Stockyard data management method and system based on clustering point cloud algorithm

Abstract

The application discloses a stockyard data management method and system based on a clustering point cloud algorithm, and belongs to the technical field of stockyard data management. The method comprises the following steps: obtaining point cloud data of a stockyard within a set time through a scanner arranged in the stockyard; inputting the point cloud data into a preset clustering point cloud algorithm to construct a stockyard image model; obtaining running time sequence data of a stacker-reclaimer in a working state within the set time according to the obtained stacker-reclaimer in the working state and the stockyard image model; obtaining detection time sequence data within the set time through a sensor arranged on the stacker-reclaimer; generating a loss value of material output of each stacker-reclaimer in the working state according to the running time sequence data and the detection time sequence data; and applying a set operation strategy to each stacker-reclaimer in the working state according to the loss value.

Description

Technical Field

[0001] The embodiments disclosed in this invention relate to the technical field of material yard data management, and more specifically, to a material yard data management method and system based on a clustering point cloud algorithm. Background Technology

[0002] With the intelligent development of manufacturing equipment, the efficiency of material collection, transportation and processing has been effectively improved.

[0003] Currently, stacker-reclaimers are commonly used to collect material data in stockyards. Sensors can be installed on the conveyor belts of these stacker-reclaimers, and the sensors output the detected data to a server. The server can then display the material output of each stacker-reclaimer on a screen for monitoring personnel. However, relying solely on monitoring personnel to adjust the stacker-reclaimers based on changes in sensor data results in low accuracy and low operating efficiency. Summary of the Invention

[0004] One objective of the embodiments disclosed in this invention is to provide a new technical solution for a material yard data management method and system based on a clustering point cloud algorithm.

[0005] According to a first aspect of the present invention, a material yard data management method based on a clustering point cloud algorithm is provided. The method includes: obtaining point cloud data of the material yard within a set time period using a scanner deployed at the material yard; inputting the point cloud data into a preset clustering point cloud algorithm to construct a material yard image model; obtaining operational sequence data of the stacker-reclaimer in operation within the set time period based on the obtained data of the stacker-reclaimer in operation and the material yard image model; wherein the operational sequence data reflects the change in the expected output of the stacker-reclaimer for materials over time; obtaining detection time-series data within the set time period using sensors configured on the stacker-reclaimer; wherein the detection time-series data reflects the change in the actual output of materials detected by the sensors over time; generating a loss value for material output for each of the stacker-reclaimers in operation based on the operational sequence data and the detection time-series data; and applying a set operating strategy to each of the stacker-reclaimers in operation based on the loss value.

[0006] Optionally, generating the loss value for material output of each stacker-reclaimer in operation based on the runtime timing data and detection timing data includes: acquiring feature information of each stacker-reclaimer in operation based on the material yard image model; extracting the conveyor belt speed and conveying distance matching the feature information from a preset equipment information database; wherein the conveying distance is the distance between the bucket wheel of different stacker-reclaimers and the corresponding sensor; for each stacker-reclaimer in operation, obtaining the time deviation value between the runtime timing data and the detection timing data based on the conveying distance and the conveyor belt speed; aligning the runtime timing data and the detection timing data based on the time deviation value to generate a set of differences for material output of each stacker-reclaimer in operation; determining the average value of the set of differences and using it as the loss value.

[0007] Optionally, the feature information includes at least one of the following: the conveyor belt length of the stacker-reclaimer, the height of the stacker-reclaimer, the number of buckets on the stacker-reclaimer's bucket wheel, and the ratio of the distance from the bucket wheel of the stacker-reclaimer to the height of the stacker-reclaimer to the height of the stacker-reclaimer.

[0008] Optionally, before aligning the runtime timing data with the detection timing data based on the time deviation value to generate a set of differences in material output for each of the stacker-reclaimers in operation, the method includes: obtaining the start time of operation of the stacker-reclaimers in operation; determining the first stacker-reclaimer whose start time is within a set time from the stacker-reclaimers; the step of aligning the runtime timing data with the detection timing data based on the time deviation value to generate a set of differences in material output for each of the stacker-reclaimers in operation includes: for the first stacker-reclaimer, obtaining the inflection point of the material output curve that changes over time corresponding to the detection timing data; extracting the detection timing data located after the inflection point and using it as the first detection timing data; aligning the runtime timing data with the first detection timing data based on the time deviation value to generate a set of differences in material output for the first stacker-reclaimer.

