Coal conveying belt coal blockage image recognition method and control system

Abstract

The invention provides a coal conveying belt coal blockage image recognition method and a control system, and relates to the technical field of image recognition, and the method comprises the steps: 1, dividing a working region of a coal conveying belt into a plurality of detection regions, and obtaining a monitoring image of each monitoring region; 2, monitoring parameters are preset for each detection area, the monitoring parameters comprise a monitoring position and a scene type, and a corresponding identification method is called based on the scene type to determine an identification result of each detection area; and step 3, based on the identification result of each detection area, automatically adjusting the operation parameters of the coal conveying belt and giving an alarm. The corresponding coal blockage recognition method is selected according to the scene type of each detection area, and the corresponding recognition method is formulated according to the coal blockage characteristics of each scene type, so that the recognition accuracy of the coal blockage conditions under various scene types is improved, and the coal blockage recognition efficiency is improved by automatically adjusting the operation parameters of the coal conveying belt. And the function of automatically eliminating the coal blockage condition or reducing the deterioration speed of the coal blockage condition is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and in particular to an image recognition method and control system for coal conveyor belt blockage. Background Art

[0002] During the operation of the coal conveyor belt, coal blockage of the coal conveyor belt has always been the main source of failure during the operation of the machinery. However, the coal blockage of the belt is caused by various reasons, and it is difficult to detect or predict it in a fixed way. Therefore, the traditional method can only monitor the running coal conveyor belt based on the camera. When the belt is adjusted and repaired, the coal blockage has developed to a certain extent. It is difficult to make timely adjustments in the early stage of coal blockage and complete the repair in a short time. Summary of the invention

[0003] The present invention provides a coal conveyor belt coal blockage image recognition method and control system, which are used to solve the defect in the prior art that when the belt is adjusted and repaired, the coal blockage has developed to a certain extent, and it is difficult to make timely adjustments in the early stage of coal blockage and complete the repair in a short time.

[0004] In one aspect, the present invention provides a method for identifying an image of a coal conveyor belt being blocked by coal, comprising:

[0005] Step 1: Divide the working area of ​​the coal conveyor belt into several detection areas, and obtain the monitoring image of each monitoring area;

[0006] Step 2: Preset monitoring parameters for each detection area, the monitoring parameters include monitoring location and scene type, and call the corresponding recognition method based on the scene type to determine the recognition result of each detection area, the scene type parameters include hopper scene and entrance and exit scene;

[0007] Step 3: Based on the recognition results of each detection area, the operating parameters of the coal conveyor belt are automatically adjusted and an alarm is issued.

[0008] Preferably, step 3 comprises:

[0009] Step 31: Obtain the identification result of each detection area, screen out the detection area with coal blocking potential as the potential danger detection area, and obtain the coal conveyor belt number corresponding to each potential danger detection area as the potential danger coal conveyor belt number;

[0010] Step 32: based on the identification results of the hidden danger monitoring areas on each hidden danger coal conveyor belt, determine the hidden danger severity corresponding to each hidden danger coal conveyor belt number;

[0011] Step 33: Adjust the operating parameters of the corresponding potential danger coal conveyor belt based on the potential danger severity corresponding to each potential danger coal conveyor belt number.

[0012] On the other hand, the present invention also provides a coal conveyor belt coal blocking image recognition control system, which is applied to the above-mentioned coal conveyor belt coal blocking image recognition method, comprising:

[0013] The area configuration module is used to divide the working area of ​​the coal conveyor belt into several monitoring areas and generate monitoring parameters for each monitoring area;

[0014] An image acquisition module, used to acquire a number of monitoring images of each monitoring area as analysis images;

[0015] A hidden danger identification module is used to evaluate the hidden danger situation of each monitoring area based on the analysis image of each monitoring area;

[0016] A comprehensive assessment module is used to determine the severity of hidden dangers on each coal conveyor belt according to the hidden danger conditions in the hidden danger monitoring area on each coal conveyor belt;

[0017] The parameter adjustment module is used to adjust the operating parameters of the coal conveyor belt according to the severity of the hidden dangers of each coal conveyor belt.

