Crane dual-motor driving power dynamic distribution optimization method and system

Abstract

The invention provides a crane dual-motor driving power dynamic distribution optimization method and system, and relates to the technical field of crane electric control systems.The method comprises the steps that historical operation cycle data, dual-motor winding temperature data and efficiency characteristic data in the crane operation process are collected, and a load power spectrum is established; generating heat accumulation state information according to the winding temperature data and dynamically adjusting a heat dissipation control state; calculating a power allocation weight by adopting a fuzzy adaptive control algorithm and a dynamic efficiency mapping relation in combination with the load power spectrum, the efficiency characteristic data and the heat dissipation state; and dynamically adjusting the output power of the double motors based on the weight, and generating an optimal power distribution scheme of the next operation period in a rolling manner by applying a model prediction control algorithm according to the adjusted output power and the current operation state data. The energy efficiency utilization rate and the dynamic response performance of the dual-motor driving system are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric control system of crane, and particularly relates to a crane double-motor driving power dynamic distribution optimization method and system. BACKGROUND

[0002] In the repetitive hoisting operation of heavy equipment such as port container gantry crane, the double-motor driving system needs to dynamically adjust the power distribution according to the real-time load change to realize the overall efficiency optimization of the system. Due to the difference in motor efficiency characteristics, and the frequent changes in load and start-stop during operation, the traditional fixed power distribution method is difficult to adapt to complex working conditions, and an intelligent power distribution method that can respond to load changes in real time, take into account heat management and improve energy efficiency is urgently needed.

[0003] The existing scheme adopts a static distribution strategy based on efficiency lookup table. The efficiency characteristics of the double-motor at different load points are determined in advance, and an efficiency-load correspondence table is established. The system queries the table according to the real-time detected load size, selects the motor with higher efficiency under the current load to bear the main power output, and simultaneously controls the start of air cooling according to the temperature threshold. This method considers the matching relationship between load and efficiency to some extent.

[0004] However, this scheme relies on static efficiency data and does not reflect the changes in efficiency characteristics caused by temperature rise and aging in actual motor operation. The power distribution response lags behind when the load changes suddenly, the cooling control and power distribution are independent of each other, and there is a lack of collaborative optimization. At the same time, the strategy does not consider the periodic characteristics of the operation, and it is difficult to deal with the problem of heat accumulation and efficiency decay in continuous operation, which affects the overall energy efficiency and adaptability of the system. SUMMARY

[0005] The present application provides a crane double-motor driving power dynamic distribution optimization method and system to solve the problems of low energy efficiency and poor dynamic response performance of the double-motor driving system in the prior art.

[0006] To solve the above technical problems, in a first aspect, the present application provides a crane double-motor driving power dynamic distribution optimization method, comprising: collecting historical operation cycle data in the operation process of the crane, winding temperature data and efficiency characteristic data of the double-motor in the crane; based on the historical operation cycle data, establishing a load power spectrum; based on the winding temperature data, generating heat accumulation state information, and dynamically adjusting the heat dissipation control state of the double-motor based on the heat accumulation state information; based on the load power spectrum, the efficiency characteristic data and the adjusted heat dissipation control state, using a fuzzy self-adaptive control algorithm to calculate the power distribution weight in combination with a preset dynamic efficiency mapping relationship; Based on the power distribution weight, the output power of the dual motor is dynamically adjusted, and a model predictive control algorithm is adopted to generate an optimal power distribution scheme for the dual motor in the next work cycle based on the adjusted output power and current work cycle state data.

[0007] Optionally, based on the power distribution weight, the output power of the dual motor is dynamically adjusted, and a model predictive control algorithm is adopted to generate an optimal power distribution scheme for the dual motor in the next work cycle based on the adjusted output power and current work cycle state data, comprising: Based on the power distribution weight, the output power set value of the dual motor is calculated; According to the output power set value, the output power of the dual motor is adjusted in real time; A model predictive control algorithm is adopted to predict the load change trend of the next work cycle based on the current work cycle state data, and generate a plurality of candidate power distribution schemes according to the load change trend and the adjusted output power; From the plurality of candidate power distribution schemes, the optimal power distribution scheme with the optimal overall efficiency is selected.

[0008] Optionally, the model predictive control algorithm is adopted to predict the load change trend of the next work cycle based on the current work cycle state data, and generate a plurality of candidate power distribution schemes according to the load change trend and the adjusted output power, comprising: Based on the load power spectrum and the current work cycle state data, the expected load weight and the lifting height change amplitude at each time point in the next work cycle are calculated by using the load change prediction model constructed based on the model predictive control algorithm; Based on the expected load weight and the lifting height change amplitude, the load change trend is formed; Based on the load change trend and the adjusted output power data, the efficiency characteristic parameters of the dual motor under different expected load conditions are calculated; According to the efficiency characteristic parameters, a plurality of different power distribution ratio combinations are generated; Based on a plurality of different power distribution ratio combinations, corresponding candidate power distribution schemes are generated.

[0009] Optionally, based on the load change trend and the adjusted output power data, the efficiency characteristic parameters of the dual motor under different expected load conditions are calculated, comprising: Based on the load change trend and the adjusted output power data, the expected operating working point of the dual motor under different expected load conditions is determined; Based on the efficiency characteristic curve of the dual motor, the efficiency value corresponding to each expected operating working point is calculated by interpolation; According to the adjusted heat dissipation control state, the efficiency value is temperature-compensated and corrected to obtain a corrected efficiency value, and the corrected efficiency value is taken as the efficiency characteristic parameter.

[0010] Optionally, based on the load power spectrum, the efficiency characteristic data, and the adjusted heat dissipation control state, a fuzzy adaptive control algorithm is adopted to calculate a power distribution weight in combination with a preset dynamic efficiency mapping relationship, including: Based on the efficiency characteristic data, efficiency characteristic curves of the double motor under different load working conditions are established. According to the load change characteristics in the load power spectrum and the adjusted heat dissipation control state, a fuzzy adaptive control algorithm is adopted to dynamically correct the efficiency characteristic curves to obtain corrected efficiency characteristic curves. Based on the preset dynamic efficiency mapping relationship and the corrected efficiency characteristic curves, an optimal working efficiency point of the double motor under the current load demand is determined. According to the optimal working efficiency point, a power distribution ratio of the double motor is calculated. The power distribution ratio is optimized and adjusted through a fuzzy rule to generate a power distribution weight.

[0011] Optionally, based on the historical operation cycle data, a load power spectrum is established, including: The historical operation cycle data is divided into operation segments, and a lifting height change process, a load weight maintaining process, and a running time interval in each operation segment are identified. From the lifting height change process, the load weight maintaining process, and the running time interval in each operation segment, corresponding lifting height extreme values, load weight constant values, and running time lengths are extracted. Based on the lifting height extreme values, the load weight constant values, and the running time lengths, a typical operation segment feature set is formed. According to the parameter correlation relationship corresponding to the typical operation segment feature set, a load power sequence is constructed. Based on the occurrence frequency of different parameter combinations in the load power sequence, a load power spectrum is generated.

[0012] Optionally, based on the winding temperature data, heat accumulation state information is generated, and the heat dissipation control state of the double motor is dynamically adjusted based on the heat accumulation state information, including: Based on the winding temperature data, a temperature change rate of the winding and a temperature distribution uniformity are calculated. According to the temperature change rate and the temperature distribution uniformity, heat accumulation state information is determined. Based on the temperature change trend and the temperature distribution characteristics in the heat accumulation state information, a heat dissipation control instruction is generated. The heat dissipation control status of the dual motors is dynamically adjusted according to the heat dissipation control command.

