A method and device for multi-point synchronous hoisting control of a hoisting mechanism

Abstract

The application provides a kind of multi-point synchronous lifting control method and device of lifting mechanism, it is related to crane control technical field, comprising: obtaining the load structure information of load to be lifted, lifting point optimization is carried out;Multi-point load balancing analysis, generate first multi-point control parameter group;Establish multi-point environment-lifting abnormal probability space and multi-point offset compensation space;Screening first abnormal lifting point with greater than preset probability abnormal probability;Carrying out offset compensation control, update first multi-point control parameter group, generate second multi-point control parameter group;Input control terminal carries out lifting control.The application can solve the technical problems of instability in the lifting process due to insufficient adaptability to changes in the working environment in the prior art, by obtaining the load structure information of the load to be lifted, optimizing the lifting point, and combining load balancing analysis and environmental offset influence analysis, the stability and safety of the lifting process are improved.

Description

Technical Field

[0001] This application relates to the field of crane control technology, and in particular to a multi-point synchronous lifting control method and device for a lifting mechanism. Background Technology

[0002] With the rapid development of industrial automation and logistics, lifting mechanisms are increasingly widely used in various fields. The main function of a lifting mechanism is to achieve vertical transportation of heavy objects, and its efficiency and safety directly affect production efficiency and worker safety. In many application scenarios, such as the installation of large equipment and the handling of heavy goods, multiple lifting points need to work together to achieve smooth and synchronous lifting of the load. Existing lifting mechanisms have some technical problems in multi-point synchronous lifting. Due to the lack of accurate acquisition and utilization of load structure information, existing lifting mechanisms are insufficiently adaptable to changes in the working environment, such as wind speed changes and uneven ground, leading to instability and safety hazards during the lifting process. Specifically, this manifests as uneven load distribution during lifting, difficulty in synchronous control, and susceptibility to environmental influences, resulting in low lifting efficiency and poor safety.

[0003] In summary, existing technologies suffer from insufficient adaptability to changes in the working environment during the lifting process, leading to instability in the lifting process. Summary of the Invention

[0004] The purpose of this application is to provide a multi-point synchronous lifting control method and device for a lifting mechanism, in order to solve the technical problem in the prior art that the lifting process is unstable due to insufficient adaptability to changes in the working environment.

[0005] In view of the above problems, this application provides a multi-point synchronous lifting control method and device for a lifting mechanism.

[0006] In a first aspect, this application provides a multi-point synchronous lifting control method for a lifting mechanism. This method is implemented using a multi-point synchronous lifting control device for the lifting mechanism. The method includes: acquiring load structure information of the load to be lifted; optimizing lifting points to minimize environmental impact indicators; generating multiple lifting points; wherein the lifting point optimization identifies key support points and generates lifting points based on the structural design drawings of the load to be lifted; performing similarity screening and overlap analysis based on historical lifting data; constructing a screening sample set and evaluating environmental impact indicators to complete the lifting point distribution optimization; and performing multi-point load balancing analysis based on the multiple lifting points and the load structure information to generate a first multi-point control parameter set. Based on the first multi-point control parameter group, an offset impact analysis of the working environment is performed on the multiple lifting points to establish a multi-point environment-lifting anomaly probability space and a multi-point offset compensation space. Environmental monitoring is performed at the lifting start node, and the multi-point environment-lifting anomaly probability space is called to identify the anomaly probability of any lifting point, filtering out the first abnormal lifting point with an anomaly probability greater than a preset probability. Offset compensation control is performed on the first abnormal lifting point based on the multi-point offset compensation space, and the first multi-point control parameter group is updated with offset compensation parameters to generate a second multi-point control parameter group. After configuring the lifting points of the target lifting mechanism according to the multiple lifting points, the second multi-point control parameter group is input into the control terminal of the target lifting mechanism to perform lifting control on the load to be lifted.

