Flotation froth state detection method and device
By employing a dual detection method that combines distance and image analysis, the flow state of flotation foam can be accurately assessed, solving the problem of misjudgment of flowability in existing technologies and enabling more precise adjustment of the aeration volume.
Patent Information
- Application Number
- CN202511246497.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-02
- Publication Date
- 2025-11-21
AI Technical Summary
Existing flotation foam state detection methods have the problem of misjudging fluidity, which leads to inaccurate adjustment of aeration volume.
A dual detection method is adopted. First, the vertical distance of the foam layer is measured by a non-contact distance sensor, and the flow state is evaluated based on the distance change. If the flow state is abnormal, the foam motion is analyzed by a camera and optical flow algorithm, and the foam flowability is further judged by combining optical flow field and target tracking algorithm.
It improves the accuracy and reliability of flotation foam state detection, avoids misjudgment caused by a single evaluation dimension, and ensures the precision of aeration volume adjustment.
Smart Images
Figure CN120992412A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of flotation process detection technology, and more specifically, relates to a method and apparatus for detecting the state of flotation foam. Background Technology
[0002] Flotation technology is an important process for separating metals, non-metals, and fine-grained coal minerals. During flotation, in addition to adding flotation reagents, it is also necessary to match an appropriate aeration rate. Currently, the main method for detecting whether the flotation aeration rate is appropriate is to use automated detection technology to monitor the fluidity and state of the flotation froth layer. For example, the fluidity of the flotation froth can be characterized by detecting its flow velocity, thereby determining whether the aeration rate needs to be adjusted.
[0003] Most existing methods for detecting the state of flotation foam determine that the current flotation foam has insufficient fluidity when the characterization value is abnormal. However, since fluidity itself is determined by other characteristics, misjudgments of fluidity may occur.
[0004] Therefore, an accurate and reliable method for detecting the state of flotation foam is needed. Summary of the Invention
[0005] The purpose of this application is to provide a method and apparatus for detecting the state of flotation foam, so as to improve the accuracy and reliability of flotation foam state detection.
[0006] A first aspect of this application provides a method for detecting the state of flotation foam, comprising: Based on the detection results of the first device on the first area, multiple target distances are determined; where the target distance is the vertical distance between the first device and the flotation foam in the first area; The flow state characterization value of flotation foam in the first region is determined based on multiple target distances; In response to a flow state characterization value being greater than or equal to a preset threshold, the flow state of flotation foam in the first region is determined to be good; In response to the flow state characterization value being less than a preset threshold, the first region is detected based on the second device to obtain a first image group, and the first image group is processed based on the optical flow algorithm to determine the flow state of the flotation foam in the first region. An inflation strategy is sent to external equipment based on the flow state of the flotation foam in the first region.
[0007] A second aspect of this application provides a flotation foam state detection device, comprising: The distance detection module determines multiple target distances based on the detection results of the first device on the first area; wherein the target distance is the vertical distance between the first device and the flotation foam in the first area; The flow characterization module determines the flow state characterization value of flotation foam in the first region based on multiple target distances; The first judgment module determines that the flow state of the flotation foam in the first region is good in response to the flow state characterization value being greater than or equal to a preset threshold. The second judgment module, in response to the flow state characterization value being less than a preset threshold, detects the first region based on the second device to obtain a first image group, and processes the first image group based on the optical flow algorithm to determine the flow state of the flotation foam in the first region; The decision module sends an inflation strategy to external devices based on the flow state of the flotation foam in the first region.
[0008] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the flotation foam state detection method described above.
[0009] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the flotation foam state detection method described above.
[0010] The beneficial effects of the flotation foam state detection method and apparatus provided in this application are as follows: This application uses a first device to perform preliminary detection on the area to be detected, to preliminarily determine the state of flotation foam in the area to be detected, and decides whether to perform further detection on the area to be detected using a second device based on the detection results. That is, this application improves the accuracy and reliability of flotation foam state detection through dual detection. Secondly, the dual detection is based on distance and image respectively, to prevent the inaccuracy of flotation foam state detection by a single evaluation dimension, thereby improving the accuracy of flotation foam state detection. Attached Figure Description
[0011] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0012] Figure 1 This is a schematic flowchart of a flotation foam state detection method provided in an embodiment of this application; Figure 2 A schematic diagram of a distance measurement device provided in an embodiment of this application; Figure 3This is a structural block diagram of a flotation foam state detection device provided in an embodiment of this application; Figure 4 This is a schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation
[0013] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.
