A material flow detection method, device, equipment and medium

Abstract

The application discloses a kind of material flow detection method, device, equipment and medium, by obtaining the depth calibration data under no flow state;In the process of material transmission, the point cloud data of material transmission area is collected, and the instantaneous material flow data of the material transmission area is determined based on the point cloud data of the material transmission area and the depth calibration data;Based on the instantaneous material flow data, the material transmission state of the material transmission area is determined;In the case where the material transmission state of the material transmission area is no material state, the depth calibration data is updated based on the point cloud data of the material transmission area, and the instantaneous material flow data is determined based on the updated depth calibration data and the subsequent point cloud data of the material transmission area.The embodiment of the present application can adaptively correct the depth calibration data to adapt to the relative position change of the material flow detection environment in the structured working surface, improve the accuracy and stability of instantaneous material flow detection.

Description

Technical Field

[0001] This invention relates to the field of material flow control technology, and in particular to a material flow detection method, apparatus, equipment and medium. Background Technology

[0002] In a fully mechanized coal mining face (hereinafter referred to as a fully mechanized mining face), coal mining and production are achieved by controlling the coal cutting machine, hydraulic support, and scraper conveyor.

[0003] Currently, fully mechanized mining faces typically have detection devices installed on belt conveyors to monitor the coal flow on the belt. The belt conveyor speed is controlled based on the coal flow rate, or the coal mining speed of the mining machine on the fully mechanized mining face is controlled in reverse.

[0004] However, in actual coal mining operations, there is a situation where the coal flow detection device and the coal conveying device are misaligned, requiring manual maintenance. Summary of the Invention

[0005] This invention provides a material flow detection method, apparatus, equipment, and medium to solve the problem of inaccurate instantaneous material flow detection results when the relative position of the material flow detection environment changes in a structured working face, thereby improving the accuracy and stability of material flow detection.

[0006] According to one aspect of the present invention, a method for detecting material flow rate is provided, comprising:

[0007] Acquire depth calibration data under no-traffic conditions;

[0008] During the material transfer process, point cloud data of the material transfer area is collected, and instantaneous material flow data of the material transfer area is determined based on the point cloud data and depth calibration data of the material transfer area.

[0009] The material transfer status of the material transfer area is determined based on instantaneous material flow data;

[0010] When the material transfer status in the material transfer area is no material, the depth calibration data is updated based on the point cloud data of the material transfer area, and the instantaneous material flow data is determined based on the updated depth calibration data and the subsequent point cloud data of the material transfer area.

[0011] According to another aspect of the present invention, a material flow detection device is provided, comprising:

[0012] The depth calibration data acquisition module is used to acquire depth calibration data under no-traffic conditions.

[0013] The instantaneous material flow rate data determination module is used to collect point cloud data of the material transport area during the material transport process, and determine the instantaneous material flow rate data of the material transport area based on the point cloud data and depth calibration data of the material transport area.

[0014] The material transfer status determination module is used to determine the material transfer status of the material transfer area based on instantaneous material flow data;

[0015] The depth calibration data update module is used to update the depth calibration data based on the point cloud data of the material transport area when the material transport status of the material transport area is no material, and to determine the instantaneous material flow data based on the updated depth calibration data and the subsequent point cloud data of the material transport area.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory that is communicatively connected to at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the material flow detection method of any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the material flow detection method of any embodiment of the present invention.

[0021] The technical solution of this invention acquires depth calibration data under no-flow conditions; during material transfer, it collects point cloud data of the material transfer area and determines instantaneous material flow data of the material transfer area based on the point cloud data and depth calibration data; it determines the material transfer status of the material transfer area based on the instantaneous material flow data; when the material transfer status of the material transfer area is no-material, it updates the depth calibration data based on the point cloud data of the material transfer area, and determines the instantaneous material flow data based on the updated depth calibration data and subsequent point cloud data of the material transfer area, adaptively correcting the depth calibration data to adapt to the relative position changes of the material flow detection environment in the structured working face. This solves the problem of inaccurate instantaneous material flow detection results when there are relative position changes in the material flow detection environment in the structured working face, and improves the accuracy and stability of material flow detection.