[0007] Secondly, this application also provides a multi-point synchronous lifting control device for a lifting mechanism, used to execute a multi-point synchronous lifting control method for a lifting mechanism as described in the first aspect, wherein the multi-point synchronous lifting control device for a lifting mechanism includes: a load information acquisition module, which is used to acquire the load structure information of the load to be lifted, optimize the lifting points with the goal of minimizing environmental impact indicators, and generate multiple lifting points, wherein the lifting point optimization identifies key support points and generates lifting points by combining the structural design drawings of the load to be lifted, performs similarity screening and overlap analysis with historical lifting data, constructs a screening sample set and evaluates environmental impact indicators, and completes the lifting point distribution optimization; a load balancing analysis module, which is used to perform multi-point load balancing analysis by combining the multiple lifting points and the load structure information, and generates a first multi-point control parameter group; and an offset influence analysis module, which is used to perform multi-point load balancing analysis based on the load structure information of the load to be lifted. The system comprises: a first multi-point control parameter group for analyzing the impact of environmental offsets on the multiple lifting points, establishing a multi-point environment-lifting anomaly probability space and a multi-point offset compensation space; an anomaly probability identification module for monitoring the environment at the lifting start node and using the multi-point environment-lifting anomaly probability space to identify the anomaly probability of any lifting point, selecting a first abnormal lifting point with an anomaly probability greater than a preset probability; an offset compensation control module for performing offset compensation control on the first abnormal lifting point based on the multi-point offset compensation space, updating the first multi-point control parameter group with offset compensation parameters to generate a second multi-point control parameter group; and a lifting point configuration module for configuring the lifting points of the target lifting mechanism according to the multiple lifting points, inputting the second multi-point control parameter group into the control terminal of the target lifting mechanism to perform lifting control on the load to be lifted.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By acquiring the load structure information of the load to be lifted, and aiming to minimize environmental impact indicators, the lifting point optimization is performed, generating multiple lifting points. The lifting point optimization process involves identifying key support points from the structural design drawings of the load to be lifted and generating lifting points. Similarity screening and overlap analysis are performed using historical lifting data to construct a screening sample set and evaluate environmental impact indicators, thus completing the lifting point distribution optimization. Multi-point load balancing analysis is then performed using the multiple lifting points and the load structure information to generate a first multi-point control parameter set. Based on the first multi-point control parameter set, the operational environment offset impact analysis is conducted on the multiple lifting points to establish a multi-point environment-lifting anomaly analysis. The system employs a probability space and a multi-point offset compensation space. At the lifting start-up node, environmental monitoring is performed, and the multi-point environment-lifting anomaly probability space is invoked to identify the anomaly probability of any lifting point. A first abnormal lifting point with an anomaly probability greater than a preset probability is selected. Based on the multi-point offset compensation space, offset compensation control is applied to the first abnormal lifting point, and the first multi-point control parameter group is updated with offset compensation parameters to generate a second multi-point control parameter group. After configuring the lifting points of the target lifting mechanism according to the multiple lifting points, the second multi-point control parameter group is input into the control terminal of the target lifting mechanism to control the lifting of the load to be lifted. In other words, by acquiring the load structure information of the load to be lifted, optimizing the lifting points, and combining load balancing analysis and environmental offset impact analysis, the stability and safety of the lifting process are improved.

[0010] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating a multi-point synchronous lifting control method for a lifting mechanism according to this application.

[0013] Figure 2 This is a schematic diagram of the structure of a multi-point synchronous lifting control device for a lifting mechanism according to this application.

[0014] Figure labeling: 11 Load information acquisition module, 12 Load balancing analysis module, 13 Offset impact analysis module, 14 Anomaly probability identification module, 15 Offset compensation control module, 16 Lifting point configuration module. Detailed Implementation

[0015] This application provides a multi-point synchronous lifting control method and device for a lifting mechanism, which solves the technical problem in the prior art where insufficient adaptability to changes in the working environment during the lifting process leads to instability. By obtaining the load structure information of the load to be lifted, the lifting point is optimized, and combined with load balancing analysis and environmental offset influence analysis, the stability and safety of the lifting process are improved.

[0016] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0017] Example 1, please refer to the appendix. Figure 1 This application provides a multi-point synchronous lifting control method for a hoisting mechanism, wherein the multi-point synchronous lifting control method for a hoisting mechanism is applied to a multi-point synchronous lifting control device for a hoisting mechanism, and the multi-point synchronous lifting control method for a hoisting mechanism specifically includes the following steps:

[0018] Step 1: Obtain the load structure information of the load to be lifted. With the goal of minimizing environmental impact indicators, optimize the lifting points and generate multiple lifting points. The lifting point optimization is carried out by identifying key support points and generating lifting points through the structural design drawings of the load to be lifted. Similarity screening and overlap analysis are performed by combining historical lifting data to construct a screening sample set and evaluate environmental impact indicators, thus completing the lifting point distribution optimization.

[0019] Specifically, the physical limitations and operating range of the lifting mechanism during lifting operations are determined, including the location and number of lifting points and the maximum load each point can withstand. The structural design drawings of the load to be lifted are read to identify key support points and the mass distribution network. Preliminary lifting points are determined, and the weight distribution network of the load to be lifted is compared with historical weight distributions to extract historical records with high similarity. Historical lifting points with the smallest distance from the preliminary lifting points are selected to generate a filter sample set. Based on historical environmental parameters and offset records, the minimum environmental parameters that satisfy the preset abnormal offset are selected from the sample set. Combined with the environmental impact index set, the distribution of historical lifting points with the smallest environmental impact is obtained. These are then replaced with lifting points with the smallest overlap distance from the first group of lifting points, ultimately resulting in multiple lifting points. By combining historical data and environmental impact indicators, the selection of lifting points is optimized to improve lifting efficiency and safety.

[0020] Step 2: Combine the multiple lifting points and the load structure information to perform multi-point load balancing analysis and generate the first multi-point control parameter group.

[0021] Specifically, the center of gravity of the load, i.e., the equilibrium point of the load weight, is identified based on the load structure information. The distances and orientations of multiple lifting points relative to the load's center of gravity are calculated, yielding multiple relative positional information. Combining the load's weight distribution network and the relative positional information of the lifting points, a torque balance model is used to generate the first multi-point control parameter set. Load balancing analysis calculates the load proportion that each lifting point should bear, ensuring the entire load is evenly distributed across multiple lifting points to prevent overloading or underloading, thereby improving lifting safety and efficiency. The first multi-point control parameter set includes multiple control parameters corresponding to each lifting point, such as load distribution, lifting speed, and acceleration. Through load balancing analysis, stability and safety during the lifting process are ensured.

[0022] Step 3: Based on the first multi-point control parameter group, perform an offset impact analysis on the multiple lifting points to establish a multi-point environment-lifting anomaly probability space and a multi-point offset compensation space.