[0009] Optionally, obtaining the operational sequence data of the stacker-reclaimer in operation within the set time period based on the obtained stacker-reclaimer in operation and the material yard image model includes: identifying the bucket wheel of the stacker-reclaimer in operation and the raw material pile adjacent to the bucket wheel in the material yard image model through a preset recognition model; for each stacker-reclaimer in operation, obtaining the operational sequence data within the set time period based on the working status of the corresponding bucket wheel in the material yard image model and the relative position between the corresponding bucket wheel and the adjacent raw material pile; wherein the working status reflects the rotation direction and rotation amplitude of the bucket wheel within the set time period.

[0010] Optionally, the set operation strategy includes at least one of the following: a first operation strategy for a first loss range, a second operation strategy for a second loss range, and a third operation strategy for a third loss range; wherein, the first operation strategy is to adjust the position of the bucket wheel of the stacker-reclaimer based on a set offset angle; the second operation strategy is to maintain the current working state of the stacker-reclaimer; and the third operation strategy is to stop the stacker-reclaimer from working.

[0011] Optionally, after generating the material output loss value for each stacker-reclaimer in operation based on the runtime timing data and the detection timing data, the method further includes: obtaining observation timing data of the raw material pile corresponding to the stacker-reclaimer in operation through the material yard image model, wherein the observation timing data reflects the degree of change in the volume of the raw material pile within a set time period; obtaining the consumption value of the raw material pile corresponding to all stacker-reclaimers in operation based on the observation timing data and the detection timing data; obtaining the current dust emission amount based on the weighting coefficients set for the consumption value and the loss value respectively; and outputting a drive command to the dust removal equipment configured in the material yard when the sum of the pre-stored historical dust emission amount and the current dust emission amount exceeds a preset threshold, so that the dust removal equipment performs dust removal treatment on the material yard.

[0012] According to a second aspect of the present invention, a material yard data management system based on a clustering point cloud algorithm is also provided. The system includes a server, a scanner, sensors, and a stacker-reclaimer. The server includes: a point cloud data acquisition module, used to acquire point cloud data of the material yard within a set time period using a scanner deployed in the material yard; an image model construction module, used to input the point cloud data into a preset clustering point cloud algorithm to construct a material yard image model; and a first data acquisition module, used to obtain the operating sequence data of the stacker-reclaimer within the set time period based on the obtained stacker-reclaimer in operation and the material yard image model. The runtime timing data reflects the change in the expected output of the stacker-reclaimer for materials over time; the second data acquisition module is used to obtain detection timing data within the set time period through sensors configured on the stacker-reclaimer; wherein, the detection timing data reflects the change in the actual output of materials detected by the sensors over time; the loss value generation module is used to generate a loss value for the material output of each stacker-reclaimer in the working state based on the runtime timing data and the detection timing data; the operation strategy application module is used to apply a set operation strategy to each stacker-reclaimer in the working state based on the loss value.

[0013] According to a third aspect of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the computer program to implement the method described in the first aspect of the present invention.

[0014] According to a fourth aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, the computer program implementing the method described in the first aspect of the present invention when executed by a processor.

[0015] One beneficial effect of the embodiments disclosed in this invention is that scanning the material yard with a scanner can obtain point cloud data of the material yard, and then a material yard image model can be obtained through a clustering point cloud algorithm. The material yard image model can reflect the runtime sequence data of the stacker-reclaimer in operation. Combined with the detection time sequence data obtained by the sensors configured on the stacker-reclaimer, a loss value of the stacker-reclaimer in operation can be generated, and different operating strategies can be applied to the stacker-reclaimer based on the loss value. By using the loss value obtained by combining the material yard image model and sensors to adjust the stacker-reclaimer, the accuracy of stacker-reclaimer control is effectively improved, thereby improving the working efficiency of the stacker-reclaimer.

[0016] Other features and advantages of the disclosed embodiments of the present invention will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the invention disclosed herein and, together with their description, serve to explain the principles of these embodiments.

[0018] Figure 1 This is a schematic diagram of the composition structure of a material yard data management system based on a clustering point cloud algorithm, which can apply a material yard data management method based on a clustering point cloud algorithm according to an embodiment.

[0019] Figure 2 This is a flowchart illustrating a material yard data management method based on a clustering point cloud algorithm according to another embodiment;

[0020] Figure 3 This is a schematic diagram illustrating the structure of a stacker-reclaimer according to another embodiment;

[0021] Figure 4 This is a block diagram of a server according to another embodiment;

[0022] Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to another embodiment. Detailed Implementation

[0023] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention.