[0022] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a material flow rate detection method provided in Embodiment 1 of the present invention;

[0025] Figure 2 This is a schematic diagram of the material testing environment;

[0026] Figure 3 This is a flowchart of a material flow rate detection method provided in Embodiment 2 of the present invention;

[0027] Figure 4 This is a flowchart of a material flow detection method;

[0028] Figure 5 This is a schematic diagram of the structure of a material flow detection device provided in Embodiment 3 of the present invention;

[0029] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the material flow detection method of this invention. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] Example 1

[0033] Figure 1 This is a flowchart of a material flow detection method provided in Embodiment 1 of the present invention. This embodiment is applicable to the real-time detection of material flow on a material conveying device. The method can be executed by a material flow detection device, which can be implemented in hardware and / or software.

[0034] The material flow detection method provided in this embodiment realizes real-time material flow detection in the material transmission area through a material flow detection device. The material flow detection device has point cloud data acquisition and point cloud data-based material flow detection functions. It is used to collect point cloud data and perform real-time flow detection on the material in the material transmission area, enabling local real-time flow detection. Compared to non-local flow detection methods, this reduces detection latency caused by the time consumed in point cloud data transmission. For example, the material can be coal. Optionally, the point cloud data is obtained by scanning with a LiDAR scanner positioned above the material transmission area. The LiDAR scanner includes a LiDAR sensor, and the point cloud data is obtained by the LiDAR sensor collecting point cloud data within the environmental range, including the point cloud data of the material transmission area. Since the material flow detection result is only related to the point cloud data of the material transmission area, optionally, a portion of the point cloud data from the fixed LiDAR scanner is removed from the point cloud data collected by the LiDAR sensor to eliminate interfering point cloud data. For example, Figure 2 This is a schematic diagram of the material testing environment, such as... Figure 2 As shown, the lidar scanner 220 is installed below the top plate 210 of the hydraulic support. The point cloud data collected includes the point cloud data of the material transfer area 230 and the point cloud data of the hydraulic support. Based on the coordinate information of the point cloud data, the point cloud data of the hydraulic support is removed, and only the point cloud data of the material transfer area is retained.

[0035] like Figure 1 As shown, the method includes:

[0036] S110. Obtain depth calibration data under no-traffic conditions.

[0037] In this embodiment, the no-flow state can be understood as a state where there is no material in the material transport area. Depth calibration data is data characterizing the depth of the material transport area, representing the maximum depth at which the material transport area can carry material. Depth calibration data is determined by calibrating the point cloud data of the material transport area collected during the no-flow state when the material transport area is not operating. Alternatively, the depth calibration data from the no-flow state can be updated when the material transport area is operating, based on the depth calibration data from the no-flow state.

[0038] The calibration process in the no-flow state when the material transport area is not working can be as follows: acquire point cloud data of the material transport area in the no-flow state, segment the point cloud data of the material transport area to obtain bottom point cloud data and top point cloud data of the material transport area; determine depth calibration data based on the depth data of the bottom point cloud data and the depth data of the top cloud data.

[0039] In this embodiment, point cloud data of the material transport area collected under no-flow conditions is calibrated to determine the spatial transformation relationship of the point cloud data from the lidar coordinate system to the global coordinate system, thus obtaining point cloud data in the global coordinate system. Based on the known morphological parameters of the material transport area, the calibrated point cloud data is segmented to determine the material transport area structure label corresponding to each point in the point cloud data. Bottom point cloud data represents the bottom load-bearing structure of the material transport area, while top point cloud data represents the top protective structure of the material transport area. Taking a fully mechanized coal mining face as an example, the material transport area is a scraper conveyor area; the bottom load-bearing structure can be the cable trough structure of the scraper conveyor, and the top protective structure can be the guardrail structure of the scraper conveyor. Based on a clustering algorithm, point cloud data with bottom structure labels are clustered into bottom point cloud data, and point cloud data with top structure labels are clustered into top point cloud data. The average Z-coordinate information of the bottom point cloud data is calculated respectively. The average Z-coordinate information of the top point cloud data The Z-coordinate information refers to the Z-axis coordinate of the point cloud data in the YOZ plane of the global coordinate system. Depth data identified as bottom-end point cloud data will Determined to be depth data of the top point cloud data. For depth calibration data.

