to provide a real-time monitoring system for an intelligent manufacturing plant, the system including:
the information acquisition module is used for acquiring monitoring video information and infrared monitoring information, wherein the monitoring video information is continuously acquired video information, and the infrared monitoring information is an intermittently acquired infrared image;
the visibility analysis module is used for analyzing the environment visibility according to the monitoring video information to obtain a visibility analysis result;
the temperature analysis module is used for analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain a temperature analysis result;
and the alarm judging module is used for judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result.
Preferably, the visibility analysis module includes:
the frame extraction unit is used for dividing the monitoring video information according to a preset time step and randomly extracting a preset number of picture frames from the monitoring video information;
the picture identification unit is used for identifying target identification points in each group of picture frames to obtain an identification result;
and the data statistics unit is used for counting the number of the target recognition points which can be successfully recognized and included in the recognition result, calculating the successful recognition rate and generating a visibility analysis result.
Preferably, the temperature analysis module includes:
the data reading unit is used for reading each infrared image in the infrared monitoring information;
the overtemperature region judging unit is used for judging the infrared image according to a preset standard image and judging whether a region with the temperature exceeding a preset temperature value exists in the infrared image;
and the result generating unit is used for marking the area exceeding the preset temperature value and generating a temperature analysis result.
According to the real-time monitoring method for the intelligent manufacturing workshop, the collected information is analyzed in real time, so that whether the visibility abnormality and the temperature abnormality exist in the current workshop or not can be judged in real time, whether the workshop has dangerous situations or not can be judged according to the abnormal conditions, the dangerous situations are timely corresponding, and the problem that the timeliness of manual analysis is poor is solved.
Drawings
FIG. 1 is a flow chart of a real-time monitoring method for an intelligent manufacturing shop according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating steps for analyzing environmental visibility and obtaining a visibility analysis result according to monitoring video information according to an embodiment of the present application;
FIG. 3 is a flowchart showing steps for analyzing an abnormal condition of an ambient temperature and obtaining a temperature analysis result according to infrared monitoring information according to an embodiment of the present application;
FIG. 4 is a flowchart showing steps for judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result according to the embodiment of the present application;
FIG. 5 is a block diagram of a real-time monitoring system of an intelligent manufacturing shop according to an embodiment of the present application;
FIG. 6 is a block diagram of a visibility analysis module according to an embodiment of the present application;
fig. 7 is a schematic diagram of a temperature analysis module according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of this disclosure.
In current workshops, monitoring is widely used. The monitoring in the workshop can be used for monitoring the factory environment in real time so as to ensure the safety of the working environment, and the operation condition of the workshop can be mastered in real time by utilizing the monitoring. However, in the existing workshops, the monitoring is mainly used for collecting pictures, and the content can be judged only by manually analyzing the pictures in the later period, so that the manual judgment has hysteresis, and the timeliness of identifying the picture content is affected.
According to the application, the acquired information is analyzed in real time, so that whether the visibility abnormality and the temperature abnormality exist in the current workshop or not can be judged in real time, and therefore, whether the workshop has dangerous situations or not can be judged according to the abnormal conditions, and the situation is corresponding in time, and the problem of poor timeliness of manual analysis is solved.
As shown in fig. 1, a flowchart of a real-time monitoring method for an intelligent manufacturing shop according to an embodiment of the present application includes:
s100, acquiring monitoring video information and infrared monitoring information, wherein the monitoring video information is continuously acquired video information, and the infrared monitoring information is an intermittently acquired infrared image.
In the current intelligent manufacturing workshops, such as flour production workshops, a large amount of dust exists in the workshops, and the particle size of the flour is small, so that the flour is easy to suspend in the air, and the flour has combustibility and can be fully contacted with the air when suspended in the air, so that dust explosion is extremely easy to occur when the concentration of the dust exceeds a certain value, and the two requirements of the dust explosion are that the concentration of the dust reaches a certain value, and the condition that open fire or high temperature in the environment can cause rapid combustion of the dust, so that the risk of explosion phenomenon is extremely high; in the intelligent manufacturing workshop, a plurality of cameras are arranged, so that the condition of dust explosion can be monitored by using the cameras, and the production safety is ensured.
