Automatic discharging control method and system for automatic concrete production

Abstract

The invention relates to the technical field of concrete production, in particular to an automatic discharging control method and system for automatic concrete production, and the method comprises the steps: collecting an image of a feeding hopper, and carrying out the graying of the image, and obtaining a gray image; constructing an edge recognition model and a key point detection model based on a neural network, and training the constructed edge recognition model and key point detection model by using the sample data; and inputting the preprocessed grayscale image into the trained edge recognition model, and outputting a recognition result. According to a neural network method, the discharging condition of the feeding hopper is monitored, and the problem that too much concrete overflows from the feeding hopper is greatly avoided. And in another embodiment, the blanking condition of the feeding hopper is monitored in combination with data measured by a sensor, so that the detection accuracy is further improved, and the problem of inaccurate detection caused by only relying on image processing is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete production, and more specifically, to an automatic material feeding control method and system for automated concrete production. Background Art

[0002] Concrete is a general term for engineering composite materials that are cemented into a whole by cementitious materials. The term concrete usually refers to cement concrete that is obtained by mixing cement as a cementitious material, sand and stone as aggregates, and water in a certain proportion. It is also called ordinary concrete and is widely used in civil engineering. As a basic material in the modern construction industry, concrete has a huge market demand, so concrete production lines came into being. Concrete production lines are an industrial production equipment that integrates automation and mechanization, and are mainly used to produce prefabricated concrete parts and cast-in-place concrete components.

[0003] Concrete production lines include conveying equipment, which is used to transport raw materials and mixed concrete to various production links. Common conveying equipment includes conveyor belts, concrete pump trucks, mixer trucks, etc. These equipment can ensure the timely supply of raw materials and concrete and improve production efficiency.

[0004] At present, when the concrete production line transports the mixed concrete to the mixer truck through the conveyor belt, it is necessary to use the feed hopper on the mixer truck to receive the material. However, since the small material speed of the cement production device is relatively fast, if the staff makes a supervision error during work, it is easy to cause the cement in the feed hopper to overflow, thereby causing the concrete in the feed hopper to overflow, resulting in material waste. Summary of the invention

[0005] The present invention provides an automatic material feeding control method for automated concrete production, aiming to solve the problem in the related art that due to the fast speed of small materials in the cement production device, if the staff makes a supervision error at work, it is easy to cause the cement in the feed hopper to overflow, thereby causing the concrete in the feed hopper to overflow and cause material waste.

[0006] In a first aspect, the present invention provides an automatic unloading control method for automated concrete production, comprising: collecting an image of a feed hopper, graying the image to obtain a grayscale image; constructing an edge recognition model and a key point detection model based on a neural network, and training the constructed edge recognition model and key point detection model using sample data; inputting the preprocessed grayscale image into the trained edge recognition model, and outputting a recognition result, wherein the recognition result includes a boundary edge between a concrete area and a feed hopper area, and an upper edge of the feed hopper in the feed hopper area; for the grayscale image after the recognition result, inputting it into the trained key point detection model, and outputting a first key point on the boundary edge and a second key point on the upper edge of the feed hopper; calculating the material capacity of the feed hopper, wherein the material capacity is positively correlated with the distance between the first key point and the second key point and the area of ​​the concrete area; and controlling the opening and closing of the closing plate on the feed hopper according to the size of the material capacity of the feed hopper.

[0007] Furthermore, the preprocessed grayscale image includes: preprocessing the grayscale image using a semantic segmentation method to obtain a concrete area and a feed hopper area.

[0008] Furthermore, an edge recognition model is constructed based on a neural network, including: obtaining multiple preprocessed grayscale image samples; marking the boundary edge between the concrete area and the feed hopper area and the upper edge of the feed hopper in the feed hopper area in the grayscale image samples to obtain marked grayscale image samples; iteratively training the neural network using the marked grayscale image samples as training samples; determining the difference between the edge prediction information output by the neural network and the marked grayscale image sample information during each iteration; adjusting the network parameters of the neural network based on the difference, and terminating the training until the training stop condition is met to obtain the edge detection model.

