Method and system for analyzing internal temperature data of asphalt melting furnace

Abstract

The invention discloses an asphalt melting furnace internal temperature data analysis method and system, relates to the technical field of asphalt melting furnace temperature analysis, and particularly discloses a virtual three-dimensional cavity, and temperature acquisition points corresponding to temperature sensing devices are set in the virtual three-dimensional cavity; acquiring temperature distribution characteristics of the side part of the asphalt melting furnace, and mapping the temperature distribution characteristics to the virtual three-dimensional cavity; a temperature distribution parameter characteristic array is constructed, the temperature distribution parameter characteristic array comprises temperature values and inter-point distances, and the position of each parameter in the array is determined; sequencing the parametric characteristic arrays based on a time sequence relationship, establishing a correlation between a parametric characteristic array sequence and the asphalt melting mass, and forming an asphalt melting mass relationship library; the characteristic analysis model is used for carrying out characteristic factor configuration on the temperature distribution parameter characteristic array, an index relation of the asphalt melting quality relation library is established, and powerful support is provided for accurate control and optimization of the asphalt melting quality.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature analysis of asphalt melting furnaces, and in particular to a method and system for analyzing internal temperature data of an asphalt melting furnace. Background Art

[0002] In the field of asphalt processing, asphalt melting furnaces are key production equipment, and the control of their internal temperature has a decisive influence on the melting quality of asphalt. However, traditional asphalt melting furnace temperature data analysis methods face many challenges and limitations. These methods often fail to accurately and comprehensively obtain the temperature distribution characteristics in the furnace, resulting in insufficient accuracy of temperature control; at the same time, traditional methods also find it difficult to effectively associate the temperature collection point with the center point of the temperature distribution, and fail to establish a quantitative relationship between temperature data and asphalt melting quality. Summary of the invention

[0003] The purpose of the present invention is to provide a method for analyzing internal temperature data of an asphalt melting furnace, which can accurately establish the corresponding relationship between the temperature distribution characteristics in the furnace and the asphalt melting quality, and further provide data support for the optimization of the furnace temperature control of the asphalt melting furnace.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for analyzing temperature data inside an asphalt melting furnace, comprising:

[0006] The cavity structure of the asphalt melting furnace is determined, and a virtual three-dimensional cavity is constructed based on the cavity structure, and corresponding temperature collection points are set in the virtual three-dimensional cavity based on the relative position of the temperature sensor device in the cavity structure of the asphalt melting furnace;

[0007] The temperature distribution characteristics of the side of the asphalt melting furnace are determined by using thermal imaging detection technology, and the determined temperature distribution characteristics are mapped on the virtual three-dimensional cavity. In addition, the temperature values ​​collected at different temperature collection points are combined to determine the distance between each temperature collection point of the virtual three-dimensional cavity and the center point of the temperature distribution to which it belongs.

[0008] According to the relative positions of different temperature collection points, a temperature distribution parameter characteristic array is constructed. The temperature distribution parameters include the temperature values ​​collected by the temperature collection points and the distances between the points. Based on the positions of the temperature collection points in the virtual three-dimensional cavity, the corresponding temperature distribution parameters are determined in the temperature distribution parameter characteristic array.

[0009] Based on the time series relationship, the temperature distribution parameter feature array is sorted to obtain the parameter feature array sequence, and the association between the parameter feature array sequence and the asphalt melting quality is established, and several association relationships are integrated to obtain the asphalt melting quality relationship library;

[0010] By using the characteristic analysis model, characteristic factors are configured for the characteristic array of temperature distribution parameters, and the index relationship of the asphalt melting quality relationship library is established based on the characteristic factors.

[0011] In some embodiments disclosed in the present invention, a method for determining the distance between each temperature collection point of a virtual three-dimensional cavity and the center point of the temperature distribution to which each temperature collection point belongs includes:

[0012] Using thermal imaging detection technology, a thermal image of the side of the asphalt melting furnace is formed, and based on the grayscale change gradient in the thermal image and the preset grayscale boundary, a threshold is determined to divide several temperature center blocks in the thermal image;

[0013] Based on the temperature center blocks in the thermal imaging images at different orientations, three-dimensional temperature center blocks are divided in the virtual three-dimensional cavity;

[0014] Determine the block boundary of each stereo temperature center block, and randomly select a number of boundary mapping points on the block boundary, obtain the boundary coordinates of each boundary mapping point, and calculate the average coordinates of all boundary coordinates, and identify the mapping point corresponding to the average coordinate as the center point of the temperature distribution;

[0015] The temperature collection points within the same three-dimensional temperature center block are determined, and the distance between each temperature collection point and the temperature distribution center point is calculated and recorded as the distance between points.