[0018] Preferably, the image acquisition module includes:

[0019] An image capture unit, used to capture a number of analysis images at the same time interval in the surveillance video;

[0020] The image preprocessing unit is used to preprocess the analysis image.

[0021] Preferably, the hidden danger identification module includes:

[0022] An algorithm selection unit, used to select a recognition method to be called according to the scene type parameters of the monitoring area;

[0023] A first hidden danger analysis unit, which performs hidden danger analysis of a monitoring area belonging to the hopper scene based on a first recognition algorithm;

[0024] The second hidden danger analysis unit performs hidden danger analysis on the monitoring area belonging to the entrance and exit scene based on the second recognition algorithm.

[0025] Preferably, the first hidden danger analysis unit includes:

[0026] A feature extraction subunit is used to extract abnormal feature patterns in each analysis image and obtain the characteristic properties of each abnormal feature pattern;

[0027] A first calculation subunit, used to calculate a first hidden danger assessment parameter of a monitoring area belonging to a hopper scene;

[0028]

[0029] Among them, P iis the first hidden danger assessment parameter of the i-th monitoring area belonging to the hopper scene; α and β are the weighted calculation coefficients of the actual number of abnormal feature graphics and the change trend of the number of abnormal feature graphics, respectively; M i is the total number of analyzed images of the i-th monitoring area belonging to the hopper scene; G ij A is the total number of abnormal feature graphics in the jth analysis image of the i-th monitoring area belonging to the hopper scene; ijk is the area of ​​the kth abnormal feature graph in the jth analysis image of the i-th monitoring area belonging to the hopper scene; A0 is the minimum discrimination area of ​​the abnormal feature graph; G b G is the preset maximum allowable amount of abnormal characteristic patterns; i(j-1) A is the total number of abnormal feature graphics in the j-1th analysis image of the i-th monitoring area belonging to the hopper scene; i(j-1)k is the area of ​​the kth abnormal feature pattern in the j-1th analysis image of the i-th monitoring area belonging to the hopper scene;

[0030] The first result judgment subunit determines that there is a coal blocking hazard in the monitored area when the first hidden danger assessment parameter is greater than a preset first hidden danger threshold, otherwise it determines that there is no coal blocking hazard in the monitored area.

[0031] Preferably, the second hidden danger analysis unit includes:

[0032] An image partitioning subunit, used for dividing the analysis image into a coal-containing area and a coal-free area;

[0033] A second calculation subunit, used to calculate a second hidden danger assessment parameter of a monitoring area belonging to an entrance and exit scene;

[0034] Among them, P b is the second hidden danger assessment parameter of the bth monitoring area belonging to the entrance and exit scene; γ and δ are the weighted calculation coefficients of the actual proportion of coal-burning area and the change rate of the proportion of coal-burning area, respectively; N b is the total number of analyzed images of the bth monitoring area belonging to the entrance and exit scene; A mby A is the area of ​​the coal-containing region in the yth analysis image of the bth monitoring area belonging to the entrance and exit scene; wby A is the total area of ​​the yth analysis image of the bth monitoring area belonging to the entrance and exit scene; mb(y-1) A is the area of ​​the coal-containing region in the y-1th analysis image of the bth monitoring area belonging to the entrance and exit scene; wb(y-1) is the total area of ​​the y-1th analysis image of the bth monitoring area belonging to the entrance and exit scene;

[0035] The second result judgment subunit determines that there is a coal blocking hazard in the monitoring area when the second hidden danger assessment parameter of the monitoring area is greater than a preset second hidden danger threshold, otherwise it determines that there is no coal blocking hazard in the monitoring area.

