AI-based dust reduction control method and system for mechanical excavation operations

Abstract

This invention provides an AI-based dust reduction control method and system for mechanical excavation operations, relating to the field of artificial intelligence technology. The control method uses information about the excavated object, the excavation direction of the excavating components, excavation information, and weather information to predict a first dust diffusion range generated during excavation. The method then controls the pan / tilt platform and power drive unit to create a first water mist area covering the first dust diffusion range. This reduces dust concentration generated during excavation operations, avoids environmental pollution, and ensures a healthy working environment for on-site construction personnel.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an AI-based dust reduction control method and system for mechanical excavation operations. Background Art

[0002] During the on-site construction process, open-air excavation operations are inevitably involved. Dust will inevitably be generated during the excavation process. The dust will affect the surrounding air environment and cause pollution. It will also cause certain damage to the health of on-site construction workers. How to reduce dust during excavation operations is a technical problem that technical personnel in this field urgently need to solve. Summary of the Invention

[0003] In order to at least overcome the above-mentioned deficiencies in the prior art, in a first aspect, the present invention provides an AI-based dust reduction control method for mechanical excavation operations, which is applied to an AI-based dust reduction control system for mechanical excavation operations. The system includes a computer device, a mechanical excavation device, a dust reduction device, and an image acquisition device in communication connection. The mechanical excavation device includes a mechanical base, a mechanical arm, and an excavation component. One end of the mechanical arm is connected to the mechanical base, and the other end of the mechanical arm is connected to the excavation component. The image acquisition device is arranged on the mechanical excavation device. The dust reduction device is at least partially arranged on the mechanical arm. The dust reduction device includes a water storage container, a power drive unit, a pan-tilt head, and a water spray head. The water spray head is fixed to the mechanical arm via a pan-tilt head, and the water storage container is connected to the water spray head via a water pipe. The method includes:

[0004] Acquiring video image information of the mechanical excavation equipment during operation captured by the image acquisition device, and identifying, based on the video image information, information of an excavated object at an excavation position during excavation, an excavation direction of an excavation component in the mechanical excavation equipment, and excavation information of the excavation component;

[0005] Obtaining geographic location information of the mechanical excavation equipment, and obtaining weather information of the corresponding location area in real time based on the geographic location information, wherein the weather information includes temperature, humidity, wind speed, and wind direction;

[0006] Inputting the excavated object information, the excavation direction of the excavating component, the excavation information, and the weather information into a pre-trained excavation dust prediction model for prediction to obtain a first dust diffusion range generated during excavation;

[0007] The movement of the pan / tilt head is controlled to adjust the water spraying direction of the sprinkler head, and the working power of the power drive unit is controlled to adjust the water outlet speed of the sprinkler head to form a first water mist area that at least covers the first dust diffusion range.

[0008] In a possible implementation, the method further includes:

[0009] Acquiring transfer information of the excavated object transferred by the mechanical excavation equipment through the excavation component through the video image information, the transfer information including a transfer speed and a transfer direction of the excavation component;

[0010] Inputting the excavated object information, the weather information, the transfer speed and transfer direction of the excavating component into a pre-trained transfer dust prediction model to perform prediction to obtain a second dust diffusion range generated during the transfer;

[0011] The movement of the pan / tilt platform is controlled to adjust the water spraying direction of the sprinkler head, and the working power of the power drive unit is controlled to adjust the speed of water discharge from the sprinkler head, so as to form a second water mist area that at least covers the second dust diffusion range.

[0012] In a possible implementation, the method further includes:

[0013] Obtaining unloading information of the excavated object by the mechanical excavation equipment through the video image information, wherein the unloading information includes the distance between the excavated object in the excavation component and the bearing surface during unloading, the unloading direction of the excavated object in the excavation component, and the unloading speed of the excavation component;

[0014] Inputting the excavated object information, the weather information, and the unloading information into a pre-trained unloading dust prediction model for prediction, thereby obtaining a third dust diffusion range generated during unloading;

[0015] The movement of the pan / tilt head is controlled to adjust the water spraying direction of the sprinkler head, and the working power of the power drive unit is controlled to adjust the water outlet speed of the sprinkler head to form a third water mist area that at least covers the third dust diffusion range.

[0016] In one possible implementation, the step of obtaining video image information of the mechanical excavation equipment captured by the image acquisition device during operation, and identifying, based on the video image information, information of the excavated object at the excavation position during excavation, the excavation direction of the excavation component in the mechanical excavation equipment, and excavation information of the excavation component includes:

[0017] Acquiring video image information captured by the image acquisition device while the mechanical excavation device is in operation, wherein the video image information at least includes an excavation component of the mechanical excavation device and an excavation location area toward which the excavation component is directed during excavation;

[0018] Inputting the image corresponding to the excavation location area into the object recognition model for recognition, thereby obtaining information about the excavated object at the excavation location, wherein the information about the excavated object includes the composition type and dryness degree of the excavated object;

[0019] The excavation components of the mechanical excavation equipment in the video image information are identified to obtain the size of the excavation components, and the excavation speed of the excavation components is obtained by target tracking calculation on the excavation components in the video image information. The excavation information of the excavation components is composed of the size of the excavation components and the excavation speed.

