Method and device for detecting state of aerated concrete brick production line

Abstract

The invention discloses a state detection method and device for an aerated concrete brick production line, and is applied to the technical field of aerated concrete brick production, and the method comprises the steps: obtaining a power utilization data set, an equipment data set and a brick data set of the aerated concrete brick production line; performing feature extraction on the power utilization data set, the equipment data set and the brick data set to obtain a power utilization feature sequence, an equipment operation feature sequence and a production quality feature sequence of the aerated concrete brick production line; performing imaging processing based on the power utilization characteristic sequence, the equipment operation characteristic sequence and the production quality characteristic sequence to obtain a state image of the aerated concrete brick production line; and detecting the state of the aerated concrete brick production line according to the state image to obtain a state detection result of the aerated concrete brick production line. According to the invention, the state detection precision of the aerated concrete brick production line can be effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of aerated concrete brick production, and in particular to a state detection method and device for an aerated concrete brick production line. Background Art

[0002] Aerated concrete (ACC) bricks are a type of aerated concrete product produced through a high-temperature autoclaving process. The specific production process is as follows: First, there is a crushing and grinding process, in which raw materials such as quicklime and gypsum are crushed by crushing equipment, or raw materials such as fly ash are ball-milled by ball milling equipment. Crushing equipment and ball milling equipment can be collectively referred to as crushing and grinding equipment. Then there is a mixing process, in which the ground raw materials are fed into the mixing equipment according to the preset batching ratio, and the ground raw materials are mixed and poured into slurry through the mixing equipment. It should be noted that the temperature of the slurry should meet the process requirements (about 45 degrees Celsius) before pouring. If the temperature of the slurry does not meet the process requirements, it can be heated by steam. In addition, aluminum powder suspension should be added 0.5 to 1 minute before pouring the material. The last step is the forming process. The mold after pouring is sent to the initial curing room for gasification and initial setting. After the initial curing is completed, the green body is sent to the cutting equipment. The cutting equipment performs operations such as cross-cutting, longitudinal cutting, and breading of the green body according to actual production needs to obtain the cut green body. The cut green body is placed on the autoclave, and the autoclave is sent to the autoclave for curing. The formed brick products can be obtained through the curing process. Of course, in some cases, the gasification and initial setting process can be omitted.

[0003] For the production of aerated concrete bricks, it is crucial to regularly evaluate the status of the aerated concrete brick production line. Through regular evaluation, bottlenecks and problems in the aerated concrete brick production line can be discovered in time, so as to optimize and adjust, making the production process of aerated concrete bricks smoother and improving overall production efficiency. In related technologies, the status of the aerated concrete brick production line is usually detected based on information such as the weight of the aerated concrete bricks combined with a deep learning model. However, related technologies often only select data of a single dimension, but ignore other key factors, resulting in a decrease in the accuracy of status detection of the aerated concrete brick production line. Summary of the invention

[0004] The embodiments of the present application provide a method and device for detecting the state of an aerated concrete brick production line, which are used to improve the accuracy of detecting the state of an aerated concrete brick production line.

[0005] On the one hand, an embodiment of the present application provides a state detection method for an aerated concrete brick production line, comprising the following steps: Obtaining an electricity consumption dataset, an equipment dataset, and a brick dataset of the aerated concrete brick production line; Performing feature extraction on the electricity consumption data set, the equipment data set, and the brick data set to obtain an electricity consumption feature sequence, an equipment operation feature sequence, and a production quality feature sequence of the aerated concrete brick production line; Performing image processing based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain a state image of the aerated concrete brick production line; The state of the aerated concrete brick production line is detected according to the state image to obtain a state detection result of the aerated concrete brick production line.

[0006] On the other hand, an embodiment of the present application provides a state detection device for an aerated concrete brick production line, comprising: An acquisition module, used to acquire an electricity consumption data set, an equipment data set and a brick data set of the aerated concrete brick production line; A feature extraction module, used to extract features from the power consumption data set, the equipment data set and the brick data set to obtain a power consumption feature sequence, an equipment operation feature sequence and a production quality feature sequence of the aerated concrete brick production line; An image processing module, used for performing image processing based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain a state image of the aerated concrete brick production line; A detection module is used to detect the state of the aerated concrete brick production line according to the state image to obtain a state detection result of the aerated concrete brick production line.

[0007] According to a state detection method and device for an aerated concrete brick production line of the present application, taking into account the correlation between the power consumption, equipment operation and brick quality of the aerated concrete brick production line and the actual state of the aerated concrete brick production line, feature extraction is performed on multi-dimensional data such as power consumption data, equipment data, brick data, etc., to effectively capture the key time series features associated with the actual state of the aerated concrete brick production line, and then image generation is performed based on the time series features of the three modes. The image generation can reconstruct the time series feature sequence associated with the actual state of the aerated concrete brick production line while retaining the time dependency of the data, thereby converting the one-dimensional time series feature sequence into a state image for depicting the actual state of the aerated concrete brick production line. This can better mine the feature information hidden in the data of each dimension and capture more real state expressions. Finally, the state of the aerated concrete brick production line is detected based on the state image, thereby giving full play to the advantages of the deep learning model in image processing tasks and effectively improving the state detection accuracy of the aerated concrete brick production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1It is a flow chart of a state detection method of an aerated concrete brick production line provided by the present application; Figure 2 It is a schematic diagram of a state detection method of an aerated concrete brick production line provided by the present application; Figure 3 It is a flow chart of the feature extraction of electricity consumption dimension provided by this application; Figure 4 It is a flow chart of the device operation dimension feature extraction provided by this application; Figure 5 It is a flow chart of production quality dimension feature extraction provided by this application; Figure 6 It is a flow chart of the feature interaction and visualization provided by this application; Figure 7 It is a structural diagram of a state detection device for an aerated concrete brick production line provided in this application. DETAILED DESCRIPTION

[0009] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0010] In the following description, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0011] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0012] In view of the problems and defects existing in the related technologies, the embodiments of the present application provide a method and device for detecting the state of an aerated concrete brick production line, aiming to improve the accuracy of state detection of the aerated concrete brick production line.

[0013] A state detection method for an aerated concrete brick production line provided by an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0014] A state detection method for an aerated concrete brick production line provided in an embodiment of the present application can be applied to a terminal, a server, or software running in a terminal or a server. The terminal can be a tablet computer, a laptop computer, a desktop computer, etc., but is not limited to this. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms. In addition, the server can also be a node server in a blockchain network, but is not limited to this. Among them, blockchain is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.

[0015] Reference Figure 1 , Figure 1 1 is a flow chart of a method for detecting the state of an aerated concrete brick production line provided in an embodiment of the present application. The method for detecting the state of an aerated concrete brick production line may include the following steps S101-S104.

[0016] S101, obtaining an electricity consumption dataset, an equipment dataset, and a brick dataset of an aerated concrete brick production line.

[0017] It should be noted that the electricity consumption data set is associated with the electricity consumption of the aerated concrete brick production line, which may include electricity consumption impact data and process electricity consumption data at several preset moments. Among them, the electricity consumption impact data is used to indicate the environmental data that affects the power load of the aerated concrete brick production line. The electricity consumption impact data may include the cloud cover information, air pressure information and relative humidity information of the environment in which the aerated concrete brick production line is located. The process electricity consumption data is used to indicate the electricity consumption data of various processes in the aerated concrete brick production line. The process electricity consumption data may include the electricity consumption data of the crushing and grinding process, the mixing process and the molding process in the aerated concrete brick production line. The process electricity consumption data refers to the electricity consumption data of the equipment used to execute the process, such as the power consumption, etc. Furthermore, the equipment data set is associated with the equipment operation status of the aerated concrete brick production line, which may include the equipment operation data of the crushing and grinding process, the mixing process and the molding process in the aerated concrete brick production line. The process equipment operation data refers to the operation data of the equipment used to execute the process, such as work efficiency, etc. In addition, the brick data set is associated with the production quality of the aerated concrete brick production line, which may include image data, weight information and thickness information of at least one brick at several preset moments. The number of bricks can be flexibly set according to actual conditions. For example, the number of bricks can be 100, but is not limited to this.

[0018] It can be understood that the power usage dataset, the device dataset, and the brick dataset may be datasets that are detected and stored in advance.

[0019] In this step, the status of the aerated concrete brick production line can often be measured from two aspects: production frequency and production quality. The higher the production frequency and the better the production quality of the aerated concrete brick production line, the better the status of the aerated concrete brick production line. Among them, for production frequency, according to prior knowledge, the more electricity the aerated concrete brick production line consumes and the more frequently the equipment of the aerated concrete brick production line runs, the higher the production frequency of the aerated concrete brick production line; for production quality, according to prior knowledge, the better the quality of bricks of the aerated concrete brick production line, the better the production quality of the aerated concrete brick production line. It can be seen that the electricity consumption, equipment operation and brick quality of the aerated concrete brick production line are closely related to the actual status of the aerated concrete brick production line. Therefore, this step obtains the three-dimensional data sets of electricity consumption data set, equipment data set and brick data set of the aerated concrete brick production line as the data source for status detection.

[0020] Optionally, the preset time can be flexibly set according to actual conditions, and the embodiments of the present application do not limit this. For example, if the state of the aerated concrete brick production line in the past hour is detected, the past hour can be evenly divided into sixty preset moments, and one preset moment represents one minute. For another example, if the state of the aerated concrete brick production line in the previous day is detected, the previous day can be evenly divided into twenty-four preset moments, and one preset moment represents one hour.

