High-precision early warning and positioning method for dark cabin of screw ship unloader

Abstract

The invention relates to a screw ship unloader dark cabin high-precision early warning positioning method which specifically comprises the following steps: S1, firstly, measuring and marking the position of the coordinate of a wharf through radar positioning, and then determining the ship type pose of an operation ship of a screw ship unloader; s2, the movement track of a feeding head of the spiral ship unloader is known, and then the vertical pose of the feeding head of the spiral ship unloader is set according to the bin opening position of the discharging bin; by arranging the trigger sensor and the distance sensor, collision generated during operation of the spiral ship unloader dark cabin can be accurately sensed, the stability of anti-collision pre-judgment of the spiral ship unloader dark cabin is improved, the probability of accidents is reduced, and the applicability is high; meanwhile, a triggered signal is detected, so that the situation that the screw ship unloader stops running due to the fact that the screw ship unloader is abnormal is effectively prevented, the running accuracy of the screw ship unloader is improved, and collision early warning is achieved.

Description

Technical Field

[0001] The invention relates to an early warning positioning method, in particular to a high-precision early warning positioning method for a dark compartment of a screw ship unloader, and belongs to the technical field of ship unloader anti-collision. Background Art

[0002] The screw ship unloader is a ship unloading machine with a horizontal screw conveyor without flexible traction components, a vertical screw conveyor and a special material reclaiming device as the main working mechanism. It is a high-efficiency continuous bulk cargo ship unloader. The screw ship unloader is mainly composed of a gantry, a turntable, a horizontal screw, a vertical screw and a feeder. It is generally used to unload bulk materials such as coal, cement, bulk grain, fertilizer, potassium salt, etc. At present, unmanned screw ship unloaders often need to be equipped with a dark cabin anti-collision function when working to prevent damage to the ship unloader;

[0003] In the Chinese patent application number CN202210571670.3, a method for predicting collision prevention in the dark compartment of an unmanned screw ship unloader is mentioned. By determining the ship type and ship posture of the operating ship of the ship unloader and setting the time for the feeding head, and performing the same action within the set time, a safety distance threshold is set according to the ship type and ship posture of the operating ship of the ship unloader when the feeding head moves. When the feeding head performs the set action within the set time, if it deviates from the safety distance threshold of the action, an alarm is issued, and a stop instruction and a buffering action are executed, thereby realizing accurate prediction of the impending collision of the feeding hopper, effectively preventing the occurrence of collision actions, and having a good use effect;

[0004] However, in actual use, it was found that the above patent lacks the function of warning signal detection, so that when the screw ship unloader fails and the feeding head operates normally, an alarm is also generated, which affects the normal operation of the screw ship unloader and reduces the working efficiency of the screw ship unloader. For this reason, the staff proposed a method for predicting collision prevention in the dark compartment of an unmanned screw ship unloader. Summary of the invention

[0005] The present invention is aimed at the problems existing in the prior art and provides a high-precision early warning positioning method for the dark compartment of a screw ship unloader to solve the problem that the prior art lacks the function of warning signal detection, so that when a malfunction of the screw ship unloader causes the normal operation of the feeding head, an alarm is also generated, affecting the normal operation of the screw ship unloader and reducing the working efficiency of the screw ship unloader.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows: a high-precision early warning positioning method for a screw ship unloader dark compartment, the method comprising the following steps:

[0007] S1. First, the staff measures and marks the coordinates of the wharf through radar positioning. Then, the staff understands the swing angle, walking position and pitch angle of the boom during the operation of the screw ship unloader. According to the coordinates of the wharf and the draft of the ship unloader, the staff adapts the ship type and posture of the operating ship of the screw ship unloader through the mobile path algorithm model, and then determines the ship type and posture of the operating ship of the screw ship unloader.

