Coal mine belt water coal identification analysis method based on AI technology

Abstract

The invention belongs to the technical field of coal water identification, and particularly discloses a coal mine belt coal water identification analysis method based on an AI technology, which comprises the following steps: acquiring an image signal of a coal conveying belt in real time, and quickly monitoring a coal water signal; interference analysis is carried out on dust, humidity and geological condition signals, and the influence of interference signals on the blocking effect of shooting sight lines in the mining process of an underground coal mine is eliminated; by detecting the gravity deviation trend of the coal conveying belt, analyzing the state signal of the coal conveying belt at each image signal acquisition moment, and screening the reference impact area and the reference sputtering area of the corresponding coal flow impact at each image signal acquisition moment, the quantitative analysis of the coal flow impact is facilitated; and identifying the point domain area condition of the coal water component by identifying the reflective component of each identification point domain to which each image signal belongs, and further analyzing the dynamic behavior of the coal flow in the transportation process according to the corresponding point domain area of each reflective point domain in an impact state and a sputtering state in each image signal.

Description

Technical Field

[0001] The invention belongs to the technical field of water-coal identification and relates to a method for identifying and analyzing water-coal in a coal mine belt based on AI technology. Background Art

[0002] With the increasing depth of coal mining, hydrogeological conditions are becoming more and more complex, and water inrush disasters in coal mines occur from time to time, causing serious casualties and property losses. In order to ensure the safety of coal mining and operation management, it is necessary to use various advanced image detection and recognition technologies to improve the production environment of coal mines and enhance their risk resistance. Among them, water-coal monitoring is an important part of coal mine monitoring. However, water-coal monitoring usually requires manual inspections, mainly relying on underground workers to report to the dispatching room through underground telephones after water inrush, which is inefficient and information is delayed. Therefore, the development of an automatic water-coal monitoring system has important practical significance and application value.

[0003] There are also some solutions related to water-coal identification in the existing technology, but there are still the following limitations: the existing technology does not take interference signals into consideration when identifying water-coal signals. Due to the influence of vibration and dust accumulation during the mining process in coal mines, the water-coal detection signal has a certain degree of camera line of sight obstruction, and the actual water-coal content may be higher than the content detected in the collected image.

[0004] In addition, when identifying the water content of coal on the coal transport belt, the existing technology does not conduct in-depth analysis based on the impact and sputtering state of the coal flow and the reflective state of the coal blocks. Due to the different weight distribution of the coal blocks and the running speed of the belt, different impact and sputtering effects will be produced. These dynamic changes put higher requirements on the capture and processing of the image. If this factor is not fully considered, inaccurate recognition results may result. Summary of the invention

[0005] In view of this, in order to solve the problems raised in the above background technology, a method for identifying and analyzing water coal in coal mine belts based on AI technology is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a method for identifying and analyzing water and coal in a coal mine belt based on AI technology, including: E1, image acquisition unit: using an industrial camera to collect a real-time picture of the water and coal situation on the coal transport belt, and transmit it as an image signal to an image processing unit.

[0007] E2, image processing unit: receiving the coal transport image set sent by the image acquisition unit, and numbering each image signal in the coal transport image set as , and detect the image interference signal and transmit it to the water-coal signal detection unit.

[0008] E3. Identify the alarm triggering mechanism of the terminal device.

[0009] E4. Belt status detection: detect the operation data of the coal transport belt at each image signal collection moment, and generate the coal transport belt status signal at each image signal collection moment, and import it into the water-coal signal detection unit.

[0010] E5. Water-coal signal detection unit: monitors the water-coal signal in each image signal, and generates an alarm signal when continuous water-coal signals are detected in the coal transport image set.

[0011] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention collects the image signal of the coal conveyor belt in real time, monitors and analyzes the coal conveyor belt in the coal mine in real time, can quickly monitor the water-coal signal, and solves the problems existing in manual inspection.

[0012] (2) The present invention can identify and eliminate signal attenuation and distortion caused by interference factors by performing interference analysis on dust and moisture condition signals and geological condition signals, eliminate the influence of interference signals on the camera's line of sight during the mining process in coal mines, and improve the accuracy and reliability of water-coal signal monitoring, thereby improving production dispatchers' accurate grasp of on-site conditions. At the same time, the alarm triggering mechanism of the terminal device can be identified, so that the invalidity of equipment detection by the industrial camera can be discovered in a timely manner.