[0009] Furthermore, the capacity of the feed hopper is calculated using the following formula: ; In the formula, Indicates the capacity of the feed hopper. represents the distance between the first key point and the second key point, Represents the area of ​​the concrete area.

[0010] Furthermore, the opening and closing of the closing plate on the feed hopper is controlled according to the capacity of the feed hopper, including: in response to the capacity of the feed hopper being greater than a first preset threshold, closing the closing plate on the feed hopper; in response to the capacity of the feed hopper being less than or equal to the first preset threshold, not closing the closing plate on the feed hopper.

[0011] Furthermore, the opening and closing of the closing plate on the feed hopper is controlled according to the material capacity of the feed hopper, and it also includes: in response to the material capacity of the feed hopper being greater than a second preset threshold, the closing plate on the feed hopper is controlled to move to reduce the material unloading gap between the feed hopper and the closing plate.

[0012] In response to the filling level of the feed hopper being greater than a third preset threshold, the closing plate on the feed hopper is closed.

[0013] Furthermore, it also includes: collecting the current pressure of the sensor; calculating the storage coefficient of the feed hopper according to the difference between the current pressure and the maximum bearing capacity, wherein the maximum bearing capacity is the pressure value measured after the feed hopper bears concrete to the maximum capacity, and the storage coefficient is inversely correlated with the difference; and controlling the opening and closing of the closing plate on the feed hopper according to the sum of the storage coefficient of the feed hopper and the material capacity of the feed hopper.

[0014] Further, according to the sum of the storage coefficient of the feed hopper and the material capacity of the feed hopper, controlling the opening and closing of the closing plate on the feed hopper includes: calculating the average value between the storage coefficient of the feed hopper and the material capacity of the feed hopper; in response to the average value being greater than a fourth preset threshold, closing the closing plate on the feed hopper; Furthermore, an empirical value of the first preset threshold is 0.7.

[0015] In a second aspect of the present invention, there is also provided an automatic unloading control system for automated concrete production, characterized in that it comprises a processor and a memory, the memory stores a computer program, and the processor executes the computer program to implement any of the above-described automatic unloading control methods for automated concrete production.

[0016] Beneficial effect: According to the neural network method, the material discharge situation of the feed hopper is monitored, which greatly avoids the problem of excessive concrete overflow in the feed hopper. In another embodiment, the material discharge situation of the feed hopper is monitored in combination with the data measured by the sensor, which further improves the accuracy of the detection and avoids the problem of inaccurate detection by relying solely on image processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The following detailed description is read with reference to the accompanying drawings, which illustrate several embodiments of the present invention in an exemplary and non-limiting manner, and in which like or corresponding reference numerals represent like or corresponding parts, wherein: Figure 1 FIG. 4 is a flow chart schematically illustrating the calculation of the material capacity of the feed hopper according to an embodiment of the present invention.

[0018] Figure 2 is a flow chart schematically illustrating calculation of a storage factor of a feed hopper according to an embodiment of the present invention.

[0019] Figure 3 FIG. 1 is a schematic diagram schematically showing a system structure according to an embodiment of the present invention.

[0020] Figure 4 Schematic diagram of the structure of a feed hopper according to an embodiment of the present invention.

[0021] Reference numerals 1. Processor; 2. Communication bus; 3. Memory; 4. Communication interface. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0023] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] The concrete production line includes conveying equipment, which is used to transport raw materials and mixed concrete to various production links. Common conveying equipment includes conveyor belts, concrete pump trucks, transport trucks, etc. These equipment can ensure the timely supply of raw materials and concrete and improve production efficiency. At present, when the concrete production line transports the mixed concrete to the transport truck through the production device, the production device is used to produce cement, and a baffle plate is installed at the discharge port of the production device. However, due to the unstable discharge speed on the production device and the manual monitoring of the discharge process, when manual supervision is neglected, the concrete in the feed hopper overflows, resulting in material waste. Therefore, the following method is proposed to solve the above problem.

