State monitoring system for guide chute of belt conveyor

Abstract

The invention provides a belt conveyor guide chute state monitoring system, and relates to the technical field of intelligent monitoring systems, and the system comprises a video collection module which is used for collecting videos of a plurality of areas, prone to coal accumulation, of a guide chute; the fire hazard to-be-monitored area identification module is used for judging whether the coal accumulation-prone area is a coal accumulation area or not, and identifying the fire hazard to-be-monitored area based on the plurality of coal accumulation areas; the guide chute emergency hidden danger monitoring pre-alarm module is used for judging whether to carry out early warning prompt or not based on the fire hazard prediction matrix of each fire hazard to-be-monitored area, and judging whether to carry out fire hazard alarm prompt or not based on the fire hazard condition of the fire hazard to-be-monitored area; and the guide groove mechanical performance monitoring module is used for evaluating the mechanical performance of the guide groove. The temperature, the coal accumulation condition and the mechanical performance of the guide chute can be automatically monitored, so that fire hazards are effectively prevented, the production safety and efficiency are improved, the accident prevention capacity is enhanced, and losses caused by shutdown and maintenance are remarkably reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent monitoring systems, and in particular relates to a material guide trough state monitoring system for a belt conveyor. Background Art

[0002] In modern industrial production, the belt conveyor trough plays a vital role in the fuel transportation process. However, belt conveyors face many challenges, especially when handling lignite. Lignite is prone to spontaneous combustion due to its high moisture and high volatility. In addition, the closed structure of the trough limits the possibility of manual observation and cleaning, which makes the coal accumulation inside the trough a potential fire hazard.

[0003] The traditional material guide chute design lacks a status monitoring system, which makes it impossible to detect and deal with coal accumulation in a timely manner, easily causing spontaneous combustion and fire, posing a huge risk to the company's safe production. Summary of the invention

[0004] The present invention provides a belt conveyor material guide trough state monitoring system, which is used to solve at least one of the above-mentioned technical problems.

[0005] In order to solve the above technical problems, the present invention discloses a belt conveyor trough state monitoring system, comprising:

[0006] A video acquisition module, used to acquire videos of several areas prone to coal accumulation in the guide chute in a working state as initial videos of several areas prone to coal accumulation;

[0007] A fire hazard monitoring area identification module is used to determine whether several coal-prone areas are coal-accumulating areas based on initial videos of several coal-prone areas, and to identify fire hazard monitoring areas based on several coal-accumulating areas;

[0008] The guide chute emergency hidden danger monitoring and pre-alarm module is used to construct a fire hazard prediction matrix for each fire hazard to be monitored area, and determine whether to issue a warning prompt based on the fire hazard prediction matrix for each fire hazard to be monitored area, while monitoring the fire hazard situation in the fire hazard to be monitored area, and determine whether to issue a fire hazard alarm prompt based on the fire hazard situation in the fire hazard to be monitored area;

[0009] The guide trough mechanical performance monitoring module is used to collect the material flow rate and working noise of the guide trough during the monitoring period, and input them into the trained guide trough mechanical performance evaluation model to evaluate the mechanical performance of the guide trough and issue a mechanical performance alarm when the mechanical performance is poor.

[0010] Preferably, the fire hazard monitoring area identification module includes:

[0011] A single-frame extraction submodule is used to extract all single video frames of the initial video of several coal-prone areas within a preset acquisition time period;

[0012] The coal accumulation region identification submodule is used to input all video single frames of the initial videos of several coal accumulation-prone regions within a preset acquisition time period into the trained coal accumulation identification model to identify whether the corresponding coal accumulation-prone regions are coal accumulation regions;

[0013] The fire hazard monitoring area identification submodule is used to identify the fire hazard monitoring area based on all video single frames of the initial video of several coal accumulation areas within a preset acquisition time period.

[0014] Preferably, the fire hazard to-be-monitored area identification submodule includes:

[0015] The auxiliary pixel acquisition unit is used to acquire the pixel value of each pixel position of all single video frames in a preset acquisition time period of the initial video of the coal accumulation area, calculate the average of the pixel values ​​of the adjacent pixels of each pixel of each single video frame, and use it as the auxiliary pixel of the corresponding pixel of the single video frame to obtain a number of auxiliary pixels at each pixel position;

[0016] The background acquisition unit of the fire hazard monitoring area takes the sum of several auxiliary pixels at each pixel position and the quotient of the total number of all video single frames of the initial video of each coal accumulation area within a preset acquisition time period as the background pixel value of each pixel position, and takes the image constructed by the background pixel value as the background of the fire hazard monitoring area at the current moment;

[0017] The fire hazard monitored area identification unit calculates the absolute value of the difference between the background pixel value of each pixel position in the background of the fire hazard monitored area at the current moment and the pixel value of the corresponding pixel position in the single frame of video at the current moment. When the absolute value of the difference corresponding to the pixel position is greater than the preset pixel difference, the pixel position is marked as the pixel position of the fire hazard monitored area, and the area composed of all the pixel positions of the fire hazard monitored area is the fire hazard monitored area.

[0018] Preferably, the guide chute emergency hidden danger monitoring and early warning module includes:

[0019] The material guide chute emergency hazard warning submodule is used to obtain the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period based on the fire hazard prediction matrix of each fire hazard to be monitored area, and determine whether to issue an early warning prompt based on the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period;

[0020] The material guide chute emergency hidden danger alarm submodule is used to evaluate the fire situation in the fire hazard monitored area, and determine whether to issue a fire alarm prompt based on the fire situation in the fire hazard monitored area.

