Production and preparation method of special concrete

Abstract

The invention relates to a production and preparation method of special concrete. The method comprises the following steps: acquiring a stirring state image of a first cement mixture through a camera, and carrying out feature analysis and multi-scale perception enhancement on the stirring state image by adopting an image recognition and analysis technology based on computer vision, on the basis of fusion expression between the stirring state strengthening characteristics of the first scale and the second scale, whether the stirring state meets the preset requirement or not is automatically judged, and whether stone and strengthening fibers are added into the first cement mixture in batches or not is determined. Therefore, the stirring state can be objectively evaluated, the uniformity difference caused by human factors is reduced, the uniformity of concrete is ensured, meanwhile, the adding opportunity of the material can be accurately controlled, and too early or too late adding is avoided, so that the intelligent degree of material mixing and stirring is improved.

Description

Technical Field

[0001] The present application relates to the field of construction technology, and in particular to a method for producing special concrete. Background Art

[0002] With the increasing intensity of mineral resource development, tailings emissions continue to rise. Although traditional harmless storage can control the spread of pollution, it has not fundamentally solved the accumulation problem. At the same time, the large amount of fly ash produced by the thermal power industry also poses an environmental challenge. Although the resource utilization of tailings and fly ash is imminent, due to their complex composition and large processing volume, there is currently a lack of efficient, environmentally friendly, economical and practical large-scale comprehensive utilization technology. In addition, the existing concrete production equipment is insufficient in material grinding and screening, resulting in limited concrete quality and difficulty in meeting diversified construction needs.

[0003] In response to the above technical problems, Chinese patent CN113816656B proposes a special concrete based on waste resource utilization and its preparation process, which grinds quartz-containing tailings to promote the transformation of its quartz molecules into an active disordered glass state, enhances the gelling reaction with cement, and improves the hardness and wear resistance of concrete. At the same time, fly ash is added in an appropriate amount to optimize the comprehensive performance of special concrete, thereby solving the problems of insufficient traditional resource utilization and heavy environmental burden, realizing the resource utilization of tailings and fly ash, and reducing production costs.

[0004] In the preparation process of the above-mentioned special concrete, it is necessary to add stones and reinforcing fibers to the cement mixture and stir it to enhance the bonding force between the mixtures and improve the overall performance of the concrete. However, the above-mentioned patent monitors the mixing state to determine when to add stones and reinforcing fibers. This process may rely on the operator's observation and experience and the use of a basic timer for evaluation and judgment. However, since each operator may have deviations in their understanding and judgment of the mixing uniformity and the degree of material mixing, this will directly affect the uniformity and strength of the concrete. In addition, although the use of a timer to control the timing of material addition provides a certain degree of standardization, this method ignores the changes in the actual mixing state and cannot flexibly adjust the addition time point, which may lead to uneven mixing of materials or over-mixing, thereby affecting the final performance of the concrete.

[0005] Therefore, an optimized production method of special concrete is desired. Summary of the invention

[0006] This summary is provided to introduce concepts in a brief form that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] In a first aspect, the present application provides a method for producing special concrete, the method comprising: Grinding and pulverizing the tailings and fly ash to obtain ground tailings and ground fly ash; The ground tailings and the ground fly ash are mixed and stirred to obtain an original mixed material; Adding cement to the original mixture and stirring to obtain a first cement mixture; adding stone and reinforcing fiber to the first cement mixture and stirring to obtain a second cement mixture; After the water reducing agent and water are evenly mixed, the mixture is added to the second cement mixture, and stirred to obtain special concrete; The step of adding stone and reinforcing fiber to the first cement mixture and stirring the mixture to obtain a second cement mixture comprises: Acquire a mixing state image of the first cement mixture captured by a camera; Passing the stirring state image through a stirring state feature extractor to obtain a stirring state feature map; Performing multi-scale perception enhancement expression on the stirring state feature map to obtain a first-scale stirring state perception enhancement feature map and a second-scale stirring state perception enhancement feature map; Performing global mean pooling processing on each feature matrix along the channel dimension in the first-scale stirring state perception enhancement feature map to obtain a first-scale stirring state perception enhancement feature vector; The first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are passed through a gated multi-scale stirring state fusion module based on an imitation converter structure to obtain a stirring state multi-scale fusion expression feature map; Based on the multi-scale fusion expression feature map of the mixing state, a recognition result is obtained, and based on the recognition result, it is determined whether to add stone and reinforcing fiber to the first cement mixture in batches.

[0008] Optionally, passing the stirring state image through a stirring state feature extractor to obtain a stirring state feature map includes: passing the stirring state image through a stirring state feature extractor based on a hole convolutional neural network model to obtain the stirring state feature map.

[0009] Optionally, the stirring state characteristic map is subjected to multi-scale perceptual enhancement expression to obtain a first-scale stirring state perceptual enhancement feature map and a second-scale stirring state perceptual enhancement feature map, including: performing convolution processing on the stirring state characteristic map based on a multi-layer convolutional layer to obtain a shallow stirring state characteristic map and a deep stirring state characteristic map; performing convolution encoding on the deep stirring state characteristic map to obtain the first-scale stirring state perceptual enhancement feature map; performing upsampling processing on the first-scale stirring state perceptual enhancement feature map to obtain an upsampled first-scale stirring state perceptual enhancement feature map; performing multi-scale feature perception processing on the shallow stirring state characteristic map to obtain the second-scale stirring state perceptual enhancement feature map.

[0010] Optionally, multi-scale feature perception processing is performed on the shallow stirring state feature map to obtain the second-scale stirring state perception enhanced feature map, including: inputting the shallow stirring state feature map into a multi-scale perception network to obtain a shallow multi-scale perception stirring state feature map; performing feature fusion on the shallow multi-scale perception stirring state feature map and the upsampled first-scale stirring state perception enhanced feature map, and performing convolution encoding on the obtained stirring state fusion feature map to obtain the second-scale stirring state perception enhanced feature map.