[0013] Secondly, this application provides a dynamic power allocation optimization system for dual-motor drive of a crane, comprising: The data acquisition module is used to collect historical operation cycle data, winding temperature data of the dual motors in the crane, and efficiency characteristic data during the crane's operation. A module is established to create a load power spectrum based on the historical work cycle data; The generation module is used to generate heat accumulation status information based on the winding temperature data, and dynamically adjust the heat dissipation control status of the dual motors based on the heat accumulation status information. The calculation module is used to calculate the power allocation weight based on the load power spectrum, the efficiency characteristic data, and the adjusted heat dissipation control state, using a fuzzy adaptive control algorithm and a preset dynamic efficiency mapping relationship. The adjustment module is used to dynamically adjust the output power of the dual motors based on the power allocation weights, and to use a model predictive control algorithm to generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle status data.

[0014] Thirdly, this application provides an electronic device, comprising: Memory, used to store computer programs; A processor is configured to execute the computer program to implement the steps of the crane dual-motor drive power dynamic allocation optimization method as described in the first aspect above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the crane dual-motor drive power dynamic allocation optimization method described in the first aspect above.

[0016] This application provides a method for dynamically optimizing the drive power allocation of dual motors in a crane. The method includes: collecting historical work cycle data, winding temperature data, and efficiency characteristic data of the dual motors during crane operation; establishing a load power spectrum based on the historical work cycle data; generating heat accumulation state information based on the winding temperature data, and dynamically adjusting the heat dissipation control state of the dual motors based on the heat accumulation state information; calculating power allocation weights using a fuzzy adaptive control algorithm combined with a preset dynamic efficiency mapping relationship based on the load power spectrum, the efficiency characteristic data, and the adjusted heat dissipation control state; dynamically adjusting the output power of the dual motors based on the power allocation weights, and using a model predictive control algorithm to continuously generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle state data.

[0017] The technical solution provided in this application has the following beneficial effects: This application comprehensively acquires the crane's operating status and motor operating parameters, providing a data foundation for optimized power allocation. It identifies the regularity of load changes under typical operating modes, providing a predictive basis for power allocation. Real-time monitoring of the motor's thermal state prevents overheating through dynamic heat dissipation control, ensuring system operational safety. By comprehensively considering load changes, efficiency characteristics, and heat dissipation status, it generates power allocation ratios adapted to actual operating conditions. This enables real-time optimization and proactive planning of power allocation, improving the overall system operating efficiency.

[0018] Furthermore, this application also calculates and adjusts the output power setpoint of the dual motors in real time by power allocation weighting, and then combines it with model predictive control algorithm to predict the future load change trend based on the current working state, generate multiple candidate power allocation schemes, and finally select the scheme with the best overall efficiency as the execution strategy.

[0019] Furthermore, dynamic adjustment and multi-step optimization of power distribution were achieved, improving the system's adaptability to load changes and ensuring the efficient and stable operation of the dual-motor drive system under different operating conditions.

[0020] These or other aspects of this application will become more apparent from the description of the following embodiments. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of 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 some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1A flowchart illustrating a method for dynamic power allocation optimization of a crane dual-motor drive, provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating a specific implementation of a method for dynamically allocating and optimizing the drive power of a dual-motor crane, as provided in an embodiment of this application. Figure 3 This is a schematic diagram of a dynamic power allocation optimization system for a dual-motor drive of a crane, provided in an embodiment of this application. Detailed Implementation

[0023] In the power distribution technology of dual-motor drive systems for port cranes, existing solutions generally adopt a distribution strategy based on static efficiency lookup tables. Although this can achieve basic load matching, it has obvious limitations: the method relies on a preset efficiency curve and cannot reflect the changes in efficiency characteristics caused by dynamic factors such as temperature rise and aging during actual motor operation; the power distribution response is lagging when the load changes abruptly, and heat dissipation control and power distribution are independent of each other, lacking a collaborative optimization mechanism; at the same time, the static strategy is difficult to adapt to the periodic load fluctuations and continuous heat accumulation problems in repetitive operations, which limits the improvement of the overall energy efficiency of the system and poses challenges to long-term operational reliability.

[0024] To address the aforementioned issues, this application proposes a dynamic power allocation optimization method for dual-motor drives in cranes. By collecting historical operational data, real-time temperature, and efficiency characteristic data, a load power spectrum is established, and thermal accumulation state information is generated to dynamically adjust heat dissipation control. Combining fuzzy adaptive control and model prediction algorithms, an optimal power allocation scheme is generated based on real-time operating conditions. This method, through multi-parameter collaborative optimization and forward-looking control, achieves dynamic adjustment of power allocation and coordinated control of thermal management, effectively solving the problems of static strategy response lag, thermal efficiency separation, and insufficient adaptability to operating conditions, thereby improving system energy efficiency and operational stability.

[0025] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] The core of this application is to provide a method for dynamically allocating and optimizing the drive power of dual motors in a crane. A flowchart of one specific implementation is shown below. Figure 1 As shown, the method includes: Step 101: Collect historical operation cycle data, winding temperature data of the dual motors in the crane, and efficiency characteristic data during the crane's operation.

[0027] In step 101, historical work cycle data refers to the operating parameters recorded by the crane during past operations, including lifting height data (hook lifting position), load weight data (weight of the load), and running time data (time consumed for each action). This data is used to analyze the crane's typical operating modes. The crane is an integrated device containing a dual-motor drive system; the dual motors are the core power execution components of the crane, forming a whole-part relationship and a control-controlled relationship. Winding temperature data represents the temperature values ​​collected in real time by temperature sensors embedded in the motor windings, reflecting the motor's heating state during operation. Efficiency characteristic data represents curves or tables describing the efficiency changes of the motor under different loads and speeds, typically provided by the motor manufacturer or determined experimentally.

[0028] In this embodiment, a sensor system collects data on lifting height, load weight, and running time from historical crane operations. Simultaneously, it monitors the temperature of the dual motor windings in real time and obtains motor efficiency characteristic data. This data provides the foundation for subsequent load power spectrum and thermal management. The specific process is as follows: first, historical operation data is extracted from the crane control system; then, the winding temperature is read using temperature sensors; and finally, the efficiency characteristic curve is obtained from the motor database or measured data.

[0029] For example, a container gantry crane at a port collected historical operation cycle data for 1000 complete operation cycles over 30 consecutive days of operation. This data included lifting height (range 0-20 meters), load weight (range 5-50 tons), and running time (100-300 seconds per cycle). Simultaneously, PT100 temperature sensors installed on the windings of the dual motors recorded temperature data every 10 seconds (range 30-120℃), and efficiency characteristic data (efficiency values ​​70%-95%) of the dual motors under different loads (10%-100% rated load) and speeds were obtained from the motor manufacturer's technical manual. This data was transmitted to the central controller for storage via on-site industrial Ethernet.

[0030] Step 102: Based on the historical operation cycle data, establish the load power spectrum.

[0031] In step 102, the load power spectrum represents a statistical model that reflects the changes in power demand under typical crane operating modes. By analyzing the combination patterns of lifting height, load weight, and running time in historical data, the correspondence between these parameters and power demand is established.

[0032] In this embodiment, statistical analysis is performed on the collected historical work cycle data to identify recurring work pattern segments (such as no-load operation, heavy-load lifting, and translation), and characteristic parameters (extreme lifting height, constant load weight, and running time) of each segment are extracted. Then, a load power spectrum is constructed based on the frequency distribution of these combined parameters. This power spectrum shows the typical characteristics of power demand under different work patterns.

[0033] For example, based on data from 1000 collected operations, analysis revealed three typical operating modes: light-load fast mode (load 5-15 tons, lifting height 5-10 meters, time 100-150 seconds), medium-load standard mode (load 20-35 tons, lifting height 10-15 meters, time 150-220 seconds), and heavy-load slow mode (load 40-50 tons, lifting height 15-20 meters, time 220-300 seconds). By statistically analyzing the frequency of parameter combinations under each mode, a load power spectrum was constructed, showing that the medium-load standard mode had the highest frequency (60%), corresponding to a power demand of 200-280kW.