[0021] Preferably, the guide chute emergency hidden danger warning submodule includes:

[0022] A temperature collection unit is used to collect the ambient temperature of each fire hazard to be monitored area in each monitoring period;

[0023] A temperature prediction matrix construction unit, for constructing a temperature prediction matrix A for each fire hazard to be monitored area based on the ambient temperature of each fire hazard to be monitored area in each monitoring period;

[0024] The coal accumulation speed acquisition unit is used to input the first moment video single frame and the last moment video single frame of the initial video corresponding to the fire hazard to be monitored area in each monitoring period into the trained coal accumulation speed judgment model to obtain the coal accumulation speed of each fire hazard to be monitored area in each monitoring period;

[0025] A coal accumulation speed prediction matrix construction unit is used to construct a coal accumulation speed prediction matrix B for each monitoring area to be monitored based on the coal accumulation speed of each fire hazard to be monitored area in each monitoring period;

[0026] A fire hazard prediction matrix construction unit for each fire hazard to be monitored area, used for constructing a fire hazard prediction matrix C for each fire hazard to be monitored area based on a temperature prediction matrix for each fire hazard to be monitored area and a coal accumulation speed prediction matrix for each fire hazard to be monitored area;

[0027] A fire hazard occurrence index obtaining unit, for obtaining the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period based on the fire hazard prediction matrix of each fire hazard to be monitored area;

[0028] The early warning judgment unit is used to judge whether to issue an early warning based on the fire hazard occurrence index of the area to be monitored for fire hazards in the next monitoring period.

[0029] Preferably, based on the ambient temperature of each fire hazard to be monitored area in each monitoring period, a temperature prediction matrix A for each fire hazard to be monitored area is constructed:

[0030] Among them, A αi is the temperature prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, a α1 is the ambient temperature of the αth fire hazard monitoring area in the first monitoring cycle, a α2 is the ambient temperature of the αth fire hazard monitoring area in the second monitoring cycle, a α3 is the ambient temperature of the αth fire hazard monitoring area in the third monitoring cycle, a α(i-1)is the ambient temperature of the αth fire hazard monitoring area in the i-1th monitoring period, a αi is the ambient temperature of the αth fire hazard to be monitored area in the ith monitoring period;

[0031] Based on the coal accumulation rate of each fire hazard to be monitored area in each monitoring period, a coal accumulation rate prediction matrix B is constructed for each fire hazard to be monitored area:

[0032] Among them, B αi is the coal accumulation speed prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, b α1 is the coal accumulation speed of the αth fire hazard monitoring area in the first monitoring cycle, b α2 is the coal accumulation speed of the αth fire hazard monitoring area in the second monitoring cycle, b α3 is the coal accumulation speed of the αth fire hazard monitoring area in the third monitoring cycle, b α(i-1) is the coal accumulation speed of the αth fire hazard monitoring area in the i-1th monitoring period, b αi is the coal accumulation rate of the αth fire hazard to be monitored area in the ith monitoring period.

[0033] Preferably, based on the temperature prediction matrix of each fire hazard to be monitored area and the coal accumulation speed prediction matrix of each fire hazard to be monitored area, a fire hazard prediction matrix C of each fire hazard to be monitored area is constructed:

[0034] Among them, C αi is the fire hazard prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, β α1 is the weight value of the ambient temperature of the αth fire hazard monitoring area, β α2 is the weight value of the coal accumulation speed of the αth fire hazard monitoring area;

[0035] Obtain the mean of all matrix elements in the third column of the fire hazard prediction matrix of the αth fire hazard to be monitored area in the ith monitoring period, and replace the values ​​of all matrix elements in the third row of the fire hazard prediction matrix of the αth fire hazard to be monitored area in the ith monitoring period with the mean of all matrix elements in the third column to obtain a new matrix, and use the quotient of the rank of the new matrix and the preset rank as the fire hazard occurrence index of the αth fire hazard to be monitored area in the i+1th monitoring period;

[0036] If the fire occurrence index of the αth fire hazard to be monitored area in the i+1th monitoring period is greater than the preset fire occurrence index of the αth fire hazard to be monitored area, an early warning prompt will be issued.

[0037] Preferably, the guide chute emergency hidden danger alarm submodule includes:

[0038] The flame pixel acquisition unit to be tested is used to compare the pixel value of each pixel in the fire hazard to be monitored area at the current moment with the preset flame pixel value. If the pixel value of each pixel in the fire hazard to be monitored area at the current moment is greater than the preset flame pixel value, the pixel is used as the flame pixel to be tested;

[0039] The predicted flame pixel determination unit uses optical flow analysis to extract the motion information of the flame pixel to be tested, including the motion speed and direction, to obtain the motion trajectory of the flame pixel to be tested, and matches the motion trajectory of the flame pixel to be tested with the preset flame motion characteristics, and selects the flame pixel to be tested that meets the preset characteristics as the predicted flame pixel points. If there are a preset number of predicted flame pixel points, an alarm prompt is given.

