Boiler steel belt type slag drying machine deviation monitoring method and system, medium and electronic equipment

Abstract

The invention relates to a deviation monitoring method and system for a boiler steel belt type slag drying machine, a medium and electronic equipment, and relates to the technical field of equipment monitoring, and the method comprises the steps that the lateral thrust moment of target ash on a steel belt is determined; based on the actual running speed, the lateral thrust moment, the historical speed interval and the historical time period, the influence coefficient of ash accumulation on steel belt deviation is determined, and a target deviation threshold value corresponding to the steel belt is determined according to the influence coefficient; the actual offset and the actual offset direction of the steel belt are obtained, when the actual offset exceeds a target offset threshold value, if the actual offset direction is consistent with the moment direction of the lateral thrust moment, first early warning information is sent to a terminal of a person, and if the actual offset direction is opposite to the moment direction of the lateral thrust moment, second early warning information is sent to the terminal of the person; and if so, sending second early warning information to the terminal of the person. The method and the device have the effect of accurately carrying out steel belt deviation early warning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of equipment monitoring, in particular to a boiler steel belt type dry slag machine offset monitoring method, system, medium and electronic equipment. BACKGROUND

[0002] The boiler steel belt type dry slag machine is the core equipment of the boiler bottom slag treatment system, mainly used for receiving, cooling and conveying high-temperature bottom slag discharged from the bottom of the boiler furnace, and is a key device for realizing automatic and clean treatment of boiler ash and slag. In recent years, in order to protect the environment and effectively regenerate and utilize the ash and slag, the wet-to-dry conveying mode is being carried out in the power plant slag discharge system, and the boiler steel belt type dry slag machine is widely used in coal-fired power plants, industrial boilers and other scenes. With the change of the coal quality burned by the boiler and the change of the frequency and depth of the boiler load, the change range and frequency of the boiler slag discharge are increased, and the steel belt offset problem of the boiler steel belt type dry slag machine is increasingly prominent due to the impact of the ash and slag impact load, the non-uniform load of the conveyed ash and slag and the installation adjustment error. The steel belt offset is a common fault or working condition phenomenon in the running process of the boiler steel belt type dry slag machine, that is, the force balance in the width direction of the steel belt is broken, which causes the displacement of the steel belt to one side or both sides, thereby causing three major problems of equipment component damage, system operation efficiency reduction and safety risk increase. Therefore, a steel belt offset monitoring and early warning method is urgently needed.

[0003] At present, the steel belt offset monitoring and early warning is usually carried out by measuring the real-time offset of the steel belt in the dry slag machine and setting a fixed offset threshold. Once the real-time offset reaches or exceeds the fixed offset threshold, the steel belt offset early warning is triggered. However, in the actual running process of the steel belt, various complex scenes will be encountered, and the generation of the steel belt offset is also different. Therefore, the fixed threshold method cannot accurately perform steel belt offset early warning. SUMMARY

[0004] In order to more accurately perform steel belt offset early warning, the present application provides a boiler steel belt type dry slag machine offset monitoring method, system, medium and electronic equipment.

[0005] In a first aspect of the present application, a boiler steel belt type dry slag machine offset monitoring method is provided, which specifically comprises: obtaining the actual gravity center position, the actual falling speed and the actual running speed of the steel belt of the target ash and slag falling on the surface of the steel belt, the actual falling speed being the instantaneous speed of the target ash and slag falling on the surface of the steel belt; determining the side thrust torque of the target ash and slag on the steel belt according to the actual gravity center position and the actual falling speed; The system acquires multiple historical speed ranges in which the steel strip accumulates ash due to its operating speed, and multiple historical time periods in which the steel strip exhibits abnormal deviation due to ash accumulation within the historical speed ranges. Based on the actual operating speed, the lateral thrust torque, the historical speed range, and the historical time period, the influence coefficient of ash accumulation on the steel strip offset is determined, and the target offset threshold corresponding to the steel strip is determined according to the influence coefficient. The actual offset and actual offset direction of the steel strip are obtained by a laser ranging sensor. When the actual offset exceeds the target offset threshold, if the actual offset direction is consistent with the torque direction of the lateral thrust torque, a first warning message is sent to the personnel's terminal. If the actual offset direction is opposite to the torque direction of the lateral thrust torque, a second warning message is sent to the personnel's terminal. The first warning message is used to warn of offset of working condition type, and the second warning message is used to warn of offset of fault type.

[0006] By employing the aforementioned technical solution, based on the obtained actual center of gravity position and actual falling speed, the lateral thrust torque generated on the steel strip surface when the target ash falls onto it is analyzed and determined. This provides a more intuitive reflection of the lateral impact of the ash on the steel strip or the situation of slag discharge eccentricity. Furthermore, the lateral thrust torque is also a factor influencing steel strip deflection; the larger the lateral thrust torque, the greater the lateral impact of the ash on the steel strip, and the greater the possibility of steel strip deflection. Further, by combining historical speed ranges and historical time periods, the probability of abnormal steel strip deflection due to ash accumulation at the current actual operating speed is analyzed. Combined with the influence of the lateral thrust torque, the influence coefficient of ash accumulation on steel strip deflection is comprehensively determined. The larger the influence coefficient, the greater the risk of abnormal steel strip deflection due to ash accumulation. Then, based on the influence coefficient, the current target deflection threshold of the steel strip is determined more accurately. Furthermore, if the actual offset exceeds the target offset threshold, it indicates a high risk of an abnormal offset, requiring an offset warning. If the actual offset direction is consistent with the torque direction, it indicates that the offset is caused by falling ash and belongs to the working condition type of offset, so the first warning message is sent. If the actual offset direction is opposite to the torque direction, it indicates that the offset is not caused by falling ash and is most likely caused by equipment failure of the dry slag machine, so the second warning message is issued, thus providing a more accurate warning for steel belt offset.

[0007] In one embodiment, the method further includes: When the lateral thrust torque is zero, the thermal imaging video stream of the ash conveyed on the steel belt during the unloading process is obtained by the infrared thermal imager preset on the unloading hopper of the dry slag machine; Based on the thermal imaging video stream, the two-dimensional distribution profile of the ash and slag conveyed on the steel belt during the unloading process is determined; Determine the geometric center point of the two-dimensional distribution contour, and determine the offset pixel amount between the geometric center point and the preset reference unloading center line; Based on the preset calibration parameters of the infrared thermal imager, the offset pixel amount is converted into a physical space offset. Calculate the absolute value of the difference between the physical space offset and the actual offset. If the absolute value is less than a preset absolute value threshold, then the actual offset is verified to be correct.