[0036] Preferably, the comprehensive assessment module includes:

[0037] Belt hidden danger assessment unit, used to assess the severity of hidden dangers of each coal conveyor belt;

[0038] Among them, T x F is the severity of hidden dangers of the xth coal conveyor belt; x is the total number of monitoring areas on the x-th coal conveyor belt; max is the maximum value function; P xv is the first hidden danger assessment parameter or the second hidden danger assessment parameter of the vth monitoring area on the xth coal conveyor belt; is the correlation coefficient between the vth monitoring area on the xth coal conveyor belt and the monitoring area with the most serious coal blockage on the xth coal conveyor belt; R0 is the preset maximum allowable fluctuation deviation of the hidden danger level; || is the absolute value symbol; is the rounding symbol;

[0039] Anomaly assessment unit, used to assess the hidden dangers and abnormalities in each monitoring area;

[0040] Among them, Q xy is the abnormal situation evaluation parameter of the vth monitoring area on the xth coal conveyor belt; || is the absolute value symbol; P xv is the first hidden danger assessment parameter or the second hidden danger assessment parameter of the vth monitoring area on the xth coal conveyor belt; is the correlation coefficient between the vth monitoring area on the xth coal conveyor belt and the monitoring area with the most serious coal blockage on the xth coal conveyor belt; R0 is the maximum allowable fluctuation deviation of the preset hidden danger level;

[0041] When Q xy If it is greater than or equal to 0, it is considered that there is no abnormality in the vth monitoring area on the xth coal conveyor belt; otherwise, it is considered that there is an abnormality in the vth monitoring area on the xth coal conveyor belt and an alarm is issued.

[0042] Preferably, the parameter adjustment module includes:

[0043] An alarm unit, when the severity of the hidden danger of the coal conveyor belt is greater than or equal to a preset fault threshold, the coal conveyor belt is shut down and an alarm is sounded;

[0044] The automatic adjustment unit adjusts the operating parameters of the coal conveyor belt when the severity of the hidden danger of the coal conveyor belt is less than a preset fault threshold.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The corresponding coal blockage identification method is selected according to the scene type of each detection area, and the corresponding identification method is formulated according to the coal blockage characteristics of each scene type, thereby improving the recognition accuracy of coal blockage situations in various scene types. When a coal blockage is identified, the operating parameters of the coal conveyor belt are automatically adjusted to automatically eliminate the coal blockage or slow down the deterioration of the coal blockage, providing sufficient reaction time for the staff to eliminate the coal blockage. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0048] Figure 1 It is a schematic flow chart of the method for recognizing coal blockage images on a coal conveyor belt according to the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are 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.

[0050] 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.

[0051] Example 1

[0052] The embodiment of the present invention provides a method for identifying coal conveyor belt blockage images, such as Figure 1 As shown, including:

[0053] Step 1: Divide the working area of ​​the coal conveyor belt into several detection areas, and obtain the monitoring image of each monitoring area;

[0054] Step 2: Preset monitoring parameters for each detection area, the monitoring parameters include monitoring location and scene type, and call the corresponding recognition method based on the scene type to determine the recognition result of each detection area, the scene type parameters include hopper scene and entrance and exit scene;

[0055] Step 3: Based on the recognition results of each detection area, the operating parameters of the coal conveyor belt are automatically adjusted and an alarm is issued.

[0056] Preferably, step 3 comprises:

[0057] Step 31: Obtain the identification result of each detection area, screen out the detection area with coal blocking potential as the potential danger detection area, and obtain the coal conveyor belt number corresponding to each potential danger detection area as the potential danger coal conveyor belt number;

[0058] Step 32: based on the identification results of the hidden danger monitoring areas on each hidden danger coal conveyor belt, determine the hidden danger severity corresponding to each hidden danger coal conveyor belt number;

[0059] Step 33: Adjust the operating parameters of the corresponding potential danger coal conveyor belt based on the potential danger severity corresponding to each potential danger coal conveyor belt number.

[0060] In this embodiment, the monitoring position refers to the coal conveyor belt number corresponding to the detection area and the position of the monitoring point on the coal conveyor belt.

[0061] In this embodiment, the operating parameters of the coal conveyor belt include the belt running speed and the coal falling speed.

[0062] The beneficial effects of the above technical solution are:

[0063] The corresponding coal blockage identification method is selected according to the scene type of each detection area, and the corresponding identification method is formulated according to the coal blockage characteristics of each scene type, thereby improving the recognition accuracy of coal blockage situations in various scene types. When a coal blockage is identified, the operating parameters of the coal conveyor belt are automatically adjusted to automatically eliminate the coal blockage or slow down the deterioration of the coal blockage, providing sufficient reaction time for the staff to eliminate the coal blockage.