[0025] In one embodiment, a transport vehicle is provided with a tank body for storing concrete, and a feed hopper is provided on the tank body. Meanwhile, a baffle plate is installed at the discharge port of the production device, and the movement of the baffle plate is controlled by a hydraulic mechanism to realize the opening and closing of the discharge port of the production device, thereby avoiding the overflow of concrete in the feed hopper and causing waste of materials.

[0026] S101: Collect an image of the feed hopper.

[0027] In one embodiment, an image acquisition device is installed on a production device so that a camera can take a bird's-eye view of the image of the front of the feed hopper on the transport vehicle. Specifically, in the front image, the feed hopper is located in the middle of the image. In this embodiment, the image acquisition device can be a camera or an industrial camera. With the above structure, an image of the feed hopper can be obtained. The image of the feed hopper is then grayed to obtain a grayscale image. In addition, noise in the grayscale image can also be removed. For example, filtering technology (such as Gaussian filtering) is applied to remove image noise to facilitate subsequent processing of the grayscale image. It should be noted that Gaussian filtering is a linear smoothing filtering technology widely used in image processing, which is mainly used to eliminate Gaussian noise in images, improve the signal-to-noise ratio (SNR) of the image, and thus improve image quality.

[0028] In one embodiment, after obtaining the grayscale image, the grayscale image needs to be preprocessed, wherein the preprocessing refers to segmenting the grayscale image to obtain the concrete area and the feed hopper area in the grayscale image. Specifically, the grayscale image can be segmented by a semantic segmentation method to obtain the concrete area and the feed hopper area, so as to facilitate the subsequent determination of whether the feed hopper is full of concrete.

[0029] In one embodiment, the feed hopper area and the concrete area in the grayscale image are segmented by semantic segmentation. Semantic segmentation is a task in the field of computer vision, which aims to assign a specific semantic label to each pixel in the image, that is, to classify each pixel so that each object area in the image can be accurately divided and identified. Specifically, semantic segmentation includes the following steps. Construct a data set. Specifically, collect images of all types of feed hoppers and images of concrete involved to form a data set. Data labeling. Specifically, the labels of the pixels in the image are divided into two categories, in which the pixels in the concrete area of ​​the image are labeled as 1, and the pixels in the feed hopper area are labeled as 0. Network selection. Specifically, the Unet network structure is used in this embodiment, and the implementer can select the network module in the Unet network structure according to the actual situation.

[0030] S102: Constructing an edge recognition model and a key point detection model based on a neural network.

[0031] In one embodiment, an edge recognition model is constructed based on a neural network, including: first obtaining a plurality of preprocessed grayscale image samples; marking the boundary edge between the concrete area and the feed hopper area and the upper edge of the feed hopper in the feed hopper area in the grayscale image samples to obtain marked grayscale image samples; iteratively training a neural network using the marked grayscale image samples as training samples; determining the difference between the edge prediction information output by the neural network and the marked grayscale image sample information during each iteration; adjusting the network parameters of the neural network based on the difference until the training is terminated when a training stop condition is met, thereby obtaining the edge detection model.

[0032] In another embodiment, in addition to using a neural network algorithm, an edge detection algorithm can be used to directly detect the edge of a grayscale image. The specific steps are as follows: edge detection is performed on the grayscale image to obtain the boundary edge between the concrete area and the feed hopper area and the upper edge of the feed hopper in the feed hopper area. In this embodiment, the grayscale image can be segmented using the Canny edge detection method. The Canny detection algorithm, i.e., the Canny edge detection algorithm, is a widely used image processing algorithm. The algorithm aims to find the optimal edge in an image to ensure good detection, positioning, and minimum response. The characteristics of using the Canny detection algorithm are: high accuracy: the Canny algorithm can accurately detect the real edge in the image and carefully locate the edge pixels. Noise suppression: through steps such as Gaussian filtering and non-maximum suppression, the Canny algorithm can effectively suppress noise interference and improve the accuracy of edge detection. Edge connection: using dual threshold detection and edge connection algorithms, the Canny algorithm can connect weak edges with strong edges to obtain continuous edge lines. Parameter adjustability: The parameters of the Canny algorithm (such as the size of the Gaussian filter, the setting of the dual threshold, etc.) can be adjusted according to the specific application to obtain edge detection results that meet the requirements.

