Lightweight building material loss prediction method and system

Abstract

The invention discloses a loss prediction method and system for a light building material. The method comprises the steps of data acquisition, data screening, loss prediction modeling and loss prediction of the light building material. The invention belongs to the field of data processing, and particularly relates to a loss prediction method and system for a light building material.According to the scheme, an environment difference measurement function is introduced for sub-region merging, and local loss characteristics are captured; a local space autocorrelation index is introduced, and abnormal loss data is identified based on local abnormal value scoring optimization, so that the quality of the loss data is effectively improved; an environment penalty correction factor is introduced to weight an abnormal environment region sample, so that a prediction error in an environment dramatic change region is amplified, and the sensitivity to serious loss is improved; a compensation correction term is designed, grading punishment is carried out on different loss grades through the loss gradient, and therefore the model has better distinguishing power when the light loss, the medium loss and the serious loss are predicted; and the final material loss prediction effect is improved.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a loss prediction method and system for lightweight building materials. Background Art

[0002] Material loss prediction method refers to the process of modeling and predicting the gradual degradation or loss of materials during use by using techniques such as statistics, machine learning or deep learning. Its main goal is to identify potential serious loss areas in advance through the analysis of historical data and multi-dimensional influencing factors such as environment and force, so as to provide a scientific basis for maintenance, inspection and safety management. However, the general material loss prediction method ignores the interactive effects of spatial and environmental factors, and thus cannot capture local loss characteristics and effectively identify abnormal losses, which leads to poor accuracy in material loss prediction; the general material loss prediction method ignores the particularity of loss in areas with drastic environmental changes or local uneven force, and does not introduce a special compensation mechanism. When faced with serious losses, the prediction error is not given enough attention, which leads to the problem of poor final prediction effect. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a method and system for predicting the loss of lightweight building materials. In view of the problem that the general material loss prediction method ignores the interactive effects of spatial and environmental factors, thus failing to capture local loss characteristics and effectively identify abnormal losses, which leads to poor accuracy in material loss prediction, this scheme introduces an environmental difference measurement function to merge sub-regions to capture local loss characteristics; by introducing a local spatial autocorrelation index to identify noise caused by sensor failure or external factors, and optimizing the identification of abnormal loss data based on local outlier scores, the quality of loss data is effectively improved, thereby improving the accuracy of the final material loss prediction; in view of the problem that the general material loss prediction method ignores the interactive effects of spatial and environmental factors, thus failing to capture local loss characteristics and effectively identify abnormal losses, thereby leading to poor accuracy in material loss prediction Due to the particularity of losses in areas with drastic environmental changes or local uneven stress, no special compensation mechanism has been introduced. When faced with severe losses, the prediction error has not received sufficient attention, which has led to the problem of poor final prediction effect. This scheme introduces an environmental penalty correction factor to weight the samples in abnormal environmental areas, so that the prediction error in areas with drastic environmental changes is amplified, thereby increasing the sensitivity to severe losses; additional compensation correction items are designed for areas with local uneven stress and drastic environmental changes to ensure that the loss prediction of lightweight building materials in these areas is additionally compensated; graded penalties are imposed on different loss levels through the loss loss gradient, so that the model is more discriminatory when predicting mild, moderate and severe losses, thereby improving the final material loss prediction effect.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides a method for predicting the loss of lightweight building materials, the method comprising the following steps:

[0005] Step S1: data acquisition;

[0006] Step S2: data screening;

[0007] Step S3: loss prediction modeling;

[0008] Step S4: Prediction of lightweight building material loss.

[0009] Furthermore, in step S1, the data acquisition is to acquire historical environmental data and historical lightweight building material loss data; and to clarify the loss level, and use the loss level as a data label; and to obtain an initial loss data set based on feature engineering.

