Industrial production monitoring management system based on thermal imaging identification

Abstract

The invention relates to the technical field of industrial production monitoring, and discloses an industrial production monitoring management system based on thermal imaging recognition, which is characterized in that temperature distribution images of industrial equipment and environment temperature and humidity in an industrial production process are acquired through a thermal imaging sensor based on a data acquisition module; the data processing module obtains temperature change data of each key area of the industrial equipment according to the temperature distribution image of the industrial equipment, then calculates a temperature anomaly risk coefficient of each area of the industrial equipment according to process temperature data periodically collected in the industrial production process in combination with the influence of environment temperature and humidity, and calculates the temperature anomaly risk coefficient of each area of the industrial equipment; accurate monitoring of temperature distribution of industrial equipment in the industrial production process is achieved, meanwhile, the temperature abnormal condition of each area is judged according to the temperature abnormal risk coefficient of each area, and then the power of cooling equipment corresponding to each area is adjusted according to the abnormal condition of each area, so that the cooling efficiency is improved. The influence on industrial production due to over-high or over-low temperature is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial production monitoring, and in particular to an industrial production monitoring and management system based on thermal imaging recognition. Background Art

[0002] In modern industrial production, it is crucial to ensure the safe, stable and efficient operation of the production line. Traditional monitoring methods often rely on contact sensors, which have safety hazards and delayed response. With the rapid development of thermal imaging technology, its non-contact, large-scale and real-time characteristics provide new solutions for industrial production monitoring.

[0003] Most of the existing industrial production monitoring and management systems based on thermal imaging recognition set a reasonable temperature threshold and compare the monitored temperature with the temperature threshold. If the threshold requirement is not met, it means that the current temperature of the equipment is abnormal. However, this monitoring system has the following problems: 1. It does not take into account the impact of environmental factors on the temperature of the equipment; 2. It can only judge obvious abnormal points, and does not rule out human or other factors that cause temperature abnormalities at a certain moment, which is prone to misjudgment. Therefore, the present invention proposes an industrial production monitoring and management system based on thermal imaging recognition. Summary of the invention

[0004] The purpose of the present invention is to provide an industrial production monitoring and management system based on thermal imaging recognition to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An industrial production monitoring and management system based on thermal imaging recognition, including a data acquisition module, a data processing module, a data analysis module, a temperature state evaluation and alarm module and a cooling management module;

[0007] The data acquisition module includes a plurality of thermal imaging sensors for collecting temperature distribution images of industrial equipment during industrial production, and also includes a temperature sensor and a humidity sensor for collecting ambient temperature and ambient humidity of the industrial equipment;

[0008] The data processing module is used to obtain temperature data of various positions of industrial equipment during the production process according to the temperature distribution image of the industrial equipment;

[0009] The data analysis module is used to analyze the temperature data of various positions of the industrial equipment, the ambient temperature data of the industrial equipment, and the ambient humidity data;

[0010] The temperature status evaluation and alarm module is used to evaluate the temperature status of the industrial equipment according to the analysis results of the data analysis module. Once an abnormal temperature is found, an alarm is immediately triggered and relevant personnel are notified by SMS, email, etc.;

[0011] The cooling management module includes a plurality of cooling devices, which are used to adjust the cooling power according to the temperature state of the industrial equipment.

[0012] As a further description of the solution of the present invention, the working process of the data acquisition module includes:

[0013] Divide the industrial equipment into several areas and number each area in the following order: 1, 2, ..., n;

[0014] Acquire temperature distribution images of various areas through thermal imaging sensors;

[0015] Get the threshold range set for each area.