[0040] Taking a fully mechanized coal mining face as an example, before mining, when there is no coal on the scraper conveyor, point cloud data of the scraper conveyor area is collected and calibrated. Based on the structure of the scraper conveyor, the calibrated point cloud data is segmented. Based on the DBSCAN clustering algorithm, point cloud data with the same segmentation label are clustered. The resulting point cloud clusters include point cloud clusters of the coal flow area in the coal-free state and point cloud clusters of the scraper conveyor guardrail area, which are respectively determined as bottom point cloud data and top point cloud data. The average Z coordinate information of all points in the bottom point cloud data is calculated. And the average Z-coordinate information of all points in the top point cloud data. Determine the average relative height difference between the coal flow area and the scraper conveyor guardrail area under coal-free conditions. For depth calibration data.

[0041] S120. During the material transfer process, point cloud data of the material transfer area is collected, and instantaneous material flow data of the material transfer area is determined based on the point cloud data and depth calibration data of the material transfer area.

[0042] In this embodiment, based on the spatial transformation relationship of point cloud data from the lidar coordinate system to the global coordinate system determined under no-flow conditions, the point cloud data of the material transport area at the current time t is collected in real time. The collected point cloud data is segmented based on the known morphological parameters of the material transport area to determine the material transport area structure label corresponding to each point in the point cloud data. Notably, during material transport, material surface labels are used instead of the bottom load-bearing structure labels of the material transport area. The instantaneous material shape is determined based on the point cloud data and depth calibration data related to the material transport area structure labels. Instantaneous material flow data is then determined based on the shape of the instantaneous material, where the instantaneous material flow data is the real-time flow data of material passing through the material transport area, and can be material volume data.

[0043] The instantaneous material shape in the material transfer area can be a cuboid. Optionally, the point cloud data of the material transfer area is clustered to obtain material point cloud data and top point cloud data of the material transfer area. Based on the material point cloud data and top point cloud data, the width data, length data, and distance data between the material surface and the top of the material in the material transfer area are determined. Based on the distance data between the material surface and the top of the material and the depth calibration data, the material depth data is determined. Based on the width data, length data, and depth data of the material, the instantaneous material flow rate data of the material transfer area is determined.

[0044] Specifically, the point cloud data of the material transport area at time t is clustered. Point cloud data with the material surface label as the structural label of the material transport area are identified as material point cloud data. Here, material point cloud data represents the shape and position of the material surface layer in the material transport area. Point cloud data with the top structural label as the structural label of the material transport area are identified as top point cloud data. The average Z-coordinate information of each material point cloud data is calculated. The average Z-coordinate information of the top point cloud data z3 is defined as the depth data of the material point cloud. The depth data of the top point cloud during material transport was determined. This involves determining the distance between the material surface and the top. Based on the coordinate range of the material point cloud data and the top point cloud data, the width and length of the material within the material transport area are determined. The material depth data is determined by calculating the difference between the distance between the material surface and the top and the depth calibration data; this material depth data characterizes the height of the material flow. Based on the width, length, and depth data, the material volume data within the material transport area is determined, thus establishing the instantaneous material flow rate.

[0045] Taking a fully mechanized coal mining face as an example, during material transfer, point cloud data at time t (determined when the coal mining face is not operating and there is no coal) is collected in real time from the lidar coordinate system to the global coordinate system based on the spatial transformation relationship between the point cloud data at time t and the global coordinate system. The point cloud data of the scraper conveyor area is then segmented based on the structure of the scraper conveyor. The DBSCAN clustering algorithm is used to cluster point cloud data with the same segmentation labels, resulting in point cloud clusters including the coal flow area cluster and the scraper conveyor guardrail point area cluster, which are respectively identified as material point cloud data and top point cloud data. The average Z-coordinate information of all point cloud data in the material point cloud data is calculated. And the average Z-coordinate information of all point cloud data in the top point cloud data. Determine the average relative height difference between the coal flow area and the scraper conveyor guardrail area under the working conditions of the fully mechanized coal mining face. The distance data between the material surface and the top is used. Based on the coordinate range of the material point cloud data and the top point cloud data, the width and length data of the coal flow are determined. By calculating the difference between the distance data between the material surface and the top and the depth calibration data, the material depth data is determined, and the height data of the coal flow is obtained. The point cloud coordinates are converted into actual length units to obtain the length, width, and height data of the coal flow in the actual scenario, thereby determining the coal flow volume and obtaining the instantaneous material flow rate data of the material transmission area.