[0011] Optionally, the shallow stirring state feature map is input into a multi-scale perception network to obtain a shallow multi-scale perception stirring state feature map, including: copying the shallow stirring state feature map to obtain a shallow stirring state copy feature map; firstly passing the shallow stirring state feature map through a convolution layer with a convolution kernel of 1×1, and then sequentially performing activation processing and standardized batch processing based on the ReLU function on the obtained feature map to obtain a first-scale shallow stirring state feature map; firstly passing the first-scale shallow stirring state feature map through a convolution layer with a convolution kernel of 3×3, and then sequentially performing activation processing and standardized batch processing based on the ReLU function on the obtained feature map activation processing and standardized batch processing to obtain a second-scale shallow stirring state feature map; the second-scale shallow stirring state feature map is first passed through a convolution layer with a convolution kernel of 5×5, and then the obtained feature map is successively subjected to the activation processing based on the ReLU function and standardized batch processing to obtain a third-scale shallow stirring state feature map; the shallow stirring state copy feature map, the first-scale shallow stirring state feature map, the second-scale shallow stirring state feature map and the third-scale shallow stirring state feature map are cascaded through a feature fusion layer to obtain the shallow multi-scale perception stirring state feature map.

[0012] Optionally, the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are subjected to a gated multi-scale stirring state fusion module based on an imitation converter structure to obtain a stirring state multi-scale fusion expression feature map, including: performing layer normalization and convolution coding processing on the second-scale stirring state perception enhancement feature map to obtain a second-scale stirring state perception enhancement space deepening feature map; copying the second-scale stirring state perception enhancement space deepening feature map to obtain a backup second-scale stirring state perception enhancement space deepening feature map; and reshaping the backup second-scale stirring state perception enhancement space deepening feature map and the second-scale stirring state perception enhancement space deepening feature map to obtain a backup second-scale stirring state perception enhancement space deepening feature matrix and a second-scale stirring state perception enhancement space deepening feature matrix as an imitation query feature matrix and an imitation key feature matrix. matrix; calculating the product between the imitation query feature matrix and the transposed matrix of the imitation key feature matrix to obtain a second-scale stirring state perception enhancement local autocorrelation library matrix; inputting the second-scale stirring state perception enhancement local autocorrelation library matrix into the attention gating unit based on the GELU function to obtain a second-scale stirring state perception enhancement gated local autocorrelation library weight matrix; taking the first-scale stirring state perception enhancement feature vector as the value feature vector, calculating the product between the second-scale stirring state perception enhancement gated local autocorrelation library weight matrix and the first-scale stirring state perception enhancement feature vector to obtain a stirring state multi-scale cross-modal interactive fusion condition weight vector; taking the stirring state multi-scale cross-modal interactive fusion condition weight vector as the weight vector, weighting the second-scale stirring state perception enhancement feature map channel by channel to obtain the stirring state multi-scale fusion expression feature map.

[0013] Optionally, the second-scale stirring state perception enhancement feature map is layer-normalized and convolutionally encoded to obtain a second-scale stirring state perception enhancement spatial deepening feature map, including: performing layer-normalization processing on the second-scale stirring state perception enhancement feature map to obtain a layer-normalized second-scale stirring state perception enhancement feature map; performing point convolution encoding and spatial convolution encoding on the layer-normalized second-scale stirring state perception enhancement feature map to obtain the second-scale stirring state perception enhancement spatial deepening feature map.

[0014] Optionally, based on the multi-scale fusion expression feature map of the mixing state, an identification result is obtained, and based on the identification result, it is judged whether to add stone and reinforcing fiber to the first cement mixture in batches, including: passing the multi-scale fusion expression feature map of the mixing state through a classifier-based mixing state identifier to obtain the identification result, and the identification result is used to indicate whether the mixing state meets predetermined requirements; in response to the identification result that the mixing state meets the predetermined requirements, adding stone and reinforcing fiber to the first cement mixture in batches.

[0015] Optionally, the multi-scale fusion expression feature map of the stirring state is passed through a classifier-based stirring state identifier to obtain the recognition result, including: expanding the multi-scale fusion expression feature map of the stirring state into a classification feature vector according to a row vector or a column vector; using multiple fully connected layers of the classifier-based stirring state identifier to fully connect encode the classification feature vector to obtain an encoded classification feature vector; and passing the encoded classification feature vector through the Softmax classification function of the classifier-based stirring state identifier to obtain the recognition result.

[0016] By adopting the above technical solution, the stirring state image of the first cement mixture is collected by a camera, and the image recognition and analysis technology based on computer vision is used to perform feature analysis and multi-scale perception enhancement of the stirring state image, so as to automatically judge whether the stirring state meets the predetermined requirements based on the fusion expression between the stirring state enhancement features of the first scale and the second scale, and determine whether to add stone and reinforcing fiber to the first cement mixture in batches. In this way, the stirring state can be objectively evaluated, the uniformity difference caused by human factors can be reduced, the uniformity of concrete can be ensured, and the timing of adding materials can be accurately controlled to avoid adding too early or too late, thereby improving the intelligent degree of mixing.

[0017] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings: Figure 1 The present invention is a flow chart of a method for producing special concrete according to an exemplary embodiment.

[0019] Figure 2 The invention is a block diagram of a production and preparation system of special concrete according to an exemplary embodiment.

[0020] Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment.

[0021] Figure 4 It is an application scenario diagram of a production and preparation method of special concrete according to an exemplary embodiment. DETAILED DESCRIPTION

[0022] The embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes and are not intended to limit the scope of protection of the present application.

