CN111667673B

CN111667673B - A heat pipe network system for intelligent leak detection

Info

Publication number: CN111667673B
Application number: CN202010547633.XA
Authority: CN
Inventors: 周守军; 张林华; 魏建平
Original assignee: Shandong Jianzhu University
Current assignee: Shandong Jianzhu University
Priority date: 2019-03-15
Filing date: 2019-03-15
Publication date: 2021-07-27
Anticipated expiration: 2039-03-15
Also published as: CN109915736B; CN111667673A; CN109915736A

Abstract

The invention provides a heat pipe network system for intelligently detecting leakage detection, wherein the heat supply pipe network is provided with a plurality of nodes, and a thermal imager is arranged at least one node, and the system comprises: the system comprises a data acquisition and monitoring subsystem, a data transmission subsystem, a well lid integrity detection subsystem and an infrared data processing and alarming subsystem. The invention provides a novel heat pipe network system for intelligently detecting leakage, which automatically detects leakage by monitoring the change of an infrared temperature field at a node of a heat supply pipe network in real time through a thermal infrared imager, and can achieve good effects of energy conservation and environmental protection.

Description

Hot pipe network system for intelligently detecting leakage

Technical Field

The invention relates to a heating system, in particular to the technical field of an intelligent leakage detection heating pipe network.

Background

The leakage of the centralized heat supply pipe network can directly cause a large amount of loss of high-temperature media in the pipe, the heat pollutes the environment, the leakage is serious, even geological collapse can be caused, and casualties are caused, so that the leakage is always a main fault influencing the safe and economic operation of the pipe network. Along with the rapid development of domestic centralized heat supply in recent years, the scale of a heat supply area and a pipe network is continuously enlarged, particularly, the traditional thermal power plant actively develops cogeneration (such as low vacuum modification, circulating water waste heat utilization and the like) under the guidance of national energy conservation and emission reduction policies, so that the safe operation of a power plant unit is more and more influenced by the operation safety of the heat network, and once the heat supply pipe network has large leakage, the unit is directly tripped, and major safety accidents are caused. And heat supply pipe network branch node, owing to connect the branch pipeline, need on-the-spot trompil welding, heat preservation, processingquality is difficult to reach prefabricated heat preservation pipeline technology level in the factory to branch node still installs branch pipeline valve and instrument, especially pipeline stress concentration point, thereby causes pipe network branch node to take place the probability of leaking the trouble and is greater than ordinary pipeline far away. According to engineering practice statistics, more than 60% -80% of leakage faults of the heat supply pipe network occur at branch nodes.

The research and application of the leakage detection of the heat supply pipe network, particularly the real-time leakage fault monitoring method, have always been focused by scholars at home and abroad and heating power pipe network operation units. The methods can be classified into direct methods and indirect methods. The direct method mainly comprises a direct-buried early warning line method, a distributed optical fiber temperature measurement method and an infrared imaging detection method. At present, the European direct-buried warning line monitoring system has a mature design and process method. The method is divided into an impedance type and a resistance type, alarm lines are buried in a prefabricated heat insulation layer, fault points and positions of the fault points are diagnosed by detecting pulse reflection signals and resistance values respectively, and internal leakage and external leakage can be detected. However, in the method, detection points need to be arranged within a certain distance (500 m is recommended in China), and the requirements on the field installation process of the detection points and the pipe network design and process of the whole monitoring system are high; the distributed optical fiber temperature measurement method is mainly based on Raman light reflection, Brillouin light reflection and fiber grating principles, senses temperature change generated by leakage through a temperature measurement system which is arranged on the outer side of a pipeline and is composed of serially connected temperature measurement optical fiber sensors, and therefore leakage can be found and accurate positioning can be carried out. The distributed optical fiber temperature sensing system of York corporation in England based on Raman light reflection is applied more, but compared with a direct-buried early warning line method, the system has higher cost and low technical maturity; the infrared imaging detection method adopts a thermal infrared imaging technology to convert an infrared radiation energy distribution image of a detected target into a standard video signal of a temperature field of the detected target. The method is used as one of the manual inspection methods of the heat supply pipe network, does not affect the operation of the pipe network, and is mainly used for burying shallow directly-buried heat distribution pipelines. At present, the unmanned aerial vehicle is researched at home and abroad, the unmanned aerial vehicle carries out leakage monitoring on the whole urban pipe network by adopting an infrared camera, but the leakage and the ambient temperature of the pipeline caused by the thermal insulation damage cannot be distinguished, and the high-altitude flight of the unmanned aerial vehicle is controlled by national safety at present, so that the implementation difficulty is high. The indirect method mainly comprises a model method, a neural network method and a statistical detection method at present. The model method is to establish a steady-state or transient model of the heat supply pipe network, compare and analyze the simulation value of the pipe network with actual operation data (flow or pressure) to determine whether leakage occurs, and the accuracy of the method mainly depends on the precision of the pipe network model; the neural network method relies on learning normal and fault operation data of the pipe network, autonomously analyzes the operation state of the pipe network and establishes the capacity of judging leakage of the pipe network. The method has strong anti-interference capability, but needs a large amount of leakage data to learn and model; the statistical detection method is based on statistical theory, analyzes the leakage working condition operation data, and establishes a functional relation with the normal working condition to estimate the leakage amount and the leakage position. The method does not need to establish a model, only needs a small amount of pressure and flow probability calculation, has wide adaptability, and has strict requirements on the precision of the instrument. Because the heat supply pipe network on-line monitoring system is widely applied in China and the accuracy of the instrument is continuously improved, a better material basis is laid for the application of a statistical detection method, and the method has obtained continuous attention in the field of leakage detection of the water supply pipe network at present.

