Method and system for detecting performance of high-strength refractory material

Abstract

A performance testing method and system for high-strength refractory materials relates to the field of material testing. The method is applied to a testing system and includes: performing a first performance test on the refractory material to obtain first performance test data, wherein the first performance test data is composed of performance data from multiple detection areas of the refractory material; performing a signal accuracy evaluation on the performance data of the multiple detection areas to obtain the signal accuracy of the multiple detection areas; determining the detection paths of the multiple detection areas based on the signal accuracy of the multiple detection areas; performing a second performance test on the refractory material based on the detection path to obtain second performance test data; matching the second performance test data with a preset refractory material performance evaluation index table, and outputting the performance test results of the refractory material. Implementing the technical solution provided by this application solves the problem that the complex internal structure of the refractory material makes it difficult to accurately judge the true internal condition of the material based on the detection signal.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of material detection, and in particular to a performance detection method and system of high-strength refractory material. BACKGROUND

[0002] The working environment of a kiln is relatively harsh, often accompanied by ultra-high temperature, easy corrosion, thermal stress impact, etc. Therefore, the building materials of the kiln generally use high-strength refractory materials. However, the performance of such high-strength refractory materials is unstable, and needs to be regularly detected to ensure that it can continue to be used normally.

[0003] At present, the performance detection of high-strength refractory materials mostly uses non-destructive testing technology, which detects whether there are cracks, pores and other defects in the material by emitting ultrasonic or infrared detection signals to the refractory material. However, refractory materials are usually composed of multiple mineral components and additives, and their internal structure and component distribution are often uneven, which will lead to complex and variable propagation and reflection of detection signals during non-destructive testing, making it difficult to accurately determine the true condition of the material inside and prone to misjudgment. SUMMARY

[0004] In view of the problem that the internal structure of refractory material is complex, making it difficult to accurately determine the true condition of the material inside according to the detection signal, the present application provides a performance detection method and system of high-strength refractory material.

[0005] In the first aspect, the present application provides a performance detection method of high-strength refractory material, applied to a detection system, comprising:

[0006] Performing a first performance test on the refractory material to obtain first performance test data, wherein the first performance test data is composed of performance data of multiple detection areas of the refractory material;

[0007] Performing signal accuracy evaluation on the performance data of the multiple detection areas to obtain signal accuracy of the multiple detection areas;

[0008] Determining a detection path of the multiple detection areas according to the signal accuracy of the multiple detection areas;

[0009] Performing a second performance test on the refractory material based on the detection path to obtain second performance test data;

[0010] Matching the second performance test data with a preset refractory material performance evaluation index table to output a performance detection result of the refractory material.

[0011] Optionally, the signal accuracy evaluation on the performance data of the multiple detection areas to obtain the signal accuracy of the multiple detection areas is specifically:

[0012] performing spectrum analysis on the performance data of the plurality of detection regions to obtain signal-to-noise ratios of the plurality of detection regions;

[0013] performing clustering on the plurality of detection regions based on the signal-to-noise ratios of the plurality of detection regions to obtain a plurality of clustering clusters;

[0014] calculating a coefficient of variation of the plurality of clustering clusters;

[0015] normalizing the coefficient of variation of the plurality of clustering clusters to obtain signal accuracies of the plurality of clustering clusters.

[0016] Optionally, the determining of the detection path of the plurality of detection regions according to the signal accuracies of the plurality of detection regions specifically comprises:

[0017] determining detection priorities of the plurality of clustering clusters based on the signal accuracies of the plurality of clustering clusters;

[0018] comparing the signal accuracies of the plurality of clustering clusters with a preset signal accuracy threshold, so as to divide the plurality of clustering clusters into a high-accuracy clustering set and a low-accuracy clustering set;

[0019] if a first clustering cluster belongs to the high-accuracy clustering set, calculating a center coordinate of the first clustering cluster, the first clustering cluster being any one of the plurality of clustering clusters;

[0020] identifying a detection region corresponding to the center coordinate of the first clustering cluster;

[0021] determining the detection path of the second performance test according to the detection priorities of the plurality of clustering clusters and the detection region corresponding to the center coordinate of the first clustering cluster.