[0040] Preferably, it also includes:

[0041] A fire severity calculation unit, for calculating a fire severity assessment value of the fire hazard to be monitored area at the current moment based on the motion trajectory parameters and motion feature parameters of each predicted flame pixel point in the fire hazard to be monitored area at the current moment, the motion trajectory parameters and motion feature parameters including motion speed and direction change frequency;

[0042] The fire spread degree calculation unit is used to obtain the fire spread degree assessment value of the fire hazard to be monitored area at the current moment based on the trained fire spread degree assessment model, the motion trajectory parameters and motion feature parameters of each predicted flame pixel point in the fire hazard to be monitored area at the current moment, and the density of the predicted flame pixel points at the current moment;

[0043] The alarm level calculation unit is used to obtain the fire alarm level of the fire hazard to be monitored area at the current moment based on the fire severity assessment value and the fire spread assessment value of the fire hazard to be monitored area at the current moment.

[0044] Preferably, the fire severity assessment value of the fire hazard to be monitored area at the current moment is:

[0045] Among them, D is the fire severity assessment value of the fire hazard to be monitored area at the current moment, γ1 is the weight value of the movement speed of the flame pixel point at the current moment, and θ x is to predict the movement speed of the flame pixel at the current moment, θ0 is the benchmark movement speed of the flame pixel, γ2 is the weight value of the direction change frequency of the flame pixel at the current moment, and f x is to predict the direction change frequency of the flame pixel at the current moment, and f0 is the reference direction change frequency of the flame pixel;

[0046] The fire alarm level of the fire hazard monitoring area at the current moment:

[0047] Among them, Δ is the fire alarm level of the fire hazard to be monitored area at the current moment, E is the fire spread degree assessment value of the fire hazard to be monitored area at the current moment, D0 is the fire severity benchmark assessment value of the fire hazard to be monitored area, and E0 is the fire spread benchmark assessment value of the fire hazard to be monitored area.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention can automatically monitor the temperature, coal accumulation and mechanical properties of the material guide chute, and use intelligent algorithms to perform real-time analysis to achieve coal accumulation warning, fire hazard alarm and mechanical property alarm prompts, thereby effectively preventing fire hazards, improving production safety and efficiency, enhancing accident prevention capabilities, and significantly reducing losses caused by downtime and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 This is a schematic diagram of a belt conveyor trough status monitoring system proposed by the present invention. DETAILED DESCRIPTION

[0052] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0053] In addition, in the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes, and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only used to distinguish components or operations described with the same technical terms, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the technical solutions and technical features between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in the field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0054] The present invention provides the following embodiments

[0055] Example 1

[0056] The embodiment of the present invention provides a belt conveyor trough state monitoring system, such as Figure 1 As shown, including:

[0057] A video acquisition module, used to acquire videos of several areas prone to coal accumulation in the guide chute in a working state as initial videos of several areas prone to coal accumulation;

[0058] A fire hazard monitoring area identification module is used to determine whether several coal-prone areas are coal-accumulating areas based on initial videos of several coal-prone areas, and to identify fire hazard monitoring areas based on several coal-accumulating areas;

[0059] The guide chute emergency hidden danger monitoring and pre-alarm module is used to construct a fire hazard prediction matrix for each fire hazard to be monitored area, and determine whether to issue a warning prompt based on the fire hazard prediction matrix for each fire hazard to be monitored area, while monitoring the fire hazard situation in the fire hazard to be monitored area, and determine whether to issue a fire hazard alarm prompt based on the fire hazard situation in the fire hazard to be monitored area;

[0060] The guide trough mechanical performance monitoring module is used to collect the material flow rate and working noise of the guide trough during the monitoring period, and input them into the trained guide trough mechanical performance evaluation model to evaluate the mechanical performance of the guide trough and issue a mechanical performance alarm when the mechanical performance is poor.

[0061] In this embodiment, the plurality of areas prone to coal accumulation include a bottom area of ​​the material guide trough, a side wall area of ​​the material guide trough, a top area of ​​the material guide trough, a turning area of ​​the material guide trough, and a transition area of ​​the material guide trough.

[0062] In this embodiment, the trained guide groove mechanical property evaluation model is a model obtained by training the neural network model using a large number of guide groove material flow rates and working noises as model inputs of training samples and the guide groove mechanical property values ​​as model outputs.

[0063] In this embodiment, a status monitoring system is installed in the material guide trough. When the coal conveyor belt is running, the status monitoring probe will automatically withdraw from the material guide trough. When the belt stops, the status monitoring probe is extended to monitor the inside of the material guide trough in real time. A hole is opened above the original belt conveyor material guide trough, 1m behind the coal drop pipe, and the monitoring probe is inserted 10cm into the material guide trough. The probe is fixed to the material guide trough by a telescopic mechanism, and the lead-out line is connected to the outdoor monitoring box to transmit the monitoring system to the monitoring computer in the control room, thereby achieving the effect of extending the fire alarm system to the inside of the belt conveyor material guide trough.

[0064] The working principle and beneficial effects of the above technical solution are as follows: the belt conveyor guide trough state monitoring system of the present invention obtains the initial video of each area of ​​the guide trough through the video acquisition module, and determines which areas are accumulating coal through the fire hazard to be monitored area identification module, and identifies the fire hazard to be monitored area, then, constructs a fire hazard prediction matrix for each fire hazard to be monitored area through the guide trough emergency hazard monitoring pre-alarm module, and determines whether to issue an early warning prompt based on the prediction matrix, and handles the accumulated coal. At the same time, the module also monitors the fire situation, and determines whether to issue a fire hazard alarm prompt based on the situation. In addition, the guide trough mechanical performance monitoring module monitors the guide flow and working noise of the guide trough, and evaluates the mechanical performance of the guide trough through the trained guide trough mechanical performance evaluation model, and issues a mechanical performance alarm prompt when the mechanical performance is poor;

[0065] The system can monitor the status of the chute in real time, including fire hazards and mechanical performance, detect potential problems in advance, and take timely measures for early warning and alarm, which helps to improve the safety of the chute, reduce fire risks, and ensure the normal operation of the conveyor;

[0066] The present invention can automatically monitor the temperature, coal accumulation and mechanical properties of the material guide chute, and use intelligent algorithms to perform real-time analysis to achieve coal accumulation warning, fire hazard alarm and mechanical property alarm prompts, thereby effectively preventing fire hazards, improving production safety and efficiency, enhancing accident prevention capabilities, and significantly reducing losses caused by downtime and maintenance.