[0023] Specifically, a lifting simulation model is constructed using load structure information and control parameters. Various environmental test samples, such as different wind speeds and temperatures, are created and loaded into the simulation model for testing. The simulation model is run, recording the offset data of each lifting point under various environmental conditions. The offset of each lifting point in the current lifting operation is input into the probability transformation model and compared with historical data in the probability transformation sample library. Historical samples matching the current offset are found in the probability transformation sample library, and their corresponding anomaly probabilities are extracted. The offset sample of each lifting point and its corresponding anomaly probability identifier are stored in a space. Based on environmental testing and anomaly probability transformation, a multi-point environment-lifting anomaly probability space is established, describing the probability distribution of anomalies occurring during the lifting process under different environmental conditions. Using the lifting simulation model, a feedback correction test of the offset is performed based on the multi-point environment-lifting anomaly probability space. Based on the results of the feedback correction test, a multi-point offset compensation space is generated, describing the control parameters that need to be adjusted to reduce offset under different environmental conditions. Through offset impact analysis and the establishment of the offset compensation space, the lifting control strategy is optimized, improving lifting efficiency and safety.

[0024] Step 4: Perform environmental monitoring at the lifting start node, and call the multi-point environment-lifting anomaly probability space to identify the anomaly probability of any lifting point, and select the first abnormal lifting point with an anomaly probability greater than the preset probability.

[0025] Specifically, environmental monitoring is performed at the start point of the lifting operation to measure and record current operating environmental conditions, such as temperature, humidity, and wind speed. Based on the current environmental parameters, a multi-point environment-lifting anomaly probability space is invoked, and the current environmental parameters are compared with the data in the multi-point environment-lifting anomaly probability space to identify lifting points that may experience anomalies. Based on the environmental data and the anomaly probability space, the anomaly probability of each lifting point is identified. Anomaly probability refers to the probability that a lifting point will experience an anomaly (such as swaying or tilting) under the current environmental conditions. A preset anomaly probability threshold is set; only when the anomaly probability of a lifting point exceeds this threshold is it considered an abnormal lifting point. Lifting points with anomaly probabilities greater than the preset probability are filtered out. These points have a high risk of anomalies under the current environmental conditions and require special attention or measures. For example, suppose the preset probability is set to 0.3. Environmental monitoring at the lifting start point yields current environmental parameters of wind speed 6 m / s and temperature 25℃. The multi-point environment-lifting anomaly probability space is invoked to find the anomaly probability corresponding to the current environmental parameters. Based on the multi-point environment-lifting anomaly probability space, the anomaly probability of point P1 is 0.4, that of point P2 is 0.25, and that of point P3 is 0.2. Since the anomaly probability of point P1 is greater than the preset anomaly probability threshold of 0.2, it is selected as the first abnormal lifting point. By identifying abnormal lifting points, potential anomalies can be detected in advance, allowing for targeted adjustments to control parameters and improving operational stability.

[0026] Step 5: Perform offset compensation control on the first abnormal lifting point based on the multi-point offset compensation space, update the first multi-point control parameter group with the offset compensation parameters, and generate the second multi-point control parameter group.

[0027] Specifically, offset compensation control is applied to the first abnormal lifting point based on the multi-point offset compensation space. Control parameters, such as lifting speed and acceleration, are adjusted to reduce or eliminate the offset. Offset compensation parameters are specific parameters used to adjust the lifting control strategy to reduce or eliminate lifting point offset caused by various factors (such as uneven load distribution and environmental conditions). These parameters typically include lifting speed, acceleration, and lifting torque, and are determined through simulation and experimental data to achieve a more accurate and stable lifting process. The first multi-point control parameter set is updated based on the offset compensation parameters to generate a second multi-point control parameter set, which includes optimized control parameters for the first abnormal lifting point. By adjusting the control parameters, the abnormal risks during the lifting process are reduced, thus lowering losses and risks.

[0028] Step Six: After configuring the lifting points of the target lifting mechanism according to the multiple lifting points, input the second multi-point control parameter group into the control terminal of the target lifting mechanism to control the lifting of the load to be lifted.

[0029] Specifically, the target lifting mechanism is physically configured with multiple lifting points. Lifting points are set at their actual locations on the target lifting mechanism, ensuring that the position of each lifting point conforms to the optimized scheme. A second set of multi-point control parameters is input to the control terminal of the target lifting mechanism via a control terminal or the interface of the control system. The control terminal, acting as the brain of the lifting mechanism, is responsible for receiving instructions and parameters and executing lifting operations. Based on the input control parameters, the control terminal performs lifting control on the load to be lifted. The lifting process is initiated, and the lifting speed, acceleration, etc., are adjusted according to the parameters to ensure the load is lifted smoothly and safely. Precise control enhances the safety of the lifting operation, reduces the risk of abnormalities, and, combined with the input of the parameter set and the execution of the control terminal, improves the accuracy and stability of the lifting control.

[0030] Furthermore, step one of this application includes:

[0031] The system reads the structural design drawings of the load to be lifted, identifies key support points and weight distribution networks, generates a first set of lifting points based on the key support points, connects to the log storage platform of the target lifting mechanism, and receives the historical weight distribution network of historical lifting control loads. It compares the historical weight distribution network with the weight distribution network, extracts historical lifting control records corresponding to lifting control loads with similarity scores meeting a preset similarity threshold, and establishes a lifting control sample dataset. This dataset includes historical lifting point distributions, historical environmental parameters, and historical offset records. It performs overlap distance analysis between the historical lifting point distributions and the first set of lifting points, filters the lifting control sample dataset based on the overlap distance, and generates a filtered sample set. Based on historical environmental parameters and historical offset records, it performs environmental impact index analysis on the filtered sample set, generating an environmental impact index set. It then performs environmental impact index minimization screening based on the environmental impact index set, obtains the historical lifting point distribution with the smallest environmental impact index, and replaces the lifting points with the smallest overlap distance according to the first set of lifting points, generating the multiple lifting points.

