Intelligent load balancing control system of automobile elevator

Abstract

The invention relates to the technical field of automobile lifters, in particular to an intelligent load balancing control system for an automobile lifter, which comprises a data acquisition and analysis module, a load state evaluation module, a load balancing adjustment module, a lifter monitoring module and a safety protection control module. According to the invention, the load condition of each fulcrum can be reflected by collecting the weight data of the pressure sensor and the vehicle in real time, and the load difference between the fulcrums and the overall balance state of the vehicle can be deeply analyzed and identified according to the load condition, so that the pressure value needing to be adjusted is automatically calculated, and the servo motor is matched for accurate adjustment, thereby greatly improving the dynamic management efficiency of the load. A real-time monitoring and feedback mechanism can recognize load deviation exceeding a threshold value in real time, safety early warning is automatically triggered, necessary adjustment measures are taken, absolute safety of the lifting process is ensured, and especially when heavy vehicles are processed, operation safety is improved, and high efficiency and accuracy of maintenance operation are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile lifts, and in particular to an intelligent load balancing control system for automobile lifts. Background Art

[0002] A car lift primarily involves equipment used to lift vehicles for repair and inspection. The equipment can be mechanical, hydraulic, or electric, allowing the vehicle to be safely lifted into the air, providing technicians with ample clearance to perform repairs on the chassis, tires, and underbody. Car lifts are designed with load-bearing capacity, stability, and safety in mind, ensuring the vehicle remains balanced and stable during the lift.

[0003] Among them, the intelligent load balancing control system of the car lift is a system that integrates sensors and control algorithms. It is used to automatically monitor and adjust the load distribution on the lift. Its main purpose is to ensure that the weight of the vehicle is evenly distributed on each fulcrum during the car lifting process, preventing mechanical failure or safety accidents caused by center of gravity shift. The use of this system can significantly improve maintenance efficiency and safety, especially when handling heavy vehicles.

[0004] Existing technologies have obvious shortcomings in load monitoring and dynamic adjustment, and lack real-time data support and automatic feedback mechanisms. As a result, when handling complex or irregular loads, the stability and overload protection of the lift rely on the operator's judgment, which to a certain extent increases the operational risk and potential for mechanical failure. For example, in the absence of a real-time monitoring system, abnormal loads at any fulcrum are not discovered in time, resulting in damage to the lift structure or serious safety accidents, such as vehicle slipping or overturning, which not only affects the service life of the lift, but also endangers the safety of maintenance personnel. Especially in emergency situations, the response speed and processing accuracy of the existing system are difficult to meet the needs of modern automobile maintenance. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent load balancing control system for a car lift.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical solution: an intelligent load balancing control system for a car lift, the system comprising: The data acquisition and analysis module obtains data from the pressure sensors installed at the lift fulcrum and the vehicle weight data, receives the data stream through the lift control system, analyzes the pressure sensor and vehicle weight data, and establishes a real-time load data set; The load state assessment module compares the load data of each support point based on the real-time load data set, identifies the load distribution that deviates from the normal range, and analyzes the load difference between the overall load center of the vehicle and each support point to obtain load offset analysis information; The load balancing adjustment module adjusts the force of the lifting fulcrum based on the load offset analysis information and compares it with the current balance threshold of the lift. If a deviation outside the normal range is detected, the pressure adjustment value is automatically recalculated and the lift servo motor is controlled to adjust the fulcrum pressure to obtain the balance state parameters. The lift monitoring module monitors the balance state parameters, collects the adjusted pressure sensor data, and compares it with the original state in real time. If the error exceeds the design allowable range, it automatically triggers a safety warning, records the relevant data, and obtains a load adjustment feedback log.

[0007] The present invention has the following improvements: the real-time load data set includes pressure value, total weight, and acquisition time; the load offset analysis information includes offset value, balance assessment result, and difference index; the balance state parameters include adjustment pressure, threshold comparison information, and adjustment index; and the load adjustment feedback log includes error results, deviation amount, and warning mark.