In the step, monitoring video information and infrared monitoring information are acquired, wherein the monitoring video information is continuously acquired video information, the infrared monitoring information is an intermittently acquired infrared image, monitoring is arranged in an intelligent manufacturing workshop, the intelligent manufacturing workshop is monitored in real time by utilizing the monitoring, so that continuous video information, namely the monitoring video information, is obtained, an infrared camera is arranged in the intelligent manufacturing workshop, the temperature of each part in the intelligent manufacturing workshop can be perceived by utilizing the infrared camera, and the temperature is required to be increased, so that the intelligent manufacturing workshop can be subjected to infrared image acquisition in an intermittent acquisition mode, and the infrared image is acquired once every time; the monitoring video information and the infrared monitoring information all contain the acquired time information.
And S200, analyzing the environment visibility according to the monitoring video information to obtain a visibility analysis result.
In this step, according to monitoring video information analysis environment visibility, at the beginning, indoor dust concentration is not high, only in ventilation system trouble or the in-process that leaks appears, indoor dust concentration can rise to the required concentration of explosion, and the concentration of dust rises also can influence indoor visibility, consequently through detecting indoor visibility, just can reverse judgement current intelligent manufacturing shop in dust concentration information, obtains the visibility analysis result.
S300, analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain a temperature analysis result.
In the step, the abnormal condition of the ambient temperature is analyzed according to the infrared monitoring information, and the infrared images are intermittently acquired and contain the temperature conditions of all the positions, so that the specific temperature condition of each position and the temperature rising speed of each position can be judged according to the continuous infrared images, and the temperature change of the infrared images in the future time can be reasonably predicted according to the current temperature and the temperature rising speed to obtain a temperature analysis result.
S400, judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result.
In the step, the dust explosion is judged according to the visibility analysis result and the temperature analysis result, and two necessary conditions are met, so that when at most one of the two conditions reaches a numerical value, the dust explosion can not occur, and when the temperature and the dust concentration in the intelligent manufacturing workshop exceed the conditions required by the dust explosion, alarm information is sent out to remind related personnel to process, thereby playing a role in alarming in time.
As shown in fig. 2, as a preferred embodiment of the present application, the step of analyzing the environmental visibility according to the surveillance video information to obtain a visibility analysis result specifically includes:
s201, dividing the monitoring video information according to a preset time step, and randomly extracting a preset number of picture frames from the monitoring video information.
In this step, the monitoring video information is divided according to a preset time step, and because the monitoring video information is a continuous video, it is difficult to process, the monitoring video information is divided into short videos of one segment, the divided time step can be selected according to the data processing capability, after the short videos of one segment are obtained, the number of the picture frames is randomly extracted from each short video, the number of the picture frames is selected according to the length and the frame rate of the short video, the larger the short video length is, the larger the number of the extracted picture frames is, the larger the frame rate of the short video is, and the larger the number of the extracted picture frames is.
S202, identifying target identification points in each group of picture frames to obtain an identification result.
In this step, the target identification point in each group of picture frames is identified, the target detection point is set at the key position of the intelligent manufacturing shop, specifically, the monitoring area of each monitoring device is limited, so that the target identification point can be set in the monitoring area of the monitoring device, the target identification point can be a printed mark point or a fixed article in the monitoring area, and when the picture frames are identified, the target identification point in the picture is identified, thus obtaining the identification result.
S203, counting the number of target recognition points which can be successfully recognized and included in the recognition result, calculating the successful recognition rate, and generating a visibility analysis result.
In this step, the number of target recognition points that can be successfully recognized included in the recognition result is counted, and in the process of recognizing the target recognition points, due to the influence of dust, part of the target recognition points cannot be recognized, so that the concentration of dust can be determined according to the probability that the target recognition points are successfully recognized, the total number of the target recognition points is calculated first, then the successful recognition rate is calculated according to the number of the target recognition points that can be successfully recognized, and a visibility analysis result is generated.
As shown in fig. 3, as a preferred embodiment of the present application, the step of analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain the temperature analysis result specifically includes:
s301, reading each infrared image in the infrared monitoring information.