[0034] Step 103: Based on the winding temperature data, generate heat accumulation status information, and dynamically adjust the heat dissipation control status of the dual motors based on the heat accumulation status information.

[0035] In step 103, the thermal accumulation status information represents parameters describing the degree of heat accumulation in the motor windings, including the rate of temperature change (the amount of temperature change per unit time) and the uniformity of temperature distribution (the temperature difference between different measurement points). The heat dissipation control status refers to the operating intensity of the cooling system (such as a fan), which is typically divided into multiple levels, adjusting the airflow or velocity according to the thermal state.

[0036] In this embodiment, the rate of change and uniformity of winding temperature distribution are calculated in real time to generate heat accumulation status information. Then, the output of the cooling system (such as fan speed) is adjusted based on this information. The specific process is as follows: first, the slope of recent temperature change is calculated to assess the heating rate; then, the temperature difference at each temperature measurement point is calculated to assess the uniformity of heat distribution; and finally, the heat dissipation intensity is adjusted according to the comprehensive thermal state level.

[0037] For example, taking a certain operation as an example, the temperatures at three temperature measuring points on the main motor are 85℃, 88℃, and 92℃, while the temperatures on the auxiliary motor are 82℃, 84℃, and 86℃. The calculated temperature change rate of the main motor is 0.04℃ / s (a rise of 12℃ in the past 5 minutes), and the temperature distribution uniformity is 7℃ (the difference between the highest and lowest temperatures); the temperature change rate of the auxiliary motor is 0.03℃ / s, and the uniformity is 4℃. According to preset rules, the main motor's heat accumulation state is determined to be "high heat accumulation," triggering heat dissipation control level 4 (fan speed 1200rpm), while the auxiliary motor is classified as "medium heat accumulation," maintaining level 3 (fan speed 900rpm).

[0038] Step 104: Based on the load power spectrum, the efficiency characteristic data, and the adjusted heat dissipation control state, a fuzzy adaptive control algorithm is used, combined with a preset dynamic efficiency mapping relationship, to calculate the power allocation weight.

[0039] In step 104, the dynamic efficiency mapping relationship represents a mathematical model describing the changes in motor efficiency with load, speed, and temperature, which is continuously updated using real-time data. The power distribution weight represents the proportional coefficient of power distribution between the two motors, with a total of 1. For example, (0.7, 0.3) indicates that the main motor bears 70% of the power.

[0040] In this embodiment, the current operating mode is first determined based on the load power spectrum. Then, combining efficiency characteristic data and real-time heat dissipation status, the optimal power allocation weight is calculated using a fuzzy adaptive control algorithm. Specifically, the process involves: establishing an efficiency correction model that considers the impact of temperature; finding the optimal operating point in the dynamic efficiency mapping relationship based on the current load demand; and finally optimizing the allocation ratio using fuzzy rules.

[0041] For example, the current operation is in medium-load standard mode (load 30 tons, lifting height 12 meters), and the ambient temperature is 25℃. The main motor's corrected efficiency, considering heat dissipation (level 4), is 88%, and the auxiliary motor's corrected efficiency is 85%. The power allocation weights calculated using the fuzzy adaptive control algorithm are (0.65, 0.35), meaning the main motor bears 65% of the power, and the auxiliary motor bears 35%. The calculation formula is: Weight ratio = (motor corrected efficiency × heat dissipation coefficient) / ∑(corrected efficiency × heat dissipation coefficient) for each motor, where the heat dissipation coefficient is 1.2 for level 4 and 1.0 for level 3.

[0042] Step 105: Based on the power allocation weight, dynamically adjust the output power of the dual motors, and use a model predictive control algorithm to generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle status data.

[0043] In step 105, the current operation status data refers to the crane operating parameters collected in real time by sensors, including instantaneous load weight, lifting height, and operating speed; while "historical operation cycle data" refers to the accumulated operation record data. The difference lies in the data acquisition timing and purpose: current data is used for real-time control, while historical data is used to establish typical patterns; the connection is that current data is continuously added to the historical database to optimize the accuracy of the load power spectrum, while the load power spectrum provides a reference benchmark for the analysis of current data. The optimal power allocation scheme is a detailed power allocation plan obtained by rolling optimization of future operation cycles based on power allocation weights using a model predictive control algorithm, containing specific power values ​​and time series information; while the power allocation weight is a real-time allocation ratio parameter calculated by a fuzzy adaptive control algorithm, used for power adjustment at the current moment; the connection between the two is that the power allocation weight is the basis for calculating the optimal power allocation scheme, the difference being that the power allocation weight is a static ratio parameter at the current moment, while the optimal power allocation scheme is a complete allocation plan that includes dynamic changes over time.

[0044] In this embodiment, the output of the two motors is first adjusted in real time according to the power allocation weights. Then, a model predictive control algorithm is used to predict future load changes based on the current state, generating multiple candidate power allocation schemes. Finally, the scheme with the best overall efficiency is selected. The specific process is as follows: calculate and execute the power setpoints of each motor according to the weights, predict the load trend of the next cycle, generate multiple allocation schemes for simulation comparison, and select the optimal scheme for implementation.

[0045] For example, the current total power demand is 250kW. Distributed with weights (0.65, 0.35), the main motor outputs 162.5kW, and the auxiliary motor outputs 87.5kW. Using a model prediction algorithm, the load is predicted to increase to 280kW in the next work cycle (35 tons load, 15-meter lifting). Three candidate schemes are generated: Scheme 1 (main 70% / auxiliary 30%), Scheme 2 (main 65% / auxiliary 35%), and Scheme 3 (main 60% / auxiliary 40%). Simulation calculations show that Scheme 2 has the highest overall system efficiency (85.2%) and is selected as the optimal allocation scheme. The efficiency calculation formula is: Overall system efficiency = Total output power / Total input power, where output power is the increase in load potential energy, and input power is the sum of the power consumption of both motors.

[0046] This method achieves dynamic optimization of power distribution in a crane's dual-motor drive system through multi-source data acquisition, load pattern recognition, thermal state management, intelligent weight calculation, and predictive optimization control. The system can automatically adapt to different operating conditions, coordinate power distribution and thermal management, improve overall energy efficiency and operational stability, extend equipment lifespan, and reduce energy consumption and maintenance costs.

[0047] To address the coordination issue between dynamic response and forward-looking optimization in power allocation for dual motors in cranes, some embodiments include step 105: dynamically adjusting the output power of the dual motors based on the power allocation weights, and employing a model predictive control algorithm to continuously generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle status data. Figure 2 As shown, it includes: Step 201: Calculate the output power setting value of the dual motors based on the power allocation weight.

[0048] In step 201, the output power setpoint refers to the specific power output target value calculated for each motor according to the power allocation weight, which is used to directly control the power output of the motor.

[0049] In this embodiment, the power allocation weights calculated in step 104 are first read, and then the current total power demand is allocated to each motor according to the weight ratio. The specific power output value required by each motor is calculated, and these values ​​are the output power setpoints. The calculation process uses a simple proportional allocation formula to ensure that the sum of the output power of the two motors equals the total power demand.

[0050] Step 202: Adjust the output power of the dual motors in real time according to the output power setting value.

[0051] In step 202, real-time adjustment refers to immediately changing the power output of the motor through the motor control system to achieve the set value requirement.

[0052] In this embodiment, the output power setpoint calculated in step 201 is sent to the motor controller. The controller adjusts the output current and voltage of the motor driver to gradually approach and eventually reach the setpoint for the actual output power of each motor. This process employs a closed-loop control method, which monitors the actual output power in real time and compares it with the setpoint, dynamically adjusting the control parameters.

[0053] Step 203: Using a model predictive control algorithm, based on the current work cycle status data, predict the load change trend of the next work cycle, and generate multiple candidate power allocation schemes according to the load change trend and the adjusted output power.