[0008] The present invention is improved in that the data acquisition and analysis module includes: The data acquisition submodule acquires data from the pressure sensor installed at the lift fulcrum and the vehicle weight data, parses and extracts the measured values, removes abnormal data through unit conversion and data correction, and generates accurate sensor and vehicle data; The load state calculation submodule analyzes the deviation between the sensor pressure data and the vehicle weight based on the accurate sensor and vehicle data, using the formula: ; Calculating Load Error Percentage , get the current load state of the lift, where Representative The pressure value of a pressure sensor, represents the normalized vehicle weight value, Represents the number of sensors; The load data establishment submodule continuously monitors the load changes of the lift based on the current load status of the lift and combines the timestamp recorded data to establish a real-time load data set.

[0009] The present invention is improved in that the load state evaluation module includes: The load data comparison submodule compares the load data of each fulcrum based on the real-time load data set, filters out the load data that deviates from the normal range, and calculates the difference between the fulcrum load and the global load mean to obtain abnormal load data; The load center analysis submodule identifies the overall load center of the vehicle based on the abnormal load data using the formula: ; and ; Get the horizontal coordinate of the load center and the longitudinal coordinate of the load center , and analyze the load differences between multiple support points to obtain the vehicle equilibrium state, where Representative Load data of each support point, and Representative The coordinate position of the pivot point, Represents the number of fulcrums; The load offset calculation submodule calls the vehicle balance state, calculates the difference between the load center coordinates and the vehicle theoretical symmetry center, analyzes the offset of each support point, and obtains load offset analysis information.

[0010] The present invention is improved in that the load balancing adjustment module includes: The fulcrum pressure adjustment submodule adjusts the force of the lifting fulcrum based on the load offset analysis information, calculates the current pressure value of each fulcrum, compares it with the current balance threshold of the lift, filters out the fulcrum pressure data that exceeds the normal range, and obtains the over-limit fulcrum pressure data; The pressure deviation calculation submodule recalculates the pressure adjustment value and corrects the deviation based on the over-limit support pressure data, using the formula: ; Get the Pressure adjustment value of each pivot point , get the pivot pressure adjustment parameter, where, Representative The current pressure of the pivot point, Represents the current balance threshold of the lift. Representative The pressure of other fulcrums, Represents the number of fulcrums, It means that in the summation process, except for the The pressure on all other fulcrums except include in; The servo motor control submodule sends a control instruction to the lift servo motor according to the fulcrum pressure adjustment parameter, adjusts the pressure of each fulcrum to match the expected load balance state, and obtains the balance state parameter.

[0011] The present invention is improved in that the lift monitoring module includes: The equilibrium state monitoring submodule monitors the equilibrium state parameters, collects the adjusted pressure sensor data and real-time pressure data, and calls the original state pressure data for comparison to obtain the pressure change value; The regulation error analysis submodule analyzes the regulation error and regulation deviation based on the pressure change value, using the formula: ; Calculate the adjustment error value , analyze whether it exceeds the allowable range of the lift design and obtain the error judgment result, among which, Representative The pressure after the fulcrum is adjusted, Representative The pressure of the fulcrum in its original state, Represents the number of fulcrums; The safety warning triggering submodule calls the error determination result. If the adjustment error exceeds the design allowable range, a safety warning is automatically triggered, and related data is recorded to obtain a load adjustment feedback log.

[0012] The present invention is improved in that the system further comprises: The safety protection control module analyzes the pressure change data of the lifting fulcrum based on the load adjustment feedback log, calculates the stability of the load distribution, and determines whether it is an abnormal state. If abnormal, it adjusts the working state of the lift and limits its lifting height to obtain a lifting safety state; The lifting safety status includes operation restrictions, status adjustments, and height limit indicators.