[0020] In one possible implementation, the method further includes a step of training the mining dust prediction model, which includes:

[0021] Creating a first training sample set, the first training sample set including a plurality of first training samples and a first dust diffusion mark range corresponding to each first training sample, wherein the first training samples include excavated object information samples, excavation direction samples of excavating components, excavation information samples, and weather information samples;

[0022] Inputting the first training sample into a first neural network model for training to obtain a first dust predicted diffusion range, and calculating a first loss function value of the first neural network model based on the first dust predicted diffusion range and the first dust diffusion marked range;

[0023] When the first loss function value is greater than a preset first loss function threshold, adjusting the network parameters of the first neural network model;

[0024] Repeatedly input the first training sample in the first training sample set into the first neural network for training until the first loss function value is no greater than the preset first loss function domain value. The training is completed and the first neural network obtained by training is used as the excavation dust prediction model.

[0025] In one possible implementation, the method further includes a step of training the transferred dust prediction model, which includes:

[0026] Creating a second training sample set, the second training sample set including a plurality of second training samples and a second dust diffusion mark range corresponding to each second training sample, wherein the second training samples include excavated object information samples, weather information samples, and excavation component transfer speed samples and transfer direction samples;

[0027] Inputting the second training sample into a second neural network model for training to obtain a second dust predicted diffusion range, and calculating a second loss function value of the second neural network model based on the second dust predicted diffusion range and the second dust diffusion marked range;

[0028] When the second loss function value is greater than a preset second loss function threshold, adjusting the network parameters of the second neural network model;

[0029] Repeatedly input the second training sample in the second training sample set into the second neural network for training until the second loss function value is no greater than the preset second loss function domain value. The training is completed and the second neural network obtained by training is used as the transferred dust prediction model.

[0030] In one possible implementation, the method further includes a step of training the unloading dust prediction model, which includes:

[0031] Creating a third training sample set, the third training sample set including a plurality of third training samples and a third dust diffusion mark range corresponding to each third training sample, wherein the third training sample includes an excavated object information sample, a weather information sample, and an unloading information sample;

[0032] Inputting the third training sample into a third neural network model for training to obtain a third dust predicted diffusion range, and calculating a third loss function value of the third neural network model based on the third dust predicted diffusion range and the third dust diffusion marked range;

[0033] When the third loss function value is greater than a preset third loss function threshold, adjusting the network parameters of the third neural network model;

[0034] Repeatedly input the third training sample in the third training sample set into the third neural network for training until the third loss function value is no greater than the preset third loss function domain value. The training is completed and the third neural network obtained by training is used as the unloading dust prediction model.

[0035] In a possible implementation, the method further includes:

[0036] comparing the first dust diffusion range with a preset maximum dust diffusion range, and reducing the excavation speed of the excavating component when the first dust diffusion range is greater than the preset maximum dust diffusion range; and / or,

[0037] comparing the second dust diffusion range with a preset maximum dust diffusion range, and reducing the transfer speed of the excavating component when the second dust diffusion range is greater than the preset maximum dust diffusion range; and / or,

[0038] The third dust diffusion range is compared with a preset maximum dust diffusion range. When the third dust diffusion range is greater than the preset maximum dust diffusion range, the distance between the excavated object in the excavation component and the bearing surface is reduced, and / or the unloading speed of the excavation component is reduced.

[0039] In a second aspect, the present invention further provides an AI-based dust reduction control system for mechanical excavation operations, the system comprising a communicatively connected computer device, a mechanical excavation device, a dust reduction device, and an image acquisition device, the mechanical excavation device comprising a mechanical base, a mechanical arm, and an excavation component, one end of the mechanical arm being connected to the mechanical base, and the other end of the mechanical arm being connected to the excavation component, the image acquisition device being disposed on the mechanical excavation device, the dust reduction device being at least partially disposed on the mechanical arm, the dust reduction device comprising a water storage container, a power drive unit, a pan / tilt platform, and a water spray head, the water spray head being fixed to the mechanical arm via a pan / tilt platform, and the water storage container being connected to the water spray head via a water pipe;

[0040] The image acquisition device is used to acquire video image information at the excavation position when the mechanical excavation equipment is working;

[0041] The computer device is used to identify and obtain information of an excavated object at an excavation location, an excavation direction of an excavation component in the mechanical excavation device, and excavation information of the excavation component based on the video image information;

[0042] The computer device is further configured to obtain geographic location information of the mechanical excavation equipment, and obtain weather information of the corresponding location area in real time based on the geographic location information, wherein the weather information includes temperature, humidity, wind speed, and wind direction;

[0043] The computer device is further configured to input the excavated object information, the excavation direction of the excavation component, the excavation information, and the weather information into a pre-trained excavation dust prediction model to perform prediction, thereby obtaining a first dust diffusion range generated during excavation.

[0044] The computer device is also used to control the movement of the pan-tilt platform to adjust the water spraying direction of the sprinkler head, and control the working power of the power drive unit to adjust the speed of water discharge from the sprinkler head, so as to form a first water mist area that at least covers the first dust diffusion range.

[0045] In a third aspect, the present invention also provides a computer device, comprising a processor, a computer-readable storage medium, and a communication interface, wherein the computer-readable storage medium, the communication interface, and the processor are connected via a bus system, the computer-readable storage medium is used to store programs, instructions, or codes, and the processor is used to execute the programs, instructions, or codes in the computer-readable storage medium to execute the AI-based mechanical excavation dust reduction control method described in any possible implementation of the first aspect.

[0046] In a fourth aspect, the present invention also provides a computer-readable storage medium, in which instructions are stored, which, when executed, enable the computer to execute the AI-based dust reduction control method for mechanical excavation operations in any possible implementation of the first aspect.