[0064] Example 2

[0065] The embodiment of the present invention further provides a coal conveyor belt coal blocking image recognition control system, which is applied to a coal conveyor belt coal blocking image recognition method as described in Example 1, comprising:

[0066] The area configuration module is used to divide the working area of ​​the coal conveyor belt into several monitoring areas and generate monitoring parameters for each monitoring area;

[0067] An image acquisition module, used to acquire a number of monitoring images of each monitoring area as analysis images;

[0068] A hidden danger identification module is used to evaluate the hidden danger situation of each monitoring area based on the analysis image of each monitoring area;

[0069] A comprehensive assessment module is used to determine the severity of hidden dangers on each coal conveyor belt according to the hidden danger conditions in the hidden danger monitoring area on each coal conveyor belt;

[0070] The parameter adjustment module is used to adjust the operating parameters of the coal conveyor belt according to the severity of the hidden dangers of each coal conveyor belt.

[0071] Preferably, the image acquisition module includes:

[0072] An image capture unit, used to capture a number of analysis images at the same time interval in the surveillance video;

[0073] The image preprocessing unit is used to preprocess the analysis image.

[0074] Preferably, the hidden danger identification module includes:

[0075] An algorithm selection unit, used to select a recognition method to be called according to the scene type parameters of the monitoring area;

[0076] A first hidden danger analysis unit, which performs hidden danger analysis of a monitoring area belonging to the hopper scene based on a first recognition algorithm;

[0077] The second hidden danger analysis unit performs hidden danger analysis on the monitoring area belonging to the entrance and exit scene based on the second recognition algorithm.

[0078] In this embodiment, the preprocessing method of analyzing the image includes: adjusting contrast, brightness, and removing noise.

[0079] The beneficial effects of the above technical solution are:

[0080] By setting up several monitoring areas on each coal conveyor belt, the key working nodes of the coal conveyor belt are monitored, and then the severity of the overall hidden dangers of the coal conveyor belt is evaluated through the working conditions of each working node on the coal conveyor belt, so as to ensure the accuracy of automatic adjustment of the coal conveyor belt operating parameters, and improve the effect of adjusting the coal conveyor belt operating parameters on alleviating coal blockage on the coal conveyor belt. By formulating corresponding recognition methods for the coal blockage characteristics of different scenario types, the recognition accuracy of coal blockage in each scenario type is improved, thereby improving the recognition accuracy of the overall coal blockage of the coal conveyor belt.

[0081] Example 3

[0082] Based on Example 2, the first hidden danger analysis unit includes:

[0083] A feature extraction subunit is used to extract abnormal feature patterns in each analysis image and obtain the characteristic properties of each abnormal feature pattern;

[0084] A first calculation subunit, used to calculate a first hidden danger assessment parameter of a monitoring area belonging to a hopper scene;

[0085]

[0086] Among them, P i is the first hidden danger assessment parameter of the i-th monitoring area belonging to the hopper scene; α and β are weighted calculation coefficients of the actual number of abnormal feature graphics and the change trend of the number of abnormal feature graphics, respectively; M i is the total number of analyzed images of the monitoring area belonging to the hopper scene; G ij A is the total number of abnormal feature graphics in the jth analysis image of the i-th monitoring area belonging to the hopper scene; ijk is the area of ​​the kth abnormal feature graph in the jth analysis image of the i-th monitoring area belonging to the hopper scene; A0 is the minimum discrimination area of ​​the abnormal feature graph; G b G is the preset maximum allowable amount of abnormal characteristic patterns; i(j-1) A is the total number of abnormal feature graphics in the j-1th analysis image of the i-th monitoring area belonging to the hopper scene; i(j-1)k is the area of ​​the kth abnormal feature pattern in the j-1th analysis image of the i-th monitoring area belonging to the hopper scene;

[0087] The first result judgment subunit determines that there is a coal blocking hazard in the monitored area when the first hidden danger assessment parameter is greater than a preset first hidden danger threshold, otherwise it determines that there is no coal blocking hazard in the monitored area.