[0008] In one embodiment, determining the influence coefficient of ash accumulation on the steel strip offset based on the actual operating speed, the lateral thrust torque, the historical speed range, and the historical time period specifically includes: From the plurality of historical velocity ranges, at least one target velocity range is determined, wherein the target velocity range is a historical velocity range that is prone to causing ash and slag accumulation; Based on multiple historical periods in which the steel strip experienced abnormal displacement due to ash accumulation within the target speed range, at least one target period corresponding to the target speed range is determined. The target period is a historical period in which abnormal displacement is easily caused by ash accumulation. Determine the interval weight of the target speed interval, and determine the time period weight of each target time period; The influence coefficient of ash accumulation on the steel strip offset is determined based on the time period weight, the interval weight, the lateral thrust torque, and the actual operating speed.

[0009] In one embodiment, determining the influence coefficient of ash accumulation on the steel strip offset based on the time period weight, the interval weight, the lateral thrust torque, and the actual operating speed specifically includes: Determine whether the actual operating speed is within the target speed range. If the actual operating speed is within the target speed range, then determine the target speed range as a reference speed range, and determine whether the target time period corresponding to the reference speed range includes the current time. If the target time period corresponding to the reference speed range includes the current time, then the corresponding target time period is determined as the reference time period; The interval weight of the reference speed interval is multiplied by the time period weight of the corresponding reference time period to obtain the weight product. Based on the lateral thrust torque and the weight product, the initial influence coefficient of ash accumulation on the steel strip offset is determined. The initial influence coefficient is corrected to obtain the influence coefficient of ash accumulation on the steel strip offset.

[0010] In one embodiment, correcting the initial influence coefficient to obtain the influence coefficient of ash accumulation on the steel strip offset specifically includes: The first number of historical high-load periods included in the reference period is counted, and the number of historical high-load periods included in all the target periods is summed to obtain the first summation result. The historical high-load period is the period in which the ratio of boiler load to rated load exceeds a preset ratio threshold. Calculate the first ratio between the first number and the first summation result; The second number of historical high-load periods that include the reference period is counted, and the number of historical load periods that include each target period is summed to obtain the second summation result; Calculate the second ratio of the second number to the second summation result, perform a weighted summation of the first ratio and the second ratio to obtain the final ratio, and determine the first correction coefficient based on the final ratio, wherein the first correction coefficient is a positive number not less than 1; Multiplying the first correction coefficient by the initial influence coefficient yields the influence coefficient of ash accumulation on the steel strip offset.

[0011] In one embodiment, the step of correcting the initial influence coefficient to obtain the influence coefficient of ash accumulation on the steel strip offset further includes: Multiply the interval weight of the target speed interval by the time period weight of each target time period to obtain multiple multiplication results corresponding to the target speed interval; The multiplication results corresponding to all the target speed intervals are summed, and the multiplication results corresponding to at least one key time period are summed to obtain a comprehensive result. The key time period is the target time period that includes the current time. Based on the comprehensive results, a second correction coefficient is determined, wherein the second correction coefficient is a positive number not less than 1; Multiplying the second correction coefficient by the initial influence coefficient yields the influence coefficient of ash accumulation on the steel strip offset.

[0012] In one embodiment, the method further includes: When the actual offset exceeds the target offset threshold, if the target time period corresponding to the target speed range includes the current time, then the target speed range is determined as the key speed range, and the target time period including the current time is determined as the key time period. Multiply the interval weight of at least one of the key speed intervals with the time period weight of the corresponding key time period to obtain the product result; If the influence coefficient exceeds a preset coefficient threshold, the minimum product result is selected from all the product results, and the key speed range corresponding to the minimum product result is determined as the appropriate speed range. The actual operating speed is adjusted to the maximum value within the suitable speed range.

[0013] A second aspect of this application provides a boiler steel belt dry slag machine offset monitoring system, specifically including: The information acquisition module is used to acquire the actual center of gravity position, actual falling speed, and actual running speed of the target ash falling on the surface of the steel strip. The actual falling speed is the instantaneous speed of the target ash falling on the surface of the steel strip. The torque determination module is used to determine the lateral thrust torque of the target ash on the steel strip based on the actual center of gravity position and the actual falling speed. The data acquisition module is used to acquire multiple historical speed intervals in which the steel strip accumulates ash due to its operating speed, and multiple historical time periods in which the steel strip deviates abnormally due to ash accumulation within the historical speed intervals. The threshold determination module is used to determine the influence coefficient of ash accumulation on the steel strip offset based on the actual operating speed, the lateral thrust torque, the historical speed range and the historical time period, and to determine the target offset threshold of the steel strip according to the influence coefficient. The offset warning module is used to obtain the actual offset and actual offset direction of the steel strip through a laser rangefinder. When the actual offset exceeds the target offset threshold, if the actual offset direction is consistent with the torque direction of the lateral thrust torque, a first warning message is sent to the personnel's terminal. If the actual offset direction is opposite to the torque direction of the lateral thrust torque, a second warning message is sent to the personnel's terminal. The first warning message is used to warn of offset of working condition type, and the second warning message is used to warn of offset of fault type.

[0014] By adopting the above technical solution, the information acquisition module acquires the actual center of gravity position, actual falling speed, and actual running speed. The torque determination module determines the lateral thrust torque of the target ash on the steel strip based on the actual center of gravity position and actual falling speed. Then, the data acquisition module acquires the historical speed range and historical time period. The threshold determination module determines the influence coefficient of ash accumulation on the steel strip offset and determines the target offset threshold corresponding to the steel strip based on the influence coefficient. Finally, the offset warning module sends a first warning message to the personnel's terminal when the actual offset direction is consistent with the torque direction of the lateral thrust torque, and sends a second warning message to the personnel's terminal when the actual offset direction is opposite to the torque direction of the lateral thrust torque.

[0015] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when loaded and executed by a processor, performs the steps of the method described in any one of the first aspects.

[0016] A fourth aspect of this application provides an electronic device, specifically comprising: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, the processor being configured to load and execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of the first aspects.