[0016] In some embodiments disclosed in the present invention, the method of establishing the association between the parameter feature array sequence and the asphalt melting quality includes:

[0017] The penetration test, softening point test and viscosity test are used to judge the melting quality of asphalt, and the judgment results are quantified to obtain the melting quality parameters;

[0018] The methods for quantifying the judgment results include:

[0019] For the penetration test, a corresponding group of penetration intervals is set, wherein each penetration interval is set with a first preset melting quality parameter; for the softening point test, a corresponding group of softening point intervals is set, wherein each softening point interval is set with a second preset melting quality parameter; for the viscosity test, a corresponding group of viscosity intervals is set, wherein each annual interval is set with a third preset melting quality parameter;

[0020] Based on the relationship among the penetration test results, softening point test results and viscosity test results in their respective interval corresponding groups, the corresponding first preset melting mass parameter, second preset melting mass parameter and third preset melting mass parameter are called for comprehensive comparison and calculation to obtain the melting mass parameter.

[0021] In some embodiments of the present invention, the expression for calculating the melting quality parameter is:

[0022] H=K1×h1+K2×h2+K3×h3;

[0024] Among them, H is the melting mass parameter, K1 is the weight coefficient of the first preset melting mass parameter, h1 is the first preset melting mass parameter, K2 is the weight coefficient of the second preset melting mass parameter, h2 is the second preset melting mass parameter, K3 is the weight coefficient of the third preset melting mass parameter, and h3 is the third preset melting mass parameter.

[0025] In some embodiments of the present invention, a method for configuring a characteristic factor for a temperature distribution parameter characteristic array using a characteristic analysis model includes:

[0026] For a preset difference interval, the quantized melting quality parameters are classified, and based on the classification of the melting quality parameters, the parameter feature array sequences are classified to obtain a plurality of parameter feature array sequence sets;

[0027] Align each temperature distribution parameter feature array sequence in the parameter feature array sequence set, and compare the temperature distribution parameter feature arrays in the temperature distribution parameter feature array sequence one by one. If the difference characteristics between the temperature distribution parameter feature arrays of the same sequence meet the equivalent standard, the temperature distribution parameter feature array of the sequence is marked as a weak characteristic temperature distribution parameter feature array, and the remaining other temperature distribution feature arrays are marked as medium characteristic temperature distribution parameter feature arrays;

[0028] Analyze the difference characteristics of the medium characteristic temperature distribution parameter characteristic arrays of the same order between the parameter characteristic array sequences, determine the difference characteristic parameters of each order, and continuously analyze the difference characteristic parameters. If the continuous difference characteristic parameters show a monotonic change trend, the temperature distribution parameter characteristic array corresponding to the difference characteristic parameters is identified as a strong characteristic temperature distribution parameter characteristic array;

[0029] The order of the strong characteristic temperature distribution parameter characteristic array in the parameter characteristic array sequence is identified as the first characteristic factor.

[0030] In some embodiments of the present invention, the temperature values ​​at different points in the characteristic array of the strong characteristic temperature distribution parameter are identified as the second characteristic factor.

[0031] In some embodiments disclosed in the present invention, a method for analyzing difference characteristics of medium characteristic temperature distribution parameter characteristic arrays of equal order between parameter characteristic array sequences includes:

[0032] Randomly select medium characteristic temperature distribution parameter feature arrays of equal order to combine in pairs, record as parameter feature array random group, compare the temperature values ​​and inter-point distances of equal points between medium characteristic temperature distribution parameter feature arrays of parameter feature array random group, determine the temperature value difference and inter-point distance of equal points, and determine the average temperature value difference and average inter-point distance between parameter feature array random groups;

[0033] A first difference characteristic operator is constructed based on the difference in average temperature values ​​of equivalent points, and a second difference characteristic operator is constructed based on the average distance between equivalent points. Based on the first difference characteristic operator and the second difference characteristic operator, a difference characteristic parameter is determined.

[0034] In some embodiments disclosed in the present invention, the difference characteristic parameter expression is:

[0035]

[0036] Among them, T is the difference characteristic parameter, Δx i is the average temperature difference between the i-th equivalent points in the medium characteristic temperature distribution parameter feature array, Δl i is the average distance between the i-th equivalent points between the characteristic arrays of the medium characteristic temperature distribution parameters, n is the total number of points in the characteristic arrays of the medium characteristic temperature distribution parameters, r is the average distance between points multiplier adjustment coefficient, and b is the average distance between points multiplier adjustment constant.