[0047] In the above-mentioned solution provided by the present invention, first, based on the video image information of the mechanical excavation equipment during operation, information about the object being excavated, the excavation direction of the excavation components, and excavation information are obtained; then, the geographic location information of the mechanical excavation equipment is obtained, and weather information of the corresponding location area is obtained in real time based on the geographic location information; then, the information about the object being excavated, the excavation direction of the excavation components, the excavation information, and the weather information are input into a pre-trained excavation dust prediction model for prediction, thereby obtaining a first dust diffusion range generated during excavation; finally, the pan-tilt platform and the power drive unit are controlled to form a first water mist area that at least covers the first dust diffusion range. The above-mentioned solution predicts the first dust diffusion range generated during excavation based on the information about the object being excavated, the excavation direction of the excavation components, the excavation information, and the weather information, and forms the first water mist area that covers the first dust diffusion range by controlling the pan-tilt platform and the power drive unit. This can reduce the dust concentration generated during the excavation operation, avoid environmental pollution, and ensure a healthy working environment for on-site construction personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 A block diagram of an AI-based dust reduction control system for mechanical excavation operations provided by an embodiment of the present invention;

[0050] Figure 2 A schematic flow chart of an AI-based dust reduction control method for mechanical excavation operations provided in an embodiment of the present invention;

[0051] Figure 3 One of the partial flow diagrams of the AI-based dust reduction control method for mechanical excavation operations provided by an embodiment of the present invention;

[0052] Figure 4 The second partial flow chart of the AI-based dust reduction control method for mechanical excavation operations provided by an embodiment of the present invention;

[0053] Figure 5 A schematic diagram of the structural framework of a computer device for implementing the above-mentioned AI-based mechanical excavation operation dust reduction control method provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be described in detail below with reference to the accompanying drawings. The specific operating methods in the method embodiments can also be applied to the device embodiments or system embodiments.

[0055] Before introducing the specific solution provided by this embodiment, we first introduce the application scenario of the AI-based mechanical excavation dust reduction control system to which this specific solution is applicable. Please refer to Figure 1 In this embodiment, an AI-based mechanical excavation dust reduction control system 1 includes a communicatively connected computer device 10, a mechanical excavation device 20, a dust reduction device 30, and an image acquisition device 40. The mechanical excavation device 20 includes a mechanical base, a mechanical arm, and an excavation component. One end of the mechanical arm is connected to the mechanical base, the other end of the mechanical arm is connected to the excavation component, and the excavation component is movably connected to the mechanical arm. The image acquisition device 40 is disposed on the mechanical excavation device 20. For example, the image acquisition device 40 is disposed on the mechanical arm. The dust reduction device 30 includes a water storage container, a power drive unit, a pan / tilt platform, and a sprinkler head. Multiple pan / tilt platforms can be arranged in an array and fixed to the mechanical arm. The sprinkler head is fixed to the mechanical arm via the pan / tilt platform. The spray direction of the sprinkler head can be controlled by the pan / tilt platform, and the spray height of the sprinkler head can be controlled by the operating power of the power drive unit. Generally, the greater the operating power of the power drive unit, the greater the speed of water discharge from the sprinkler head and the greater the spray height. In this embodiment, the water storage container and the sprinkler head can be connected by a water pipe.

[0056] The following combination Figure 1 The application scenario shown is an exemplary description of the AI-based mechanical excavation dust control method provided in the embodiment of the present application. Figure 2The AI-based mechanical excavation dust reduction control method provided in the embodiment of the present application can be executed by the aforementioned computer device 10. In other embodiments, the order of some steps in the AI-based mechanical excavation dust reduction control method in the embodiment of the present application can be interchanged according to actual needs, or some of the steps can be omitted or deleted. The detailed steps of the AI-based mechanical excavation dust reduction control method executed by the computer device 10 are introduced as follows.

[0057] Step S10, obtaining video image information of the mechanical excavation equipment at work captured by the image acquisition device, and identifying the excavated object information at the excavation position during excavation, the excavation direction of the excavation components in the mechanical excavation equipment, and the excavation information of the excavation components based on the video image information.

[0058] In this embodiment, step S10 can be implemented in the following manner.

[0059] First, video image information collected by the image acquisition device 40 during the operation of the mechanical excavation device 20 is obtained. The video image information at least includes the excavation components of the mechanical excavation device 20 and the excavation location area to which the excavation components are directed during excavation.

[0060] Next, the image corresponding to the excavation location is input into the object recognition model for identification, obtaining information about the excavated object at the excavation location. This information includes the composition and dryness of the object. Composition types can include gravel, sand, soil, and various mixtures. Composition types can also be differentiated based on whether they are prone to generating dust, such as sticky and non-sticky objects. Dryness can be measured by the moisture content of the object; higher dryness increases the likelihood of generating dust during excavation.

[0061] Then, the excavation components of the mechanical excavation equipment in the video image information are identified to obtain the size of the excavation components, and the excavation components in the video image information are tracked and calculated to obtain the excavation speed of the excavation components. The excavation information of the excavation components is composed of the size of the excavation components and the excavation speed.

[0062] Step S20: obtaining geographical location information of the mechanical excavation equipment, and obtaining weather information of the corresponding location area in real time based on the geographical location information.