[0022] Optionally, the calculating of the coefficient of variation of the plurality of clustering clusters specifically further comprises:

[0023] calculating a region volume of a second clustering cluster, the second clustering cluster being any one of the plurality of clustering clusters;

[0024] determining an accuracy weight of the second clustering cluster in the plurality of clustering clusters according to the region volume of the second clustering cluster;

[0025] multiplying the accuracy weight of the second clustering cluster by a corresponding coefficient of variation to obtain a weighted coefficient of variation of a third clustering cluster, and taking the weighted coefficient of variation as the coefficient of variation of the second clustering cluster.

[0026] Optionally, performing a second performance test on the refractory material based on the detection path to obtain second performance test data, specifically:

[0027] obtaining an application environment parameter of the refractory material, the application environment parameter including an environmental temperature, an environmental thermal shock force, and an environmental corrosion degree;

[0028] generating a signal test strength of the plurality of detection regions according to the application environment parameter and signal accuracies of the plurality of detection regions.

[0029] Optionally, the signal test strength of the plurality of detection regions is generated according to the application environment parameter and the signal accuracies of the plurality of detection regions, and specifically can be generated according to the following formula:

[0030]

[0031] wherein, is a signal test strength of an i-th detection region, is an initial signal test strength, is an environmental temperature representation value, S is an environmental thermal shock force representation value, and C is an environmental corrosion degree representation value, is a signal accuracy of the i-th detection region, is a signal accuracy standard deviation, is a signal accuracy expectation value.

[0032] In a second aspect, the application provides a performance detection system of high-strength refractory material, the system is a detection system, and the detection system includes a test module, a processing module, and a sending module, wherein:

[0033] The test module is configured to perform a first performance test on the refractory material to obtain first performance test data, the first performance test data being composed of performance data of a plurality of detection regions of the refractory material.

[0034] The processing module is configured to perform signal accuracy evaluation on the performance data of the plurality of detection regions to obtain signal accuracies of the plurality of detection regions, and determine detection paths of the plurality of detection regions according to the signal accuracies of the plurality of detection regions.

[0035] The test module is further configured to perform a second performance test on the refractory material based on the detection paths to obtain second performance test data.

[0036] The sending module is configured to match the second performance test data with a preset refractory material performance evaluation index table and output a performance detection result of the refractory material.

[0037] In a third aspect, the present application provides an electronic device, comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method according to any one of the first aspect.

[0038] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions, when the instructions are executed, the method according to any one of the first aspect is performed.

[0039] To sum up, the one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0040] 1、The present application firstly obtains the first performance test data by performing the conventional first performance test on the refractory material, so as to preliminarily understand the internal structure information of the refractory material. However, at this time, due to the uneven internal structure and composition distribution of the refractory material, the first performance test data is not accurate. Therefore, the signal accuracy of each detection area is understood by performing precision analysis on the first performance test data. Then, the refractory material is detected according to the detection accuracy, the multiple detection areas are divided into a high-precision clustering group and a low-precision clustering group, the detection areas in the low-precision clustering group are detected one by one, and the detection areas in the high-precision clustering group are combined for detection, so as to avoid excessive consumption of detection resources in the area with high signal accuracy, and improve the detection efficiency. Then, the signal detection strength of each detection area is set according to the signal accuracy of each detection area, and the second performance test is performed to obtain the second performance test data, so as to improve the performance test accuracy of the refractory material. Finally, the comprehensive performance detection result is made according to the second performance test data, the analysis workload of the workers is reduced, and the detection efficiency is improved.

[0041] 2、When evaluating the signal accuracy of multiple detection areas, due to the change of the internal structure of the refractory material after long-term use, the signal accuracy of each detection area has high dispersion degree, so that it is difficult to evaluate the multiple detection areas as a whole by using a fixed accuracy evaluation standard. The present application obtains the signal-to-noise ratio by performing frequency spectrum analysis on the performance data of the detection areas. Then, the detection areas with similar accuracy are reclassified according to the signal-to-noise ratio clustering, so as to adapt to the structural change. Then, the variation coefficient of the clustering cluster is calculated to measure the data dispersion degree in each clustering cluster, so as to understand the signal accuracy stability of each clustering cluster. Finally, the variation coefficient is normalized to obtain the relative signal accuracy of each clustering cluster, so as to reduce the influence of the internal structure change of the refractory material on the signal accuracy evaluation. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 is a flowchart of a performance detection method of a high-strength refractory material provided by an embodiment of the present application.