[0010] Furthermore, in step S2, the data screening specifically includes the following steps:

[0011] Step S21: Sub-region merging process; introduce the environmental difference measurement function D(·); continuously merge regions based on the similarity between regions until the number of sub-regions is reached, and the initial sub-region is the data point; the similarity between regions It is expressed as: ; ;in, and Represents a set of loss data for two adjacent regions; and Represents the weight coefficient of two adjacent regions; represents the comprehensive loss effect after regional merger; and is the adjustment parameter; where and Represented as environmental data vectors of two regions; T is the transpose operation; It is a diagonal matrix, and the diagonal elements represent the sensor reliability of each environmental indicator; is the covariance matrix of the environmental data;

[0012] Step S22: Optimize the number of sub-regions and introduce penalties for small sample regions; the optimal number of sub-regions P is expressed as: ; Where K is the total number of sub-regions, k is the sub-region index; N k is the number of samples in the kth sub-region; is the variance of loss data in the kth sub-region; N is the total sample size; is the variance of the overall data; is the additional penalty coefficient;

[0013] Step S23: Abnormal loss identification; calculation of local spatial autocorrelation index , expressed as: ;in, , and are the i-th, j-th and s-th loss data respectively; i and j are data indexes; n is the total number of measurement points in the current investigation area; is the average loss value of all measurement points; is the spatial weight of the point; is the loss data neighborhood, k is the neighborhood data index; is a smoothing term; yes The median of the loss data of all measuring points in the neighborhood; Represents the median of the absolute deviations of all measurement points in the neighborhood;

[0014] Step S24: Outlier score optimization; bias adjustment based on local loss and introduction of local sensor reliability factor ; Outlier score It is expressed as: ; ; ;in, is the local loss deviation of the ith measuring point; is the average loss deviation of the measuring point; is the standard error inherent in the sensor; It is the local dispersion degree of loss data in the current sub-region; a threshold is set in advance, and data with an outlier score higher than the threshold is identified as abnormal loss data; the abnormal loss data is eliminated to obtain a screened loss prediction data set.

[0015] Furthermore, in step S3, the loss prediction modeling specifically includes the following steps:

[0016] Step S31: Feature construction: Perform principal component analysis on the filtered data to extract the global feature vector ; Local linear embedding is used for the data in the neighborhood to extract local nonlinear features ; The two parts of information are combined to form the final feature vector;

[0017] Step S32: dual-branch deep learning model design; loss prediction is performed based on the deep learning model, one branch captures the spatial distribution characteristics of material loss, and the other branch focuses on environmental variables and global features. The two branches are fused to jointly predict the loss; the model input is the extracted feature vector, and the output is the loss prediction value of lightweight building materials; the screened data is used as a normal loss data set and divided into a training set and a validation set; the model parameters are optimized using gradient descent and back propagation algorithms; the K-fold cross-validation method is used to evaluate the generalization performance of the model;

[0018] Step S33: Design the loss function; design the penalty loss function LE and introduce the environmental penalty correction factor , expressed as: ; ; Where N is the total number of samples; is the predicted loss value of the sample; is the environmental data vector; is the mean of the overall environment vector; is the environmental adjustment coefficient; c is the critical threshold of the loss level; is the penalty index, which is used to adjust the weights of different levels of loss errors; and the loss gradient is defined as ; Introducing the loss correction, the final loss loss function LC is expressed as: ; ;in, is the loss correction; and They are the environmental compensation weight coefficient and the force compensation weight coefficient; and is the exponential parameter; is the standard deviation of the environmental deviation; is the average load stress in the neighborhood; is the average value of the load stress over the entire area.

[0019] Furthermore, in step S4, the lightweight building material loss prediction is to collect environmental data and lightweight building material loss data in real time, input them into the loss prediction model after feature engineering processing, and use the loss level output by the loss prediction model as the loss prediction result.

[0020] The invention provides a lightweight building material loss prediction system, comprising a data acquisition module, a data screening module, a loss prediction modeling module and a lightweight building material loss prediction module;

[0021] The data acquisition module acquires historical environmental data and lightweight building material loss data, and constructs an initial loss data set based on feature engineering;

[0022] The data screening module screens the initial data based on local anomaly detection to remove abnormal data;

[0023] The loss prediction modeling module realizes the modeling of material loss prediction based on the screened data by constructing global and local features, designing a dual-branch deep learning model and a dedicated loss loss function;

[0024] The lightweight building material loss prediction module uses loss prediction modeling to predict the loss of lightweight building materials based on real-time collected environmental and loss data.