[0017] In summary, this application includes at least one of the following beneficial technical effects: Based on the obtained actual center of gravity position and actual falling speed, the lateral thrust torque generated on the steel strip surface when the target ash falls onto the steel strip surface is analyzed and determined. This provides a more intuitive reflection of the lateral impact of the ash on the steel strip or the situation of slag discharge eccentricity. Furthermore, the lateral thrust torque is also a factor influencing steel strip deflection; the larger the lateral thrust torque, the greater the lateral impact of the ash on the steel strip, and the greater the possibility of steel strip deflection. Further, by combining historical speed ranges and historical time periods, the probability of abnormal steel strip deflection due to ash accumulation at the current actual operating speed is analyzed. Combined with the influence of the lateral thrust torque, the influence coefficient of ash accumulation on steel strip deflection is comprehensively determined. The larger the influence coefficient, the greater the risk of abnormal steel strip deflection due to ash accumulation. Then, based on the influence coefficient, the current target deflection threshold of the steel strip is determined more accurately. Furthermore, if the actual offset exceeds the target offset threshold, it indicates a high risk of an abnormal offset, requiring an offset warning. If the actual offset direction is consistent with the torque direction, it indicates that the offset is caused by falling ash and belongs to the working condition type of offset, so the first warning message is sent. If the actual offset direction is opposite to the torque direction, it indicates that the offset is not caused by falling ash and is most likely caused by equipment failure of the dry slag machine, so the second warning message is issued, thus providing a more accurate warning for steel belt offset. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for monitoring the offset of a boiler steel belt dry slag machine according to an embodiment of this application; Figure 2 This is a scenario architecture diagram of a boiler steel belt dry slag machine offset monitoring method provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the relationship between a target speed range and a target time period, provided in an embodiment of this application. Figure 4 This is a schematic diagram of the structure of a boiler steel belt dry slag machine offset monitoring system provided in an embodiment of this application; Figure 5 This is a schematic diagram of another boiler steel belt dry slag machine offset monitoring system provided in the embodiments of this application.

[0019] Explanation of reference numerals in the attached diagram: 11. Information acquisition module; 12. Torque determination module; 13. Data acquisition module; 14. Threshold determination module; 15. Offset warning module; 16. Offset verification module; 17. Speed ​​adjustment module. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0021] In the description of the embodiments of this application, words such as "exemplarily," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplarily," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily," "for example," or "for instance" is intended to present the relevant concepts in a specific manner.

[0022] In the description of the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, B existing alone, or A and B existing simultaneously. Furthermore, unless otherwise stated, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized.

[0023] See Figure 1 This application discloses a flowchart illustrating a method for monitoring the offset of a boiler steel belt dry slag machine, which can be implemented using a computer program or run on a boiler steel belt dry slag machine offset monitoring system based on the von Neumann architecture. This computer program can be integrated into an application or run as a standalone utility application, specifically including: S101: Obtain the actual center of gravity, actual falling speed, and actual running speed of the target ash falling on the surface of the steel belt.

[0024] Specifically, in this embodiment, the steel belt is a component in the boiler steel belt dry ash machine used to transport ash (high-temperature bottom ash) generated by boiler combustion. The boiler steel belt dry ash machine is the core equipment of the boiler bottom ash treatment system, mainly used to receive, cool, and transport the high-temperature bottom ash discharged from the bottom of the boiler furnace. It is a key device for realizing automated and clean treatment of boiler ash and ash, and is widely used in coal-fired power plants, industrial boilers, and other scenarios. The target ash is the ash currently falling from the cold ash hopper of the boiler furnace onto the surface of the steel belt. The actual falling speed is the instantaneous speed of the target ash when it falls onto the surface of the steel belt. The actual running speed is the current running speed of the steel belt.

[0025] This application discloses a method for monitoring the offset of a boiler steel belt dry slag conveyor. The executing entity is a server, which is wirelessly connected to a user's terminal. The terminal can be a personal computer or tablet computer, and it has an offset monitoring client installed. The server is the backend server for the client, and can be a standalone physical server or a cluster of multiple physical servers. One implementation scenario is as follows: When it is necessary to monitor the offset of the steel belt conveyor for ash and slag to avoid problems caused by the offset (such as damage to equipment components or decreased operating efficiency), the user sends a monitoring start command to the server through the client on the terminal. Based on the start command, the server monitors the steel belt offset, obtains the offset amount in real time, and sends corresponding warning information to the user's terminal if an abnormal offset is detected. See [link to relevant documentation] for details. Figure 2 .

[0026] Furthermore, the actual operating speed of the steel strip can be obtained through a preset speed sensor; the actual falling speed of the target ash can be obtained through a preset microwave radar; additionally, a feasible method to obtain the actual center of gravity position of the target ash is as follows: real-time images of the target ash are captured by a preset high-speed camera, and then a threshold segmentation algorithm (such as the Otsu algorithm) is used to distinguish the ash from the steel strip and remove background noise. Next, a contour extraction algorithm (such as the Canny edge detection algorithm) is used to extract the contour of the target ash accumulation area, eliminating interference from individual fine particles (setting a minimum area threshold, such as ≥5cm²), thereby determining the ash region of the target ash. Finally, the geometric center of gravity is calculated using a pixel-weighted average, with the formula x. c =∑x i p i / ∑p i ;y c =∑y i p i / ∑p i , where x i y i These are the coordinates of the pixels within the ash / slag area, p i It is the grayscale weight of the pixel, x c and yc The actual center of gravity of the target ash is located, which is existing technology and will not be elaborated here.

[0027] S102: Determine the lateral thrust torque of the target ash on the steel strip based on the actual center of gravity position and the actual falling speed.

[0028] Specifically, the lateral thrust torque can be understood as a mechanical quantitative index of the impact force of falling ash on the steel strip in the lateral direction, reflecting the eccentric loading of the steel strip during slag discharge. Once the actual center of gravity and actual falling velocity of the target ash are determined, a feasible method for determining the lateral thrust torque is as follows: The weight of the target ash is obtained through a pre-set array of weight sensors. Then, the actual falling velocity is decomposed into vertical and lateral components. Using the momentum theorem, the lateral impact force F of the target ash impacting the steel strip is calculated based on the weight and lateral components of the target ash. 侧 Next, calculate the lateral coordinate of the actual center of gravity position, subtract the preset lateral coordinate of the steel strip centerline, and take the absolute value to obtain the lateral offset distance L (lever arm) of the target ash center of gravity. Finally, combine L and F 侧 Substituting these values ​​into the preset lateral moment calculation formula, the lateral thrust moment of the target ash on the steel strip is obtained. The lateral moment calculation formula is: M = F 侧 *L and M represent lateral moments. This is existing technology and will not be elaborated further here.

[0029] S103: Obtain multiple historical speed ranges where ash and slag accumulates due to the running speed of the steel strip, and multiple historical periods during which the steel strip deviates abnormally due to ash and slag accumulation within the historical speed ranges.

[0030] S104: Based on the actual operating speed, lateral thrust torque, historical speed range and historical time period, determine the influence coefficient of ash accumulation on steel strip offset, and determine the target offset threshold of the steel strip according to the influence coefficient.