[0021] S102, extracting features from the electricity consumption dataset, the equipment dataset and the brick dataset to obtain an electricity consumption feature sequence, an equipment operation feature sequence and a production quality feature sequence of the aerated concrete brick production line.

[0022] It should be noted that the power consumption feature sequence is a time feature sequence associated with the power consumption of the aerated concrete brick production line, which may include target power consumption features at several preset moments. Furthermore, the equipment operation feature sequence is a time feature sequence associated with the equipment operation of the aerated concrete brick production line, which may include target equipment operation features at several preset moments. In addition, the production quality feature sequence is a time feature sequence used to indicate the production quality of the aerated concrete brick production line. The production quality can be measured by the cracks, shape, and weight of the finished bricks. Therefore, the production quality feature sequence may include a first quality feature sequence, a second quality feature sequence, and a third quality feature sequence, wherein the first quality feature sequence is a time feature sequence associated with the cracks of the bricks produced by the aerated concrete brick production line, and the first quality feature sequence may include target crack features at several preset moments; the second quality feature sequence is a time feature sequence associated with the shape of the bricks produced by the aerated concrete brick production line, and the second quality feature sequence may include target shape features at several preset moments; the third quality feature sequence is a time feature sequence associated with the weight of the bricks produced by the aerated concrete brick production line, and the third quality feature sequence may include target weight features at several preset moments.

[0023] In this step, after obtaining the data source used for status detection, the electricity consumption feature sequence of the aerated concrete brick production line is obtained by performing feature extraction on the electricity consumption data set, and the equipment operation feature sequence of the aerated concrete brick production line is obtained by performing feature extraction on the equipment data set, and the production quality feature sequence of the aerated concrete brick production line is obtained by performing feature extraction on the brick data set. In this way, the feature information of the electricity consumption dimension, the equipment operation dimension and the production quality dimension can be effectively captured, thereby realizing feature extraction.

[0024] S103, performing image processing based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain a state image of the aerated concrete brick production line.

[0025] In this step, images are generated based on the feature sequence of the electricity consumption dimension, the feature sequence of the equipment operation dimension, and the feature sequence of the production quality dimension to obtain a portrait indicating the real state of the aerated concrete brick production line, namely, a state image, thereby upgrading the one-dimensional time series feature sequence to a two-dimensional image.

[0026] S104, detecting the state of the aerated concrete brick production line according to the state image, and obtaining a state detection result of the aerated concrete brick production line.

[0027] In this step, the state image is input into a pre-trained state detection model, and state detection is performed through the state detection model to obtain the state detection result of the aerated concrete brick production line, thereby giving full play to the advantages of the deep learning model in image processing and realizing the state detection of the aerated concrete brick production line.

[0028] Optionally, the state detection model is a deep learning model, and its type can be flexibly set according to actual conditions. For example, the state detection model can be a YOLOv8 model or an Adaboost model, but is not limited to this. In addition, the data set used in the training of the state detection model may include multiple preset state sample images and label information corresponding to each state sample image, wherein the generation of the state sample image is the same as the above steps S101-S103, that is, the state sample image is generated based on multiple data in the electricity data sample set, the equipment data sample set and the brick data sample set, and the label information corresponding to the state sample image is the same as the state detection result of the aerated concrete brick production line, and the embodiments of the present application will not be repeated here.

[0029] Optionally, the state detection result of the aerated concrete brick production line depends on the type of state detection task in the embodiment of the present application. For example, the state detection task in the embodiment of the present application may be a binary classification task, that is, the state detection model is used to detect whether the state of the aerated concrete brick production line is qualified, and the state detection result of the aerated concrete brick production line may include any one of qualified or unqualified. For another example, the state detection task in the embodiment of the present application may be a multi-classification task, that is, the state detection model is used to detect the type of state of the aerated concrete brick production line, and the state detection result of the aerated concrete brick production line may include any one of excellent, good, qualified or poor, but is not limited to this. It can be seen that the embodiment of the present application takes into account the correlation between the power consumption, equipment operation and brick quality of the aerated concrete brick production line and the actual state of the aerated concrete brick production line, and effectively captures the key time series features associated with the actual state of the aerated concrete brick production line by extracting features of multi-dimensional data such as power consumption data, equipment data, and brick data. Then, image generation is performed based on the time series features of the three modes. The image generation can reconstruct the time series feature sequence associated with the actual state of the aerated concrete brick production line while retaining the time dependency of the data, thereby converting the one-dimensional time series feature sequence into a state image for depicting the actual state of the aerated concrete brick production line. This can better mine the feature information hidden in the data of each dimension and capture more real state expressions. Finally, based on the state image, the state of the aerated concrete brick production line is detected, thereby giving full play to the advantages of the deep learning model in image processing and solving the limitations brought about by single data prediction, which can effectively improve the state detection accuracy of the aerated concrete brick production line.

[0030] In some embodiments, reference Figure 2 and Figure 3 In terms of feature extraction of the electricity consumption dimension, this embodiment extracts feature information associated with the electricity consumption of the aerated concrete brick production line from the two dimensions of electricity consumption influencing factors and internal electricity consumption factors, and integrates these feature information into a corresponding time feature sequence. The specific implementation process of the above step S102 may include but is not limited to the following steps S201-S203.

[0031] S201, obtaining the electricity consumption impact characteristics at each preset moment according to the electricity consumption impact data at each preset moment.

[0032] It should be noted that the electricity consumption of the aerated concrete brick production line can be further subdivided into electricity influencing factors and internal electricity factors. The electricity influencing factors are used to indicate the degree of influence of the environment in which the aerated concrete brick production line is located on the electricity consumption of the aerated concrete brick production line. The electricity influencing factors are characterized by the electricity influence characteristics.

[0033] In this step, under normal circumstances, environmental factors will indirectly affect the electricity consumption of the aerated concrete brick production line, thereby affecting the status detection of the aerated concrete brick production line. For example, in hot weather, it is often necessary to turn on additional air-conditioning equipment to maintain a comfortable working environment, and in precipitation weather, it is often necessary to turn on additional drainage equipment for drainage. The operation of these equipment will increase the electricity consumption of the aerated concrete brick production line. However, these equipment are not related to the production of the aerated concrete brick production line. Therefore, it is possible that the electricity consumption of the aerated concrete brick production line is high but the state of the aerated concrete brick production line is not good, thereby affecting the status detection of the aerated concrete brick production line. For another example, the relative humidity is high during the rainy season. In this environment, the corrosion and aging process of the equipment of the aerated concrete brick production line will be accelerated, further increasing the electricity consumption of the aerated concrete brick production line. Although these additional electricity consumptions are related to the production of the aerated concrete brick production line, these additional electricity consumptions do not play a substantial role in the production process of the aerated concrete brick production line. Therefore, it is still possible that the electricity consumption of the aerated concrete brick production line is high but the state of the aerated concrete brick production line is not good, thereby affecting the status detection of the aerated concrete brick production line.

[0034] In this regard, in order to improve the state detection accuracy of the aerated concrete brick production line, in terms of feature extraction of the electricity consumption dimension, this step needs to consider the electricity consumption influencing factors. Specifically, according to prior knowledge, cloud cover information, air pressure information and relative humidity information can jointly measure the actual weather conditions, and the environment in which the aerated concrete brick production line is located can be measured by the actual weather conditions. Therefore, as described in the above embodiment, the electricity consumption influencing data may include cloud cover information, air pressure information and relative humidity information of the environment in which the aerated concrete brick production line is located. Among them, the cloud cover information refers to the area of ​​the cloud and the percentage of the cloud occupying the sky. When the cloud cover is between 0 and 20%, the weather is likely to be sunny, when the cloud cover is between 20 and 50%, the weather is likely to be less cloudy, when the cloud cover is between 50 and 80%, the weather is likely to be cloudy, and when the cloud cover is between 80 and 100%, the weather is likely to be cloudy. In the above case, if the air pressure drops, there is a possibility of rain. For sunny days and few clouds, the probability of rain is small and generally light rain; for cloudy and overcast days, the probability of rain is large and generally moderate rain or heavy rain. In addition, on rainy days outside the rainy season or in the rainy season, the relative humidity will rise. Based on this, this step constructs a comprehensive weather model, which can reflect the impact of cloud cover, air pressure and relative humidity on the electricity consumption of the aerated concrete brick production line. For each preset moment, this step inputs the cloud cover information, air pressure information and relative humidity information of the current preset moment into the comprehensive weather model. The electricity consumption impact characteristics of the current preset moment can be obtained by indexing the comprehensive weather model, as shown in the following formula (1): (1); In formula (1), Indicates the characteristics of electricity consumption impact; Indicates air pressure information; Indicates cloud cover information; Relative humidity information; , , and Respectively represent the influence coefficients of atmospheric pressure information on the electricity consumption of aerated concrete brick production line under sunny, lightly cloudy, cloudy and overcast days; , , and Respectively represent the influence coefficients of relative humidity information on the electricity consumption of aerated concrete brick production line under sunny, lightly cloudy, cloudy and overcast days; , , and Respectively represent the influence coefficients of cloud cover information on the electricity consumption of aerated concrete brick production line under sunny, few clouds, cloudy and overcast days.