[0067] Example 2

[0068] Based on Example 1, the fire hazard to-be-monitored area identification module includes:

[0069] A single-frame extraction submodule is used to extract all single video frames of the initial video of several coal-prone areas within a preset acquisition time period;

[0070] The coal accumulation region identification submodule is used to input all video single frames of the initial videos of several coal accumulation-prone regions within a preset acquisition time period into the trained coal accumulation identification model to identify whether the corresponding coal accumulation-prone regions are coal accumulation regions;

[0071] The fire hazard monitoring area identification submodule is used to identify the fire hazard monitoring area based on all video single frames of the initial video of several coal accumulation areas within a preset acquisition time period.

[0072] In this embodiment, the trained coal accumulation determination model is a model obtained by training a neural network model using a large number of single video frames at several consecutive moments within a monitoring time period as model input samples, and using the coal accumulation determination results as model outputs.

[0073] The working principle and beneficial effects of the above technical solution are as follows: first, the single-frame extraction submodule is used to extract all video single frames within a preset acquisition time period from the initial videos of several areas prone to coal accumulation; next, the coal accumulation area discrimination submodule inputs all the extracted video single frames into the trained coal accumulation judgment model to judge whether the corresponding area prone to coal accumulation is an area undergoing coal accumulation; finally, the fire hazard to-be-monitored area recognition submodule identifies the fire hazard to-be-monitored area based on all video single frames of the initial videos of several areas undergoing coal accumulation within a preset acquisition time period;

[0074] Being able to more accurately determine which areas are accumulating coal and thus identify areas with fire hazards to be monitored will help improve the accuracy and reliability of the system and effectively prevent fire risks.

[0075] Example 3

[0076] Based on Example 2, the fire hazard to-be-monitored area identification submodule includes:

[0077] The auxiliary pixel acquisition unit is used to acquire the pixel value of each pixel position of all single video frames in a preset acquisition time period of the initial video of the coal accumulation area, calculate the average of the pixel values ​​of the adjacent pixels of each pixel of each single video frame, and use it as the auxiliary pixel of the corresponding pixel of the single video frame to obtain a number of auxiliary pixels at each pixel position;

[0078] The background acquisition unit of the fire hazard monitoring area takes the sum of several auxiliary pixels at each pixel position and the quotient of the total number of all video single frames of the initial video of each coal accumulation area within a preset acquisition time period as the background pixel value of each pixel position, and takes the image constructed by the background pixel value as the background of the fire hazard monitoring area at the current moment;

[0079] The fire hazard monitored area identification unit calculates the absolute value of the difference between the background pixel value of each pixel position in the background of the fire hazard monitored area at the current moment and the pixel value of the corresponding pixel position in the single frame of video at the current moment. When the absolute value of the difference corresponding to the pixel position is greater than the preset pixel difference, the pixel position is marked as the pixel position of the fire hazard monitored area, and the area composed of all the pixel positions of the fire hazard monitored area is the fire hazard monitored area.

[0080] The working principle and beneficial effects of the above technical solution are as follows: first, the auxiliary pixel acquisition unit is used to obtain the pixel value of each pixel position in all video single frames of the initial video of several coal accumulation areas within a preset acquisition time period, and then, the average of the pixel values ​​of adjacent pixels of each pixel of each video single frame is calculated, and it is used as the auxiliary pixel of the corresponding pixel of the video single frame. Through such processing, several auxiliary pixels are obtained at each pixel position. After that, the background acquisition unit of the fire hazard monitored area takes the quotient of the sum of the several auxiliary pixels at each pixel position and the total number of all video single frames of the initial video of each coal accumulation area within the preset acquisition time period as the value of each pixel position. Background pixel value, construct the background pixel value into an image as the background of the fire hazard to be monitored area at the current moment. Finally, the fire hazard to be monitored area identification unit calculates the absolute value of the difference between the background pixel value of each pixel position in the background of the fire hazard to be monitored area at the current moment and the pixel value of the corresponding pixel position of the single frame of the video at the current moment. If the absolute value of the difference corresponding to the pixel position is greater than the preset pixel difference, the pixel position is marked as the pixel position of the fire hazard to be monitored area. The area composed of all the pixel positions of the fire hazard to be monitored area is the fire hazard to be monitored area, which can more accurately identify the fire hazard to be monitored area and improve the accuracy and reliability of the system.