In this step, each infrared image in the infrared monitoring information is read, and because the infrared images are intermittently collected, the number of the infrared images is relatively small, each infrared image can be processed, if the collection interval time is relatively small, an intermittent mode can be adopted, and one infrared image can be selected every interval.
S302, judging the infrared image according to a preset standard image, and judging whether a region with the temperature exceeding a preset temperature value exists in the infrared image.
In this step, the infrared image is determined according to the preset standard image, and the temperature is represented by the color in the infrared image, so that the temperature of each region can be determined according to the color distribution in the infrared image, thereby determining whether the region with the temperature exceeding the preset temperature value exists in the infrared image.
S303, marking a region exceeding a preset temperature value, and generating a temperature analysis result.
In the step, a region exceeding a preset temperature value is marked, so that the risk of overtemperature existing in the current region is indicated, and if the regulation is not performed, dust explosion or fire is easily caused; and marking the area exceeding the preset temperature value according to the temperature analysis result.
As shown in fig. 4, as a preferred embodiment of the present application, the step of determining whether to issue alarm information according to the visibility analysis result and the temperature analysis result specifically includes:
s401, inquiring a preset visibility data comparison table according to the visibility analysis result to obtain a first inquiry result.
In this step, a preset visibility data comparison table is queried according to the visibility analysis result, and the value of the dust concentration corresponding to each visibility is recorded in the visibility data comparison table, namely, the dust concentration in the reaction chamber can be reversely pushed according to the visibility analysis result, so as to obtain a first query result.
S402, inquiring a preset temperature data comparison table according to the temperature analysis result to obtain a second inquiry result.
In this step, a preset temperature data comparison table is queried according to the temperature analysis result, and similarly, the temperature value corresponding to each color is recorded in the temperature data comparison table, so that the temperature of each area in the room can be reversely pushed according to the color to obtain a second query result.
S403, judging whether the threshold value is reached at the same time according to the first query result and the second query result, and if so, sending out alarm information.
In the step, whether the threshold value is simultaneously reached is judged according to the first query result and the second query result, if the indoor dust concentration and the indoor dust temperature reach the threshold value, the dust explosion is likely to happen, and the processing is needed immediately, namely alarm information is sent out to inform related personnel of evacuation.
In this step, whether the highest threshold is reached is further determined according to the first query result and/or the second query result, if yes, an alarm message is sent out, and when the temperature exceeds the highest threshold, it is indicated that a fire disaster may occur, so that the processing is required, and when the dust concentration is too high, even if the indoor temperature is insufficient for ignition, the high concentration of the dust will affect the visibility and the health of workers, so that an alarm message is sent out to notify related personnel to perform the processing.
As shown in fig. 5, a real-time monitoring system for an intelligent manufacturing shop according to an embodiment of the present application is characterized in that the system includes:
the information acquisition module 100 is configured to acquire monitoring video information and infrared monitoring information, where the monitoring video information is continuously acquired video information, and the infrared monitoring information is an intermittently acquired infrared image.
In the system, an information acquisition module 100 acquires monitoring video information and infrared monitoring information, wherein the monitoring video information is continuously acquired video information, the infrared monitoring information is an intermittently acquired infrared image, monitoring is arranged in an intelligent manufacturing workshop, real-time monitoring is performed on the intelligent manufacturing workshop by using the monitoring, so that continuous video information, namely the monitoring video information, is obtained, an infrared camera is arranged in the intelligent manufacturing workshop, and the temperature of each part in the intelligent manufacturing workshop can be perceived by using the infrared camera, and the intermittent acquisition mode can be adopted to acquire the infrared image of the intelligent manufacturing workshop once every interval of time because the temperature is increased; the monitoring video information and the infrared monitoring information all contain the acquired time information.
The visibility analysis module 200 is configured to analyze the environmental visibility according to the monitoring video information, and obtain a visibility analysis result.
In this system, the visibility analysis module 200 analyzes the environmental visibility according to the monitoring video information, and at the beginning, the indoor dust concentration is not high, and only in the process of the ventilation system failure or leakage, the indoor dust concentration can rise to the concentration required by explosion, and the concentration rise of the dust can also affect the indoor visibility, so that the current dust concentration information in the intelligent manufacturing workshop can be reversely judged by detecting the indoor visibility, and the visibility analysis result is obtained.