[0035] Optionally, cloud cover information, air pressure information, and relative humidity information can be obtained in real time by crawling local meteorological data websites. In addition, the specific values ​​of the influence coefficients of various types of information on the electricity consumption of the aerated concrete brick production line can be pre-set according to actual conditions, and this embodiment is not limited here. Among them, a little cloud and cloudy days have a lower impact on the electricity consumption of the aerated concrete brick production line, and sunny and cloudy days have a greater impact on the electricity consumption of the aerated concrete brick production line. Therefore, the influence coefficient under a little cloud and cloudy days can be smaller than the influence coefficient under sunny and cloudy days. For example, the value range of the influence coefficient under a little cloud and cloudy days can be 0.1~0.5, while the value range of the influence coefficient under sunny and cloudy days can be 0.05~0.1, but it is not limited to this.

[0036] S202, obtaining the power consumption characteristics of the process at each preset time according to the power consumption data of the process at each preset time.

[0037] It should be noted that, according to the above steps, the electricity consumption of the aerated concrete brick production line can be further subdivided into electricity influencing factors and internal electricity consumption factors. The internal electricity consumption factors are used to indicate the electricity consumption of the equipment of the aerated concrete brick production line. The internal electricity consumption factors are characterized by the process electricity consumption characteristics. The process electricity consumption characteristics can include the electricity consumption characteristics of the grinding process, the electricity consumption characteristics of the mixing process and the electricity consumption characteristics of the molding process.

[0038] In this step, under normal circumstances, the electricity consumption of various processes of the aerated concrete brick production line is used to measure the electricity consumption of the aerated concrete brick production line. According to the production process of aerated concrete bricks, the aerated concrete brick production line can include three types of processes: grinding process, mixing process and forming process. Therefore, for each preset moment, the following operations are performed: For the crushing and grinding process, the equipment includes a crusher and a ball mill. The crusher is used to process large-particle raw materials such as quicklime and gypsum, and the ball mill is used to process small-particle raw materials such as fly ash. When the equipment is not aged, the crushing and grinding capacity of the equipment has not degraded, and the equipment is fully capable of coping with the maximum weight of the raw materials that can be processed; as the running time increases, the equipment gradually ages, and its crushing and grinding capacity degrades accordingly, which is usually manifested as a decrease in the maximum weight of the raw materials that the equipment can process. It can be seen that the running time of the equipment will directly affect the maximum weight of the raw materials that the equipment can process (crushing and grinding capacity). Affected by the degradation of the crushing and grinding capacity, compared with the past, the equipment often takes more time to process the same weight of raw materials, which causes the power consumption of the equipment to increase on the original basis. However, according to the above content, the greater the power consumption of the equipment, the better the state of the production line, which is obviously contrary to the above-mentioned situation of equipment aging, that is, the power consumption at this time cannot truly reflect the state of the equipment or production line. It can be seen that the maximum weight of raw materials that can be processed (crushing and grinding capacity) of the equipment will directly affect the power consumption of the equipment and cause the power consumption of the equipment to be distorted (too large).

[0039] Accordingly, the power consumption data of the crushing and grinding process at the current preset moment may include the power consumption of the crusher at the current preset moment, the power consumption influence coefficient of the crusher, the power consumption of the ball mill and the power consumption influence coefficient of the ball mill, wherein the power consumption of the equipment (i.e., the crusher or the ball mill) at the current preset moment refers to the difference between the cumulative power consumption of the equipment at the current preset moment and the cumulative power consumption of the equipment at the previous preset moment, and the power consumption influence coefficient of the equipment at the current preset moment is used to correct the power consumption of the equipment at the current preset moment. In this step, based on the power consumption data of the crushing and grinding process at the current preset moment, combined with the following formula (2), the power consumption characteristics of the crushing and grinding process at the current preset moment can be obtained: (2); In formula (2), Indicates the electricity consumption characteristics of the crushing and grinding process, and is used to measure the electricity consumption of the crushing and grinding process; Indicates the electricity consumption influence coefficient of the crusher; Indicates the power consumption of the crusher; Indicates the electricity consumption influence coefficient of the ball mill; Indicates the power consumption of the ball mill; and Must be greater than zero and less than or equal to one.

[0040] It is worth noting that regarding the power consumption coefficient of the ball mill and crusher, it can be seen from the above content that the operating time of the equipment will directly affect the maximum weight of the raw materials that the equipment can process (crushing capacity), and the maximum weight of the raw materials that the equipment can process (crushing capacity) will directly affect the power consumption of the equipment and cause the power consumption of the equipment to be distorted (too large). In order to correct the power consumption of the equipment, this step first calculates the maximum weight of the raw materials that the equipment can process at the current preset time, which can be expressed as , Indicates the maximum weight of raw materials that can be processed by the equipment, which is pre-calibrated at the factory and is always greater than zero. Indicates the operating time of the device. The operating time refers to the difference between the year to which the current preset time belongs and the year when the device was manufactured. , Represents the raw material decay parameter, which represents the weight of processable raw materials that decreases each year. It is a preset value and is greater than zero. Then, based on the maximum processable raw material weight of the equipment at the current preset moment, combined with the preset mapping table, the power consumption impact coefficient of the equipment at the current preset moment is dynamically determined. Among them, the preset mapping table pre-stores multiple preset maximum processable raw material weights and the power consumption impact coefficient corresponding to each preset maximum processable raw material weight. The table is pre-prepared based on historical data. The dynamic power consumption impact coefficient can be determined by looking up the table, thereby correcting the power consumption of the equipment. It should be noted that the power consumption impact coefficient is positively correlated with the maximum processable raw material weight, that is, the smaller the maximum processable raw material weight, the smaller the power consumption impact coefficient.

[0041] For the mixing process, its equipment includes a mixer, and the raw material ratio will have a certain impact on the mixing process. Generally speaking, a suitable raw material ratio can speed up the mixing process, thereby reducing the electricity consumption of the mixer, while an improper raw material ratio slows down the mixing process, thereby increasing the electricity consumption of the mixer. Therefore, the electricity consumption data of the mixing process at the current preset time may include the electricity consumption of the mixer at the current preset time and the influence coefficient of the raw material ratio on the electricity consumption of the mixer, and these data can be collected in advance. Among them, the electricity consumption here is the same as the above-mentioned electricity consumption, which will not be repeated here. This step is based on the electricity consumption data of the mixing process at the current preset time, combined with the following formula (3), to obtain the electricity consumption characteristics of the mixing process at the current preset time: (3); In formula (3), Indicates the electricity consumption characteristics of the mixing process and is used to measure the electricity consumption of the mixing process; Indicates the influence coefficient of raw material ratio on the power consumption of the mixer; Indicates the power usage of the blender. Optionally, The specific value of can be preset according to the actual situation, and this implementation mode is not limited here, but it should be noted that Must be greater than zero and less than or equal to one.

[0042] For the molding process, considering that gasification and initial condensation are not required in some cases, the equipment for the molding process only considers the cutting machine and the steam press. In addition, since the molding process is relatively simple and there are no additional factors that have a significant impact on its process, the power consumption data of the molding process at the current preset time can include the power consumption of the cutting machine and the power consumption of the steam press at the current preset time, and these data can be collected in advance. Among them, the power consumption here is the same as the above-mentioned power consumption, which will not be repeated here. This step is based on the power consumption data of the molding process at the current preset time, combined with the following formula (4), to obtain the power consumption characteristics of the molding process at the current preset time: (4); In formula (4), Indicates the electricity consumption characteristics of the molding process and is used to measure the electricity consumption of the molding process; represents the power consumption weight of the cutting machine, Indicates the power consumption of the cutting machine; Indicates the power consumption weight of the autoclave, Indicates the power consumption of the autoclave. Optionally, and The specific value of can be preset according to the actual situation, and this implementation mode is not limited here, but it should be noted that the sum of these two power consumption weights is one, and and Must be greater than zero and less than or equal to one.

[0043] S203, obtaining target power consumption characteristics at a plurality of preset moments according to the power consumption impact characteristics and process power consumption characteristics at a plurality of preset moments, and further obtaining a power consumption characteristic sequence.

[0044] It should be noted that the target power consumption characteristics at the preset time are used to measure the power consumption of the aerated concrete brick production line at the preset time.

[0045] In this step, for each preset moment, the power consumption impact characteristics and process power consumption characteristics of the current preset moment are fused to obtain the target power consumption characteristics of the current preset moment; the power consumption characteristic sequence can be constructed through the target power consumption characteristics of several preset moments. , For the The target power consumption characteristics at a preset time, is the number of preset moments, thereby capturing the key timing characteristics of the power consumption dimension.

[0046] Exemplarily, for each preset moment, a machine learning method is used to process the electricity consumption impact characteristics and process electricity consumption characteristics of the current preset moment to obtain the target electricity consumption characteristics of the current preset moment. The machine learning method can be set according to actual conditions, for example, the machine learning method can be a support vector machine or a random forest, but is not limited thereto.

[0047] As another example, the process power consumption characteristics at the preset time are used to measure the power consumption of various processes in the aerated concrete brick production line at the preset time. For each preset time, the comprehensive power consumption characteristics at the current preset time are obtained according to the proportion of the time of each process in the production time of the aerated concrete brick production line and the power consumption characteristics of each process at the current preset time. The comprehensive power consumption characteristics can measure the power consumption of the aerated concrete brick production line itself at the current preset time. However, since the power consumption of the aerated concrete brick production line is affected by external environmental factors, the power consumption represented by the comprehensive power consumption characteristics contains false power consumption caused by environmental factors, which is not the real power consumption.

