Ultrahigh-precision positioning control system and method for box-type double-beam truss vehicle

Abstract

The invention discloses an ultrahigh-precision positioning control system and method for a box-type double-beam joist barrow, relates to the technical field of joist barrow positioning control, and solves the technical problems that in joist barrow positioning control, high-precision positioning regulation and control are difficult to carry out, and the influence of wind power and cargo characteristics on positioning deviation is difficult to analyze in a self-adaptive mode. The cargo transaction coefficient is obtained through the weight and the shape of the cargo, the environment compensation model is generated based on the historical cargo transaction coefficient and the historical environment data, the cargo transaction coefficient of the cargo and the environment data are input into the environment compensation model to obtain the environment compensation parameter of the joist barrow, and the positioning deviation value is obtained through the environment compensation parameter. Analyzing the putting point and the positioning deviation value to obtain a dynamic positioning point; according to the method, deviation of positioning caused by influence between wind power and goods can be adaptively analyzed, and the positioning precision of the joist barrow is improved.

Description

Technical Field

[0001] The present invention belongs to the field of automatic control, relates to the positioning control technology of a truss crane, and specifically is a super-high-precision positioning control system and method for a box-type double-girder truss crane. Background Art

[0002] A truss crane, that is, a bridge crane, mainly consists of a bridge, a trolley, a car and a hoisting mechanism. The trolley refers to the longitudinal moving device of the entire truss crane structure along the factory building track, usually laid on the ground or high-altitude track. The movement of the trolley is realized by the drive wheel sets installed on both sides of the truss crane, and usually needs to bear the weight of the entire truss crane, the trolley and the lifting appliance; the car refers to the transverse moving mechanism installed on the box-type double girder of the truss crane, that is, along the length direction of the main girder of the truss crane, usually driven by wheel sets or rack and pinion. A hoisting mechanism such as a winch and a hook is installed on the car to be responsible for transverse precise positioning; the hoisting mechanism is used to lift and lower goods. During the operation of the truss crane, precise positioning is a key technology to ensure the working efficiency of the truss crane.

[0003] At present, for most super-high-precision positioning control systems of box-type double-girder truss cranes, it is difficult to perform positioning regulation with high precision during the positioning control of the truss crane, resulting in reduced working efficiency and potential safety hazards caused by inaccurate placement positions of goods; at the same time, for most super-high-precision positioning control systems of box-type double-girder truss cranes, it is difficult to adaptively analyze the influence of wind force and the characteristics of goods themselves on the positioning deviation, resulting in a decrease in positioning accuracy, unable to meet the requirements of high-precision operations, and the system cannot adapt to environmental factors, restricting the application range of the truss crane.

[0004] Therefore, the present invention discloses a super-high-precision positioning control system and method for a box-type double-girder truss crane to solve the above technical problems. Summary of the Invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present invention provides a super-high-precision positioning control system and method for a box-type double-girder truss crane to solve the technical problems that it is difficult to perform positioning regulation with high precision during the positioning control of the truss crane and it is difficult to adaptively analyze the influence of wind force and the characteristics of goods themselves on the positioning deviation. The present invention obtains the goods movement coefficient based on the weight and shape of the goods, generates an environment compensation model based on the historical goods movement coefficient and historical environmental data, inputs the goods movement coefficient and environmental data of the goods into the environment compensation model to obtain the environment compensation parameters of the truss crane, obtains the positioning deviation value through the environment compensation parameters, and analyzes the drop point and the positioning deviation value to obtain the dynamic positioning point, thus solving the above problems.

[0006] To achieve the above object, the first aspect of the present invention provides a super-high-precision positioning control system for a box-type double-girder truss crane, including: an intelligent analysis module, a data collection module, an execution control module and a database;

[0007] The data collection module: used to obtain the target data and environmental data of the gantry crane; among them, the target data includes the dropping point, the weight and shape of the goods; the environmental data includes the wind speed and wind direction;

[0008] The intelligent analysis module: used to obtain the goods movement coefficient based on the weight and shape of the goods, generate an environmental compensation model based on the historical goods movement coefficient and historical environmental data, input the goods movement coefficient and environmental data of the goods into the environmental compensation model to obtain the environmental compensation parameters of the gantry crane, obtain the positioning deviation value through the environmental compensation parameters, and analyze the dropping point and the positioning deviation value to obtain the dynamic positioning point;

[0009] The execution control module: used to obtain the latest position points of the trolley and the crab of the gantry crane through the dynamic positioning point;

[0010] The database: used to store the data generated and required by the system.