[0043] Figure 2 is a structural diagram of a performance detection system of a high-strength refractory material provided by an embodiment of the present application.

[0044] Figure 3 is a structural diagram of an electronic device provided by an embodiment of the present application.

[0045] Legend: 1, test module; 2, processing module; 3, sending module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0046] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in combination with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0047] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0048] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means 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 indicating the indicated technical features. Therefore, the features defined with "first" and "second" can explicitly or implicitly include one or more features. The terms "include", "contain", "have" and their variants mean "include but are not limited to", unless otherwise specifically emphasized.

[0049] With the continuous development of material science, many processing technologies that are difficult to achieve in industry have been effectively supported. Especially through continuous research and improvement of refractory materials, the development process of the national economy in the fields of steel, non-ferrous metals, glass, cement, ceramics, petrochemicals, machinery, boilers, light industry, electric power, military industry, etc. has been greatly improved.

[0050] Currently, kilns are essential equipment in the processing of ceramics and glass. However, kilns operate in a harsh environment, often characterized by ultra-high temperatures, corrosion, and strong thermal stress shocks. Therefore, kilns are generally constructed from high-strength refractory materials. However, the performance of these high-strength refractory materials is unstable, requiring regular performance testing to ensure their continued normal use.

[0051] The performance testing of high-strength refractory materials is mostly carried out using non-destructive testing (NDT). This involves transmitting ultrasonic or infrared signals to the refractory material to detect defects such as cracks and pores. However, refractory materials are typically composed of a variety of mineral components and additives, and their internal structure and composition distribution are often uneven. This results in complex and variable signal propagation and reflection during NDT, making it difficult to accurately determine the true condition within the material and prone to misjudgment.

[0052] In order to solve the above problems, the present application provides a method for detecting the performance of high-strength refractory materials, which is applied to a detection system, such as Figure 1 As shown, the method includes steps S101 to S105, which are as follows:

[0053] S101. Perform a first performance test on a refractory material to obtain first performance test data, where the first performance test data is composed of performance data of multiple detection areas of the refractory material.

[0054] In the above steps, the first performance test is a conventional performance test, which transmits a preset ultrasonic signal or infrared detection signal to the refractory material to be tested, and then analyzes the internal structure of the refractory material to be tested for defects such as pores and cracks that affect performance based on the received feedback signal. In order to conduct a more refined analysis of the refractory material's performance, the refractory material is divided into multiple detection areas, each with the same detection area. Therefore, after the first performance test, the first performance test data obtained includes performance data from multiple detection areas, and the performance data of each detection area reflects its corresponding internal structure.

[0055] S102: Perform signal accuracy evaluation on the performance data of the multiple detection areas to obtain the signal accuracy of the multiple detection areas.

[0056] In the above steps, due to the influence of the high-temperature working environment of the kiln on the refractory material during long-term use, the internal structure and organizational composition of the refractory material have undergone unknown changes, which can cause the preset detection signal to be absorbed or offset, resulting in more noise or incomplete signal in the received feedback signal, and thus lower signal accuracy, which cannot accurately determine the real internal structure. Therefore, the test results of the first performance test cannot be directly used to evaluate the performance of the refractory material, and further evaluation of the signal accuracy of the performance data of multiple detection areas is required to ensure the accuracy of the test results. Specifically, first, the performance data of multiple detection areas are subjected to frequency spectrum analysis, converting the first performance test data from time domain signal to frequency domain signal to amplify the difference between signal and noise, then calculating the signal-to-noise ratio of multiple detection areas to determine the degree of noise interference on the detection signal; At this time, the signal-to-noise ratio of each detection area is affected by the internal structure, but it cannot be determined whether the influence is caused by defects or normal complex organizational composition. In order to distinguish, the present application clusters multiple detection areas according to the signal-to-noise ratio of each detection area, and divides detection areas with similar signal-to-noise ratio into the same cluster. It can be understood that if the signal-to-noise ratios of multiple detection areas divided into the same cluster are similar and continuous, it means that the signal-to-noise ratio in the detection area is caused by normal complex organizational composition, and it can also be further explained that the signal accuracy in the cluster is high. Therefore, the present application further calculates the coefficient of variation of multiple clusters to determine the continuity of multiple detection areas in the cluster. The coefficient of variation is the ratio of standard deviation to mean, which can effectively measure the dispersion degree of data in the cluster. Finally, the coefficient of variation of multiple clusters is normalized to obtain the signal accuracy of multiple clusters, thereby providing an improved basis for further detection. Wherein, the smaller the coefficient of variation of the cluster, the lower the dispersion degree of the data, and the higher the signal accuracy.