[0048] According to the above steps, the electricity consumption impact feature is used to indicate the impact of the environment in which the aerated concrete brick production line is located on the electricity consumption of the aerated concrete brick production line. Therefore, after obtaining the comprehensive electricity consumption feature, this example first performs maximum-minimum normalization on the electricity consumption impact features of several preset moments so that the electricity consumption impact features of each preset moment are all between 0 and 1. Then, for each preset moment, the product of the normalized electricity consumption impact feature at the current preset moment and the comprehensive electricity consumption feature at the current preset moment is obtained as the electricity consumption feature generated by the environmental impact at the current preset moment, and then the difference between the comprehensive electricity consumption feature at the current preset moment and the electricity consumption feature generated by the environmental impact at the current preset moment is obtained as the target electricity consumption feature at the current preset moment. The target electricity consumption feature can indicate the actual electricity consumption of the aerated concrete brick production line at the current preset moment, so as to reduce the impact of false electricity consumption on the feature extraction process of the electricity consumption dimension, improve the feature accuracy associated with the electricity consumption of the aerated concrete brick production line, and effectively improve the state detection accuracy of the aerated concrete brick production line. Among them, the target electricity consumption feature at the preset moment satisfies the following formula (5): (5); In formula (5), Indicates the target power consumption characteristics at a preset time; Indicates the normalized electricity consumption impact characteristics at the current preset time; Indicates the proportion of the grinding process in the production time of the aerated concrete brick production line; Indicates the proportion of the mixing process in the production time of the aerated concrete brick production line; Indicates the proportion of the time spent in the forming process in the production time of the aerated concrete brick production line. Optionally, the proportion of the time spent in each process in the production time of the aerated concrete brick production line can be set according to actual conditions, and no specific limitation is made to this.

[0049] In some embodiments, reference Figure 2 and Figure 4In terms of feature extraction of the equipment operation dimension, this embodiment extracts feature information associated with the equipment operation status of the aerated concrete brick production line in units of processes, and integrates these feature information into a corresponding time feature sequence. The specific implementation process of the above step S102 may also include but is not limited to the following steps S301-S302.

[0050] S301, obtaining the process equipment operation characteristics at each preset time according to the equipment operation data at each preset time.

[0051] It should be noted that the process equipment operation characteristics may include the equipment operation characteristics of the grinding process, the equipment operation characteristics of the stirring process and the equipment operation characteristics of the forming process.

[0052] In this step, the equipment operation status of each process jointly measures the equipment operation status of the aerated concrete brick production line. For the equipment of each process, if the equipment is operated for a long time, the equipment will be subject to a certain degree of wear and tear, thereby affecting the operation of the equipment. It can be seen that the operation time of the equipment directly affects the operation status of the equipment. In addition, the working efficiency and operation status of the equipment can indicate the operation status of the equipment to a certain extent. Therefore, for each preset moment, the following operations are performed: For the crushing and grinding process, its equipment includes a crusher and a ball mill. The equipment operation data of the crushing and grinding process at the current preset time may include the working efficiency, operation time influence coefficient and crushing rate of the crusher at the current preset time, and the working efficiency, operation time influence coefficient and ball mill rate of the ball mill. In this step, the equipment operation characteristics of the crushing and grinding process at the current preset time can be obtained based on the equipment operation data of the crushing and grinding process at the current preset time and combined with the following formula (6): (6); In formula (6), Indicates the equipment operation characteristics of the crushing and grinding process; Indicates the working efficiency of the crusher; Indicates the impact coefficient of the crusher's operating time; Indicates the crushing rate of the crusher; Indicates the working efficiency of the ball mill; Indicates the influence coefficient of ball mill operation time; Indicates the ball milling rate of the ball mill; and Must be greater than zero and less than or equal to one.

[0053] For the mixing process, the equipment includes a mixer. The equipment operation data of the mixing process at the current preset time may include the working efficiency, operation time influence coefficient and mixing rate of the mixer at the current preset time. In this step, the equipment operation characteristics of the mixing process at the current preset time can be obtained based on the equipment operation data of the mixing process at the current preset time and combined with the following formula (7): (7); In formula (7), Indicates the equipment operation characteristics of the mixing process; Indicates the working efficiency of the mixer; Indicates the influence coefficient of the mixer's running time; Indicates the stirring rate of the mixer; Must be greater than zero and less than or equal to one.

[0054] Optionally, the equipment operation data of the grinding process and the equipment operation data of the stirring process can be pre-calibrated according to actual conditions, and this embodiment does not specifically limit this.

[0055] Exemplarily, for the equipment used in the crushing and grinding process, the difference between the current preset moment and the previous preset moment is defined as the current time interval, and the difference between the discharge weight of the equipment at the current preset moment and the discharge weight of the equipment at the previous preset moment is defined as the discharge value of the equipment at the current preset moment. Then, the working efficiency of the equipment at the current preset moment can be the ratio of the discharge value of the equipment at the current preset moment to the current time interval; in addition, the difference between the weight of the raw materials inside the equipment at the current preset moment and the weight of the raw materials inside the equipment at the previous preset moment is defined as the raw material value of the equipment at the current preset moment. Then, the operation (i.e., crushing or ball milling) rate of the equipment at the current preset moment can be the ratio of the discharge value of the equipment at the current preset moment to the raw material value of the equipment at the current preset moment.

[0056] As another example, for a mixer in a mixing process, the difference between the current preset moment and the previous preset moment is defined as the current time interval, then the working efficiency of the mixer at the current preset moment may be the ratio of the mixing batches completed within the current time interval to the current time interval; in addition, the mixing rate of the mixer at the current preset moment may be the homogeneity of the concrete mixture at the current preset moment, and the concept and measurement of the homogeneity of the concrete mixture may follow the prior knowledge based on industry standards, which belongs to the prior art, but is not limited to this.

[0057] For the molding process, considering that gasification and initial condensation are not required in some cases, the equipment of the molding process only considers the cutting machine and the autoclave. The equipment operation data of the molding process at the current preset time may include the working efficiency, operating time influence coefficient and cutting rate of the cutting machine, and the working efficiency, operating time influence coefficient and autoclave rate of the autoclave. In this step, the equipment operation characteristics of the molding process at the current preset time can be obtained by processing the equipment operation data of the molding process at the current preset time, as shown in the following formula (8): (8); In formula (8), Indicates the equipment operation characteristics of the molding process; Indicates the working efficiency of the cutting machine; Indicates the impact coefficient of the cutting machine's operating time; Indicates the cutting rate of the cutting machine; Indicates the working efficiency of the autoclave; Indicates the influence coefficient of the running time of the autoclave; Indicates the autoclave rate of the autoclave; and Must be greater than zero and less than or equal to one.

[0058] Optionally, the equipment operation data of the forming process can be pre-calibrated according to actual conditions, and this embodiment does not specifically limit this. Exemplarily, the difference between the current preset moment and the previous preset moment is defined as the current time interval, the difference between the weight of the billet of the cutting machine at the current preset moment and the weight of the billet of the cutting machine at the previous preset moment is defined as the current cut billet difference, and the difference between the weight of the billet of the autoclave at the current preset moment and the weight of the billet of the autoclave at the previous preset moment is defined as the current autoclaved billet difference. Then, the working efficiency of the cutting machine at the current preset moment can be the ratio of the current cut billet difference to the current time interval, and the working efficiency of the autoclave at the current preset moment can be the ratio of the current autoclaved billet difference to the current time interval; in addition, the working efficiency of the cutting machine at the previous preset moment is defined as the current autoclaved billet difference. The difference between the volume of the blank and the volume of the blank of the cutting machine at the current preset moment is determined as the cutting value of the blank at the current preset moment, then the cutting rate of the cutting machine at the current moment may be the ratio of the cutting value of the blank at the current preset moment to the volume of the blank of the cutting machine at the previous preset moment; the difference between the density of the blank of the autoclave at the previous moment and the density of the blank of the autoclave at the current preset moment is determined as the autoclaving value of the blank at the current preset moment, then the autoclaving rate of the autoclave at the current preset moment may be the ratio of the autoclaving value of the blank at the current preset moment to the density of the blank of the autoclave at the previous moment, and the blank density may be the ratio of the blank weight to the blank volume, but is not limited to this.

[0059] It is worth noting that regarding the equipment operation time influence coefficient, the equipment's working efficiency and operating conditions are positively correlated with the equipment's operating conditions, that is, the greater the working efficiency, the better the operating conditions. The operating conditions are mapped by parameters such as crushing rate and ball milling rate. The greater the operating conditions, the better the operating conditions. However, the equipment's operation time is negatively correlated with the equipment's operating conditions, that is, the greater the operation time, the worse the operating conditions. If only the operating conditions reflected by the equipment's working efficiency and operating conditions are considered, then the operating conditions are difficult to reflect the actual operating conditions. In order to correct the operating conditions reflected by the equipment's working efficiency and operating conditions, this embodiment introduces the above-mentioned operation time influence coefficient, which is expressed as an exponential function , is the running time of the device (defined as above), , is the coefficient attenuation parameter, which is a preset value (for example, 2). Assuming that the operating time starts from zero (the equipment is put into use in the first year after leaving the factory), the impact of the operating time on the operating conditions of the equipment is almost zero, and the operating conditions reflected by the equipment's working efficiency and operating conditions are the real conditions. Therefore, the operating time impact coefficient at this time is one, and the operating conditions reflected by the equipment's working efficiency and operating conditions are not corrected; and as the operating time increases, the impact of the operating time on the equipment's operating conditions gradually increases, and the operating conditions reflected by the equipment's working efficiency and operating conditions are distorted (too large), and because the operating time impact coefficient gradually grows exponentially, the operating time impact coefficient gradually decreases from one and approaches zero (but will not be equal to zero), thereby correcting the operating conditions reflected by the equipment's working efficiency and operating conditions, making it smaller, and thus correcting the operating conditions.