[0024] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use.

[0025] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0026] In all the examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0027] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0028] <System Implementation>

[0029] Figure 1This is a schematic diagram of the structural composition of a material yard data management system based on a clustering point cloud algorithm, which can apply a material yard data management method based on a clustering point cloud algorithm according to one embodiment. For example... Figure 1 As shown, the system includes a server 100, a scanner, sensors, and a stacker-reclaimer 200. The system can be applied to scenarios involving material yard data management.

[0030] Server 100 can communicate with the scanner, sensor, and stacker-reclaimer 200 respectively. Users can control the scanner through a terminal that communicates with server 100. The terminal can be a mobile phone, tablet, or computer, and there is no specific limitation here.

[0031] The scanner can be an unmanned aerial vehicle (UAV) laser scanner, which allows the scanner to scan the entire material yard and feed the scanned point cloud data back to the server.

[0032] The stacker-reclaimer 200 can receive work tasks sent by the server 100. After receiving the work task, the stacker-reclaimer 200 can perform tasks such as digging on the raw material pile.

[0033] The sensor can be configured on the conveyor belt of the stacker-reclaimer 200 and used to scan the material being transported by the conveyor belt and send the data back to the server 100.

[0034] In the embodiments disclosed in this invention, the memory of server 100 is used to store a computer program that controls the processor of server 100 to operate in order to implement a material yard data management method based on a clustering point cloud algorithm according to any embodiment. Those skilled in the art can design the computer program according to the scheme of the disclosed embodiments of this invention. How the computer program controls the processor to operate is well known in the art and will not be described in detail here.

[0035] <Method Implementation>

[0036] Figure 2 This is a flowchart illustrating a material yard data management method based on a clustering point cloud algorithm according to one embodiment. The implementing entity is, for example, the server 100 described above.

[0037] Among them, such as Figure 3 As shown, the stacker-reclaimer 200 may include a sensor 250, a bucket wheel 220, a bucket 230, and a conveyor belt 240. The rotation of the bucket wheel 220 of the stacker-reclaimer 200 can transport the material in the bucket 230 onto the conveyor belt 240, and the conveyor belt 240 can transport the material toward the sensor 250.

[0038] like Figure 2 As shown, the material yard data management method based on clustering point cloud algorithm in this embodiment may include the following steps S210 to S260:

[0039] Step S210: Obtain point cloud data of the material yard within a set time period by using a scanner deployed in the material yard.

[0040] Specifically, a scanner can be deployed on the material yard, which can be a drone-borne laser scanner. The scanner can scan the material yard within a set time period and output point cloud data of the material yard for that time period to the server, allowing the server to obtain the point cloud data of the entire material yard from the scanner output within the set time period. The set time period can be 30 seconds, 5 minutes, or 10 minutes, and is not specifically limited here.

[0041] Step S220: Input the point cloud data into the preset clustering point cloud algorithm to construct the material yard image model.

[0042] The server is pre-configured with a point cloud clustering algorithm, which is used to process point cloud data, such as denoising, to construct an image model. This point cloud clustering algorithm is existing technology and will not be described in detail here.

[0043] Specifically, the server can input point cloud data into a pre-defined clustering point cloud algorithm to construct a material yard image model.

[0044] Step S230: Based on the obtained image model of the stacker-reclaimer in working state and the material yard, obtain the running sequence data of the stacker-reclaimer in working state within a set time period; wherein, the running sequence data is the data reflecting the change of the expected output of the stacker-reclaimer for the material over time.

[0045] Users can deploy tasks for the stacker-reclaimers in the stockyard through terminals connected to the server. These tasks can include moving to a specific location within the stockyard, excavating a stockpile of raw materials within a given time period, and so on. Upon receiving a task, the stacker-reclaimer in the stockyard can either execute the task proactively or be manually operated.

[0046] Among them, the stacker-reclaimer can be the stacker-reclaimer mentioned above, and the rotation of the bucket wheel of the stacker-reclaimer can drive the bucket on the bucket wheel to scoop out the material.