[0013] (3) The present invention can accurately capture the dynamic impact of coal flow impact on the belt by detecting the gravity deviation trend of the coal conveyor belt at each image signal acquisition moment, combining the acceleration at each image signal acquisition moment, and analyzing the coal conveyor belt status signal at each image signal acquisition moment. Based on this, the baseline impact area and baseline splashing area of ​​the corresponding coal flow impact at each image signal acquisition moment can be screened, and the coal flow impact can be quantitatively analyzed to determine the baseline impact area and baseline splashing area, thereby providing an accurate basis for subsequent monitoring and early warning.

[0014] (4) The present invention identifies the reflective components of each identification point domain to which each image signal belongs, identifies the point domain area of ​​the water-coal component, and further analyzes the dynamic behavior of the coal flow during transportation, especially the flow characteristics of the water-coal mixture, based on the corresponding point domain area of ​​each reflective point domain in the impact state and the sputtering state in each image signal. This helps to more accurately determine the presence and proportion of water-coal. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 The present invention is a schematic flow chart of the steps for implementing the method.

[0017] Figure 2 This is a physical picture of the water-coal situation of the present invention.

[0018] Figure 3 It is a schematic diagram of the content of the image interference signal of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 , Figure 3 As shown, the present invention provides a method for identifying and analyzing water and coal on a coal mine belt based on AI technology, including: E1, an image acquisition unit: using an industrial camera to collect a real-time picture of the water and coal situation on the coal transport belt, and transmitting it as an image signal to an image processing unit.

[0021] See also Figure 2 As shown, in a preferred embodiment, the image interference signal includes a dust and moisture condition signal and a geological condition signal.

[0022] The dust and moisture condition signal includes a dust concentration signal and a water vapor content signal.

[0023] The geological condition signals include transport distortion signals and route structure signals.

[0024] The present invention collects the image signal of the coal conveyor belt in real time, monitors and analyzes the coal conveyor belt in the coal mine in real time, can quickly monitor the water-coal signal, and solves the problems existing in manual inspection.

[0025] E2, image processing unit: receiving the coal transport image set sent by the image acquisition unit, and numbering each image signal in the coal transport image set as , and detect the image interference signal and transmit it to the water-coal signal detection unit.

[0026] In a preferred embodiment, the detection of image interference signals includes: E2-11, analyzing dust concentration signals: obtaining the acquisition time of each image signal in the coal transport image set, monitoring the dust distribution spectrum image of the area near the coal transport belt at each image signal acquisition time through a portable spectrometer, and evenly dividing the area near the coal transport belt into sub-areas, recorded as micro-areas, identifying the dust concentration of each micro-area corresponding to the dust distribution spectrum image at each image signal acquisition time , and then As the dust blur signal of each micro-area corresponding to each image signal acquisition moment, Indicates the preset reference concentration of dust, Indicates the number of the micro-area, .

[0027] The dust concentration of each micro-area is determined by the image chromaticity value of the corresponding micro-area portion in the dust distribution spectrum image. Specifically, the image chromaticity value of each micro-area portion is identified from the dust distribution spectrum image, and the image chromaticity value of each micro-area portion is matched with the preset chromaticity value corresponding to each dust concentration to obtain the dust concentration of each micro-area.

[0028] E2-12. Analyze water vapor content signal: Use infrared spectroscopy technology to detect the water vapor content of each micro-area at the time of image signal acquisition. , and detect the lens wetness characteristics of industrial cameras at each image signal acquisition moment , evaluate the water vapor blur signal of each micro-area corresponding to each image signal acquisition time ,in Indicates the preset reference content of water vapor.

[0029] Specifically, the sensor scans the area within the image acquisition range, obtains the radiation data in each band and presents it in the form of an image, where each pixel represents the spectral radiation information of a certain area. Water vapor has strong absorption characteristics in certain bands of the infrared spectrum, so the sensor can identify the water vapor content by measuring the radiation intensity in these bands.

[0030] The method for acquiring the lens wetness characteristics of the industrial camera at each image signal acquisition moment is specifically as follows: the industrial camera lens is scanned by sensor technology to identify the water vapor content on the surface of the industrial camera lens at each image signal acquisition moment, and then the water vapor content on the surface of the industrial camera lens at each image signal acquisition moment is compared with a preset reference water vapor content to obtain the lens wetness characteristics of the industrial camera at each image signal acquisition moment.