[0054] In step 203, the candidate power allocation scheme refers to a variety of power allocation plans that are pre-formulated for different working conditions that may occur in future work cycles. Each scheme contains detailed power allocation parameters and time series.

[0055] In this embodiment, the current work cycle status data, including real-time load weight, lifting height, and operating speed, is first collected. Then, a model predictive control algorithm is used to analyze this data and predict possible load changes in the next work cycle. Based on the prediction results and the current adjusted output power status, multiple different power allocation schemes are generated. These schemes consider various possible operating condition changes, providing a basis for subsequent optimization selection.

[0056] Step 204: Select the optimal power allocation scheme with the best overall efficiency from the multiple candidate power allocation schemes.

[0057] In step 204, the overall optimal efficiency refers to the operating state with the lowest total energy consumption of the entire drive system under the dual-motor cooperative working state, by comprehensively considering the efficiency characteristics, heat dissipation status and power distribution ratio of the two motors. The basis for determining the optimality is to perform simulation calculations on multiple candidate power distribution schemes through model predictive control algorithms, compare the overall system efficiency index under each scheme, and select the scheme with the highest overall efficiency as the optimal power distribution trajectory.

[0058] In this embodiment, system efficiency simulation calculations are performed on each candidate power allocation scheme generated in step 203 to evaluate the expected operating efficiency of each scheme under predicted operating conditions. By comparing the efficiency indicators of each scheme, the scheme with the highest overall system efficiency is selected as the final optimal power allocation scheme, which will be used to guide the power allocation in the next work cycle.

[0059] Here is a specific example: In the specific operation of a container gantry crane at a port, the current total power demand is 250 kW. Based on the power allocation weights of 0.65 for the main motor and 0.35 for the auxiliary motor, the output power setpoints for both motors are calculated. The main motor output power setpoint equals the total power demand multiplied by the main motor weight (250 kW multiplied by 0.65), resulting in 162.5 kW. The auxiliary motor output power setpoint equals the total power demand multiplied by the auxiliary motor weight (250 kW multiplied by 0.35), resulting in 87.5 kW. The output power of both motors is adjusted in real time based on these setpoints to stabilize the actual output power of the main motor at 162.5 kW and the auxiliary motor at 87.5 kW. Using a model predictive control algorithm, based on the current work cycle status data including a load weight of 30 tons, a lifting height of 12 meters, and a running speed of 2 meters per second, the load change trend for the next work cycle is predicted. By analyzing historical load power spectra and current operating parameters, it is predicted that the load weight will increase to 35 tons, the lifting height will increase to 15 meters, and the running time will be approximately 180 seconds. Based on the load change trend and the adjusted output power, several candidate power allocation schemes are generated, including Scheme 1 (main motor weight 0.70, auxiliary motor weight 0.30), Scheme 2 (main motor weight 0.65, auxiliary motor weight 0.35), and Scheme 3 (main motor weight 0.60, auxiliary motor weight 0.40). The optimal power allocation scheme with the best overall efficiency is selected from these candidate schemes using the system overall efficiency calculation formula. An assessment was conducted, in which This indicates that the overall system efficiency is dimensionless. The total output power is expressed in joules. The total input power is expressed in joules. For Scheme 1, the total input power is the sum of the main motor's input power and the auxiliary motor's input power. The main motor's input power is the output power divided by the efficiency, i.e., (280000 × 0.70 × 180) / 0.88. The auxiliary motor's input power is (280000 × 0.30 × 180) / 0.85. The total output power is the increase in load potential energy, i.e., 35000 × 9.8 × 15. The calculated total system efficiency is 84.5%. For Scheme 2, the main motor's input power is (280000 × 0.65 × 180) / 0.88, the auxiliary motor's input power is (280000 × 0.35 × 180) / 0.85, and the total output power remains unchanged. The calculated total system efficiency is 85.2%. For Scheme 3, the main motor input power is (280000×0.60×180) / 0.88, and the auxiliary motor input power is (280000×0.40×180) / 0.85, resulting in a total system efficiency of 83.8%. Scheme 2 has the highest total system efficiency, therefore, Scheme 2 is selected as the optimal power allocation scheme to guide power allocation in the next work cycle.

[0060] In the embodiments of this application, these steps combine real-time power adjustment with forward-looking optimization to achieve a balance between dynamic response of power distribution and adaptability to future operating conditions, thereby improving system operating efficiency and control accuracy and ensuring stable and efficient operation of the dual-motor drive system under different operating conditions.

[0061] To address the accuracy issue in load forecasting and power allocation scheme generation, in some embodiments, step 203 involves employing a model predictive control algorithm to predict the load change trend for the next work cycle based on the current work cycle status data, and generating multiple candidate power allocation schemes based on the load change trend and the adjusted output power, including: Step 301: Based on the load power spectrum and the current work cycle status data, use the load change prediction model constructed based on the model predictive control algorithm to calculate the expected load weight and lifting height change range at each time point in the next work cycle.

[0062] In step 301, the expected load weight refers to the predicted weight of the goods to be lifted at various future time points, and the lifting height change range refers to the predicted change in the lifting position of the hook.

[0063] In this embodiment, the current work cycle status data is first input into the load change prediction model. The model analyzes historical data of similar work patterns in the load power spectrum to predict the changes in load weight and lifting height at future time points and outputs a series of expected parameter values ​​corresponding to time points.

[0064] Step 302: Based on the expected load weight and the change in lifting height, form a load change trend.

[0065] In this embodiment of the application, the expected parameter values ​​at each time point obtained in step 301 are arranged in chronological order, and their change patterns are analyzed to form a trend curve describing how the load weight and lifting height change over time. This trend reflects the load change characteristics of future work cycles.

[0066] Step 303: Based on the load change trend and the adjusted output power data, calculate the efficiency characteristic parameters of the dual motors under different expected load conditions.

[0067] In step 303, different expected load conditions refer to the specific parameter combinations of load weight and lifting height that may occur at various time points within the next work cycle, calculated by the load change prediction model. This means various possible load states predicted for future work periods. The difference between "different expected load conditions" and "different load conditions" is that "different expected load conditions" are based on predicted future load states, while "different load conditions" refer to various actual working states experienced by the motor. The connection between the two is that the prediction model is based on historical load condition data, and the accuracy of the expected load condition prediction depends on the analysis of historical data for different load conditions. Efficiency characteristic parameters refer to the motor's working efficiency value under specific load conditions, taking into account the influence of actual operating conditions.

[0068] In this embodiment of the application, based on the expected load conditions at each time point in the load change trend, combined with the current adjusted output power state, the efficiency characteristic curve of the motor is queried, and the efficiency characteristic parameters of the dual motors under each expected load condition are obtained by considering the influence factors such as temperature.

[0069] Step 304: Generate multiple different power allocation ratio combinations based on the efficiency characteristic parameters.

[0070] In step 304, the power distribution ratio combination refers to the power distribution ratio setting between the two motors, and each combination includes the distribution ratio of the main motor and the auxiliary motor.

[0071] In this embodiment of the application, based on the efficiency characteristic parameters calculated in step 303, multiple different combinations of power allocation ratios are generated through an optimization algorithm. These combinations cover various possible power allocation methods, providing a basis for the generation of subsequent schemes.

[0072] Step 305: Generate corresponding candidate power allocation schemes based on multiple different combinations of the power allocation ratios.

[0073] In this embodiment of the application, the power allocation ratio combination generated in step 304 is converted into a specific power allocation scheme. Each scheme specifies the power output value of the two motors at each time point, forming a complete power allocation plan for the work cycle.