[0014] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.
[0015] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a flotation foam state detection method according to an embodiment of this application. The method includes: S101: Based on the detection of the first area by the first device, determine multiple target distances; wherein the target distance is the vertical distance between the first device and the flotation foam in the first area.
[0016] In this embodiment, please refer to Figure 2 , Figure 2 This is a schematic diagram of a distance measuring device provided in an embodiment of this application. The first device can be a non-contact distance sensor 4, used to measure the vertical distance between itself and the foam directly below it. The first device is arranged at a certain distance directly above the flotation cell. The first area can refer to the area in the flotation cell 1, or it can be other areas where flotation is required.
[0017] In this embodiment, the target distance is the vertical distance between the first device and the flotation foam in the first area, which is also the distance measured by the non-contact sensor 4 to the liquid surface on the foam layer 3. Below the foam layer is the slurry 2 to be flotated.
[0018] S102: Determine the flow state characterization value of flotation foam in the first region based on multiple target distances.
[0019] In this embodiment, determining the flow state characterization value of flotation foam in the first region based on multiple target distances includes: Determine multiple distance differences based on the distances to multiple targets; Substituting multiple distance differences into the first formula yields the flow state characterization value of flotation foam in the first region; The first formula is: ; in, Indicates the detection cycle Characteristic values of the flow state of internal flotation foam. Indicates the calculation of the detection cycle; , , ; express Distance value at time, express Distance value at time, express Distance value at time, express Distance value at time, express Distance value at time, , indicating in Time and The absolute value of the difference between the time distance values. , indicating in Time and The absolute value of the difference between the time distance values. , indicating in Time and The absolute value of the difference between the time distance values.
[0020] This embodiment takes into account that in actual production, the foam layer is uneven and exhibits random variations. When the foam layer has poor fluidity, the target distance (i.e., ...) within a certain time period... Figure 2 middle The change in distance is relatively small, while when the foam layer is highly fluid, the target distance within a certain time period will change drastically with the peaks and troughs of the foam layer. Therefore, in this embodiment, a first formula is set to use the sum of the distance differences at each detection time as the distance during the detection cycle. Characteristic values of the flow state of internal flotation foam.
[0021] In this embodiment, Indicates the detection cycle The flow state characterization value of internal flotation foam, that is, during the detection cycle. The sum of the absolute values of the distance differences at various times within the time frame. It should be noted that in this embodiment... time, time,…… All times are within the detection cycle The moment within.
[0022] S103: In response to the flow state characterization value being greater than or equal to a preset threshold, the flow state of the flotation foam in the first region is determined to be good.
[0023] In this embodiment, corresponding to the aforementioned logic, when the flow state characterization value is greater than or equal to a preset threshold, it indicates that the distance between the upper surface of the foam layer and the first device changes relatively quickly during this detection cycle, which in turn indicates that the flow state of the flotation foam in the first region is relatively good. The preset threshold can be obtained based on multiple experiments or determined based on experience.
[0024] S104: In response to the flow state characterization value being less than a preset threshold, the first region is detected based on the second device to obtain a first image group, and the first image group is processed based on the optical flow algorithm to determine the flow state of the flotation foam in the first region.
[0025] In this embodiment, the second device can be a camera, which can be positioned above the first area along with the first device. However, it should be noted that the second device's position should not obstruct the ranging port of the first device. The first image group can consist of multiple images of flotation foam in the first area.
[0026] In this embodiment, consistent with the judgment logic of S103, when the flow state characterization value is less than the preset threshold, it indicates that the distance between the upper surface of the foam layer and the first device changes relatively slowly during this detection cycle, which in turn indicates that the flow state of the flotation foam in the first region is relatively poor.
[0027] This embodiment takes into account that since fluidity itself is characterized by other features, there may be misjudgments of fluidity. Therefore, in this embodiment, when the fluidity state characterization value is abnormal, that is, less than the preset threshold, the fluidity state of the flotation foam in the first region can be detected and judged based on the second relatively accurate method.