[0025] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0026] (1) In view of the problem that general material loss prediction methods ignore the interactive effects of spatial and environmental factors, thus failing to capture local loss characteristics and effectively identify abnormal losses, which in turn leads to poor accuracy in material loss prediction, this scheme introduces an environmental difference measurement function to merge sub-regions and capture local loss characteristics; by introducing a local spatial autocorrelation index to identify noise caused by sensor failure or external factors, and optimizing the identification of abnormal loss data based on local outlier scores, the quality of loss data is effectively improved, thereby improving the accuracy of the final material loss prediction.

[0027] (2) In view of the fact that general material loss prediction methods ignore the particularity of loss in areas with drastic environmental changes or local uneven stress, and do not introduce a special compensation mechanism, when faced with severe loss, the prediction error is not given enough attention, which leads to the problem of poor final prediction effect. This scheme introduces an environmental penalty correction factor to weight the samples in abnormal environmental areas, so that the prediction error in areas with drastic environmental changes is amplified, thereby increasing the sensitivity to severe loss; for areas with local uneven stress and drastic environmental changes, additional compensation correction items are designed to ensure that the loss prediction of lightweight building materials in these areas is additionally compensated; different loss levels are graded and punished by the loss loss gradient, so that the model is more discriminatory when predicting mild, moderate and severe losses; thereby improving the final material loss prediction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of a process flow of a method for predicting loss of lightweight building materials provided by the present invention;

[0029] Figure 2 A schematic diagram of a loss prediction system for lightweight building materials provided by the present invention;

[0030] Figure 3 It is a schematic diagram of the process of step S2.

[0031] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0033] In the description of the present invention, it is necessary to understand that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the referred system or element must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0034] Example 1, see Figure 1 The present invention provides a method for predicting the loss of lightweight building materials, the method comprising the following steps:

[0035] Step S1: Data acquisition: historical environmental data and lightweight building material loss data are acquired, and an initial loss data set is constructed based on feature engineering;

[0036] Step S2: Data screening: Screening the initial data based on local anomaly detection to remove abnormal data;

[0037] Step S3: loss prediction modeling: Based on the screened data, material loss prediction modeling is achieved by constructing global and local features, designing a dual-branch deep learning model and a dedicated loss loss function;

[0038] Step S4: Prediction of lightweight building material loss; using loss prediction modeling to predict lightweight building material loss based on real-time collected environmental and loss data.

[0039] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, data acquisition is based on sensors to acquire historical environmental data and historical lightweight building material loss data; and the loss level is clarified and the loss level is used as a data label; an initial loss data set is obtained based on feature engineering; sensors are installed at key locations of walls and beams of the building structure to collect environmental data and loss data of the lightweight building materials themselves; the historical environmental data includes humidity, temperature, precipitation, wind speed, air quality, ultraviolet radiation intensity and load stress; the historical lightweight building material loss data includes material type, crack width and material shedding rate; the loss levels include no loss, slight loss, moderate loss and severe loss.

[0040] Example 3, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S2, data screening introduces sub-region merging and local anomaly detection to screen the initial loss data set to ensure that a normal loss data set is screened out to provide high-quality data for subsequent modeling. Specifically, the following steps are included:

[0041] Step S21: Sub-region merging processing; the material loss data is divided into several sub-regions according to the spatial distribution, so that the loss in the same region has a high consistency, which is convenient for capturing the local loss characteristics caused by environmental factors such as humidity, load, temperature gradient, etc.; the environmental difference measurement function D(·) is introduced, so that the sub-region merging not only considers the material loss data, but also fully reflects the environmental characteristics of each sub-region, so as to improve the ability to distinguish local loss characteristics; the regions are continuously merged based on the similarity between regions until the number of sub-regions is reached, and the initial sub-region is the data point; the similarity between regions It is expressed as: ; ;in, and Represents a set of loss data for two adjacent regions; and Indicates the weight coefficient of two adjacent areas, reflecting the importance of loss data in each area; represents the comprehensive loss effect after regional merger; and are adjustment parameters that balance the impact of loss data and environmental data on the sub-region merging results; and Represented as environmental data vectors of two regions; T is the transpose operation; It is a diagonal matrix, and the diagonal elements represent the sensor reliability of each environmental indicator; is the covariance matrix of environmental data, reflecting the statistical correlation between environmental indicators;