[0088] In this embodiment, the shape of the characteristic graphic is judged based on color and texture. In the analysis image, a graphic whose area is greater than a preset judgment value is an abnormal characteristic graphic, such as a large piece of material, which has a specific material color, material texture and an area greater than a preset judgment value. The characteristic properties of the characteristic image include: shape, size, color, and texture of the target.

[0089] In this embodiment, the minimum discrimination area of ​​the abnormal characteristic pattern refers to the area judgment value used to judge whether the characteristic pattern belongs to the abnormal characteristic pattern.

[0090] In this embodiment, the maximum allowable number of abnormal characteristic patterns refers to the maximum allowable number of abnormal characteristic patterns that will not cause coal blockage on the coal conveyor belt when the area of ​​each abnormal pattern is the minimum discrimination area.

[0091] The beneficial effects of the above technical solution are:

[0092] When performing hidden danger analysis on the monitoring area belonging to the hopper scene, the probability of blockage in the hopper area is judged according to the number of abnormal feature graphics by extracting and analyzing the abnormal feature graphics in the image, and each abnormal feature graphic is replaced with an equivalent number according to the area of ​​the abnormal feature image, thereby improving the calculation accuracy of the first hidden danger assessment parameter, and then by comprehensively considering the current number of abnormal feature graphics and the changing trend of the number of abnormal feature graphics, the first hidden danger assessment parameter can not only reflect the current coal blockage situation, but also realize the prediction of the trend of coal blockage, so as to complete the adjustment of the operating parameters of the coal conveyor belt in advance, thereby reducing the occurrence of coal blockage.

[0093] Example 4

[0094] Based on any one of Embodiments 2-3, the second hidden danger analysis unit includes:

[0095] An image partitioning subunit, used for dividing the analysis image into a coal-containing area and a coal-free area;

[0096] A second calculation subunit, used to calculate a second hidden danger assessment parameter of a monitoring area belonging to an entrance and exit scene;

[0097] Among them, P b is the second hidden danger assessment parameter of the bth monitoring area belonging to the entrance and exit scene; γ and δ are the weighted calculation coefficients of the actual proportion of coal-burning area and the change rate of the proportion of coal-burning area, respectively; N b is the total number of analyzed images of the bth monitoring area belonging to the entrance and exit scene; A mby A is the area of ​​the coal-containing region in the yth analysis image of the bth monitoring area belonging to the entrance and exit scene; wby A is the total area of ​​the yth analysis image of the bth monitoring area belonging to the entrance and exit scene; mb(y-1) A is the area of ​​the coal-containing region in the y-1th analysis image of the bth monitoring area belonging to the entrance and exit scene; wb(y-1) is the total area of ​​the y-1th analysis image of the bth monitoring area belonging to the entrance and exit scene;

[0098] The second result judgment subunit determines that there is a coal blocking hazard in the monitoring area when the second hidden danger assessment parameter of the monitoring area is greater than a preset second hidden danger threshold, otherwise it determines that there is no coal blocking hazard in the monitoring area.

[0099] The beneficial effects of the above technical solution are:

[0100] By considering the current actual area ratio of the coal-containing area in the monitoring area of ​​the entrance and exit scenes and the development trend of the area ratio of the coal-containing area, we can comprehensively consider the current coal blockage situation and predict the impact of the coal blockage trend, thereby discovering the tendency of coal blockage in advance. By adjusting the working parameters of the coal conveyor belt before coal blockage actually occurs, we can slow down or avoid the occurrence of coal blockage and ensure the normal operation of the coal conveyor belt.