[0016] As a further description of the solution of the present invention, the working process of the data analysis module includes:

[0017] Get the set temperature threshold interval [T i1 , T i2 ];

[0018] With Δt as the interval period, the temperature data of the i-th area is collected every Δt;

[0019] Obtain the temperature variation data of the ith area of ​​industrial equipment over time and fit the function T i (t);

[0020] The temperature anomaly risk coefficient μ of the i-th region is calculated by the following formula: i :

[0021]

[0022] Where t1 is the initial monitoring time, t2 is the current monitoring time, and y is the temperature less than or equal to T i1 The number of times, x is the temperature greater than or equal to T i2 The number of times, T(t j ) is the temperature data collected in the jth cycle, T(t m ) is the temperature data collected in the mth cycle, where j belongs to x, m belongs to y, T e (t2) is the environmental impact at the current moment, α, β and γ are the preset weight coefficients, μ i is the temperature anomaly risk coefficient of the ith region.

[0023] As a further description of the solution of the present invention, the current environmental impact temperature T e The acquisition process of (t2) includes:

[0024] Get the current ambient temperature data T W (t2) and ambient humidity data H W (t2);

[0025] Environmental impact at the construction time T e (t2) Mathematical calculation model, the expression is:

[0026]

[0027] Where, F() is the ambient humidity-ambient temperature conversion function, T W (t2) is the ambient temperature at time t2, H W (t2) is the ambient humidity at time t2, and ω are preset weight coefficients.

[0028] As a further description of the solution of the present invention, the working process of the temperature status assessment and alarm module includes:

[0029] The temperature anomaly risk coefficient μ of the i-th region i Compared with the preset standard interval of temperature anomaly risk factor of the ith region [μ i1 , μ i2 ]Compare;

[0030] If the temperature anomaly risk coefficient μ of the i-th region i Does not belong to the interval [μ i1 , μ i2 ], it is judged that the temperature of the i-th area is abnormal, the working equipment may fail, and an alarm is triggered immediately, and relevant personnel are notified through SMS, email, etc.;

[0031] Otherwise, it is determined that the temperature of the i-th area is abnormal.

[0032] As a further description of the solution of the present invention, the working process of the cooling management module includes:

[0033] When the temperature anomaly risk coefficient μ of the i-th region i Less than μ i1 When , it means that the temperature of the i-th area is too low, and the cooling power P of the cooling equipment in the i-th area is obtained. i1 ;

[0034] The adjusted power of the cooling equipment in the i-th zone is calculated by the following formula:

[0035]

[0036] Where T0 is the preset standard environmental impact temperature and δ is the conversion coefficient.

[0037] As a further description of the solution of the present invention, the working process of the cooling management module also includes:

[0038] When the temperature anomaly risk coefficient μ of the i-th region i Greater than μ i2 When , it means that the temperature of the i-th area is too high, and the cooling power P of the cooling equipment in the i-th area is obtained. i2 ;

[0039] The adjusted power of the cooling equipment in the i-th zone is calculated by the following formula:

[0040]

[0041] Where T0 is the preset standard environmental impact temperature and δ is the conversion coefficient.

[0042] As a further description of the solution of the present invention, the system also includes a thermal imaging sensor self-test module, which is used to perform self-test on all thermal imaging sensors before industrial production. The self-test process includes:

[0043] Obtain the temperature variation data of the hth thermal imaging sensor during the self-test process, and fit the temperature variation function T h (t);

[0044] The thermal imaging sensor self-test coefficient τ is calculated by the following formula:

[0045]

[0046] Where z is the number of thermal imaging sensors, t3 is the start time of self-test, t4 is the end time of self-test, T h (t4) is the temperature data collected by the hth thermal imaging sensor at the end of the self-test, and k is the preset weight coefficient;

[0047] The thermal imaging sensor self-detection coefficient τ and the preset thermal imaging sensor self-detection coefficient threshold τ th If the thermal imaging sensor self-test coefficient τ is greater than or equal to the thermal imaging sensor self-test coefficient threshold τ th , it means that the hth thermal imaging sensor is faulty.