[0011] Preferably, obtaining the target data and environmental data of the gantry crane includes:

[0012] Obtaining the dropping point of the current goods through the database, obtaining the shape of the current goods through the monitoring camera, obtaining the weight of the current goods through the weight sensor; obtaining the wind speed through the wind speed sensor, and obtaining the wind direction through the wind vane.

[0013] It should be noted that the installation positions of the wind speed sensor and the wind vane are obtained by manual setting, generally at a fixed height from the ground; for example: the fixed height is taken as 2 meters.

[0014] Preferably, obtaining the goods movement coefficient based on the weight and shape of the goods includes:

[0015] Extracting the weight W of the goods, and obtaining the weight influence factor α based on formula (1):

[0016]

[0017] where, W re is the average value of the historical goods weight, W th is the weight threshold set manually;

[0018] Obtaining the wind direction in the environmental data and the wind direction angle θ formed by the goods, and obtaining the equivalent windward area A based on formula (2):

[0019] A = max(L·H·∣cosθ∣ + L·H·∣sinθ∣, W·H·∣cos(θ - 90°)∣) (2);

[0020] where, L is the length of the goods, W is the width of the goods, and H is the height of the goods;

[0021] Obtain the asymmetry degree C1 and the edge effect coefficient C2, and obtain the shape influence factor β based on formula (5):

[0022]

[0023] where A b is the standard cargo area, which is obtained according to manual setting; γ1 and γ2 are the proportion adjustment coefficients set manually, and their value ranges are both (0, 1];

[0024] Obtain the cargo movement coefficient D based on formula (6):

[0025] D = α·β·(1 + δ·Cov(α, β)) (6);

[0026] where δ is the gain coefficient set manually.

[0027] It should be noted that the wind direction angle θ is formed by the wind direction of the dominant wind and the cargo; where the dominant wind is the wind with the maximum wind speed, which can be identified by a wind speed sensor.

[0028] It should be noted that max() is the symbol for taking the maximum value.

[0029] It should be noted that the gain coefficient δ is a control variable used to adjust the statistical correlation between the weight and shape parameters, and its role is to balance the contribution degree of the combined effect of the weight influence factor and the shape influence factor to the movement coefficient.

[0030] Preferably, the obtaining of the asymmetry degree C1 and the edge effect coefficient C2 includes:[[]]

[0031] Obtain the asymmetry degree C1 based on formula (3):

[0032]

[0033] where

[0034] Obtain the edge effect coefficient C2 based on formula (4):

[0035]

[0036] where BS is the number of surface protrusions and LB is the standard deviation of the edge length.

[0037] It should be noted that the asymmetry degree C1 is a parameter that quantifies the three-dimensional shape symmetry of the cargo and is used to measure the sensitivity of the distribution uniformity of the length, width, and height of the cargo to wind load disturbance.

[0038] It should be noted that the edge effect coefficient C2 is a parameter that quantifies the irregularity and geometric complexity of the cargo surface, and is used to measure the influence of local features such as protrusions on the cargo surface and roughness of edges on the airflow separation and dynamic stability.

[0039] It should be noted that for regular shapes: the volume can be directly calculated using L×W×H, such as cuboids and cylinders; for irregular shapes: the volume needs to be calculated through three-dimensional integration or laser scanning point cloud reconstruction, and L×W×H is still used as the equivalent volume in the formula.

[0040] Preferably, the generation of the environmental compensation model based on the historical cargo movement coefficient and historical environmental data includes:

[0041] Extracting the historical cargo movement coefficient, historical environmental data, and environmental compensation parameters from the historical reference data; where the historical reference data includes the historical cargo movement coefficient and historical environmental data, as well as the environmental compensation parameters of the gantry crane set by experts based on the historical cargo movement coefficient and historical environmental data;

[0042] Integrating the historical cargo movement coefficient, historical environmental data, and environmental compensation parameters into a number of training data and test data; training the artificial intelligence model using the training data, testing the trained artificial intelligence model using the test data, and adjusting the artificial intelligence model according to the test results; finally obtaining an environmental compensation model with the cargo movement coefficient and environmental data as the input and the environmental compensation parameters of the gantry crane as the output; where the artificial intelligence model includes an RNN neural network model and a Transformer neural network model.

