Multi-feature input flow-state solidified soil fluidity prediction method and multi-feature input flow-state solidified soil fluidity prediction device

Abstract

The invention relates to the technical field of flow-state solidified soil, in particular to a flow-state solidified soil fluidity prediction method and device based on multi-feature input. The invention discloses a multi-feature input flow-state solidified soil fluidity prediction method. The method comprises the following steps: S1, collecting raw material features; s2, collecting current characteristics; s3, collecting unloading characteristics; s4, constructing a prediction model; and S5, production line application. According to the method, the multi-input flow state solidified soil fluidity prediction model based on the raw material characteristics, the current characteristics and the unloading characteristics is pre-established and is applied to the production line, so that in the actual stirring flow state solidified soil process of the production line, the corresponding characteristic data is input into the mature prediction model; therefore, the fluidity of the current batch of fluid-state solid soil is pre-judged, the accuracy of predicting the fluidity of the fluid-state solidified soil through the multi-input characteristic is high, meanwhile, the problem that in the prior art, the fluid-state solidified soil can only be measured off line is solved, and online measurement of the fluidity of the fluid-state solidified soil is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of fluid-solidified soil, and in particular to a method and device for predicting the fluidity of fluid-solidified soil with multi-feature input. Background Art

[0002] Fluid-solidified soil is a new type of building material, which is widely used in engineering such as foundation reinforcement, trench backfilling, road subgrade, foundation pit support curtain wall, and mine goaf backfilling. Its main characteristics are good fluidity, can be constructed by pumping or pouring, has a fast filling and laying speed, can completely fill all small voids and irregular shapes in the space, ensure full contact between the filler and the surrounding soil, and provide stable support and uniform distributed load.

[0003] However, there are some deficiencies in the existing fluidity detection methods for fluid-solidified soil. For example, when detecting according to the cement mortar fluidity test method of GB / T 2419-2005, the accuracy of fluidity measurement is related to the operation specification of workers, the detection time is relatively long, about six minutes, and the detection has hysteresis, and the change of fluidity during the production process cannot be detected. Therefore, it is of great significance to develop a fast and accurate online fluidity prediction method for fluid-solidified soil. Summary of the Invention

[0004] Other features and advantages of the present invention will be described in the following specification, and will be partially obvious from the specification, or understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained through the structures specifically pointed out in the specification and other specification drawings.

[0005] The objective of the present invention is to overcome the above deficiencies, and provide a method and device for predicting the fluidity of fluid-solidified soil with multi-feature input. By pre-establishing a multi-input fluidity prediction model for fluid-solidified soil based on raw material characteristics, current characteristics, and discharging characteristics and applying it to the production line, during the actual mixing process of fluid-solidified soil on the production line, by inputting the corresponding characteristic data into the mature prediction model, the fluidity of the current batch of fluid-solidified soil can be pre-determined. The prediction accuracy of the fluidity of fluid-solidified soil by multi-input characteristics is high. At the same time, the mature fluidity prediction model has high graftability and wide applicability, solves the problem that a large amount of labor is required for the fluidity of fluid-solidified soil in the prior art and can only be measured offline, and realizes the online measurement of the fluidity of fluid-solidified soil.

[0006] The present invention provides a method for predicting the fluidity of fluid-solidified soil with multi-feature input, including: S1. Collect raw material characteristics: For multiple batches of fluid-solidified soil after production, trace their corresponding production formula data, and obtain the raw material characteristics of the fluid-solidified soil after processing the production formula data; S2. Collect current characteristics: For multiple batches of fluid-solidified soil, trace the current curves during their respective mixing processes, and obtain current characteristics based on the current curves. S3. Collect discharge characteristics: For multiple batches of fluid-solidified soil, trace the discharge images after their respective mixing is completed, and form discharge characteristics after processing the discharge images. S4. Build a prediction model: Build a fluid-solidified soil fluidity prediction model based on a neural network, and train the prediction model with training data to obtain a mature fluid-solidified soil fluidity prediction model. The training data specifically includes one-dimensional arrays formed by raw material characteristics, current characteristics, discharge characteristics, and corresponding fluidity labels. S5. Application in the production line: Integrate the multi-characteristics obtained during the production process of the production line into a one-dimensional array and input it into the mature fluid-solidified soil fluidity prediction model to output the fluid-solidified soil fluidity prediction result.