[0031] Specifically, in this embodiment, based on the historical operating records of the steel strip cached in the database, multiple historical operating speeds at which ash and slag accumulation occurred due to the operating speed of the steel strip are obtained. Ash and slag accumulation due to operating speed can be understood as a mismatch between the operating speed of the steel strip and the rate at which ash and slag are produced by boiler combustion, resulting in ash and slag accumulation on the surface of the steel strip. The historical operating records include, but are not limited to, the operating speeds that caused ash and slag accumulation in the steel strip, different historical periods of abnormal deviation due to ash and slag accumulation, and the operating speed of the steel strip within those historical periods.

[0032] Based on a clustering algorithm, multiple historical operating speeds are clustered to obtain multiple historical speed intervals that cover all historical operating speeds. The clustering algorithm can be K-Means or hierarchical clustering. Then, the number of historical operating speeds contained in each historical speed interval is counted. If the number exceeds a preset threshold, the corresponding historical speed interval is determined as the target speed interval, i.e., the historical speed interval most likely to cause ash and slag accumulation. At least one target speed interval exists.

[0033] Furthermore, from multiple historical periods where deviation anomalies occurred due to ash accumulation, several historical periods with operating speeds within a single target speed range are selected. The frequency of occurrence of each individual historical period is statistically analyzed. If the frequency exceeds a preset frequency threshold, the historical period is designated as the target period corresponding to that target speed range, i.e., a historical period prone to deviation anomalies due to ash accumulation. At least one target period exists. Then, the interval weight of each target speed range is determined. The interval weight is the ratio of the number of occurrences corresponding to a single target speed range to the sum of the number of occurrences corresponding to all target speed ranges. The interval weight represents the likelihood of ash accumulation occurring within that target speed range. Next, the time period weight of each target time period corresponding to that target speed range is determined. The time period weight is the ratio of the frequency of occurrence of a single target time period to the sum of the frequencies of occurrence of all target time periods. The time period weight represents the likelihood of deviation anomalies due to ash accumulation within the corresponding target time period. For example, there are target speed intervals B, C, and D. Target speed interval B corresponds to 10 intervals, target speed interval C corresponds to 30 intervals, and target speed interval D corresponds to 60 intervals. Therefore, the interval weight of target speed interval B is: 10 / (10 + 30 + 60) = 0.1. Further, target speed interval B corresponds to target time periods B1, B2, and B3; target speed interval C corresponds to target time periods C1, C2, etc.; and target speed interval D corresponds to target time periods D1, D2, etc. Target time period B1 occurs 30 times, target time period B2 occurs 20 times, and target time period B3 occurs 50 times. Therefore, the time period weight of target time period B1 is: 30 / (30 + 20 + 50) = 0.3. See details in [link to relevant documentation]. Figure 3 .

[0034] Based on the interval weight of a single target speed interval, the time period weight of each target time period corresponding to the single target speed interval, the current actual operating speed of the steel strip, and the lateral thrust torque, the influence coefficient of ash accumulation on steel strip deflection is determined. The larger the influence coefficient, the greater the possibility of abnormal deflection of the steel strip due to ash accumulation. In this embodiment, a feasible method for determination is as follows: If the actual operating speed falls within a single target speed range, then that target speed range is designated as the reference speed range. Furthermore, if the target time period corresponding to the reference speed range includes the current time, then the target time period including the current time is designated as the reference time period. Next, the interval weight of this reference speed range is multiplied by the time period weight of the corresponding reference time period to obtain a weighted product. This weighted product represents the likelihood of the steel strip exhibiting abnormal deviation due to ash accumulation at the current actual operating speed. Since the lateral thrust torque reflects the magnitude of the impact of falling ash on the steel strip in the lateral direction, the larger the lateral thrust torque, the greater the exacerbation of the possibility of abnormal steel strip displacement due to ash accumulation. Under actual operating speed, the steel strip is more prone to abnormal displacement. Therefore, it is necessary to combine the lateral thrust torque and the weight product to determine the initial influence coefficient of ash accumulation on steel strip displacement. The specific process is as follows: determine the correction factor corresponding to the lateral thrust torque according to the preset mapping relationship set. The correction factor is not less than 1. The larger the lateral thrust torque, the larger the correction factor. The mapping relationship set includes the mapping relationship of different lateral thrust torques and their corresponding correction factors. Based on the regression analysis of historical data of lateral thrust torque and correction factor or expert experience, the mapping relationship can be a linear function, a piecewise function, or an exponential function.

[0035] Next, the correction factor is multiplied by the weight product to obtain the initial influence coefficient of ash accumulation on steel strip displacement, thus initially optimizing the possibility of abnormal steel strip displacement due to ash accumulation. Further, the initial influence coefficient is corrected a second time to obtain the influence coefficient of ash accumulation on steel strip displacement. One feasible implementation method is as follows: Based on the boiler's historical load records, multiple historical high-load periods are identified. These are periods when the ratio of boiler load to rated load exceeds a preset threshold. In other words, historical high-load periods are when the boiler is operating at high load. Boiler load refers to the amount of steam output by the boiler per unit time. The ratio threshold can be set to 0.8. Furthermore, when the boiler is under high load, a large amount of coal needs to be fed in, and the air volume needs to be increased. This leads to a rapid increase in combustion intensity and furnace temperature. Large areas of coking and ash buildup inside the furnace are prone to detachment, causing a large amount of ash and slag to fall onto the steel belt. This increases the likelihood of ash and slag accumulation and impact-induced uneven loading, leading to stress imbalance and a higher probability of abnormal belt shift.

[0036] Furthermore, the first number of historical high-load periods included within the reference period is counted. The higher the number of these first numbers, the greater the likelihood of a sudden load increase after the current time, and the greater the possibility of abnormal strip offset. The number of historical high-load periods included in each target period is then summed to obtain a first summation result. The first ratio of the first number to the first summation result is calculated, thus assessing the likelihood of the boiler's high load on strip offset from the perspective of historical high-load periods included in the reference period. Next, the second number of historical high-load periods including the reference period is counted, and the number of historical high-load periods including each target period is summed to obtain a second summation result. The second ratio of the second number to the second summation result is then calculated, thus assessing the likelihood of the boiler's high load on strip offset from the perspective of historical high-load periods including the reference period. Finally, the first and second ratios are weighted and summed to obtain a final ratio, where the first ratio is assigned a weight of 0.4 and the second ratio is assigned a weight of 0.6. The final ratio characterizes the overall likelihood of the blast furnace's high load on strip offset. Furthermore, from the first coefficient matching table cached in the database, the first correction coefficient corresponding to this final ratio is matched. The first correction coefficient is not less than 1, and the larger the final ratio, the larger the corresponding first correction coefficient. The first coefficient matching table includes different ratio ranges and their corresponding correction coefficients, all set based on subjective experience. Finally, the first correction coefficient is multiplied by the initial influence coefficient to obtain the influence coefficient.