[0043] Preferably, obtaining the positioning deviation value through the environmental compensation parameter includes:

[0044] Extracting the environmental compensation parameter HB of the gantry crane, and determining whether the environmental compensation parameter HB is greater than the standard compensation parameter; if so, sending a warning signal that the wind influence is too large; if not, based on the formula Obtaining the positioning deviation value DC of the gantry crane; where BC is the standard compensation parameter, and BCZ is the standard deviation value corresponding to the standard compensation parameter.

[0045] Preferably, analyzing the dropping point and the positioning deviation value to obtain the dynamic positioning point includes:

[0046] Obtaining the original position point of the gantry crane corresponding to the dropping point, extracting the current wind direction, taking the original position point as the starting point, and taking the reverse direction of the wind direction as the forward direction, and marking the position point with a distance value of the positioning deviation value DC from the starting point in the forward direction as the dynamic positioning point.

[0047] Preferably, obtaining the latest position points of the trolley and the hoist of the gantry crane through the dynamic positioning point includes:

[0048] After moving the trolley and the hoist to the corresponding position points according to the original position points of the hoist, obtain the dynamic positioning points of the hoist corresponding to the goods. Through reverse analysis of the dynamic positioning points, obtain the latest position points corresponding to the trolley and the hoist, and move the trolley and the hoist to the latest position points for discharging the goods.

[0049] It should be noted that moving the trolley and the hoist to the corresponding position points according to the original position points of the hoist can be understood as: the position points of the trolley and the hoist designed according to the original position points of the hoist corresponding to the discharging point without considering the influence of wind force.

[0050] Preferably, the step of obtaining the latest position points corresponding to the trolley and the hoist through reverse analysis of the dynamic positioning points includes:

[0051] Mark the moving direction of the trolley as the y-axis and the moving direction of the hoist as the x-axis. Taking the fixed position point as the origin, establish a two-dimensional coordinate system of the current hoist based on the x-axis, y-axis and the origin. Mark the x-axis value of the dynamic positioning point in the two-dimensional coordinate system as the target x value, and mark the y-axis value of the dynamic positioning point in the two-dimensional coordinate system as the target y value. Move the trolley to the target y value on the y-axis and move the hoist to the target x value on the x-axis; wherein, the fixed position point is obtained by manual setting.

[0052] The second aspect of the present invention provides a super-high-precision positioning control method for a box-type double-girder hoist, including the following steps:

[0053] S1: Obtain the target data and environmental data of the hoist;

[0054] S2: Obtain the goods movement coefficient based on the weight and shape of the goods. Generate an environmental compensation model based on the historical goods movement coefficient and historical environmental data. Input the goods movement coefficient and environmental data of the goods into the environmental compensation model to obtain the environmental compensation parameters of the hoist. Obtain the positioning deviation value through the environmental compensation parameters, and analyze the discharging point and the positioning deviation value to obtain the dynamic positioning point;

[0055] S3: Obtain the latest position points of the trolley and the hoist of the hoist through the dynamic positioning point.

[0056] Compared with the prior art, the beneficial effects of the present invention are:

[0057] 1. The present invention obtains the coefficient of cargo movement by the weight and shape of the cargo, generates an environmental compensation model based on the historical coefficient of cargo movement and historical environmental data, inputs the coefficient of cargo movement and environmental data of the cargo into the environmental compensation model to obtain the environmental compensation parameters of the overhead crane, obtains the positioning deviation value through the environmental compensation parameters, analyzes the placement point and the positioning deviation value to obtain the dynamic positioning point, and solves the technical problems that it is difficult to perform high-precision positioning control in the positioning control of the overhead crane and it is difficult to adaptively analyze the influence of wind force and the characteristics of the cargo itself on the positioning deviation; the present invention can adaptively analyze the deviation caused by the influence between the wind force and the cargo on the positioning, and improve the positioning accuracy of the overhead crane.

[0058] 2. When calculating the weight influence factor, the present invention distinguishes the non-linear effects of light load and heavy load through a piecewise function. For light goods, the hyperbolic tangent function is used to enhance the sensitivity, enhancing the sensitivity to small changes in weight in the light load range. For heavy goods, linear attenuation is used to suppress overfitting and reduce the calculation error in the heavy load range; it enables the system to be applicable to the transportation of precision instruments, avoiding positioning jitter caused by small fluctuations in weight, and applicable to the hoisting of heavy machinery, suppressing potential safety hazards caused by inertial overshoot. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0060] Figure 1 is a schematic diagram of the operation steps of the present invention;

[0061] Figure 2 is a schematic diagram of the system module of the present invention;