[0060] S302, obtaining target equipment operation characteristics at a plurality of preset moments according to process equipment operation characteristics at a plurality of preset moments, and further obtaining an equipment operation characteristic sequence.

[0061] It should be noted that the target equipment operation characteristics at the preset time are used to measure the equipment operation status of the aerated concrete brick production line at the preset time.

[0062] In this step, for each preset moment, the equipment operation characteristics of each process at the current preset moment are integrated to obtain the target equipment operation characteristics at the current preset moment. The equipment operation characteristic sequence can be constructed through the target equipment operation characteristics of several preset moments. , Indicates The target equipment operation characteristics at a preset moment can be obtained to obtain the key timing characteristics of the equipment operation dimension. By fully considering the equipment operation conditions of the grinding process, mixing process and molding process in the aerated concrete brick production line, the feature accuracy associated with the equipment operation conditions of the aerated concrete brick production line can be effectively improved, thereby improving the state detection accuracy of the aerated concrete brick production line.

[0063] Exemplarily, for each preset moment, a machine learning method can be used to process the equipment operation characteristics of the crushing and grinding process, the equipment operation characteristics of the stirring process, and the equipment operation characteristics of the molding process at the current preset moment to obtain the target equipment operation characteristics at the current preset moment. Exemplarily, for each preset moment, the equipment operation characteristics of the crushing and grinding process, the equipment operation characteristics of the stirring process, and the equipment operation characteristics of the molding process at the current preset moment are weighted to obtain the target equipment operation characteristics at the current preset moment, as shown in the following formula (9): (9); In formula (9), Indicates the target device operation characteristics at a preset time; Indicates the operation weight of the crushing and grinding process; Indicates the operation weight of the mixing process; Indicates the operation weight of the forming process. Optionally, the operation weights of various processes can be preset according to actual conditions. Exemplarily, the operation weights of various processes can be set according to the proportion of the time spent on each process in the production time of the aerated concrete brick production line. For example, the operation weight of the grinding process can be the proportion of the time spent on the grinding process in the production time of the aerated concrete brick production line. The same is true for other processes, but it is not limited to this.

[0064] In some embodiments, reference Figure 2 and Figure 5 In terms of feature extraction of production quality dimension, the specific implementation process of the above step S102 may also include but is not limited to the following steps S401-S403: S401, obtaining target crack features at a plurality of preset moments according to image data of each brick at a plurality of preset moments, and further obtaining a first quality feature sequence; S402, obtaining target morphological features at a plurality of preset moments according to the image data and thickness information of each brick at a plurality of preset moments, and further obtaining a second quality feature sequence; S403, according to the weight information of each brick at several preset moments, the target weight characteristics at several preset moments are obtained, and then the third quality characteristic sequence is obtained.

[0065] It should be noted that the target crack characteristics, target morphological characteristics and target weight characteristics at the preset moment are all used to measure the production quality of the aerated concrete brick production line at the preset moment.

[0066] In this embodiment, in terms of feature extraction of the production quality dimension, this embodiment captures feature information associated with the production quality of the aerated concrete brick production line from three aspects: cracks, morphology, and weight of the finished bricks, and integrates these feature information into a corresponding time feature sequence. Specifically, for each preset moment, the target crack feature at the current preset moment is obtained through the image data of each brick at the current preset moment, and the target morphological feature at the current preset moment is obtained through the image data and thickness information of each brick at the current preset moment, and the target weight feature at the current preset moment is obtained through the weight information of each brick at the current preset moment. This embodiment constructs a first quality feature sequence through the target crack features at several preset moments. The first quality feature sequence can be expressed as , Indicates The target crack features at a preset time are used to construct a second quality feature sequence through the target morphological features at several preset times. The second quality feature sequence can be expressed as , Indicates The target shape features at a preset moment, and the third quality feature sequence are constructed by the target weight features at several preset moments. The third quality feature sequence can be expressed as , Indicates The target weight feature at a preset moment can be used to measure the production quality of the aerated concrete brick production line by fully considering the cracks, shape and weight of the finished bricks. This can effectively improve the feature accuracy associated with the production quality of the aerated concrete brick production line, thereby improving the state detection accuracy of the aerated concrete brick production line.

[0067] In some embodiments, in the above step S401, obtaining target crack features at several preset moments according to the image data of each brick at several preset moments may include the following steps S01-S02: S01, for each preset moment, crack detection is performed on each brick based on the image data of each brick at the preset moment, and the number and position of cracks of each brick at the preset moment are obtained. For each brick at the preset moment, crack characteristics of the brick are obtained based on the center point position, number and position of cracks of the brick; S02, obtaining target crack characteristics at each preset moment according to the crack characteristics of each brick at each preset moment.

[0068] It should be noted that the number of cracks refers to the number of cracks contained in a brick, and the crack position includes the position of at least one crack contained in the brick. In addition, the center point position refers to the coordinate position of the center point of the image data of the brick in the image coordinate system, and the image coordinate system refers to a two-dimensional coordinate system constructed with the lower left corner of the image as the coordinate origin, the width of the image as the horizontal axis, and the length of the image as the vertical axis.

[0069] In this embodiment, in terms of cracks in finished bricks, the following operations are performed for each preset moment: In the first step, the bricks produced by the aerated concrete brick production line between the last preset moment and the current preset moment are defined as the finished bricks at the current preset moment. The finished bricks at the current preset moment are photographed by a camera set just above the bricks, and the image data of the finished bricks at the current preset moment can be obtained. Among them, the length of the brick is approximately parallel or completely parallel to the length of the image, and the width of the brick is approximately parallel or completely parallel to the width of the image. Optionally, the image data of each brick is preprocessed such as denoising to ensure the quality of the image data.

[0070] In the second step, a pre-trained crack detection model is used to identify cracks in the image data of each brick at the current preset moment, and the crack position of each brick at the current preset moment is obtained. The crack position is the position of the candidate box of the crack in the image data in the image coordinate system. The number of cracks can be obtained by counting the crack positions. Among them, the crack detection model is trained by multiple preset brick sample images and label information corresponding to each brick sample image. The label information corresponding to the brick sample image includes the crack state and crack position of the brick sample image. The crack state includes either containing cracks or not containing cracks. Optionally, the crack detection model is a deep learning model, and its type can be set according to actual conditions. For example, the crack detection model can be a FasterRCNN model, but is not limited to this.

[0071] The third step is to obtain the crack characteristics of each brick at the current preset moment through the center point position, number of cracks and crack position of the brick. Specifically, if the crack is located at the edge of the brick, the crack will affect the appearance or local strength of the brick, but if the crack is close to the center line of the brick, the crack will affect the bearing capacity of the brick, thereby seriously affecting the quality of the brick, and the quality of the brick can indirectly reflect the production quality of the aerated concrete brick production line. Therefore, in order to further measure the quality of bricks, and thus accurately measure the production quality of aerated concrete brick production lines, first, for each crack contained in the brick, the straight-line distance between the crack position of the crack and the center point position of the brick is obtained as the crack distance of the crack; then, cracks with a crack distance less than a preset distance threshold are screened from all the cracks in the brick as abnormal cracks, and the ratio of the number of abnormal cracks in the brick to the number of cracks in the brick is obtained as the abnormal crack distribution information of the brick, and the distribution information can reflect the distribution of cracks that affect the bearing capacity of the brick; at the same time, according to prior knowledge, the more cracks a brick has, the worse the quality of the brick, and the worse the production quality of the aerated concrete brick production line, so the relative quantity error between the number of cracks in the brick and the maximum tolerable crack number threshold is obtained, which is specifically manifested in obtaining the difference between the number of cracks in the brick and the maximum tolerable crack number threshold, and then obtaining the ratio of the difference to the maximum tolerable crack number threshold as the relative quantity error; finally, the abnormal crack distribution information and the relative quantity error of the brick are weighted to obtain the crack characteristics of the brick. In this way, when determining that a brick contains cracks, this embodiment determines the final crack characteristics by considering the influence of the crack position and the number of cracks on the quality of the brick, which can accurately measure the quality of the brick, thereby accurately measuring the production quality of the aerated concrete brick production line, which is beneficial to the state detection accuracy of the aerated concrete brick production line. Among them, the crack characteristics of the brick are shown in the following formula (10): (10); In formula (10), Indicates crack characteristics of bricks; The weight representing the abnormal crack distribution information; Indicates the number of abnormal cracks in the bricks; Indicates the number of cracks in the brick; Indicates the maximum tolerable crack number threshold; Represents the weight of the relative quantity error.

[0072] Optionally, the probability threshold, the distance threshold, and the maximum tolerable crack quantity threshold can be flexibly set according to actual conditions. In addition, the weight of the abnormal crack distribution information and the weight of the relative quantity error can also be set according to actual conditions, and this embodiment does not specifically limit this. For example, the weight of the abnormal crack distribution information and the weight of the relative quantity error can be 0.5, but is not limited to this. It should be noted that the sum of the weights of the abnormal crack distribution information and the relative quantity error is one.