[0057] In a possible implementation, when calculating the coefficient of variation of the clustering cluster, the shape of the refractory material can be irregular, resulting in inconsistent volumes of the respective detection regions. For detection regions with low volume, the presence of structural defects inside has less impact on performance than high volume, and thus the signal accuracy requirement is not high. Based on this characteristic, when calculating the coefficient of variation of the clustering cluster, the area volume of each clustering cluster is calculated to determine the accuracy weight of the plurality of clustering clusters. Specifically, the accuracy weight of the plurality of clustering clusters can be obtained by normalizing the ratio of the area volume of each clustering cluster. Then, the accuracy weight of each clustering cluster is multiplied by the corresponding coefficient of variation to obtain the weighted coefficient of variation of each clustering cluster. Finally, the weighted coefficient of variation of each clustering cluster is taken as the coefficient of variation of each clustering cluster, so as to more accurately identify which regions have greater impact on the overall performance and which regions have less impact on the overall performance, and provide a reliable basis for subsequent adjustment of the detection signal in the second performance detection.

[0058] S103, determining the detection path of the plurality of detection regions according to the signal accuracy of the plurality of detection regions.

[0059] In the above step, after the signal accuracy of each clustering cluster is determined, in order to avoid excessive consumption of detection resources in the region with high signal accuracy, thereby reducing the detection efficiency, the application sets the detection path of the refractory material according to the signal accuracy of the plurality of detection regions. First, based on the signal accuracy of the plurality of clustering clusters, the detection priority of each clustering cluster is determined, that is, the higher the signal accuracy, the lower the detection priority; the lower the signal accuracy, the higher the detection priority. Then, the signal accuracy of the plurality of clustering clusters is compared with the preset signal accuracy threshold, so as to divide the plurality of clustering clusters into a high-accuracy clustering set and a low-accuracy clustering set. The high-accuracy clustering set includes a plurality of high-accuracy clustering clusters with high signal accuracy, and the low-accuracy clustering set includes a plurality of low-accuracy clustering clusters with low signal accuracy. For the clustering clusters in the high-accuracy clustering set, the signal accuracy is high enough, so no excessive detection resources are needed. Therefore, the center coordinates of the high-accuracy clustering clusters are calculated, and then the high-accuracy detection regions corresponding to the center coordinates of the high-accuracy clustering clusters are identified. At this time, the high-accuracy detection regions are taken as the representative regions of the plurality of detection regions in the high-accuracy clustering clusters, and only the high-accuracy detection regions need to be detected when detecting the detection regions in the high-accuracy clustering clusters, so as to avoid excessive consumption of detection resources in the region with high signal accuracy, thereby improving the detection efficiency. For the plurality of clustering clusters in the low-accuracy clustering set, each detection region in the low-accuracy clustering cluster needs to be detected because the signal accuracy is low. When planning the detection path of the plurality of detection regions in the low-accuracy clustering cluster, the application is based on the shortest path principle to further improve the detection efficiency.

[0060] To make the detection path of the second performance test more clear, the application illustrates the following example: the existing high-precision clusters A and B, low-precision clusters C and D, the high-precision cluster A includes detection areas a1 and a2, the high-precision cluster B includes detection areas b1 and b2, the low-precision cluster C includes detection areas c1, c2 and c3, and the low-precision cluster D includes detection areas d1, d2 and d3, wherein a1 is the characteristic area of the high-precision cluster A, b2 is the characteristic area of the high-precision cluster B, the shortest detection path of the low-precision cluster C is c2, c1 and c3, the shortest detection path of the low-precision cluster D is d1, d2 and d3, and the detection priority of the four clusters is C, D, B and A in sequence. The detection path of the second performance test is c2, c1, c3, d1, d2, d3, b2 and a1.

[0061] In S104, the refractory is subjected to the second performance test based on the detection path, and second performance test data is obtained.