[0062] Figure 3 is a schematic diagram of the operation steps for generating the environmental compensation model of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0064] Please refer to Figure 1 - Figure 2, an embodiment of the first aspect of the present invention provides a super-high-precision positioning control system for a box-type double-beam overhead crane, including: an intelligent analysis module, a data collection module, an execution control module, and a database;

[0065] Data collection module: used to obtain the target data and environmental data of the overhead crane; among them, the target data includes the dropping point, the weight and shape of the goods; the environmental data includes wind speed and wind direction;

[0066] Intelligent analysis module: used to obtain the goods movement coefficient based on the weight and shape of the goods, generate an environmental compensation model based on the historical goods movement coefficient and historical environmental data, input the goods movement coefficient and environmental data of the goods into the environmental compensation model to obtain the environmental compensation parameters of the overhead crane, obtain the positioning deviation value through the environmental compensation parameters, and analyze the dropping point and the positioning deviation value to obtain the dynamic positioning point;

[0067] Execution control module: used to obtain the latest position points of the trolley and the crab of the overhead crane through the dynamic positioning point;

[0068] Database: used to store the data generated and required by the system.

[0069] In this embodiment, the operation steps of the super-high-precision positioning control system for the box-type double-beam overhead crane can be:

[0070] Obtain the original position point of the overhead crane corresponding to the dropping point, move the trolley and the crab to the corresponding position points according to the original position point of the overhead crane, then obtain the dynamic positioning point of the overhead crane corresponding to the goods, reversely analyze through the dynamic positioning point to obtain the latest position points corresponding to the trolley and the crab, and move the trolley and the crab to the latest position points to drop the goods.

[0071] In this application, obtaining the target data and environmental data of the overhead crane includes:

[0072] Obtain the dropping point of the current goods through the database, obtain the shape of the current goods through the monitoring camera, obtain the weight of the current goods through the weight sensor; obtain the wind speed through the wind speed sensor, and obtain the wind direction through the wind vane.

[0073] It should be noted that the installation positions of the wind speed sensor and the wind vane are obtained by manual setting, generally at a fixed height from the ground; for example: the fixed height value is 2 meters.

[0074] In this application, obtaining the goods movement coefficient based on the weight and shape of the goods includes:

[0075] Extract the weight W of the goods, and obtain the weight influence factor α based on formula (1):

[0076]

[0077] Among them, Wre is the average value of historical cargo weights, W th is the manually set weight threshold;

[0078] Obtain the wind direction angle θ formed by the wind direction in the environmental data and the cargo, and obtain the equivalent windward area A based on formula (2):

[0079] A = max(L·W·∣cosθ∣ + L·H·∣sinθ∣, W·H·∣cos(θ - 90°)∣) (2);

[0080] where L is the length of the cargo, W is the width of the cargo, and H is the height of the cargo;

[0081] Obtain the asymmetry degree C1 and the edge effect coefficient C2, and obtain the shape influence factor β based on formula (5):

[0082]

[0083] where A b is the standard cargo area, which is obtained according to manual setting; γ1 and γ2 are manually set proportional adjustment coefficients, and their value ranges are both (0, 1];

[0084] Obtain the cargo movement coefficient D based on formula (6):

[0085] D = α·β·(1 + δ·Cov(α, β)) (6);

[0086] where δ is the manually set gain coefficient.

[0087] It should be noted that the present invention can enhance the sensitivity to small weight changes in the light load range and reduce the calculation error in the heavy load range by performing different analyses on the cargo through the establishment of a light load range and a heavy load range, enabling the system to be applicable to the transportation of precision instruments, avoiding positioning jitter caused by small weight fluctuations, and being applicable to the hoisting of heavy machinery, suppressing potential safety hazards caused by inertial overshoot.

[0088] It should be noted that the present invention analyzes the correlation between weight and shape influence through the covariance term Cov(α, β), avoiding the dominance of errors by a single factor of weight or shape, and improving the stability under complex working conditions.

[0089] It should be noted that when calculating the weight influence factor α, the present invention distinguishes the non - linear effects of light load and heavy load through a piece - wise function. For light cargo, the sensitivity is enhanced through the hyperbolic tangent function, enhancing the sensitivity to small weight changes in the light load range. For heavy cargo, over - fitting is suppressed through linear attenuation, reducing the calculation error in the heavy load range.

[0090] It should be noted that the larger the cargo weight W, the exponential term The more significant the attenuation, the greater the inertia of the heavy-load goods and the relatively smaller the influence of external disturbances such as wind force; the smaller the weight W of the goods, the slower the attenuation, and the sensitivity of the light-load goods to disturbances is retained.