[0032] Furthermore, this application also includes the following steps:

[0033] Based on the historical environmental parameters and the historical offset records, the minimum environmental parameters that satisfy the preset abnormal offset are selected; the environmental impact index set is generated using the minimum environmental parameters, wherein the environmental impact index is inversely proportional to the minimum environmental parameters.

[0034] Specifically, the structural design drawings of the load to be lifted are read using professional CAD software or similar tools. These drawings contain detailed structural information about the load, such as dimensions, materials, and connection methods. By analyzing the drawings, the key support points of the load are identified; these are the points where the load can withstand the lifting force. The load to be lifted may be equipment, process products, etc., and some locations are process assembly points or support points. To avoid damage to the equipment, lifting points are set for these points. Simultaneously, the weight distribution of the load is analyzed, including determining the center of gravity and how the weight is distributed among different parts. Suitable points are selected from the key support points as lifting points, generating the first set of lifting points. The control system of the lifting mechanism is connected to a log storage platform to receive historical lifting control load records, including weight distribution network information, from the log storage platform.

[0035] The weight distribution network of the load to be lifted is compared with the historical weight distribution network, and the similarity between the two networks is calculated using Euclidean distance, cosine similarity, etc. A similarity threshold is set, and only historical records of lifting control loads whose similarity meets the threshold are extracted. The extracted historical lifting control records are integrated to form a lifting control sample dataset, including historical lifting point distribution, historical environmental parameters, and historical offset records. Historical lifting point distribution refers to the specific positional distribution of the lifting point relative to the load during past lifting operations, including the specific position of the lifting point set for each lifting operation. Historical environmental parameters include environmental conditions recorded during historical lifting operations, such as wind speed, temperature, humidity, and ground conditions. Historical offset records refer to the deviation between the actual lifting point position and the predetermined position during past lifting operations. These deviations are due to various factors, such as equipment performance, environmental conditions, or operational errors.

[0036] For each point in the first group of lifting points, find the closest point in the historical lifting point distribution; this distance is called the overlap distance. Since a point cannot be reused, a unique historical lifting point is found for each point in the first group. For each point in the first group of lifting points, calculate its distance to the nearest historical lifting point. Set a preset minimum distance threshold. Only when all overlap distances meet this minimum distance threshold will the relevant historical lifting control record be retained, thus generating a filter sample set. For example, suppose there are 3 lifting points in the first group, A, B, and C. There are multiple points in the historical lifting point distribution. The preset minimum distance threshold is 0.5 meters. For point A, find the closest point in the historical lifting point distribution to A, and calculate the distance as 0.3 meters; for point B, find the closest point to B, and calculate the distance as 0.4 meters; for point C, find the closest point to C, and calculate the distance as 0.2 meters. Since all overlap distances are less than 0.5 meters, the relevant historical lifting control record is retained, generating a filter sample set.

[0037] A preset abnormal offset threshold is set to determine whether historical offset records exceed the normal range. Historical environmental parameters and offset records in the hoisting control sample dataset are analyzed to identify the minimum environmental parameter causing the abnormal offset. Based on the identified minimum environmental parameter, an environmental impact index set is generated to assess the hoisting process's resistance to environmental factors such as wind speed and temperature changes. The generated environmental impact index is ensured to be inversely proportional to the minimum environmental parameter. The smaller the environmental parameter value, the higher the environmental impact index, indicating stronger resistance to environmental factors during the hoisting process. Based on the environmental impact index set, the hoisting point distribution with the least environmental impact is selected from historical data. The first group of hoisting points is compared with the selected historical hoisting points, and the point with the smallest overlap distance is replaced. Through this replacement operation, a new combination of hoisting points is generated, exhibiting optimal performance and minimized environmental impact under specific environmental parameters. By identifying key support points, it can be ensured that these points receive appropriate support during the hoisting process, avoiding equipment damage due to insufficient support. By analyzing historical data and current load structure information, the selection of lifting points is optimized to minimize environmental impact and ensure the safety and efficiency of lifting operations.

[0038] Furthermore, step two of this application includes:

[0039] The load center of gravity position is identified based on the load structure information; the distance and orientation of the multiple lifting points relative to the load center of gravity position are calculated to generate multiple relative position information; the first multi-point control parameter set is generated by combining the weight distribution network and the multiple relative position information through the torque balance model; wherein, the torque balance model is used to balance the forces and torques of multiple lifting points and is trained through torque balance samples.