[0063] For example, a positioning device can be configured on the mechanical excavation equipment 20, and the computer device 10 obtains the geographic location information of the mechanical excavation equipment 20 through the positioning device. The computer device 10 can obtain the weather information of the location area in real time through the geographic location information, where the weather information includes temperature, humidity, wind force and wind direction, etc.

[0064] Step S30 , inputting the excavated object information, the excavation direction of the excavation components, the excavation information and the weather information into a pre-trained excavation dust prediction model for prediction, thereby obtaining a first dust diffusion range generated during excavation.

[0065] The excavation dust prediction model can predict and output a first dust diffusion range generated during excavation based on input information about the excavated object, the excavation direction of the excavating components, excavation information, and weather information. The first dust diffusion range represents the potential diffusion range of dust generated by the mechanical excavation equipment 20 during the excavation process. This diffusion range is closely related to the excavated object information, the excavation direction of the excavating components, the excavation information, and weather information. For example, when the excavated object is prone to dust generation, the first dust diffusion range is generally larger. For another example, when the excavation direction and wind direction are the same, dust diffusion is also more likely to occur, and the first dust diffusion range is generally larger. In this embodiment, the first dust diffusion range is generally a spatial range that covers the excavation components.

[0066] Step S40, controlling the movement of the pan / tilt to adjust the water spraying direction of the sprinkler head, and controlling the working power of the power drive unit to adjust the speed of water outflow from the sprinkler head, to form a first water mist area that at least covers the first dust diffusion range.

[0067] By adjusting the working power of the pan / tilt platform and the power drive unit, the formed water mist area can be changed, thereby forming a first water mist area that can cover the first dust diffusion range, so as to achieve the purpose of reducing dust generated during excavation.

[0068] The above-mentioned solution provided in this embodiment predicts the first dust diffusion range generated during excavation based on the information of the excavated object, the excavation direction of the excavation components, the excavation information and the weather information, and forms a first water mist area covering the first dust diffusion range by controlling the pan-tilt head and the power drive unit. This can reduce the dust concentration generated during the excavation operation, avoid environmental pollution, and ensure that on-site construction personnel have a healthy working environment.

[0069] Furthermore, before step S30, the method provided in this embodiment further includes a step of training an excavation dust prediction model, which can be implemented in the following manner.

[0070] First, a first training sample set is created, which includes multiple first training samples and a first dust diffusion mark range corresponding to each first training sample, wherein the first training sample includes an excavated object information sample, an excavation direction sample of an excavation component, an excavation information sample, and a weather information sample.

[0071] Next, the first training sample is input into the first neural network model for training to obtain a first dust predicted diffusion range, and a first loss function value of the first neural network model is calculated based on the first dust predicted diffusion range and the first dust diffusion mark range.

[0072] Then, when the first loss function value is greater than a preset first loss function threshold, the network parameters of the first neural network model are adjusted.

[0073] Finally, the first training sample in the first training sample set is repeatedly input into the first neural network for training until the first loss function value is no greater than the preset first loss function domain value. The training is completed, and the first neural network obtained by training is used as the mining dust prediction model.

[0074] Furthermore, the inventors found that dust is generated not only during the excavation process of the mechanical excavation equipment 20, but also during the process of transferring the excavated objects by the mechanical excavation equipment 20. In order to solve this technical problem, please refer to Figure 3 , the method provided in this embodiment also includes the following steps.

[0075] Step S51 : obtaining transfer information of the excavated object transferred by the mechanical excavation equipment 20 through the excavation components through video image information, wherein the transfer information includes the transfer speed and transfer direction of the excavation components.

[0076] In detail, the transfer information for transferring the excavated object may be determined based on a target tracking method of the excavation component in the video image.

[0077] In step S52, the excavated object information, weather information, transfer speed and transfer direction of the excavating components are input into a pre-trained transfer dust prediction model for prediction to obtain a second dust diffusion range generated during the transfer.

[0078] During transfer, factors such as the object being excavated, weather conditions, and the speed and direction of the excavation components all influence the amount of dust generated. For example, when the excavated object is sandy and easily generates dust, faster transfer speeds increase dust generation. Stronger winds also increase dust generation, especially when the transfer direction is opposite to the wind. By combining these factors to predict dust during transfer, we can estimate the secondary dust dispersion range generated during transfer.

[0079] Step S53, controlling the movement of the pan / tilt to adjust the water spraying direction of the sprinkler head, and controlling the working power of the power drive unit to adjust the speed of water outflow from the sprinkler head, to form a second water mist area that at least covers the second dust diffusion range.

[0080] By adjusting the working power of the pan / tilt head and the power drive unit, the formed water mist area can be changed, thereby forming a second water mist area that can cover the second dust diffusion range, so as to achieve the purpose of reducing dust generated during the transfer process.

[0081] Furthermore, before step S52, the method provided in this embodiment further includes a step of training a transferred dust prediction model, which can be implemented in the following manner.

[0082] First, a second training sample set is created, comprising a plurality of second training samples and a second dust diffusion marker range corresponding to each second training sample. The second training samples include excavated object information samples, weather information samples, and excavation component transfer speed and transfer direction samples.

[0083] Next, the second training sample is input into the second neural network model for training to obtain a second dust predicted diffusion range, and a second loss function value of the second neural network model is calculated based on the second dust predicted diffusion range and the second dust diffusion mark range.

[0084] Then, when the second loss function value is greater than a preset second loss function threshold, the network parameters of the second neural network model are adjusted.