[0007] In some embodiments, in step S1, the specific steps for collecting raw material characteristics are as follows: S11. Obtain the actual production formula from the raw material metering equipment, which specifically includes muck, curing agent, admixture, and water. S12. Obtain the water mass characteristic based on the water content of the muck raw material and the water consumption, obtain the soil mass characteristic by calculating based on the mass of the muck raw material and the water content of the muck raw material, obtain the curing agent mass characteristic based on the type and mass of the curing agent, and obtain the admixture mass characteristic based on the type and mass of the admixture. S13. Combine the water mass characteristic, soil mass characteristic, curing agent mass characteristic, and admixture mass characteristic to form the raw material characteristic.

[0008] In some embodiments, in step S2, the specific steps for collecting current characteristics are as follows: S21. Collect the current values in the mixer at a fixed frequency to obtain the current curve, smooth the current curve, and take the current value at no-load as the first current value. S22. When the mixer stirs the fluid-solidified soil, the current curve rises from the no-load current. As the fluid-solidified soil is stirred evenly, the current value continuously decreases. Take the stage where the current value no longer decreases as the sign of the evenly stirred stage, and take the current value at the evenly stirred stage as the second current value. S23. Use the difference between the second current value and the first current value as the current characteristic of the current batch of fluid-solidified soil.

[0009] In some embodiments, in step S3, the specific steps for collecting discharge characteristics are as follows: S31. Obtain the discharge image at the moment when the discharge port is opened as the first image. S32. Obtain the discharging image at the second after the discharging port is opened as the second image; S33. Calculate the absolute value of the difference in pixel values between the first image and the second image, and obtain the third image after converting it into a grayscale image; S34. Threshold the third image, and count the number of non-zero pixel points as the discharging feature of the current batch of fluidized solidified soil.

[0010] In some embodiments, in step S4, the fluidized solidified soil fluidity prediction model uses a fully connected neural network. The specific structure of the fully connected neural network includes an input layer, a first hidden layer, a second hidden layer, and an output layer. Among them, the number of neurons in the first hidden layer is 64, the number of neurons in the second hidden layer is 32, the activation function is ReLU, and the number of neurons in the output layer is 1 and has a linear activation function.

[0011] In some embodiments, the optimizer in the training process of the fully connected neural network uses the Adam optimizer, and the loss function uses the mean absolute error evaluation.

[0012] In some embodiments, in step S4, for the produced fluidized solidified soil, the producer divides the grades with reference to the fluidity standard and adds the fluidity label into the corresponding array.

[0013] A fluidized solidified soil fluidity prediction device with multi-feature input, the device includes: A double-shaft mixing bin, which is used to uniformly mix raw materials to form fluidized solidified soil; A temporary storage bin, which is used to temporarily store the fluidized solidified soil and guide it to a tanker. A discharging port is arranged at the bottom side of the temporary storage bin; A camera, which is located on the side of the double-shaft mixing bin and the temporary storage bin, and is used to take a horizontal picture of the fluidized solidified soil below the discharging port; A control center, which is data-connected to the double-shaft mixing bin and the camera; The rotating shaft of the double-shaft mixing bin is connected to the control center for obtaining the current feature. After the camera takes a horizontal picture, the picture is input into the control center, and the actual production formula obtained from the raw material metering equipment is stored in the control center.

[0014] In some embodiments, the control center includes a model construction module and a prediction module. The model construction module uses training data to train the prediction model to form a mature fluidized solidified soil fluidity prediction model, and inputs the mature fluidized solidified soil fluidity prediction model into the prediction module. After inputting a multi-feature one-dimensional array, the predicted value of the fluidized solidified soil fluidity is output.