[0023] It should be understood that the various steps described in the method implementation of the present application can be performed in different orders and / or performed in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0024] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0025] It should be noted that the concepts such as "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0026] It should be noted that the modifications of "one" and "plurality" mentioned in the present application are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0027] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0028] The specific implementation of the present application is described in detail below with reference to the accompanying drawings.

[0029] Figure 1FIG. 1 is a flow chart showing a method for producing special concrete according to an exemplary embodiment. Figure 1 As shown, the method includes: Step S101, grinding and pulverizing the tailings and fly ash to obtain ground tailings and ground fly ash; Step S102, stirring and mixing the ground tailings and the ground fly ash to obtain an original mixture; Step S103, adding cement to the original mixture and stirring to obtain a first cement mixture; Step S104, adding stone and reinforcing fiber to the first cement mixture, and stirring to obtain a second cement mixture; Step S105, after the water reducing agent and water are evenly mixed, the mixture is added to the second cement mixture, and the mixture is stirred to obtain special concrete; Wherein, step S104, adding stone and reinforcing fiber to the first cement mixture and stirring to obtain a second cement mixture, comprises: Step S1041, obtaining a stirring state image of the first cement mixture captured by a camera; Step S1042, passing the stirring state image through a stirring state feature extractor to obtain a stirring state feature map; Step S1043, performing multi-scale perception enhancement expression on the stirring state feature map to obtain a first-scale stirring state perception enhancement feature map and a second-scale stirring state perception enhancement feature map; Step S1044, performing global mean pooling processing on each feature matrix along the channel dimension in the first-scale stirring state perception enhancement feature map to obtain a first-scale stirring state perception enhancement feature vector; Step S1045, the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are passed through a gated multi-scale stirring state fusion module based on an imitation converter structure to obtain a stirring state multi-scale fusion expression feature map; Step S1046: obtaining a recognition result based on the multi-scale fusion expression feature map of the mixing state, and judging whether to add stone and reinforcing fiber to the first cement mixture in batches based on the recognition result.

[0030] It should be understood that the raw materials of special concrete can be tailings, fly ash, cement, water reducing agent, stone, reinforcing fiber and water, and mixed according to precise proportions. Among them, step S101, grinding and pulverizing the tailings and fly ash to obtain ground tailings and ground fly ash, grinding the tailings and fly ash to a certain fineness to increase the contact area with other materials such as cement, thereby enhancing the reactivity and bonding between the materials. The quartz molecules in the tailings are transformed into an active disordered glass state during the grinding process, which helps to improve the hardness and wear resistance of the concrete. Step S102, stirring and mixing the ground tailings and the ground fly ash to obtain an original mixture, mixing the ground tailings and fly ash to form an original mixture, the purpose of this step is to ensure that the two materials are evenly mixed, and lay the foundation for subsequent cement addition and further mixing. Step S103, adding cement to the original mixture and stirring to obtain the first cement mixture. Cement is added to the original mixture and stirred to form the first cement mixture. Cement is the main cementitious material in concrete, and its addition and uniform mixing are the key to the hardening and strength formation of concrete. Step S104, adding stone and reinforcing fiber to the first cement mixture and stirring to obtain the second cement mixture, adding stone and reinforcing fiber to the first cement mixture, and stirring to form the second cement mixture. Stone provides the volume and structural support of concrete, while reinforcing fiber is used to improve the tensile strength and toughness of concrete and enhance its overall performance. Step S105, after mixing the water reducer and water evenly, add it to the second cement mixture and stir to obtain special concrete, after mixing the water reducer and water evenly, add it to the second cement mixture, and finally stir to obtain special concrete. The addition of water reducer can reduce the water demand of cement paste, thereby improving the strength and durability of concrete without reducing the working performance. Water is a necessary condition for cement hydration reaction. An appropriate amount of water can ensure that the cement is fully hydrated to form a solid concrete structure.

[0031] Accordingly, in the process of adding stone and reinforcing fiber to the first cement mixture and stirring to obtain the second cement mixture, the technical concept of the present application is to collect the stirring state image of the first cement mixture by a camera, and use the image recognition and analysis technology based on computer vision to perform feature analysis and multi-scale perception enhancement of the stirring state image, so as to automatically judge whether the stirring state meets the predetermined requirements based on the fusion expression between the stirring state enhancement features of the first scale and the second scale, and determine whether to add stone and reinforcing fiber to the first cement mixture in batches. In this way, the stirring state can be objectively evaluated, the uniformity difference caused by human factors can be reduced, the uniformity of concrete can be ensured, and the timing of adding materials can be accurately controlled to avoid adding too early or too late, thereby improving the intelligent degree of mixing.

[0032] Specifically, in the technical solution of the present application, first, the stirring state image of the first cement mixture captured by the camera is obtained. Then, considering that the stirring state image contains the state and distribution of the mixture during the stirring process, these state information are crucial for the judgment of the stirring state. Based on this, in the technical solution of the present application, the stirring state image is passed through a stirring state feature extractor based on a hole convolutional neural network model to capture and extract the key feature information of the halving process, and obtain a stirring state feature map.

[0033] In one embodiment of the present application, the stirring state image is passed through a stirring state feature extractor to obtain a stirring state feature map, including: passing the stirring state image through a stirring state feature extractor based on a void convolutional neural network model to obtain the stirring state feature map.