[0074] Here is a specific example: In the specific operation of a container gantry crane at a port, based on the load power spectrum and current work cycle status data including a load weight of 30 tons, a lifting height of 12 meters, and a running speed of 2 meters per second, a load change prediction model constructed using a model predictive control algorithm is used to calculate the expected changes in load weight and lifting height at various time points within the next work cycle. Specifically, by analyzing the changing patterns of similar work modes in historical data, it is predicted that within a 180-second work cycle, the load weight will linearly increase from 30 tons to 35 tons, and the lifting height will linearly increase from 12 meters to 15 meters. These values ​​are calculated using a time series prediction model, the formula of which is as follows: ,in express The unit of load weight at any given time is tons. This indicates an initial load weight of 30 tons. This indicates a maximum load weight of 35 tons, and t represents the time unit, seconds. The total operation time is 180 seconds. Based on these expected load weight and lifting height variations, a load change trend is formed, showing that both the load and lifting height increase at a constant rate. Based on this load change trend and the adjusted output power data (main motor 162.5 kW, auxiliary motor 87.5 kW), the efficiency characteristic parameters of the two motors under different expected load conditions are calculated. When the load is 35 tons, the efficiency parameter of the main motor is 0.88, and the efficiency parameter of the auxiliary motor is 0.85. These efficiency values ​​are obtained by querying the motor efficiency characteristic curves and considering the current heat dissipation status. Based on these efficiency characteristic parameters, several different power allocation ratio combinations are generated, including combination one [0.70, 0.30], combination two [0.65, 0.35], and combination three [0.60, 0.40]. These combinations are possible allocation ratios generated by an optimization algorithm under efficiency constraints. Based on these different power allocation ratios, corresponding candidate power allocation schemes are generated. Scheme 1 adopts a combination ratio of 70% for the main motor and 30% for the auxiliary motor. Scheme 2 adopts a combination ratio of 65% for the main motor and 35% for the auxiliary motor. Scheme 3 adopts a combination ratio of 60% for the main motor and 40% for the auxiliary motor. Each scheme specifies the specific power allocation values ​​at each time point in the entire operation cycle. These values ​​are calculated by allocating the total power demand of 280 kilowatts according to the corresponding ratios.

[0075] In the embodiments of this application, these steps generate multiple feasible power allocation schemes through accurate load prediction and efficiency calculation, providing ample choice space for selecting the optimal scheme and ensuring the scientific nature and adaptability of the power allocation scheme.

[0076] To address the accuracy issue in calculating efficiency parameters under expected load conditions, in some embodiments, step 303: calculating the efficiency characteristic parameters of the dual motors under different expected load conditions based on the load change trend and adjusted output power data, includes: Step 401: Based on the load change trend and the adjusted output power data, determine the expected operating points of the dual motors under different expected load conditions.

[0077] In step 401, the expected operating point refers to the operating condition point that the dual motors may operate in the future work cycle based on the load change trend prediction, reflecting the motor operating state under the predicted load conditions; the "optimal operating efficiency point" refers to the operating state of the dual motors with the highest efficiency obtained through efficiency optimization calculation under specific load conditions; the connection between the two is that the expected operating point is the basic operating condition point for efficiency optimization, while the optimal operating efficiency point is the best operating state obtained through efficiency optimization calculation based on the expected operating point. The expected operating point provides an analytical basis and optimization direction for finding the optimal operating efficiency point.

[0078] In this embodiment, the expected load weight and lifting height data in the load change trend are first analyzed. Combined with the current adjusted output power state, the speed and torque values ​​that the dual motors need to reach under each expected load condition are calculated through the motor working characteristic model. The combination of these speeds and torques is the expected operating point.

[0079] Step 402: Based on the efficiency characteristic curves of the dual motors, interpolate to calculate the efficiency value corresponding to each expected operating point.

[0080] In step 402, the efficiency characteristic curve is a two-dimensional curve describing the relationship between motor efficiency and speed and torque. Interpolation calculation refers to the method of estimating the efficiency value of unknown points between known data points on the curve.

[0081] In this embodiment of the application, the speed and torque values ​​of each expected operating point obtained in step 401 are interpolated on the efficiency characteristic curves of the two motors to find the efficiency values ​​of the corresponding operating points. These efficiency values ​​reflect the working efficiency of the motors under ideal conditions.

[0082] Step 403: Based on the adjusted heat dissipation control state, perform temperature compensation correction on the efficiency value to obtain the corrected efficiency value, and use the corrected efficiency value as the efficiency characteristic parameter.

[0083] In step 403, temperature compensation correction is a process of adjusting the ideal efficiency value by taking into account the impact of the actual operating temperature on the motor efficiency.

[0084] In this embodiment of the application, the efficiency value calculated in step 402 is corrected according to the temperature influence coefficient corresponding to the current heat dissipation control state to obtain an efficiency value that is more in line with the actual working conditions. These corrected efficiency values ​​are the efficiency characteristic parameters finally used for power distribution calculation.

[0085] Here is a specific example: During the specific operation of a container gantry crane at a port, based on the load change trend indicating that the load weight will increase from 30 tons to 35 tons and the lifting height will increase from 12 meters to 15 meters, and combined with the adjusted output power data of 162.5 kW for the main motor and 87.5 kW for the auxiliary motor, the expected operating points of the two motors under different anticipated load conditions were determined. Specifically, when the load is 35 tons, the power calculation formula was used... Calculate the working point, where The unit of power is kilowatt. The unit for rotational speed is revolutions per minute. The torque is expressed in Newton-meters (Nm). The expected operating point of the main motor is a torque of 1000 revolutions per minute. The auxiliary motor operates at a speed of 950 rpm with a torque of 955 × 87.5 / 950 ≈ 87.8 Nm. Based on the efficiency characteristic curves of the dual motors, a linear interpolation method is used to calculate the efficiency value corresponding to each expected operating point. The efficiency value of the main motor at 1000 rpm (155.1 Nm) is 0.89 through table lookup interpolation, and the efficiency value of the auxiliary motor at 950 rpm (87.8 Nm) is 0.86. According to the adjusted heat dissipation control state, the temperature compensation coefficient for the main motor's heat dissipation level 4 is 0.98, and the temperature compensation coefficient for the auxiliary motor's heat dissipation level 3 is 0.99. Temperature compensation correction is applied to the efficiency values ​​using the following formula: ,in This indicates that the corrected efficiency value has no unit. This indicates that the initial efficiency value is dimensionless. This indicates that the temperature compensation coefficient has no unit, and the corrected efficiency value of the main motor is... The corrected efficiency value of the auxiliary motor is 0.86 × 0.99 = 0.8514. These corrected efficiency values ​​are used as efficiency characteristic parameters for subsequent power distribution calculations.

[0086] In the embodiments of this application, these steps, through precise operating point determination, curve interpolation, and temperature compensation, yield efficiency characteristic parameters that better reflect actual working conditions, providing accurate basic data for power allocation and improving the reliability and practicality of the power allocation scheme.

[0087] To address the issues of accuracy and adaptability in power allocation weight calculation, in some embodiments, step 104: calculating the power allocation weight based on the load power spectrum, the efficiency characteristic data, and the adjusted heat dissipation control state using a fuzzy adaptive control algorithm, combined with a preset dynamic efficiency mapping relationship, includes: Step 501: Based on the efficiency characteristic data, establish the efficiency characteristic curves of the dual motors under different load conditions.

[0088] In step 501, different load conditions refer to the various torque output requirements and speed changes encountered by the dual motors when driving the crane, including various motor operating states such as no-load operation, light-load operation, rated load operation, and overload operation.

[0089] In this embodiment of the application, efficiency characteristic data provided by the motor manufacturer is first obtained, and these data points are connected to form a smooth efficiency characteristic curve, which shows the change law of motor efficiency from no load to full load.

[0090] Step 502: Based on the load change characteristics in the load power spectrum and the adjusted heat dissipation control state, a fuzzy adaptive control algorithm is used to dynamically correct the efficiency characteristic curve to obtain the corrected efficiency characteristic curve.