[0046] S130. Determine the material transfer status of the material transfer area based on instantaneous material flow data.

[0047] In this embodiment, the material transfer status includes a material-present status and a material-free status. A material-present status can be understood as the presence of material in the material transfer area during the material transfer process; a material-free status can be understood as the absence of material in the material transfer area during the material transfer process. During material transfer, the material transfer status to which the instantaneous material flow rate data of the material transfer area belongs is determined based on the instantaneous material flow rate data range corresponding to the material-present and material-free statuses.

[0048] For example, if the instantaneous material flow data at the current moment is within the range of instantaneous material flow data corresponding to the material presence state, the material transmission state at the current moment is determined to be a material presence state; if the instantaneous material flow data at the current moment is within the range of instantaneous material flow data corresponding to the material absence state, the material transmission state at the current moment is determined to be a material absence state.

[0049] S140. When the material transfer status in the material transfer area is no material, update the depth calibration data based on the point cloud data of the material transfer area, and determine the instantaneous material flow data based on the updated depth calibration data and the subsequent point cloud data of the material transfer area.

[0050] Specifically, when the material transfer status of the material transfer area at time t is "no material," a depth calibration data update operation is triggered to redetermine the spatial transformation relationship of the point cloud data from the LiDAR coordinate system to the global coordinate system at time t, thus obtaining point cloud data in the global coordinate system. Based on the known morphological parameters of the material transfer area, the recalibrated point cloud data is segmented to determine the structural label of the material transfer area corresponding to each point in the point cloud data. Bottom and top point cloud data are obtained using a clustering algorithm, and the average Z-coordinate information of the bottom point cloud data is calculated for each. The average Z-coordinate information of the top point cloud data The Z-coordinate information refers to the Z-axis coordinate of the point cloud data in the YOZ plane of the global coordinate system. Depth data identified as bottom-end point cloud data will Depth data identified as top point cloud data is used. Update the depth calibration data. During subsequent material transfer, based on the spatial transformation relationship from the lidar coordinate system to the global coordinate system of the point cloud data at time t, the point cloud data of the material transfer area is collected in real time. Based on the known morphological parameters of the material transfer area, the real-time collected point cloud data is segmented to determine the material transfer area structure label corresponding to each point in the point cloud data. Based on the point cloud data with relevant material transfer area structure labels and the updated depth calibration data, the instantaneous material shape is determined, and the instantaneous material flow rate data is determined based on the shape of the instantaneous material.

[0051] Taking a fully mechanized coal mining face as an example, during material transfer, the material transfer state of the material transfer area at time t is a no-material state. The spatial transformation relationship of the point cloud data from the lidar coordinate system to the global coordinate system at time t is redefined, and the point cloud data is recalibrated. Based on the structure of the scraper conveyor, the recalibrated point cloud data is segmented. Using the DBSCAN clustering algorithm, point cloud data with the same segmentation labels are clustered, resulting in point cloud clusters including the coal flow area point cloud cluster at time t (no coal) and the scraper conveyor guardrail point area point cloud cluster, which are respectively identified as the bottom point cloud data and the top point cloud data. The average Z-coordinate information of all points in the bottom point cloud data is calculated. And the average Z-coordinate information of all points in the top point cloud data. Determine the average relative height difference between the coal flow region and the scraper conveyor guardrail region at time t under the condition of no coal. The updated depth calibration data is used. Based on the redefined spatial transformation relationship of the point cloud data at time t from the lidar coordinate system to the global coordinate system, the point cloud data of the scraper transport area after time t is collected in real time. Based on the point cloud data of the scraper transport area and the updated depth calibration data, the instantaneous material flow rate data is determined.

[0052] The technical solution of this embodiment acquires depth calibration data under no-flow conditions; during material transfer, it collects point cloud data of the material transfer area and determines instantaneous material flow data of the material transfer area based on the point cloud data and depth calibration data; it determines the material transfer status of the material transfer area based on the instantaneous material flow data; when the material transfer status of the material transfer area is no-material, it updates the depth calibration data based on the point cloud data of the material transfer area, and determines the instantaneous material flow data based on the updated depth calibration data and subsequent point cloud data of the material transfer area, adaptively correcting the depth calibration data to adapt to the relative position changes of the material flow detection environment in the structured working face. This solves the problem of inaccurate instantaneous material flow detection results when there are relative position changes in the material flow detection environment in the structured working face, and improves the accuracy and stability of material flow detection.