[0015] In some embodiments, the control center further includes a feature extraction module, which is used to obtain the actual production formula from the raw material metering device and convert it into raw material features, convert the current value into current features, and convert the horizontal picture into discharging features.

[0016] By adopting the above technical solution, the beneficial effects of the present invention are as follows: The present invention pre-establishes a multi-input fluid-solidified soil fluidity prediction model based on raw material features, current features, and discharging features and applies it to the production line. During the actual mixing process of fluid-solidified soil, by inputting the corresponding feature data into the mature prediction model, the fluidity of the current batch of fluid-solidified soil can be pre-determined. The prediction of the fluidity of fluid-solidified soil by multi-input features is highly accurate. At the same time, the mature fluidity prediction model has high graftability and wide applicability, solving the problem in the prior art that the fluidity of fluid-solidified soil requires a large amount of manual work and can only be measured offline, and realizing the online measurement of the fluidity of fluid-solidified soil.

[0017] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure.

[0018] Undoubtedly, such objects of the present invention and other objects will become more apparent after the detailed description of the preferred embodiments described in the following with multiple drawings and illustrations.

[0019] To make the above and other objects, features, and advantages of the present invention more obvious and understandable, one or several preferred embodiments are specifically given below, and in conjunction with the accompanying drawings shown, the detailed description is as follows. Description of the Drawings

[0020] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention.

[0021] In the drawings, the same components are denoted by the same reference numerals, and the drawings are schematic and not necessarily drawn to actual scale.

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only one or several embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on such drawings without creative efforts.

[0023] Figure 1 It is a schematic diagram of the overall process of the multi-feature input fluid-solidified soil fluidity prediction method in some embodiments of the present invention; Figure 2 Schematic diagram of the overall structure of the fluidity prediction device for multi-feature input in some embodiments of the present invention; Figure 3 Schematic diagram of the internal data flow of the control center in some embodiments of the present invention.

[0024] Description of the main reference numerals: 1. Twin-shaft mixing bin; 2. Temporary storage bin; 3. Camera. Detailed implementation manners

[0025] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific implementation manners. It should be understood that the specific implementation manners described herein are only used to explain the present invention, but not to limit the present invention.

[0026] In addition, in the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "axial", "radial", "circumferential", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0027] In the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the internal communication of two elements or the interaction relationship between two elements. However, indicating a direct connection means that there is no connection relationship constructed by a transition structure between the two connected main bodies, and only a connection structure is used to connect them to form a whole. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0028] In the present invention, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0029] Referring to Figures 1 - 3 , Figure 1 is a schematic diagram of the overall process of the fluidity prediction method of fluid-solidified soil with multi-feature input in some embodiments of the present invention; Figure 2 is a schematic diagram of the overall structure of the fluidity prediction device of fluid-solidified soil with multi-feature input in some embodiments of the present invention; Figure 3 is a schematic diagram of the internal data flow of the control center in some embodiments of the present invention.

[0030] According to some embodiments of the present invention, as Figure 1 shown, the present invention provides a fluidity prediction method of fluid-solidified soil with multi-feature input, including: S1. Collect raw material characteristics: For multiple batches of fluid-solidified soil that have been produced, trace their corresponding production formula data, and obtain the raw material characteristics of the fluid-solidified soil after processing the production formula data; The specific steps for collecting raw material characteristics are: S11. Obtain the actual production formula from the raw material metering equipment, specifically including construction waste, solidifying agent, admixture, and water; the actual production formula includes the actual raw material usage. Among them, construction waste generally includes construction waste, engineering slurry, etc., the solidifying agent generally includes slag powder, fly ash, cement, etc., and the admixture generally includes expansion agent, water reducing agent, etc. Different production formulas are selected corresponding to different types and batches of the actually produced fluid-solidified soil; S12. Obtain the water mass characteristic based on the water content of the construction waste raw material and the water usage, obtain the soil mass characteristic by calculating the mass of the construction waste raw material and the water content of the construction waste raw material, obtain the solidifying agent mass characteristic based on the type and mass of the solidifying agent, and obtain the admixture mass characteristic based on the type and mass of the admixture; S13. Combine the water mass characteristic, soil mass characteristic, solidifying agent mass characteristic, and admixture mass characteristic to form the raw material characteristic.