[0085] Finally, the second training sample in the second training sample set is repeatedly input into the second neural network for training until the second loss function value is no greater than the preset second loss function domain value. The training is completed and the second neural network obtained by training is used as the transferred dust prediction model.

[0086] Furthermore, the inventors have found that dust will also be generated during the process of unloading the excavated objects by the mechanical excavation equipment 20. Specifically, during the process of unloading the excavated objects from the excavation component to the bearing surface, due to the distance between the excavation component and the bearing surface, dust will be generated when the excavated objects fall to the bearing surface. In order to solve this technical problem, please refer to Figure 4 , the method provided in this embodiment also includes the following steps.

[0087] Step S61: obtaining unloading information of the excavated object by the mechanical excavation equipment through video image information.

[0088] In this embodiment, the unloading information includes the distance between the excavated object in the excavating component and the load-bearing surface during unloading, the unloading direction of the excavated object in the excavating component, and the unloading speed of the excavating component.

[0089] Step S62: input the excavated object information, weather information and unloading information into a pre-trained unloading dust prediction model to perform prediction, thereby obtaining a third dust diffusion range generated during unloading.

[0090] During unloading, the amount of dust generated is affected by the object being excavated, weather conditions, and unloading information. By combining these factors to predict dust during unloading, we can predict the third dust dispersion range generated during unloading.

[0091] Step S63, controlling the movement of the pan / tilt to adjust the water spraying direction of the sprinkler head, and controlling the working power of the power drive unit to adjust the speed of water outflow from the sprinkler head, to form a third water mist area that at least covers the third dust diffusion range.

[0092] By adjusting the working power of the pan / tilt head and the power drive unit, the formed water mist area can be changed, thereby forming a third water mist area that can cover the third dust diffusion range, so as to achieve the purpose of reducing dust generated during unloading.

[0093] Furthermore, before step S62, the method provided in this embodiment further includes a step of training an unloading dust prediction model, which can be implemented in the following manner.

[0094] First, a third training sample set is created, which includes a plurality of third training samples and a third dust diffusion mark range corresponding to each third training sample. The third training samples include excavated object information samples, weather information samples, and unloading information samples.

[0095] Next, the third training sample is input into the third neural network model for training to obtain a third dust predicted diffusion range, and a third loss function value of the third neural network model is calculated based on the third dust predicted diffusion range and the third dust diffusion mark range.

[0096] Then, when the third loss function value is greater than a preset third loss function threshold, the network parameters of the third neural network model are adjusted.

[0097] Finally, the third training sample in the third training sample set is repeatedly input into the third neural network for training until the third loss function value is no greater than the preset third loss function domain value. The training is completed, and the third neural network obtained by training is used as the unloading dust prediction model.

[0098] Furthermore, in order to prevent the dust generated during the construction process from spreading beyond the water mist area, thereby reducing the dust reduction effect, the method adopted in this embodiment further includes the following steps.

[0099] comparing the first dust diffusion range with a preset maximum dust diffusion range, and reducing the excavation speed of the excavation component when the first dust diffusion range is greater than the preset maximum dust diffusion range; and / or,

[0100] comparing the second dust diffusion range with a preset maximum dust diffusion range, and reducing the transfer speed of the excavating component when the second dust diffusion range is greater than the preset maximum dust diffusion range; and / or,

[0101] The third dust diffusion range is compared with a preset maximum dust diffusion range. When the third dust diffusion range is greater than the preset maximum dust diffusion range, the distance between the excavated object in the excavation component and the bearing surface is reduced, and / or the unloading speed of the excavation component is reduced.

[0102] In this embodiment, the preset maximum dust diffusion range can correspond to the maximum water mist area that can be formed by the dust reduction device 30. Through the above control, it can be ensured that the water mist area formed by the dust reduction device 30 can cover the dust diffusion range generated during the construction process, thereby achieving the purpose of reducing dust.

[0103] Please refer again Figure 1 This embodiment provides an AI-based dust reduction control system for mechanical excavation operations. The AI-based dust reduction control system 1 for mechanical excavation operations includes a communicatively connected computer device 10, a mechanical excavation device 20, a dust reduction device 30, and an image acquisition device 40. The mechanical excavation device 20 includes a mechanical base, a mechanical arm, and an excavation component. One end of the mechanical arm is connected to the mechanical base, and the other end of the mechanical arm is connected to the excavation component. The excavation component is movably connected to the mechanical arm. The image acquisition device 40 is disposed on the mechanical excavation device 20. For example, the image acquisition device 40 is disposed on the mechanical arm. The dust reduction device 30 is at least partially disposed on the mechanical arm. The dust reduction device 30 includes a water storage container, a power drive unit, a pan / tilt platform, and a sprinkler. Multiple pan / tilt platforms can be arranged in an array and fixed to the mechanical arm. The sprinkler is fixed to the mechanical arm via the pan / tilt platform. The spray direction of the sprinkler can be controlled by the pan / tilt platform, and the spray height of the sprinkler can be controlled by the operating power of the power drive component. Generally, the greater the operating power of the power drive component, the greater the water velocity of the sprinkler and the greater the spray height. In this embodiment, the water storage container and the water spray head can be connected through a water pipe.

[0104] The image acquisition device 40 is used to acquire video image information at the excavation position when the mechanical excavation device 20 is working, and send the video image information to the computer device 10.

[0105] The computer device 10 is used to identify and obtain information of an excavated object at an excavation location, an excavation direction of an excavation component in a mechanical excavation device, and excavation information of the excavation component based on video image information.