[0073] In the fourth step, since the crack features of multiple bricks are included at the current preset moment, in order to obtain the final target crack features, it is necessary to aggregate the crack features of multiple bricks at the current preset moment. Exemplarily, the crack features of multiple bricks at the current preset moment can be weighted to obtain the target crack features at the current preset moment. Exemplarily, the mean of the crack features of multiple bricks at the current preset moment can be calculated as the target crack features at the current preset moment.

[0074] By traversing several preset moments in the above manner, target crack features at several preset moments can be obtained, which can improve the feature accuracy associated with the production quality of the aerated concrete brick production line, thereby improving the state detection accuracy of the aerated concrete brick production line.

[0075] In some embodiments, in the above step S402, obtaining the target morphological features at several preset moments according to the image data and thickness information of each brick at several preset moments may include the following steps S11-S12: S11, for each preset moment, according to the image data of each brick at the preset moment, a binary segmentation mask of each brick at the preset moment is obtained, for each brick at the preset moment, morphological detection is performed on the binary segmentation mask of the brick to obtain width information, length information and angle information of the brick as morphological information of the brick, and according to the morphological information and thickness information, a morphological feature of the brick is obtained; S12, obtaining target morphological features at each preset moment according to the morphological features of each brick at each preset moment.

[0076] In this embodiment, in terms of the shape of the finished bricks, for each preset moment, the following operations are performed: The first step is to obtain the image data and thickness information of each brick at the current preset moment. The thickness information is pre-determined information. The acquisition of the image data of the brick at the current preset moment can be referred to the above embodiment, which will not be described in detail. Optionally, the image data of each brick is pre-processed such as denoising to ensure the quality of the image data.

[0077] In the second step, the image data of each brick at the current preset moment is segmented using a pre-trained brick segmentation model to obtain a binary segmentation mask of each brick at the current preset moment. The brick segmentation model is obtained by training a plurality of preset brick sample images and the brick areas corresponding to each brick sample image. Optionally, the brick segmentation model is a deep learning model, and its type can be set according to actual conditions. For example, the brick segmentation model can be a U-Net model or an ECA-Net model, but is not limited thereto.

[0078] In the third step, for each brick at the current preset moment, first perform morphological operations such as closing operations on the binary segmentation mask of the brick to clean the segmentation result, then use edge detection operators such as Sobel operator and Canny operator to perform edge detection on the binary segmentation mask of the brick and extract the edge of the brick, and then determine the width information, length information and angle information of the brick based on the edge of the brick as the morphological information of the brick. Finally, if the width information, length information, angle information and thickness information of the brick are all within the corresponding preset range, it means that the shape of the brick is qualified, and the morphological feature of the brick is determined to be one. Otherwise, it means that the shape of the brick is unqualified, and the morphological feature of the brick is determined to be zero. In this way, the morphological condition of the brick can be accurately measured, thereby accurately measuring the production quality of the aerated concrete brick production line, which is beneficial to the state detection accuracy of the aerated concrete brick production line.

[0079] Specifically, in terms of determining the morphological information, according to the aforementioned embodiments, the image coordinate system refers to a two-dimensional coordinate system constructed with the lower left corner of the image as the coordinate origin and the two sides of the image as the horizontal axis and the vertical axis. After edge detection is completed, the maximum and minimum values ​​of the edge of the brick on the horizontal axis in the image coordinate system can be obtained as the horizontal axis extreme value of the brick, and the maximum and minimum values ​​of the edge of the brick on the vertical axis can be obtained as the vertical axis extreme value of the brick. The absolute value of the difference between the maximum and minimum values ​​of the edge of the brick on the horizontal axis in the image coordinate system is obtained as the width information of the brick, and the absolute value of the difference between the maximum and minimum values ​​of the edge of the brick on the vertical axis is obtained as the length information of the brick. At the same time, the vectors of the four sides of the brick can be determined according to the horizontal axis extreme value and the vertical axis extreme value of the brick. For two adjacent sides of the brick, the angle information of the two sides can be obtained by the dot product and cross product of the vectors. In this way, the four angle information of the brick can be obtained, which belongs to the prior art and will not be described in detail. In this way, the width, length and angle of the brick can be determined only by the edge of the brick, thereby effectively improving the efficiency of obtaining the brick shape information. Optionally, the preset ranges corresponding to the width information, length information, angle information and thickness information can be pre-set according to actual conditions, and this embodiment does not specifically limit this.

[0080] In the fourth step, since the current preset moment contains morphological features of multiple bricks, in order to obtain the final target morphological features, it is necessary to aggregate the morphological features of multiple bricks at the current preset moment. Exemplarily, the morphological features of multiple bricks at the current preset moment can be weighted to obtain the target morphological features at the current preset moment. Exemplarily, the average of the morphological features of multiple bricks at the current preset moment can be calculated as the target morphological features at the current preset moment.

[0081] By traversing several preset moments in the above manner, the target morphological features at several preset moments can be obtained, which can improve the feature extraction accuracy associated with the production quality of the aerated concrete brick production line, thereby improving the state detection accuracy of the aerated concrete brick production line.

[0082] In some embodiments, in the above step S403, according to the weight information of each brick at a plurality of preset moments, the target weight characteristics at a plurality of preset moments are obtained, which may include S21-S22: S21, for each brick at each preset time, if the weight information of the brick is within the preset weight range, the weight feature of the brick is determined to be one, otherwise the weight feature of the brick is determined to be zero; S22, obtaining the target weight characteristics at each preset moment according to the weight characteristics of each brick at each preset moment.

[0083] In this embodiment, in terms of the weight of the finished bricks, for each preset moment, the following operations are performed: the first step is to obtain the weight information of each brick at the current preset moment, and the weight information is pre-determined information. The second step is to compare the weight information of each brick at the current moment with the preset weight range. If the weight information is within the preset weight range, it means that the weight of the brick is qualified, and the weight feature of the brick is determined to be one. Otherwise, it means that the weight of the brick is unqualified, and the weight feature of the brick is determined to be zero. The third step is that since the current preset moment contains the weight features of multiple bricks, in order to obtain the final target weight feature, it is necessary to aggregate the weight features of multiple bricks at the current preset moment. Exemplarily, the weight features of multiple bricks at the current preset moment can be weighted to obtain the target weight feature at the current preset moment. Exemplarily, the mean of the weight features of multiple bricks at the current preset moment can be calculated as the target weight feature at the current preset moment. By traversing several preset moments in the above manner, the target weight features at several preset moments can be obtained, which can improve the feature extraction accuracy associated with the production quality of the aerated concrete brick production line, thereby improving the state detection accuracy of the aerated concrete brick production line.

[0084] In some embodiments, reference Figure 2 and Figure 6In the above step S103, image processing is performed based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain the state image of the aerated concrete brick production line, which may include the following steps S501-S505.

[0085] S501, normalizing the power consumption feature sequence, the equipment operation feature sequence, and the production quality feature sequence to obtain a normalized power consumption feature sequence, a normalized equipment operation feature sequence, and a normalized production quality feature sequence.

[0086] In this step, since the internal eigenvalues ​​of each feature sequence are different, the internal eigenvalues ​​of each feature sequence need to be normalized before feature fusion so that the internal eigenvalues ​​of each feature sequence are between zero and one.

[0087] S502: Fusing the normalized electricity consumption feature sequence and the normalized production quality feature sequence to obtain a first fused feature sequence.

[0088] In this step, among the three dimensions of electricity consumption, production quality and equipment operation, the production quality dimension can usually directly map the state of the aerated concrete brick production line, which is a direct dimension, while the electricity consumption dimension and the equipment operation dimension often indirectly map the state of the aerated concrete brick production line, which are indirect dimensions. In order to promote the feature fusion between the direct dimension and the indirect dimension, according to the above-mentioned embodiment, the production quality dimension can be subdivided into three aspects: cracks, shape and weight of the finished bricks. Based on this, this step uses the normalized first quality feature sequence as the query, and the normalized electricity feature sequence as the key and value, and uses the self-attention mechanism to fuse the above two feature sequences to obtain the first fused feature sequence, which aims to promote the feature fusion between the electricity consumption dimension and the production quality dimension, so that the electricity consumption feature sequence and the first quality feature sequence can perceive each other's feature information related to the state of the aerated concrete brick production line, thereby obtaining a more comprehensive and accurate time feature sequence, which can better mine the feature information hidden in the data of each dimension, capture more real state expressions, and help improve the state detection accuracy of the aerated concrete brick production line.

[0089] S503, fusing the normalized equipment operation feature sequence and the normalized production quality feature sequence to obtain a second fused feature sequence.

[0090] In this step, in order to promote the feature fusion between the direct dimension and the indirect dimension, according to the above-mentioned embodiment, the production quality dimension can be subdivided into three aspects: cracks, shape and weight of finished bricks. Based on this, this step uses the normalized second quality feature sequence as the query, and the normalized equipment operation feature sequence as the key and value, and uses the self-attention mechanism to fuse the above two feature sequences to obtain the second fused feature sequence, which aims to promote the feature interaction and fusion between the electricity consumption dimension and the production quality dimension, so that the equipment operation feature sequence and the second quality feature sequence can perceive each other's feature information related to the state of the aerated concrete brick production line, thereby obtaining a more comprehensive and accurate time feature sequence, which can better mine the feature information hidden in the data of each dimension, capture more real state expressions, and help improve the state detection accuracy of the aerated concrete brick production line.

[0091] S504: Fusing the normalized power consumption feature sequence, the normalized equipment operation feature sequence, and the normalized production quality feature sequence to obtain a third fused feature sequence.