[0031] E2-13, through the relationship formula , analyze the dust and wetness interference rate of each image signal ,in They respectively represent the corresponding interference proportions of the preset dust blur signal and water vapor blur signal.

[0032] In a further preferred embodiment, the detection of image interference signals also includes: E2-21, analyzing the transport distortion signal: obtaining the engineering load of the coal mine belt transport area at each image signal acquisition time , generate the transport distortion signal at each image signal acquisition moment ,in Represents the preset reference loads for the project.

[0033] The engineering load specifically refers to the unit construction volume of the coal mine belt transportation project in unit time.

[0034] E2-22. Analyze route structure signals: Install laser rangefinders and angle sensors on the coal conveyor belt to monitor the changes in the corresponding slope and curvature of the coal conveyor belt in real time, and extract the route slope of the coal conveyor belt at each image signal collection time. and the curvature of the route , generate the corresponding route distortion signal at each image signal acquisition moment ,in They respectively represent the preset route reference slope and route reference curvature.

[0035] E2-23. Through the relational formula , analyze the geological condition interference rate of each image signal ,in They respectively represent the corresponding interference proportions of the preset transport distortion signal and route distortion signal.

[0036] E3. Identify the alarm triggering mechanism of the terminal device.

[0037] In a preferred embodiment, the alarm triggering mechanism of the identification terminal device is specifically as follows: comparing the dust and moisture condition interference rate of each image signal with a preset dust and moisture condition interference rate threshold; when the dust and moisture condition interference rate of each image signal exceeds the preset dust and moisture condition interference rate threshold, the alarm mechanism of the industrial camera deployment location is triggered.

[0038] The present invention can identify and eliminate signal attenuation and distortion caused by interference factors by performing interference analysis on dust and moisture condition signals and geological condition signals, eliminate the influence of interference signals on the camera's line of sight during the mining process in underground coal mines, and improve the accuracy and reliability of water-coal signal monitoring, thereby improving production dispatchers' accurate grasp of on-site conditions. At the same time, the alarm triggering mechanism of the terminal device can be identified, so that the invalidity of equipment detection of the industrial camera can be discovered in time.

[0039] E4. Belt status detection: A sensor device is installed on the coal conveyor belt to detect the operation data of the coal conveyor belt at each image signal collection moment, and a coal conveyor belt status signal at each image signal collection moment is generated and imported into the water-coal signal detection unit.

[0040] The water-coal signal refers to the percentage of moisture content in the coal on the coal conveyor belt.

[0041] In a preferred embodiment, the operation data of the coal conveyor belt at each image signal acquisition moment includes the acceleration and actual offset amplitude of the coal conveyor belt at each image signal acquisition moment.

[0042] In a further preferred embodiment, the specific generation method of the coal conveyor belt status signal at the moment of acquisition of each image signal is: the position, brightness component and saturation component of each micro-area corresponding to each image signal are identified by image recognition technology, and each reflective point domain in each image signal is obtained therefrom, and adjacent reflective point domains are integrated to obtain the corresponding point domain area and point domain position of each reflective point domain in each image signal.

[0043] The reflective point domain is specifically a micro-area whose brightness component is significantly higher than that of the surrounding area and whose saturation component is lower than a preset saturation threshold.

[0044] The belt surface monitoring area of ​​the coal conveyor belt in the image signal is obtained, and the area belonging to the gravity offset direction is divided. For example, the belt surface monitoring area is divided into the area belonging to the left offset direction and the area belonging to the right offset direction with the midline of the belt surface monitoring area as the dividing line. The identification point domains whose reflective components are coal blocks within the corresponding monitoring area of ​​each image signal are marked. Based on the volume analysis method, the gravity offset trend of the coal conveyor belt at each image signal acquisition moment is detected, and the gravity offset difference volume at each image signal acquisition moment is obtained. .

[0045] Specifically, the volume analysis method refers to statistically analyzing the total volume of coal blocks corresponding to the areas belonging to the left offset direction and the areas belonging to the right offset direction in the monitoring area, comparing and selecting the maximum value, and then obtaining the gravity offset direction to which the maximum value belongs, that is, the gravity offset trend of the coal conveyor belt at the time of image signal acquisition.