[0091] It should be noted that in the light-load range W < W th :

[0092] When W << W th , tanh(negative number) ≈ -1, resulting in (1 - 1) = 0, suppressing the movement coefficient of the light-load goods;

[0093] When W → W th , tanh(0) = 0, and at this time smoothly transition to the heavy-load mode;

[0094] In the heavy-load range W ≥ W th :

[0095] When W = W th , the correction term is 1, smoothly connecting with the light-load range;

[0096] When W >> W th , the correction term approaches linearly suppress overshoot.

[0097] It should be noted that the weight threshold W th can be obtained by multiplying the average value W re of the historical goods weights by a proportionality coefficient; among them, the value of the proportionality coefficient is (0, 1].

[0098] It should be noted that the wind direction angle θ is formed by the wind direction of the dominant wind and the goods; among them, the dominant wind is the wind with the maximum wind speed and can be identified by a wind speed sensor.

[0099] It should be noted that max() is the symbol for taking the maximum value.

[0100] In another embodiment, if the length L of the goods is 2m, the width W is 1m, the height H is 1.5m, and the wind direction angle θ is 30°, then: A1 = 2 × 1 × ∣cos30°∣ + 2 × 1.5 × ∣sin30°∣ ≈ 1.732 + 1.5 = 3.232m 2 , A2 = 1 × 1.5 × ∣cos(-60°)∣ = 1.5 × 0.5 = 0.75m 2 , A = max(3.232, 0.75) = 3.232m 2 .

[0101] It should be noted that the gain coefficient δ is a control variable used to adjust the statistical correlation between the weight and shape parameters, and its role is to balance the contribution degree of the combined effect of the weight influence factor and the shape influence factor to the anomaly coefficient.

[0102] In this application, obtaining the asymmetry degree C1 and the edge effect coefficient C2 includes:

[0103] The asymmetry degree C1 is obtained based on formula (3):

[0104]

[0105] Among them,

[0106] The edge effect coefficient C2 is obtained based on formula (4):

[0107]

[0108] Among them, BS is the number of surface protrusions, and LB is the standard deviation of the edge length.

[0109] It should be noted that based on the analysis of the windward area and roughness, the present invention jointly quantifies the number of protrusions and the edge regularity, quantifies the influence of the geometric details of the goods on the positioning stability, and realizes cross-scale comparability by combining volume normalization, thereby improving the accuracy of goods positioning.

[0110] It should be noted that the asymmetry degree C1 is a parameter that quantifies the symmetry of the three-dimensional shape of the goods and is used to measure the sensitivity of the distribution uniformity of the length, width, and height of the goods to wind load disturbance;

[0111] For example: for symmetric goods, such as when L≈W≈H, C1→0, indicating that the shape is close to a cube and the wind load acts relatively uniformly in all directions;

[0112] For asymmetric goods, such as long and flat shapes, C1>>0, indicating that the dimension in a certain direction significantly deviates from the average value, resulting in uneven distribution of the wind load moment.

[0113] It should be noted that the edge effect coefficient C2 is a parameter that quantifies the surface irregularity and geometric complexity of the goods and is used to measure the influence of local features such as surface protrusions and edge roughness of the goods on the air flow separation and dynamic stability.

[0114] It should be noted that BS is the number of surface protrusions, and the specific explanation is: the number of non-smooth protrusion structures existing on the surface of the goods, such as bolts on mechanical parts, reinforcement strips on packaging boxes, etc., which can be obtained through three-dimensional laser scanning or image recognition technology;

[0115] For example: if a standard container has 4 longitudinal reinforcing ribs on both sides, the number of surface protrusions is 4.

[0116] It should be noted that LB is the standard deviation of edge lengths, and the specific explanation is: the standard deviation of the lengths of all the edges of the goods, which is used to measure the degree of dispersion of the edge lengths;

[0117] It can be expressed by the formula: where i is the edge length number, l i is the length of the i-th edge, Pl is the average edge length, and N is the total number of edges;

[0118] For example: a cuboid has a length L = 3m, a width W = 1m, and a height H = 1m. Its edge lengths include: 4 long edges (3m) and 8 short edges (1m), and the standard deviation

[0119] It should be noted that for regular shapes: the volume can be directly calculated using L×W×H, such as cuboids and cylinders; for irregular shapes: the volume needs to be calculated through three-dimensional integration or laser scanning point cloud reconstruction, and L×W×H is still used as the equivalent volume in the formula.