[0040] Specifically, by utilizing load structure information, including the load's size, shape, material, and connection method, the load's center of gravity is calculated or estimated. The center of gravity is the equilibrium point of the load's weight and is crucial for stability and safety during the lifting process. Geometric or mathematical methods are used to calculate the distance and orientation of each lifting point relative to the load's center of gravity, determining the specific positional relationship of each lifting point relative to the center of gravity. Distance refers to the straight-line distance from the lifting point to the center of gravity, while orientation describes the spatial positional relationship of the lifting point relative to the center of gravity. Using a torque balance model, combined with the weight distribution network and the relative position information of the lifting points relative to the center of gravity, control parameters are calculated. The torque at each lifting point is calculated using a mathematical model to ensure that the total torque is zero, maintaining load balance. The torque balance model is used to balance the forces and torques at multiple lifting points, ensuring stability and safety during the lifting process. The first multi-point control parameter group includes multiple control parameters corresponding to each of the multiple lifting points. For example, given three lifting points P1, P2, and P3, the distance and orientation of each lifting point relative to the load's center of gravity are calculated. Combining the weight distribution network and this relative position information, a torque balance model is used to generate the first set of multi-point control parameters. If the load weight distribution on the X-axis is W1, W2, W3, then the torques M1, M2, and M3 generated by each lifting point P1, P2, and P3 should satisfy the following condition: M1 + M2 + M3 = 0 (total torque is 0). By balancing the forces and torques at multiple lifting points, offset and swaying during the lifting process are reduced, improving operational accuracy.

[0041] Furthermore, step three of this application includes:

[0042] Simulation modeling is performed based on the load structure information and the first multi-point control parameter group to generate a lifting simulation model; multiple sets of environmental test samples are loaded onto the lifting simulation model for anti-environmental interference testing, generating offsets corresponding to multiple lifting points under each set of environmental test samples; lifting anomaly probability conversion is performed based on the offsets to generate the multi-point environment-lifting anomaly probability space; based on the multi-point environment-lifting anomaly probability space, feedback correction testing of the offsets is performed through the lifting simulation model to generate the multi-point offset compensation space.

[0043] Specifically, using specialized simulation software, a lifting simulation model is constructed using load structure information and control parameters to simulate the lifting process, including the dynamic response of the load and the actions of the lifting points. Multiple sets of environmental test samples are established to represent different environmental conditions, such as temperature, humidity, and wind speed, which may interfere with the lifting process. The simulation model is run to test the actual offset of each lifting point in the lifting model under different environmental conditions. Based on the generated offsets, a lifting anomaly probability conversion is performed. The offset is input into the probability conversion model and compared with historical data in the probability conversion sample library. Historical samples matching the current offset are found in the probability conversion sample library, and their corresponding anomaly probabilities are extracted. A multi-point environment-lifting anomaly probability space is established, storing the offset sample of each lifting point and its corresponding anomaly probability identifier information in the space. Using the lifting simulation model, a feedback correction test of the offset is performed based on the multi-point environment-lifting anomaly probability space. By adjusting the control parameters, the offset is minimized, thereby optimizing the lifting control strategy. Based on the results of the feedback correction test, a multi-point offset compensation space is generated. The multi-point offset compensation space describes the control parameters that need to be adjusted to reduce offset under different environmental conditions.

[0044] In a specific example, in the multi-point environment-lifting anomaly probability space, the probability of lifting anomaly is 0.2 under a wind speed of 5 m / s. A feedback correction test of the offset was performed using a lifting simulation model. In the simulation model, offsets in actual operation were simulated: offset of 0.1 mm at point P1, 0.2 mm at point P2, and 0.3 mm at point P3. Based on the information from the multi-point environment-lifting anomaly probability space, these offsets could lead to lifting anomalies. Therefore, control parameters were adjusted to reduce or eliminate these offsets. The lifting speed at point P1 was increased, the lifting speed at point P2 was decreased, and the lifting angle at point P3 was adjusted. Through the feedback correction test, the compensation parameters for each lifting point under different offsets were recorded. For point P1, the compensation parameter was an increase in lifting speed of 0.05 mm / s; for point P2, the compensation parameter was a decrease in lifting speed of 0.1 mm / s; and for point P3, the compensation parameter was an adjustment of the lifting angle to 3 degrees. Through the feedback correction test, anomalies caused by offsets in actual operation were predicted and avoided, thereby improving operational safety.

[0045] Furthermore, this application also includes the following steps:

[0046] Based on the offset, a probability transformation model is invoked to perform offset sample matching, identify the abnormal probability corresponding to the matched sample, and establish the multi-point environment-lifting abnormal probability space; wherein, the probability transformation model is connected to the probability transformation sample library, and the probability transformation sample library includes multiple sets of offset samples corresponding to the multiple lift points, as well as the corresponding abnormal probability identification information.

[0047] Specifically, a probability transformation model is prepared to identify and convert corresponding anomaly probabilities based on offset samples. This model is then connected to a probability transformation sample library, which contains multiple sets of offset samples and their corresponding anomaly probability identifiers. The probability transformation model is used to analyze the current offset data, comparing it with historical data in the sample library. Real-time offset data is compared with existing samples in the library to find the most similar sample. Based on the information in the sample library, the probability transformation model determines the anomaly probability corresponding to the matching sample. A multi-point environment-lifting anomaly probability space is established based on the identified anomaly probabilities. This space describes the probability distribution of anomalies occurring during the lifting process under different environmental conditions. By monitoring and predicting anomaly probabilities in real time, lifting operations are effectively controlled. Based on the multi-point environment-lifting anomaly probability space, lifting operation strategies are optimized to improve the stability of the lifting process.

[0048] Furthermore, step six of this application includes:

[0049] Continuous environmental monitoring is performed to generate an environmental monitoring sequence under continuous time-series nodes; the multi-point environmental-lifting anomaly probability space is invoked, and continuous offset probability identification is performed on the multiple lift points based on the environmental monitoring sequence to generate a continuous offset probability index; the second abnormal lift point is located based on the continuous offset probability index; offset compensation control is performed on the second abnormal lift point based on the multi-point offset compensation space, and the second multi-point control parameter group is updated.