[0053] Example 2

[0054] Figure 2 This is a flowchart of a material flow rate detection method provided in Embodiment 2 of the present invention. The technical solution of the present invention is further optimized based on any of the above embodiments. Figure 2 As shown, the method includes:

[0055] S210. Obtain depth calibration data under no-traffic conditions.

[0056] S220. During the material transfer process, point cloud data of the material transfer area is collected, and instantaneous material flow data of the material transfer area is determined based on the point cloud data and depth calibration data of the material transfer area.

[0057] S230. If the instantaneous material flow rate data is greater than or equal to the flow rate threshold, the material transfer status of the material transfer area is determined to be a material-containing state; if the instantaneous material flow rate data is less than the flow rate threshold, the material transfer status of the material transfer area is determined to be a material-free state.

[0058] In this embodiment, the flow threshold is a threshold value for instantaneous material flow data used to determine whether the material transport status of the material transport area is "with material" or "without material". By comparing the instantaneous material flow data with the flow threshold, if the instantaneous material flow data of the material transport area at the current moment is greater than or equal to the flow threshold, the material transport status of the material transport area at the current moment is determined to be "with material"; if the instantaneous material flow data of the material transport area at the current moment is less than the flow threshold, the material transport status of the material transport area at the current moment is determined to be "without material". Determining the material transport status of the material transport area using the flow threshold ensures the accuracy of material flow detection.

[0059] Taking a fully mechanized coal mining face as an example, during the coal transportation process, the instantaneous material flow rate data of the scraper transport area is compared with the flow rate threshold. If the instantaneous material flow rate data is greater than or equal to the flow rate threshold, it can be considered that there is coal in the scraper transport area, and the coal transportation status of the scraper transport area is determined to be a coal-containing state; if the instantaneous material flow rate data is greater than or equal to the flow rate threshold, it can be considered that there is no coal in the scraper transport area, and the coal transportation status of the scraper transport area is determined to be a coal-free state.

[0060] Since there may be situations where the instantaneous material flow rate is less than the flow rate threshold but there is still material in the material transport area, optionally, when the instantaneous material flow rate is less than the flow rate threshold, the point cloud data of the material transport area is registered with the point cloud data of the no-flow state to determine the registration degree data. When the registration degree data is greater than the registration degree threshold, the material transport state of the material transport area is determined to be the no-material state.

[0061] In this embodiment, when the instantaneous material flow rate data is less than the flow rate threshold, a registration operation is triggered. The point cloud data of the current material transport area is registered with the point cloud data in the no-flow state to obtain registration accuracy data. The registration accuracy threshold is the threshold used to determine whether the material transport state of the material transport area is a no-material state. By comparing the registration accuracy data with the registration accuracy threshold, the material transport state of the material transport area is determined. If the registration accuracy data is greater than the registration accuracy threshold, it can be considered that there is no material in the material transport area, and the material transport state is determined to be a no-material state; if the registration accuracy data is less than or equal to the registration accuracy threshold, it can be considered that there is material in the material transport area, and the material transport state is determined to be a material-containing state.

[0062] Taking a fully mechanized coal mining face as an example, when the instantaneous material flow rate data is less than the flow rate threshold, the point cloud data of the current material transmission area is registered with the point cloud data in the no-flow state based on the point cloud registration (ICP) model to obtain the matching degree data. The matching degree data is compared with the registration degree threshold. Assuming that the registration degree threshold is 90% and the matching degree data is 93%, the material transmission state is determined to be the no-material state.