[0037] In other embodiments, a feasible way to correct the initial influence coefficient is as follows: multiply the interval weight of a single target speed interval by the time period weight of each corresponding target time period to obtain multiple multiplication results corresponding to the target speed interval. The multiplication result characterizes the probability of an abnormal shift due to ash accumulation in the corresponding target time period when the steel strip running speed is within the target speed interval. Further, sum the multiplication results corresponding to at least one key time period among all the multiplication results corresponding to all target speed intervals to obtain a comprehensive result. The key time period is the target time period corresponding to each target speed interval that includes the current time. The comprehensive result characterizes the overall probability of an abnormal shift in the steel strip at the current time (assessing the probability of an abnormal shift from a time period perspective). Then, according to a preset second coefficient matching table, match the second correction coefficient corresponding to this comprehensive result. The larger the comprehensive result, the larger the second correction coefficient. The second correction coefficient is not less than 1. The second coefficient matching table includes different comprehensive result ranges and corresponding correction coefficients. The second coefficient matching table is determined based on regression analysis of historical data, aiming to map the comprehensive result to a second correction coefficient not less than 1. The mapping relationship can be a linear function or a piecewise function. For example, the second coefficient matching table includes a comprehensive result range of 0-0.2 with a corresponding correction coefficient of 1.1; a comprehensive result range of 0.2-0.4 with a corresponding correction coefficient of 1.2, and so on. If the comprehensive result is 0.3, then the second correction coefficient is 1.2. Finally, the second correction coefficient is multiplied by the initial influence coefficient to obtain the influence coefficient of ash accumulation on the steel strip offset.

[0038] Further, based on the influence coefficient, the target offset threshold corresponding to the steel strip is determined. The target offset threshold is the critical value for judging abnormal steel strip offset. One feasible method for determination is as follows: according to a preset threshold correction factor mapping table, the threshold correction factor corresponding to this influence coefficient is matched. The threshold correction factor is a positive number not greater than 1. The larger the influence coefficient, the smaller the corresponding threshold correction factor. The threshold correction factor mapping table includes the mapping relationship between different influence coefficient ranges and the corresponding threshold correction factors. It is determined based on regression analysis of historical data of influence coefficients and threshold correction factors. The mapping relationship can be a linear function or an exponential function. For example, the threshold correction factor mapping table includes: influence coefficient range 0-0.5, corresponding threshold correction factor is 0.9; influence coefficient range 0.5-1, corresponding threshold correction factor is 0.8. Finally, the threshold correction factor is multiplied by the preset offset threshold to obtain the target offset threshold. For example, if the preset offset threshold is 2mm and the threshold correction factor is 0.8, then the target offset threshold is 1.6mm; if the threshold correction factor is 0.9, then the target offset threshold is 1.8mm. It can be seen that the larger the influence coefficient, the greater the risk of offset anomaly, requiring earlier warning, and therefore the smaller the corresponding target offset threshold should be. The preset offset threshold is an initial critical value pre-set by the user to determine abnormal steel strip offset.

[0039] S105: The actual offset and actual offset direction of the steel strip are obtained by the laser rangefinder. When the actual offset exceeds the target offset threshold, if the actual offset direction is consistent with the torque direction of the lateral thrust torque, a first warning message is sent to the personnel's terminal. If the actual offset direction is opposite to the torque direction of the lateral thrust torque, a second warning message is sent to the personnel's terminal.

[0040] Specifically, the first warning information is used to warn of deviations in operating conditions, and the second warning information is used to warn of deviations in fault conditions. A laser rangefinder installed on one side of the steel strip determines the distance from the sensor probe to the current edge of the steel strip. This distance is subtracted from the distance from the sensor probe to the edge of the steel strip under normal conditions (when the steel strip is not deviated), and the absolute value of the difference is used to determine the actual deviation of the steel strip. Furthermore, if the difference is less than 0, it indicates that the actual deviation direction is closer to the side of the laser rangefinder. Furthermore, the actual offset is compared with the target offset threshold. If the actual offset exceeds the target offset threshold, it indicates that the current steel strip is likely to have an abnormal offset, requiring timely warning. Then, the actual offset direction is compared with the direction of the lateral thrust torque. If the two directions are consistent (both pointing to the same side of the steel strip centerline), it indicates that the steel strip offset is caused by fluctuations in the operating conditions during normal operation of the dry slag machine, i.e., an offset of the operating condition type. In this case, the first warning message is sent to the personnel terminal. Conversely, if the actual offset direction is opposite to the direction of the lateral thrust torque (pointing to different sides of the steel strip centerline), it indicates that the steel strip offset is caused by a malfunction of the dry slag machine equipment components, and not by fluctuations in operating conditions such as ash and slag falling. In this case, the second warning message is sent to the personnel terminal.

[0041] In one embodiment, if the actual offset exceeds zero and the lateral thrust torque is zero, it indicates to some extent that there is no off-center slag discharge and the ash is falling normally in the center. Then, an infrared thermal imager pre-installed on the discharge hopper of the dry slag machine acquires a thermal imaging video stream of the ash conveyed on the steel belt during the discharge process. The discharge hopper is an important component of the dry slag machine system, mainly used to guide the ash on the steel belt to subsequent conveying equipment or storage devices. Further, A temperature threshold T_th (e.g., 250°C) is set to distinguish high-temperature red slag from the background. For each frame of the thermal imaging video stream, all pixels with a temperature value higher than T_th are identified as candidate points for "high-temperature red slag," generating a binary image (slag area as 1, background as 0). Next, an opening operation (erosion followed by dilation) is performed on the binary image to eliminate isolated noise points caused by a few high-temperature floating ash particles and to fill small voids that may be caused by uneven temperature within the slag group, improving the connectivity of the slag group region. Then, an edge detection algorithm (such as the Canny algorithm) or a connected component analysis algorithm is used to extract all closed contours from the processed binary image. Based on engineering experience, an area threshold is set to remove contours with too small an area (e.g., less than 100 pixels), which may be small splashed slag particles, retaining the main slag group contours. Finally, the largest retained connected component or the union of all connected components is determined as the two-dimensional distribution contour of the ash slag during the unloading process.