[0050] Specifically, during the lifting operation, environmental sensors and monitoring equipment continuously measure and record environmental parameters, such as wind speed, temperature, and humidity, to obtain continuous time-series data on environmental changes. A multi-point environment-lifting anomaly probability space is invoked to analyze the environmental monitoring sequence and understand how current environmental conditions change over time. The corresponding anomaly probability is searched within the multi-point environment-lifting anomaly probability space to assess the offset risk of each lifting point under different environmental conditions. Based on the offset probability identification results, a continuous offset probability index is generated, describing the probability distribution of offset at each lifting point under continuous time-series nodes. Based on the continuous offset probability index, the second abnormal lifting point is located, identifying lifting points with high offset probabilities, which have a high offset risk in the current and future period. A multi-point offset compensation space is applied to perform offset compensation control on the second abnormal lifting point, adjusting control parameters such as lifting speed and acceleration to reduce or eliminate offset. Based on the offset compensation control results, the second multi-point control parameter group is updated, including optimized control parameters for the second abnormal lifting point. By continuously monitoring the environment and identifying the probability of deviation, control parameters can be adjusted in real time to adapt to the impact of environmental changes on the lifting operation, which helps to ensure that the load remains stable during the lifting process and reduces operational risks.

[0051] In summary, the multi-point synchronous lifting control method for a lifting mechanism provided in this application has the following technical advantages:

[0052] By acquiring the load structure information of the load to be lifted, and aiming to minimize environmental impact indicators, the lifting point optimization is performed, generating multiple lifting points. The lifting point optimization process involves identifying key support points from the structural design drawings of the load to be lifted and generating lifting points. Similarity screening and overlap analysis are performed using historical lifting data to construct a screening sample set and evaluate environmental impact indicators, thus completing the lifting point distribution optimization. Multi-point load balancing analysis is then performed using the multiple lifting points and the load structure information to generate a first multi-point control parameter set. Based on the first multi-point control parameter set, the operational environment offset impact analysis is conducted on the multiple lifting points to establish a multi-point environment-lifting anomaly analysis. The system employs a probability space and a multi-point offset compensation space. At the lifting start-up node, environmental monitoring is performed, and the multi-point environment-lifting anomaly probability space is invoked to identify the anomaly probability of any lifting point. A first abnormal lifting point with an anomaly probability greater than a preset probability is selected. Based on the multi-point offset compensation space, offset compensation control is applied to the first abnormal lifting point, and the first multi-point control parameter group is updated with offset compensation parameters to generate a second multi-point control parameter group. After configuring the lifting points of the target lifting mechanism according to the multiple lifting points, the second multi-point control parameter group is input into the control terminal of the target lifting mechanism to control the lifting of the load to be lifted. In other words, by acquiring the load structure information of the load to be lifted, optimizing the lifting points, and combining load balancing analysis and environmental offset impact analysis, the stability and safety of the lifting process are improved.

[0053] Example 2: Based on the same inventive concept as the multi-point synchronous lifting control method for a lifting mechanism in the foregoing examples, this application also provides a multi-point synchronous lifting control device for a lifting mechanism. Please refer to the appendix. Figure 2 The multi-point synchronous lifting control device for the lifting mechanism includes:

[0054] The load information acquisition module 11 is used to acquire the load structure information of the load to be lifted, optimize the lifting point with the goal of minimizing environmental impact indicators, and generate multiple lifting points. The lifting point optimization is carried out by identifying key support points and generating lifting points through the structural design drawings of the load to be lifted, and performing similarity screening and overlap analysis in combination with historical lifting data to construct a screening sample set and evaluate environmental impact indicators, thereby completing the lifting point distribution optimization.

[0055] The load balancing analysis module 12 is used to perform multi-point load balancing analysis by combining the multiple lifting points and the load structure information, and generate a first multi-point control parameter group.

[0056] The offset impact analysis module 13 is used to perform offset impact analysis on the multiple lifting points based on the first multi-point control parameter group, and to establish a multi-point environment-lifting anomaly probability space and a multi-point offset compensation space.

[0057] Anomaly probability identification module 14 is used to perform environmental monitoring at the lifting start node, and call the multi-point environment-lifting anomaly probability space to identify the anomaly probability of any lifting point, and filter the first abnormal lifting point with an anomaly probability greater than a preset probability.

[0058] The offset compensation control module 15 is used to perform offset compensation control on the first abnormal lifting point based on the multi-point offset compensation space, and to update the first multi-point control parameter group with offset compensation parameters to generate a second multi-point control parameter group.

[0059] The lifting point configuration module 16 is used to configure the lifting points of the target lifting mechanism according to the plurality of lifting points, and then input the second multi-point control parameter group into the control terminal of the target lifting mechanism to perform lifting control on the load to be lifted.