[0048] Beneficial effects of the present invention: 1. In order to avoid the danger and error caused by contact sensor measurement, the present invention collects the temperature distribution image and the ambient temperature and humidity of the industrial equipment in the industrial production process through the thermal imaging sensor based on the data acquisition module. The data processing module obtains the temperature change data of each key area of ​​the industrial equipment according to the temperature distribution image of the industrial equipment. Then, combined with the influence of the ambient temperature and humidity, the temperature anomaly risk coefficient of each area of ​​the industrial equipment is calculated according to the process temperature data periodically collected during the industrial production process, so as to realize the accurate monitoring of the temperature distribution of the industrial equipment in the industrial production process. At the same time, the temperature anomaly situation of each area is judged according to the temperature anomaly risk coefficient of each area. Then, according to the abnormal situation of each area, the power of the cooling equipment corresponding to each area is adjusted to avoid the influence of excessively high or low temperature on industrial production.

[0049] 2. The present invention performs self-inspection on all thermal imaging sensors before the start of industrial production to avoid affecting the monitoring of industrial equipment during the industrial production process. The temperature monitored by the thermal imaging sensor during the self-inspection process is the ambient temperature. The thermal imaging sensor self-inspection coefficient is calculated by the difference between the average cumulative change of the collected temperature of each thermal imaging sensor during the self-inspection process and the cumulative change of the collected temperature of the corresponding thermal imaging sensor and the difference between the real-time collected temperature of each thermal imaging sensor and the collected temperature of the corresponding thermal imaging sensor. The thermal imaging sensor self-inspection coefficient is compared with a preset thermal imaging sensor self-inspection coefficient threshold. If the thermal imaging sensor self-inspection coefficient is greater than or equal to the thermal imaging sensor self-inspection coefficient threshold, it means that the current thermal imaging sensor is faulty. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below in conjunction with the accompanying drawings.

[0051] Figure 1 It is a structural schematic diagram of the industrial production monitoring and management system based on thermal imaging recognition provided by the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 , the present invention is an industrial production monitoring and management system based on thermal imaging recognition, including a data acquisition module, a data processing module, a data analysis module, a temperature state evaluation and alarm module and a cooling management module;

[0054] The data acquisition module includes a plurality of thermal imaging sensors for collecting temperature distribution images of industrial equipment during industrial production, and also includes a temperature sensor and a humidity sensor for collecting ambient temperature and ambient humidity of the industrial equipment;

[0055] The data processing module is used to obtain temperature data of various positions of industrial equipment during the production process according to the temperature distribution image of the industrial equipment;

[0056] The data analysis module is used to analyze the temperature data of various positions of the industrial equipment, the ambient temperature data of the industrial equipment, and the ambient humidity data;

[0057] The temperature status evaluation and alarm module is used to evaluate the temperature status of the industrial equipment according to the analysis results of the data analysis module. Once an abnormal temperature is found, an alarm is immediately triggered and relevant personnel are notified by SMS, email, etc.;

[0058] The cooling management module includes a plurality of cooling devices, which are used to adjust the cooling power according to the temperature state of the industrial equipment.

[0059] Through the above technical scheme, in order to avoid the dangers and errors caused by contact sensor measurement, the present invention collects the temperature distribution image and the ambient temperature and humidity of the industrial equipment in the industrial production process through the thermal imaging sensor based on the data acquisition module. The data processing module obtains the temperature change data of each key area of ​​the industrial equipment according to the temperature distribution image of the industrial equipment. Then, combined with the influence of ambient temperature and humidity, the temperature anomaly risk coefficient of each area of ​​the industrial equipment is calculated according to the process temperature data periodically collected in the industrial production process, so as to realize the accurate monitoring of the temperature distribution of the industrial equipment in the industrial production process. At the same time, the temperature anomaly situation of each area is judged according to the temperature anomaly risk coefficient of each area. Then, according to the abnormal situation of each area, the power of the cooling equipment corresponding to each area is adjusted to avoid the influence of excessively high or low temperature on industrial production.

[0060] As a further description of the solution of the present invention, the working process of the data acquisition module includes:

[0061] Divide the industrial equipment into several areas, including motors, batteries, production lines, etc., and number each area in the following order: 1, 2, ..., n;

[0062] Acquire temperature distribution images of various areas through thermal imaging sensors;

[0063] Get the threshold range set for each area.