[0042] Step S22: Optimize the number of sub-regions to prevent excessive sub-region merging from causing detail loss while enabling the loss at different locations to be reasonably divided to ensure that classification details are preserved; and introduce penalties for small sample areas to ensure that there is sufficient data in each sub-region and that statistics are reliable; the optimal number of sub-regions P is expressed as: ; Where K is the total number of sub-regions, k is the sub-region index; N k is the number of samples in the kth sub-region; is the variance of loss data in the kth sub-region; N is the total sample size; is the variance of the overall data; is the additional penalty coefficient;

[0043] Step S23: Abnormal loss identification: by measuring the spatial correlation of the material loss area, the abnormal loss points caused by statistical noise and actual structural defects are identified, so as to eliminate the abnormal data; calculate the local spatial autocorrelation index , expressed as: ;in, , and are the i-th, j-th and s-th loss data respectively; i and j are data indexes; n is the total number of measurement points in the current investigation area; is the average loss value of all measurement points; is the spatial weight of the point; is the loss data neighborhood, k is the neighborhood data index; is a smoothing term; yes The median of the loss data of all measuring points in the neighborhood; Represents the median of the absolute deviations of all measurement points in the neighborhood;

[0044] Step S24: Outlier scoring optimization; for areas with small data fluctuations, enhance the outlier scoring ability and improve the recognition accuracy of abnormal losses; perform deviation adjustment based on local losses and introduce local sensor reliability factors , so that the score can reflect the credibility of sensor data, thereby more accurately identifying abnormal losses; outlier scoring It is expressed as: ; ; ;in, is the local loss deviation of the ith measuring point; is the average loss deviation of the measuring point; is the standard error inherent in the sensor; It is the local dispersion degree of loss data in the current sub-region; a threshold is set in advance, and data with an outlier score higher than the threshold is identified as abnormal loss data; the abnormal loss data is eliminated to obtain a screened loss prediction data set.

[0045] By performing the above operations, this scheme introduces an environmental difference measurement function to merge sub-regions and capture local loss characteristics, aiming at the problem that general material loss prediction methods ignore the interactive effects of spatial and environmental factors, thus failing to capture local loss characteristics and effectively identify abnormal losses, which leads to poor accuracy in material loss prediction. The local spatial autocorrelation index is introduced to identify noise caused by sensor failure or external factors, and abnormal loss data is optimized based on local outlier scores, thereby effectively improving the quality of loss data and improving the accuracy of the final material loss prediction.

[0046] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the loss prediction modeling uses the screened data to establish a loss prediction model. The loss prediction model not only considers the input of environmental variables, but also incorporates the spatial distribution characteristics of local loss data, and finely punishes the prediction error through the designed loss function to ensure that the identification of severe losses is more sensitive. Specifically, the following steps are included:

[0047] Step S31: Feature construction: Perform principal component analysis on the filtered data to extract the global feature vector ; Local linear embedding is used for the data in the neighborhood to extract local nonlinear features ; The two parts of information are combined to form the final feature vector;

[0048] Step S32: dual-branch deep learning model design; loss prediction is performed based on the deep learning model, one branch captures the spatial distribution characteristics of material loss, and the other branch focuses on environmental variables and global features. The two branches are fused to jointly predict the loss; the model input is the extracted feature vector, and the output is the loss prediction value of lightweight building materials; the screened data is used as a normal loss data set and divided into a training set and a validation set; the model parameters are optimized using gradient descent and back propagation algorithms; the K-fold cross-validation method is used to evaluate the generalization performance of the model;