[0028] For example, an optical flow algorithm could be used, which calculates the optical flow field of pixels in an image, i.e., the velocity vector field of the pixels on the image plane. By analyzing the optical flow field, the motion of the foam surface can be obtained, and its fluidity can be evaluated.
[0029] In this embodiment, the optical flow field can display information such as the velocity and direction of movement of the flotation foam, which can be used as the basis for determining the flow state of the flotation foam in the first region. Therefore, it can be understood that the flowability judgment basis of the optical flow field is the motion information such as the velocity and direction of movement of the flotation foam, while the flowability judgment basis of the first formula is the distance between the flotation foam and the first device. That is, the flowability judgment basis based on the optical flow field is different from the flowability judgment basis based on the first formula.
[0030] In this embodiment, it is also considered that under normal circumstances, the detection of the first region by the first device can basically meet the flow judgment under normal conditions. Therefore, the second device can not work when the flow state characterization value is greater than or equal to the preset threshold, that is, when the flow state of the flotation foam in the first region detected by the first device is good. It can only work when the flow state characterization value is less than the preset threshold, that is, when the flow state of the flotation foam in the first region detected by the first device may be abnormal.
[0031] S105: Send an inflation strategy to an external device based on the flow state of the flotation foam in the first region.
[0032] In this embodiment, the flow state of the flotation foam can be good or partially abnormal. The corresponding aeration strategies can be not to aerate or to aerate the abnormal area until the flow state of the flotation foam can be good or a first time period is reached. The first time period can be determined based on experience to prevent detection errors from causing continuous aeration.
[0033] As can be seen from the above, this application uses a first device to perform preliminary detection on the area to be detected, to preliminarily determine the state of flotation foam in the area to be detected, and decides whether to perform further detection on the area to be detected using a second device based on the detection results. That is, this application improves the accuracy and reliability of flotation foam state detection through dual detection. Secondly, the dual detection is based on distance and image respectively, which prevents the inaccuracy of flotation foam state detection by a single evaluation dimension, thereby improving the accuracy of flotation foam state detection.
[0034] In one embodiment of this application, processing a first image group based on an optical flow algorithm to determine the flow state of flotation foam in a first region includes: The first image group is processed based on the optical flow algorithm to obtain the optical flow field of the first region; the first image group consists of multiple images of flotation foam in the first region; Since there is no region in the optical flow field that meets the abnormal conditions, the flow state of the flotation foam in the first region is good; In response to the existence of regions satisfying abnormal conditions in the optical flow field, the flotation foam in the second region corresponding to the region satisfying the abnormal conditions is tracked and identified to obtain the flow state of the flotation foam in the second region; wherein, the second region is a region in the first region; The flow state of flotation foam in the first region is determined based on the flow state of flotation foam in the second region.
[0035] In this embodiment, the first image group consists of multiple images of flotation foam in the first region, containing the state information of the flotation foam at different times. The second region refers to the region in the real scene corresponding to the abnormal region in the optical flow field that meets the abnormal conditions; that is, each region in the optical flow field corresponds to the real scene, therefore the second region is a region in the first region.
[0036] This embodiment applies an optical flow algorithm to process the first image group. Its function is to calculate the motion information of each pixel in the first image group. By analyzing information such as the brightness changes of pixels between adjacent image frames, the optical flow field of the first region is obtained. The optical flow field can visually demonstrate the movement direction and velocity distribution of the flotation foam within this region.
[0037] In this embodiment, the first optical flow region can be determined to meet the abnormal conditions based on the following method: The first optical flow region satisfies the abnormal condition when the average optical flow velocity of the first optical flow region is not in the preset velocity range and / or the difference in the optical flow direction is greater than the first difference. The first optical flow region is the region within the optical flow field.
[0038] In this embodiment, the degree of difference in optical flow direction can be determined by calculating the variance of the optical flow direction in the optical flow field. The larger the variance, the greater the degree of difference in optical flow direction.