[0046] The gravity offset difference volume is the difference between the total volumes of the corresponding coal blocks in the area belonging to the left offset direction and the area belonging to the right offset direction.

[0047] Analysis of the expected deviation amplitude of the coal conveyor belt under the corresponding acceleration at each image signal acquisition time , and then determine the coal conveyor belt status signal at each image signal acquisition moment ,in Indicates the preset reference difference volume, Indicates The acceleration at the time of image signal acquisition, represents the preset reference acceleration, Indicates the preset offset amplitude corresponding to the unit difference volume of the coal conveyor belt. It indicates the actual deviation amplitude of the coal conveyor belt at each image signal acquisition moment.

[0048] The present invention can accurately capture the dynamic influence of coal flow impact on the belt by detecting the gravity deviation trend of the coal conveyor belt at each image signal acquisition moment, combining the acceleration at each image signal acquisition moment, and analyzing the coal conveyor belt status signal at each image signal acquisition moment. The benchmark impact area and benchmark spattering area of ​​the corresponding coal flow impact at each image signal acquisition moment are screened accordingly, and the coal flow impact can be quantitatively analyzed to determine the benchmark impact area and benchmark spattering area, thereby providing an accurate basis for subsequent monitoring and early warning.

[0049] E5. Water-coal signal detection unit: monitors the water-coal signal in each image signal, and generates an alarm signal when continuous water-coal signals are detected in the coal transport image set.

[0050] In a preferred embodiment, the monitoring of water-coal signals in each image signal includes: comparing the point domain area of ​​each reflective point domain in each image signal with a preset reference area, screening out each reflective point domain in each image signal whose point domain area exceeds the preset reference area, recording them as each identification point domain to which each image signal belongs, and then identifying the reflective components of each identification point domain to which each image signal belongs, and the reflective components are divided into coal blocks and water-coal. If a boundary area of ​​a certain identification point domain to which a certain image signal belongs has a preset boundary texture feature of a coal block, then the reflective component of the identification point domain to which the image signal belongs is a coal block, otherwise the reflective component of the identification point domain to which the image signal belongs is water-coal. Based on this, each identification point domain in each image signal whose reflective component is water-coal is obtained, and the corresponding point domain area of ​​each identification point domain in each image signal whose reflective component is water-coal is counted. .

[0051] The corresponding point domain positions of each identification point domain whose reflective component is water coal in each image signal are compared with each other, and the mean distance between the corresponding point domain positions of all identification point domains in each image signal is identified. .

[0052] Due to the uneven distribution of coal blocks, the conveyor belt is bumpy, which causes coal flow impact and sputtering in coal mines with high moisture content. Therefore, based on each reflection point domain in each image signal, the coal flow impact rate in each image signal is detected. It is one of the evaluation methods to infer the moisture content in coal mines, which helps to increase the stability and uniformity of coal flow detection.

[0053] Determine the water-coal signal in each image signal , where Indicates the preset reference area, represents the preset reference distance, and e is a natural constant.

[0054] In a further preferred embodiment, the detection of the coal flow impact rate in each image signal includes: identifying the relative state of the corresponding point domain position of each reflective point domain in each image signal and the position of the coal conveyor belt, including the impact state and the sputtering state, and counting the corresponding point domain areas of each reflective point domain in the impact state and the sputtering state in each image signal, respectively recorded as , Indicates the number of the reflective point domain, .

[0055] The sputtering state refers to the situation where the corresponding point domain position of the reflective point domain in the image signal is disconnected from the position of the coal conveyor belt, and the impact state refers to the situation where the corresponding point domain position of the reflective point domain in the image signal is connected to the position of the coal conveyor belt.

[0056] The coal conveyor belt state signal at each image signal acquisition moment is matched with the preset reference impact area and reference splashing area corresponding to each coal conveyor belt state signal, and the reference impact area corresponding to the coal flow impact at each image signal acquisition moment is screened out. and the reference sputtering area .

[0057] Combined with the geological conditions interference rate of each image signal , analyze the coal flow impact rate in each image signal , where It means that there is no sputtering state between the relative state between the corresponding point domain position of each reflective point domain in each image signal and the position of the coal conveyor belt. It indicates that there is a sputtering state between the corresponding point domain position of each reflective point domain in each image signal and the relative state of the coal conveyor belt position.