Among the two methods, the direct burial early warning line method in the first direct method has mature technical process and higher detection efficiency, but has high technical requirement and higher manufacturing cost, and is difficult to popularize and apply in China in a short time. Even if a newly-built pipe network can be considered to be adopted, the heat supply pipe network which is built and operated at present is more difficult to apply and implement due to overhigh cost; although the distributed optical fiber temperature measurement method has been accumulated in certain research and engineering application, and the method has high detection efficiency, compared with a direct-buried early warning line method, the method has higher cost and lower technical maturity; the infrared imaging detection method has been widely applied in the field of manual detection due to its simple and rapid characteristics. However, even if domestic conditions permit, the current unmanned airborne infrared camera detection method researched and developed can be adopted, and the purpose of regular inspection can only be achieved, and the method also needs to solve the problem of how to distinguish and confirm the leakage point of the heat supply pipeline under the complex background and environmental interference; in the second type of indirect method, a model method is used first. In spite of a steady-state or transient model method, the model precision needs to be further improved, and how to quickly and effectively establish a specific heat supply pipeline model is researched; the main problems faced by the neural network method are how to obtain effective operation data and research an optimization algorithm for ensuring the quick and effective convergence of the neural network; the statistical detection method is simple in operation and wide in adaptability, and a large number of on-line monitoring systems are adopted in domestic heat supply pipe networks at present, so that a relatively solid application basis is provided for the on-line monitoring systems. However, the accuracy of the instrument needs to be further improved by means of the instrument industry, and a proper method needs to be researched and developed, so that the method can be applied to the field of large-scale complex heat supply pipe networks.

The project is based on the existing mature infrared thermal imaging technology (the infrared thermal imaging technology is that invisible infrared energy emitted by an object is converted into a thermal image visible to human eyes through optics and a detector), a visible light image processing method, a thermal infrared image processing method and a mode recognition technology are organically fused, a heat supply pipe network node leakage real-time detection system and a method based on the infrared thermal imaging technology are provided, corresponding software and hardware systems are researched and developed, and the overall heat supply pipe network leakage detection efficiency is improved by taking a branch node with the highest leakage fault occurrence probability as a breach, so that the safe operation of a heat supply pipe network and a power plant unit is ensured.

Disclosure of Invention

The invention provides a heating system and a heating method for intelligently detecting leakage, aiming at the defects in the prior art, and the heating system and the method are used for detecting the leakage of nodes of a pipe network in real time so as to solve the technical problem of detecting the leakage of the nodes of the heating pipe network in real time.

In order to achieve the purpose, the invention adopts the following technical scheme:

the utility model provides a heating power heating system, includes boiler, heat exchanger and heat supply radiator, and boiler, heat exchanger and heat supply radiator pass through the heat supply pipe network and link to each other, the steam that the boiler produced gets into the heat exchanger, carries out the heat transfer with the water in the heat exchanger, then heats in the water entering heat supply radiator, the heat supply pipe network has a plurality of nodes, sets up the thermal imager in at least one node.