[0063] In one specific embodiment Figure 4 This is a flowchart of a material flow detection method. For example... Figure 4 As shown, the collected point cloud data is first assessed for whether it has been labeled. If the point cloud data is unlabeled, it is then calibrated, and the labeling process is completed. If the point cloud data is calibrated, it is then preprocessed, including but not limited to outlier removal, sampling, and smoothing. Instantaneous material flow rate is detected on the preprocessed point cloud data to obtain instantaneous material flow rate data, which is then output. The material transport status of the material transport area is then determined, comparing the instantaneous material flow rate data with a flow rate threshold. If the instantaneous material flow rate data is less than the flow rate threshold, the registration accuracy is calculated between the current point cloud data in the material transport area and the point cloud data in the no-flow state. The registration accuracy is then compared with the registration accuracy threshold. If the registration accuracy is greater than the registration accuracy threshold, the material transport status is determined to be a no-material state. The point cloud data in the no-material state is then recalibrated, and the recalibrated point cloud data is then assessed for whether it has been labeled.

[0064] The control platform controls the operating speed of relevant material acquisition equipment based on instantaneous material flow data. Point cloud data is large in volume; transmitting unprocessed point cloud data to the control platform can cause network communication instability. Optionally, the instantaneous material flow data of the material transmission area can be transmitted to the control platform, enabling the control platform to control the material acquisition equipment based on this instantaneous material flow data.

[0065] In this embodiment, the microprocessor is a microprocessor that performs instantaneous material flow detection and processing on point cloud data. By processing the point cloud data locally on the microprocessor instead of on the control platform, and directly transmitting the detected instantaneous material flow data to the control platform, the data load on the communication network is reduced, enabling real-time, stable, and reliable network communication. The control platform coordinates the working speed of the material acquisition equipment based on the instantaneous material flow data of the material transmission area. The material acquisition equipment refers to one or more devices that collect and transmit materials on the work surface, achieving safe and efficient production on the work surface.

[0066] Taking the instantaneous coal flow detection of a scraper conveyor as an example, a lidar scanner, using an embedded ARM computer as a microprocessor, collects point cloud data of the scraper conveyor area through a high-precision lidar sensor. The embedded ARM computer calculates the instantaneous coal flow data at a rate of 10Hz, obtaining high-precision instantaneous coal flow data. The detected instantaneous coal flow data is transmitted to the control platform via wired transmission to control the coal mining machine, scraper conveyor, transfer conveyor, and belt conveyor in the structured fully mechanized mining face. By detecting the instantaneous coal flow data on the scraper conveyor near the coal mining machine, rather than detecting the instantaneous coal flow data on the subsequent belt conveyor, the control platform can adjust the coal mining volume of the fully mechanized mining face in real time based on the instantaneous material flow data of the scraper conveyor area, avoiding the problem of control lag.

[0067] S240. When the material transfer status in the material transfer area is no material, update the depth calibration data based on the point cloud data of the material transfer area, and determine the instantaneous material flow data based on the updated depth calibration data and the subsequent point cloud data of the material transfer area.

[0068] It is understandable that the process of determining new depth calibration data based on point cloud data of the material transport area is the same as the calibration process of depth calibration data, and will not be described in detail here.

[0069] The technical solution of this embodiment acquires depth calibration data under no-flow conditions; during material transfer, point cloud data of the material transfer area is collected, and instantaneous material flow data of the material transfer area is determined based on the point cloud data and depth calibration data; if the instantaneous material flow data is greater than or equal to a flow threshold, the material transfer status of the material transfer area is determined to be a material-containing state; if the instantaneous material flow data is less than the flow threshold, the material transfer status of the material transfer area is determined to be a material-free state; when the material transfer status of the material transfer area is a material-free state, the depth calibration data is updated based on the point cloud data of the material transfer area, and the instantaneous material flow data is determined based on the updated depth calibration data and subsequent point cloud data of the material transfer area. The material transfer status is accurately determined based on the flow threshold, improving the accuracy of adaptive correction of depth calibration data and ensuring the stability of instantaneous material flow detection.

[0070] Example 3

[0071] Figure 5 This is a schematic diagram of the structure of a material flow detection device provided in Embodiment 3 of the present invention.

[0072] like Figure 5 As shown, the device includes:

[0073] The depth calibration data acquisition module 310 is used to acquire depth calibration data under no-traffic conditions;

[0074] The instantaneous material flow rate data determination module 320 is used to collect point cloud data of the material transport area during the material transport process, and determine the instantaneous material flow rate data of the material transport area based on the point cloud data and depth calibration data of the material transport area.