[0034] Then, considering that the stirring state feature map contains features of different scales about the mixing state of the mixture, such as color, texture, shape, etc. in the shallow large-scale mixing process, and high-level semantic features of deep small scales, such as the uniformity of mixing, there is also some interference from background information. In order to be able to more centrally and meticulously analyze the stirring state feature information of different scales, so as to perceive the stirring state of different scales in the stirring process, avoid the omission of small-scale stirring states, and enhance the multi-layer semantic perception ability of the stirring state, so as to better understand the complexity of the stirring process, in the technical solution of the present application, the stirring state feature map is subjected to multi-scale perception enhancement expression to obtain a first-scale stirring state perception enhancement feature map and a second-scale stirring state perception enhancement feature map. It should be understood that the multi-scale perception enhancement expression enhances the characterization ability of stirring states of different scales by expressing the features of the stirring state feature map at different scales, so as to highlight the most important features in the stirring state, such as the uniformity of material mixing or the potential separation area, reduce the interference of background noise, and thus improve the understanding of the overall situation and local details. Specifically, the stirring state feature map is firstly subjected to convolution processing based on different scales to obtain shallow and deep features. The deep features are then subjected to convolution coding processing to further highlight more representative and distinguishing stirring semantic features, so as to better capture the essential attributes of the stirring process and obtain the first-scale stirring state perception enhancement feature map.

[0035] In one embodiment of the present application, a multi-scale perceptual enhancement expression is performed on the stirring state characteristic map to obtain a first-scale stirring state perceptual enhancement feature map and a second-scale stirring state perceptual enhancement feature map, including: performing convolution processing on the stirring state characteristic map based on a multi-layer convolutional layer to obtain a shallow stirring state characteristic map and a deep stirring state characteristic map; performing convolution encoding on the deep stirring state characteristic map to obtain the first-scale stirring state perceptual enhancement feature map; performing upsampling processing on the first-scale stirring state perceptual enhancement feature map to obtain an upsampled first-scale stirring state perceptual enhancement feature map; performing multi-scale feature perception processing on the shallow stirring state characteristic map to obtain the second-scale stirring state perceptual enhancement feature map.

[0036] Furthermore, in one embodiment of the present application, multi-scale feature perception processing is performed on the shallow stirring state feature map to obtain the second-scale stirring state perception enhanced feature map, including: inputting the shallow stirring state feature map into a multi-scale perception network to obtain a shallow multi-scale perception stirring state feature map; performing feature fusion on the shallow multi-scale perception stirring state feature map and the upsampled first-scale stirring state perception enhanced feature map, and performing convolution encoding on the obtained stirring state fusion feature map to obtain the second-scale stirring state perception enhanced feature map.

[0037] Next, the shallow features are input into the multi-scale perception network to further capture the features of different scales in the shallow stirring features. In particular, the multi-scale perception network first copies the initial shallow features to retain the original stirring state information as the input of the two processing branches respectively. Then, one branch directly uses the original features, while the other branch sequentially performs 1x1, 3x3 and 5x5 convolution kernels to progressively learn the features extracted by the previous layer layer by layer to extract more semantic features, while retaining the output features of each convolution layer, and performing activation processing based on the ReLU function and standardized batch processing after each convolution layer to extract features of different levels layer by layer to enhance the expression ability of the model. Afterwards, the output of each convolution layer and the original features are cascaded to realize the multi-scale perception fusion of features to obtain the shallow multi-scale perception stirring state feature map.

[0038] In one embodiment of the present application, the shallow stirring state feature map is input into a multi-scale perception network to obtain a shallow multi-scale perception stirring state feature map, including: copying the shallow stirring state feature map to obtain a shallow stirring state copy feature map; firstly passing the shallow stirring state feature map through a convolution layer with a convolution kernel of 1×1, and then successively performing activation processing and standardized batch processing based on the ReLU function on the obtained feature map to obtain a first-scale shallow stirring state feature map; firstly passing the first-scale shallow stirring state feature map through a convolution layer with a convolution kernel of 3×3, and then successively performing the activation processing and standardized batch processing based on the ReLU function on the obtained feature map; LU function activation processing and standardized batch processing are performed to obtain a second-scale shallow stirring state feature map; the second-scale shallow stirring state feature map is first passed through a convolution layer with a convolution kernel of 5×5, and then the obtained feature map is sequentially subjected to the ReLU function-based activation processing and standardized batch processing to obtain a third-scale shallow stirring state feature map; the shallow stirring state copy feature map, the first-scale shallow stirring state feature map, the second-scale shallow stirring state feature map and the third-scale shallow stirring state feature map are cascaded through a feature fusion layer to obtain the shallow multi-scale perception stirring state feature map.

[0039] Then, the first-scale stirring state perception enhancement feature map is upsampled to make the feature maps of different scales consistent in spatial size to obtain the upsampled first-scale stirring state perception enhancement feature map. Finally, the shallow multi-scale perception stirring state feature map and the upsampled first-scale stirring state perception enhancement feature map are feature fused and convolutionally encoded to obtain the second-scale stirring state perception enhancement feature map. In this way, by performing multi-scale perception on the stirring state feature map, different aspects of the stirring state can be captured more carefully and comprehensively, thereby helping to improve the accuracy of stirring state recognition.

[0040] Then, in order to enable the network to focus on the more important global information of the stirring state, in the technical solution of the present application, global mean pooling is performed on each feature matrix along the channel dimension in the first-scale stirring state perception enhancement feature map to obtain a first-scale stirring state perception enhancement feature vector. That is, through global mean pooling, high-dimensional features can be compressed into a lower-dimensional feature vector, effectively reducing the feature dimension while retaining important stirring state feature information.