[0039] In this embodiment, when the average optical flow velocity of the first optical flow region is less than a preset velocity and / or the difference in optical flow direction is greater than a first difference, the first optical flow region is determined to meet the abnormal condition. The logic is as follows: If the average optical flow velocity in the first optical flow region is less than the preset velocity, it indicates that the flow velocity of the flotation foam in the corresponding real-world scenario region is relatively slow, suggesting insufficient aeration. Therefore, this is used as a criterion for identifying an anomaly. The preset velocity is determined based on experience or historical data on the normal foam flow velocity during flotation. If the difference in optical flow direction is greater than the first difference, it indicates that the flow direction of the foam is disordered or inconsistent, resulting in poor foam fluidity. Appropriately increasing the aeration volume can change the gas-liquid mixing state in the flotation cell, generating more bubbles. These bubbles, during their ascent, will drive the surrounding liquid flow, making the flow field more uniform, thereby improving the consistency of the foam flow direction and enhancing foam fluidity. Therefore, the difference in optical flow direction can also be used as one of the criteria for judging whether the optical flow region meets abnormal conditions. The first difference can be based on a threshold for solving similar problems or set empirically. In this embodiment, the flotation foam in the second region can be tracked and identified based on a target tracking algorithm to obtain the flow state of the flotation foam.
[0040] In this embodiment, determining the flow state of the flotation foam in the first region based on the flow state of the flotation foam in the second region includes: If the flow state of the flotation foam in the second region is abnormal, then the flow state of the flotation foam in the first region is partially abnormal. If the flow state of the flotation foam in the second region is normal, then the flow state of the flotation foam in the first region is good.
[0041] In this embodiment, when this action is performed, the first device has already detected the first region. Only when the first device determines that an anomaly may occur will the second device be activated for detection. When the second device detects, it once again detects and judges the flowability of the flotation foam in the first region from other judgment criteria and dimensions. Therefore, when this step is executed, it can be considered that all regions that are considered to be possibly abnormal (i.e., the second region) have been detected. Therefore, when the flow state of the flotation foam in the second region is abnormal, it can be considered that the flow state of the flotation foam in the first region is partially abnormal, that is, the second region in the first region is abnormal. When the flow state of the flotation foam in the second region is normal, it indicates that the detection result of the first device is a misjudgment, and the flow state of the flotation foam in the first region is determined to be good.
[0042] As can be seen from the above, this application evaluates the flow state of flotation foam from two different dimensions: distance and image, which avoids the limitations of a single data source and improves the accuracy of flow state detection.
[0043] In one embodiment of this application, the flotation foam in a second region corresponding to a region that meets abnormal conditions is tracked and identified to obtain the flow state of the flotation foam in the second region, including: Based on the target tracking algorithm, the flotation foam in the second region corresponding to the region that meets the abnormal conditions is tracked and identified to obtain the motion information of the flotation foam. The flow state of the flotation foam in the second region is determined based on the motion information of the flotation foam.
[0044] In this embodiment, the target tracking algorithm can be Kalman filtering, particle filtering, mean-shift algorithm, or deep learning-based tracking algorithm, etc. The motion information of the flotation foam can include velocity, acceleration, and direction, etc.
[0045] In this embodiment, it is also considered that in the actual detection process, the flotation foam is not necessarily independent, but also consists of many clumps of foam. Therefore, in this embodiment, the flotation foam includes: independent foam and foam clumps. Based on the target tracking algorithm, the flotation foam in the second region corresponding to the region that meets the abnormal conditions is tracked and identified to obtain the motion information of the flotation foam, including: Since the acquisition time of the first image group is in the early stage of flotation, the target tracking algorithm is used to track and identify M independent bubbles and N foam clusters in the second region that meet the abnormal conditions, as the motion information of the flotation foam; where M and N are both natural numbers, and M is greater than N; Since the acquisition time of the first image group does not belong to the early stage of flotation, the target tracking algorithm is used to track and identify P independent bubbles and Q foam clusters in the second region corresponding to the abnormal conditions, as the motion information of the flotation foam; where P and Q are both natural numbers, and P is less than Q.
[0046] In this embodiment, considering that in the early stage of flotation, bubbles and mineral particles in the slurry begin to adhere, the number of independent bubbles is relatively large, and the behavior of a single bubble has a significant impact on the flotation effect, when the acquisition time of the first image group is in the early stage of flotation, the number of independent bubbles detected should be greater than the number of bubbles detected, that is, M is greater than N.