[0042] Furthermore, the geometric center point of the two-dimensional distribution contour is determined using a polygon centroid algorithm. Then, the x-axis coordinate of the geometric center point is subtracted from the x-axis coordinate of the preset reference unloading center line (the x-axis coordinate of the reference unloading center line in the image coordinate system of the infrared thermal imager) to obtain the offset pixel amount. Then, based on the preset calibration parameters of the infrared thermal imager (such as focal length, object distance, image resolution, etc.), the offset pixel amount is converted into a physical space offset using OpenCV, thereby indirectly inferring the steel plate offset from the "deviation" during the ash unloading process. Finally, the absolute value of the difference between the physical space offset and the actual offset is calculated. If the absolute value is less than a preset absolute value threshold, it indicates that the steel strip offset inferred from the offset during the ash unloading process is close to the directly measured actual steel strip offset, indicating that the actual offset is accurate with a small error. When the actual offset exceeds the target offset threshold, the accuracy of the offset warning can be further verified, avoiding false alarms. It should be noted that the reference unloading centerline is a preset, static spatial reference benchmark. Essentially, it is a vertical line that is clearly defined in physical space and image coordinate system, representing the theoretical center of the unloading trajectory of the steel strip under ideal alignment.

[0043] In other embodiments, when the actual offset exceeds the target offset threshold, it indicates that the steel strip is highly likely to experience an abnormal offset. If the target time period corresponding to a single target speed interval includes the current time, then the target speed interval is determined as a key speed interval, and the target time period including the current time is determined as a key time period. The interval weight of at least one key speed interval is then multiplied by the time period weight of the corresponding key time period to obtain the product result corresponding to that key speed interval. The smaller the product result, the lower the probability of the current steel strip experiencing an abnormal offset due to ash accumulation within that key speed interval. Next, the influence coefficient is compared with a preset coefficient threshold. If the influence coefficient exceeds the coefficient threshold, it indicates a higher risk of the current steel strip experiencing an abnormal offset due to ash accumulation. This verifies that the target offset threshold corresponding to the steel strip is reasonable, making the early warning for steel strip offset anomalies more accurate. It also confirms that the steel strip offset anomaly is caused by a working condition problem (ash accumulation) rather than a malfunction of the dry slag machine itself. Furthermore, the minimum product result is selected from all product results, and the key speed range corresponding to the minimum product result is determined as the appropriate speed range. The actual running speed of the steel strip is then adjusted to the maximum value within the appropriate speed range, thereby reducing the possibility of ash accumulation and thus reducing the risk of deviation anomalies.

[0044] In one embodiment, at least one previous operating speed and its corresponding duration are obtained from the historical adjustment records of the steel strip speed. The historical adjustment records include, but are not limited to, the historical operating speed of the steel plate and its corresponding duration. The target speed interval in which a single previous operating speed is located is determined as an important speed interval. The product of the interval weight of a single important speed interval and the time period weight of the corresponding important time period is calculated and summed to obtain the product summation result. The product summation result represents the accumulated offset anomaly risk before the current time, where the important time period is the target time period containing the duration of a single previous operating speed. If the product summation result is greater than a preset summation result threshold, it indicates that the accumulated offset anomaly risk is large, and the probability of an offset anomaly occurring at present is high. In this case, the target offset threshold is verified to be correct.

[0045] The implementation principle of the boiler steel belt dry slag machine offset monitoring method in this application embodiment is as follows: Based on the actual center of gravity position and actual falling speed, the lateral thrust torque generated on the steel belt surface when the target ash falls onto the steel belt is analyzed and determined. This provides a more intuitive reflection of the lateral impact of the ash on the steel belt or the slag discharge off-center loading. Furthermore, the lateral thrust torque is also a factor affecting steel belt offset; the larger the lateral thrust torque, the greater the lateral impact of the ash on the steel belt, and the greater the possibility of steel belt offset. Further, by combining historical speed ranges and historical time periods, the probability of abnormal steel belt offset due to ash accumulation at the current actual operating speed is analyzed. Combined with the influence of the lateral thrust torque, the influence coefficient of ash accumulation on steel belt offset is comprehensively determined. The larger the influence coefficient, the greater the risk of abnormal steel belt offset due to ash accumulation. Then, based on the influence coefficient, the current target offset threshold of the steel belt is determined more accurately. Furthermore, if the actual offset exceeds the target offset threshold, it indicates a high risk of an abnormal offset, requiring an offset warning. If the actual offset direction is consistent with the torque direction, it indicates that the offset is caused by falling ash and belongs to the working condition type of offset, so the first warning message is sent. If the actual offset direction is opposite to the torque direction, it indicates that the offset is not caused by falling ash and is most likely caused by equipment failure of the dry slag machine, so the second warning message is issued, thus providing a more accurate warning for steel belt offset.

[0046] The following are system embodiments of this application, which can be used to execute the method embodiments of this application. For details not disclosed in the system embodiments of this application, please refer to the method embodiments of this application.

[0047] Please see Figure 4This is a schematic diagram of the boiler steel belt dry slag machine offset monitoring system provided in this embodiment of the application. This offset monitoring system for boiler steel belt dry slag machines can be implemented as all or part of a system through software, hardware, or a combination of both. The system includes an information acquisition module 11, a torque determination module 12, a data acquisition module 13, a threshold determination module 14, and an offset early warning module 15.

[0048] Information acquisition module 11 is used to acquire the actual center of gravity position, actual falling speed and actual running speed of the target ash falling on the surface of the steel strip. The actual falling speed is the instantaneous speed of the target ash falling on the surface of the steel strip. The torque determination module 12 is used to determine the lateral thrust torque of the target ash on the steel strip based on the actual center of gravity position and the actual falling speed. The data acquisition module 13 is used to acquire multiple historical speed ranges in which ash and slag accumulates in the steel strip due to its running speed, and multiple historical time periods in which the steel strip deviates abnormally due to ash and slag accumulation within the historical speed ranges. The threshold determination module 14 is used to determine the influence coefficient of ash accumulation on steel strip offset based on actual operating speed, lateral thrust torque, historical speed range and historical time period, and to determine the target offset threshold of steel strip according to the influence coefficient. The offset warning module 15 is used to obtain the actual offset and actual offset direction of the steel strip through the laser range sensor. When the actual offset exceeds the target offset threshold, if the actual offset direction is consistent with the torque direction of the lateral thrust torque, a first warning message is sent to the personnel terminal. If the actual offset direction is opposite to the torque direction of the lateral thrust torque, a second warning message is sent to the personnel terminal. The first warning message is used to warn of offset of working condition type, and the second warning message is used to warn of offset of fault type.