[0041] Further, considering that the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map respectively express stirring states at different levels of different scales, and can effectively process and fuse features of different scales to ensure that the information captured from multiple levels is fully utilized, in the technical solution of the present application, the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are passed through a gated multi-scale stirring state fusion module based on an imitation transformer structure to obtain a stirring state multi-scale fusion expression feature map. It is worth mentioning that the gated multi-scale stirring state fusion module based on the imitation transformer structure achieves effective fusion of features of different scales by imitating the self-attention mechanism of the Transformer structure and combining the gating unit. Specifically, first, the second-scale stirring state perception enhancement feature map is layer-normalized to eliminate the scale difference between different feature maps, so that features from different scales can be compared and fused at the same scale. Then, the normalized features are point-convolutionally encoded and spatially convolutionally encoded to capture and extract the spatial context feature information of the stirring feature, and the second-scale stirring state perception enhancement spatial deepening feature map is obtained, and the feature is copied to retain the spatial information. Next, the copied features and spatial features are reshaped to obtain the backup second-scale stirring state perception enhanced spatial deepening feature matrix and the second-scale stirring state perception enhanced spatial deepening feature matrix as the imitation query feature matrix and the imitation key feature matrix. Then, the autocorrelation matrix between the imitation query feature matrix and the imitation key feature matrix is ​​calculated to capture the stirring state correlation of different scales between the two to obtain the second-scale stirring state perception enhanced local autocorrelation library matrix. Further, the attention gating unit based on the GELU function is used to adaptively adjust the autocorrelation weight of each eigenvalue in the autocorrelation matrix to achieve dynamic selection and enhancement of features to obtain the second-scale stirring state perception enhanced gated local autocorrelation library weight matrix. Subsequently, the autocorrelation library weight matrix and the first-scale stirring state perception enhanced feature vector are matrix-multiplied to achieve multi-scale feature fusion to obtain the stirring state multi-scale cross-modal interactive fusion conditional weight vector. Finally, the conditional weight vector and the second-scale stirring state perception enhanced feature map are weighted channel by channel to obtain the stirring state multi-scale fusion expression feature map that integrates stirring information of different scales.

[0042] In one embodiment of the present application, the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are subjected to a gated multi-scale stirring state fusion module based on an imitation converter structure to obtain a stirring state multi-scale fusion expression feature map, including: performing layer normalization and convolution coding on the second-scale stirring state perception enhancement feature map to obtain a second-scale stirring state perception enhancement space deepening feature map; copying the second-scale stirring state perception enhancement space deepening feature map to obtain a backup second-scale stirring state perception enhancement space deepening feature map; and reshaping the backup second-scale stirring state perception enhancement space deepening feature map and the second-scale stirring state perception enhancement space deepening feature map to obtain a backup second-scale stirring state perception enhancement space deepening feature matrix and a second-scale stirring state perception enhancement space deepening feature matrix as an imitation query feature matrix and The invention relates to a pseudo-key feature matrix; calculating the product between the pseudo-query feature matrix and the transposed matrix of the pseudo-key feature matrix to obtain a second-scale stirring state perception enhancement local autocorrelation library matrix; inputting the second-scale stirring state perception enhancement local autocorrelation library matrix into the attention gating unit based on the GELU function to obtain a second-scale stirring state perception enhancement gated local autocorrelation library weight matrix; taking the first-scale stirring state perception enhancement feature vector as the value feature vector, calculating the product between the second-scale stirring state perception enhancement gated local autocorrelation library weight matrix and the first-scale stirring state perception enhancement feature vector to obtain a stirring state multi-scale cross-modal interactive fusion condition weight vector; taking the stirring state multi-scale cross-modal interactive fusion condition weight vector as the weight vector, weighting the second-scale stirring state perception enhancement feature map channel by channel to obtain the stirring state multi-scale fusion expression feature map.

[0043] Furthermore, in one embodiment of the present application, the second-scale stirring state perception enhancement feature map is layer-normalized and convolutionally encoded to obtain a second-scale stirring state perception enhancement spatial deepening feature map, including: performing layer-normalization processing on the second-scale stirring state perception enhancement feature map to obtain a layer-normalized second-scale stirring state perception enhancement feature map; performing point convolution encoding and spatial convolution encoding on the layer-normalized second-scale stirring state perception enhancement feature map to obtain the second-scale stirring state perception enhancement spatial deepening feature map.

[0044] Specifically, the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are processed by a gated multi-scale stirring state fusion module based on an imitation converter structure using the following fusion formula to obtain the stirring state multi-scale fusion expression feature map; wherein the fusion formula is: ;in, is the second scale stirring state perception enhancement feature map, is the layer normalization operation, is point convolutional coding, The convolution kernel is The convolution operation, The second scale stirring state perception enhanced spatial deepening feature map, For copy operation, The backup second scale stirring state perception enhanced space deepening feature map, For the reshape operation, is the transpose of the feature matrix, is matrix multiplication, is the GELU function, is the second scale stirring state-aware enhanced gated local autocorrelation library weight matrix, is the first scale stirring state perception enhancement feature vector, is the weight vector of the stirring state multi-scale cross-modal interaction fusion condition, It is the multi-scale fusion expression feature map of the stirring state.

[0045] Then, the multi-scale fusion expression feature map of the mixing state is passed through a mixing state identifier based on a classifier to obtain a recognition result, and the recognition result is used to indicate whether the mixing state meets the predetermined requirements. In response to the recognition result that the mixing state meets the predetermined requirements, stones and reinforcing fibers are added to the first cement mixture in batches. That is, the multi-scale fusion expression feature map of the mixing state obtained by multi-scale fusion of the first-scale mixing state perception reinforcement feature vector and the second-scale mixing state perception reinforcement feature map is classified and processed to automatically determine whether the mixing state meets the predetermined requirements and whether to add stones and reinforcing fibers to the first cement mixture in batches. In this way, the mixing state can be objectively evaluated, the uniformity difference caused by human factors can be reduced, the uniformity of concrete can be ensured, and the timing of adding materials can be accurately controlled to avoid adding too early or too late, thereby improving the intelligent degree of mixing of materials.

[0046] In one embodiment of the present application, based on the multi-scale fusion expression feature map of the mixing state, a recognition result is obtained, and based on the recognition result, it is judged whether to add stone and reinforcing fiber to the first cement mixture in batches, including: passing the multi-scale fusion expression feature map of the mixing state through a classifier-based mixing state identifier to obtain the recognition result, and the recognition result is used to indicate whether the mixing state meets predetermined requirements; in response to the recognition result that the mixing state meets the predetermined requirements, adding stone and reinforcing fiber to the first cement mixture in batches.