[0120] It should be noted that the cube root of the volume represents the equivalent characteristic size of the goods, which is used to normalize the influence of protrusions and edge lengths.

[0121] Please refer to Figure 3 , in this application, an environment compensation model is generated based on historical goods movement coefficients and historical environment data, including:

[0122] Extracting the historical goods movement coefficient, historical environment data, and environment compensation parameters from the historical reference data; where the historical reference data includes the historical goods movement coefficient and historical environment data, as well as the environment compensation parameters of the gantry crane set by experts according to the historical goods movement coefficient and historical environment data;

[0123] Integrating the historical goods movement coefficient, historical environment data, and environment compensation parameters into several training data and test data; using the training data to train the artificial intelligence model, using the test data to test the trained artificial intelligence model, and adjusting the artificial intelligence model according to the test results; finally obtaining an environment compensation model with the goods movement coefficient and environment data as the input and the environment compensation parameters of the gantry crane as the output; where the artificial intelligence model includes an RNN neural network model and a Transformer neural network model.

[0124] It should be noted that the environment compensation parameter is a parameter set by experts according to the historical goods movement coefficient and historical environment data, which reflects the degree of compensation required by the gantry crane affected by the wind speed. The larger the historical goods movement coefficient and historical environment data, the greater the degree of compensation required by the gantry crane, and the larger the environment compensation parameter.

[0125] Specifically, the specific steps of using the test data to test the trained artificial intelligence model and adjusting the artificial intelligence model according to the test results are:

[0126] Input the historical cargo movement coefficient and historical environmental data in the inspection data into the trained artificial intelligence model to obtain the corresponding environmental compensation parameters. Compare the corresponding environmental compensation parameters with the environmental compensation parameters in the inspection data. When the gap between the two is less than the set threshold, no parameter adjustment is required, and the next set of inspection data is inspected; when the gap between the two is not less than the set threshold, the corresponding parameters are adjusted until the gap between the two is less than the set threshold, and then the next set of inspection data is inspected. When the number of inspection data with the gap between the environmental compensation parameters obtained from all inspection data less than the threshold accounts for 90% or more of the total amount of inspection data, an environmental compensation model with the input of cargo movement coefficient and environmental data and the output of environmental compensation parameters is obtained.

[0127] In this application, the positioning deviation value is obtained through the environmental compensation parameter, including:

[0128] Extract the environmental compensation parameter HB of the overhead crane, and determine whether the environmental compensation parameter HB is greater than the standard compensation parameter; if so, send a warning signal that the wind influence is too large; if not, based on the formula Obtain the positioning deviation value DC of the overhead crane; where BC is the standard compensation parameter, and BCZ is the standard deviation value corresponding to the standard compensation parameter.

[0129] In this application, the dynamic positioning point is obtained by analyzing the dropping point and the positioning deviation value, including:

[0130] Obtain the original position point of the overhead crane corresponding to the dropping point, extract the current wind direction, take the original position point as the starting point, and take the reverse direction of the wind direction as the forward direction. Mark the position point with the distance value from the starting point in the forward direction equal to the positioning deviation value DC as the dynamic positioning point.

[0131] It should be noted that the position point with the distance value from the starting point in the forward direction equal to the positioning deviation value DC is marked as the dynamic positioning point, specifically limited to: the position point corresponding to the dynamic positioning point is in the reverse direction of the wind direction.

[0132] In this application, the latest position points of the trolley and the crab of the overhead crane are obtained through the dynamic positioning point, including:

[0133] After moving the trolley and the crab to the corresponding position points according to the original position point of the overhead crane, obtain the dynamic positioning point of the overhead crane corresponding to the cargo, and inversely analyze through the dynamic positioning point to obtain the latest position points corresponding to the trolley and the crab, and move the trolley and the crab to the latest position points for cargo dropping.

[0134] It should be noted that moving the trolley and the hoist to the corresponding position points according to the original position points of the hoist can be understood as: the position points of the trolley and the hoist designed according to the original position points of the hoist corresponding to the dropping points without considering the influence of wind force.

[0135] In this application, the latest position points corresponding to the trolley and the hoist are obtained through reverse analysis of the dynamic positioning points, including:

[0136] Mark the moving direction of the trolley as the y-axis, and the moving direction of the hoist as the x-axis. With the fixed position point as the origin, a two-dimensional coordinate system of the current hoist is established based on the x-axis, y-axis, and the origin. Mark the x-axis value of the dynamic positioning point in the two-dimensional coordinate system as the target x value, and mark the y-axis value of the dynamic positioning point in the two-dimensional coordinate system as the target y value. Move the trolley to the target y value on the y-axis, and move the hoist to the target x value on the x-axis; among them, the fixed position point is obtained through manual setting.