[0075] The material transfer status determination module 330 is used to determine the material transfer status of the material transfer area based on instantaneous material flow data;

[0076] The depth calibration data update module 340 is used to update the depth calibration data based on the point cloud data of the material transport area when the material transport status of the material transport area is no material, and to determine the instantaneous material flow data based on the updated depth calibration data and the subsequent point cloud data of the material transport area.

[0077] The technical solution of this embodiment acquires depth calibration data under no-flow conditions; during material transfer, it collects point cloud data of the material transfer area and determines instantaneous material flow data of the material transfer area based on the point cloud data and depth calibration data; it determines the material transfer status of the material transfer area based on the instantaneous material flow data; when the material transfer status of the material transfer area is no-material, it updates the depth calibration data based on the point cloud data of the material transfer area, and determines the instantaneous material flow data based on the updated depth calibration data and subsequent point cloud data of the material transfer area, adaptively correcting the depth calibration data to adapt to the relative position changes of the material flow detection environment in the structured working face. This solves the problem of inaccurate instantaneous material flow detection results when there are relative position changes in the material flow detection environment in the structured working face, and improves the accuracy and stability of material flow detection.

[0078] Based on the above embodiments, optionally, the depth calibration data acquisition module 310 is specifically used for:

[0079] Acquire point cloud data of the material transport area in the absence of material, segment the point cloud data of the material transport area to obtain the bottom point cloud data and the top point cloud data of the material transport area.

[0080] Depth calibration data is determined based on depth data from bottom cloud data and top cloud data.

[0081] Based on the above embodiments, optionally, the instantaneous material flow rate data determination module 320 is specifically used for:

[0082] Clustering is performed on the point cloud data of the material transport area to obtain the material point cloud data and the top point cloud data of the material transport area.

[0083] The width, length, and distance between the material surface and the top of the material within the material transport area are determined based on the material point cloud data and the top point cloud data.

[0084] Material depth data is determined based on the distance data between the material surface and the top and the depth calibration data. Instantaneous material flow rate data of the material transport area is determined based on the material width data, length data and depth data.

[0085] Based on the above embodiments, optionally, after the instantaneous material flow rate data determination module 320, it further includes:

[0086] The material acquisition equipment control module is used to transmit the instantaneous material flow data of the material transmission area to the control platform, so that the control platform can control the material acquisition equipment based on the instantaneous material flow data of the material transmission area.

[0087] Based on the above embodiments, optionally, the material transfer status determination module 330 includes:

[0088] The material status determination unit is used to determine the material transmission status of the material transmission area as having material if the instantaneous material flow data is greater than or equal to the flow threshold.

[0089] The no-material state unit is used to determine the material transport state of the material transport area as no-material state if the instantaneous material flow data is less than the flow threshold.

[0090] Based on the above embodiments, optionally, the no-material state unit is specifically used for:

[0091] The point cloud data of the material transport area is registered with the point cloud data of the no-flow state to determine the registration degree data. If the registration degree data is greater than the registration degree threshold, the material transport state of the material transport area is determined to be the no-material state.

[0092] The material flow detection device provided in the embodiments of the present invention can execute the material flow detection method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0093] Example 4

[0094] Figure 6 This is a schematic diagram of an electronic device implementing the material flow detection method of this invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The connections and relationships shown herein, as well as their functions, are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0095] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0096] The electronic device 10 includes multiple connections to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0097] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as material flow detection methods.

[0098] In some embodiments, the material flow detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the material flow detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the material flow detection method by any other suitable means (e.g., by means of firmware).

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] Computer programs for implementing the material flow detection method of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] Example 5

[0102] Embodiment 5 of the present invention also provides a computer-readable storage medium storing computer instructions for causing a processor to execute a material flow detection method, the method comprising:

[0103] Acquire depth calibration data under no-flow conditions; during material transfer, collect point cloud data of the material transfer area, and determine the instantaneous material flow rate data of the material transfer area based on the point cloud data and depth calibration data of the material transfer area; determine the material transfer status of the material transfer area based on the instantaneous material flow rate data; when the material transfer status of the material transfer area is no-material, update the depth calibration data based on the point cloud data of the material transfer area, and determine the instantaneous material flow rate data based on the updated depth calibration data and subsequent point cloud data of the material transfer area.