[0047] Furthermore, in one embodiment of the present application, the multi-scale fusion expression feature map of the stirring state is passed through a classifier-based stirring state identifier to obtain the recognition result, including: expanding the multi-scale fusion expression feature map of the stirring state into a classification feature vector according to a row vector or a column vector; using multiple fully connected layers of the classifier-based stirring state identifier to fully connect encode the classification feature vector to obtain an encoded classification feature vector; and passing the encoded classification feature vector through the Softmax classification function of the classifier-based stirring state identifier to obtain the recognition result.

[0048] Preferably, considering that the first-scale stirring state perception enhancement feature map and the second-scale stirring state perception enhancement feature map respectively represent image semantic features of different scales of perception enhancement of the stirring state image, when the first-scale stirring state perception enhancement feature map is subjected to global mean pooling processing of the feature matrix to obtain a first-scale stirring state perception enhancement feature vector, and the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are passed through a gated multi-scale stirring state fusion module based on an analog converter structure, there will also be image semantic feature aggregation mapping offset caused by inconsistent distribution of image semantic features at different scales and different distribution dimensions, thereby expecting to further improve the image semantic feature fusion understandability of the stirring state multi-scale fusion expression feature map, thereby improving the accuracy of the recognition result obtained by the stirring state multi-scale fusion expression feature map based on the classifier-based stirring state identifier.

[0049] Therefore, when the stirring state multi-scale fusion expression feature map is passed through a stirring state identifier based on a classifier, the present application optimizes the stirring state multi-scale fusion expression feature map, including the steps of: Expanding the stirring state multi-scale fusion expression feature map into a stirring state multi-scale fusion expression feature vector; Subtracting the zero norm of the stirring state multi-scale fusion expression feature vector from the length of the stirring state multi-scale fusion expression feature vector to obtain a stirring state multi-scale fusion expression isolated representation value; Calculate the logarithm of the square of the isolated representation value of the stirring state multi-scale fusion expression and the sum of the isolated representation value of the stirring state multi-scale fusion expression to obtain the information order value of the stirring state multi-scale fusion expression; Calculate a power function with each eigenvalue of the stirring state multi-scale fusion expression feature vector as the base, the difference of the stirring state multi-scale fusion expression isolated representation value minus one as the exponent, and perform a point multiplication with the stirring state multi-scale fusion expression information order value to obtain the stirring state multi-scale fusion expression leading vector; After performing a dot multiplication on the stirring state multi-scale fusion expression feature vector and the difference between the stirring state multi-scale fusion expression isolated representation value minus one, the dot multiplication is performed on the inverse of the stirring state multi-scale fusion expression isolated representation value to obtain a stirring state multi-scale fusion expression field constraint vector; Calculating an exponential function with a natural constant as the base and each eigenvalue of the stirring state multi-scale fusion expression field constraint vector as the exponent to obtain a stirring state multi-scale fusion expression field bias vector; and The stirring state multi-scale fusion expression leading vector and the stirring state multi-scale fusion expression field bias vector are added to obtain an optimized stirring state multi-scale fusion expression feature vector.

[0050] Among them, the multi-scale fusion expression feature vector of the stirring state is, for example, recorded as The optimization process is expressed as: ; To calculate a power function with each eigenvalue of the stirring state multi-scale fusion expression feature vector as the base and the difference between the stirring state multi-scale fusion expression isolated representation value minus one as the exponent, is the multi-scale fusion expression feature vector of the stirring state, is the zero norm of the vector, is the length of the multi-scale fusion expression feature vector of the stirring state, is the isolated representation value of the multi-scale fusion expression of the stirring state, It is an exponential function that calculates the exponential function of each eigenvalue of a vector with a natural constant as the base. The stirring state multi-scale fusion expression is the inverse of the isolated representation value, is the logarithm with base 2, is the optimized multi-scale fusion expression feature vector of the stirring state, It is added by location point. Multiply by position.

[0051] Therefore, for the high-dimensional feature manifold of the multi-scale fusion expression feature map of the mixing state, the feature value of the feature set is used as the vector field representation of the aggregation dimension, and the superposition value of the vector field of the multi-scale fusion expression feature vector of the mixing state is used as the order information to fix the local position of the feature value of the feature set, and add the bias of the reversibility of the feature regression distribution field of the multi-scale fusion expression feature vector of the mixing state as a reward to achieve the mapping target tracking of the regression distribution of the multi-scale fusion expression feature vector of the mixing state for the feature value position, so that the feature set of the multi-scale fusion expression feature map of the mixing state perceives the mapping migration to the aggregation distribution, thereby improving the accuracy of the recognition result obtained by the mixing state identifier based on the classifier by improving the image semantic feature fusion mapping comprehensibility of the multi-scale fusion expression feature map of the mixing state. In this way, the mixing state can be objectively evaluated, the uniformity difference caused by human factors can be reduced, the uniformity of concrete can be ensured, and the timing of adding materials can be accurately controlled to avoid adding too early or too late, thereby improving the intelligence of mixing materials.

[0052] In summary, the above scheme is adopted to collect the stirring state image of the first cement mixture by the camera, and to perform feature analysis and multi-scale perception enhancement of the stirring state image by using image recognition and analysis technology based on computer vision, so as to automatically judge whether the stirring state meets the predetermined requirements based on the fusion expression between the stirring state enhancement features of the first scale and the second scale, and determine whether to add stone and reinforcing fiber to the first cement mixture in batches. In this way, the stirring state can be objectively evaluated, the uniformity difference caused by human factors can be reduced, the uniformity of concrete can be ensured, and the timing of adding materials can be accurately controlled to avoid adding too early or too late, thereby improving the intelligent degree of mixing.