[0062] In the above step, when the second performance test is performed, the signal strength of the detection signal needs to be adjusted again due to the different signal accuracies of the detection areas, so as to adapt to the internal structure change of the refractory. Specifically, the application environment parameters of the refractory are first obtained. Since the refractory in the application is applied to the construction of a kiln, the working environment of the kiln is characterized by high temperature, strong thermal shock force and strong slag corrosion. Therefore, the application environment parameters include environmental temperature, environmental thermal shock force and environmental corrosion degree. Then, the adjusted signal test strength of each detection area is generated according to the application environment parameters and the signal accuracy of each detection area. Specifically, the adjusted signal test strength of each detection area can be calculated by the following formula:

[0063]

[0064] wherein, is the signal test strength of the i th detection area, is the initial signal test strength, is the environmental temperature characteristic value, S is the environmental thermal shock force characteristic value, and C is the environmental corrosion degree characteristic value, is the signal accuracy of the i th detection area, is the signal accuracy standard deviation, is the signal accuracy expected value.

[0065] In the above formula, the initial signal test strength is the signal strength of the detection signal at the first performance test, and the environmental thermal shock force characteristic value, the environmental corrosion degree characteristic value, and the environmental temperature characteristic value can be understood as data obtained by normalizing the current application environment parameters of the kiln compared to the boundary conditions of the application environment parameters. The data converts different dimensions of kiln environment evaluation indexes into unified kiln environment evaluation indexes, that is, it is mapped to the interval of 0 to 1. For example, if the environmental temperature characteristic value is 1, it means that the current working environment temperature of the kiln has reached the highest tolerable temperature. If the environmental temperature characteristic value is 0, it means that the current working environment temperature of the kiln has reached the minimum effective working temperature. From the above formula, it can be understood that when the signal accuracy of the detection area is equal to the signal accuracy expectation value, the adjusted signal test strength is equal to the initial signal test, which means that the internal structure of the detection area has not changed much under the influence of the application environment parameters. In actual application, this situation is too ideal. In most cases, the higher the environmental temperature, the greater the thermal shock force, and the higher the corrosion degree, the greater the impact on the performance of the refractory material. Therefore, the corresponding signal test strength requirement is also higher, which can ensure that the final detection result is more accurate.

[0066] After obtaining the adjusted signal detection strength of each detection area according to the above formula, the second performance test is performed. The second performance test detects the multiple detection areas in sequence according to the detection path to obtain second performance test data.

[0067] S105, matching the second performance test data with the preset refractory material performance evaluation index table, and outputting the performance detection result of the refractory material.

[0068] In the above step, the preset refractory performance evaluation index table includes evaluation range indexes of possible defects of the refractory, for example, can include evaluation range indexes of pore defects "excellent if the pore diameter is less than 1 mm", "good if the pore diameter is greater than 1 mm and less than 3 mm", and "poor if the pore diameter is greater than 3 mm"; the second performance test data is converted into structure data of each detection area, and then the structure data of each detection area is matched with the preset refractory performance evaluation index table, and defect analysis is performed to obtain defect information of the plurality of detection areas, the defect information including defect type, defect quantity, and defect evaluation (excellent, good, or poor); then the proportion of detection areas containing major defects in all detection areas is counted, the detection areas containing major defects referring to detection areas containing defect evaluation "poor" in the defect information; if the proportion of detection areas containing major defects is greater than or equal to a preset threshold, it is determined that the performance test result of the refractory is "unqualified", and the refractory needs to be replaced, if the proportion of detection areas containing major defects is less than the preset threshold, it is determined that the performance test result of the refractory is "qualified", and the existing defects are counted and output to the display terminal together with the performance test result.

[0069] Reference Figure 2 The application also provides a performance detection system of high-strength refractory, which is a detection system and includes a test module 1, a processing module 2, and a sending module 3.

[0070] The test module 1 is configured to perform first performance testing on the refractory to obtain first performance test data, the first performance test data being composed of performance data of a plurality of detection areas of the refractory.

[0071] The processing module 2 is configured to perform signal accuracy evaluation on the performance data of the plurality of detection areas to obtain signal accuracy of the plurality of detection areas, and determine detection paths of the plurality of detection areas according to the signal accuracy of the plurality of detection areas.

[0072] The test module 1 is further configured to perform second performance testing on the refractory based on the detection paths to obtain second performance test data.

[0073] The sending module 3 is configured to match the second performance test data with a preset refractory performance evaluation index table, and output a performance detection result of the refractory.