[0049] Optional, such as Figure 5 As shown, the system also includes an offset verification module 16, which is specifically used for: When the lateral thrust torque is zero, the infrared thermal imager preset on the unloading hopper of the dry slag machine acquires the thermal imaging video stream of the ash conveyed on the steel belt during the unloading process. Based on the thermal imaging video stream, the two-dimensional distribution profile of the ash and slag conveyed on the steel belt during the unloading process was determined; Determine the geometric center point of the two-dimensional distribution contour, and determine the offset in pixels between the geometric center point and the preset reference unloading center line; Based on the preset calibration parameters of the infrared thermal imager, the offset pixel amount is converted into a physical space offset. Calculate the absolute value of the difference between the physical space offset and the actual offset. If the absolute value is less than the preset absolute value threshold, then the actual offset is verified to be correct.

[0050] Optionally, the threshold determination module 14 is specifically used for: From multiple historical velocity ranges, at least one target velocity range is determined. The target velocity range is the historical velocity range that is prone to causing ash and slag accumulation. Based on multiple historical periods in which the steel strip deviated abnormally due to ash and slag accumulation within the target speed range, at least one target period corresponding to the target speed range is determined. The target period is a historical period in which deviation abnormalities are easily caused by ash and slag accumulation. Determine the interval weights for the target speed ranges and the time period weights for each target time period; The influence coefficient of ash accumulation on steel strip offset is determined based on time period weight, interval weight, lateral thrust torque and actual operating speed.

[0051] Optionally, the threshold determination module 14 is specifically used for: Determine whether the actual operating speed is within the target speed range. If the actual operating speed is within the target speed range, then determine the target speed range as the reference speed range, and determine whether the target time period corresponding to the reference speed range includes the current time. If the target time period corresponding to the reference speed range includes the current time, then the corresponding target time period is determined as the reference time period; The interval weight of the reference speed interval is multiplied by the time period weight of the corresponding reference time period to obtain the weight product. Based on the lateral thrust torque and the weight product, the initial influence coefficient of ash accumulation on the steel strip offset is determined. The initial influence coefficient was corrected to obtain the influence coefficient of ash accumulation on steel strip offset.

[0052] Optionally, the threshold determination module 14 is specifically used for: The first number of historical high-load periods included in the statistical reference period is counted, and the number of historical high-load periods included in all target periods is summed to obtain the first summation result. The historical high-load period is the period when the ratio of boiler load to rated load exceeds the preset ratio threshold. Calculate the first ratio of the first number to the first summation result; The second number of historical high-load periods including the reference period is counted, and the number of historical load periods including each target period is summed to obtain the second summation result; Calculate the second ratio of the second number to the second summation result, perform a weighted summation of the first and second ratios to obtain the final ratio, and determine the first correction coefficient based on the final ratio. The first correction coefficient is a positive number not less than 1. Multiply the first correction factor by the initial influence factor to obtain the influence factor of ash accumulation on steel strip offset.

[0053] Optionally, the threshold determination module 14 is specifically used for: Multiply the interval weight of the target speed interval by the time period weight of each corresponding target time period to obtain multiple multiplication results corresponding to the target speed interval; The summation of the multiplication results corresponding to all target speed ranges, including at least one key time period, yields a comprehensive result. The key time period is the target time period that includes the current time. Based on the comprehensive results, a second correction factor is determined, which is a positive number not less than 1; Multiply the second correction factor by the initial influence factor to obtain the influence factor of ash accumulation on steel strip offset.

[0054] Optionally, the system also includes a speed adjustment module 17, specifically used for: When the actual offset exceeds the target offset threshold, if the target time period corresponding to the target speed range includes the current time, then the target speed range is determined as the key speed range, and the target time period that includes the current time is determined as the key time period. Multiply the interval weight of at least one key speed interval with the time period weight of the corresponding key time period to obtain the product result; If the influence coefficient exceeds the preset coefficient threshold, the minimum product result is selected from all product results, and the key speed range corresponding to the minimum product result is determined as the appropriate speed range. Adjust the actual operating speed to the maximum value within the appropriate speed range.

[0055] It should be noted that the boiler steel belt dry ash machine offset monitoring system provided in the above embodiments is only illustrated by the division of the above functional modules when executing the boiler steel belt dry ash machine offset monitoring method. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the boiler steel belt dry ash machine offset monitoring system and the boiler steel belt dry ash machine offset monitoring method embodiment provided in the above embodiments belong to the same concept, and the implementation process is detailed in the method embodiment, which will not be repeated here.

[0056] This application also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements a boiler steel belt dry slag machine offset monitoring method according to the above embodiments.

[0057] The computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or certain middleware. The computer-readable medium includes any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the computer-readable medium includes, but is not limited to, the above-mentioned components.

[0058] The above-described method for monitoring the offset of a boiler steel belt dry slag machine is stored in the computer-readable storage medium and loaded and executed on the processor to facilitate the storage and application of the method.

[0059] This application also discloses an electronic device in which a computer program is stored in a computer-readable storage medium. When the computer program is loaded and executed by a processor, it implements the above-mentioned method for monitoring the offset of a boiler steel belt dry slag machine.

[0060] The electronic device can be a desktop computer, a laptop computer, or a cloud server, and includes, but is not limited to, a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and buses.

[0061] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it.

[0062] The memory can be an internal storage unit of an electronic device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) equipped on the electronic device. Furthermore, the memory can be a combination of an internal storage unit and an external storage device. The memory is used to store computer programs and other programs and data required by the electronic device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0063] In this electronic device, the offset monitoring method of a boiler steel belt dry slag machine according to the above embodiment is stored in the memory of the electronic device and loaded and executed on the processor of the electronic device for convenient use.

[0064] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method of monitoring the deviation of a boiler steel belt type dry slag machine, characterized by, The method comprises: acquiring an actual gravity center position of a target ash slag falling on a surface of a steel belt, an actual falling speed of the target ash slag, and an actual running speed of the steel belt, the actual falling speed being an instantaneous speed of the target ash slag when falling on the surface of the steel belt; determining a side pushing torque of the target ash slag on the steel belt according to the actual gravity center position and the actual falling speed; acquiring a plurality of historical speed intervals of the steel belt due to ash slag accumulation and a plurality of historical time periods of the steel belt due to ash slag accumulation and offset abnormality in the historical speed intervals; determining an influence coefficient of ash slag accumulation on the offset of the steel belt based on the actual running speed, the side pushing torque, the historical speed intervals, and the historical time periods, and determining a target offset threshold of the steel belt according to the influence coefficient; acquiring an actual offset amount and an actual offset direction of the steel belt by a laser ranging sensor, and when the actual offset amount exceeds the target offset threshold, if the actual offset direction is consistent with a torque direction of the side pushing torque, sending first early warning information to a terminal of a person, and if the actual offset direction is opposite to the torque direction of the side pushing torque, sending second early warning information to the terminal of the person, the first early warning information being used for early warning of a working condition type offset, and the second early warning information being used for early warning of a fault type offset.