[0053] Figure 2 FIG. 1 is a block diagram of a production and preparation system for special concrete according to an exemplary embodiment. Figure 2 As shown, the system 200 includes: The grinding and pulverizing treatment module 201 is used to grind and pulverize the tailings and fly ash to obtain ground tailings and ground fly ash; A stirring and mixing processing module 202 is used to stir and mix the ground tailings and the ground fly ash to obtain an original mixture; A first cement mixture generating module 203, configured to add cement to the original mixture and stir the mixture to obtain a first cement mixture; A second cement mixture generating module 204, configured to add stone and reinforcing fiber to the first cement mixture and stir the mixture to obtain a second cement mixture; The special concrete generation module 205 is used to mix the water reducing agent and water evenly, add them into the second cement mixture, and stir them to obtain the special concrete; Wherein, the second cement mixture generating module 204 comprises: A stirring state image acquisition unit 2041 is used to acquire a stirring state image of the first cement mixture acquired by a camera; A stirring state feature extraction unit 2042, configured to pass the stirring state image through a stirring state feature extractor to obtain a stirring state feature map; A multi-scale perception enhancement expression unit 2043 is used to perform multi-scale perception enhancement expression on the stirring state feature map to obtain a first-scale stirring state perception enhancement feature map and a second-scale stirring state perception enhancement feature map; A global mean pooling processing unit 2044 is used to perform a global mean pooling process on each feature matrix along the channel dimension in the first-scale stirring state perception enhancement feature map to obtain a first-scale stirring state perception enhancement feature vector; The gated multi-scale stirring state fusion unit 2045 is used to obtain a stirring state multi-scale fusion expression feature map by passing the first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map through a gated multi-scale stirring state fusion module based on an imitation converter structure; The recognition result determination unit 2046 is used to obtain a recognition result based on the multi-scale fusion expression feature map of the mixing state, and based on the recognition result, determine whether to add stone and reinforcing fiber to the first cement mixture in batches.

[0054] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 600 suitable for implementing the embodiment of the present application. The terminal device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0055] like Figure 3As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0056] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0057] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present application are executed.

[0058] It should be noted that the computer-readable medium mentioned above in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0059] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0060] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0061] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0062] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0063] The modules involved in the embodiments described in this application can be implemented by software or hardware. The name of the module does not limit the module itself in some cases. For example, the test parameter acquisition module can also be described as a "module for acquiring device test parameters corresponding to the target device".

[0064] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0065] In the context of the present application, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0066] Figure 4 FIG. 1 is an application scenario diagram of a method for producing special concrete according to an exemplary embodiment. Figure 4 As shown, in this application scenario, first, an image of the mixing state of the first cement mixture captured by a camera is obtained (for example, Figure 4 Then, the obtained mixing state image of the first cement mixture is input to a server (for example, Figure 4 In S) as shown in , the server is capable of processing the mixing state image of the first cement mixture based on the production and preparation algorithm of special concrete to determine whether to add stone and reinforcing fiber to the first cement mixture in batches.

[0067] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

[0068] In addition, although each operation is described in a specific order, this should not be construed as requiring these operations to be performed in the specific order shown or to be performed in a sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although some specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Some features described in the context of a separate embodiment can also be implemented in a single embodiment in combination. On the contrary, the various features described in the context of a single embodiment can also be implemented in multiple embodiments individually or in any suitable sub-combination mode.

[0069] Although the subject matter has been described in language specific to structural features and / or method logic actions, it should be understood that the subject matter defined is not necessarily limited to the specific features or actions described above. On the contrary, the specific features and actions described above are merely exemplary forms of implementation. With respect to the apparatus in the above-mentioned embodiments, the specific manner in which each module performs the operation has been described in detail in the embodiments related to the method, and will not be described in detail here.

Claims

1. A method for producing special concrete, characterized in that: include: Grinding and pulverizing the tailings and fly ash to obtain ground tailings and ground fly ash; The ground tailings and the ground fly ash are mixed and stirred to obtain an original mixed material; Adding cement to the original mixture and stirring to obtain a first cement mixture; adding stone and reinforcing fiber to the first cement mixture and stirring to obtain a second cement mixture; After the water reducing agent and water are evenly mixed, the mixture is added to the second cement mixture, and stirred to obtain special concrete; The step of adding stone and reinforcing fiber to the first cement mixture and stirring the mixture to obtain a second cement mixture comprises: Acquire a mixing state image of the first cement mixture captured by a camera; Passing the stirring state image through a stirring state feature extractor to obtain a stirring state feature map; Performing multi-scale perception enhancement expression on the stirring state feature map to obtain a first-scale stirring state perception enhancement feature map and a second-scale stirring state perception enhancement feature map; Performing global mean pooling processing on each feature matrix along the channel dimension in the first-scale stirring state perception enhancement feature map to obtain a first-scale stirring state perception enhancement feature vector; The first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are passed through a gated multi-scale stirring state fusion module based on an imitation converter structure to obtain a stirring state multi-scale fusion expression feature map; Based on the multi-scale fusion expression feature map of the mixing state, a recognition result is obtained, and based on the recognition result, it is determined whether to add stone and reinforcing fiber to the first cement mixture in batches.

2. The method for producing special concrete according to claim 1, characterized in that: Passing the stirring state image through a stirring state feature extractor to obtain a stirring state feature map, including: passing the stirring state image through a stirring state feature extractor based on a hole convolutional neural network model to obtain the stirring state feature map.