[0092] In this step, in order to promote the feature fusion between the direct dimension and the indirect dimension, in addition to fusing the direct dimension with the two indirect dimensions respectively, it is also necessary to fuse the direct dimension with the two indirect dimensions at the same time. Specifically, in this step, the normalized power consumption feature sequence and the normalized equipment operation feature sequence are first jump-connected to obtain a fourth fused feature sequence. The fourth fused feature sequence has the same size as the normalized third quality feature sequence, aiming to promote the feature interaction and fusion between the power consumption dimension and the equipment operation dimension, so that the power consumption feature sequence and the equipment operation feature sequence can perceive each other's feature information related to the state of the aerated concrete brick production line, and then use the normalized third quality feature sequence as a query, and the fourth fused feature sequence as a key and value, and use the self-attention mechanism to fuse the above two feature sequences to obtain the third fused feature sequence, aiming to achieve the feature interaction and fusion between the power consumption dimension, the equipment operation dimension and the production quality dimension, so as to obtain a more comprehensive and accurate time feature sequence, so as to better mine the state feature information hidden in the data of each dimension, capture more real state expressions, and help improve the state detection accuracy of the aerated concrete brick production line.

[0093] S505 , performing image processing based on the first fused feature sequence, the second fused feature sequence, and the third fused feature sequence to obtain a state image.

[0094] In this step, each fused feature sequence is processed by dimensionality increase through imaging, and the obtained two-dimensional image is used as data information reflecting the real state of the aerated concrete brick production line.

[0095] In some embodiments, reference Figure 2 In the above step S504, the image processing is performed based on the first fusion feature sequence, the second fusion feature sequence and the third fusion feature sequence to obtain the state image, which may include the following steps S601-S604.

[0096] S601, obtaining a red channel image according to the first fusion feature sequence.

[0097] In this step, a Gram sum angular field matrix is ​​constructed according to the first fusion feature sequence, and a red channel map is generated by the Gram sum angular field matrix, which is specifically implemented as a prior art, and no further description is given. The Gram sum angular field matrix can ensure time correlation, and as time increases, the Gram sum angular field matrix moves from the upper left to the lower right, and it represents relative correlation by superposition in the time direction. In addition, the Gram sum angular field matrix represents the correlation between each pair of points in the polar coordinate system by superposition of nonlinear cosine functions, and different time feature sequences have different features. After feature reconstruction, the feature information of the time feature sequence is enhanced, and then the difference between the feature information can be more highlighted.

[0098] S602, obtaining a green channel image according to the second fusion feature sequence.

[0099] In this step, a relative position matrix is ​​constructed according to the second fused feature sequence, and a green channel image is generated by the relative position matrix, which is specifically implemented in the prior art and will not be described in detail. The relative position matrix can contain redundant features of the original fused feature sequence, so that the similarity information between and within classes in the converted channel image is more easily captured, thereby providing richer feature information.

[0100] S603, obtaining a blue channel image according to the third fusion feature sequence.

[0101] In this step, a recursive graph matrix is ​​constructed according to the third fused feature sequence, and a blue channel graph is generated through the recursive graph matrix, which is specifically implemented in the prior art and will not be described in detail. The recursive graph matrix can intuitively capture the feature information of periodicity, chaos or other complex dynamic behaviors in the fused feature sequence, thereby providing richer feature information.

[0102] S604, obtaining a state image according to the red channel image, the green channel image and the blue channel image.

[0103] In this step, the red channel image, green channel image, and blue channel image obtained by different visualization methods are superimposed into a three-channel RGB image, which is the state image. In this way, through three different types of visualization methods, the feature information in each fused feature sequence can be retained to the maximum extent while converting the image, ensuring the accuracy of the state image, which is conducive to giving full play to the advantages of the deep learning model in image processing and effectively improving the state detection accuracy of the aerated concrete brick production line.

[0104] In addition, refer to Figure 7 The embodiment of the present application also provides a state detection device for an aerated concrete brick production line, comprising: An acquisition module 701 is used to acquire an electricity consumption dataset, a device dataset, and a brick dataset of an aerated concrete brick production line; Feature extraction module 702, used to extract features from the power consumption data set, the equipment data set and the brick data set, and obtain the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence of the aerated concrete brick production line; An image processing module 703 is used to perform image processing based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain a state image of the aerated concrete brick production line; The detection module 704 is used to detect the state of the aerated concrete brick production line according to the state image to obtain the state detection result of the aerated concrete brick production line.

[0105] The contents of the above method embodiments are all applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0106] To facilitate the understanding of the state detection method and device for the aerated concrete brick production line described above in the present application, the actual application scenario of the state detection method and device for the aerated concrete brick production line described above in the present application is illustrated herein by way of example.

[0107] In this application scenario, it is necessary to detect the status of the aerated concrete brick production line on the previous day, so the previous day is evenly divided into 24 moments, one moment represents one hour. The status detection process of the aerated concrete brick production line in this application scenario is as follows: S801, data collection: The number of bricks produced between two adjacent moments is 10,000. Due to the large number of bricks, one thousand bricks are selected from the ten thousand bricks by random sampling as finished bricks, that is, each moment corresponds to one thousand finished bricks. The finished bricks at each moment are photographed by a camera set just above the bricks, and the image data of the finished bricks at each moment can be obtained; at the same time, the weight and thickness information of the finished bricks at each moment are collected. This information can be obtained by measuring with automated equipment, thus obtaining a brick data set for the aerated concrete brick production line.

[0108] Furthermore, by measuring the data of the equipment in the crushing, mixing and molding processes in the aerated concrete brick production line, the power consumption data and equipment operation data of each process at twenty-four moments can be obtained, and by measuring the data of the environment in which the aerated concrete brick production line is located, the cloud cover information, air pressure information and relative humidity information at twenty-four moments can be obtained, thereby obtaining the power consumption data set and equipment data set of the aerated concrete brick production line.

[0109] Among them, the electricity consumption data of the crushing and grinding process includes the electricity consumption of the crusher, the electricity consumption influence coefficient of the crusher, the electricity consumption of the ball mill and the electricity consumption influence coefficient of the ball mill. The equipment operation data of the crushing and grinding process includes the working efficiency, operating time influence coefficient and crushing rate of the crusher, as well as the working efficiency, operating time influence coefficient and ball milling rate of the ball mill. The equipment operation data of the mixing process includes the working efficiency, operating time influence coefficient and mixing rate of the mixer at the current preset time. The electricity consumption data of the mixing process may include the electricity consumption of the mixer and the electricity consumption influence coefficient of the raw material ratio on the mixer. The electricity consumption data of the molding process includes the electricity consumption of the cutting machine and the electricity consumption of the autoclave. The equipment operation data of the molding process includes the working efficiency, operating time influence coefficient and cutting rate of the cutting machine, as well as the working efficiency, operating time influence coefficient and autoclave rate of the autoclave.

[0110] S802, feature extraction: For each moment, the electricity consumption impact characteristics of the current moment are obtained based on the electricity consumption impact data at the current moment combined with the above formula (1) and the maximum-minimum normalization is performed; and the electricity consumption characteristics of the grinding process, mixing process and molding process at the current moment are obtained based on the process electricity consumption data at the current moment combined with the above formulas (2)-(4), and the comprehensive electricity consumption characteristics of the current moment are obtained based on the proportion of the time of each process in the production time of the aerated concrete brick production line and the electricity consumption characteristics of each process at the current moment; then, based on the normalized electricity consumption impact characteristics and the comprehensive electricity consumption characteristics combined with the above formula (5), the target electricity consumption characteristics of the current moment are obtained. The electricity consumption characteristic sequence of the aerated concrete brick production line is constructed through the target electricity consumption characteristics of the twenty-four moments, which is a time characteristic sequence.

[0111] Secondly, for each moment, the equipment operation characteristics of the grinding process are obtained according to the equipment operation data of the grinding process combined with the above formula (6), the equipment operation characteristics of the mixing process are obtained according to the equipment operation data of the mixing process combined with the above formula (7), and the equipment operation characteristics of the molding process are obtained according to the equipment operation data of the molding process combined with the above formula (8); then, according to the proportion of the time of each process in the production time of the aerated concrete brick production line and the equipment operation characteristics of each process at the current moment, the target equipment operation characteristics at the current moment are obtained, as shown in the above formula (9). The equipment operation characteristic sequence of the aerated concrete brick production line is constructed through the target equipment operation characteristics of the twenty-four moments, which is a time characteristic sequence.

[0112] Furthermore, for each moment, the image data of each finished brick at the current moment is detected based on the trained FasterRCNN model to obtain the number of cracks and the position of cracks of each finished brick at the current moment. For each brick, for each crack contained in the finished brick, the straight-line distance between the crack position of the crack and the center point of the finished brick is obtained as the crack distance of the crack. Cracks with a crack distance less than the distance threshold are screened from all cracks in the finished brick as abnormal cracks, and the ratio of the number of abnormal cracks of the finished brick to the number of cracks of the finished brick is obtained as the abnormal crack distribution information of the finished brick. At the same time, the difference between the number of cracks of the finished brick and the maximum tolerable crack number threshold is obtained, and the ratio of the difference to the maximum tolerable crack number threshold is obtained as the relative number error. Finally, the abnormal crack distribution information and the relative number error of the finished brick are weighted to obtain the crack characteristics of the finished brick, as shown in the above formula (10). The average of the crack characteristics of multiple finished bricks at the current moment is taken as the target crack feature at the current moment. The first quality feature sequence of the aerated concrete brick production line is constructed through the target crack features at twenty-four moments, and this sequence is a time feature sequence.