[0008] S2. Then, the motion trajectory of the feeding head of the screw ship unloader is understood, and then the vertical position of the feeding head of the screw ship unloader is set according to the position of the port of the unloading bin;

[0009] S3. The maximum movement position between the feeding head of the screw ship unloader and the material cabin is set to a safe distance according to the internal shape of the unloading bin, and a buffer plate is arranged around the inner wall of the material cabin, and a trigger sensor is arranged on the buffer plate. At the same time, a distance sensor is used to detect the movement of the feeding head of the screw ship unloader in real time;

[0010] S4. By setting a warning light, a signal detection module and a control module on the screw ship unloader, when the feeding head of the screw ship unloader contacts the trigger sensor inside the material compartment or the movement of the feeding head exceeds the maximum threshold set by the distance sensor, the trigger sensor or the distance sensor is triggered, and the signal is transmitted to the signal detection module for detection. When the detected signal has no effect on the continued operation of the feeding head, the warning light does not receive the signal, and the operation of the screw ship unloader is not affected. When the detected signal has an effect on the continued operation of the feeding head, the signal of the signal detection module is transmitted to the warning light, and the warning light generates an alarm. The warning light is triggered, causing the control module to control the feeding head of the screw ship unloader to stop moving.

[0011] S5. Based on the maximum and minimum value normalization method, each trajectory feature in the feeding head trajectory data of the screw ship unloader is processed, and the historical feeding head trajectory data is processed through the trajectory data preprocessing technology, and finally the optimal driving trajectory of the feeding head is constructed; the trajectory data preprocessing technology refers to the technology of cleaning, filtering, converting and other processing operations on the original trajectory data before analyzing and mining the trajectory data, so as to improve the efficiency and accuracy of subsequent trajectory data analysis and mining;

[0012] S6. The operator can set the corresponding feeding head trajectory of the screw ship unloader according to different types of transport ships, and then automatically change the running trajectory of the feeding head when the screw ship unloader discharges materials through the feeding head.

[0013] Among them, in step S1, the steps of implementing the mobile path algorithm model are as follows:

[0014] Step 1-1, firstly, multiple position markers are set according to the position at the dock, and the draft of the ship unloader is combined for statistics to obtain multiple sample sets, and then the sample sets are divided into k clusters to obtain the number of clusters k;

[0015] Step 1-2, then according to the number of clusters k, assuming the number of iterations is R, then m objects x from the entire data set i Random Selection

[0016] k points are used as the initial algorithm model center c of the mobile path algorithm model q (r), where i = 1, 2, 3, ..., m, q = 1, 2, 3, ..., k, r = 1, 2, 3, ..., R;

[0017] Step 1-3, then calculate the similarity distance D(x i ,c q (r)):D(x i ,c q (r))=||x i -c q (r)||2, where c q (r) is the cluster center of r iterations;

[0018] According to the distance between the comparison sample and the center of different algorithm models, the category with the minimum similarity distance is selected as the cluster of the sample point.

[0019] Step 1-4: By calculating the mean of each included point in each class Wq generated by the algorithm model, k new algorithm model centers are obtained: Among them, x i (q) For category W q The pixels contained in ;

[0020] Among them, x i (q) EW q , the calculation formula of the algorithm model criterion objective function value is as follows:

[0021] |J(r+1)-J(r)<ε

[0022]

[0023] Among them, J is the objective function of the algorithm model;

[0024] Step 1-5: After multiple iterations, if the change of the objective function between the previous and the next minimization process is less than a certain set value ε, it is considered that the algorithm model criterion function has converged and the iterative task is completed; if the termination condition is not met, return to steps 1-2 and 1-3 to continue the calculation until the iterative task is completed.

[0025] In step S3, the distance sensor is set with a maximum position movement threshold.

[0026] Wherein, in step S4, the trigger sensor and the distance sensor are electrically connected to the signal detection module respectively, and the signal detection module is electrically connected to the warning light, and the warning light is electrically connected to the control module.

[0027] Wherein, in step S6, based on the optimization data of the feeding head trajectory obtained in step S5, the optimal feeding head trajectory suitable for different types of transport ships is analyzed and selected.

[0028] Compared with the prior art, the present invention has the following advantages:

[0029] 1. The present invention can accurately sense the impact generated by the screw ship unloader's dark cabin during operation by arranging trigger sensors and distance sensors, thereby improving the stability of the screw ship unloader's dark cabin anti-collision prediction. The trajectory data of the feeding head is processed by maximum and minimum value normalization, so that the trajectory of the feeding head is optimized, and the staff can differentiate the feeding head trajectory and select the feeding head trajectory that is more suitable for the transport ship during unloading, thereby reducing the probability of accidents and having high applicability.