[0058] In a further preferred embodiment, the specific identification method of the continuous water-coal signal is: comparing the water-coal signal in each image signal with a preset water-coal signal threshold. When the water-coal signal in each image signal exceeds the preset water-coal signal threshold, it indicates that there is a continuous water-coal signal in the coal transportation image set.

[0059] The present invention identifies the reflective components of each identification point domain to which each image signal belongs, identifies the point domain area of ​​the water-coal components, and further analyzes the dynamic behavior of the coal flow during transportation, especially the flow characteristics of the water-coal mixture, based on the corresponding point domain areas of each reflective point domain in the impact state and the sputtering state in each image signal. This helps to more accurately judge the presence and proportion of water-coal.

[0060] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for identifying and analyzing water-coal in coal mine belts based on AI technology, characterized in that: The method comprises the following steps: E1. Image acquisition unit: collects the real-life image of the water-coal situation on the coal conveyor belt in real time through an industrial camera, and transmits it as an image signal to the image processing unit; E2, image processing unit: receiving the coal transport image set sent by the image acquisition unit, and numbering each image signal in the coal transport image set as , and detect the image interference signal and transmit it to the water-coal signal detection unit; E3. Identify the alarm triggering mechanism of the terminal device; E4. Belt status detection: detect the operation data of the coal conveyor belt at each image signal collection moment, and generate the coal conveyor belt status signal at each image signal collection moment, and import it into the water-coal signal detection unit; E5. Water-coal signal detection unit: monitors the water-coal signal in each image signal, and generates an alarm signal when continuous water-coal signals are detected in the coal transport image set.

2. According to the method for identifying and analyzing coal in coal mine belt based on AI technology in claim 1, it is characterized in that: The image interference signal includes a dust and moisture condition signal and a geological condition signal; The dust and moisture condition signal includes a dust concentration signal and a water vapor content signal; The geological condition signals include transport distortion signals and route structure signals.

3. The method for identifying and analyzing coal in coal mine belts based on AI technology according to claim 2 is characterized in that: The detecting of the image interference signal includes: E2-11. Analyze dust concentration signals: Obtain the acquisition time of each image signal in the coal transport image set, monitor the dust distribution spectrum image of the area near the coal transport belt at each image signal acquisition time through a portable spectrometer, and divide the area near the coal transport belt into micro-areas, identify the dust concentration of each micro-area corresponding to the dust distribution spectrum image at each image signal acquisition time. , and then As the dust blur signal of each micro-area corresponding to each image signal acquisition moment, Indicates the preset reference concentration of dust, Indicates the number of the micro-area, ; E2-12. Analyze water vapor content signal: Use infrared spectroscopy technology to detect the water vapor content of each micro-area at the time of image signal acquisition. , and detect the lens wetness characteristics of industrial cameras at each image signal acquisition moment , evaluate the water vapor blur signal of each micro-area corresponding to each image signal acquisition time ,in Indicates the preset reference content of water vapor; E2-13, through the relationship formula , analyze the dust and wetness interference rate of each image signal ,in They respectively represent the corresponding interference proportions of the preset dust blur signal and water vapor blur signal.

4. The method for identifying and analyzing coal in coal mine belts based on AI technology according to claim 3 is characterized in that: The detecting of image interference signals also includes: E2-21. Analyze the transport distortion signal: Obtain the engineering load of the coal mine belt transport area at each image signal acquisition time, and generate the transport distortion signal at each image signal acquisition time ; E2-22. Analyze route structure signals: Install laser rangefinders and angle sensors on the coal conveyor belt to monitor the changes in the corresponding slope and curvature of the coal conveyor belt in real time, extract the route slope and route curvature of the coal conveyor belt at each image signal acquisition time, and generate the corresponding route distortion signal at each image signal acquisition time. ; E2-23. Through the relational formula , analyze the geological condition interference rate of each image signal ,in They respectively represent the corresponding interference proportions of the preset transport distortion signal and route distortion signal.

5. The method for identifying and analyzing water-coal in coal mine belts based on AI technology according to claim 4 is characterized in that: The alarm triggering mechanism of the terminal device is specifically as follows: comparing the dust and moisture condition interference rate of each image signal with a preset dust and moisture condition interference rate threshold. When the dust and moisture condition interference rate of each image signal exceeds the preset dust and moisture condition interference rate threshold, the alarm mechanism of the industrial camera deployment location is triggered.