[0101] Example 5

[0102] Based on any one of Embodiments 2-4, the comprehensive evaluation module includes:

[0103] Belt hidden danger assessment unit, used to assess the severity of hidden dangers of each coal conveyor belt;

[0104] Among them, T x F is the severity of hidden dangers of the xth coal conveyor belt; x is the total number of monitoring areas on the x-th coal conveyor belt; max is the maximum value function; P xv is the first hidden danger assessment parameter or the second hidden danger assessment parameter of the vth monitoring area on the xth coal conveyor belt; is the correlation coefficient between the vth monitoring area on the xth coal conveyor belt and the monitoring area with the most serious coal blockage on the xth coal conveyor belt; R0 is the preset maximum allowable fluctuation deviation of the hidden danger level; || is the absolute value symbol; is the rounding symbol;

[0105] Anomaly assessment unit, used to assess the hidden dangers and abnormalities in each monitoring area;

[0106] Among them, Q xy is the abnormal situation evaluation parameter of the vth monitoring area on the xth coal conveyor belt; || is the absolute value symbol; P xv is the first hidden danger assessment parameter or the second hidden danger assessment parameter of the vth monitoring area on the xth coal conveyor belt; is the correlation coefficient between the vth monitoring area on the xth coal conveyor belt and the monitoring area with the most serious coal blockage on the xth coal conveyor belt; R0 is the maximum allowable fluctuation deviation of the preset hidden danger level;

[0107] When Q xy If it is greater than or equal to 0, it is considered that there is no abnormality in the vth monitoring area on the xth coal conveyor belt; otherwise, it is considered that there is an abnormality in the vth monitoring area on the xth coal conveyor belt and an alarm is issued.

[0108] Preferably, the parameter adjustment module includes:

[0109] An alarm unit, when the severity of the hidden danger of the coal conveyor belt is greater than or equal to a preset fault threshold, the coal conveyor belt is shut down and an alarm is sounded;

[0110] The automatic adjustment unit adjusts the operating parameters of the coal conveyor belt when the severity of the hidden danger of the coal conveyor belt is less than a preset fault threshold.

[0111] In this embodiment, the monitoring area with the most serious coal blockage on the x-th coal conveyor belt is the monitoring area corresponding to the maximum value of the first hidden danger assessment parameter or the second hidden danger assessment parameter among all the monitoring areas on the x-th coal conveyor belt.

[0112] In this embodiment, the correlation coefficient between the vth monitoring area on the xth coal conveyor belt and the monitoring area with the most serious coal blockage on the xth coal conveyor belt is: when a coal blockage fault occurs at a certain location on the coal conveyor belt, due to the correlation between the upstream and downstream of the coal conveyor belt, it will cause coal blockages of different degrees at other locations. The correlation coefficient is a calculation coefficient for calculating the coal blockage degree of the affected location based on the coal blockage degree of the original coal blockage location. For example, a coal blockage with a severity of 0.5 occurs at location A. Under its influence, a coal blockage with a severity of 0.3 should occur at location B. Then the correlation coefficient between location A and location B is 0.6. The correlation coefficient is obtained by consulting the coal blockage association table, which is obtained by summarizing the historical data of the coal conveyor belt.

[0113] In this embodiment, the maximum allowable fluctuation deviation of the hidden danger degree refers to the influence of coal blockage at other positions on the coal conveyor belt, resulting in a certain degree of coal blockage. When the deviation between the actual degree of coal blockage at this position and the estimated degree of coal blockage is less than the maximum allowable fluctuation deviation, it is considered that the coal blockage at this position is only caused by the coal blockage at other positions, and no other abnormal conditions have occurred at this position.

[0114] The beneficial effects of the above technical solution are:

[0115] The severity of hidden dangers in the monitoring area with the most serious coal blockage on the coal conveyor belt is taken as the severity of hidden dangers of the entire coal conveyor belt (the other coal blockage locations may be coal blockages caused by the impact of coal blockage at this location), and then by considering the number of monitoring areas with abnormalities, the severity of hidden dangers of the entire coal conveyor belt is obtained (the more abnormal points, the more primary coal blockages there are on the coal conveyor belt), thereby improving the accuracy of the assessment of the severity of hidden dangers of the entire coal conveyor belt. When the severity of hidden dangers of the coal conveyor belt is greater than or equal to the preset fault threshold, it proves that there are multiple faults on the coal conveyor belt and the coal blockage cannot be suppressed by adjusting the operating parameters of the coal conveyor belt. At this time, the machine should be shut down in time and repaired at the early stage of coal blockage to avoid further deterioration of the coal blockage, thereby reducing the damage to the coal conveyor belt and increasing the service life of the coal conveyor belt.