[0060] Furthermore, the load information acquisition module 11 in the multi-point synchronous lifting control device of the lifting mechanism is also used for:

[0061] The system reads the structural design drawings of the load to be lifted, identifies key support points and weight distribution networks, generates a first set of lifting points based on the key support points, connects to the log storage platform of the target lifting mechanism, and receives the historical weight distribution network of historical lifting control loads. It compares the historical weight distribution network with the weight distribution network, extracts historical lifting control records corresponding to lifting control loads with similarity scores meeting a preset similarity threshold, and establishes a lifting control sample dataset. This dataset includes historical lifting point distributions, historical environmental parameters, and historical offset records. It performs overlap distance analysis between the historical lifting point distributions and the first set of lifting points, filters the lifting control sample dataset based on the overlap distance, and generates a filtered sample set. Based on historical environmental parameters and historical offset records, it performs environmental impact index analysis on the filtered sample set, generating an environmental impact index set. It then performs environmental impact index minimization screening based on the environmental impact index set, obtains the historical lifting point distribution with the smallest environmental impact index, and replaces the lifting points with the smallest overlap distance according to the first set of lifting points, generating the multiple lifting points.

[0062] Furthermore, the load information acquisition module 11 in the multi-point synchronous lifting control device of the lifting mechanism is also used for:

[0063] Based on the historical environmental parameters and the historical offset records, the minimum environmental parameters that satisfy the preset abnormal offset are selected; the environmental impact index set is generated using the minimum environmental parameters, wherein the environmental impact index is inversely proportional to the minimum environmental parameters.

[0064] Furthermore, the load balancing analysis module 12 in the multi-point synchronous lifting control device of the lifting mechanism is also used for:

[0065] The load center of gravity position is identified based on the load structure information; the distance and orientation of the multiple lifting points relative to the load center of gravity position are calculated to generate multiple relative position information; the first multi-point control parameter set is generated by combining the weight distribution network and the multiple relative position information through the torque balance model; wherein, the torque balance model is used to balance the forces and torques of multiple lifting points and is trained through torque balance samples.

[0066] Furthermore, the offset influence analysis module 13 in the multi-point synchronous lifting control device of the lifting mechanism is also used for:

[0067] Simulation modeling is performed based on the load structure information and the first multi-point control parameter group to generate a lifting simulation model; multiple sets of environmental test samples are loaded onto the lifting simulation model for anti-environmental interference testing, generating offsets corresponding to multiple lifting points under each set of environmental test samples; lifting anomaly probability conversion is performed based on the offsets to generate the multi-point environment-lifting anomaly probability space; based on the multi-point environment-lifting anomaly probability space, feedback correction testing of the offsets is performed through the lifting simulation model to generate the multi-point offset compensation space.

[0068] Furthermore, the offset influence analysis module 13 in the multi-point synchronous lifting control device of the lifting mechanism is also used for:

[0069] Based on the offset, a probability transformation model is invoked to perform offset sample matching, identify the abnormal probability corresponding to the matched sample, and establish the multi-point environment-lifting abnormal probability space; wherein, the probability transformation model is connected to the probability transformation sample library, and the probability transformation sample library includes multiple sets of offset samples corresponding to the multiple lift points, as well as the corresponding abnormal probability identification information.

[0070] Furthermore, the lifting point configuration module 16 in the multi-point synchronous lifting control device of the lifting mechanism is also used for:

[0071] Continuous environmental monitoring is performed to generate an environmental monitoring sequence under continuous time-series nodes; the multi-point environmental-lifting anomaly probability space is invoked, and continuous offset probability identification is performed on the multiple lift points based on the environmental monitoring sequence to generate a continuous offset probability index; the second abnormal lift point is located based on the continuous offset probability index; offset compensation control is performed on the second abnormal lift point based on the multi-point offset compensation space, and the second multi-point control parameter group is updated.

[0072] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The multi-point synchronous lifting control method and specific examples of the lifting mechanism in Embodiment 1 are also applicable to the multi-point synchronous lifting control device of the lifting mechanism in this embodiment. Through the foregoing detailed description of the multi-point synchronous lifting control method of the lifting mechanism, those skilled in the art can clearly understand the multi-point synchronous lifting control device of the lifting mechanism in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here. As for the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant details can be found in the method section.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0074] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A multi-point synchronous lifting control method for a lifting mechanism, characterized in that, include: Obtain the load structure information of the load to be lifted, and optimize the lifting points to minimize environmental impact indicators. Multiple lifting points are generated. The lifting point optimization process involves identifying key support points from the structural design drawings of the load to be lifted and generating lifting points. Similarity screening and overlap analysis are performed using historical lifting data to construct a selection sample set and evaluate environmental impact indicators, thus completing the lifting point distribution optimization. Specifically, this includes: Read the structural design drawings of the load to be lifted and identify key support points and weight distribution network; The first set of lifting points is generated using the aforementioned key support points; Connect to the log storage platform of the target lifting mechanism to receive the historical weight distribution network of historical lifting control load; The historical weight distribution network is compared with the weight distribution network to extract historical lifting control records corresponding to lifting control loads whose similarity meets the preset similarity, and a lifting control sample dataset is established. The lifting control sample dataset includes historical lifting point distribution, historical environmental parameters and historical offset records. The overlap distance between the historical lifting point distribution and the first group of lifting points is analyzed, and the lifting control sample dataset is filtered based on the overlap distance to generate a filtered sample set; Based on historical environmental parameters and historical offset records, environmental impact index analysis is performed on the selected sample set to generate an environmental impact index set. By combining the environmental impact index set, environmental impact index minimization screening is performed to obtain the historical rise point distribution with the minimum environmental impact index, and the rise point with the smallest overlap distance is replaced according to the first group of rise points to generate the multiple rise points; By combining the multiple lifting points and the load structure information, a multi-point load balancing analysis is performed to generate the first multi-point control parameter group; Based on the first multi-point control parameter group, the offset influence of the working environment on the multiple lifting points is analyzed, and a multi-point environment-lifting anomaly probability space and a multi-point offset compensation space are established. Environmental monitoring is performed at the lifting start-up node, and the multi-point environment-lifting anomaly probability space is called to identify the anomaly probability of any lifting point, and the first abnormal lifting point with an anomaly probability greater than the preset probability is selected. Based on the multi-point offset compensation space, offset compensation control is performed on the first abnormal lifting point, and the first multi-point control parameter group is updated with offset compensation parameters to generate a second multi-point control parameter group. After configuring the lifting points of the target lifting mechanism according to the multiple lifting points, the second multi-point control parameter group is input into the control terminal of the target lifting mechanism to control the lifting of the load to be lifted. The process includes inputting the second multi-point control parameter group into the control terminal of the target hoisting mechanism to control the hoisting of the load to be hoisted, and further includes: Continuous environmental monitoring is conducted to generate environmental monitoring sequences under continuous time-series nodes; The multi-point environment-lifting anomaly probability space is invoked, and the continuous offset probability of the multiple lifting points is identified based on the environmental monitoring sequence to generate a continuous offset probability index. The second abnormal rise point is located based on the continuous offset probability index; Based on the multi-point offset compensation space, offset compensation control is performed on the second abnormal lifting point, and the second multi-point control parameter group is updated.