[0047] Specifically, the service area can determine the stacker-reclaimers in operation in the material yard based on the user-inputted tasks for one or more stacker-reclaimers, or by identifying stacker-reclaimers in the material yard image model whose bucket wheels change position within a set time period. Once the server identifies the stacker-reclaimers in operation within the material yard image model, it can obtain the contact points between the buckets of each operating stacker-reclaimer and the material pile within the set time period. Based on preset bucket capacities and contact points, the server obtains the expected output of multiple buckets for the material within the set time period and can generate runtime sequence data showing the expected output over time. For example, if the runtime sequence data is set to 5 seconds, with 1 second where the buckets are not in contact with the material pile (expected output 0), and 2-5 seconds where the buckets are in contact with the material pile (expected output 1m³ of bucket capacity), then the expected output is 1m³. 3 .

[0048] In one embodiment, step S230 may specifically include the following: using a preset recognition model, identifying the bucket wheel of a stacker-reclaimer in operation and the adjacent raw material piles in the material yard image model; for each stacker-reclaimer in operation, obtaining the running sequence data within a set time period based on the working status of the corresponding bucket wheel in the material yard image model and the relative position between the corresponding bucket wheel and the adjacent raw material piles; wherein, the working status reflects the rotation direction and rotation amplitude of the bucket wheel within the set time period.

[0049] The server has a pre-set recognition model, which can be trained using deep learning to identify the bucket wheel on the stacker-reclaimer and the adjacent raw material pile.

[0050] Specifically, the server can identify the bucket wheels of a stacker-reclaimer in operation within the material yard image model, as well as the adjacent raw material piles. The server can set the bucket wheel's rotation direction as the output material direction. Based on the rotation direction and amplitude of each bucket wheel of a stacker-reclaimer in operation within a set time period, and the relative position of the corresponding bucket wheel to the adjacent raw material pile, the server can obtain runtime sequence data for that time period. For example: if the server determines that a bucket wheel of a stacker-reclaimer in operation rotates in the opposite direction to the first rotation direction for a certain time period, then the expected output for that time period is 0; if the server determines that a bucket wheel of a stacker-reclaimer in operation rotates in the first rotation direction for a certain time period and the relative position of the corresponding bucket wheel to the adjacent raw material pile exceeds a preset distance, then the expected output for that time period is 0. The preset distance can be 0m. In other words, since the bucket wheel can rotate in two different directions, more accurate runtime sequence data can be obtained based on the operating conditions and relative positions, thereby improving the accuracy of the runtime sequence data.

[0051] Step S240: The detection time sequence data within a set time period is obtained by the sensor configured on the stacker-reclaimer; wherein, the detection time sequence data is the data reflecting the change of the actual output amount of the material detected by the sensor over time.

[0052] Each stacker-reclaimer is equipped with corresponding sensors, such as Figure 3 As shown, the sensor can be set to detect the volume of material passing through area A within 1 second, meaning the sensor outputs the corresponding actual output amount every 1 second. When a stacker-reclaimer is performing a task, the corresponding sensor activates and senses the actual output amount of material transmitted by the sensor. The server can obtain the corresponding detection timing data based on the actual output amount received from the sensor and the corresponding time point.

[0053] Specifically, the server obtains the actual output of materials within a set time period, i.e., the detection time series data, by using sensors configured on the stacker-reclaimer that is in operation.

[0054] Step S250: Based on the runtime timing data and the detection timing data, generate the material output loss value for each stacker-reclaimer in operation.

[0055] Specifically, based on the first curve corresponding to the runtime sequence data of each stacker-reclaimer in operation and the second curve corresponding to the detection sequence data, the server obtains the expected output for the first curve and the actual output for the second curve at the same time point, thus obtaining the material output loss value of each stacker-reclaimer in operation.

[0056] In one embodiment, step S250 specifically includes the following: acquiring feature information of each stacker-reclaimer in operation based on the material yard image model; extracting the conveyor belt speed and conveying distance matching the feature information from a preset equipment information database; wherein, the conveying distance is the distance between the bucket wheel of different stacker-reclaimers and the corresponding sensor; for each stacker-reclaimer in operation, obtaining the time deviation value between the runtime timing data and the detection timing data based on the conveying distance and the conveyor belt speed; aligning the runtime timing data and the detection timing data based on the time deviation value to generate a set of differences for material output for each stacker-reclaimer in operation; determining the average value of the set of differences and using it as the loss value.

[0057] The server has a pre-set equipment information database, which can store information about different models of stacker-reclaimers, such as the stacker-reclaimer's volume, height, conveyor belt speed, and conveying distance.