[0081] Example 4

[0082] Based on Example 1, the guide chute emergency hidden danger monitoring and early warning module includes:

[0083] The material guide chute emergency hazard warning submodule is used to obtain the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period based on the fire hazard prediction matrix of each fire hazard to be monitored area, and determine whether to issue an early warning prompt based on the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period;

[0084] The material guide chute emergency hidden danger alarm submodule is used to evaluate the fire situation in the fire hazard monitoring area and determine whether to issue a fire alarm prompt based on the fire situation in the fire hazard monitoring area;

[0085] The guide chute emergency hazard warning submodule includes:

[0086] A temperature collection unit is used to collect the ambient temperature of each fire hazard to be monitored area in each monitoring period;

[0087] A temperature prediction matrix construction unit, for constructing a temperature prediction matrix A for each fire hazard to be monitored area based on the ambient temperature of each fire hazard to be monitored area in each monitoring period;

[0088] The coal accumulation speed acquisition unit is used to input the first moment video single frame and the last moment video single frame of the initial video corresponding to the fire hazard to be monitored area in each monitoring period into the trained coal accumulation speed judgment model to obtain the coal accumulation speed of each fire hazard to be monitored area in each monitoring period;

[0089] A coal accumulation speed prediction matrix construction unit is used to construct a coal accumulation speed prediction matrix B for each monitoring area to be monitored based on the coal accumulation speed of each fire hazard to be monitored area in each monitoring period;

[0090] A fire hazard prediction matrix construction unit for each fire hazard to be monitored area, used for constructing a fire hazard prediction matrix C for each fire hazard to be monitored area based on a temperature prediction matrix for each fire hazard to be monitored area and a coal accumulation speed prediction matrix for each fire hazard to be monitored area;

[0091] A fire hazard occurrence index obtaining unit, for obtaining the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period based on the fire hazard prediction matrix of each fire hazard to be monitored area;

[0092] The early warning judgment unit is used to judge whether to issue an early warning based on the fire hazard occurrence index of the area to be monitored for fire hazards in the next monitoring period.

[0093] In this embodiment, the trained coal accumulation speed judgment model is a model obtained by training the neural network model using a large number of single video frames at the first moment and the last moment of the monitoring time period as model inputs of training samples, and using the coal accumulation speed as the model output.

[0094] The working principle and beneficial effects of the above technical solution are as follows: First, in the guide trough emergency hazard warning submodule, the ambient temperature data in each monitoring period is obtained by the temperature acquisition unit, and the temperature prediction matrix A is formed by the temperature prediction matrix construction unit. At the same time, the coal accumulation speed acquisition unit uses the trained coal accumulation speed judgment model to analyze the time series of the initial video to obtain the coal accumulation speed of the fire hazard to be monitored area, and forms a coal accumulation speed prediction matrix B. Next, the fire hazard prediction matrix construction unit integrates the above temperature and coal accumulation speed prediction data to form a fire hazard prediction matrix C, which provides a basis for the subsequent calculation of the fire hazard occurrence index. The fire hazard occurrence index acquisition unit evaluates the fire hazard occurrence index of the next monitoring period through the fire hazard prediction matrix. Finally, the early warning prompt judgment unit decides whether to issue an early warning prompt based on the fire hazard occurrence index to ensure that necessary preventive measures are taken before the fire hazard may become serious.

[0095] By comprehensively analyzing changes in temperature and coal accumulation speed, it is possible to more accurately predict the occurrence trend of fire hazards and provide timely early warning prompts. This not only improves the response speed and accuracy of the system, but also effectively reduces the risk of fire accidents and ensures the safety of the conveyor and its surrounding environment.

[0096] Example 5

[0097] On the basis of Example 4, based on the ambient temperature of each fire hazard to be monitored area in each monitoring period, a temperature prediction matrix A for each fire hazard to be monitored area is constructed:

[0098] Among them, A αi is the temperature prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, a α1 is the ambient temperature of the αth fire hazard monitoring area in the first monitoring cycle, a α2 is the ambient temperature of the αth fire hazard monitoring area in the second monitoring cycle, a α3 is the ambient temperature of the αth fire hazard monitoring area in the third monitoring cycle, a α(i-1) is the ambient temperature of the αth fire hazard monitoring area in the i-1th monitoring period, a αi is the ambient temperature of the αth fire hazard to be monitored area in the ith monitoring period;

[0099] Based on the coal accumulation rate of each fire hazard to be monitored area in each monitoring period, a coal accumulation rate prediction matrix B is constructed for each fire hazard to be monitored area:

[0100] Among them, B αi is the coal accumulation speed prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, b α1 is the coal accumulation speed of the αth fire hazard monitoring area in the first monitoring cycle, b α2 is the coal accumulation speed of the αth fire hazard monitoring area in the second monitoring cycle, b α3 is the coal accumulation speed of the αth fire hazard monitoring area in the third monitoring cycle, b α(i-1) is the coal accumulation speed of the αth fire hazard monitoring area in the i-1th monitoring period, b αi is the coal accumulation rate of the αth fire hazard to be monitored area in the ith monitoring period.

[0101] The working principle and beneficial effects of the above technical solution are as follows: by collecting and constructing the temperature prediction matrix A and the coal accumulation speed prediction matrix B, a fire hazard prediction matrix C is formed, the fire occurrence index is calculated, and early warning judgments are made based on the index, providing multi-dimensional data analysis, improving the accuracy of predictions, providing early warnings to avoid potential fires, and enhancing safety and system robustness.