[0033] In one embodiment, a key point detection model is constructed based on a neural network, including: first obtaining multiple edge images output from an edge recognition model; and annotating the edge images, the annotation including: marking the longitudinal center line of the edge image, determining the point where the longitudinal center line intersects with the boundary edge as the first key point, and determining the point where the longitudinal center line intersects with the upper edge of the feed hopper as the second key point, thereby obtaining the annotated edge image; iteratively training the neural network using the annotated edge image as a training sample; determining the difference between the edge prediction information output by the neural network and the annotated edge image information during each iteration; adjusting the network parameters of the neural network based on the difference, until the training is terminated when the training stop condition is met, thereby obtaining the key point detection model.

[0034] S103: Calculate the material capacity of the feed hopper.

[0035] The pre-processed grayscale image is input into the trained edge recognition model, and the recognition result is output, which includes the boundary edge between the concrete area and the feed hopper area, and the upper edge of the feed hopper in the feed hopper area. The grayscale image after the recognition result is input into the trained key point detection model, and the first key point on the boundary edge and the second key point on the upper edge of the feed hopper are output.

[0036] In one embodiment, the capacity of the feed hopper is calculated, and the capacity is positively correlated with the distance between the first key point and the second key point and the area of ​​the concrete region.

[0037] In one embodiment, calculating the capacity of the feed hopper includes: calculating the capacity of the feed hopper according to the distance between the first key point and the second key point. The smaller the distance between the first key point and the second key point, the greater the capacity of the feed hopper, indicating that the concrete is closer to the top edge of the feed hopper, indicating that there is more concrete in the feed hopper.

[0038] In one embodiment, calculating the capacity of the feed hopper further includes: the calculation formula is: ; In the formula, Indicates the capacity of the feed hopper. represents the distance between the first key point and the second key point, Indicates the area of ​​the concrete area; in this embodiment, when calculating the capacity of the feed hopper, the area of ​​the concrete area is also used to correct the capacity of the feed hopper. The reason is that the shape of the feed hopper is basically a funnel shape, that is, a shape that is wide at the top and narrow at the bottom. The larger the area of ​​the concrete area, the closer the concrete is to the top of the feed hopper. At this time, the more concrete there is inside the feed hopper, and the greater the capacity of the feed hopper.

[0039] S104: Controlling the opening and closing of the sealing plate on the feed hopper according to the material capacity of the feed hopper.

[0040] In one embodiment, if the capacity of the feed hopper is greater than the first preset threshold, it means that the cement in the feed hopper is about to overflow, and the closing plate on the feed hopper is closed; if the capacity of the feed hopper is less than or equal to the first preset threshold, it means that the cement in the feed hopper is not full, and the closing plate on the feed hopper does not need to be closed. In this embodiment, the empirical value of the first preset threshold is 0.7. In other embodiments, the empirical value of the first preset threshold can be 0.8 or 0.85, etc., which can be adjusted according to actual conditions.

[0041] In another embodiment, if the capacity of the feed hopper is greater than the second preset threshold, the closing plate on the feed hopper is controlled to move to reduce the gap between the feed hopper and the closing plate. In response to the capacity of the feed hopper being greater than the third preset threshold, the closing plate on the feed hopper is closed. That is to say, in this embodiment, the closing plate can be gradually closed as the capacity of the feed hopper increases. When the cement inside the feed hopper is almost full, the amount of cement discharged in the production device is reduced to avoid the problem of cement overflowing from the feed hopper. In this embodiment, the empirical value of the second preset threshold is 0.65, and the empirical value of the third preset threshold is 0.95. In other embodiments, the empirical value of the third preset threshold can be 0.9 or 0.92, etc.