[0037] In some embodiments disclosed in the present invention, there is also disclosed a system for analyzing internal temperature data of an asphalt melting furnace, comprising:

[0038] The first module is used to determine the cavity structure of the asphalt melting furnace, and to construct a virtual three-dimensional cavity based on the cavity structure, and to set corresponding temperature collection points in the virtual three-dimensional cavity based on the relative position of the temperature sensor device in the cavity structure of the asphalt melting furnace;

[0039] The second module is used to determine the temperature distribution characteristics of the side of the asphalt melting furnace by using thermal imaging detection technology, and map the determined temperature distribution characteristics on the virtual three-dimensional cavity, and combine the temperature values ​​collected at different temperature collection points to determine the distance between each temperature collection point of the virtual three-dimensional cavity and the center point of the temperature distribution to which it belongs;

[0040] The third module is used to construct a temperature distribution parameter feature array according to the relative positions of different temperature collection points. The temperature distribution parameters include the temperature values ​​collected by the temperature collection points and the distances between the points. Based on the positions of the temperature collection points in the virtual three-dimensional cavity, the corresponding temperature distribution parameters are determined in the temperature distribution parameter feature array.

[0041] The fourth module is used to sort the temperature distribution parameter feature array based on the time series relationship to obtain the parameter feature array sequence, and establish the association between the parameter feature array sequence and the asphalt melting quality, and integrate several association relationships to obtain the asphalt melting quality relationship library;

[0042] The fifth module is used to configure characteristic factors for the characteristic array of temperature distribution parameters using the characteristic analysis model, and to establish an index relationship of the asphalt melting quality relationship library based on the characteristic factors.

[0043] The present invention discloses a method and system for analyzing internal temperature data of an asphalt melting furnace, and relates to the technical field of temperature analysis of asphalt melting furnaces. Specifically, a virtual three-dimensional cavity is constructed, and temperature collection points corresponding to temperature sensing devices are set therein; the temperature distribution characteristics of the side of the asphalt melting furnace are obtained, and mapped to the virtual three-dimensional cavity; a temperature distribution parameter feature array is constructed, including temperature values ​​and distances between points, and the position of each parameter in the array is determined; the parameter feature array is sorted based on a time series relationship, and an association between the parameter feature array sequence and the asphalt melting quality is established, forming an asphalt melting quality relationship library; the temperature distribution parameter feature array is configured with feature factors using a feature analysis model, and an index relationship of the asphalt melting quality relationship library is established, which provides strong support for the precise control and optimization of the asphalt melting quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a method step diagram of a method for analyzing internal temperature data of an asphalt melting furnace proposed by the present invention. DETAILED DESCRIPTION

[0045] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0046] The purpose of the present invention is to provide a method for analyzing internal temperature data of an asphalt melting furnace, which can accurately establish the corresponding relationship between the temperature distribution characteristics in the furnace and the asphalt melting quality, and further provide data support for the optimization of the furnace temperature control of the asphalt melting furnace.

[0047] In order to achieve the above object, the present invention adopts the following technical solutions:

[0048] See also Figure 1 , a method for analyzing temperature data inside an asphalt melting furnace, comprising:

[0049] Step S100, determining the cavity structure of the asphalt melting furnace, and constructing a virtual three-dimensional cavity based on the cavity structure, and setting corresponding temperature collection points in the virtual three-dimensional cavity based on the relative position of the temperature sensor device in the cavity structure of the asphalt melting furnace.

[0050] The core principle of this step is to convert the actual cavity structure of the asphalt melting furnace into a virtual three-dimensional cavity through precise measurement and modeling. This conversion process relies on advanced measurement technology and three-dimensional modeling software to ensure that the virtual cavity is highly consistent with the real furnace cavity in structure and size. At the same time, according to the actual position of the temperature sensor in the furnace cavity, the corresponding temperature collection point is set in the virtual cavity to provide an accurate coordinate basis for subsequent temperature data collection and analysis.

[0051] Step S200, using thermal imaging detection technology to determine the temperature distribution characteristics of the side of the asphalt melting furnace, and mapping the determined temperature distribution characteristics on the virtual three-dimensional cavity, and combining the temperature values ​​collected at different temperature collection points to determine the distance between each temperature collection point of the virtual three-dimensional cavity and the center point of the temperature distribution to which it belongs.

[0052] This step uses thermal imaging detection technology to capture the temperature distribution characteristics of the side of the asphalt melting furnace. Thermal imaging technology can measure the temperature distribution of the surface of an object in real time and accurately, and present it in the form of an image. By mapping these temperature distribution characteristics onto a virtual three-dimensional cavity, the organic combination of temperature data and furnace cavity structure can be achieved. At the same time, combined with the temperature values ​​collected at different temperature collection points, a mathematical algorithm is used to calculate the distance between each temperature collection point and the center point of its temperature distribution, providing key data for subsequent temperature analysis and quality control.