[0047] In this embodiment, as the flotation process proceeds, when the acquisition time of the first image group is not in the early stage of flotation, i.e., in the middle or late stage of flotation, the foam gradually aggregates to form foam clusters. The stability and fluidity of the foam clusters have a more significant impact on the quality and recovery rate of the flotation concentrate. Therefore, if the acquisition time of the first image group is not in the early stage of flotation, the number of individual foams detected should be less than the number of foam clusters detected, i.e., P is less than Q.
[0048] In this embodiment, the motion information of the flotation foam may include speed, acceleration, or direction, which can be determined by simple logical judgment or weighted calculation. For example, if any of the speed, acceleration, or direction does not meet the preset conditions, the flow state of the flotation foam in the second region is considered abnormal.
[0049] As can be seen from the above, this application takes into account the situation where there are independent foams and foam clusters in the actual flotation foam detection. Based on the acquisition time of the first image group, different numbers of independent foams and foam clusters are tracked and identified, which is more in line with the change law of foam state in the actual flotation process and improves the pertinence and accuracy of flotation foam state detection.
[0050] In one embodiment of this application, a method for determining the flow state of flotation foam based on the motion information of flotation foam is provided. The motion information of flotation foam includes: the velocity of flotation foam, the acceleration of flotation foam, and the direction of flotation foam; The flow state of the flotation foam in the second region is determined based on the motion information of the flotation foam, including: Determine the velocity anomaly index of flotation foam based on the velocity of flotation foam; Determine the acceleration anomaly index of flotation foam based on the acceleration of flotation foam; Determine the direction anomaly index of flotation foam based on the direction of flotation foam; The flow anomaly value is obtained by weighting the velocity anomaly index, acceleration anomaly index, and direction anomaly index. The flow state of flotation foam in the second region is determined based on flow anomalies.
[0051] In this embodiment, the velocity anomaly index reflects the degree of abnormality in the flotation foam velocity. It can be obtained by analyzing the flotation foam velocity data and comparing it with the expected velocity range of foam during normal flotation. If the foam velocity is too slow, exceeding the normal range, the value of this index will increase accordingly, indicating a velocity anomaly. For example, the velocity anomaly index = |actual velocity - mean of normal velocity range| / standard deviation of normal velocity range. The acceleration anomaly index and its calculation method are the same as the velocity anomaly index, and will not be repeated here. The direction anomaly index is calculated in the same way as the aforementioned difference in optical flow direction, i.e., by calculating the variance.
[0052] In this embodiment, the velocity anomaly index, acceleration anomaly index, and direction anomaly index are weighted and calculated to obtain the flow anomaly value, wherein the weight of each anomaly index can be determined based on experience.
[0053] In one embodiment of this application, the weighting of various abnormal indicators at different flotation stages is also taken into consideration. Specifically, the flotation foam state detection method further includes: The response time is the early stage of flotation, and the weight of the speed anomaly index is increased based on the length of the first step. In response to the first moment being the midpoint of flotation, the weight of the acceleration anomaly index is increased based on the second step size; In response to the first moment being the later stage of flotation, the weight of the directional anomaly index is increased based on the third step length; The sum of the weights of the velocity anomaly index, the acceleration anomaly index, and the direction anomaly index is a constant; the first moment is the starting moment for tracking and identifying flotation foam in the second region.
[0054] In this embodiment, considering that the starting time (first moment) for tracking and identifying flotation foam in the second region is in the early stage of flotation, the weight of the speed anomaly index should be increased at this time, because the speed of flotation foam can reflect the initial transport capacity of the foam to carry minerals. At this time, the speed has a significant impact on the overall flotation effect.
[0055] In this embodiment, when the first moment is the middle of flotation, the weight of the acceleration anomaly index should be increased, because as the flotation process proceeds, the stability and continuity of the foam become important. Stable acceleration helps to maintain the normal movement of the foam and avoid foam rupture or mineral loss.
[0056] In this embodiment, when the first moment is in the later stage of flotation, the weight of the directional anomaly index should be increased because, in the later stage of flotation, it is necessary to precisely control the flow direction of the froth to ensure the quality and recovery rate of the concentrate. The first, second, and third step lengths can be determined based on multiple experiments.