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for identifying coal-blocking images of a coal conveyor belt, characterized in that: include: Step 1: Divide the working area of ​​the coal conveyor belt into several detection areas, and obtain the monitoring image of each monitoring area; Step 2: Preset monitoring parameters for each detection area, the monitoring parameters include monitoring location and scene type, and call the corresponding recognition method based on the scene type to determine the recognition result of each detection area, the scene type parameters include hopper scene and entrance and exit scene; Step 3: Based on the recognition results of each detection area, the operating parameters of the coal conveyor belt are automatically adjusted and an alarm is issued.

2. A method for recognizing coal blocking images of a coal conveyor belt according to claim 1, characterized in that: Step 3 includes: Step 31: Obtain the identification result of each detection area, screen out the detection area with coal blocking potential as the potential danger detection area, and obtain the coal conveyor belt number corresponding to each potential danger detection area as the potential danger coal conveyor belt number; Step 32: based on the identification results of the hidden danger monitoring areas on each hidden danger coal conveyor belt, determine the hidden danger severity corresponding to each hidden danger coal conveyor belt number; Step 33: Adjust the operating parameters of the corresponding potential danger coal conveyor belt based on the potential danger severity corresponding to each potential danger coal conveyor belt number.

3. A coal conveyor belt blockage image recognition control system, applied to a coal conveyor belt blockage image recognition method as claimed in any one of claims 1-2, characterized in that: include: The area configuration module is used to divide the working area of ​​the coal conveyor belt into several monitoring areas and generate monitoring parameters for each monitoring area; An image acquisition module, used to acquire a number of monitoring images of each monitoring area as analysis images; A hidden danger identification module is used to evaluate the hidden danger situation of each monitoring area based on the analysis image of each monitoring area; A comprehensive assessment module is used to determine the severity of hidden dangers on each coal conveyor belt according to the hidden danger conditions in the hidden danger monitoring area on each coal conveyor belt; The parameter adjustment module is used to adjust the operating parameters of the coal conveyor belt according to the severity of the hidden dangers of each coal conveyor belt.

4. A coal conveyor belt blockage image recognition control system according to claim 3, characterized in that: The image acquisition module includes: An image capture unit, used to capture a number of analysis images at the same time interval in the surveillance video; The image preprocessing unit is used to preprocess the analysis image.

5. A coal conveyor belt blockage image recognition control system according to claim 4, characterized in that: The hidden danger identification module includes: An algorithm selection unit, used to select a recognition method to be called according to the scene type parameters of the monitoring area; A first hidden danger analysis unit, which performs hidden danger analysis of a monitoring area belonging to the hopper scene based on a first recognition algorithm; The second hidden danger analysis unit performs hidden danger analysis on the monitoring area belonging to the entrance and exit scene based on the second recognition algorithm.

6. A coal conveyor belt blockage image recognition control system according to claim 5, characterized in that: The first hidden danger analysis unit includes: A feature extraction subunit is used to extract abnormal feature patterns in each analysis image and obtain the characteristic properties of each abnormal feature pattern; A first calculation subunit, used to calculate a first hidden danger assessment parameter of a monitoring area belonging to a hopper scene; Among them, P i is the first hidden danger assessment parameter of the i-th monitoring area belonging to the hopper scene; α and β are the weighted calculation coefficients of the actual number of abnormal feature graphics and the change trend of the number of abnormal feature graphics, respectively; M i is the total number of analyzed images of the i-th monitoring area belonging to the hopper scene; G ij A is the total number of abnormal feature graphics in the jth analysis image of the i-th monitoring area belonging to the hopper scene; ijk is the area of ​​the kth abnormal feature graph in the jth analysis image of the i-th monitoring area belonging to the hopper scene; A0 is the minimum discrimination area of ​​the abnormal feature graph; G b G is the preset maximum allowable amount of abnormal characteristic patterns; i(j-1) A is the total number of abnormal feature graphics in the j-1th analysis image of the i-th monitoring area belonging to the hopper scene; i(j-1)k is the area of ​​the kth abnormal feature pattern in the j-1th analysis image of the i-th monitoring area belonging to the hopper scene; The first result judgment subunit determines that there is a coal blocking hazard in the monitored area when the first hidden danger assessment parameter is greater than a preset first hidden danger threshold, otherwise it determines that there is no coal blocking hazard in the monitored area.