Preferably, the thermal imager is arranged on the upright post.

Preferably, the thermal imager is arranged at the well cover and used for detecting data of the well cover.

A node leakage real-time detection method of a heating system comprises the following steps:

data acquisition and monitoring: monitoring and acquiring infrared video monitoring data and visible light video monitoring data at the well lid of the heat supply pipe network by using a thermal imager;

a data transmission step: the system is communicated with a data acquisition and monitoring subsystem, and transmits infrared video data and visible light video data of a monitoring point to a server through optical fibers;

the detection of the integrity of the well cover comprises the following steps: judging the integrity of the well lid according to the visible light video data transmitted to the server;

a leakage confirmation step: and extracting corresponding infrared temperature field data of the image frames meeting the well lid integrity detection, obtaining the temperature difference or the accumulation of temperature difference change through interframe comparison, and triggering the node leakage alarm when the temperature difference or the temperature difference change exceeds a threshold value.

Preferably, the manhole cover integrity detection comprises the following steps:

defining a standard image frame of the well lid in the visible light video data under various working conditions of each monitoring point, and calling the standard image frame as a reference frame R;

1) calculating the average value mu of the gray scale of each reference frame according to the following formula_rAnd gray scale standard deviation delta_r；

Where M, N are image resolutions, I_ijRepresenting the gray value at the corresponding coordinate

2) Get and canOne frame in the visible light monitoring video is monitored, and the gray average value mu of the current image frame T is calculated_tAnd gray scale standard deviation delta_t；

3) Calculating the gray average value difference delta mu and the gray standard difference delta between the current image frame T and the corresponding reference image frame R;

4) when the values of the Δ μ and the Δ δ are larger than the set threshold, taking the current frame as a suspected frame, and continuing the processing of the step 6); when the values of the delta mu and the delta are smaller than the set threshold value, the current frame is a normal well lid frame, and the processing of the step 4 is continued;

5) for the suspected frame, the sum S of the absolute values of the number differences of the gray level pixels of each level of the current image frame T and the corresponding reference image frame R is continuously calculated_i，

If S is_iWhen the value of the current frame is larger than the set threshold value, the current frame is considered not to pass the detection of the integrity of the well lid, the frame is discarded, and the step 3) is returned to continue the processing of the next frame;

6) and if the image frames in the specified continuous time do not pass the well lid integrity detection, triggering an integrity abnormity alarm and informing a manager to carry out manual processing.

Preferably, for the image frames meeting the well lid integrity detection, the corresponding infrared temperature field data is extracted, the temperature difference or the accumulation of the temperature difference change is obtained through inter-frame comparison, and when the temperature difference or the temperature difference change exceeds a threshold value, the node leakage alarm is triggered.

Preferably, the following two alarm modes are specifically included:

and calculating the difference D between the current temperature field matrix P and the previous frame temperature field matrix Q as P-Q, and triggering temperature difference alarm when the value of D exceeds a set threshold value.

2) Temperature difference accumulation alarm

Sequentially calculating a current temperature field matrix P_iWith the temperature field matrix Q of the previous frame_i-1Difference D of_i＝P_i-Q_i-1And for n frames of temperature difference D_iPerforming arithmetic cumulative summation

And when the value of Y exceeds a set threshold value, triggering a temperature difference accumulation alarm.

Preferably, a primary alarm, a secondary alarm and a tertiary alarm are set according to the size of the D value.

Preferably, a primary alarm, a secondary alarm and a tertiary alarm are set according to the size of the Y value.

The invention has the following advantages:

1) the invention provides a novel heat supply pipe network system capable of intelligently detecting leakage, which monitors the change of an infrared temperature field at a node well cover of the heat supply pipe network in real time through an infrared thermal imager, determines a node leakage accident through monitoring the abnormity of the well cover at first, and then according to the jump of the temperature field or the accumulated change of the temperature difference change, and alarms and informs managers.

2) The invention provides a new idea for monitoring leakage by detecting temperature change at a node, and the leakage monitoring method has the advantages of simple structure and low cost by detecting the position of the well cover and detecting the damage condition of the well cover at first.

3) In order to ensure the reliability and accuracy of the method, the invention processes the abnormal condition (damage or shielding) of the well lid of the monitoring node by utilizing the visible light data monitored by the node, thereby avoiding the generation of false alarm.