[0106] In this embodiment, the computer device 10 can implement the above process in the following manner.

[0107] First, the computer device 10 obtains video image information collected by the image acquisition device 40 when the mechanical excavation device 20 is working. The video image information at least includes the excavation components of the mechanical excavation device 20 and the excavation location area the excavation components are facing during excavation.

[0108] Next, the computer device 10 inputs the image corresponding to the excavation location into the object recognition model for recognition, thereby obtaining information about the excavated object at the excavation location. The excavated object information includes the composition type and dryness of the excavated object. The composition types of the excavated object may include gravel, sand, soil, and various mixtures. The composition types of the excavated object may also be differentiated based on whether they are prone to generating dust, such as sticky objects and non-sticky objects. The dryness can be measured by the water content of the excavated object. The higher the dryness, the more likely it is to generate dust during excavation.

[0109] Then, the computer device 10 identifies the excavation components of the mechanical excavation equipment in the video image information to obtain the size of the excavation components, performs target tracking calculation on the excavation components in the video image information to obtain the excavation speed of the excavation components, and the excavation information of the excavation components is composed of the size of the excavation components and the excavation speed.

[0110] The computer device 10 is further used to obtain geographic location information of the mechanical excavation equipment, and obtain weather information of the corresponding location area in real time based on the geographic location information, wherein the weather information includes temperature, humidity, wind force and wind direction.

[0111] For example, a positioning device can be configured on the mechanical excavation equipment 20, and the computer device 10 obtains the geographic location information of the mechanical excavation equipment 20 through the positioning device. The computer device 10 can obtain the weather information of the location area in real time through the geographic location information, where the weather information includes temperature, humidity, wind force and wind direction, etc.

[0112] The computer device 10 is further configured to input the excavated object information, the excavation direction of the excavation component, the excavation information, and the weather information into a pre-trained excavation dust prediction model to perform prediction, thereby obtaining a first dust diffusion range generated during excavation.

[0113] The excavation dust prediction model can predict and output a first dust diffusion range generated during excavation based on input information about the excavated object, the excavation direction of the excavating components, excavation information, and weather information. The first dust diffusion range represents the potential diffusion range of dust generated by the mechanical excavation equipment 20 during the excavation process. This diffusion range is closely related to the excavated object information, the excavation direction of the excavating components, the excavation information, and weather information. For example, when the excavated object is prone to dust generation, the first dust diffusion range is generally larger. For another example, when the excavation direction and wind direction are the same, dust diffusion is also more likely to occur, and the first dust diffusion range is generally larger. In this embodiment, the first dust diffusion range is generally a spatial range that covers the excavation components.

[0114] The computer device 10 is also used to control the movement of the pan-tilt head to adjust the water spraying direction of the sprinkler head, and control the working power of the power drive unit to adjust the speed of water discharge from the sprinkler head, forming a first water mist area that at least covers the first dust diffusion range.

[0115] The computer device 10 can change the formed water mist area by adjusting the working power of the pan / tilt platform and the power drive unit, thereby forming a first water mist area that can cover the first dust diffusion range, so as to achieve the purpose of reducing dust generated during the excavation process.

[0116] Please refer to Figure 5 , Figure 5 The hardware structure diagram of the computer device 10 provided by the embodiment of the present disclosure for implementing the above-mentioned AI-based mechanical excavation dust control method is shown. The computer device 10 can be implemented on a cloud server. Figure 5 As shown, the computer device 10 may include a processor 101 , a computer-readable storage medium 102 , a bus 103 , and a communication interface 104 .

[0117] During the specific implementation process, at least one processor 101 executes computer-executable instructions stored in a computer-readable storage medium 102, so that the processor 101 can execute the AI-based mechanical excavation dust reduction control method of the above method embodiment, wherein the processor 101, the computer-readable storage medium 102 and the communication interface 104 are connected through a bus 103, and the processor 101 can be used to control the sending and receiving actions of the communication interface 104.

[0118] The specific implementation process of the processor 101 can be found in the various method embodiments executed by the above-mentioned computer device 10. The implementation principles and technical effects are similar and will not be repeated here in this embodiment.

[0119] The computer-readable storage medium 102 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk storage.

[0120] The bus 103 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the bus in the drawings of the present invention is not limited to only one bus or one type of bus.

[0121] In addition, an embodiment of the present invention also provides a readable storage medium, which stores computer-executable instructions. When the processor executes the computer-executable instructions, the above-mentioned AI-based mechanical excavation operation dust reduction control method is implemented.

[0122] In summary, the technical solution provided by the embodiment of the present invention first obtains the excavated object information, the excavation direction of the excavation components, and the excavation information during excavation based on the video image information of the mechanical excavation equipment during operation; then, obtains the geographic location information of the mechanical excavation equipment, and obtains the weather information of the corresponding location area in real time based on the geographic location information; then, inputs the excavated object information, the excavation direction of the excavation components, the excavation information, and the weather information into a pre-trained excavation dust prediction model for prediction, thereby obtaining a first dust diffusion range generated during excavation; finally, controls the pan-tilt table and the power drive unit to form a first water mist area that at least covers the first dust diffusion range. The above solution predicts the first dust diffusion range generated during excavation based on the excavated object information, the excavation direction of the excavation components, the excavation information, and the weather information, and forms the first water mist area that covers the first dust diffusion range by controlling the pan-tilt table and the power drive unit. This can reduce the dust concentration generated during the excavation operation, avoid environmental pollution, and ensure a healthy working environment for on-site construction personnel.