[0058] Specifically, the server can acquire feature information for each stacker-reclaimer in operation based on the stockyard image model. This feature information can include the stacker-reclaimer's volume, height, and other details. The server can select information on a specific stacker-reclaimer model from the equipment information database that matches the feature information, and extract the conveyor belt speed and conveying distance from this information. Based on the conveying distance and conveyor belt speed of each operating stacker-reclaimer, the server can obtain the conveying time, i.e., the time deviation between the runtime timing data and the detection timing data. The server can subtract the corresponding time deviation value from the time corresponding to each time node in the runtime timing data to align the runtime timing data with the detection timing data. Subsequently, the server generates a set of differences in material output for each operating stacker-reclaimer based on the difference between the expected output and the actual output at the same time node. The average value of this set of differences is determined and used as the loss value. In other words, aligning the runtime timing data and the detection timing data can effectively improve the accuracy of the obtained loss value.

[0059] In one embodiment, the feature information includes at least one of the following: the conveyor belt length of the stacker-reclaimer, the height of the stacker-reclaimer, the number of buckets on the stacker-reclaimer's bucket wheels, and the ratio of the distance from the bucket wheels of the stacker-reclaimer to the height of the stacker-reclaimer to the height of the stacker-reclaimer.

[0060] Specifically, the server can obtain the conveyor belt length, height, number of buckets on the stacker-reclaimer's bucket wheels, and the ratio of the distance from the bucket wheels to the height of the stacker-reclaimer to the height of the stacker-reclaimer from the stacker-reclaimer image model. This allows the server to determine the conveyor belt speed and conveying distance of a specific stacker-reclaimer consistent with the pre-set equipment information database. In other words, by using different feature information, the accuracy of the extracted conveyor belt speed and conveying distance can be effectively improved.

[0061] In one embodiment, before generating the difference set, the process further includes: obtaining the start time of operation of the stacker-reclaimers in operation, and identifying the first stacker-reclaimer whose start time falls within a set time interval. Accordingly, the process of generating the difference set may specifically include: for the first stacker-reclaimer, obtaining the inflection point of the material output curve corresponding to the detection time-series data over time; extracting the detection time-series data after the inflection point and using it as the first detection time-series data; aligning the runtime time-series data with the first detection time-series data based on the time deviation value to generate the difference set for the material output of the first stacker-reclaimer.

[0062] Specifically, the server can determine the start time of the stacker-reclaimer in operation through the aforementioned tasks, and identify the first stacker-reclaimer whose start time falls within a set time frame from among multiple stacker-reclaimers. The server obtains the inflection point of the material output curve over time corresponding to the detection timing data of the first stacker-reclaimer. The server then extracts the detection timing data after the inflection point and uses it as the first detection timing data. The server aligns the runtime timing data with the first detection timing data based on the aforementioned time deviation value, generating a set of differences in material output for the first stacker-reclaimer. In other words, by eliminating the detection timing data before the inflection point of the first stacker-reclaimer, the accuracy of the difference set is effectively improved, thereby improving the accuracy of the loss value.

[0063] Step S260: For each stacker-reclaimer in operation, apply the set operation strategy based on the loss value.

[0064] The server can pre-set different operating strategies, allowing the stacker-reclaimer to work according to different strategies, thereby improving the stacker-reclaimer's working efficiency.

[0065] Specifically, the server can apply different operating strategies to different loss values, so that the server can apply different operating strategies to each stacker-reclaimer in the working state after obtaining the loss value for each stacker-reclaimer in the working state.

[0066] In one embodiment, the set operating strategy includes at least one of the following: a first operating strategy for a first loss range, a second operating strategy for a second loss range, and a third operating strategy for a third loss range; wherein, the first operating strategy is to adjust the position of the bucket wheel of the stacker-reclaimer based on a set offset angle; the second operating strategy is to maintain the current operating state of the stacker-reclaimer; and the third operating strategy is to stop the stacker-reclaimer from operating.

[0067] The server is pre-configured with different operation strategies, specifically a first operation strategy, a second operation strategy, and a third operation strategy. Different operation strategies correspond to different loss ranges; specifically, the first operation strategy corresponds to a first loss range, the first operation strategy corresponds to a first loss range, and the first operation strategy corresponds to a first loss range.

[0068] Specifically, the first loss range can be [0.05, 0.1], the second loss range can be (0, 0.05), and the third loss range can be (0.1, 1). If the loss value is within [0.05, 0.1], the server can output a first instruction to the corresponding stacker-reclaimer to adjust the position of the bucket wheel of the stacker-reclaimer based on a set offset angle, which can be 1°. If the loss value is within (0, 0.05), the server may not output an instruction to the corresponding stacker-reclaimer. If the loss value is within (0.1, 1), the server can output a second instruction to the corresponding stacker-reclaimer to stop the stacker-reclaimer from working. In other words, by setting different operating strategies, the working efficiency of the stacker-reclaimer can be effectively improved.