[0042] In one embodiment, since the camera is often at the concrete filling site, some dust or concrete may cause the camera's image to be blurred, resulting in deviations in the final calculated results and inaccurate calculation results. Therefore, another method may be used to improve the accuracy of the calculation results. The specific method is as follows.

[0043] S201: Collect the current pressure of the sensor.

[0044] In one embodiment, the sensor is a pressure sensor, and there are two sensors, which are symmetrically mounted on the inner walls of both sides of the feed hopper (such as Figure 4 As shown in the figure, the shaded part in the figure is the sensor). The greater the pressure measured by the pressure sensor, the more concrete there is in the feed hopper, and the easier it is for the concrete to overflow from the feed hopper. It should be noted that when there are multiple sensors in the feed hopper, when calculating the pressure, the average value of the pressure measured by all sensors is used as the current pressure. With the above structure, using multiple sensors to measure the pressure can reduce errors and improve the accuracy of the measurement data.

[0045] S202: Calculate the storage coefficient of the feed hopper.

[0046] In one embodiment, the storage coefficient of the feed hopper is calculated according to the difference between the current pressure and the maximum bearing capacity, and the storage coefficient is inversely correlated with the difference. Specifically, the difference between the current pressure and the maximum bearing capacity is normalized, and the normalized value is used as the storage coefficient of the feed hopper. The maximum bearing capacity is the pressure value measured after the feed hopper bears concrete to the maximum capacity, that is, the feed hopper is filled with concrete, and the pressure of the sensor at this time is collected as the maximum bearing capacity. In other embodiments, when obtaining the maximum bearing capacity, the above steps can be used multiple times, and the final average value is used as the maximum bearing capacity.

[0047] S203: Control the opening and closing of the sealing plate on the feed hopper.

[0048] In one embodiment, the opening and closing of the closing plate on the feed hopper is controlled according to the sum of the storage coefficient of the feed hopper and the material capacity of the feed hopper. Specifically, the average value between the storage coefficient of the feed hopper and the material capacity of the feed hopper is calculated; if the average value is greater than a fourth preset threshold, the opening and closing of the closing plate on the feed hopper is reduced. In this embodiment, the empirical value of the fourth preset threshold is 0.75, and in other embodiments, the empirical value of the fourth preset threshold may be 0.8 or 0.72, etc.

[0049] Exemplarily, the storage factor of the feed hopper is calculated as , the capacity of the feed hopper is , and then calculate the average value, which is equal to If the average value is greater than 0.75, the opening and closing of the closing plate on the feed hopper is reduced.

[0050] In another embodiment, the storage coefficient of the feed hopper can also be used to independently control the opening and closing of the upper closing plate of the feed hopper. Specifically, if the storage coefficient of the feed hopper is independently greater than a preset coefficient value, the upper closing plate of the feed hopper is closed. In this embodiment, the preset coefficient value is 0.9. In other embodiments, the preset coefficient value can be 0.91 or 0.92, etc.

[0051] Through the above steps, the material discharging condition of the feed hopper can be monitored according to the image processing method, which greatly avoids the problem of excessive concrete overflow in the feed hopper. In another embodiment, the material discharging condition of the feed hopper is also monitored in combination with the data measured by the sensor, thereby improving the accuracy of the detection.

[0052] The present invention also provides an automatic material feeding control method for automated concrete production. Figure 2 As shown, the system includes a processor 1 and a memory 3, wherein the memory 3 stores computer program instructions. When the computer program instructions are executed by the processor 1, an automatic unloading control method for automated concrete production according to the first aspect of the present invention is implemented.

[0053] The system also includes other components well known to those skilled in the art, such as a communication bus 2 and a communication interface 4. The configuration and functions of these components are known in the art and thus will not be described in detail herein.

[0054] In the present invention, the aforementioned memory 3 may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device. Any application or module described in the present invention may be implemented using computer-readable / executable instructions that may be stored or otherwise maintained by such a computer-readable medium.