[0091] In step 502, the load variation characteristics in the load power spectrum are obtained by analyzing the influence of parameters such as lifting height, load weight, and running time recorded in the load power spectrum on the motor torque and speed requirements. This means that the changes in these parameters affect the characteristics and patterns of the dual-motor load requirements. The corrected efficiency characteristic curve is an efficiency curve that takes into account the influence of actual working conditions, more realistically reflecting the motor's efficiency characteristics under the current operating conditions.

[0092] In this embodiment, the load variation pattern in the load power spectrum and the current heat dissipation control state are analyzed. The correction coefficient is calculated by the fuzzy adaptive control algorithm to adjust the original efficiency characteristic curve and obtain a corrected efficiency curve that is more in line with the actual working conditions.

[0093] Step 503: Based on the preset dynamic efficiency mapping relationship and the corrected efficiency characteristic curve, determine the optimal operating efficiency point of the dual motors under the current load demand.

[0094] In step 503, the current load demand refers to the real-time output torque and speed requirements that the dual motors need to provide at the current operating moment, reflecting the instantaneous power output demand of the motors. The optimal operating efficiency point is the operating state in which the motors are most efficient under specific load conditions, including the optimal combination of speed and torque.

[0095] In this embodiment, based on the current load requirements, the point with the highest efficiency is found on the modified efficiency characteristic curve, and the corresponding speed and torque values ​​are determined by combining the dynamic efficiency mapping relationship. The combination of these parameters is the optimal working efficiency point.

[0096] Step 504: Calculate the power distribution ratio of the two motors based on the optimal working efficiency point.

[0097] In step 504, the power distribution ratio refers to the relative proportion of power distribution between the two motors, expressed as a percentage or decimal.

[0098] In this embodiment of the application, the optimal power output capability of each motor is calculated based on the working efficiency of each motor at its optimal point, and then the power allocation ratio of each motor is determined according to the total power demand.

[0099] Step 505: Optimize and adjust the power allocation ratio using fuzzy rules to generate power allocation weights.

[0100] In step 505, fuzzy rules are the core component of the fuzzy adaptive control algorithm. They describe the control strategy for motor power distribution by defining linguistic variables and membership functions. The fuzzy adaptive control algorithm is a complete methodology for automatically adjusting and optimizing the power distribution parameters of the two motors through these fuzzy rules.

[0101] In this embodiment of the application, a preset fuzzy rule base is used to optimize and adjust the initially calculated power allocation ratio, taking into account factors such as system stability and response speed, to generate the final power allocation weight.

[0102] Here is a specific example: In the specific operation of a container gantry crane at a port, efficiency characteristic curves of the two motors under different load conditions were established based on efficiency characteristic data. The efficiency curve of the main motor showed 82% efficiency at 30% load, 88% efficiency at 60% load, and 85% efficiency at 100% load. The efficiency curve of the auxiliary motor showed 80% efficiency at 30% load, 85% efficiency at 60% load, and 82% efficiency at 100% load. According to the load change characteristics in the load power spectrum, the load will increase from 30 tons to 35 tons within 5 minutes. The adjusted heat dissipation control status is 4 for the main motor and 3 for the auxiliary motor. A fuzzy adaptive control algorithm was used to dynamically correct the efficiency characteristic curves, with the overall efficiency curve of the main motor being lowered by 3 percentage points and the overall efficiency curve of the auxiliary motor being lowered by 2 percentage points. The corrected efficiency characteristic curves showed that the efficiency of the main motor at 60% load was 85%, and the efficiency of the auxiliary motor at 60% load was 83%. Based on the preset dynamic efficiency mapping relationship and the corrected efficiency characteristic curve, the optimal operating efficiency points of the dual motors under the current load demand of 30 tons are determined. Specifically, the optimal operating efficiency point for the main motor is 85% at 60% load, and the optimal operating efficiency point for the auxiliary motor is 82.5% at 55% load. The power distribution ratio of the dual motors is calculated based on the optimal operating efficiency points using the formula... Calculation, where Indicates the first The power distribution ratio of the trolley motor has no unit. Indicates the first The efficiency value of the Taiwanese electric motor has no unit. This indicates that the sum of the efficiencies of the two motors is dimensionless, and the power distribution ratio of the main motor is... The auxiliary motor power distribution ratio is The power allocation ratio is optimized and adjusted using fuzzy rules. Considering the poor heat dissipation of the main motor, the load needs to be appropriately reduced. The power allocation ratio is adjusted to 0.48 for the main motor and 0.52 for the auxiliary motor. The final power allocation weight is 0.48 for the main motor and 0.52 for the auxiliary motor.

[0103] In the embodiments of this application, these steps generate power allocation weights that are more in line with actual operating conditions by dynamically correcting the efficiency characteristic curve and fuzzy optimization adjustment, thereby improving the accuracy and adaptability of power allocation and ensuring the optimal operating state of the system under different operating conditions.

[0104] To address the issues of accuracy and practicality in establishing the load power spectrum, in some embodiments, step 102: establishing the load power spectrum based on the historical work cycle data includes: Step 601: Divide the historical operation cycle data into operation segments and identify the lifting height change process, load weight maintenance process and running time interval in each operation segment.

[0105] In step 601, the operation segment division is the process of dividing continuous operation data into independent segments according to operation type. The lifting height change process describes the lifting and lowering movement of the hook, the load weight maintenance process refers to the stage in which the weight of the cargo remains stable, and the running time interval records the duration of each segment.

[0106] In this embodiment of the application, historical operation cycle data is first analyzed, and the data is divided into multiple operation segments according to the operation type change points. Then, the lifting height change trajectory, load weight stability value and running time range in each segment are identified, laying the foundation for subsequent feature extraction.

[0107] Step 602: Extract the corresponding extreme values ​​of lifting height, constant values ​​of load weight, and running time length from the lifting height change process, the load weight maintenance process, and the running time interval in each operation segment.

[0108] In step 602, the lifting height extreme value refers to the highest or lowest position reached by the hook in the segment, the load weight constant value refers to the stable value of the cargo weight in the segment, and the running time length refers to the duration of the segment from start to finish.

[0109] In the embodiments of this application, each operation segment is analyzed to extract the extreme points during the lifting height change process, the stable weight value during the load weight maintenance process, and the total duration of the running time interval. These parameter values ​​represent the typical characteristics of the operation segment.

[0110] Step 603: Based on the extreme value of the lifting height, the constant value of the load weight, and the length of the running time, form a typical operation segment feature set.

[0111] In step 603, the typical work segment feature set is a data set containing multiple work segment feature parameters, reflecting the common operation mode characteristics of cranes.

[0112] In this embodiment of the application, the feature parameters of all extracted operation segments are sorted and classified to form a feature dataset containing extreme values ​​of lifting height, constant values ​​of load weight, and length of operation time. This dataset shows the feature combinations of various typical operation modes of cranes.

[0113] Step 604: Construct a load power sequence based on the parameter correlation relationship corresponding to the feature set of the typical operation segment.

[0114] In step 604, the parameter correlations in the typical operation segment feature set are established by analyzing the statistical regularities and coordinated change patterns among three parameters—extreme lifting height, constant load weight, and running time length—in historical operation cycle data. This means that these parameters have inherent connections and mutual influences during crane operation, reflecting the combined characteristics of each parameter under a specific operation mode. The load power sequence is a power demand data sequence arranged in chronological order, reflecting the changing patterns of power demand under different operation modes.

[0115] In this embodiment of the application, the correlation between parameters in the feature set of typical work segments is analyzed, the power demand value corresponding to each work segment is calculated, and then these power values ​​are arranged in time order to form a complete load power sequence.

[0116] Step 605: Generate a load power spectrum based on the frequency of occurrence of different parameter combinations in the load power sequence.

[0117] In step 605, the frequency of occurrence of different parameter combinations in the load power sequence is obtained by statistically analyzing the number of times and distribution of various lifting height-load weight-running time parameter combinations in historical data. This means that the probability distribution of various operation parameter combinations in actual operation is used to identify the most common and typical operation modes and provide a data basis for establishing the load power spectrum.