[0057] In this embodiment, after obtaining the flow anomaly value, it can be compared with a preset anomaly threshold. If the flow anomaly value is greater than or equal to the preset anomaly threshold, the flow state of the flotation foam in the second region is abnormal; if the flow anomaly value is less than the preset anomaly threshold, the flow state of the flotation foam in the second region is normal.
[0058] As can be seen from the above, this application comprehensively evaluates the flow state of flotation foam through three dimensions of abnormal indicators: velocity, acceleration, and direction. In addition, this application can dynamically adjust the weight of each abnormal indicator according to different stages of flotation to ensure that the evaluation indicators match the actual needs of the flotation process and improve the pertinence and reliability of flotation foam state detection.
[0059] Corresponding to the flotation foam state detection method in the above embodiment, Figure 3 This is a structural block diagram of a flotation foam state detection device provided in one embodiment of this application. For ease of explanation, only the parts relevant to the embodiment of this application are shown. References Figure 3 The flotation foam state detection device 20 includes: a distance detection module 21, a flow characterization module 22, a first judgment module 23, a second judgment module 24, and a decision module 25.
[0060] The distance detection module 21 is used to detect the first area based on the first device and determine multiple target distances; wherein the target distance is the vertical distance between the first device and the flotation foam in the first area; Flow characterization module 22 is used to determine the flow state characterization value of flotation foam in the first region based on multiple target distances; The first judgment module 23 is used to determine that the flow state of flotation foam in the first region is good in response to the flow state characterization value being greater than or equal to a preset threshold. The second judgment module 24 is used to detect the first region based on the second device in response to the flow state characterization value being less than a preset threshold, obtain a first image group, and process the first image group based on the optical flow algorithm to determine the flow state of the flotation foam in the first region. Decision module 25 is used to send an inflation strategy to an external device based on the flow state of the flotation foam in the first region.
[0061] In one embodiment of this application, the flow characterization module 22 is specifically used to determine multiple distance differences based on multiple target distances; Substituting multiple distance differences into the first formula yields the flow state characterization value of flotation foam in the first region; The first formula is: ; in, Indicates the detection cycle Characteristic values of the flow state of internal flotation foam. Indicates the calculation of the detection cycle; , , ; express Distance value at time, express Distance value at time, express Distance value at time, express Distance value at time, express Distance value at time, , indicating in Time and The absolute value of the difference between the time distance values. , indicating in Time and The absolute value of the difference between the time distance values. , indicating in Time and The absolute value of the difference between the time distance values.
[0062] In one embodiment of this application, the second judgment module 24 is specifically used to process the first image group based on the optical flow algorithm to obtain the optical flow field of the first region; the first image group consists of multiple images of flotation foam in the first region; Since there is no region in the optical flow field that meets the abnormal conditions, the flow state of the flotation foam in the first region is good; In response to the existence of regions satisfying abnormal conditions in the optical flow field, the flotation foam in the second region corresponding to the region satisfying the abnormal conditions is tracked and identified to obtain the flow state of the flotation foam in the second region; wherein, the second region is a region in the first region; The flow state of flotation foam in the first region is determined based on the flow state of flotation foam in the second region.
[0063] In one embodiment of this application, the second judgment module 24 is further used to track and identify the flotation foam in the second region corresponding to the region that meets the abnormal conditions based on the target tracking algorithm, so as to obtain the motion information of the flotation foam. The flow state of the flotation foam in the second region is determined based on the motion information of the flotation foam.
[0064] In one embodiment of this application, the flotation foam includes: individual foams and foam clusters; The second judgment module 24 is further used to track and identify M independent bubbles and N foam clusters in the second region corresponding to the abnormal conditions based on the target tracking algorithm in response to the acquisition time of the first image group being the early stage of flotation, as motion information of the flotation foam; where M and N are both natural numbers, and M is greater than N; Since the acquisition time of the first image group does not belong to the early stage of flotation, the target tracking algorithm is used to track and identify P independent bubbles and Q foam clusters in the second region corresponding to the abnormal conditions, as the motion information of the flotation foam; where P and Q are both natural numbers, and P is less than Q.