[0064] As a further description of the solution of the present invention, the working process of the data analysis module includes:

[0065] Get the set temperature threshold interval [T i1 , T i2 ];

[0066] With Δt as the interval period, the temperature data of the i-th area is collected every Δt;

[0067] Obtain the temperature variation data of the ith area of ​​industrial equipment over time and fit the function T i (t);

[0068] The temperature anomaly risk coefficient μ of the i-th region is calculated by the following formula: i :

[0069]

[0070] Where t1 is the initial monitoring time, t2 is the current monitoring time, and y is the temperature less than or equal to T i1 The number of times, x is the temperature greater than or equal to T i2 The number of times, T(t j ) is the temperature data collected in the jth cycle, T(t m ) is the temperature data collected in the mth cycle, where j belongs to x, m belongs to y, T e (t2) is the environmental impact at the current moment, α, β and γ are the preset weight coefficients, μ i is the temperature anomaly risk coefficient of the ith region.

[0071] As a further description of the solution of the present invention, the current environmental impact temperature T e The acquisition process of (t2) includes:

[0072] Get the current ambient temperature data T W (t2) and ambient humidity data H W (t2);

[0073] Environmental impact at the construction time T e (t2) Mathematical calculation model, the expression is:

[0074]

[0075] Where, F() is the ambient humidity-ambient temperature conversion function, T W (t2) is the ambient temperature at time t2, H W (t2) is the ambient humidity at time t2, and ω are preset weight coefficients.

[0076] As a further description of the solution of the present invention, the working process of the temperature status assessment and alarm module includes:

[0077] The temperature anomaly risk coefficient μ of the i-th region i Compared with the preset standard interval of temperature anomaly risk factor of the ith region [μ i1 , μ i2 ]Compare;

[0078] If the temperature anomaly risk coefficient μ of the i-th region i Does not belong to the interval [μ i1 , μ i2 ], it is judged that the temperature of the i-th area is abnormal, the working equipment may fail, and an alarm is triggered immediately, and relevant personnel are notified through SMS, email, etc.;

[0079] Otherwise, it is determined that the temperature of the i-th area is abnormal.

[0080] Through the above technical solution, this embodiment collects temperature data of the i-th area every Δt time, compares the collection result with the preset temperature threshold interval of the i-th area, calculates the temperature anomaly risk coefficient of the i-th area according to the number of i-th area temperatures less than the lower limit of the interval and the number of i-th area temperatures greater than the upper limit of the interval at the monitoring time, combined with the influence of ambient temperature and ambient humidity on the ambient temperature, and compares the temperature anomaly risk coefficient of the i-th area with the preset standard interval of the temperature anomaly risk coefficient of the i-th area. If the temperature anomaly risk coefficient μ of the i-th area is i If it does not fall within the preset range, the temperature in the i-th area is judged to be abnormal, and the working equipment may fail. An alarm is triggered immediately, and relevant personnel are notified via text messages, emails, etc.

[0081] As a further description of the solution of the present invention, the working process of the cooling management module includes:

[0082] When the temperature anomaly risk coefficient μ of the i-th region i Less than μ i1 When , it means that the temperature of the i-th area is too low, and the cooling power P of the cooling equipment in the i-th area is obtained. i1 ;

[0083] The adjusted power of the cooling equipment in the i-th zone is calculated by the following formula:

[0084]

[0085] Where T0 is the preset standard environmental impact temperature and δ is the conversion coefficient.

[0086] As a further description of the solution of the present invention, the working process of the cooling management module also includes:

[0087] When the temperature anomaly risk coefficient μ of the i-th region i Greater than μ i2When , it means that the temperature of the i-th area is too high, and the cooling power P of the cooling equipment in the i-th area is obtained. i2 ;

[0088] The adjusted power of the cooling equipment in the i-th zone is calculated by the following formula:

[0089]

[0090] Where T0 is the preset standard environmental impact temperature and δ is the conversion coefficient.