[0030] 2. The present invention sets a trigger sensor and a distance sensor. When the feeding head of the screw ship unloader contacts the trigger sensor on the buffer block inside the material compartment or the movement of the feeding head exceeds the maximum threshold set by the distance sensor, the trigger sensor or the distance sensor is triggered, and the signal is transmitted to the signal detection module for detection. When the detected signal has no effect on the continued operation of the feeding head, the warning light will not receive the signal and the warning light will not generate an alarm. At this time, the operation of the screw ship unloader is not affected. When the detected signal has an effect on the continued operation of the feeding head, the signal of the signal detection module will be transmitted to the warning light, and the warning light will generate an alarm. The warning light is triggered to cause the control module to control the feeding head of the screw ship unloader to stop moving, thereby realizing early prediction of the collision. At the same time, the triggered signal is detected, which effectively prevents the screw ship unloader from stopping due to abnormality, thereby improving the accuracy of the operation of the screw ship unloader. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of the anti-collision prediction method for the dark compartment of an unattended spiral ship unloader of the present invention;

[0032] Figure 2 This is a transmission diagram of the anti-collision prediction warning signal for the dark compartment of the unmanned spiral ship unloader of the present invention. DETAILED DESCRIPTION

[0033] In order to deepen the understanding of the present invention, the present embodiment is described in detail below with reference to the accompanying drawings.

[0034] Example 1: See Figure 1 , a high-precision early warning positioning method for a screw ship unloader dark cabin, comprising the following steps:

[0035] S1. First, the staff measures and marks the coordinates of the wharf through radar positioning. Then, the staff understands the swing angle, walking position and pitch angle of the boom during the operation of the screw ship unloader. According to the coordinates of the wharf and the draft of the ship unloader, the staff adapts the ship type and posture of the operating ship of the screw ship unloader through the mobile path algorithm model, and then determines the ship type and posture of the operating ship of the screw ship unloader.

[0036] S2. Then, the motion trajectory of the feeding head of the screw ship unloader is understood, and then the vertical position of the feeding head of the screw ship unloader is set according to the position of the port of the unloading bin;

[0037] S3. The maximum movement position between the feeding head of the screw ship unloader and the material cabin is set to a safe distance according to the internal shape of the unloading bin, and a buffer plate is arranged around the inner wall of the material cabin, and a trigger sensor is arranged on the buffer plate. At the same time, a distance sensor is used to detect the movement of the feeding head of the screw ship unloader in real time;

[0038] S4. By setting a warning light, a signal detection module and a control module on the screw ship unloader, when the feeding head of the screw ship unloader contacts the trigger sensor inside the material compartment or the movement of the feeding head exceeds the maximum threshold set by the distance sensor, the trigger sensor or the distance sensor is triggered, and the signal is transmitted to the signal detection module for detection. When the detected signal has no effect on the continued operation of the feeding head, the warning light does not receive the signal, and the operation of the screw ship unloader is not affected. When the detected signal has an effect on the continued operation of the feeding head, the signal of the signal detection module is transmitted to the warning light, and the warning light generates an alarm. The warning light is triggered, causing the control module to control the feeding head of the screw ship unloader to stop moving.

[0039] S5. Based on the maximum and minimum value normalization method, each trajectory feature in the feeding head trajectory data of the screw ship unloader is processed, and combined with the historical feeding head trajectory data for comprehensive analysis, the feeding head trajectory is constructed and optimized;

[0040] S6. The operator can set the corresponding feeding head trajectory of the screw ship unloader according to different types of transport ships, and then automatically change the running trajectory of the feeding head when the screw ship unloader discharges materials through the feeding head.

[0041] From the above, it can be seen that by setting up trigger sensors and distance sensors, the impact generated by the screw ship unloader's dark compartment during operation can be accurately sensed, which improves the stability of the screw ship unloader's dark compartment anti-collision prediction, reduces the probability of accidents, and has high applicability.