7. A coal conveyor belt blockage image recognition control system according to claim 6, characterized in that: The second hidden danger analysis unit includes: An image partitioning subunit, used for dividing the analysis image into a coal-containing area and a coal-free area; A second calculation subunit, used to calculate a second hidden danger assessment parameter of a monitoring area belonging to an entrance and exit scene; Among them, P b is the second hidden danger assessment parameter of the bth monitoring area belonging to the entrance and exit scene; γ and 6 are the weighted calculation coefficients of the actual proportion of coal-burning areas and the change rate of the proportion of coal-burning areas respectively; N b is the total number of analyzed images of the bth monitoring area belonging to the entrance and exit scene; A mby A is the area of ​​the coal-containing region in the yth analysis image of the bth monitoring area belonging to the entrance and exit scene; wby A is the total area of ​​the yth analysis image of the bth monitoring area belonging to the entrance and exit scene; mb(y-1) A is the area of ​​the coal-containing region in the y-1th analysis image of the bth monitoring area belonging to the entrance and exit scene; wb(y-1) is the total area of ​​the y-1th analysis image of the bth monitoring area belonging to the entrance and exit scene; The second result judgment subunit determines that there is a coal blocking hazard in the monitoring area when the second hidden danger assessment parameter of the monitoring area is greater than a preset second hidden danger threshold, otherwise it determines that there is no coal blocking hazard in the monitoring area.

8. A coal conveyor belt blockage image recognition control system according to claim 7, characterized in that: The comprehensive assessment modules include: Belt hidden danger assessment unit, used to assess the severity of hidden dangers of each coal conveyor belt; Among them, T x F is the severity of hidden dangers of the xth coal conveyor belt; x is the total number of monitoring areas on the x-th coal conveyor belt; max is the maximum value function; P xv is the first hidden danger assessment parameter or the second hidden danger assessment parameter of the vth monitoring area on the xth coal conveyor belt; is the correlation coefficient between the vth monitoring area on the xth coal conveyor belt and the monitoring area with the most serious coal blockage on the xth coal conveyor belt; R0 is the preset maximum allowable fluctuation deviation of the hidden danger level; | | is the absolute value symbol; is the rounding symbol; Anomaly assessment unit, used to assess the hidden dangers and abnormalities in each monitoring area; Among them, Q xy is the abnormal situation evaluation parameter of the vth monitoring area on the xth coal conveyor belt; | | is the absolute value symbol; P xv is the first hidden danger assessment parameter or the second hidden danger assessment parameter of the vth monitoring area on the xth coal conveyor belt; is the correlation coefficient between the vth monitoring area on the xth coal conveyor belt and the monitoring area with the most serious coal blockage on the xth coal conveyor belt; R0 is the maximum allowable fluctuation deviation of the preset hidden danger level; When Q xy If it is greater than or equal to 0, it is considered that there is no abnormality in the vth monitoring area on the xth coal conveyor belt; otherwise, it is considered that there is an abnormality in the vth monitoring area on the xth coal conveyor belt and an alarm is issued.

9. The coal conveyor belt blockage image recognition control system according to claim 4, characterized in that: The parameter adjustment module includes: An alarm unit, when the severity of the hidden danger of the coal conveyor belt is greater than or equal to a preset fault threshold, the coal conveyor belt is shut down and an alarm is sounded; The automatic adjustment unit adjusts the operating parameters of the coal conveyor belt when the severity of the hidden danger of the coal conveyor belt is less than a preset fault threshold.

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