The temperature analysis module 300 is configured to analyze the abnormal condition of the environmental temperature according to the infrared monitoring information, and obtain a temperature analysis result.
In the system, the temperature analysis module 300 analyzes the abnormal condition of the environmental temperature according to the infrared monitoring information, and because the infrared images are intermittently acquired and contain the temperature conditions of all the positions, the specific temperature condition of each position and the temperature rising speed of each position can be judged according to the continuous infrared images, and the temperature change of the temperature analysis module in the future time can be reasonably predicted according to the current temperature and the temperature rising speed so as to obtain the temperature analysis result.
The alarm judging module 400 is configured to judge whether to issue alarm information according to the visibility analysis result and the temperature analysis result.
In this system, the alarm judging module 400 judges according to the visibility analysis result and the temperature analysis result, and the two requirements of dust explosion are satisfied, so that when at most one of the two requirements reaches a numerical value, the condition of dust explosion still cannot occur, and when the temperature and the dust concentration in the intelligent manufacturing workshop exceed the conditions required by the dust explosion, alarm information is sent out to remind related personnel to process, thereby playing a role of timely alarming.
As shown in fig. 6, as a preferred embodiment of the present application, the visibility analysis module includes:
the frame extraction unit 201 is configured to divide the monitoring video information according to a preset time step, and randomly extract a preset number of picture frames from the monitoring video information.
In this module, the frame extraction unit 201 divides the monitoring video information according to a preset time step, and because the monitoring video information is a continuous video, it is difficult to process the monitoring video information, where the monitoring video information is divided into short videos of one segment, the divided time step may be selected according to the data processing capability, after the short videos of one segment are obtained, the number of frame is selected according to the length and the frame rate of the short videos, the larger the length of the short videos is, the larger the number of frame is, and the larger the frame rate of the short videos is, the larger the number of frame is.
The picture identifying unit 202 is configured to identify the target identifying point in each group of picture frames, so as to obtain an identifying result.
In this module, the screen identifying unit 202 identifies the target identifying points in each group of screen frames, sets target detecting points at key positions of the intelligent manufacturing plant, specifically, the monitoring area of each monitoring device is limited, so that the target identifying points can be set in the monitoring area of the monitoring device, and the target identifying points can be printed mark points or fixed articles in the monitoring area, and when the screen frames are identified, the target identifying points in the identifying graph can obtain the identifying result.
And the data statistics unit 203 is configured to count the number of target recognition points that can be successfully recognized and included in the recognition result, calculate the success rate, and generate a visibility analysis result.
In this module, the data statistics unit 203 counts the number of target recognition points that can be successfully recognized and included in the recognition result, and during the process of recognizing the target recognition points, due to the influence of dust, part of the target recognition points cannot be recognized, so that the concentration of the dust can be determined according to the probability that the target recognition points are successfully recognized, and the total number of the target recognition points is calculated first, and then the successful recognition rate is calculated according to the number of the target recognition points that can be successfully recognized, so as to generate the visibility analysis result.
As shown in fig. 7, as a preferred embodiment of the present application, the temperature analysis module includes:
the data reading unit 301 is configured to read each infrared image in the infrared monitoring information.
In this module, the data reading unit 301 reads each infrared image in the infrared monitoring information, and because the infrared images are intermittently collected, the number of the infrared images is relatively small, and each infrared image can be processed, if the collection interval time is relatively small, an intermittent manner can be adopted, and one infrared image can be selected every interval.
The overtemperature area judging unit 302 is configured to judge the infrared image according to a preset standard image, and judge whether an area with a temperature exceeding a preset temperature value exists in the infrared image.
In this module, the overtemperature area determining unit 302 determines an infrared image according to a preset standard image, and in the infrared image, the temperature is represented by color, so that the temperature of each area can be determined according to the color distribution in the infrared image, and it is determined whether an area with the temperature exceeding a preset temperature value exists in the infrared image.
And a result generation unit 303 for marking a region exceeding a preset temperature value and generating a temperature analysis result.
In this module, the result generating unit 303 marks a region exceeding a preset temperature value, so that it is indicated that there is a risk of overtemperature in the current region, and if regulation is not performed, dust explosion or fire is easily caused; and marking the area exceeding the preset temperature value according to the temperature analysis result.