3) The method organically integrates the visible light image processing method, the thermal infrared image processing method and the mode identification technology, can improve the heat supply pipe network node leakage detection efficiency, and ensures the safe operation of the heat supply pipe network and the power plant unit.

Description of the drawings:

fig. 1 shows a schematic block diagram of a heat supply network node leakage real-time detection system based on an infrared thermal imaging technology;

FIG. 2 is a schematic engineering implementation diagram of a heat supply pipe network node leakage real-time detection system based on an infrared thermal imaging technology;

FIG. 3 is a flow chart showing an implementation of the heat supply pipe network node leakage real-time detection method based on the infrared thermal imaging technology;

FIG. 4 shows a flow chart of a manhole cover integrity checking algorithm in the heat supply pipe network node leakage real-time detection method based on the infrared thermal imaging technology;

FIG. 5 shows a flow chart of an infrared temperature alarm algorithm in the heat supply pipe network node leakage real-time detection method based on the infrared thermal imaging technology;

FIG. 6 is a general algorithm flowchart of the method for detecting the node leakage of the heat supply pipe network based on the infrared thermal imaging technology;

Detailed Description

The present invention will now be described in further detail with reference to the accompanying drawings. The drawings are simplified schematic views illustrating the basic structure of the present invention only in a schematic manner, and thus show only the constitution related to the present invention,

Preferably, as shown in fig. 2, the thermal imager is disposed at the manhole cover to detect data of the position of the manhole cover. The thermal imager is arranged on the upright post.

The invention provides a novel heat supply pipe network system capable of intelligently detecting leakage, which monitors the change of an infrared temperature field at a node of the heat supply pipe network in real time through an infrared thermal imager, determines the node leakage accident through the jump of the temperature field or the accumulated change of the temperature difference change, and alarms and informs managers.

The method of detection will be described in detail below.

Fig. 1 shows a schematic block diagram of a heat supply network node leakage real-time detection system based on an infrared thermal imaging technology.

As shown in fig. 1, the heat supply pipe network node leakage real-time detection system based on the infrared thermal imaging technology of the present invention includes:

the data acquisition and monitoring subsystem is used for acquiring and transmitting infrared video monitoring data and visible light video monitoring data of a heat supply pipe network node (preferably a well lid) in real time;

the data transmission subsystem is used for communicating with the data acquisition and monitoring subsystem and transmitting the infrared video data and the visible light video data of the monitoring point to the server;

the well lid integrity detection subsystem judges whether the monitored point (preferably the well lid) has damage and is shielded by utilizing the monitored visible light data, sends the data frames passing the integrity detection into the data processing and alarm subsystem, directly discards the data frames not passing the integrity detection, triggers the alarm of the abnormal integrity of the well lid if the image frames in the specified continuous time do not pass the integrity detection of the well lid, and informs a manager to carry out manual processing.

And the infrared data processing and alarming subsystem acquires the temperature change jump or the accumulated trend of the temperature change by utilizing the monitored temperature field data of the infrared imaging through interframe comparison, and triggers the node leakage alarm when the temperature change jump or the accumulated trend of the temperature change exceeds a threshold value.

Fig. 2 shows a schematic engineering implementation diagram of a heat supply pipe network node leakage real-time detection system based on an infrared thermal imaging technology.

The engineering practice statistical data show that: in the case of a heat supply network leak, the vast majority of the leaks occur at the heat supply network nodes. As shown in fig. 2, an infrared thermal image monitor is placed near a primary pipe network node (well lid) of the urban central heating system, the change information of an infrared temperature field at a monitoring point is transmitted to a server in real time through an optical fiber, and the server automatically monitors the occurrence of leakage in real time through the change of the temperature field and informs a manager.

Preferably, the invention also provides a heat supply pipe network node leakage real-time detection method based on the infrared thermal imaging technology. Fig. 3 shows an implementation flowchart of the heat supply pipe network node leakage real-time detection method based on the infrared thermal imaging technology, and as shown in fig. 3, the method specifically includes the following steps:

1) and extracting a frame of visible light image from the data transmitted from the monitoring point to the server, and carrying out the integrity detection of the well lid according to the frame of visible light image. The infrared temperature field imaging is very easily influenced by surrounding objects or environments, abnormal conditions such as well lid missing, shielding and the like can be eliminated through well lid integrity inspection, and the accuracy of infrared temperature field data transmitted back to the server from a monitoring point is ensured. The specific method of manhole cover integrity check will be described in detail later.