[0053] In some embodiments disclosed in the present invention, a method for determining the distance between each temperature collection point of a virtual three-dimensional cavity and the center point of the temperature distribution to which each temperature collection point belongs includes:

[0054] Step S201, using thermal imaging detection technology to form a thermal image of the side of the asphalt melting furnace, and based on the grayscale change gradient in the thermal image and a preset grayscale boundary, determine a threshold value to divide a number of temperature center blocks in the thermal image.

[0055] This step uses thermal imaging detection technology to capture the infrared radiation emitted from the side of the asphalt melting furnace and convert it into a visible light thermal image. Since objects at different temperatures emit different intensities of infrared radiation, in the thermal image, the area with higher temperature will appear brighter gray, while the area with lower temperature will appear darker gray. By analyzing the grayscale change gradient in the thermal image and combining it with the preset grayscale boundary threshold, the temperature center blocks representing different temperature distributions can be accurately divided.

[0056] Step S202 , dividing the virtual three-dimensional cavity into three-dimensional temperature center blocks based on the temperature center blocks in the thermal imaging images at different orientations.

[0057] The core of this step is to map the temperature center block information in the thermal image to the virtual three-dimensional cavity. Since the virtual three-dimensional cavity is constructed based on the actual cavity structure of the asphalt melting furnace, the temperature center block in the thermal image can be converted into a three-dimensional temperature center block in the virtual cavity through precise mathematical transformation and coordinate mapping. This mapping process ensures the consistency of the spatial position of the temperature data with the actual furnace cavity structure, providing an accurate spatial reference for subsequent temperature analysis and calculation.

[0058] Step S203, determine the block boundary of each three-dimensional temperature center block, and randomly select a number of boundary mapping points on the block boundary, obtain the boundary coordinates of each boundary mapping point, and calculate the average coordinates of all boundary coordinates, and identify the mapping point corresponding to the average coordinate as the center point of the temperature distribution.

[0059] The purpose of this step is to determine the exact range of each stereo temperature center block and find the center point of its temperature distribution. By defining the block boundaries and randomly selecting a number of boundary mapping points, the specific coordinates of these points can be obtained. Then, a mathematical algorithm is used to calculate the average coordinates of all boundary coordinates, and the mapping point corresponding to this average coordinate is identified as the center point of the temperature distribution. This process is based on statistical principles and estimates the center position of the block by calculating the average value of the boundary coordinates, providing an accurate reference point for the subsequent calculation of the distance between points.

[0060] Step S204, determining the temperature collection points within the same three-dimensional temperature center block, and calculating the distance between each temperature collection point and the temperature distribution center point, which is recorded as the point distance.

[0061] The core of this step is to calculate the distance between each temperature acquisition point and the center point of the temperature distribution to which it belongs. Since the temperature acquisition points represent the temperature measurement positions in the actual furnace chamber, a detailed point-to-point distance data set can be obtained by calculating the distance between them and the center point of the temperature distribution. This data set reflects the temperature distribution at different positions in the furnace chamber, providing important data support for subsequent temperature analysis, quality control, and optimization of the melting process. At the same time, by comparing the point-to-point distance data sets at different time points, the change pattern of temperature distribution over time can also be revealed, providing a strong basis for further melting process control.

[0062] Step S300, constructing a temperature distribution parameter characteristic array according to the relative positions of different temperature collection points, wherein the temperature distribution parameters include the temperature values ​​collected by the temperature collection points and the distances between the points, and determining the corresponding points of the temperature distribution parameters in the temperature distribution parameter characteristic array based on the positions of the temperature collection points in the virtual three-dimensional cavity.

[0063] The principle of this step is to construct an ordered and structured temperature distribution parameter feature array to better organize and process a large amount of temperature data. By considering the relative positions of different temperature acquisition points, key parameters such as temperature values ​​and distances between points can be organically integrated. At the same time, based on the position of the temperature acquisition points in the virtual three-dimensional cavity, the point position of each temperature distribution parameter in the feature array can be determined, thereby achieving accurate positioning and rapid retrieval of data.

[0064] Step S400, based on the time series relationship, sort the temperature distribution parameter feature array to obtain a parameter feature array sequence, and establish an association between the parameter feature array sequence and asphalt melting quality, integrate several association relationships, and obtain an asphalt melting quality relationship library.

[0065] The principle of this step is to sort and organize the temperature distribution parameter feature array using the time series relationship in order to better reveal the intrinsic relationship between temperature data and asphalt melting quality. By sorting based on the time series, an ordered parameter feature array sequence can be obtained, reflecting the change of temperature distribution parameters over time. At the same time, by establishing the correlation between the parameter feature array sequence and the asphalt melting quality, the corresponding relationship between the temperature distribution parameters at different time points and the asphalt melting quality can be found, providing strong support for subsequent quality control and prediction.