[0031] S2. Collect current characteristics: For multiple batches of fluid-solidified soil, trace the current curves during their respective mixing processes, and obtain current characteristics based on the current curves. The specific steps for collecting current characteristics are as follows: S21. Collect the current values in the mixer at a fixed frequency to obtain a current curve, smooth the current curve, and take the current value at no-load as the first current value. Preferably, the collection frequency of the current value is 5 Hz. S22. When the mixer mixes the fluid-solidified soil, the current curve rises from the no-load current. As the fluid-solidified soil is mixed evenly, the current value continuously decreases. Take the stage when the current value no longer decreases as the sign of the evenly mixed stage, and take the current value in the evenly mixed stage as the second current value. S23. Use the difference between the second current value and the first current value as the current characteristic of the current batch of fluid-solidified soil.

[0032] S3. Collect discharge characteristics: For multiple batches of fluid-solidified soil, trace the discharge images after their respective mixing is completed, and form discharge characteristics after data processing of the discharge images. The arrangement positions among the twin-shaft mixing bin 1, the temporary storage bin 2, and the camera 3 are as Figure 2 shown. The specific steps for collecting discharge characteristics are as follows: S31. Obtain the discharge image at the moment when the discharge port is opened as the first image. S32. Obtain the discharge image 2 seconds after the discharge port is opened as the second image. S33. Calculate the absolute value of the difference in pixel values between the first image and the second image, and obtain the third image after converting it into a grayscale image. S34. Perform thresholding processing on the third image, and count the number of non-0 pixel points as the discharge characteristic of the current batch of fluid-solidified soil.

[0033] S4. Build a prediction model: Build a fluid-solidified soil fluidity prediction model based on a neural network, and train the prediction model with training data to obtain a mature fluid-solidified soil fluidity prediction model. The training data specifically includes one-dimensional arrays formed by raw material characteristics, current characteristics, discharge characteristics, and corresponding fluidity labels. The fluid-solidified soil fluidity prediction model uses a fully connected neural network. The specific structure of the fully connected neural network includes an input layer, a first hidden layer, a second hidden layer, and an output layer. Among them, the number of neurons in the first hidden layer is 64, the number of neurons in the second hidden layer is 32, the activation function is ReLU, and the number of neurons in the output layer is 1 and has a linear activation function. The optimizer during the training process of the fully connected neural network uses the Adam optimizer, and the loss function uses the mean absolute error for evaluation. For the produced fluid-solidified soil, the producer classifies it according to the fluidity standard and adds the fluidity label to the corresponding array. For example, as stipulated in the national standard, the fluid-solidified soil with a fluidity less than or equal to 120 mm is a plastic mixture, and the fluid-solidified soil with a fluidity greater than 120 mm is a fluid mixture. The producer can further divide and delimit the intervals for the plastic mixture and the fluid mixture according to the actual production situation, and add the fluidity labels suitable for its own production line.

[0034] S5. Application in the production line: Integrate the multi-features obtained during the production process of the production line into a one-dimensional array and input it into a mature fluid-solidified soil fluidity prediction model to output the predicted result of the fluid-solidified soil fluidity.