[0091] Through the above technical solution, this embodiment is based on the temperature anomaly risk coefficient μ of the i-th region. i The relationship between the cooling equipment and the preset interval is used to adjust the cooling power of the cooling equipment. When the temperature abnormality risk coefficient μ of the i-th area is i Less than μ i1 When , it means that the temperature of the i-th area is too low and the cooling power needs to be reduced. Combined with the real-time environmental influence on the temperature, the formula Calculate the adjusted power when the temperature anomaly risk coefficient μ of the i-th region i Greater than μ i2 When , it means that the temperature of the i-th area is too high and the cooling power needs to be increased. Combined with the real-time environmental influence on the temperature, the formula Calculate the adjusted power to keep the temperature of each area of ​​the equipment within the set range to avoid excessively high or low temperatures that affect the normal operation of industrial equipment.

[0092] As a further description of the solution of the present invention, the system also includes a thermal imaging sensor self-test module, which is used to perform self-test on all thermal imaging sensors before industrial production. The self-test process includes:

[0093] Obtain the temperature variation data of the hth thermal imaging sensor during the self-test process, and fit the temperature variation function T h (t);

[0094] The thermal imaging sensor self-test coefficient τ is calculated by the following formula:

[0095]

[0096] Where z is the number of thermal imaging sensors, t3 is the start time of self-test, t4 is the end time of self-test, T h (t4) is the temperature data collected by the hth thermal imaging sensor at the end of the self-test, and k is the preset weight coefficient;

[0097] The thermal imaging sensor self-detection coefficient τ and the preset thermal imaging sensor self-detection coefficient threshold τ th If the thermal imaging sensor self-test coefficient τ is greater than or equal to the thermal imaging sensor self-test coefficient threshold τth , it means that the hth thermal imaging sensor is faulty.

[0098] Through the above technical solution, this embodiment performs self-inspection on all thermal imaging sensors before the start of industrial production to avoid affecting the monitoring of industrial equipment during the industrial production process. The temperature monitored by the thermal imaging sensor during the self-inspection process is the ambient temperature. The thermal imaging sensor self-inspection coefficient is calculated by the difference between the average cumulative change of the collected temperature of each thermal imaging sensor during the self-inspection process and the cumulative change of the collected temperature of the corresponding thermal imaging sensor and the difference between the real-time collected temperature of each thermal imaging sensor and the collected temperature of the corresponding thermal imaging sensor. The thermal imaging sensor self-inspection coefficient is compared with a preset thermal imaging sensor self-inspection coefficient threshold. If the thermal imaging sensor self-inspection coefficient is greater than or equal to the thermal imaging sensor self-inspection coefficient threshold, it means that the current thermal imaging sensor is faulty.

[0099] It should be noted that all weight coefficients, thresholds and threshold intervals in the present invention are obtained based on empirical data and do not require detailed description.

[0100] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An industrial production monitoring and management system based on thermal imaging recognition, characterized in that: It includes data acquisition module, data processing module, data analysis module, temperature status evaluation and alarm module and cooling management module; The data acquisition module includes a plurality of thermal imaging sensors for collecting temperature distribution images of industrial equipment during industrial production, and also includes a temperature sensor and a humidity sensor for collecting ambient temperature and ambient humidity of the industrial equipment; The data processing module is used to obtain temperature data of various positions of industrial equipment during the production process according to the temperature distribution image of the industrial equipment; The data analysis module is used to analyze the temperature data of various positions of the industrial equipment, the ambient temperature data of the industrial equipment, and the ambient humidity data; The temperature status evaluation and alarm module is used to evaluate the temperature status of the industrial equipment according to the analysis results of the data analysis module. Once an abnormal temperature is found, an alarm is immediately triggered and relevant personnel are notified by SMS, email, etc.; The cooling management module includes a plurality of cooling devices, which are used to adjust the cooling power according to the temperature state of the industrial equipment.

2. The industrial production monitoring and management system based on thermal imaging recognition according to claim 1 is characterized in that: The working process of the data acquisition module includes: Divide the industrial equipment into several areas and number each area in the following order: 1, 2, ..., n; Acquire temperature distribution images of various areas through thermal imaging sensors; Get the threshold range set for each area.