[0069] In one embodiment, after generating the loss value, the process may further include the following: obtaining observation time-series data of the raw material piles corresponding to the stacker-reclaimers in operation through a material yard image model, wherein the observation time-series data reflects the degree of change in the volume of the raw material piles within a set time period; obtaining the consumption value of the raw material piles corresponding to all stacker-reclaimers in operation based on the observation time-series data and the detection time-series data; obtaining the current dust emission amount based on the weighting coefficients set for the consumption value and the loss value respectively; and outputting a drive command to the dust removal equipment configured in the material yard when the sum of the pre-stored historical dust emission amount and the current dust emission amount exceeds a preset threshold, so that the dust removal equipment performs dust removal treatment on the material yard.

[0070] Specifically, the server, through a material yard image model, can acquire observation time-series data of raw material piles where the bucket wheel distance of a working stacker-reclaimer is 0, i.e., acquire observation time-series data of the raw material piles corresponding to working stacker-reclaimers. The server can obtain the consumption value of the raw material piles corresponding to all working stacker-reclaimers based on the difference between the observation time-series data and the detection time-series data at the same time node. The server can pre-set a weighting coefficient 'a' for the consumption value and a weighting coefficient 'b' for the loss value, where 'a' can be 0.9 and 'b' can be 0.1. Based on the weighting coefficients set for the consumption and loss values, the server obtains the current dust emission amount, i.e., current dust emission amount = 0.9 × consumption value + 0.1 × loss value. The server can also pre-store historical dust emission amounts. When the sum of the pre-stored historical dust emission amount and the current dust emission amount exceeds a preset threshold, the server outputs a drive command to the dust removal equipment configured in the material yard, causing the dust removal equipment to perform dust removal treatment on the material yard. The dust removal equipment is existing technology and will not be described in detail. The preset threshold can be 2m. 3 It can be 5m 3 It can also be 10m 3 No specific limitations are made here. In other words, by determining the current dust level through loss and consumption values, the accuracy of the dust level generated by the stacker-reclaimer during operation can be improved, thereby improving the air quality of the material yard.

[0071] <Equipment Example 1>

[0072] Figure 4 This is a block diagram of a server based on one embodiment. For example... Figure 4 As shown, the server 400 may include: a point cloud data acquisition module 410, used to acquire point cloud data of the material yard within a set time period through a scanner deployed in the material yard; an image model construction module 420, used to input the point cloud data into a preset clustering point cloud algorithm to construct a material yard image model; and a first data acquisition module 430, used to obtain the operating sequence data of the stacker-reclaimer in operation within a set time period based on the obtained image model of the stacker-reclaimer in operation; wherein, the operating sequence data reflects the expected output of the stacker-reclaimer for the material as the demand changes. The data includes: time-varying data; a second data acquisition module 440, used to obtain detection timing data within a set time period through sensors configured on the stacker-reclaimer; wherein, the detection timing data reflects the actual output of the material detected by the sensors changing over time; a loss value generation module 450, used to generate a loss value for the material output of each stacker-reclaimer in operation based on the operation timing data and the detection timing data; and an operation strategy application module 460, used to apply a set operation strategy to each stacker-reclaimer in operation based on the loss value.

[0073] Optionally, the loss value generation module 450 is further configured to: acquire feature information of each stacker-reclaimer in operation based on the material yard image model; extract the conveyor belt speed and conveying distance matching the feature information from a preset equipment information database; wherein, the conveying distance is the distance between the bucket wheel of different stacker-reclaimers and the corresponding sensor; for each stacker-reclaimer in operation, obtain the time deviation value between the runtime timing data and the detection timing data based on the conveying distance and the conveyor belt speed; align the runtime timing data and the detection timing data based on the time deviation value to generate a set of differences for material output for each stacker-reclaimer in operation; determine the average value of the set of differences and use it as the loss value.

[0074] Optionally, the loss value generation module 450 is further configured to: obtain the start time of operation of the stacker-reclaimer in operation; determine the first stacker-reclaimer whose start time is within a set time; for the first stacker-reclaimer, obtain the inflection point of the material output curve corresponding to the detection timing data over time; extract the detection timing data after the inflection point and use it as the first detection timing data; and align the running timing data with the first detection timing data based on the time deviation value to generate a set of differences in material output for the first stacker-reclaimer.