[0042] Specifically, in step S1, the steps of implementing the mobile path algorithm model are as follows:

[0043] Step 1-1, firstly, multiple position markers are set according to the position at the dock, and the draft of the ship unloader is combined for statistics to obtain multiple sample sets, and then the sample sets are divided into k clusters to obtain the number of clusters k;

[0044] Step 1-2, then according to the number of clusters k, assuming the number of iterations is R, then m objects x from the entire data set i Randomly select k points as the initial algorithm model center c of the mobile path algorithm model q (r), where i = 1, 2, 3, ..., m, q = 1, 2, 3, ..., k, r = 1, 2, 3, ..., R;

[0045] Step 1-3, then calculate the similarity distance D(x i ,c q (r)):D(x i ,c q (r))=||x i -c q (r)||2, where c q (r) is the cluster center of the rth iteration;

[0046] According to the distance between the comparison sample and the center of different algorithm models, the category with the minimum similarity distance is selected as the cluster of the sample point.

[0047] Step 1-4: By calculating the mean of each included point in each class Wq generated by the algorithm model, k new algorithm model centers are obtained: Among them, x i (q) For category W q The pixels contained in ;

[0048] Among them, x i (q) EW q , the calculation formula of the algorithm model criterion objective function value is as follows:

[0049] |J(r+1)-J(r)<ε

[0050]

[0051] Step 1-5: After multiple iterations, if the change of the objective function between the previous and the next minimization process is less than a certain set value ε, it is considered that the algorithm model criterion function has converged and the iterative task is completed; if the termination condition is not met, return to steps 1-2 and 1-3 to continue the calculation until the iterative task is completed.

[0052] From the above, it can be seen that the final effect of the operation of the mobile path algorithm model is to divide the samples with high similarity into the same cluster, while the samples that do not belong to the same cluster have low similarity. The measure of the similarity between samples is the distance between the sample and the center of the algorithm model in the cluster, so as to obtain the minimum distance from the operating ship to the dock mark position, thereby maximizing the accuracy of the ship's position and posture.

[0053] Specifically, in step S3, the distance sensor is set with a maximum position movement threshold.

[0054] Specifically, in step S4, the trigger sensor and the distance sensor are electrically connected to the signal detection module respectively, and the signal detection module is electrically connected to the warning light, and the warning light is electrically connected to the control module.

[0055] Specifically, in step S6, based on the optimization data of the feeding head trajectory obtained in step S5, the optimal feeding head trajectory suitable for different types of transport ships is analyzed and selected.

[0056] As can be seen from the above, the principle of this embodiment is: when the screw ship unloader is working normally, and the feeding head of the screw ship unloader contacts the trigger sensor on the buffer block inside the material compartment or the movement of the feeding head exceeds the maximum threshold set by the distance sensor, the trigger sensor or the distance sensor is triggered, and the signal will be transmitted to the signal detection module for detection. When the detected signal has no effect on the continued operation of the feeding head, the warning light will not receive the signal and the warning light will not generate an alarm. At this time, the operation of the screw ship unloader is not affected. When the detected signal has an impact on the continued operation of the feeding head, the signal of the signal detection module will be transmitted to the warning light, and the warning light will generate an alarm. The warning light is triggered, causing the control module to control the feeding head of the screw ship unloader to stop moving, thereby realizing the early prediction of the collision. At the same time, the triggered signal is detected, which effectively prevents the screw ship unloader from stopping due to abnormalities, thereby improving the accuracy of the operation of the screw ship unloader.

[0057] It should be noted that the above embodiments are not intended to limit the protection scope of the present invention, and equivalent changes or substitutions made on the basis of the above technical solutions all fall within the protection scope of the claims of the present invention.