3. The industrial production monitoring and management system based on thermal imaging recognition according to claim 1 is characterized in that: The working process of the data analysis module includes: Get the set temperature threshold interval [T i1 , T i2 ]; With Δt as the interval period, the temperature data of the i-th area is collected every Δt; Obtain the temperature variation data of the ith area of ​​industrial equipment over time and fit the function Ti(t); The temperature anomaly risk coefficient μ of the i-th region is calculated by the following formula: i : Where t1 is the initial monitoring time, t2 is the current monitoring time, and y is the temperature less than or equal to T i1 The number of times, x is the temperature greater than or equal to T i2 The number of times, T(t j ) is the temperature data collected in the jth cycle, T(t m ) is the temperature data collected in the mth cycle, where j belongs to x, m belongs to y, Te(t2) is the environmental impact at the current moment, α, β and γ are the preset weight coefficients, μ i is the temperature anomaly risk coefficient of the ith region.

4. The industrial production monitoring and management system based on thermal imaging recognition according to claim 3 is characterized in that: The current environment affects the temperature T e The acquisition process of (t2) includes: Get the current ambient temperature data T W (t2) and ambient humidity data H W (t2); Environmental impact at the construction time T e (t2) Mathematical calculation model, the expression is: Where F( ) is the ambient humidity-ambient temperature conversion function, T W (t2) is the ambient temperature at time t2, H W (t2) is the ambient humidity at time t2, and ω are preset weight coefficients.

5. The industrial production monitoring and management system based on thermal imaging recognition according to claim 4 is characterized in that: The working process of the temperature status assessment and alarm module includes: The temperature anomaly risk coefficient μ of the i-th region i Compared with the preset standard interval of temperature anomaly risk factor of the ith region [μ i1 , μ i2 ]Compare; If the temperature anomaly risk coefficient μ of the i-th region i Does not belong to the interval [μ i1 , μ i2 ], it is judged that the temperature of the i-th area is abnormal, the working equipment may fail, and an alarm is triggered immediately, and relevant personnel are notified through SMS, email, etc.; Otherwise, it is determined that the temperature of the i-th area is abnormal.

6. The industrial production monitoring and management system based on thermal imaging recognition according to claim 5 is characterized in that: The working process of the cooling management module includes: When the temperature anomaly risk coefficient μ of the i-th region i Less than μ i1 When , it means that the temperature of the i-th area is too low, and the cooling power P of the cooling equipment in the i-th area is obtained. i1 ; The adjusted power of the cooling equipment in the i-th zone is calculated by the following formula: Where T0 is the preset standard environmental impact temperature and δ is the conversion coefficient.

7. The industrial production monitoring and management system based on thermal imaging recognition according to claim 5 is characterized in that: The working process of the cooling management module also includes: When the temperature anomaly risk coefficient μ of the i-th region i Greater than μ i2 When , it means that the temperature of the i-th area is too high, and the cooling power P of the cooling equipment in the i-th area is obtained. i2 ; The adjusted power of the cooling equipment in the i-th zone is calculated by the following formula: Where T0 is the preset standard environmental impact temperature and δ is the conversion coefficient.

8. The industrial production monitoring and management system based on thermal imaging recognition according to claim 7 is characterized in that: The system also includes a thermal imaging sensor self-test module, which is used to perform self-test on all thermal imaging sensors before industrial production. The self-test process includes: Obtain the temperature variation data of the hth thermal imaging sensor during the self-test process, and fit the temperature variation function T h (t); The thermal imaging sensor self-test coefficient τ is calculated by the following formula: Where z is the number of thermal imaging sensors, t3 is the start time of self-test, t4 is the end time of self-test, T h (t4) is the temperature data collected by the hth thermal imaging sensor at the end of the self-test, and k is the preset weight coefficient; The thermal imaging sensor self-detection coefficient τ and the preset thermal imaging sensor self-detection coefficient threshold τ th If the thermal imaging sensor self-test coefficient τ is greater than or equal to the thermal imaging sensor self-test coefficient threshold τ th , it means that the hth thermal imaging sensor is faulty.

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