[0065] In one embodiment of this application, the motion information of the flotation foam includes: the velocity of the flotation foam, the acceleration of the flotation foam, and the direction of the flotation foam; The second judgment module 24 is specifically used to determine the flotation foam speed anomaly index based on the flotation foam speed. Determine the acceleration anomaly index of flotation foam based on the acceleration of flotation foam; Determine the direction anomaly index of flotation foam based on the direction of flotation foam; The flow anomaly value is obtained by weighting the velocity anomaly index, acceleration anomaly index, and direction anomaly index. The flow state of flotation foam in the second region is determined based on flow anomalies.
[0066] In one embodiment of this application, the flotation foam state detection device 20 includes: a weight adjustment module, used to increase the weight of the speed abnormality index based on the first step length in response to the first moment being the early stage of flotation; In response to the first moment being the midpoint of flotation, the weight of the acceleration anomaly index is increased based on the second step size; In response to the first moment being the later stage of flotation, the weight of the directional anomaly index is increased based on the third step length; The sum of the weights of the velocity anomaly index, the acceleration anomaly index, and the direction anomaly index is a constant; the first moment is the starting moment for tracking and identifying flotation foam in the second region.
[0067] See Figure 4 , Figure 4This is a schematic block diagram of an electronic device provided according to an embodiment of this application. Figure 4 The electronic device 300 in this embodiment may include one or more processors 301, one or more input devices 302, one or more output devices 303, and one or more memories 304. The processors 301, input devices 302, output devices 303, and memories 304 communicate with each other via a communication bus 305. The memories 304 store computer programs, including program instructions. The processors 301 execute the program instructions stored in the memories 304. Specifically, the processors 301 are configured to invoke the program instructions to perform the functions of each module / unit in the above-described device embodiments, for example... Figure 3 The functions of the distance detection module 21, flow characterization module 22, first judgment module 23, second judgment module 24, and decision module 25 are shown.
[0068] It should be understood that, in the embodiments of this application, the processor 301 may be a central processing unit (CPU), but it may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.
[0069] Input device 302 may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint orientation information), a microphone, etc., and output device 303 may include a display (LCD, etc.), a speaker, etc.
[0070] The memory 304 may include read-only memory and random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
[0071] In specific implementations, the processor 301, input device 302, and output device 303 described in the embodiments of this application can execute the implementation methods described in the first and second embodiments of the flotation foam state detection method provided in the embodiments of this application, or they can execute the implementation methods of the electronic devices described in the embodiments of this application, which will not be repeated here.
[0072] In another embodiment of this application, a computer-readable storage medium is provided. This computer-readable storage medium stores a computer program, which includes program instructions. When executed by a processor, the program instructions implement all or part of the processes in the methods described above. Alternatively, the computer program can instruct related hardware to complete the process. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0073] The computer-readable storage medium can be an internal storage unit of the electronic device in any of the foregoing embodiments, such as a hard disk or memory of the electronic device. The computer-readable storage medium can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the electronic device. Furthermore, the computer-readable storage medium can include both internal and external storage units of the electronic device. The computer-readable storage medium is used to store computer programs and other programs and data required by the electronic device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.
[0074] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.
[0075] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the electronic devices and units described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0076] In the several embodiments provided in this application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces or units, or it may be an electrical, mechanical, or other form of connection.
[0077] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.
[0078] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0079] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for detecting a state of a flotation froth, characterized by, The method comprises: determining a plurality of target distances based on a detection result of a first device on a first region; wherein the target distance is a vertical distance between the first device and the flotation froth in the first region; determining a flow state representation value of the flotation froth in the first region based on the plurality of target distances; in response to the flow state representation value being greater than or equal to a preset threshold, determining that the flow state of the flotation froth in the first region is good; in response to the flow state representation value being less than the preset threshold, detecting the first region based on a second device to obtain a first image group, and processing the first image group based on an optical flow algorithm to determine the flow state of the flotation froth in the first region; sending an aeration strategy to an external device based on the flow state of the flotation froth in the first region.