It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the application is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the application.
Claims (8)
1. The real-time monitoring method for the intelligent manufacturing workshop is characterized by comprising the following steps of:
acquiring monitoring video information and infrared monitoring information, wherein the monitoring video information is continuously acquired video information, and the infrared monitoring information is an intermittently acquired infrared image;
analyzing the environment visibility according to the monitoring video information to obtain a visibility analysis result;
analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain a temperature analysis result;
judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result;
the step of analyzing the environmental visibility according to the monitoring video information to obtain a visibility analysis result specifically comprises the following steps:
dividing the monitoring video information according to a preset time step, and randomly extracting a preset number of picture frames from the monitoring video information;
identifying target identification points in each group of picture frames to obtain an identification result;
counting the number of target recognition points which can be successfully recognized and included in the recognition result, calculating the successful recognition rate, and generating a visibility analysis result;
judging the specific temperature condition of each position and the heating speed of each position according to continuous infrared images, and predicting the temperature change in future time according to the current temperature and the heating speed to obtain a temperature analysis result.
2. The method for real-time monitoring an intelligent manufacturing shop according to claim 1, wherein the step of analyzing the abnormal condition of the ambient temperature according to the infrared monitoring information to obtain the temperature analysis result specifically comprises:
reading each infrared image in the infrared monitoring information;
judging the infrared image according to a preset standard image, and judging whether a region with the temperature exceeding a preset temperature value exists in the infrared image;
marking a region exceeding a preset temperature value, and generating a temperature analysis result.
3. The method for real-time monitoring an intelligent manufacturing shop according to claim 1, wherein the step of judging whether to send out alarm information according to the visibility analysis result and the temperature analysis result specifically comprises:
inquiring a preset visibility data comparison table according to the visibility analysis result to obtain a first inquiry result;
inquiring a preset temperature data comparison table according to the temperature analysis result to obtain a second inquiry result;
judging whether the threshold value is reached at the same time according to the first query result and the second query result, and if so, sending out alarm information.
4. The method of claim 1, wherein the monitoring video information and the infrared monitoring information each include collected time information.
5. The method for real-time monitoring of an intelligent manufacturing shop according to claim 3, further comprising determining whether a highest threshold is reached according to the first query result and/or the second query result, and if so, sending out an alarm message.
6. The method for real-time monitoring of an intelligent manufacturing plant according to claim 1, wherein the temperature analysis result marks a region exceeding a preset temperature value.
7. Real-time monitoring system of intelligent manufacturing shop, its characterized in that, the system includes:
the information acquisition module is used for acquiring monitoring video information and infrared monitoring information, wherein the monitoring video information is continuously acquired video information, and the infrared monitoring information is an intermittently acquired infrared image;
the visibility analysis module is used for analyzing the environment visibility according to the monitoring video information to obtain a visibility analysis result;
the temperature analysis module is used for analyzing the abnormal condition of the environmental temperature according to the infrared monitoring information to obtain a temperature analysis result;
the alarm judging module is used for judging whether alarm information is sent out according to the visibility analysis result and the temperature analysis result;
the visibility analysis module includes:
the frame extraction unit is used for dividing the monitoring video information according to a preset time step and randomly extracting a preset number of picture frames from the monitoring video information;
the picture identification unit is used for identifying target identification points in each group of picture frames to obtain an identification result;
the data statistics unit is used for counting the number of target recognition points which can be successfully recognized and included in the recognition result, calculating the successful recognition rate and generating a visibility analysis result;
judging the specific temperature condition of each position and the heating speed of each position according to continuous infrared images, and predicting the temperature change in future time according to the current temperature and the heating speed to obtain a temperature analysis result.
8. The intelligent manufacturing shop real-time monitoring system according to claim 7, wherein the temperature analysis module comprises:
the data reading unit is used for reading each infrared image in the infrared monitoring information;
the overtemperature region judging unit is used for judging the infrared image according to a preset standard image and judging whether a region with the temperature exceeding a preset temperature value exists in the infrared image;
and the result generating unit is used for marking the area exceeding the preset temperature value and generating a temperature analysis result.
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