3. The method for producing special concrete according to claim 2, characterized in that: Performing multi-scale perceptual enhancement expression on the stirring state feature map to obtain a first-scale stirring state perceptual enhancement feature map and a second-scale stirring state perceptual enhancement feature map, including: The stirring state characteristic map is subjected to convolution processing based on a multi-layer convolution layer to obtain a shallow stirring state characteristic map and a deep stirring state characteristic map; Performing convolution encoding on the deep stirring state feature map to obtain the first-scale stirring state perception enhancement feature map; Performing upsampling processing on the first-scale stirring state perception enhancement feature map to obtain an upsampled first-scale stirring state perception enhancement feature map; Multi-scale feature perception processing is performed on the shallow stirring state feature map to obtain the second-scale stirring state perception enhanced feature map.

4. The method for producing special concrete according to claim 3, characterized in that: Performing multi-scale feature perception processing on the shallow stirring state feature map to obtain the second-scale stirring state perception enhanced feature map includes: Inputting the shallow stirring state characteristic map into a multi-scale perception network to obtain a shallow multi-scale perception stirring state characteristic map; The shallow multi-scale perception stirring state feature map and the upsampled first-scale stirring state perception enhanced feature map are feature fused, and the obtained stirring state fusion feature map is convolutionally encoded to obtain the second-scale stirring state perception enhanced feature map.

5. The method for producing special concrete according to claim 4, characterized in that: Inputting the shallow stirring state feature map into a multi-scale perception network to obtain a shallow multi-scale perception stirring state feature map, including: Copying the shallow stirring state characteristic diagram to obtain a shallow stirring state copy characteristic diagram; The shallow stirring state feature map is first passed through a convolution layer with a convolution kernel of 1×1, and then the obtained feature map is sequentially subjected to activation processing based on a ReLU function and standardized batch processing to obtain a first-scale shallow stirring state feature map; The first-scale shallow stirring state feature map is first passed through a convolution layer with a convolution kernel of 3×3, and then the obtained feature map is sequentially subjected to the activation processing based on the ReLU function and the standardized batch processing to obtain a second-scale shallow stirring state feature map; The second-scale shallow stirring state feature map is first passed through a convolution layer with a convolution kernel of 5×5, and then the obtained feature map is sequentially subjected to the activation processing based on the ReLU function and the standardized batch processing to obtain a third-scale shallow stirring state feature map; The shallow stirring state replication feature map, the first-scale shallow stirring state feature map, the second-scale shallow stirring state feature map and the third-scale shallow stirring state feature map are cascaded through a feature fusion layer to obtain the shallow multi-scale perception stirring state feature map.

6. The method for producing special concrete according to claim 5, characterized in that: The first-scale stirring state perception enhancement feature vector and the second-scale stirring state perception enhancement feature map are passed through a gated multi-scale stirring state fusion module based on an imitation converter structure to obtain a stirring state multi-scale fusion expression feature map, including: performing layer normalization and convolution coding processing on the second-scale stirring state perception enhancement feature map to obtain a second-scale stirring state perception enhancement spatial deepening feature map; Copying the second-scale stirring state perception enhancement spatial deepening feature map to obtain a backup second-scale stirring state perception enhancement spatial deepening feature map; Reshaping the backup second-scale stirring state perception enhanced space deepening feature map and the second-scale stirring state perception enhanced space deepening feature map respectively to obtain a backup second-scale stirring state perception enhanced space deepening feature matrix and a second-scale stirring state perception enhanced space deepening feature matrix as a simulated query feature matrix and a simulated key feature matrix; Calculating the product of the pseudo-query feature matrix and the transposed matrix of the pseudo-key feature matrix to obtain a second-scale stirring state perception enhanced local autocorrelation library matrix; Inputting the second-scale stirring state perception enhanced local autocorrelation library matrix into the attention gating unit based on the GELU function to obtain the second-scale stirring state perception enhanced gated local autocorrelation library weight matrix; Taking the first-scale stirring state perception enhancement feature vector as the value feature vector, calculating the product between the second-scale stirring state perception enhancement gated local autocorrelation library weight matrix and the first-scale stirring state perception enhancement feature vector to obtain a stirring state multi-scale cross-modal interactive fusion condition weight vector; The stirring state multi-scale cross-modal interactive fusion condition weight vector is used as a weight vector, and the second-scale stirring state perception enhancement feature map is weighted channel by channel to obtain the stirring state multi-scale fusion expression feature map.

7. The method for producing special concrete according to claim 6, characterized in that: The second-scale stirring state perception enhancement feature map is subjected to layer normalization and convolution coding processing to obtain a second-scale stirring state perception enhancement spatial deepening feature map, including: performing layer normalization processing on the second-scale stirring state perception enhancement feature map to obtain a layer-normalized second-scale stirring state perception enhancement feature map; Point convolution encoding and spatial convolution encoding are performed on the layer-normalized second-scale stirring state perception enhancement feature map to obtain the second-scale stirring state perception enhancement spatial deepening feature map.

8. The method for producing special concrete according to claim 7, characterized in that: Obtaining a recognition result based on the multi-scale fusion expression feature map of the stirring state, and judging whether to add stone and reinforcing fiber to the first cement mixture in batches based on the recognition result, including: The stirring state multi-scale fusion expression feature map is passed through a stirring state identifier based on a classifier to obtain the recognition result, and the recognition result is used to indicate whether the stirring state meets the predetermined requirements; In response to the identification result that the mixing state meets the predetermined requirement, stone and reinforcing fiber are added to the first cement mixture in batches.

9. The method for producing special concrete according to claim 8, characterized in that: The stirring state multi-scale fusion expression feature map is passed through a stirring state identifier based on a classifier to obtain the recognition result, including: Expanding the stirring state multi-scale fusion expression feature map into a classification feature vector according to a row vector or a column vector; Performing fully connected encoding on the classification feature vector using a plurality of fully connected layers of the classifier-based stirring state identifier to obtain an encoded classification feature vector; The encoded classification feature vector is passed through the Softmax classification function of the classifier-based stirring state identifier to obtain the identification result.

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