[0104] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0106] The systems and technologies described herein can be implemented in computing systems that include a backend (e.g., as a data server), a computing system that includes middleware (e.g., an application server), a computing system that includes a frontend (e.g., a user computer with a graphical user interface or web browser through which a user can interact with the implementations of the systems and technologies described herein), or any combination of such a backend, middleware, or frontend. The systems can be interconnected via digital data communication (e.g., a communication network) of any form or medium. Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0107] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0108] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0109] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for detecting material flow rate, characterized in that, include; Acquire depth calibration data under no-traffic conditions; During the material transfer process, point cloud data of the material transfer area is collected, and instantaneous material flow data of the material transfer area is determined based on the point cloud data of the material transfer area and the depth calibration data. The material transport status of the material transport area is determined based on the instantaneous material flow data; When the material transfer status in the material transfer area is no material, the depth calibration data is updated based on the point cloud data of the material transfer area, and the instantaneous material flow rate data is determined based on the updated depth calibration data and the subsequent point cloud data of the material transfer area. Determining the material transport status of the material transport area based on the instantaneous material flow rate data includes: If the instantaneous material flow rate data is greater than or equal to the flow rate threshold, then the material transport status of the material transport area is determined to be a material-containing status. If the instantaneous material flow rate data is less than the flow rate threshold, then the material transport status of the material transport area is determined to be a no-material state. If the instantaneous material flow rate data is less than the flow rate threshold, the method further includes: The point cloud data of the material transport area is registered with the point cloud data of the no-flow state to determine the registration degree data. If the registration degree data is greater than the registration degree threshold, the material transport state of the material transport area is determined to be a no-material state.

2. The method according to claim 1, characterized in that, After determining the instantaneous material flow rate data of the material transfer area, the method further includes: The instantaneous material flow rate data of the material transfer area is transmitted to the control platform so that the control platform can control the material acquisition equipment based on the instantaneous material flow rate data of the material transfer area.

3. The method according to any one of claims 1-2, characterized in that, The point cloud data is obtained by scanning with a lidar scanner positioned above the material transport area.

4. The method according to claim 1, characterized in that, The method for determining the depth calibration data includes: Acquire the point cloud data of the material transport area in the absence of material, and perform segmentation processing on the point cloud data of the material transport area to obtain the bottom point cloud data and the top point cloud data of the material transport area. Depth calibration data is determined based on the depth data of the bottom point cloud data and the depth data of the top point cloud data.

5. The method according to claim 1, characterized in that, The determination of instantaneous material flow rate data of the material transport area based on point cloud data of the material transport area and depth calibration data includes: Clustering processing is performed on the point cloud data of the material transport area to obtain material point cloud data and top point cloud data of the material transport area. Based on the material point cloud data and the top point cloud data, the width data, length data, and distance data between the material surface and the top of the material within the material transport area are determined. Material depth data is determined based on the distance data between the material surface and the top and the depth calibration data. Instantaneous material flow rate data of the material transport area is determined based on the width data, length data and depth data of the material.

6. A material flow rate detection device, characterized in that, include: The depth calibration data acquisition module is used to acquire depth calibration data under no-traffic conditions. The instantaneous material flow rate data determination module is used to collect point cloud data of the material transport area during the material transport process, and determine the instantaneous material flow rate data of the material transport area based on the point cloud data of the material transport area and the depth calibration data. The material transport status determination module is used to determine the material transport status of the material transport area based on the instantaneous material flow data; The depth calibration data update module is used to update the depth calibration data based on the point cloud data of the material transport area when the material transport status of the material transport area is no material, and to determine the instantaneous material flow data based on the updated depth calibration data and the subsequent point cloud data of the material transport area. The material transfer status determination module specifically includes: The material status determination unit is used to determine the material transmission status of the material transmission area as having material if the instantaneous material flow data is greater than or equal to the flow threshold. The no-material state unit is used to determine the material transfer state of the material transfer area as no-material state if the instantaneous material flow data is less than the flow threshold. The no-material state unit is specifically used for: The point cloud data of the material transport area is registered with the point cloud data of the no-flow state to determine the registration degree data. If the registration degree data is greater than the registration degree threshold, the material transport state of the material transport area is determined to be the no-material state.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the material flow detection method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the material flow detection method according to any one of claims 1-5.

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