2. The multi-point synchronous lifting control method for a lifting mechanism as described in claim 1, characterized in that, Based on historical environmental parameters and historical offset records, environmental impact index analysis is performed on the selected sample set to generate an environmental impact index set, including: Based on the historical environmental parameters and the historical offset records, the minimum environmental parameters that satisfy the preset abnormal offset are selected. The environmental impact index set is generated using the minimum environmental parameter, wherein the environmental impact index is inversely proportional to the minimum environmental parameter.

3. The multi-point synchronous lifting control method for a lifting mechanism as described in claim 1, characterized in that, By combining the multiple lifting points and the load structure information, a multi-point load balancing analysis is performed to generate a first multi-point control parameter set, including: Identify the load center of gravity location based on the load structure information; Calculate the distance and orientation of the multiple lifting points relative to the load center of gravity to generate multiple relative position information; Combining the weight distribution network and the multiple relative position information, the first multi-point control parameter set is generated through a torque balance model; The torque balance model is used to balance the forces and torques at multiple lifting points and is trained using torque balance samples.

4. The multi-point synchronous lifting control method for a lifting mechanism as described in claim 1, characterized in that, Based on the first multi-point control parameter set, an analysis of the operational environment offset impact is performed on the multiple lifting points, establishing a multi-point environment-lifting anomaly probability space and a multi-point offset compensation space, including: Based on the load structure information and the first multi-point control parameter group, a simulation model is generated to perform simulation modeling and produce a lifting simulation model. Multiple sets of environmental test samples are loaded into the lifting simulation model for anti-environmental interference testing. The offsets corresponding to multiple lifting points under each set of environmental test samples are generated. The lifting anomaly probability conversion is performed based on the offsets to generate the multi-point environment-lifting anomaly probability space. Based on the multi-point environment-lifting anomaly probability space, the multi-point offset compensation space is generated by performing offset feedback correction tests through the lifting simulation model.

5. The multi-point synchronous lifting control method for a lifting mechanism as described in claim 4, characterized in that, Based on the offset, a lift anomaly probability transformation is performed to generate the multi-point environment-lift anomaly probability space, including: Based on the offset call probability transformation model, offset sample matching is performed, the abnormal probability corresponding to the matching sample is identified, and the multi-point environment-lifting abnormal probability space is established. The probability conversion model is connected to the probability conversion sample library, which includes multiple sets of offset samples corresponding to the multiple rise points, as well as corresponding anomaly probability identification information.

6. A multi-point synchronous lifting control device for a lifting mechanism, characterized in that, The step of implementing the multi-point synchronous lifting control method for a lifting mechanism according to any one of claims 1 to 5, wherein the multi-point synchronous lifting control device for the lifting mechanism comprises: The load information acquisition module is used to acquire the load structure information of the load to be lifted, optimize the lifting points with the goal of minimizing environmental impact indicators, and generate multiple lifting points. The lifting point optimization identifies key support points and generates lifting points by using the structural design drawings of the load to be lifted. It also performs similarity screening and overlap analysis by combining historical lifting data, constructs a screening sample set, evaluates environmental impact indicators, and completes the lifting point distribution optimization. A load balancing analysis module is used to perform multi-point load balancing analysis by combining the multiple lifting points and the load structure information, and generate a first multi-point control parameter group. The offset impact analysis module is used to perform offset impact analysis on the multiple lifting points based on the first multi-point control parameter group, and to establish a multi-point environment-lifting anomaly probability space and a multi-point offset compensation space. An anomaly probability identification module is used to perform environmental monitoring at the lifting start node and call the multi-point environment-lifting anomaly probability space to identify the anomaly probability of any lifting point, and filter the first abnormal lifting point with an anomaly probability greater than a preset probability. The offset compensation control module is used to perform offset compensation control on the first abnormal lifting point based on the multi-point offset compensation space, and update the first multi-point control parameter group with offset compensation parameters to generate a second multi-point control parameter group. The lifting point configuration module is used to configure the lifting points of the target lifting mechanism according to the multiple lifting points, and then input the second multi-point control parameter group into the control terminal of the target lifting mechanism to perform lifting control on the load to be lifted.

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