2) Directly discarding the data frame which does not pass the detection, and taking the next frame of visible light data;

3) and for the data frame passing the detection, extracting the infrared temperature field data corresponding to the frame, and determining whether the leakage condition occurs or not through threshold judgment. If yes, a leakage alarm is triggered to inform relevant management personnel to process, otherwise, the step 1) is directly returned to continue processing the next frame data in the monitoring video. The specific method of infrared temperature field data threshold detection alarm will be explained in detail later.

The well lid integrity detection method will be described in detail in this embodiment.

Infrared imaging data easily receives external environment influence, and well lid integrality detects the damage that can get rid of the well lid, shelters from the abnormal conditions such as, guarantees follow-up infrared temperature field distribution that can accurately acquire the monitoring point department (preferably well lid). The detection of the integrity of the manhole cover is divided into two steps of suspected frame search and suspected frame confirmation by using visible light data transmitted from a monitoring point to a server. The steps of suspected frame search are as follows:

1) defining a standard image frame of a well lid in visible light video data of each monitoring point under various working conditions, wherein the standard image frame is called as a reference frame R;

2) calculating the average value mu of the gray scale of each reference frame according to the following formula_rAnd gray scale standard deviation delta_r；

3) Visible light monitoring videoOne frame, calculating the average value mu of the gray scale of the current image frame T_tAnd gray scale standard deviation delta_t；

4) Calculating the gray average value difference delta mu and the gray standard difference delta between the current image frame T and the corresponding reference image frame R;

5) when the values of the delta mu and the delta are larger than a set threshold, taking the current frame as a suspected frame, and continuing to confirm the subsequent suspected frame; and (3) when the values of the delta mu and the delta are smaller than the set threshold value, the current frame is a normal well lid frame, and the processing of the step 3 is continued.

The steps of suspected frame confirmation are as follows:

1) for the suspected frame, the sum S of the absolute values of the number differences of the gray level pixels of each level of the current image frame T and the corresponding reference image frame R is continuously calculated_i，

If S is_iWhen the value of the infrared data frame is larger than the set threshold value, the current frame is considered to not pass the detection of the integrity of the well lid, the infrared data frame corresponding to the current frame is discarded, and the step 3 of searching the suspected frame is returned;

2) and if the image frames in the continuous time do not pass the well lid integrity detection, triggering integrity abnormity alarm and informing a manager to manually process the abnormity at the well lid.

Preferably, the infrared data processing and alarming method comprises the following steps: .

And extracting corresponding infrared temperature field data of the image frames meeting the well lid integrity detection, obtaining the temperature difference or the accumulation of temperature difference change through interframe comparison, and triggering the node leakage alarm when the temperature difference or the temperature difference change exceeds a threshold value. The method specifically comprises the following two alarm modes:

temperature difference alarm

And calculating the difference D between the current temperature field matrix P and the previous frame temperature field matrix Q as P-Q, and triggering temperature difference alarm when the value of D exceeds a set threshold value. And setting a first-level alarm, a second-level alarm and a third-level alarm according to the value of the D.

Temperature difference accumulation alarm

Sequentially calculating a current temperature field matrix P_iAnd the previous oneFrame temperature field matrix Q_i-1Difference D of_i＝P_i-Q_i-1And for n frames of temperature difference D_iPerforming arithmetic cumulative summation

And when the value of Y exceeds a set threshold value, triggering a temperature difference accumulation alarm. And setting a first-level alarm, a second-level alarm and a third-level alarm according to the Y value.

Application case

The thermal imager is arranged on an upright post with the height of 3.5 meters, is powered by a civil alternating current power supply and is connected with a server through an optical fiber. The vertical distance between the thermal imager and the well lid is 3 meters, the horizontal distance is 1.5 meters, and the monitoring angle is about 30 degrees below the oblique direction. The surveillance video resolution was 384 × 288 and the frame rate was 12 frames/sec.