[0137] It should be noted that the fixed position point is obtained through manual setting, and can be the center point of the movable position of the hoist or the fixed collection point of the goods.

[0138] The second aspect of the embodiments of the present invention provides a high-precision positioning control method for a box-type double-girder hoist, including the following steps:

[0139] S1: Obtain the target data and environmental data of the hoist;

[0140] S2: Obtain the goods movement coefficient based on the weight and shape of the goods, generate an environmental compensation model based on the historical goods movement coefficient and historical environmental data, input the goods movement coefficient and environmental data of the goods into the environmental compensation model to obtain the environmental compensation parameters of the hoist, obtain the positioning deviation value through the environmental compensation parameters, and analyze the dropping point and the positioning deviation value to obtain the dynamic positioning point;

[0141] S3: Obtain the latest position points of the trolley and the hoist of the hoist through the dynamic positioning point.

[0142] Some of the data in the above formula are calculated by removing the dimension and taking their numerical values. The formula is obtained by software simulation of a large amount of collected data to get a formula closest to the actual situation; the preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained through simulation of a large amount of data.

[0143] The working principle of the present invention:

[0144] Obtain the target data and environmental data of the gantry crane, which provides data support for subsequent analysis; obtain the cargo movement coefficient based on the weight and shape of the cargo, which can adaptively analyze the deviation of positioning caused by the influence between the wind force and the cargo, improving the positioning accuracy of the gantry crane; generate an environmental compensation model based on the historical cargo movement coefficient and historical environmental data, input the cargo movement coefficient and environmental data of the cargo into the environmental compensation model to obtain the environmental compensation parameters of the gantry crane, obtain the positioning deviation value through the environmental compensation parameters, analyze the dropping point and the positioning deviation value to obtain the dynamic positioning point; obtain the latest position points of the trolley and the hoist of the gantry crane through the dynamic positioning point.

[0145] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A super-high-precision positioning control system for a box-type double-girder overhead crane, characterized in that, Including: An intelligent analysis module, a data collection module, and an execution control module; The data collection module: is used to obtain the target data and environmental data of the gantry crane; among them, the target data includes the placement point, the weight and shape of the goods; the environmental data includes the wind speed and wind direction; The intelligent analysis module: is used to obtain the goods movement coefficient based on the weight and shape of the goods, generate an environmental compensation model based on the historical goods movement coefficient and historical environmental data, input the goods movement coefficient and environmental data of the goods into the environmental compensation model to obtain the environmental compensation parameters of the gantry crane, obtain the positioning deviation value through the environmental compensation parameters, and analyze the placement point and the positioning deviation value to obtain the dynamic positioning point; The execution control module: is used to obtain the latest position points of the trolley and the crab of the gantry crane through the dynamic positioning point.

2. The ultra-high-precision positioning control system for a box-type double-beam overhead crane according to claim 1, wherein The obtaining of the target data and environmental data of the gantry crane includes: Obtaining the placement point of the current goods through the database, obtaining the shape of the current goods through the monitoring camera, obtaining the weight of the current goods through the weight sensor; obtaining the wind speed through the wind speed sensor, and obtaining the wind direction through the wind vane.

3. The ultra-high-precision positioning control system of a box-type double-girder overhead crane according to claim 1, characterized in that, The obtaining of the goods movement coefficient based on the weight and shape of the goods includes: Extracting the weight W of the goods, and obtaining the weight influence factor α based on formula (1): Among them, W re is the average value of the historical cargo weight, and W th is the weight threshold; Obtaining the wind direction in the environmental data and the wind direction angle θ formed by the goods, and obtaining the equivalent windward area A based on formula (2): A = max(L·W·∣cosθ∣ + L·H·∣sinθ∣, W·H·∣cos(θ - 90°)∣) (2); Among them, L is the length of the goods, W is the width of the goods, and H is the height of the goods; Obtaining the asymmetry degree C1 and the edge effect coefficient C2, and obtaining the shape influence factor β based on formula (5): Among them, A b is the standard cargo area; γ1 and γ2 are proportional adjustment coefficients, and their value ranges are both (0, 1]; Obtaining the goods movement coefficient D based on formula (6): D = α·β·(1 + δ·Cov(α,β)) (6); Among them, δ is the gain coefficient.