[0102] Example 6

[0103] On the basis of Example 5, based on the temperature prediction matrix of each fire hazard to be monitored area and the coal accumulation speed prediction matrix of each fire hazard to be monitored area, a fire hazard prediction matrix C of each fire hazard to be monitored area is constructed:

[0104] Among them, C αi is the fire hazard prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, β α1 is the weight value of the ambient temperature of the αth fire hazard monitoring area, β α2 is the weight value of the coal accumulation speed of the αth fire hazard monitoring area;

[0105] Obtain the mean of all matrix elements in the third column of the fire hazard prediction matrix of the αth fire hazard to be monitored area in the ith monitoring period, and replace the values ​​of all matrix elements in the third row of the fire hazard prediction matrix of the αth fire hazard to be monitored area in the ith monitoring period with the mean of all matrix elements in the third column to obtain a new matrix, and use the quotient of the rank of the new matrix and the preset rank as the fire hazard occurrence index of the αth fire hazard to be monitored area in the i+1th monitoring period;

[0106] If the fire occurrence index of the αth fire hazard to be monitored area in the i+1th monitoring period is greater than the preset fire occurrence index of the αth fire hazard to be monitored area, an early warning prompt will be issued.

[0107] The working principle and beneficial effects of the above technical solution are as follows: based on the temperature prediction matrix of each fire hazard area to be monitored and the coal accumulation rate prediction matrix of each fire hazard area to be monitored, a fire hazard prediction matrix for each fire hazard area to be monitored is constructed. The dynamic changes of the temperature prediction matrix and the coal accumulation rate prediction matrix ensure the reliability of the prediction results. Through the prediction model combining historical data and current data, a more reliable fire risk analysis is provided, while supporting continuous monitoring and data accumulation, optimizing the system's prediction capabilities, integrating multiple factors for analysis, improving the accuracy of fire hazard prediction, and automating data processing to make the early warning system more flexible and adaptable.

[0108] Example 7

[0109] Based on Example 3, the material guide chute emergency hidden danger alarm submodule includes:

[0110] The flame pixel acquisition unit to be tested is used to compare the pixel value of each pixel in the fire hazard to be monitored area at the current moment with the preset flame pixel value. If the pixel value of each pixel in the fire hazard to be monitored area at the current moment is greater than the preset flame pixel value, the pixel is used as the flame pixel to be tested;

[0111] The predicted flame pixel determination unit uses optical flow analysis to extract the motion information of the flame pixel to be tested, including the motion speed and direction, to obtain the motion trajectory of the flame pixel to be tested, and matches the motion trajectory of the flame pixel to be tested with the preset flame motion characteristics, and selects the flame pixel to be tested that meets the preset characteristics as the predicted flame pixel points. If there are a preset number of predicted flame pixel points, an alarm prompt is given.

[0112] The working principle and beneficial effects of the above technical solution are as follows: the flame pixel point acquisition unit to be tested makes a preliminary determination of the flame pixel points to be tested, and the flame pixel point prediction determination unit uses image processing technology and optical flow analysis to identify and track the flame pixel points, and screens out areas where there is a real risk of fire through motion feature matching. The refined image analysis can more accurately capture and identify the early signs of fire, and reduce the false alarm rate and improve the accuracy of the alarm through dynamic video analysis.

[0113] Example 8

[0114] On the basis of Example 7, it also includes:

[0115] A fire severity calculation unit, for calculating a fire severity assessment value of the fire hazard to be monitored area at the current moment based on the motion trajectory parameters and motion feature parameters of each predicted flame pixel point in the fire hazard to be monitored area at the current moment, the motion trajectory parameters and motion feature parameters including motion speed and direction change frequency;

[0116] The fire spread degree calculation unit is used to obtain the fire spread degree assessment value of the fire hazard to be monitored area at the current moment based on the trained fire spread degree assessment model, the motion trajectory parameters and motion feature parameters of each predicted flame pixel point in the fire hazard to be monitored area at the current moment, and the density of the predicted flame pixel points at the current moment;

[0117] The alarm level calculation unit is used to obtain the fire alarm level of the fire hazard to be monitored area at the current moment based on the fire severity assessment value and the fire spread assessment value of the fire hazard to be monitored area at the current moment.

[0118] Preferably, the fire severity assessment value of the fire hazard to be monitored area at the current moment is:

[0119] Among them, D is the fire severity assessment value of the fire hazard to be monitored area at the current moment, γ1 is the weight value of the movement speed of the flame pixel point at the current moment, and θ x is to predict the movement speed of the flame pixel at the current moment, θ0 is the benchmark movement speed of the flame pixel, γ2 is the weight value of the direction change frequency of the flame pixel at the current moment, and f x is to predict the direction change frequency of the flame pixel at the current moment, and f0 is the reference direction change frequency of the flame pixel;

[0120] The fire alarm level of the fire hazard monitoring area at the current moment:

[0121] Among them, Δ is the fire alarm level of the fire hazard to be monitored area at the current moment, E is the fire spread degree assessment value of the fire hazard to be monitored area at the current moment, D0 is the fire severity benchmark assessment value of the fire hazard to be monitored area, and E0 is the fire spread benchmark assessment value of the fire hazard to be monitored area.

[0122] In this embodiment, the trained fire spread assessment model is a model obtained by training a neural network model using a large number of predicted motion trajectory parameters and motion feature parameters of flame pixels and the density of predicted flame pixels at the current moment as model inputs of training samples, and using the fire spread assessment value as the model output.

[0123] The working principle and beneficial effects of the above technical solution are as follows: the present invention introduces a calculation unit for the severity and spread of fire, comprehensively evaluates the current fire risk, dynamically adjusts the alarm level according to the fire parameters, provides a graded response plan, provides a dynamic and multi-level evaluation of fire hazards, supports more effective emergency response, reduces unnecessary downtime and interference, optimizes resource scheduling and risk management, and can provide more intelligent, precise and dynamic monitoring and early warning services, significantly improving the efficiency of fire prevention and safety assurance capabilities.