[0123] The foregoing description of specific embodiments of this specification describes the process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An AI-based dust control method for mechanical excavation operations, characterized in that: The invention is applied to an AI-based dust reduction control system for mechanical excavation operations, the system comprising a computer device, a mechanical excavation device, a dust reduction device, and an image acquisition device in communication connection. The mechanical excavation device comprises a mechanical base, a mechanical arm, and an excavation component. One end of the mechanical arm is connected to the mechanical base, and the other end of the mechanical arm is connected to the excavation component. The image acquisition device is arranged on the mechanical excavation device. The dust reduction device is at least partially arranged on the mechanical arm. The dust reduction device comprises a water storage container, a power drive unit, a pan-tilt head, and a water spray head. The water spray head is fixed to the mechanical arm via a pan-tilt head. The water storage container and the water spray head are connected via a water pipe. The method comprises: Acquiring video image information of the mechanical excavation equipment during operation captured by the image acquisition device, and identifying, based on the video image information, information of an excavated object at an excavation position during excavation, an excavation direction of an excavation component in the mechanical excavation equipment, and excavation information of the excavation component; Obtaining geographic location information of the mechanical excavation equipment, and obtaining weather information of the corresponding location area in real time based on the geographic location information, wherein the weather information includes temperature, humidity, wind speed, and wind direction; Inputting the excavated object information, the excavation direction of the excavating component, the excavation information, and the weather information into a pre-trained excavation dust prediction model for prediction to obtain a first dust diffusion range generated during excavation; Controlling the movement of the pan / tilt platform to adjust the water spraying direction of the water spray head, and controlling the working power of the power drive unit to adjust the speed of water discharge from the water spray head, so as to form a first water mist area that at least covers the first dust diffusion range; Acquiring transfer information of the excavated object transferred by the mechanical excavation equipment through the excavation component through the video image information, the transfer information including a transfer speed and a transfer direction of the excavation component; Inputting the excavated object information, the weather information, the transfer speed and transfer direction of the excavating component into a pre-trained transfer dust prediction model to perform prediction to obtain a second dust diffusion range generated during the transfer; Controlling the movement of the pan / tilt platform to adjust the water spraying direction of the water spray head, and controlling the working power of the power drive unit to adjust the speed of water discharge from the water spray head, so as to form a second water mist area that at least covers the second dust diffusion range; The step of obtaining video image information of the mechanical excavation equipment when it is working, acquired by the image acquisition device, and identifying information of the excavated object at the excavation position during excavation, the excavation direction of the excavation component in the mechanical excavation equipment, and the excavation information of the excavation component based on the video image information includes: Acquiring video image information captured by the image acquisition device while the mechanical excavation device is in operation, wherein the video image information at least includes an excavation component of the mechanical excavation device and an excavation location area toward which the excavation component is directed during excavation; Inputting the image corresponding to the excavation location area into the object recognition model for recognition, thereby obtaining information about the excavated object at the excavation location, wherein the information about the excavated object includes the composition type and dryness degree of the excavated object; The excavation components of the mechanical excavation equipment in the video image information are identified to obtain the size of the excavation components, and the excavation speed of the excavation components is obtained by target tracking calculation on the excavation components in the video image information. The excavation information of the excavation components is composed of the size of the excavation components and the excavation speed.

2. The AI-based dust reduction control method for mechanical excavation operations according to claim 1, characterized in that: The method further comprises: Obtaining unloading information of the excavated object by the mechanical excavation equipment through the video image information, wherein the unloading information includes the distance between the excavated object in the excavation component and the bearing surface during unloading, the unloading direction of the excavated object in the excavation component, and the unloading speed of the excavation component; Inputting the excavated object information, the weather information, and the unloading information into a pre-trained unloading dust prediction model for prediction, thereby obtaining a third dust diffusion range generated during unloading; The movement of the pan / tilt head is controlled to adjust the water spraying direction of the sprinkler head, and the working power of the power drive unit is controlled to adjust the water outlet speed of the sprinkler head to form a third water mist area that at least covers the third dust diffusion range.

3. The AI-based dust reduction control method for mechanical excavation operations according to claim 2, characterized in that: The method further includes the step of training the excavation dust prediction model, the step comprising: Creating a first training sample set, the first training sample set including a plurality of first training samples and a first dust diffusion mark range corresponding to each first training sample, wherein the first training samples include excavated object information samples, excavation direction samples of excavating components, excavation information samples, and weather information samples; Inputting the first training sample into a first neural network model for training to obtain a first dust predicted diffusion range, and calculating a first loss function value of the first neural network model based on the first dust predicted diffusion range and the first dust diffusion marked range; When the first loss function value is greater than a preset first loss function threshold, adjusting the network parameters of the first neural network model; Repeatedly input the first training sample in the first training sample set into the first neural network for training until the first loss function value is no greater than the preset first loss function domain value. The training is completed and the first neural network obtained by training is used as the excavation dust prediction model.