[0118] In this embodiment of the application, the frequency of occurrence of various combinations of lifting height, load weight and running time parameters in the load power sequence is counted, the frequency of occurrence of each combination is calculated, and a load power spectrum is generated based on the frequency distribution. This power spectrum reflects the power demand characteristics of the typical operation mode of the crane.

[0119] Here is a specific example: In the specific operation of a container gantry crane at a port, based on historical operation cycle data from 1000 complete operation cycles, the data was first divided into operation segments. Each operation cycle was divided into three segments: hoisting, traversing, and lowering. The characteristic processes within each segment were identified. Specifically, in the hoisting segment, the process of the hoisting height changing from 0 meters to 12 meters, the load weight remaining constant at 30 tons, and the running time interval of 60 seconds were identified. Corresponding feature values ​​were extracted from each operation segment. In the hoisting segment, the extreme hoisting height was identified as 12 meters, the load weight as constant at 30 tons, and the running time as 60 seconds. Based on these extracted values, a typical operation segment feature set was formed. This set contains parameter combinations for multiple operation segments, such as the hoisting segment parameters being 12 meters height, 30 tons weight, and 60 seconds; the traversing segment parameters being 12 meters height, 30 tons weight, and 90 seconds; and the lowering segment parameters being 12 meters height to 0 meters, 30 tons weight, and 50 seconds. Based on the parameter correlations corresponding to the characteristic set of typical operation segments, a load power sequence is constructed. This sequence shows that when the lifting height is 12 meters, the load weight is 30 tons, and the operation time is 60 seconds, the required power is 176.4 kilowatts. This power value is calculated by the formula: power equals weight multiplied by height divided by time and then multiplied by gravitational acceleration 9.8, that is, power = 30000 × 9.8 × 12 / 60 = 58800 watts, which equals 58.8 kilowatts. After considering the mechanical efficiency of the system, it is adjusted to 176.4 kilowatts. Based on the frequency of occurrence of different parameter combinations in the load power sequence, a load power spectrum is generated. This power spectrum shows that the frequency of occurrence of the parameter combination of 12-meter lifting height, 30-ton load, and 60-second operation time reaches 40%, the frequency of the combination of 12-meter lateral movement height, 30-ton load, and 90-second operation time is 35%, and the frequency of the combination of 30-ton descent time and 50-second operation time is 25%. These frequency values ​​are calculated by dividing the number of occurrences of each parameter combination in the historical data by the total number of occurrences of 1000. Among them, the 400 occurrences of the lifting segment correspond to a frequency of 40%, the 350 occurrences of the lateral movement segment correspond to a frequency of 35%, and the 250 occurrences of the descent segment correspond to a frequency of 25%.

[0120] In the embodiments of this application, these steps, through systematic data analysis and feature extraction, establish a load power spectrum that accurately reflects the operating characteristics of the crane, providing a reliable data foundation for subsequent power optimization allocation and improving the adaptability and accuracy of the power management system.

[0121] To address the issue of insufficient accuracy in motor thermal management, in some embodiments, step 103: generating thermal accumulation state information based on the winding temperature data, and dynamically adjusting the heat dissipation control state of the dual motors based on the thermal accumulation state information, includes: Step 701: Based on the winding temperature data, calculate the winding temperature change rate and temperature distribution uniformity.

[0122] In step 701, the rate of temperature change refers to the amount of change in winding temperature per unit time, reflecting the speed at which the temperature rises or falls; the temperature distribution uniformity refers to the degree of temperature difference between different measurement points, reflecting the uniformity of heat distribution.

[0123] In this embodiment, winding temperature data at multiple time points are first obtained, the temperature difference between adjacent time points is calculated and divided by the time interval to obtain the temperature change rate, and the standard deviation or range of the temperature at each measurement point is calculated as an index of temperature distribution uniformity.

[0124] Step 702: Determine the heat accumulation state information based on the temperature change rate and the temperature distribution uniformity.

[0125] In this embodiment of the application, the rate of temperature change and the uniformity of temperature distribution are compared with a preset threshold, and the heat accumulation state level is determined based on the comparison result, such as low heat accumulation, medium heat accumulation or high heat accumulation state.

[0126] Step 703: Generate heat dissipation control commands based on the temperature change trend and temperature distribution characteristics in the heat accumulation state information.

[0127] In step 703, the temperature change trend in the heat accumulation status information is obtained by analyzing the rate of change of winding temperature data over time. This indicates the rate and direction of temperature increase or decrease, reflecting the speed of heat accumulation or dissipation. The heat dissipation control commands are the command parameters that control the operation of the cooling system, including control parameters such as fan speed and water pump flow rate. The temperature distribution characteristics in the heat accumulation status information are obtained by comparing temperature data at different locations on the winding. This indicates the uniformity of temperature distribution within the motor winding space and the location information of hot spots, reflecting the spatial distribution of heat.

[0128] In this embodiment, the direction of heat accumulation is determined based on the temperature change trend in the heat accumulation state information, and the area that needs to be strengthened for heat dissipation is determined by combining the temperature distribution characteristics, thereby generating corresponding heat dissipation control command parameters.

[0129] Step 704: Dynamically adjust the heat dissipation control status of the dual motors according to the heat dissipation control command.

[0130] In step 704, the heat dissipation control status refers to the actual operating status of the cooling system, including parameters such as fan speed level and coolant flow rate.

[0131] In this embodiment, a heat dissipation control command is sent to the cooling system controller to adjust the fan speed or water pump flow rate in real time, so that the heat dissipation intensity matches the thermal state of the motor, thereby achieving dynamic thermal management.

[0132] Here is a specific example: In the specific operation of a container gantry crane at a port, based on winding temperature data including the temperatures of three measuring points on the main motor (85°C, 88°C, and 92°C) and three measuring points on the auxiliary motor (82°C, 84°C, and 86°C), the temperature change rate and temperature distribution uniformity of the windings were calculated. The temperature change rate of the main motor was calculated using the formula ΔT / Δt, where ΔT represents the temperature change of 12°C and Δt represents the time interval of 300 seconds, resulting in 0.04°C per second. The temperature distribution uniformity was obtained by subtracting the lowest temperature of 85°C from the highest temperature of 92°C, resulting in 7°C. The temperature change rate of the auxiliary motor was obtained by dividing 8°C by 300 seconds, resulting in 0.027°C per second, approximately equal to 0.03°C per second. The temperature distribution uniformity was obtained by subtracting 82°C from 86°C, resulting in 4°C. Based on the rate of temperature change and the uniformity of temperature distribution, the heat accumulation state information is determined. The main motor's temperature change rate (0.04°C / s) is greater than the threshold of 0.03°C / s, and its temperature distribution uniformity (7°C / s) is greater than the threshold of 5°C, indicating a high heat accumulation state. The auxiliary motor's temperature change rate (0.03°C / s) is equal to the threshold, and its temperature distribution uniformity (4°C / s) is less than the threshold, indicating a medium heat accumulation state. Based on the temperature change trend in the heat accumulation state information, which shows a rapid rise in the main motor temperature, and the temperature distribution characteristics indicating a significant hotspot area in the main motor, a heat dissipation control command is generated. This command requires the main motor's heat dissipation level to be increased to level 4, corresponding to a fan speed of 1200 rpm, while the auxiliary motor's heat dissipation level is maintained at level 3, corresponding to a fan speed of 900 rpm. According to the heat dissipation control command, the heat dissipation control status of both motors is dynamically adjusted. The main motor's cooling fan speed is increased from 900 rpm to 1200 rpm, while the auxiliary motor's cooling fan speed remains unchanged at 900 rpm. These speed values ​​are determined based on the standard values ​​corresponding to the heat dissipation level using the fan performance curves.

[0133] In the embodiments of this application, these steps, through real-time temperature monitoring and intelligent analysis, achieve precise adjustment of heat dissipation control, effectively prevent motor overheating, ensure system operation safety and reliability, and extend equipment service life.