[0074] In a possible implementation, the signal accuracy evaluation on the performance data of the plurality of detection areas to obtain the signal accuracy of the plurality of detection areas is specifically as follows:

[0075] The performance data of the plurality of detection areas is subjected to frequency spectrum analysis to obtain signal-to-noise ratios of the plurality of detection areas.

[0076] Clustering the plurality of detection regions based on signal-to-noise ratios of the plurality of detection regions to obtain a plurality of clustering clusters;

[0077] Calculating a coefficient of variation of the plurality of clustering clusters;

[0078] Normalizing the coefficient of variation of the plurality of clustering clusters to obtain signal accuracy of the plurality of clustering clusters.

[0079] In a possible implementation, a detection path of the plurality of detection regions is determined according to the signal accuracy of the plurality of detection regions, specifically including:

[0080] Determining a detection priority of the plurality of clustering clusters based on the signal accuracy of the plurality of clustering clusters;

[0081] Comparing the signal accuracy of the plurality of clustering clusters with a preset signal accuracy threshold, so as to divide the plurality of clustering clusters into a high-accuracy clustering set and a low-accuracy clustering set;

[0082] If the first clustering cluster belongs to the high-accuracy clustering set, a center coordinate of the first clustering cluster is calculated, and the first clustering cluster is any one of the plurality of clustering clusters;

[0083] Identifying a detection region corresponding to the center coordinate of the first clustering cluster;

[0084] Determining a detection path of the second performance test according to the detection priority of the plurality of clustering clusters and the detection region corresponding to the center coordinate of the first clustering cluster.

[0085] In a possible implementation, the coefficient of variation of the plurality of clustering clusters is calculated, and specifically further including:

[0086] Calculating a region volume of the second clustering cluster, and the second clustering cluster is any one of the plurality of clustering clusters;

[0087] Determining an accuracy weight of the second clustering cluster in the plurality of clustering clusters according to the region volume of the second clustering cluster;

[0088] Multiplying the accuracy weight of the second clustering cluster by a corresponding coefficient of variation to obtain a weighted coefficient of variation of the third clustering cluster, and taking the weighted coefficient of variation as the coefficient of variation of the second clustering cluster.

[0089] In a possible implementation, the refractory material is subjected to the second performance test based on the detection path to obtain second performance test data, specifically including:

[0090] Obtaining an application environment parameter of the refractory material, and the application environment parameter includes an environmental temperature, an environmental thermal shock force, and an environmental corrosion degree;

[0091] Generating signal test strengths of the plurality of detection regions according to the application environment parameter and the signal accuracy of the plurality of detection regions.

[0092] In a possible implementation, according to the application environment parameter and the signal accuracy of the plurality of detection areas, the signal test strength of the plurality of detection areas is generated, and the signal test strength can be generated by using the following formula:

[0093]

[0094] wherein, is the signal test strength of the ith detection area, is the initial signal test strength, is an environment temperature representation value, S is an environment thermal impact force representation value, and C is an environment corrosion degree representation value, is the signal accuracy of the ith detection area, is a signal accuracy standard deviation, is a signal accuracy expectation value.

[0095] It should be noted that, when the apparatus provided in the above examples implements its functions, only the division of the above functional modules is used as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method examples provided in the above examples belong to the same concept, and the specific implementation process is detailed in the method examples, which will not be described here.

[0096] The present application also discloses an electronic device. Referring to Figure 3 , Figure 3 is a structural schematic diagram of an electronic device disclosed by the embodiments of the present application. The electronic device 300 can include at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0097] The communication bus 302 is used to realize the connection and communication between the components.

[0098] The user interface 303 can include a display screen (Display) and a camera (Camera), and the optional user interface 303 can further include a standard wired interface and a wireless interface.

[0099] The network interface 304 can optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0100] The processor 301 can include one or more processing cores. The processor 301 connects various parts within the server through various interfaces and lines, performs various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Alternatively, the processor 301 can be implemented in at least one of a hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. Among them, the CPU mainly processes operating systems, user interfaces, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; and the modem is used for processing wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be realized by a separate chip.

[0101] The memory 305 can include a random access memory (RAM) and a read-only memory (ROM). Alternatively, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 can include a program storage area and a data storage area, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store data involved in the above-mentioned various method embodiments, etc. The memory 305 can also be at least one storage device located away from the aforementioned processor 301. Referring to Figure 3 The memory 305 as a computer storage medium can include an operating system, a network communication module, a user interface module, and an application program of the performance detection method of the high-strength refractory material.