2. The froth state detection method according to claim 1, wherein The method further comprises: determining a plurality of distance difference values based on the plurality of target distances; obtaining the flow state representation value of the flotation froth in the first region by substituting the plurality of distance difference values into a first formula; The first formula is: ; wherein, represents a flow state characterizing value of the flotation froth within a detection period , represents a calculation detection period; , , ; represents an instant distance value, represents an instant distance value, represents an instant distance value, represents an instant distance value, represents an instant distance value, , represents an absolute value of a distance value difference between an instant and an instant, , represents an absolute value of a distance value difference between an instant and an instant, , represents an absolute value of a distance value difference between an instant and an instant.
3. The method of claim 1, wherein the step of detecting the state of the froth comprises the steps of: determining a froth height; and determining a froth stability. The method further comprises: processing the first image group based on the optical flow algorithm to obtain an optical flow field of the first region; the first image group is composed of a plurality of images of the flotation froth in the first region; in response to the optical flow field not containing a region satisfying an abnormal condition, the flow state of the flotation froth in the first region is good; in response to the optical flow field containing a region satisfying an abnormal condition, tracking and identifying the flotation froth in a second region corresponding to the region satisfying the abnormal condition to obtain the flow state of the flotation froth in the second region; wherein the second region is a region in the first region; determining the flow state of the flotation froth in the first region based on the flow state of the flotation froth in the second region.
4. The froth state detection method according to claim 3, wherein The method further comprises: tracking and identifying the flotation froth in the second region corresponding to the region satisfying the abnormal condition based on a target tracking algorithm to obtain motion information of the flotation froth; determining the flow state of the flotation froth in the second region based on the motion information of the flotation froth.
5. The froth state detection method according to claim 4, wherein The flotation froth comprises independent froth and froth clusters. The method further comprises: in response to the collection time of the first image group being the early flotation stage, tracking and identifying M independent froths and N froth clusters in the second region corresponding to the abnormal condition based on the target tracking algorithm as the motion information of the flotation froth; wherein M and N are natural numbers, and M is greater than N. In response to the acquisition time of the first image set not belonging to the pre-floating stage, P independent foams and Q foam groups in the second region satisfying the abnormal condition are tracked and identified based on a target tracking algorithm as motion information of the flotation foam; wherein P and Q are natural numbers, and P is less than Q.
6. The froth state detection method according to claim 4, wherein The motion information of the flotation foam includes: velocity of the flotation foam, acceleration of the flotation foam, and direction of the flotation foam. The determination of the flow state of the flotation foam in the second region based on the motion information of the flotation foam includes: determining a velocity anomaly index of the flotation foam based on the velocity of the flotation foam; determining an acceleration anomaly index of the flotation foam based on the acceleration of the flotation foam; determining a direction anomaly index of the flotation foam based on the direction of the flotation foam; performing weighted calculation on the velocity anomaly index, the acceleration anomaly index, and the direction anomaly index to obtain a flow anomaly value; determining the flow state of the flotation foam in the second region based on the flow anomaly value.
7. The froth state detection method according to claim 6, wherein Further comprising: in response to the first time being the pre-floating stage, increasing the weight of the velocity anomaly index based on a first step length; in response to the first time being the middle floating stage, increasing the weight of the acceleration anomaly index based on a second step length; in response to the first time being the post-floating stage, increasing the weight of the direction anomaly index based on a third step length; the sum of the weight of the velocity anomaly index, the weight of the acceleration anomaly index, and the weight of the direction anomaly index is a constant value; and the first time is the starting time of tracking and identifying the flotation foam in the second region.
8. A flotation froth state detection apparatus characterized by comprising: Further comprising: a distance detection module, based on the detection result of the first device on the first region, determines a plurality of target distances; wherein the target distance is the vertical distance between the first device and the flotation foam in the first region; a flow representation module, based on the plurality of target distances, determines a flow state representation value of the flotation foam in the first region; a first judgment module, in response to the flow state representation value being greater than or equal to a preset threshold, determines that the flow state of the flotation foam in the first region is good; a second judgment module, in response to the flow state representation value being less than the preset threshold, detects the first region based on a second device to obtain a first image set, and processes the first image set based on an optical flow algorithm to determine the flow state of the flotation foam in the first region; a decision module, based on the flow state of the flotation foam in the first region, sends an inflation strategy to an external device.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor executes the computer program to implement the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1-9. The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 7.