6. The method for identifying and analyzing coal in coal mine belts based on AI technology according to claim 1 is characterized in that: The operation data of the coal conveyor belt at each image signal collection moment includes the acceleration and actual offset amplitude of the coal conveyor belt at each image signal collection moment.

7. The method for identifying and analyzing coal in coal mine belts based on AI technology according to claim 6 is characterized in that: The specific method for generating the coal conveyor belt status signal at the time of collecting each image signal is as follows: obtaining each reflective point domain in each image signal, integrating adjacent reflective point domains, and obtaining the corresponding point domain area and point domain position of each reflective point domain in each image signal; Taking the transverse area of ​​the belt as the monitoring area, the area belonging to the gravity offset direction is divided, and the identification point domains whose reflective components are coal blocks within the corresponding monitoring area of ​​each image signal are marked. Based on the volume analysis method, the gravity offset trend of the coal conveyor belt at each image signal acquisition time is detected, and the gravity offset difference volume at each image signal acquisition time is obtained. ; Analysis of the expected deviation amplitude of the coal conveyor belt under the corresponding acceleration at each image signal acquisition time , and then determine the coal conveyor belt status signal at each image signal acquisition moment ,in Indicates the preset reference difference volume, Indicates The acceleration at the moment of image signal acquisition, represents the preset reference acceleration, Indicates the preset offset amplitude corresponding to the unit difference volume of the coal conveyor belt. It indicates the actual deviation amplitude of the coal conveyor belt at each image signal acquisition moment.

8. The method for identifying and analyzing coal in coal mine belts based on AI technology according to claim 1 is characterized in that: The monitoring of the water-coal signal in each image signal includes: comparing the point domain area of ​​each reflective point domain in each image signal with a preset reference area, screening out each reflective point domain in each image signal whose point domain area exceeds the preset reference area, recording them as each identification point domain to which each image signal belongs, and then identifying the reflective components of each identification point domain to which each image signal belongs, the reflective components are divided into coal blocks and water-coal, if the boundary area of ​​a certain identification point domain to which a certain image signal belongs has a preset boundary texture feature of a coal block, then the reflective component of the identification point domain to which the image signal belongs is a coal block, otherwise it is water-coal, and the corresponding point domain areas of each identification point domain whose reflective components in each image signal are water-coal are counted. ; The corresponding point domain positions of each identification point domain whose reflective component is water coal in each image signal are compared with each other, and the mean distance between the corresponding point domain positions of all identification point domains in each image signal is identified. ; Based on each reflection point domain in each image signal, the coal flow impact rate in each image signal is detected ; Determine the water-coal signal in each image signal , where Indicates the preset reference area, represents the preset reference distance, and e is a natural constant.

9. The method for identifying and analyzing coal in coal mine belts based on AI technology according to claim 8 is characterized in that: The detection of the coal flow impact rate in each image signal includes: identifying the relative state of the corresponding point domain position of each reflective point domain in each image signal and the position of the coal conveyor belt, including the impact state and the sputtering state, and counting the corresponding point domain areas of each reflective point domain in the impact state and the sputtering state in each image signal, respectively recorded as , Indicates the number of the reflective point domain, ; The coal conveyor belt state signal at each image signal acquisition moment is matched with the preset reference impact area and reference splashing area corresponding to each coal conveyor belt state signal, and the reference impact area corresponding to the coal flow impact at each image signal acquisition moment is screened out. and the reference sputtering area ; Combined with the geological conditions interference rate of each image signal , analyze the coal flow impact rate in each image signal , where It means that there is no sputtering state between the relative state between the corresponding point domain position of each reflective point domain in each image signal and the position of the coal conveyor belt. It indicates that there is a sputtering state between the corresponding point domain position of each reflective point domain in each image signal and the relative state of the coal conveyor belt position.

10. The method for identifying and analyzing coal in coal mine belts based on AI technology according to claim 1 is characterized in that: The specific identification method of the continuous water-coal signal is: comparing the water-coal signal in each image signal with the preset water-coal signal threshold. When the water-coal signal in each image signal exceeds the preset water-coal signal threshold, it indicates that there is a continuous water-coal signal in the coal transportation image set.

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