[0102] In Figure 3In the electronic device 300 shown, the user interface 303 is mainly used to provide an interface for the user to input, and obtain data input by the user; and the processor 301 can be used to invoke an application program stored in the memory 305 and storing a performance detection method of a high-strength refractory material, and when executed by one or more processors 301, the electronic device 300 performs the method described in one or more of the above embodiments. It should be noted that, for the foregoing method embodiments, in order to simply describe, they are all described as a combination of a series of actions, but those skilled in the art should know that the present application is not limited to the order of the actions described, because according to the present application, certain steps can be performed in other order or at the same time. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0103] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0104] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic. The division of the units is only a logical function division. There can be another division manner for actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different parts can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical or other forms.

[0105] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0106] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0107] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable memory. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a memory and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned memory includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0108] The above-described are only exemplary embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practicing the true principles of the present disclosure.

[0109] The present application is intended to cover any variations, uses or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and examples are only considered as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for detecting the performance of high-strength refractory materials, characterized in that: Applied to a detection system, the method comprises: Performing a first performance test on the refractory material to obtain first performance test data, the first performance test data consisting of performance data of multiple detection areas of the refractory material. The first performance test is a conventional performance test that transmits a preset ultrasonic signal or infrared detection signal to the refractory material to be tested, and then analyzing the internal structure of the refractory material to be tested for defects such as pores and cracks that affect performance based on the received feedback signal; Performing signal accuracy evaluation on the performance data of the plurality of detection areas to obtain signal accuracy of the plurality of detection areas; determining detection paths for the plurality of detection areas according to signal accuracy of the plurality of detection areas; Based on the detection path, performing a second performance test on the refractory material to obtain second performance test data; Matching the second performance test data with a preset refractory material performance evaluation index table, outputting the performance test results of the refractory material, The signal accuracy evaluation of the performance data of the plurality of detection areas is performed to obtain the signal accuracy of the plurality of detection areas, specifically: performing spectrum analysis on the performance data of the plurality of detection areas to obtain signal-to-noise ratios of the plurality of detection areas; Clustering the plurality of detection areas based on signal-to-noise ratios of the plurality of detection areas to obtain a plurality of clusters; Calculating the coefficient of variation of the plurality of clusters; Normalizing the coefficients of variation of the plurality of clusters to obtain the signal accuracies of the plurality of clusters; The determining of the detection paths of the plurality of detection areas according to the signal accuracy of the plurality of detection areas specifically includes: determining detection priorities of the plurality of clusters based on the signal accuracy of the plurality of clusters; Comparing the signal accuracy of the plurality of clusters with a preset signal accuracy threshold, thereby dividing the plurality of clusters into a high-precision cluster set and a low-precision cluster set, wherein the high-precision cluster set includes a plurality of high-precision clusters with higher signal accuracy, and the low-precision cluster set includes a plurality of low-precision clusters with lower signal accuracy; If the first cluster belongs to the high-precision cluster set, calculating the center coordinates of the first cluster, where the first cluster is any one of the multiple clusters; Identifying a detection area corresponding to the center coordinates of the first cluster; Determining a detection path for the second performance test based on the detection priorities of the multiple clusters and the detection areas corresponding to the center coordinates of the first cluster, wherein the high-precision detection area corresponding to the center coordinates of the high-precision cluster is used as a representation area of ​​the multiple detection areas in the high-precision cluster, and the multiple detection areas in any one of the low-precision clusters determine a detection path based on the shortest path principle; Based on the detection path, a second performance test is performed on the refractory material to obtain second performance test data, specifically: Obtaining application environment parameters of the refractory material, wherein the application environment parameters include ambient temperature, ambient thermal shock force, and ambient corrosion degree; generating signal test strengths of the plurality of detection areas according to the application environment parameters and the signal accuracy of the plurality of detection areas; According to the signal test strengths of the plurality of detection areas, the plurality of detection areas are detected in sequence along the detection paths of the plurality of detection areas to obtain the second performance test data.

2. The method according to claim 1, characterized in that The calculating of the coefficient of variation of the plurality of clusters further comprises: calculating a regional volume of a second cluster, where the second cluster is any one of the plurality of clusters; determining, according to the area volume of the second cluster, a precision weight of the second cluster among the plurality of clusters; The precision weight of the second cluster is multiplied by its corresponding coefficient of variation to obtain a weighted coefficient of variation of the second cluster, and the weighted coefficient of variation is used as the coefficient of variation of the second cluster.