2. The method of boiler steel belt type dry slag machine deviation monitoring according to claim 1, characterized in that, The method further comprises: when the side pushing torque is zero, acquiring a thermal imaging video stream of ash slag conveyed on the steel belt in a discharging process by a preset infrared thermal imager on a discharging hopper of a dry ash machine; determining a two-dimensional distribution profile of the ash slag conveyed on the steel belt in the discharging process according to the thermal imaging video stream; determining a geometric center point of the two-dimensional distribution profile and an offset pixel amount between the geometric center point and a preset reference discharging center line; converting the offset pixel amount into a physical space offset amount according to a preset calibration parameter of the infrared thermal imager; calculating an absolute value of a difference between the physical space offset amount and the actual offset amount, and if the absolute value is less than a preset absolute value threshold, verifying that the actual offset amount is correct.

3. The method of claim 1, wherein the offset is determined by: The determination of the influence coefficient of ash slag accumulation on the offset of the steel belt based on the actual running speed, the side pushing torque, the historical speed intervals, and the historical time periods specifically comprises: determining at least one target speed interval from the plurality of historical speed intervals, the target speed interval being a historical speed interval prone to causing ash slag accumulation; determining at least one target time period corresponding to the target speed interval according to the plurality of historical time periods of the steel belt due to ash slag accumulation and offset abnormality in the target speed interval, the target time period being a historical time period prone to causing offset abnormality due to ash slag accumulation; determining an interval weight of the target speed interval and a time period weight of each target time period; determining the influence coefficient of ash slag accumulation on the offset of the steel belt according to the time period weight, the interval weight, the side pushing torque, and the actual running speed.

4. The method of boiler steel belt type dry slag conveyer deviation monitoring according to claim 3, characterized in that, The method further comprises: determining whether the actual running speed is in the target speed interval, if the actual running speed is in the target speed interval, determining the target speed interval as a reference speed interval, and determining whether the current time is included in a target time period corresponding to the reference speed interval; if the current time is included in the target time period corresponding to the reference speed interval, determining the corresponding target time period as a reference time period; multiplying the interval weight of the reference speed interval and the time period weight of the corresponding reference time period to obtain a weight product, and determining an initial influence coefficient of the slag accumulation on the steel belt deviation according to the side thrust torque and the weight product; correcting the initial influence coefficient to obtain the influence coefficient of the slag accumulation on the steel belt deviation.

5. The method of boiler steel belt type dry slag conveyer deviation monitoring according to claim 4, characterized in that, The method further comprises: statistically counting a first number of historical high-load time periods included in the reference time period, and summing up the number of historical high-load time periods included in all the target time periods to obtain a first summation result, the historical high-load time period being a time period in which the ratio of the boiler load to the rated load exceeds a preset ratio threshold value; calculating a first ratio of the first number and the first summation result; statistically counting a second number of historical high-load time periods included in the reference time period, and summing up the number of historical high-load time periods included in each of the target time periods to obtain a second summation result; calculating a second ratio of the second number and the second summation result, and performing weighted summation on the first ratio and the second ratio to obtain a final ratio, and determining a first correction coefficient according to the final ratio, the first correction coefficient being a positive number not less than 1; multiplying the first correction coefficient by the initial influence coefficient to obtain the influence coefficient of the slag accumulation on the steel belt deviation.

6. The method of boiler steel belt type dry slag conveyer deviation monitoring according to claim 4, characterized in that, The method further comprises: multiplying the interval weight of the target speed interval and the time period weight of each corresponding target time period to obtain a plurality of multiplication results corresponding to the target speed interval; summing up the multiplication results corresponding to at least one key time period among all the multiplication results corresponding to the target speed intervals to obtain a comprehensive result, the key time period being a target time period including the current time; determining a second correction coefficient according to the comprehensive result, the second correction coefficient being a positive number not less than 1; multiplying the second correction coefficient by the initial influence coefficient to obtain the influence coefficient of the slag accumulation on the steel belt deviation.

7. The method of boiler steel belt type dry slag conveyer deviation monitoring according to claim 3, characterized in that, The method further comprises: if the actual deviation exceeds the target deviation threshold, and if the current time is included in the target time period corresponding to the target speed interval, determining the target speed interval as a key speed interval, and determining the target time period including the current time as a key time period; The interval weight of at least one of the key speed intervals is multiplied by the period weight of the corresponding key period to obtain a product result; When the influence coefficient exceeds a preset coefficient threshold, the minimum product result is selected from the product results, and a key speed interval corresponding to the minimum product result is determined as a suitable speed interval; The actual running speed is adjusted to a maximum value in the suitable speed interval.

8. A boiler steel belt type dry slag machine deviation monitoring system characterized by, The method comprises the following steps: An information acquisition module (11) is configured to acquire an actual gravity center position of a target ash falling on a surface of a steel belt, an actual falling speed of the target ash, and an actual running speed of the steel belt, the actual falling speed being an instantaneous speed of the target ash when falling on the surface of the steel belt; A torque determination module (12) is configured to determine a side-pushing torque of the target ash on the steel belt according to the actual gravity center position and the actual falling speed; A data acquisition module (13) is configured to acquire a plurality of historical speed intervals of the steel belt in which ash accumulation occurs due to a running speed, and a plurality of historical periods in which the steel belt deviates abnormally due to ash accumulation in the historical speed intervals; A threshold determination module (14) is configured to determine an influence coefficient of ash accumulation on deviation of the steel belt based on the actual running speed, the side-pushing torque, the historical speed intervals, and the historical periods, and determine a target deviation threshold of the steel belt according to the influence coefficient; A deviation warning module (15) is configured to acquire an actual deviation amount and an actual deviation direction of the steel belt by a laser ranging sensor, and when the actual deviation amount exceeds the target deviation threshold, if the actual deviation direction is consistent with a torque direction of the side-pushing torque, a first warning information is sent to a terminal of a person, and if the actual deviation direction is opposite to the torque direction of the side-pushing torque, a second warning information is sent to the terminal of the person, the first warning information being used for warning a working condition type deviation, and the second warning information being used for warning a fault type deviation.

9. A computer-readable storage medium having stored therein a computer program, characterized in that, The computer program is loaded and executed by the processor to implement the method of any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The processor loads and executes the computer program to implement the method of any one of claims 1-7.

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