[0035] As Figure 2 shown, the present invention also provides a fluid-solidified soil fluidity prediction device with multi-feature input, and the device includes: A double-shaft mixing bin 1, which is used to uniformly mix the raw materials to form fluid-solidified soil; A temporary storage bin 2, which is used to temporarily store the fluid-solidified soil and guide it to a tanker. A discharge port is arranged at the bottom side of the temporary storage bin 2; A camera 3, which is located on the side of the double-shaft mixing bin 1 and the temporary storage bin 2, and is used to take a horizontal picture of the fluid-solidified soil below the discharge port; A control center, which is data-connected to the double-shaft mixing bin 1 and the camera 3; The rotating shaft of the double-shaft mixing bin 1 is connected to the control center to obtain the current feature. After the camera 3 takes a horizontal picture, the picture is input into the control center, and the actual production formula obtained from the raw material metering equipment is stored in the control center; Preferably, in the actual application process, there are requirements for the opening time of the discharge port of the double-shaft mixing bin 1. Each time the discharge port is about to be opened, it will wait until the main shaft of the mixer rotates to a certain fixed position before opening.

[0036] According to some embodiments of the present invention, optionally, as Figure 3 shown, the control center includes a model construction module and a prediction module. The model construction module uses the training data to train the prediction model to form a mature fluid-solidified soil fluidity prediction model, and inputs the mature fluid-solidified soil fluidity prediction model into the prediction module. After inputting the multi-feature one-dimensional array, the predicted value of the fluid-solidified soil fluidity is output.

[0037] The control center also includes a feature extraction module, which is used to obtain the actual production formula from the raw material metering equipment and convert it into raw material features, used to convert the current value into current features, and used to convert the horizontal picture into discharge features.

[0038] It should be understood that the embodiments disclosed in the present invention are not limited to the specific processing steps or materials disclosed herein, but should extend to equivalent alternatives of such features understood by those of ordinary skill in the relevant art. It should also be understood that the terms used herein are for the purpose of describing particular embodiments only and do not imply limitation.

[0039] As used herein, the term "embodiment" means that a particular feature or characteristic described in connection with an embodiment is included in at least one embodiment of the present invention. Thus, the phrase "an embodiment" or "embodiments" that appear throughout the specification are not necessarily all referring to the same embodiment.

[0040] In addition, the described features or characteristics may be combined in any other suitable manner into one or more embodiments. In the above description, some specific details, such as thickness, quantity, etc., are provided to provide a comprehensive understanding of the embodiments of the present invention. However, those skilled in the relevant art will understand that the present invention may be implemented without one or more of the above specific details or may also be implemented using other methods, components, materials, etc.

Claims

1. A method for predicting the fluidity of fluidized solidified soil with multi-feature input, characterized in that: include S1. Characteristics of raw materials collected: For multiple batches of fluidized solidified soil produced, trace the corresponding production formula data, and obtain the raw material characteristics of the fluidized solidified soil after processing the production formula data; S2. Collecting current characteristics: For multiple batches of fluidized solidified soil, trace the current curves during their respective mixing processes and obtain current characteristics based on the current curves; S3. Collecting unloading characteristics: For multiple batches of fluidized solidified soil, trace the unloading images after each mixing is completed, and form unloading characteristics after data processing of the unloading images; S4. Constructing a prediction model: constructing a prediction model for the fluidity of fluidized solidified soil based on a neural network, and using training data to train the prediction model to obtain a mature prediction model for the fluidity of fluidized solidified soil, wherein the training data specifically includes a one-dimensional array formed by raw material characteristics, current characteristics, discharge characteristics, and corresponding fluidity labels; S5. Production line application: The multiple features obtained during the production process of the production line are integrated into a one-dimensional array and input into a mature fluidity prediction model for fluidized solidified soil, and the fluidity prediction results of fluidized solidified soil are output.

2. The method for predicting fluidity of fluidized solidified soil with multiple feature inputs according to claim 1 is characterized in that: In step S1, the specific steps for collecting raw material characteristics are: S11. Obtain the actual production formula from the raw material metering equipment, including slag, curing agent, admixture and water; S12. Obtaining water quality characteristics according to the water content of the slag raw material and the water consumption, obtaining soil quality characteristics according to the quality of the slag raw material and the water content of the slag raw material, obtaining curing agent quality characteristics according to the type and quality of the curing agent, and obtaining admixture quality characteristics according to the type and quality of the admixture; S13. The water quality characteristics, soil quality characteristics, curing agent quality characteristics and admixture quality characteristics are combined to form the raw material characteristics.