[0113] Also, for each moment, the image data of each finished brick at the current moment is segmented based on the trained ECA-Net model to obtain the binary segmentation mask of each finished brick at the current moment. For each finished brick, the binary segmentation mask of the finished brick is subjected to morphological operations and edge detection to obtain the edge of the finished brick. The length, width and angle of the finished brick are calculated based on the edge of the finished brick. If the width information, length information, angle information and thickness information of the finished brick are all within the corresponding preset range, the morphological feature of the finished brick is one, otherwise the morphological feature of the finished brick is zero. The average of the morphological features of multiple finished bricks at the current moment is taken as the target morphological feature at the current moment. The second quality feature sequence of the aerated concrete brick production line is constructed through the target morphological features at twenty-four moments, and this sequence is a time feature sequence.

[0114] And, for each finished brick at each moment, if the weight information of the finished brick is within the preset weight range, the weight feature of the finished brick is one, otherwise the weight feature of the finished brick is zero. The average of the weight features of multiple finished bricks at the current moment is used as the target weight feature at the current moment. The third quality feature sequence of the aerated concrete brick production line is constructed through the target weight features at twenty-four moments, which is a time feature sequence.

[0115] S803, feature fusion: The power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence are normalized so that the internal feature value of each feature sequence is between zero and one. The normalized first quality feature sequence is used as the query, the normalized power consumption feature sequence is used as the key and value, and the above two feature sequences are fused using the self-attention mechanism to obtain the first fused feature sequence. Also, the normalized second quality feature sequence is used as the query, the normalized equipment operation feature sequence is used as the key and value, and the above two feature sequences are fused using the self-attention mechanism to obtain the second fused feature sequence. Also, the normalized power consumption feature sequence and the normalized equipment operation feature sequence are first jump-connected to obtain the fourth fused feature sequence, and then the normalized third quality feature sequence is used as the query, the fourth fused feature sequence is used as the key and value, and the above two feature sequences are fused using the self-attention mechanism to obtain the third fused feature sequence.

[0116] S804, Image: A Gram sum angular field matrix is ​​constructed according to the first fused feature sequence, and a red channel is generated through the Gram sum angular field matrix. A relative position matrix is ​​constructed according to the second fused feature sequence, and a green channel map is generated through the relative position matrix. A recursive graph matrix is ​​constructed according to the third fused feature sequence, and a blue channel map is generated through the recursive graph matrix. Finally, the red channel map, the green channel map and the blue channel map are superimposed into a three-channel RGB image to obtain a status image of the aerated concrete brick production line.

[0117] S805, status detection: The state image of the aerated concrete brick production line is input into the pre-trained AdaBoost model to obtain the detection result that characterizes whether the aerated concrete brick production line is qualified, thereby realizing the state detection of the aerated concrete brick production line.

[0118] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0119] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the described embodiments. Technical personnel familiar with the field may make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A state detection method for an aerated concrete brick production line, characterized in that: The following steps are involved: Obtaining an electricity consumption dataset, an equipment dataset, and a brick dataset of the aerated concrete brick production line; Performing feature extraction on the electricity consumption data set, the equipment data set, and the brick data set to obtain an electricity consumption feature sequence, an equipment operation feature sequence, and a production quality feature sequence of the aerated concrete brick production line; Performing image processing based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain a state image of the aerated concrete brick production line; The state of the aerated concrete brick production line is detected according to the state image to obtain a state detection result of the aerated concrete brick production line.

2. The method according to claim 1, characterized in that The power consumption data set includes power consumption impact data and process power consumption data at a plurality of preset moments, wherein the power consumption impact data is used to indicate environmental data that affects the power load of the aerated concrete brick production line, and the process power consumption data includes power consumption data of a grinding process, a mixing process and a forming process in the aerated concrete brick production line; The feature extraction of the power consumption data set, the equipment data set and the brick data set to obtain the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence of the aerated concrete brick production line includes: Obtaining the electricity consumption impact characteristics of each preset moment according to the electricity consumption impact data at each preset moment; According to the process power consumption data at each of the preset moments, the process power consumption characteristics at each of the preset moments are obtained; wherein the process power consumption characteristics include the power consumption characteristics of the crushing and grinding process, the power consumption characteristics of the stirring process and the power consumption characteristics of the forming process; According to the power consumption influencing characteristics and process power consumption characteristics at the preset moments, target power consumption characteristics at the preset moments are obtained, and then the power consumption characteristic sequence is obtained.

3. The method according to claim 1, characterized in that The equipment data set includes equipment operation data at a plurality of preset moments, and the equipment operation data includes equipment operation data of a grinding process, a mixing process and a molding process in the aerated concrete brick production line; The feature extraction of the power consumption data set, the equipment data set and the brick data set to obtain the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence of the aerated concrete brick production line includes: According to the equipment operation data at each of the preset moments, the process equipment operation characteristics at each of the preset moments are obtained; wherein the process equipment operation characteristics include the equipment operation characteristics of the crushing and grinding process, the equipment operation characteristics of the stirring process and the equipment operation characteristics of the forming process; According to the process equipment operation characteristics at the preset moments, the target equipment operation characteristics at the preset moments are obtained, and then the equipment operation characteristic sequence is obtained.

4. The method according to claim 1, characterized in that: The brick data set includes image data, weight information and thickness information of at least one brick at a number of preset moments, and the production quality feature sequence includes a first quality feature sequence, a second quality feature sequence and a third quality feature sequence; The feature extraction of the power consumption data set, the equipment data set and the brick data set to obtain the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence of the aerated concrete brick production line includes: According to the image data of each brick at the preset time, the target crack features at the preset time are obtained, and then the first quality feature sequence is obtained; According to the image data and thickness information of each brick at the preset time, the target morphological features at the preset time are obtained, and then the second quality feature sequence is obtained; According to the weight information of each brick at several preset moments, the target weight characteristics at several preset moments are obtained, and then the third quality characteristic sequence is obtained.

5. The method according to claim 4, characterized in that The method of obtaining target crack features at a plurality of preset moments according to the image data of each brick at a plurality of preset moments comprises: For each of the preset moments, crack detection is performed on each of the bricks based on the image data of each of the bricks at the preset moments to obtain the number of cracks and the position of the cracks of each of the bricks at the preset moments. For each of the bricks at the preset moments, the crack characteristics of the bricks are obtained based on the center point position of the bricks and the number and position of the cracks; According to the crack characteristics of each brick at each preset moment, the target crack characteristics at each preset moment are obtained.

6. The method according to claim 4, characterized in that The target morphological features at several preset moments are obtained according to the image data and thickness information of each brick at several preset moments, including: For each of the preset moments, a binary segmentation mask of each of the bricks at the preset moment is obtained according to the image data of each of the bricks at the preset moment, and for each of the bricks at the preset moment, morphological detection is performed on the binary segmentation mask of the brick to obtain width information, length information and angle information of the brick as morphological information of the brick, and morphological features of the brick are obtained according to the morphological information and the thickness information; According to the morphological features of each of the bricks at each of the preset moments, the target morphological features at each of the preset moments are obtained.

7. The method according to claim 4, characterized in that The target weight characteristics at the preset moments are obtained according to the weight information of each brick at the preset moments, including: For each of the bricks at each of the preset moments, if the weight information of the brick is within a preset weight range, the weight feature of the brick is determined to be one, otherwise the weight feature of the brick is determined to be zero; According to the weight characteristics of each brick at each preset moment, the target weight characteristics at each preset moment are obtained.

8. The method according to claim 1, characterized in that: The image processing based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain the state image of the aerated concrete brick production line includes: Normalizing the power consumption feature sequence, the equipment operation feature sequence, and the production quality feature sequence to obtain a normalized power consumption feature sequence, a normalized equipment operation feature sequence, and a normalized production quality feature sequence; Fusing the normalized power consumption feature sequence and the normalized production quality feature sequence to obtain a first fused feature sequence; fusing the normalized equipment operation feature sequence and the normalized production quality feature sequence to obtain a second fused feature sequence; fusing the normalized power consumption feature sequence, the normalized equipment operation feature sequence, and the normalized production quality feature sequence to obtain a third fused feature sequence; The state image is obtained by performing image processing based on the first fusion feature sequence, the second fusion feature sequence and the third fusion feature sequence.

9. The method according to claim 8, characterized in that The performing image processing based on the first fusion feature sequence, the second fusion feature sequence and the third fusion feature sequence to obtain the state image includes: According to the first fusion feature sequence, a red channel image is obtained; According to the second fusion feature sequence, a green channel image is obtained; According to the third fusion feature sequence, a blue channel map is obtained; The state image is obtained according to the red channel image, the green channel image and the blue channel image.

10. A state detection device for an aerated concrete brick production line, characterized in that: include: An acquisition module, used to acquire an electricity consumption data set, an equipment data set and a brick data set of the aerated concrete brick production line; A feature extraction module, used to extract features from the power consumption data set, the equipment data set and the brick data set to obtain a power consumption feature sequence, an equipment operation feature sequence and a production quality feature sequence of the aerated concrete brick production line; An image processing module, used for performing image processing based on the power consumption feature sequence, the equipment operation feature sequence and the production quality feature sequence to obtain a state image of the aerated concrete brick production line; A detection module is used to detect the state of the aerated concrete brick production line according to the state image to obtain a state detection result of the aerated concrete brick production line.

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