[0075] Optionally, the first data acquisition module 430 is further configured to identify the bucket wheel of the stacker-reclaimer in operation and the adjacent raw material pile in the material yard image model through a preset recognition model; for each stacker-reclaimer in operation, the running sequence data within a set time period is obtained based on the working status of the corresponding bucket wheel in the material yard image model and the relative position between the corresponding bucket wheel and the adjacent raw material pile; wherein, the working status reflects the rotation direction and rotation amplitude of the bucket wheel within the set time period.

[0076] Optionally, the data acquisition module is used to acquire observation time-series data of the raw material piles corresponding to the stacker-reclaimers in operation through the material yard image model. The observation time-series data reflects the degree of change in the volume of the raw material piles within a set time period. The consumption value acquisition module is used to obtain the consumption value of the raw material piles corresponding to all stacker-reclaimers in operation based on the observation time-series data and the detection time-series data. The dust emission acquisition module is used to obtain the current dust emission based on the weighting coefficients set for the consumption value and the loss value respectively. The drive command output module is used to output drive commands to the dust removal equipment configured in the material yard when the sum of the pre-stored historical dust emission and the current dust emission exceeds a preset threshold, so that the dust removal equipment performs dust removal treatment on the material yard.

[0077] The server 400 can be the same as the server 100 mentioned above.

[0078] <Equipment Example 2>

[0079] Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to another embodiment.

[0080] like Figure 5 As shown, the electronic device 500 includes a processor 510 and a memory 520, the memory 520 being used to store an executable computer program, and the processor 510 being used to execute methods as described in any of the above method embodiments under the control of the computer program.

[0081] The electronic device 500 can be the aforementioned server 100.

[0082] Each module of the server 400 described above can be implemented by the processor 510 in this embodiment executing the computer program stored in the memory 520, or it can be implemented by other structures, which are not limited here.

[0083] This invention can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of the invention.

[0084] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0085] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0086] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0087] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0088] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0089] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0090] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. It will be known to those skilled in the art that implementation in hardware, implementation in software, and implementation using a combination of software and hardware are equivalent.

[0091] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.

Claims

1. A material yard data management method based on clustering point cloud algorithm, characterized in that, The method includes: Point cloud data of the material yard is obtained within a set time period by a scanner deployed in the material yard; The point cloud data is input into a preset clustering point cloud algorithm to construct a material yard image model; Based on the obtained stacker-reclaimer in operation and the material yard image model, the operation sequence data of the stacker-reclaimer in operation within the set time period is obtained; wherein, the operation sequence data is data reflecting the change of the expected output of the stacker-reclaimer for the material over time; By using sensors configured on the stacker-reclaimer, detection time-series data is obtained within the set time period; wherein, the detection time-series data is data reflecting the change of the actual output amount of material detected by the sensors over time; Based on the runtime timing data and detection timing data, generate the material output loss value for each of the stacker-reclaimers in the working state; For each of the stacker-reclaimers in operation, a set operating strategy is applied based on the loss value; The step of generating the material output loss value for each of the stacker-reclaimers in operation based on the runtime timing data and detection timing data includes: Based on the material yard image model, obtain the feature information of each stacker-reclaimer in operation; Extract the conveyor belt speed and conveying distance that match the feature information from the preset equipment information database; wherein, the conveying distance is the distance between the bucket wheel of different stacker-reclaimers and the corresponding sensor; For each of the stacker-reclaimers in operation, the time deviation between the runtime timing data and the detection timing data is obtained based on the conveying distance and the conveyor belt speed. Based on the time deviation value, the runtime timing data is aligned with the detection timing data to generate a set of differences in material output for each stacker-reclaimer in operation. Determine the average value of the set of differences and use it as the loss value.

2. The method according to claim 1, characterized in that, The feature information includes at least one of the following: the conveyor belt length of the stacker-reclaimer, the height of the stacker-reclaimer, the number of buckets on the stacker-reclaimer's bucket wheel, and the ratio of the distance from the bucket wheel of the stacker-reclaimer to the height of the stacker-reclaimer to the height of the stacker-reclaimer.