[0134] Figure 3 A schematic diagram of a dynamic power allocation optimization system for a dual-motor drive of a crane, provided in this application embodiment, is described in the detailed implementation section as follows: The data acquisition module 31 is used to collect historical operation cycle data, winding temperature data of the dual motors in the crane, and efficiency characteristic data during the crane's operation.

[0135] Module 32 is used to establish a load power spectrum based on the historical operation cycle data.

[0136] The generation module 33 is used to generate heat accumulation status information based on the winding temperature data, and dynamically adjust the heat dissipation control status of the dual motors based on the heat accumulation status information.

[0137] The calculation module 34 is used to calculate the power allocation weight based on the load power spectrum, the efficiency characteristic data and the adjusted heat dissipation control state, using a fuzzy adaptive control algorithm and a preset dynamic efficiency mapping relationship.

[0138] The adjustment module 35 is used to dynamically adjust the output power of the dual motors based on the power allocation weight, and to use a model predictive control algorithm to generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle status data.

[0139] The crane dual-motor drive power dynamic allocation optimization system of this application embodiment is used to implement the aforementioned crane dual-motor drive power dynamic allocation optimization method. Therefore, the specific implementation of the crane dual-motor drive power dynamic allocation optimization system can be found in the embodiment section of the crane dual-motor drive power dynamic allocation optimization method above. The specific implementation can be referred to the description of the corresponding embodiment, and will not be repeated here.

[0140] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the above-described crane dual-motor drive power dynamic allocation optimization method.

[0141] This application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described crane dual-motor drive power dynamic allocation optimization method.

[0142] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0143] The embodiments of this application also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the embodiments of the dynamic allocation optimization method for dual-motor drive power of a crane.

[0144] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0145] The foregoing has provided a detailed description of a method and system for dynamic power allocation optimization of dual-motor drive for cranes, as provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for dynamic allocation optimization of dual-motor drive power in a crane, characterized in that, include: Collect historical operation cycle data, winding temperature data and efficiency characteristic data of the dual motors in the crane during operation; Based on the aforementioned historical operating cycle data, a load power spectrum is established; Based on the winding temperature data, heat accumulation status information is generated, and the heat dissipation control status of the dual motors is dynamically adjusted based on the heat accumulation status information. Based on the load power spectrum, the efficiency characteristic data, and the adjusted heat dissipation control state, a fuzzy adaptive control algorithm is used, combined with a preset dynamic efficiency mapping relationship, to calculate the power allocation weight. Based on the power allocation weights, the output power of the dual motors is dynamically adjusted, and a model predictive control algorithm is used to generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle status data.

2. The method according to claim 1, characterized in that, The process of dynamically adjusting the output power of the dual motors based on the power allocation weights, and using a model predictive control algorithm to generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle status data, includes: Based on the power allocation weights, calculate the output power setpoints for the two motors; The output power of the two motors is adjusted in real time according to the output power setting value. The model predictive control algorithm is used to predict the load change trend of the next work cycle based on the current work cycle status data, and generate multiple candidate power allocation schemes according to the load change trend and the adjusted output power. The optimal power allocation scheme with the best overall efficiency is selected from the multiple candidate power allocation schemes.

3. The method according to claim 2, characterized in that, The aforementioned model predictive control algorithm predicts the load change trend for the next work cycle based on the current work cycle status data, and generates multiple candidate power allocation schemes based on the load change trend and the adjusted output power, including: Based on the load power spectrum and the current work cycle status data, the expected load weight and lifting height change range at each time point in the next work cycle are estimated using a load change prediction model constructed based on model predictive control algorithm. Based on the expected load weight and the range of change in lifting height, a load change trend is formed; Based on the load change trend and the adjusted output power data, the efficiency characteristic parameters of the dual motors under different expected load conditions are calculated. Based on the efficiency characteristic parameters, multiple different power allocation ratio combinations are generated; Based on multiple different combinations of the power allocation ratios, corresponding candidate power allocation schemes are generated.

4. The method according to claim 3, characterized in that, Based on the load change trend and the adjusted output power data, the efficiency characteristic parameters of the dual motors under different expected load conditions are calculated, including: Based on the load change trend and the adjusted output power data, the expected operating points of the dual motors under different expected load conditions are determined. Based on the efficiency characteristic curves of the two motors, the efficiency value corresponding to each expected operating point is calculated by interpolation. Based on the adjusted heat dissipation control state, the efficiency value is corrected by temperature compensation to obtain the corrected efficiency value, which is then used as the efficiency characteristic parameter.

5. The method according to claim 1, characterized in that, The process involves using a fuzzy adaptive control algorithm, based on the load power spectrum, efficiency characteristic data, and adjusted heat dissipation control state, to calculate power allocation weights in conjunction with a preset dynamic efficiency mapping relationship. This includes: Based on the efficiency characteristic data, efficiency characteristic curves of the dual motors under different load conditions are established. Based on the load variation characteristics in the load power spectrum and the adjusted heat dissipation control state, a fuzzy adaptive control algorithm is used to dynamically correct the efficiency characteristic curve to obtain the corrected efficiency characteristic curve. Based on the preset dynamic efficiency mapping relationship and the corrected efficiency characteristic curve, the optimal working efficiency point of the dual motors under the current load demand is determined. Calculate the power distribution ratio of the two motors based on the optimal operating efficiency point; The power allocation ratio is optimized and adjusted using fuzzy rules to generate power allocation weights.

6. The method according to claim 1, characterized in that, The process of establishing a load power spectrum based on the historical work cycle data includes: The historical operation cycle data is divided into operation segments, and the lifting height change process, load weight maintenance process and running time interval in each operation segment are identified; Extract the corresponding extreme values ​​of lifting height, constant values ​​of load weight, and running time length from the lifting height change process, the load weight maintenance process, and the running time interval in each operation segment; Based on the extreme lifting height, the constant load weight, and the running time, a set of typical operation segment features is formed. Based on the parameter correlations corresponding to the feature set of the typical work segments, a load power sequence is constructed; A load power spectrum is generated based on the frequency of occurrence of different parameter combinations in the load power sequence.

7. The method according to claim 1, characterized in that, The step of generating heat accumulation state information based on the winding temperature data, and dynamically adjusting the heat dissipation control state of the dual motors based on the heat accumulation state information, includes: Based on the winding temperature data, the temperature change rate and temperature distribution uniformity of the winding are calculated. Based on the rate of temperature change and the uniformity of temperature distribution, the thermal accumulation state information is determined; Based on the temperature change trend and temperature distribution characteristics in the heat accumulation state information, a heat dissipation control command is generated. The heat dissipation control status of the dual motors is dynamically adjusted according to the heat dissipation control command.

8. A dynamic power allocation optimization system for dual-motor drive of a crane, characterized in that, include: The data acquisition module is used to collect historical operation cycle data, winding temperature data of the dual motors in the crane, and efficiency characteristic data during the crane's operation. A module is established to create a load power spectrum based on the historical work cycle data; The generation module is used to generate heat accumulation status information based on the winding temperature data, and dynamically adjust the heat dissipation control status of the dual motors based on the heat accumulation status information. The calculation module is used to calculate the power allocation weight based on the load power spectrum, the efficiency characteristic data, and the adjusted heat dissipation control state, using a fuzzy adaptive control algorithm and a preset dynamic efficiency mapping relationship. The adjustment module is used to dynamically adjust the output power of the dual motors based on the power allocation weights, and to use a model predictive control algorithm to generate the optimal power allocation scheme for the dual motors in the next work cycle based on the adjusted output power and the current work cycle status data.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the dynamic power allocation optimization method for dual-motor drive of a crane as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the implementation of the dynamic power allocation optimization method for dual-motor drive of a crane as described in any one of claims 1 to 7.

Images