Claims

1. A high-precision early warning positioning method for a screw ship unloader dark compartment, characterized in that: The method comprises the following steps: S1. First, the staff measures and marks the coordinates of the wharf through radar positioning. Then, the staff understands the swing angle, walking position and pitch angle of the boom during the operation of the screw ship unloader. According to the coordinates of the wharf and the draft of the ship unloader, the staff adapts the ship type and posture of the operating ship of the screw ship unloader through the mobile path algorithm model, and then determines the ship type and posture of the operating ship of the screw ship unloader. S2. Then, the motion trajectory of the feeding head of the screw ship unloader is understood, and then the vertical position of the feeding head of the screw ship unloader is set according to the position of the port of the unloading bin; S3. The maximum movement position between the feeding head of the screw ship unloader and the material cabin is set to a safe distance according to the internal shape of the unloading bin, and a buffer plate is arranged around the inner wall of the material cabin, and a trigger sensor is arranged on the buffer plate. At the same time, a distance sensor is used to detect the movement of the feeding head of the screw ship unloader in real time; S4. By setting a warning light, a signal detection module and a control module on the screw ship unloader, when the feeding head of the screw ship unloader contacts the trigger sensor inside the material compartment or the movement of the feeding head exceeds the maximum threshold set by the distance sensor, the trigger sensor or the distance sensor is triggered, and the signal is transmitted to the signal detection module for detection. When the detected signal has no effect on the continued operation of the feeding head, the warning light does not receive the signal, and the operation of the screw ship unloader is not affected. When the detected signal has an effect on the continued operation of the feeding head, the signal of the signal detection module is transmitted to the warning light, and the warning light generates an alarm. The warning light is triggered, causing the control module to control the feeding head of the screw ship unloader to stop moving. S5. Based on the maximum and minimum value normalization method, each trajectory feature in the feeding head trajectory data of the screw ship unloader is processed, and the historical feeding head trajectory data is processed through the trajectory data preprocessing technology, and finally the optimal driving trajectory of the feeding head is constructed; S6. The operator can set the corresponding feeding head trajectory of the screw ship unloader according to different types of transport ships, and then automatically change the running trajectory of the feeding head when the screw ship unloader discharges materials through the feeding head.

2. The high-precision early warning positioning method for the dark compartment of a screw ship unloader according to claim 1 is characterized in that: In step S1, the steps of implementing the mobile path algorithm model are as follows: Step 1-1, firstly, multiple position markers are set according to the position at the dock, and the draft of the ship unloader is combined for statistics to obtain multiple sample sets, and then the sample sets are divided into k clusters to obtain the number of clusters k; Step 1-2, then according to the number of clusters k, assuming the number of iterations is R, then m objects x from the entire data set i Randomly select k points as the initial algorithm model center cq(r) of the mobile path algorithm model, where i=1,2,3,...,m,q=1,2,3,...,k,r=1,2,3,...,R; Step 1-3, then calculate the similarity distance D(x i ,cq(r)):D(x i ,c q (r))=||x i -c q (r)||2, where c q (r) is the cluster center of the rth iteration; According to the distance between the comparison sample and the center of different algorithm models, the category with the minimum similarity distance is selected as the cluster of the sample point. Step 1-4: By calculating the mean of each included point in each class Wq generated by the algorithm model, k new algorithm model centers are obtained: Among them, x i (q) For category W q The pixels contained in ; Among them, x i (q) EW q , the calculation formula of the algorithm model criterion objective function value is as follows: |J(r+1)-J(r)<ε Step 1-5: After multiple iterations, if the change of the objective function between the previous and the next minimization process is less than a certain set value ε, it is considered that the algorithm model criterion function has converged and the iterative task is completed; if the termination condition is not met, return to steps 1-2 and 1-3 to continue the calculation until the iterative task is completed.

3. The high-precision early warning positioning method for the dark compartment of a screw ship unloader according to claim 2 is characterized in that: In step S3, the distance sensor is set with a maximum position movement threshold.

4. The high-precision early warning positioning method for the dark compartment of a screw ship unloader according to claim 3 is characterized in that: In step S4, the trigger sensor and the distance sensor are electrically connected to the signal detection module respectively, and the signal detection module is electrically connected to the warning light, and the warning light is electrically connected to the control module.

5. The high-precision early warning positioning method for the dark compartment of a screw ship unloader according to claim 3 or 4, characterized in that: In step S6, based on the optimization data of the feed head trajectory obtained in step S5, the optimal feed head trajectory suitable for different types of transport ships is analyzed and selected.

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