[0066] In some embodiments disclosed in the present invention, the method of establishing the association between the parameter feature array sequence and the asphalt melting quality includes:

[0067] Step S401, using the penetration test, softening point test and viscosity test to judge the melting quality of the asphalt, and quantify the judgment result to obtain the melting quality parameter.

[0068] The methods for quantifying the judgment results include:

[0069] Step S4011, a corresponding group of needle penetration intervals is set for the needle penetration test, wherein each needle penetration interval is set with a first preset melting quality parameter; a corresponding group of softening point intervals is set for the softening point test, wherein each soft point interval is set with a second preset melting quality parameter; a corresponding group of viscosity intervals is set for the viscosity test, wherein each annual interval is set with a third preset melting quality parameter.

[0070] Step S4012, based on the relationship between the needle penetration test results, the softening point test results and the viscosity test results in the corresponding groups of their respective intervals, call the corresponding first preset melting mass parameter, the second preset melting mass parameter and the third preset melting mass parameter for comprehensive comparison and calculation to obtain the melting mass parameter.

[0071] In some embodiments of the present invention, the expression for calculating the melting quality parameter is:

[0072] H=K1×h1+K2×h2+K3×h3.

[0074] Among them, H is the melting mass parameter, K1 is the weight coefficient of the first preset melting mass parameter, h1 is the first preset melting mass parameter, K2 is the weight coefficient of the second preset melting mass parameter, h2 is the second preset melting mass parameter, K3 is the weight coefficient of the third preset melting mass parameter, and h3 is the third preset melting mass parameter.

[0075] Step S500: using a characteristic analysis model, configuring characteristic factors for the temperature distribution parameter characteristic array, and establishing an index relationship of an asphalt melting quality relationship library based on the characteristic factors.

[0076] The principle of this step is to use the characteristic analysis model to deeply mine and analyze the characteristic array of temperature distribution parameters to extract key characteristic factors that have an important impact on the asphalt melting quality. These characteristic factors can reflect the core characteristics and properties of the temperature distribution parameters, which are of great significance for understanding and controlling the asphalt melting process. At the same time, the index relationship of the asphalt melting quality relationship library is established based on these characteristic factors, which can realize the rapid retrieval and efficient use of data, and provide strong technical support for the precise control and optimization of asphalt melting quality.

[0077] In some embodiments of the present invention, a method for configuring a characteristic factor for a temperature distribution parameter characteristic array using a characteristic analysis model includes:

[0078] Step S501 , classifying the quantized melting quality parameters according to the preset difference intervals, and classifying the parameter feature array sequences based on the classification of the melting quality parameters to obtain a plurality of parameter feature array sequence sets.

[0079] The core of this step is to classify the quantified melting quality parameters and group the parameter feature array sequences based on these classifications. The preset difference interval is used to determine the different categories of melting quality parameters, thereby ensuring the accuracy and effectiveness of the classification. By classifying the parameter feature array sequences, several sets of parameter feature array sequences with similar characteristics can be obtained, providing a basis for subsequent feature analysis and factor configuration.

[0080] Step S502, align each temperature distribution parameter feature array sequence in the parameter feature array sequence set, and compare the temperature distribution parameter feature arrays in the temperature distribution parameter feature array sequence one by one. If the difference characteristics between the temperature distribution parameter feature arrays of the same sequence meet the equivalent standard, the temperature distribution parameter feature array of the sequence is marked as a weak characteristic temperature distribution parameter feature array, and the remaining other temperature distribution feature arrays are marked as medium characteristic temperature distribution parameter feature arrays.

[0081] In this step, the temperature distribution parameter feature arrays in each parameter feature array sequence set are aligned and compared, with the aim of identifying arrays with similar features. If the difference features between the temperature distribution parameter feature arrays of the same sequence meet the equivalent standard, these arrays are marked as weak feature temperature distribution parameter feature arrays, indicating that they are relatively weak in features and may have little impact on the melting quality. The remaining other temperature distribution feature arrays are marked as medium feature temperature distribution parameter feature arrays, indicating that they are relatively strong in features and may have a certain impact on the melting quality.

[0082] Step S503, analyze the difference characteristics of the medium characteristic temperature distribution parameter feature arrays of equal order between the parameter feature array sequences, determine the difference characteristic parameters of each order, and continuously analyze the difference characteristic parameters. If the continuous difference characteristic parameters show a monotonic change trend, the temperature distribution parameter feature array corresponding to the difference characteristic parameters is identified as a strong characteristic temperature distribution parameter feature array.