[0055] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0056] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above-mentioned embodiments only express several implementation methods of the present invention, and the description is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent application. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. An automatic material feeding control method for automated concrete production, characterized in that: include: Collecting an image of the feed hopper, and graying the image to obtain a grayscale image; Construct an edge recognition model and a key point detection model based on a neural network, and use sample data to train the constructed edge recognition model and key point detection model; Inputting the preprocessed grayscale image into a trained edge recognition model, and outputting a recognition result, wherein the recognition result includes a boundary edge between the concrete area and the feed hopper area, and an upper edge of the feed hopper in the feed hopper area; For the grayscale image after the recognition result, input it into the trained key point detection model, and output the first key point on the boundary edge and the second key point on the upper edge of the feed hopper; Calculating the material capacity of the feed hopper, wherein the material capacity is positively correlated with the distance between the first key point and the second key point and the area of ​​the concrete region; The opening and closing of the sealing plate on the feed hopper is controlled according to the material capacity of the feed hopper.

2. The automatic material feeding control method for automated concrete production according to claim 1, characterized in that: Preprocessed grayscale image, including: The grayscale image is preprocessed using a semantic segmentation method to obtain a concrete area and a feed hopper area.

3. The automatic material feeding control method for automated concrete production according to claim 2, characterized in that: Build an edge recognition model based on a neural network, including: Obtain multiple preprocessed grayscale image samples; Marking the boundary edge between the concrete area and the feed hopper area and the upper edge of the feed hopper in the feed hopper area in the grayscale image sample to obtain a marked grayscale image sample; Iteratively train the neural network using the labeled grayscale image samples as training samples; Determine the difference between the edge prediction information output by the neural network and the labeled grayscale image sample information during each iteration; The network parameters of the neural network are adjusted based on the difference until the training stop condition is met to terminate the training and obtain the edge recognition model.

4. The automatic material feeding control method for automated concrete production according to claim 1, characterized in that: Calculate the capacity of the feed hopper using the following formula: ； In the formula, Indicates the capacity of the feed hopper. represents the distance between the first key point and the second key point, Represents the area of ​​the concrete area.

5. The automatic material feeding control method for automated concrete production according to claim 1, characterized in that: According to the material capacity of the feed hopper, the opening and closing of the closing plate on the feed hopper is controlled, including: In response to the filling level of the feed hopper being greater than a first preset threshold, closing a closing plate on the feed hopper; In response to the filling level of the feed hopper being less than or equal to the first preset threshold, the closing plate on the feed hopper does not need to be closed.

6. The automatic material feeding control method for automated concrete production according to claim 1, characterized in that: According to the material capacity of the feed hopper, the opening and closing of the sealing plate on the feed hopper is controlled, and the method further comprises: In response to the material capacity of the feed hopper being greater than a second preset threshold, controlling the closing plate on the feed hopper to move so as to reduce the material discharge gap between the feed hopper and the closing plate; In response to the filling level of the feed hopper being greater than a third preset threshold, the closing plate on the feed hopper is closed.

7. The automatic material feeding control method for automated concrete production according to claim 1, characterized in that: Also includes: Collect the current pressure of the sensor; According to the difference between the current pressure and the maximum bearing capacity, wherein the maximum bearing capacity is the pressure value measured after the feed hopper bears the concrete to the maximum capacity, the storage coefficient of the feed hopper is calculated, and the storage coefficient is inversely correlated with the difference; The opening and closing of the closing plate on the feed hopper is controlled according to the sum of the storage coefficient of the feed hopper and the material capacity of the feed hopper.

8. The automatic material feeding control method for automated concrete production according to claim 7, characterized in that: According to the sum of the storage coefficient of the feed hopper and the material capacity of the feed hopper, the opening and closing of the closing plate on the feed hopper is controlled, including: Calculating an average value between a storage coefficient of the feed hopper and a capacity of the feed hopper; In response to the average value being greater than a fourth preset threshold, the closing plate on the feed hopper is closed.

9. An automatic unloading control system for automated concrete production, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the automatic unloading control method for automated concrete production according to any one of claims 1 to 8.

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