[0049] Step S33: Design a loss loss function; To ensure the model's ability to distinguish different loss levels, especially the sensitivity to severe loss, design a special loss function and its gradient penalty term; Design a penalty loss function LE and introduce an environmental penalty correction factor , so that the loss of samples in abnormal environmental areas is amplified, expressed as: ; ; Where N is the total number of samples; is the predicted loss value of the sample; is the environmental data vector; is the mean of the overall environment vector; is the environmental adjustment coefficient; c is the critical threshold of the loss level; is the penalty index, which is used to adjust the weights of different levels of loss errors; and the loss gradient is defined as ; In order to deal with the uneven stress area of ​​building materials, it is necessary to improve the ability to distinguish different loss levels and introduce loss correction to compensate for the loss prediction of lightweight building materials in the area of ​​local uneven stress and environmental drastic changes. The final loss loss function LC is expressed as: ; ;in, It is the loss correction amount, which provides additional compensation for areas where the material is subjected to uneven forces or where the environment changes drastically; and They are the environmental compensation weight coefficient and the force compensation weight coefficient; and is the exponential parameter; is the standard deviation of the environmental deviation; is the average load stress in the neighborhood; is the average value of the load stress over the entire area.

[0050] By performing the above operations, in view of the fact that general material loss prediction methods ignore the particularity of losses in areas with drastic environmental changes or local uneven stress, and do not introduce special compensation mechanisms, when faced with severe losses, the prediction errors are not given enough attention, which leads to the problem of poor final prediction results. This scheme introduces an environmental penalty correction factor to weight samples in abnormal environmental areas, so that the prediction errors in areas with drastic environmental changes are amplified, thereby increasing the sensitivity to severe losses; additional compensation correction items are designed for areas with local uneven stress and drastic environmental changes to ensure that the loss prediction of lightweight building materials in these areas is additionally compensated; graded penalties are imposed on different loss levels through loss loss gradients, so that the model is more discriminatory when predicting mild, moderate and severe losses; thereby improving the final material loss prediction effect.

[0051] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the loss prediction of lightweight building materials is to collect environmental data and lightweight building material loss data in real time, input them into the loss prediction model after feature engineering processing, and use the loss level output by the loss prediction model as the loss prediction result; when the loss level is severe loss or moderate loss, the management personnel are warned.

[0052] Example 6, see Figure 2 , this embodiment is based on the above embodiment, and the present invention provides a lightweight building material loss prediction system, including a data acquisition module, a data screening module, a loss prediction modeling module and a lightweight building material loss prediction module;

[0053] The data acquisition module acquires historical environmental data and lightweight building material loss data, constructs an initial loss data set based on feature engineering, and sends the data to the data screening module;

[0054] The data screening module screens the initial data based on local anomaly detection to remove abnormal data; and sends the data to the loss prediction modeling module;

[0055] The loss prediction modeling module realizes the modeling of material loss prediction based on the screened data by constructing global and local features, designing a dual-branch deep learning model and a dedicated loss loss function; and sends the data to the lightweight building material loss prediction module;

[0056] The lightweight building material loss prediction module uses loss prediction modeling to predict the loss of lightweight building materials based on real-time collected environmental and loss data.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0058] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0059] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A method for predicting the loss of lightweight building materials, characterized in that: The method comprises the following steps: Step S1: Data acquisition: historical environmental data and lightweight building material loss data are acquired, and an initial loss data set is constructed based on feature engineering; Step S2: data screening; Step S3: loss prediction modeling; Step S4: lightweight building material loss prediction: using loss prediction modeling to predict lightweight building material loss based on real-time collected environmental and loss data; Step S2 includes step S21: sub-region merging process; introducing environmental difference measurement function D(·); continuously merging regions based on inter-region similarity until the number of sub-regions is reached, and the initial sub-region is a data point; inter-region similarity It is expressed as: ; ;in, and Represents a set of loss data for two adjacent regions; and Represents the weight coefficient of two adjacent regions; represents the comprehensive loss effect after regional merger; and is the adjustment parameter; where and Represented as environmental data vectors of two regions; T is the transpose operation; It is a diagonal matrix, and the diagonal elements represent the sensor reliability of each environmental indicator; is the covariance matrix of the environmental data.