3. The method for predicting fluidity of fluidized solidified soil with multiple feature inputs according to claim 1 is characterized in that: In step S2, the specific steps of collecting current characteristics are: S21, collecting the current value in the mixer at a fixed frequency and obtaining a current curve, smoothing the current curve, and taking the current value when no-load as the first current value; S22, when the mixer is stirring the fluidized solidified soil, the current curve starts to rise from the no-load current, and as the fluidized solidified soil is stirred evenly, the current value continues to decrease, and the current value no longer decreases as the stirring is evenly staged. The current value of the stirring evenly stage is taken as the second current value; S23. Use the difference between the second current value and the first current value as the current characteristic of the current batch of fluidized solidified soil.

4. The method for predicting fluidity of fluidized solidified soil with multiple feature inputs according to claim 1, characterized in that: In step S3, the specific steps for collecting unloading characteristics are: S31, acquiring a discharge image at the time when the discharge port is opened as a first image; S32, acquiring a discharge image taken two seconds after the discharge port is opened as a second image; S33, calculating the absolute value of the difference between the pixel values ​​of the first image and the second image, and converting the difference into a grayscale image to obtain a third image; S34, performing threshold processing on the third image, and counting the number of non-zero pixels as the unloading characteristics of the current batch of fluidized solidified soil.

5. The method for predicting fluidity of fluidized solidified soil with multiple feature inputs according to claim 1, characterized in that: In step S4, the fluidity prediction model of fluidized solidified soil uses a fully connected neural network. The specific structure of the fully connected neural network includes an input layer, a first hidden layer, a second hidden layer, and an output layer. The number of neurons in the first hidden layer is 64, the number of neurons in the second hidden layer is 32, the activation function is ReLU, and the number of neurons in the output layer is 1 with a linear activation function.

6. The method for predicting fluidity of fluidized solidified soil with multiple feature inputs according to claim 5, characterized in that: The optimizer used in the fully connected neural network training process is the Adam optimizer, and the loss function is evaluated using the mean absolute error.

7. The method for predicting fluidity of fluidized solidified soil with multiple feature inputs according to claim 1, characterized in that: In step S4, the producer classifies the produced fluidized solidified soil according to the fluidity standard and adds the fluidity label into the corresponding array.

8. A multi-feature input fluidity prediction device for fluidized solidified soil, characterized in that: The method for predicting fluidity of fluidized solidified soil using multi-feature input as described in any one of claims 1 to 7 comprises: A double-shaft mixing chamber is used to evenly mix the raw materials to form fluidized solidified soil; A temporary storage bin, which is used to temporarily store the fluidized solidified soil and guide it to a tank truck, and a discharge port is provided at the bottom of the temporary storage bin; A camera, located at the side of the dual-shaft mixing bin and the temporary storage bin, for taking a lateral picture of the fluidized solidified soil below the discharge port; A control center, which is connected to the dual-axis stirring chamber and the camera data; The rotating shaft of the dual-axis mixing bin is connected to the control center for obtaining current characteristics. The camera takes a horizontal picture and inputs the picture into the control center. The control center stores the actual production formula obtained from the raw material metering equipment.

9. The fluidity prediction device for fluidized solidified soil with multiple feature inputs according to claim 8, characterized in that: The control center includes a model building module and a prediction module. The model building module uses training data to train the prediction model to form a mature prediction model for the fluidity of fluidized solidified soil. The mature prediction model for the fluidity of fluidized solidified soil is input into the prediction module, and after inputting a multi-feature one-dimensional array, the prediction value of the fluidity of fluidized solidified soil is output.

10. The fluidity prediction device for fluidized solidified soil with multiple feature inputs according to claim 9, characterized in that: The control center also includes a feature extraction module, which is used to obtain the actual production formula from the raw material metering equipment and convert it into raw material characteristics, to convert the current value into current characteristics, and to convert the horizontal image into unloading characteristics.

Images