[0083] This step further analyzes the difference characteristics of the medium characteristic temperature distribution parameter feature array, with the goal of finding arrays with significant characteristics. By continuously analyzing the difference characteristic parameters, if the continuous difference characteristic parameters show a monotonic change trend, it means that the temperature distribution parameter feature arrays corresponding to these characteristic parameters have strong performance in characteristics, so they are identified as strong characteristic temperature distribution parameter feature arrays. This step helps to identify the key characteristic factors that have an important impact on the melting quality, and provides strong support for subsequent quality control and optimization.

[0084] In some embodiments disclosed in the present invention, a method for analyzing difference characteristics of medium characteristic temperature distribution parameter characteristic arrays of equal order between parameter characteristic array sequences includes:

[0085] Step S5031, randomly select medium characteristic temperature distribution parameter feature arrays of equal order for pair combination, recorded as parameter feature array random group, compare the temperature values ​​of equal points and the distances between points between the medium characteristic temperature distribution parameter feature arrays in the parameter feature array random group, determine the temperature value difference and the distance between points at equal points, and determine the average temperature value difference and the average distance between points between the parameter feature array random groups.

[0086] The purpose of this step is to compare the differences in temperature values ​​and inter-point distances at equivalent points by randomly combining parameter feature arrays of medium characteristic temperature distribution of equal order. By comparison, the difference in temperature values ​​and inter-point distances of each pair of parameter feature arrays at equivalent points can be determined. Furthermore, by calculating the average temperature value difference and average inter-point distance between all random groups of parameter feature arrays, a quantitative difference metric can be obtained, providing a basis for subsequent difference feature analysis.

[0087] Step S5032, constructing a first difference characteristic operator based on the difference in average temperature values ​​of equivalent points, and constructing a second difference characteristic operator based on the average distance between equivalent points, and determining the difference characteristic parameters based on the first difference characteristic operator and the second difference characteristic operator.

[0088] In some embodiments disclosed in the present invention, the difference characteristic parameter expression is:

[0089]

[0090] Among them, T is the difference characteristic parameter, Δx i is the average temperature difference between the i-th equivalent points in the medium characteristic temperature distribution parameter feature array, Δl i is the average distance between the i-th equivalent points between the characteristic arrays of the medium characteristic temperature distribution parameters, n is the total number of points in the characteristic arrays of the medium characteristic temperature distribution parameters, r is the average distance between points multiplier adjustment coefficient, and b is the average distance between points multiplier adjustment constant.

[0091] Step S504: The order of the strong characteristic temperature distribution parameter characteristic array in the parameter characteristic array sequence is identified as the first characteristic factor.

[0092] In some embodiments of the present invention, the temperature values ​​at different points in the characteristic array of the strong characteristic temperature distribution parameter are identified as the second characteristic factor.

[0093] In some embodiments disclosed in the present invention, there is also disclosed a system for analyzing internal temperature data of an asphalt melting furnace, comprising:

[0094] The first module is used to determine the intracavity structure of the asphalt melting furnace, and to construct a virtual three-dimensional cavity based on the intracavity structure, and to set corresponding temperature collection points in the virtual three-dimensional cavity based on the relative position of the temperature sensor device in the intracavity structure of the asphalt melting furnace.

[0095] The second module is used to determine the temperature distribution characteristics of the side of the asphalt melting furnace using thermal imaging detection technology, and map the determined temperature distribution characteristics onto the virtual three-dimensional cavity, and combine the temperature values ​​collected at different temperature collection points to determine the distance between each temperature collection point in the virtual three-dimensional cavity and the center point of the temperature distribution to which they belong.

[0096] The third module is used to construct a temperature distribution parameter characteristic array according to the relative positions of different temperature collection points. The temperature distribution parameters include the temperature values ​​collected by the temperature collection points and the distances between the points. Based on the positions of the temperature collection points in the virtual three-dimensional cavity, the corresponding temperature distribution parameters are determined in the temperature distribution parameter characteristic array.

[0097] The fourth module is used to sort the temperature distribution parameter feature array based on the time series relationship to obtain the parameter feature array sequence, and to establish the association between the parameter feature array sequence and the asphalt melting quality, and to integrate several association relationships to obtain the asphalt melting quality relationship library.

[0098] The fifth module is used to configure characteristic factors for the characteristic array of temperature distribution parameters using the characteristic analysis model, and to establish an index relationship of the asphalt melting quality relationship library based on the characteristic factors.