4. The ultra-high-precision positioning control system of a box-type double-beam overhead crane according to claim 3, characterized in that, The obtaining of the asymmetry degree C1 and the edge effect coefficient C2 includes: Obtaining the asymmetry degree C1 based on formula (3): Among them, Obtaining the edge effect coefficient C2 based on formula (4): Among them, BS is the number of surface protrusions, and LB is the standard deviation of the edge length.

5. The ultra-high precision positioning control system of a box-type double-girder overhead crane according to claim 1, characterized in that, The generating of the environmental compensation model based on the historical goods movement coefficient and historical environmental data includes: Extracting the historical goods movement coefficient, historical environmental data, and environmental compensation parameters in the historical reference data; among them, the historical reference data includes the historical goods movement coefficient and historical environmental data, and the environmental compensation parameters of the gantry crane set by experts according to the historical goods movement coefficient and historical environmental data; Integrating the historical goods movement coefficient, historical environmental data, and environmental compensation parameters into a number of training data and test data; training the artificial intelligence model with the training data, testing the trained artificial intelligence model with the test data, and adjusting the artificial intelligence model according to the test results; finally obtaining an environmental compensation model with the goods movement coefficient and environmental data as the input and the environmental compensation parameters of the gantry crane as the output; among them, the artificial intelligence model includes an RNN neural network model and a Transformer neural network model.

6. The ultra-high precision positioning control system of a box-type double-girder overhead crane according to claim 1, characterized in that, The obtaining of the positioning deviation value through the environmental compensation parameters includes: Extract the environmental compensation parameter HB of the gantry crane, and determine whether the environmental compensation parameter HB is greater than the standard compensation parameter; if so, issue a warning signal that the wind influence is too large; if not, based on the formula Obtain the positioning deviation value DC of the gantry crane; where BC is the standard compensation parameter, and BCZ is the standard deviation value corresponding to the standard compensation parameter.

7. A super-high-precision positioning control system for a box-type double-beam overhead crane according to claim 1, characterized in that, Analyzing the dropping point and the positioning deviation value to obtain the dynamic positioning point includes: Obtaining the original position point of the overhead crane corresponding to the dropping point, extracting the current wind direction, taking the original position point as the starting point, and taking the reverse direction of the wind direction as the forward direction. Mark the position point with a distance value of the positioning deviation value DC from the starting point in the forward direction as the dynamic positioning point.

8. A super-high-precision positioning control system for a box-type double-girder overhead crane according to claim 7, characterized in that, Obtaining the latest position points of the trolley and the crab of the overhead crane through the dynamic positioning point includes: After moving the trolley and the crab to the corresponding position points according to the original position point of the overhead crane, obtaining the dynamic positioning point of the overhead crane corresponding to the goods, and reversely analyzing through the dynamic positioning point to obtain the latest position points corresponding to the trolley and the crab, and moving the trolley and the crab to the latest position points to drop the goods.

9. The ultra-high precision positioning control system for a box-shaped double-girder overhead crane according to claim 8, characterized in that, Reversely analyzing through the dynamic positioning point to obtain the latest position points corresponding to the trolley and the crab includes: Mark the moving direction of the trolley as the y-axis, and mark the moving direction of the crab as the x-axis. Taking the fixed position point as the origin, establish a two-dimensional coordinate system of the current overhead crane based on the x-axis, the y-axis and the origin. Mark the x-axis value of the dynamic positioning point in the two-dimensional coordinate system as the target x value, and mark the y-axis value of the dynamic positioning point in the two-dimensional coordinate system as the target y value. Move the trolley to the target y value on the y-axis, and move the crab to the target x value on the x-axis.

10. A method for ultra-high precision positioning control of a box-type double-girder overhead crane, which operates based on the ultra-high precision positioning control system of a box-type double-girder overhead crane according to any one of claims 1 to 9, characterized in that: S1: Obtaining the target data and environmental data of the overhead crane; S2: Obtaining the goods movement coefficient based on the weight and shape of the goods, generating an environmental compensation model based on the historical goods movement coefficient and historical environmental data, inputting the goods movement coefficient and environmental data of the goods into the environmental compensation model to obtain the environmental compensation parameters of the overhead crane, obtaining the positioning deviation value through the environmental compensation parameters, and analyzing the dropping point and the positioning deviation value to obtain the dynamic positioning point; S3: Obtaining the latest position points of the trolley and the crab of the overhead crane through the dynamic positioning point.

Images