[0124] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A belt conveyor trough status monitoring system, characterized in that: include: A video acquisition module is used to acquire videos of several areas prone to coal accumulation in the guide chute in a working state as initial videos of several areas prone to coal accumulation; A fire hazard monitoring area identification module is used to determine whether several coal-prone areas are coal-accumulating areas based on initial videos of several coal-prone areas, and to identify fire hazard monitoring areas based on several coal-accumulating areas; The guide chute emergency hidden danger monitoring and pre-alarm module is used to construct a fire hazard prediction matrix for each fire hazard to be monitored area, and determine whether to issue a warning prompt based on the fire hazard prediction matrix for each fire hazard to be monitored area, while monitoring the fire hazard situation in the fire hazard to be monitored area, and determine whether to issue a fire hazard alarm prompt based on the fire hazard situation in the fire hazard to be monitored area; The guide trough mechanical performance monitoring module is used to collect the material flow rate and working noise of the guide trough during the monitoring period, and input them into the trained guide trough mechanical performance evaluation model to evaluate the mechanical performance of the guide trough and issue a mechanical performance alarm when the mechanical performance is poor.

2. A belt conveyor trough state monitoring system according to claim 1, characterized in that: The fire hazard monitoring area identification module includes: A single-frame extraction submodule is used to extract all single video frames of the initial video of several coal-prone areas within a preset acquisition time period; The coal accumulation region identification submodule is used to input all video single frames of the initial videos of several coal accumulation-prone regions within a preset acquisition time period into the trained coal accumulation identification model to identify whether the corresponding coal accumulation-prone regions are coal accumulation regions; The fire hazard monitoring area identification submodule is used to identify the fire hazard monitoring area based on all video single frames of the initial video of several coal accumulation areas within a preset acquisition time period.

3. A belt conveyor trough state monitoring system according to claim 2, characterized in that: The fire hazard monitoring area identification submodule includes: The auxiliary pixel acquisition unit is used to acquire the pixel value of each pixel position of all single video frames in a preset acquisition time period of the initial video of the coal accumulation area, calculate the average of the pixel values ​​of the adjacent pixels of each pixel of each single video frame, and use it as the auxiliary pixel of the corresponding pixel of the single video frame to obtain a number of auxiliary pixels at each pixel position; The background acquisition unit of the fire hazard monitoring area takes the sum of several auxiliary pixels at each pixel position and the quotient of the total number of all video single frames of the initial video of each coal accumulation area within a preset acquisition time period as the background pixel value of each pixel position, and takes the image constructed by the background pixel value as the background of the fire hazard monitoring area at the current moment; The fire hazard monitored area identification unit calculates the absolute value of the difference between the background pixel value of each pixel position in the background of the fire hazard monitored area at the current moment and the pixel value of the corresponding pixel position in the single frame of video at the current moment. When the absolute value of the difference corresponding to the pixel position is greater than the preset pixel difference, the pixel position is marked as the pixel position of the fire hazard monitored area, and the area composed of all the pixel positions of the fire hazard monitored area is the fire hazard monitored area.

4. A belt conveyor trough state monitoring system according to claim 1, characterized in that: The emergency hidden danger monitoring and early warning module of the guide chute includes: The material guide chute emergency hazard warning submodule is used to obtain the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period based on the fire hazard prediction matrix of each fire hazard to be monitored area, and determine whether to issue an early warning prompt based on the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period; The material guide chute emergency hidden danger alarm submodule is used to evaluate the fire situation in the fire hazard monitored area, and determine whether to issue a fire alarm prompt based on the fire situation in the fire hazard monitored area.

5. A belt conveyor trough state monitoring system according to claim 4, characterized in that: The guide chute emergency hazard warning submodule includes: A temperature collection unit is used to collect the ambient temperature of each fire hazard to be monitored area in each monitoring period; A temperature prediction matrix construction unit, for constructing a temperature prediction matrix A for each fire hazard to be monitored area based on the ambient temperature of each fire hazard to be monitored area in each monitoring period; The coal accumulation speed acquisition unit is used to input the first moment video single frame and the last moment video single frame of the initial video corresponding to the fire hazard to be monitored area in each monitoring period into the trained coal accumulation speed judgment model to obtain the coal accumulation speed of each fire hazard to be monitored area in each monitoring period; A coal accumulation speed prediction matrix construction unit is used to construct a coal accumulation speed prediction matrix B for each monitoring area to be monitored based on the coal accumulation speed of each fire hazard to be monitored area in each monitoring period; A fire hazard prediction matrix construction unit for each fire hazard to be monitored area, used for constructing a fire hazard prediction matrix C for each fire hazard to be monitored area based on a temperature prediction matrix for each fire hazard to be monitored area and a coal accumulation speed prediction matrix for each fire hazard to be monitored area; A fire hazard occurrence index obtaining unit, for obtaining the fire hazard occurrence index of the fire hazard to be monitored area in the next monitoring period based on the fire hazard prediction matrix of each fire hazard to be monitored area; The early warning judgment unit is used to judge whether to issue an early warning based on the fire hazard occurrence index of the area to be monitored for fire hazards in the next monitoring period.