[0099] The present invention discloses a method and system for analyzing internal temperature data of an asphalt melting furnace, and relates to the technical field of temperature analysis of asphalt melting furnaces. Specifically, a virtual three-dimensional cavity is constructed, and temperature collection points corresponding to temperature sensing devices are set therein; the temperature distribution characteristics of the side of the asphalt melting furnace are obtained, and mapped to the virtual three-dimensional cavity; a temperature distribution parameter feature array is constructed, including temperature values ​​and distances between points, and the position of each parameter in the array is determined; the parameter feature array is sorted based on a time series relationship, and an association between the parameter feature array sequence and the asphalt melting quality is established, forming an asphalt melting quality relationship library; the temperature distribution parameter feature array is configured with feature factors using a feature analysis model, and an index relationship of the asphalt melting quality relationship library is established, which provides strong support for the precise control and optimization of the asphalt melting quality.

[0100] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for analyzing temperature data inside an asphalt melting furnace, characterized in that: include: The cavity structure of the asphalt melting furnace is determined, and a virtual three-dimensional cavity is constructed based on the cavity structure, and corresponding temperature collection points are set in the virtual three-dimensional cavity based on the relative position of the temperature sensor device in the cavity structure of the asphalt melting furnace; The temperature distribution characteristics of the side of the asphalt melting furnace are determined by using thermal imaging detection technology, and the determined temperature distribution characteristics are mapped on the virtual three-dimensional cavity. In addition, the temperature values ​​collected at different temperature collection points are combined to determine the distance between each temperature collection point of the virtual three-dimensional cavity and the center point of the temperature distribution to which it belongs. According to the relative positions of different temperature collection points, a temperature distribution parameter characteristic array is constructed. The temperature distribution parameters include the temperature values ​​collected by the temperature collection points and the distances between the points. Based on the positions of the temperature collection points in the virtual three-dimensional cavity, the corresponding temperature distribution parameters are determined in the temperature distribution parameter characteristic array. Based on the time series relationship, the temperature distribution parameter feature array is sorted to obtain the parameter feature array sequence, and the association between the parameter feature array sequence and the asphalt melting quality is established, and several association relationships are integrated to obtain the asphalt melting quality relationship library; By using the characteristic analysis model, characteristic factors are configured for the characteristic array of temperature distribution parameters, and the index relationship of the asphalt melting quality relationship library is established based on the characteristic factors.

2. The method for analyzing internal temperature data of an asphalt melting furnace according to claim 1, characterized in that: The method for determining the distance between each temperature collection point of the virtual three-dimensional cavity and the center point of the temperature distribution to which each temperature collection point belongs includes: Using thermal imaging detection technology, a thermal image of the side of the asphalt melting furnace is formed, and based on the grayscale change gradient in the thermal image and the preset grayscale boundary, a threshold is determined to divide several temperature center blocks in the thermal image; Based on the temperature center blocks in the thermal imaging images at different orientations, three-dimensional temperature center blocks are divided in the virtual three-dimensional cavity; Determine the block boundary of each stereo temperature center block, and randomly select a number of boundary mapping points on the block boundary, obtain the boundary coordinates of each boundary mapping point, and calculate the average coordinates of all boundary coordinates, and identify the mapping point corresponding to the average coordinate as the center point of the temperature distribution; The temperature collection points within the same three-dimensional temperature center block are determined, and the distance between each temperature collection point and the temperature distribution center point is calculated and recorded as the distance between points.

3. The method for analyzing internal temperature data of an asphalt melting furnace according to claim 1, characterized in that: The method of establishing the association between the parameter feature array sequence and the asphalt melting quality includes: The penetration test, softening point test and viscosity test are used to judge the melting quality of asphalt, and the judgment results are quantified to obtain the melting quality parameters; The methods for quantifying the judgment results include: For the penetration test, a corresponding group of penetration intervals is set, wherein each penetration interval is set with a first preset melting quality parameter; for the softening point test, a corresponding group of softening point intervals is set, wherein each softening point interval is set with a second preset melting quality parameter; for the viscosity test, a corresponding group of viscosity intervals is set, wherein each annual interval is set with a third preset melting quality parameter; Based on the relationship among the penetration test results, softening point test results and viscosity test results in their respective interval corresponding groups, the corresponding first preset melting mass parameter, second preset melting mass parameter and third preset melting mass parameter are called for comprehensive comparison and calculation to obtain the melting mass parameter.

4. The method for analyzing the internal temperature data of an asphalt melting furnace according to claim 3, characterized in that: The expression for calculating the melting mass parameter is: H=K1×h1+K2×h2+K3×h3; Among them, H is the melting mass parameter, K1 is the weight coefficient of the first preset melting mass parameter, h1 is the first preset melting mass parameter, K2 is the weight coefficient of the second preset melting mass parameter, h2 is the second preset melting mass parameter, K3 is the weight coefficient of the third preset melting mass parameter, and h3 is the third preset melting mass parameter.