Other parameter settings for the thermal imager are shown in the following table:

parameter item	Value range
		Temperature range	-20℃---150℃
Emissivity	0.81
		Reflection temperature	5℃
Atmospheric temperature	10℃
		Relative humidity	0.33
Transmittance of light	0.80

The thresholds used for the manhole cover integrity check are shown in the following table:

parameter item	Threshold value
		Mean difference in gray level Δ μ	30
Difference of gray standard deviation delta	15
		Sum of absolute values of difference of number of pixels per gradation level S_i	5500

The temperature difference alarm threshold is shown in the following table:

alarm level	Threshold value D
		First-level alarm	5
Two-stage alarm	8
		Three-level alarm	12

The temperature difference accumulation alarm threshold is shown in the following table:

alarm level	Threshold value Y
		First-level alarm	15
Two-stage alarm	25
		Three-level alarm	35

Although the present invention has been described with reference to the preferred embodiments, it is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims

1. A thermal pipeline network system for intelligently detecting leakage, the thermal pipeline network has a plurality of nodes, and a thermal imager is set at at least one node;

The system includes:

The data acquisition and monitoring subsystem is used to collect and transmit the infrared video monitoring data and visible light video monitoring data of the manhole cover at the nodes of the heating pipe network in real time;

The data transmission subsystem is used to communicate with the data acquisition and monitoring subsystem, and transmit the infrared video monitoring data and visible light video monitoring data of the monitoring point to the server;

The manhole cover integrity detection subsystem uses the monitored visible light video monitoring data to determine whether the manhole cover is damaged or blocked at the monitoring point. The data frames that pass the integrity test are sent to the data processing and alarm subsystem. The detected data frames are discarded directly. If the image frames within the specified continuous time fail to pass the manhole cover integrity test, the abnormal manhole cover integrity alarm will be triggered, and the management personnel will be notified for manual processing;

The infrared data processing and alarm subsystem uses the temperature field data of the infrared imaging monitored to obtain the temperature change jump or the cumulative trend of the temperature change through the comparison between frames. When the threshold value is exceeded, the node leakage alarm is triggered;

The manhole cover integrity detection subsystem includes the following steps:

Define the standard image frame of the manhole cover in the visible light video monitoring data under various working conditions of each monitoring point, which is called the reference image frame R;

1) Calculate the grayscale mean μ _r and the grayscale standard deviation δ _r of each reference image frame according to the following formulas respectively;

where M, N are the image resolutions, and I _ij represents the grayscale value at the corresponding coordinate

2) take a frame in the visible light monitoring video, calculate the grayscale mean μ _t and the grayscale standard deviation δ _t of the current image frame T;

3) Calculate the difference Δμ of the mean gray value and the difference Δδ of the standard deviation of gray between the current image frame T and the corresponding reference image frame R;

4) When the values of Δμ and Δδ are greater than the set threshold, the current frame is regarded as a suspected frame, and the processing of step 5) is continued; when the values of Δμ and Δδ are less than the set threshold, the current frame is a normal manhole cover frame, and step 2 is continued. );

5) For the suspected frame, continue to calculate the sum S _i of the absolute value of the difference in the number of grayscale pixels of each level of the current image frame T and the corresponding reference image frame R,

If the value of S _i is greater than the set threshold, it is considered that the current frame has not passed the integrity detection of the manhole cover, the frame is discarded, and the process is returned to step 2) to continue the processing of the next frame;

6) If the image frames within the specified continuous time fail to pass the integrity detection of the manhole cover, an abnormal integrity alarm will be triggered, and the management personnel will be notified for manual processing.

2 . The thermal pipe network system according to claim 1 , wherein the thermal imager is arranged at the manhole cover to detect the data of the position of the manhole cover. 3 .

3. The thermal pipe network system according to claim 1, characterized in that, for the image frame satisfying the integrity detection of the manhole cover, the corresponding infrared temperature field data is extracted, and the temperature difference or the temperature difference change is obtained by comparing between frames. Accumulated, when the threshold is exceeded, the node leakage alarm is triggered.

4. heat pipe network system as claimed in claim 1, is characterized in that, specifically comprises following two kinds of alarm modes:

1) Temperature difference alarm:

Calculate the difference D=P-Q between the current temperature field matrix P and the previous frame temperature field matrix Q, when the value of D exceeds the set threshold, trigger the temperature difference alarm;

2) Temperature difference accumulative alarm:

Calculate the difference D _i =P _i -Q _i-1 of the current temperature field matrix P _i and the previous frame temperature field matrix Q _i-1 in turn, and perform an arithmetic cumulative summation on the temperature difference D _i of n frames

When the value of Y exceeds the set threshold, the temperature difference accumulation alarm is triggered.