3. The method according to claim 1, characterized in that, Before aligning the runtime timing data with the detection timing data based on the time deviation value to generate a set of differences for material output for each of the stacker-reclaimers in operation, the process includes: Obtain the start time of operation of the stacker-reclaimer when it is in operation; The first stacker-reclaimer whose start time is within the set time is determined from the stacker-reclaimers; The step of aligning the runtime timing data with the detection timing data based on the time deviation value to generate a set of differences in material output for each stacker-reclaimer in operation includes: For the first stacker reclaimer, obtain the inflection point of the material output curve that changes over time corresponding to the detection timing data; Extract the detection time series data located after the inflection point and use it as the first detection time series data; Based on the time deviation value, the runtime timing data is aligned with the first detection timing data to generate a set of differences in material output for the first stacker-reclaimer.

4. The method according to claim 1, characterized in that, The step of obtaining the operational sequence data of the stacker-reclaimer within the set time period based on the obtained image model of the stacker-reclaimer in operation includes: The bucket wheel of the stacker-reclaimer in operation and the adjacent raw material piles in the material yard image model are identified using a preset recognition model. For each stacker-reclaimer in operation, the running sequence data within the set time period is obtained based on the working status of the corresponding bucket wheel in the material yard image model and the relative position of the corresponding bucket wheel with the adjacent raw material pile; wherein, the working status reflects the rotation direction and rotation amplitude of the bucket wheel within the set time period.

5. The method according to claim 1, characterized in that, The set operation strategy includes at least one of the following: a first operation strategy for a first loss range, a second operation strategy for a second loss range, and a third operation strategy for a third loss range; The first operating strategy involves adjusting the position of the bucket wheel of the stacker-reclaimer based on a set offset angle. The second operating strategy is to maintain the current operating status of the stacker-reclaimer; The third operating strategy is to stop the stacker-reclaimer from operating.

6. The method according to claim 4, characterized in that, After generating the material output loss value for each of the stacker-reclaimers in the working state based on the runtime timing data and the detection timing data, the method further includes: The observation time series data of the raw material pile corresponding to the stacker-reclaimer in operation is obtained through the material yard image model. The observation time series data reflects the degree of change in the volume of the raw material pile within a set time period. Based on the observation time series data and the detection time series data, the consumption value of the raw material pile corresponding to all stacker-reclaimers in operation is obtained; The current dust emission level is obtained based on the weighting coefficients set for the consumption value and the loss value, respectively. If the sum of the pre-stored historical dust volume and the current dust volume exceeds a preset threshold, a drive command is output to the dust removal equipment configured in the material yard, so that the dust removal equipment performs dust removal treatment on the material yard.

7. A material yard data management system based on clustering point cloud algorithm, characterized in that, The system includes a server, a scanner, sensors, and a stacker-reclaimer, wherein the server includes: The point cloud data acquisition module is used to acquire point cloud data of the material yard within a set time period by using a scanner deployed in the material yard. The image model construction module is used to input the point cloud data into a preset clustering point cloud algorithm to construct a material yard image model; The first data acquisition module is used to obtain the operation sequence data of the stacker-reclaimer in operation within the set time period based on the obtained stacker-reclaimer in operation and the material yard image model; wherein, the operation sequence data is data reflecting the change of the expected output of the stacker-reclaimer for the material over time. The second data acquisition module is used to obtain detection time-series data within the set time period through sensors configured on the stacker-reclaimer; wherein, the detection time-series data is data reflecting the change of the actual output amount of the material detected by the sensor over time; The loss value generation module is used to generate a loss value for material output for each of the stacker-reclaimers in the working state based on the runtime sequence data and the detection sequence data. The calculation of the loss value includes: obtaining the feature information of each of the stacker-reclaimers in the working state based on the material yard image model. Extract the conveyor belt speed and conveying distance that match the feature information from the preset equipment information database; wherein, the conveying distance is the distance between the bucket wheel of different stacker-reclaimers and the corresponding sensor; For each of the stacker-reclaimers in operation, the time deviation between the runtime timing data and the detection timing data is obtained based on the conveying distance and the conveyor belt speed. Based on the time deviation value, the runtime timing data is aligned with the detection timing data to generate a set of differences in material output for each stacker-reclaimer in operation. Determine the average value of the set of differences and use it as the loss value; The operation strategy application module is used to apply a set operation strategy to each of the stacker-reclaimers in operation, based on the loss value.

8. An electronic device, characterized in that, It includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the material yard data management method based on clustering point cloud algorithm as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The readable storage medium stores a program or instructions, which, when executed by a processor, implement the steps of the material yard data management method based on clustering point cloud algorithm as described in any one of claims 1-6.

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