5. The method for analyzing internal temperature data of an asphalt melting furnace according to claim 1, characterized in that: The method of configuring characteristic factors for the characteristic array of temperature distribution parameters using the characteristic analysis model includes: For a preset difference interval, the quantized melting quality parameters are classified, and based on the classification of the melting quality parameters, the parameter feature array sequences are classified to obtain a plurality of parameter feature array sequence sets; Align each temperature distribution parameter feature array sequence in the parameter feature array sequence set, and compare the temperature distribution parameter feature arrays in the temperature distribution parameter feature array sequence one by one. If the difference characteristics between the temperature distribution parameter feature arrays of the same sequence meet the equivalent standard, the temperature distribution parameter feature array of the sequence is marked as a weak characteristic temperature distribution parameter feature array, and the remaining other temperature distribution feature arrays are marked as medium characteristic temperature distribution parameter feature arrays; Analyze the difference characteristics of the medium characteristic temperature distribution parameter characteristic arrays of the same order between the parameter characteristic array sequences, determine the difference characteristic parameters of each order, and continuously analyze the difference characteristic parameters. If the continuous difference characteristic parameters show a monotonic change trend, the temperature distribution parameter characteristic array corresponding to the difference characteristic parameters is identified as a strong characteristic temperature distribution parameter characteristic array; The order of the strong characteristic temperature distribution parameter characteristic array in the parameter characteristic array sequence is identified as the first characteristic factor.

6. The method for analyzing internal temperature data of an asphalt melting furnace according to claim 5, characterized in that: The temperature values ​​at different points in the characteristic array of the strong characteristic temperature distribution parameter are identified as the second characteristic factor.

7. The method for analyzing internal temperature data of an asphalt melting furnace according to claim 5, characterized in that: The method for analyzing the difference characteristics of medium characteristic temperature distribution parameter characteristic arrays of equal order between parameter characteristic array sequences includes: Randomly select medium characteristic temperature distribution parameter feature arrays of equal order to combine in pairs, record as parameter feature array random group, compare the temperature values ​​and inter-point distances of equal points between medium characteristic temperature distribution parameter feature arrays of parameter feature array random group, determine the temperature value difference and inter-point distance of equal points, and determine the average temperature value difference and average inter-point distance between parameter feature array random groups; A first difference characteristic operator is constructed based on the difference in average temperature values ​​of equivalent points, and a second difference characteristic operator is constructed based on the average distance between equivalent points. Based on the first difference characteristic operator and the second difference characteristic operator, a difference characteristic parameter is determined.

8. The method for analyzing the internal temperature data of an asphalt melting furnace according to claim 7, characterized in that: The expression of the difference characteristic parameter is: Among them, T is the difference characteristic parameter, Δx i is the average temperature difference between the i-th equivalent points in the medium characteristic temperature distribution parameter feature array, Δl i is the average distance between the i-th equivalent points between the characteristic arrays of the medium characteristic temperature distribution parameters, n is the total number of points in the characteristic arrays of the medium characteristic temperature distribution parameters, r is the average distance between points multiplier adjustment coefficient, and b is the average distance between points multiplier adjustment constant.

9. An analysis system based on the asphalt melting furnace internal temperature data analysis method of claim 1, characterized in that: include: The first module is used to determine the cavity structure of the asphalt melting furnace, and to construct a virtual three-dimensional cavity based on the cavity structure, and to set corresponding temperature collection points in the virtual three-dimensional cavity based on the relative position of the temperature sensor device in the cavity structure of the asphalt melting furnace; The second module is used to determine the temperature distribution characteristics of the side of the asphalt melting furnace by using thermal imaging detection technology, and map the determined temperature distribution characteristics on the virtual three-dimensional cavity, and combine the temperature values ​​collected at different temperature collection points to determine the distance between each temperature collection point of the virtual three-dimensional cavity and the center point of the temperature distribution to which it belongs; The third module is used to construct a temperature distribution parameter feature array according to the relative positions of different temperature collection points. The temperature distribution parameters include the temperature values ​​collected by the temperature collection points and the distances between the points. Based on the positions of the temperature collection points in the virtual three-dimensional cavity, the corresponding temperature distribution parameters are determined in the temperature distribution parameter feature array. The fourth module is used to sort the temperature distribution parameter feature array based on the time series relationship to obtain the parameter feature array sequence, and establish the association between the parameter feature array sequence and the asphalt melting quality, and integrate several association relationships to obtain the asphalt melting quality relationship library; The fifth module is used to configure characteristic factors for the characteristic array of temperature distribution parameters using the characteristic analysis model, and to establish an index relationship of the asphalt melting quality relationship library based on the characteristic factors.

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