6. A belt conveyor trough state monitoring system according to claim 5, characterized in that: Based on the ambient temperature of each fire hazard monitored area in each monitoring period, a temperature prediction matrix A is constructed for each fire hazard monitored area: Among them, A αi is the temperature prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, a α1 is the ambient temperature of the αth fire hazard monitoring area in the first monitoring cycle, a α2 is the ambient temperature of the αth fire hazard monitoring area in the second monitoring cycle, a α3 is the ambient temperature of the αth fire hazard monitoring area in the third monitoring cycle, a α(i-1) is the ambient temperature of the αth fire hazard monitoring area in the i-1th monitoring period, a αi is the ambient temperature of the αth fire hazard to be monitored area in the ith monitoring period; Based on the coal accumulation rate of each fire hazard to be monitored area in each monitoring period, a coal accumulation rate prediction matrix B is constructed for each fire hazard to be monitored area: Among them, B αi is the coal accumulation speed prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, b α1 is the coal accumulation speed of the αth fire hazard monitoring area in the first monitoring cycle, b α2 is the coal accumulation speed of the αth fire hazard monitoring area in the second monitoring cycle, b α3 is the coal accumulation speed of the αth fire hazard monitoring area in the third monitoring cycle, b α(i-1) is the coal accumulation speed of the αth fire hazard monitoring area in the i-1th monitoring period, b αi is the coal accumulation rate of the αth fire hazard to be monitored area in the ith monitoring period.

7. A belt conveyor trough state monitoring system according to claim 6, characterized in that: Based on the temperature prediction matrix of each fire hazard area to be monitored and the coal accumulation speed prediction matrix of each fire hazard area to be monitored, a fire hazard prediction matrix C of each fire hazard area to be monitored is constructed: Among them, C αi is the fire hazard prediction matrix of the αth fire hazard monitoring area in the ith monitoring period, β α1 is the weight value of the ambient temperature of the αth fire hazard monitoring area, β α2 is the weight value of the coal accumulation speed of the αth fire hazard monitoring area; Obtain the mean of all matrix elements in the third column of the fire hazard prediction matrix of the αth fire hazard to be monitored area in the ith monitoring period, and replace the values ​​of all matrix elements in the third row of the fire hazard prediction matrix of the αth fire hazard to be monitored area in the ith monitoring period with the mean of all matrix elements in the third column to obtain a new matrix, and use the quotient of the rank of the new matrix and the preset rank as the fire hazard occurrence index of the αth fire hazard to be monitored area in the i+1th monitoring period; If the fire occurrence index of the αth fire hazard to be monitored area in the i+1th monitoring period is greater than the preset fire occurrence index of the αth fire hazard to be monitored area, an early warning prompt will be issued.

8. A belt conveyor trough state monitoring system according to claim 4, characterized in that: The guide chute emergency hidden danger alarm submodule includes: The flame pixel acquisition unit to be tested is used to compare the pixel value of each pixel in the fire hazard to be monitored area at the current moment with the preset flame pixel value. If the pixel value of each pixel in the fire hazard to be monitored area at the current moment is greater than the preset flame pixel value, the pixel is used as the flame pixel to be tested; The predicted flame pixel determination unit uses optical flow analysis to extract the motion information of the flame pixel to be tested, including the motion speed and direction, to obtain the motion trajectory of the flame pixel to be tested, and matches the motion trajectory of the flame pixel to be tested with the preset flame motion characteristics, and selects the flame pixel to be tested that meets the preset characteristics as the predicted flame pixel points. If there are a preset number of predicted flame pixels, an alarm prompt is given.

9. A belt conveyor trough state monitoring system according to claim 8, characterized in that: Also includes: A fire severity calculation unit, for calculating a fire severity assessment value of the fire hazard to be monitored area at the current moment based on the motion trajectory parameters and motion feature parameters of each predicted flame pixel point in the fire hazard to be monitored area at the current moment, the motion trajectory parameters and motion feature parameters including motion speed and direction change frequency; The fire spread degree calculation unit is used to obtain the fire spread degree assessment value of the fire hazard to be monitored area at the current moment based on the trained fire spread degree assessment model, the motion trajectory parameters and motion feature parameters of each predicted flame pixel point in the fire hazard to be monitored area at the current moment, and the density of the predicted flame pixel points at the current moment; The alarm level calculation unit is used to obtain the fire alarm level of the fire hazard to be monitored area at the current moment based on the fire severity assessment value and the fire spread assessment value of the fire hazard to be monitored area at the current moment.

10. A belt conveyor trough state monitoring system according to claim 9, characterized in that: The fire severity assessment value of the fire hazard monitoring area at the current moment: Among them, D is the fire severity assessment value of the fire hazard to be monitored area at the current moment, γ1 is the weight value of the movement speed of the flame pixel point at the current moment, and θ x is to predict the movement speed of the flame pixel at the current moment, θ0 is the benchmark movement speed of the flame pixel, γ2 is the weight value of the direction change frequency of the flame pixel at the current moment, and f x is to predict the direction change frequency of the flame pixel at the current moment, and f0 is the reference direction change frequency of the flame pixel; The fire alarm level of the fire hazard monitoring area at the current moment: Among them, Δ is the fire alarm level of the fire hazard to be monitored area at the current moment, E is the fire spread degree assessment value of the fire hazard to be monitored area at the current moment, D0 is the fire severity benchmark assessment value of the fire hazard to be monitored area, and E0 is the fire spread benchmark assessment value of the fire hazard to be monitored area.

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