2. The method for predicting loss of lightweight building materials according to claim 1, characterized in that: In step S2, the data screening further includes the following steps: Step S22: Optimize the number of sub-regions and introduce penalties for small sample regions; the optimal number of sub-regions P is expressed as: ; Where K is the total number of sub-regions, k is the sub-region index; N k is the number of samples in the kth sub-region; is the variance of loss data in the kth sub-region; N is the total sample size; is the variance of the overall data; is the additional penalty coefficient; Step S23: Abnormal loss identification; calculation of local spatial autocorrelation index , expressed as: ;in, , and are the i-th, j-th and s-th loss data respectively; i and j are data indexes; n is the total number of measurement points in the current investigation area; is the average loss value of all measurement points; is the spatial weight of the point; is the loss data neighborhood, k is the neighborhood data index; is a smoothing term; yes The median of the loss data of all measuring points in the neighborhood; Represents the median of the absolute deviations of all measurement points in the neighborhood; Step S24: outlier score optimization.

3. The method for predicting loss of lightweight building materials according to claim 2, characterized in that: In step S2, the outlier score optimization is based on local loss to adjust the deviation and introduce the local sensor reliability factor ; Outlier score It is expressed as: ; ; ;in, is the local loss deviation of the ith measuring point; is the average loss deviation of the measuring point; is the standard error inherent in the sensor; It is the local dispersion degree of loss data in the current sub-region; a threshold is set in advance, and data with an outlier score higher than the threshold is identified as abnormal loss data; the abnormal loss data is eliminated to obtain a screened loss prediction data set.

4. The method for predicting loss of lightweight building materials according to claim 3, characterized in that: In step S3, the loss prediction modeling specifically includes the following steps: Step S31: Feature construction: Perform principal component analysis on the filtered data to extract the global feature vector ; Local linear embedding is used for the data in the neighborhood to extract local nonlinear features ; The two parts of information are combined to form the final feature vector; Step S32: dual-branch deep learning model design; loss prediction is performed based on the deep learning model, one branch captures the spatial distribution characteristics of material loss, and the other branch focuses on environmental variables and global features. The two branches are fused to jointly predict the loss; the model input is the extracted feature vector, and the output is the loss prediction value of lightweight building materials; the screened data is used as a normal loss data set and divided into a training set and a validation set; the model parameters are optimized using gradient descent and back propagation algorithms; the K-fold cross-validation method is used to evaluate the generalization performance of the model; Step S33: Design a loss function.

5. The method for predicting loss of lightweight building materials according to claim 4, characterized in that: In step S3, the design loss function is the design penalty loss function LE, and the environmental penalty correction factor is introduced. , expressed as: ; ; Where N is the total number of samples; is the predicted loss value of the sample; is the environmental data vector; is the mean of the overall environment vector; is the environmental adjustment coefficient; c is the critical threshold of the loss level; is the penalty index, which is used to adjust the weights of different levels of loss errors; and the loss gradient is defined as ; Introducing the loss correction, the final loss loss function LC is expressed as: ; ;in, is the loss correction; and They are the environmental compensation weight coefficient and the force compensation weight coefficient; and is the exponential parameter; is the standard deviation of the environmental deviation; is the average load stress in the neighborhood; is the average value of the load stress over the entire area.

6. A method for predicting loss of lightweight building materials according to claim 5, characterized in that: In step S4, the lightweight building material loss prediction is to collect environmental data and lightweight building material loss data in real time, input them into the loss prediction model after feature engineering processing, and use the loss level output by the loss prediction model as the loss prediction result.

7. A loss prediction system for lightweight building materials, used to implement a loss prediction method for lightweight building materials as claimed in any one of claims 1 to 6, characterized in that: It includes a data acquisition module, a data screening module, a loss prediction modeling module and a lightweight building material loss prediction module; The data acquisition module acquires historical environmental data and lightweight building material loss data, and constructs an initial loss data set based on feature engineering; The data screening module screens the initial data based on local anomaly detection to remove abnormal data; The loss prediction modeling module realizes the modeling of material loss prediction based on the screened data by constructing global and local features, designing a dual-branch deep learning model and a dedicated loss loss function; The lightweight building material loss prediction module uses loss prediction modeling to predict the loss of lightweight building materials based on real-time collected environmental and loss data.

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