3. The method according to claim 1, characterized in that The signal test strengths of the plurality of detection areas are generated according to the application environment parameters and the signal accuracy of the plurality of detection areas, and can be specifically generated using the following formula: ； in, is the signal test intensity of the i-th detection area, is the initial signal test strength, is the environmental temperature characterization value, S is the environmental thermal shock characterization value, C is the environmental corrosion characterization value, is the signal accuracy of the i-th detection area, is the signal accuracy standard deviation, is the expected value of signal accuracy, among which the environmental thermal shock force characterization value, environmental corrosion degree characterization value and environmental temperature characterization value can be understood as the data obtained by normalizing the current application environment parameters of the kiln compared with the boundary conditions of the application environment parameters. This data converts the kiln environment evaluation indicators of different dimensions into a unified kiln environment evaluation indicator, that is, they are all mapped to the range of 0 to 1.

4. A performance detection system for high-strength refractory materials, characterized in that: The system is a detection system, which includes a testing module, a processing module and a sending module, wherein: The testing module is used to perform a first performance test on the refractory material to obtain first performance test data, wherein the first performance test data is composed of performance data of multiple detection areas of the refractory material. The first performance test is a conventional performance test, which transmits a preset ultrasonic signal or infrared detection signal to the refractory material to be tested, and then analyzes the internal structure of the refractory material to be tested for defects such as pores and cracks that affect performance based on the received feedback signal; The processing module is configured to perform signal accuracy evaluation on the performance data of the plurality of detection areas to obtain the signal accuracy of the plurality of detection areas; and determine the detection paths of the plurality of detection areas based on the signal accuracy of the plurality of detection areas, wherein the performing signal accuracy evaluation on the performance data of the plurality of detection areas to obtain the signal accuracy of the plurality of detection areas is specifically as follows: performing spectrum analysis on the performance data of the plurality of detection areas to obtain signal-to-noise ratios of the plurality of detection areas; Clustering the plurality of detection areas based on signal-to-noise ratios of the plurality of detection areas to obtain a plurality of clusters; Calculating the coefficient of variation of the plurality of clusters; Normalizing the coefficients of variation of the plurality of clusters to obtain the signal accuracies of the plurality of clusters; The determining of the detection paths of the plurality of detection areas according to the signal accuracy of the plurality of detection areas specifically includes: determining detection priorities of the plurality of clusters based on the signal accuracy of the plurality of clusters; Comparing the signal accuracy of the plurality of clusters with a preset signal accuracy threshold, thereby dividing the plurality of clusters into a high-precision cluster set and a low-precision cluster set, wherein the high-precision cluster set includes a plurality of high-precision clusters with higher signal accuracy, and the low-precision cluster set includes a plurality of low-precision clusters with lower signal accuracy; If the first cluster belongs to the high-precision cluster set, calculating the center coordinates of the first cluster, where the first cluster is any one of the multiple clusters; Identifying a detection area corresponding to the center coordinates of the first cluster; Determining a detection path for a second performance test based on the detection priorities of the plurality of clusters and the detection areas corresponding to the center coordinates of the first cluster, wherein the high-precision detection area corresponding to the center coordinates of the high-precision cluster is used as a representation area of ​​the plurality of detection areas in the high-precision cluster, and the detection path of the plurality of detection areas in any one of the low-precision clusters is determined based on the shortest path principle; The testing module is further configured to perform a second performance test on the refractory material based on the detection path to obtain second performance test data, wherein the second performance test is performed on the refractory material based on the detection path to obtain the second performance test data, specifically: Obtaining application environment parameters of the refractory material, wherein the application environment parameters include ambient temperature, ambient thermal shock force, and ambient corrosion degree; generating signal test strengths of the plurality of detection areas according to the application environment parameters and the signal accuracy of the plurality of detection areas; According to the signal test strengths of the plurality of detection areas, the plurality of detection areas are tested in sequence along the detection paths of the plurality of detection areas to obtain the second performance test data; The sending module is used to match the second performance test data with a preset refractory material performance evaluation index table and output the performance test result of the refractory material.

5. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 3 is performed.

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