4. The AI-based dust reduction control method for mechanical excavation operations according to claim 2, characterized in that: The method further includes the step of training the transferred dust prediction model, the step comprising: Creating a second training sample set, the second training sample set including a plurality of second training samples and a second dust diffusion mark range corresponding to each second training sample, wherein the second training samples include excavated object information samples, weather information samples, and excavation component transfer speed samples and transfer direction samples; Inputting the second training sample into a second neural network model for training to obtain a second dust predicted diffusion range, and calculating a second loss function value of the second neural network model based on the second dust predicted diffusion range and the second dust diffusion marked range; When the second loss function value is greater than a preset second loss function threshold, adjusting the network parameters of the second neural network model; Repeatedly input the second training sample in the second training sample set into the second neural network for training until the second loss function value is no greater than the preset second loss function domain value. The training is completed and the second neural network obtained by training is used as the transferred dust prediction model.

5. The AI-based dust reduction control method for mechanical excavation operations according to claim 2, characterized in that: The method further includes the step of training the unloading dust prediction model, which comprises: Creating a third training sample set, the third training sample set including a plurality of third training samples and a third dust diffusion mark range corresponding to each third training sample, wherein the third training sample includes an excavated object information sample, a weather information sample, and an unloading information sample; Inputting the third training sample into a third neural network model for training to obtain a third dust predicted diffusion range, and calculating a third loss function value of the third neural network model based on the third dust predicted diffusion range and the third dust diffusion marked range; When the third loss function value is greater than a preset third loss function threshold, adjusting the network parameters of the third neural network model; Repeatedly input the third training sample in the third training sample set into the third neural network for training until the third loss function value is no greater than the preset third loss function domain value. The training is completed and the third neural network obtained by training is used as the unloading dust prediction model.

6. The AI-based dust reduction control method for mechanical excavation operations according to claim 2, characterized in that: The method further comprises: comparing the first dust diffusion range with a preset maximum dust diffusion range, and reducing the excavation speed of the excavating component when the first dust diffusion range is greater than the preset maximum dust diffusion range; and / or, comparing the second dust diffusion range with a preset maximum dust diffusion range, and reducing the transfer speed of the excavating component when the second dust diffusion range is greater than the preset maximum dust diffusion range; and / or, The third dust diffusion range is compared with a preset maximum dust diffusion range. When the third dust diffusion range is greater than the preset maximum dust diffusion range, the distance between the excavated object in the excavation component and the bearing surface is reduced, and / or the unloading speed of the excavation component is reduced.

7. An AI-based dust reduction control system for mechanical excavation operations, characterized in that: The system includes a computer device, a mechanical excavation device, a dust suppression device, and an image acquisition device that are communicatively connected. The mechanical excavation device includes a mechanical base, a mechanical arm, and an excavation component. One end of the mechanical arm is connected to the mechanical base, and the other end of the mechanical arm is connected to the excavation component. The image acquisition device is disposed on the mechanical excavation device. The dust suppression device is at least partially disposed on the mechanical arm. The dust suppression device includes a water storage container, a power drive unit, a pan / tilt platform, and a water spray head. The water spray head is fixed to the mechanical arm via a pan / tilt platform. The water storage container and the water spray head are connected via a water pipe. The image acquisition device is used to acquire video image information at the excavation position when the mechanical excavation equipment is working; The computer device is used to identify, based on the video image information, information of an excavated object at an excavation location, an excavation direction of an excavation component in the mechanical excavation equipment, and excavation information of the excavation component; obtain geographic location information of the mechanical excavation equipment, and obtain weather information of a corresponding location area in real time based on the geographic location information, wherein the weather information includes temperature, humidity, wind force, and wind direction; input the information of the excavated object, the excavation direction of the excavation component, the excavation information, and the weather information into a pre-trained excavation dust prediction model for prediction, thereby obtaining a first dust diffusion range generated during excavation; control the movement of the pan / tilt table to adjust the water spraying direction of the water sprinkler, and control the working power of the power drive unit to adjust the water outlet speed of the water sprinkler, thereby forming a first water mist area that at least covers the first dust diffusion range; The computer device is further configured to obtain, through the video image information, transfer information of the excavated object transferred by the mechanical excavation equipment via the excavation component, the transfer information including the transfer speed and transfer direction of the excavation component; input the excavated object information, the weather information, the transfer speed and transfer direction of the excavation component into a pre-trained transfer dust prediction model for prediction, thereby obtaining a second dust diffusion range generated during the transfer; control the movement of the pan / tilt table to adjust the water spray direction of the water sprinkler, and control the working power of the power drive unit to adjust the water discharge speed of the water sprinkler, thereby forming a second water mist area that at least covers the second dust diffusion range; Wherein, the computer device is also used to obtain video image information collected by the image acquisition device when the mechanical excavation equipment is working, wherein the video image information at least includes the excavation components of the mechanical excavation equipment and the excavation position area to which the excavation components are directed during excavation; the image corresponding to the excavation position area is input into the object recognition model for identification to obtain information of the excavated object at the excavation position, wherein the excavated object information includes the composition type and dryness degree of the excavated object; the excavation components of the mechanical excavation equipment in the video image information are identified to obtain the size of the excavation components, the excavation components in the video image information are target tracked and calculated to obtain the excavation speed of the excavation components, and the excavation information of the excavation components is composed of the size and excavation speed of the excavation components.

8. A computer device, characterized in that: The computer device includes a processor, a readable storage medium and a communication interface. The readable storage medium, the communication interface and the processor are connected via a bus system. The readable storage medium is used to store programs, instructions or codes. The processor is used to execute the programs, instructions or codes in the readable storage medium to execute the AI-based mechanical excavation dust reduction control method described in any one of claims 1-6.

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