[0013] The present invention is improved in that the security protection control module includes: The pressure change analysis submodule analyzes the pressure change data of the lifting fulcrum based on the load adjustment feedback log, calculates the pressure change trend, and obtains the fulcrum pressure change value; The load stability calculation submodule calls the fulcrum pressure change value to analyze the stability of the load distribution using the formula: ; Calculate load stability factor , determine whether it is an abnormal state and obtain the stability judgment result, where, Representative The pressure after the fulcrum is adjusted, Represents the average value of the fulcrum pressure, represents the standard deviation of the pivot pressure, Represents the total number of pivot points; The lifting safety adjustment submodule calls the stability determination result, and if it is determined to be an abnormal state, adjusts the working state of the lift, limits the lifting height, and obtains a lifting safety state.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by collecting pressure sensor and vehicle weight data in real time, it is possible to reflect the load conditions of each fulcrum, and conduct in-depth analysis based on this, identify the load differences between the fulcrums and the overall balance state of the vehicle, and automatically calculate the pressure value that needs to be adjusted. Combined with the precise adjustment of the servo motor, the dynamic management efficiency of the load is greatly improved. The real-time monitoring and feedback mechanism can instantly identify load deviations that exceed the threshold, and automatically trigger safety warnings, take necessary adjustment measures, and ensure the absolute safety of the lifting process. Especially when handling heavy vehicles, it not only improves operational safety, but also ensures the efficiency and accuracy of maintenance operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the data acquisition and analysis module in the present invention; Figure 3 is a flow chart of the load status evaluation module in the present invention; Figure 4 This is a flow chart of the load balancing adjustment module in the present invention; Figure 5 This is a flow chart of the lift monitoring module of the present invention; Figure 6 This is a flow chart of the security protection control module in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined. Example

[0018] See also Figure 1 The present invention provides a technical solution: an intelligent load balancing control system for a car lift includes: The data acquisition and analysis module obtains data from the pressure sensors installed at the lift fulcrum and the vehicle weight data. The lift control system receives the data stream, analyzes the pressure sensor and vehicle weight data, determines the current load status of the lift, and establishes a real-time load data set. The load status assessment module compares the load data of each support point based on the real-time load data set, identifies load distribution that deviates from the normal range, and analyzes the load difference between the vehicle's overall load center and each support point to determine the vehicle's balance state and obtain load offset analysis information; The load balance adjustment module adjusts the lifting fulcrum force based on the load offset analysis information and compares it with the lift's current balance threshold. If a deviation outside the normal range is detected, the pressure adjustment value is automatically recalculated and the lift servo motor is controlled to adjust the fulcrum pressure to match the expected load balance and obtain the balance state parameters. The lift monitoring module monitors balance parameters, collects adjusted pressure sensor data, compares it with the original state in real time, and analyzes adjustment errors and deviations. If the error exceeds the design allowable range, it automatically triggers a safety warning and records the associated data to obtain a load adjustment feedback log. The safety protection control module analyzes the pressure change data of the lifting fulcrum based on the load adjustment feedback log, calculates the stability of the load distribution, and determines whether it is in an abnormal state. If it is abnormal, it adjusts the working state of the lift and limits its lifting height to obtain a safe lifting state.

[0019] The real-time load data set includes pressure value, total weight, and collection time. The load offset analysis information includes offset value, balance assessment result, and difference index. The balance status parameters include adjustment pressure, threshold comparison information, and adjustment index. The load adjustment feedback log includes error results, deviation amount, and warning mark. The lifting safety status includes operation restrictions, status adjustment, and height limit indicators.

[0020] See also Figure 2 , the data acquisition and analysis modules include: The data acquisition submodule acquires data from the pressure sensor installed at the lift fulcrum and the vehicle weight data, parses and extracts the measured values, removes abnormal data through unit conversion and data correction, and generates accurate sensor and vehicle data; During the reading process, the collected pressure values ​​are converted to standard units. For example, if the sensor output is mV / V, it needs to be converted to N or kgf and stored. At the same time, vehicle weight data is collected using a weighing sensor or a floor scale to obtain the vehicle weight value in standard units. During the data processing stage, preliminary data screening is performed to remove abnormal values ​​that appear during the collection process, such as sudden changes in sensor data, outliers, or obviously unreasonable data points. A threshold range is set. For example, for a lift with a rated load of less than 10,000 N, if the pressure data of a single sensor exceeds ±20% of this range, it is determined to be abnormal data. After eliminating the abnormal data, the remaining data is normalized, for example, using the maximum and minimum normalization method to normalize the data to the range [0, 1] to ensure comparability of data from different sensors. At the same time, noise filtering is performed, such as using a mean filter or a Kalman filter, to reduce data fluctuations caused by environmental interference and improve data accuracy. After all data has been screened, normalized, and filtered, accurate sensor and vehicle data is generated.

[0021] The load state calculation submodule analyzes the deviation between the sensor pressure data and the vehicle weight based on accurate sensor and vehicle data, using the formula: ; Calculating Load Error Percentage , get the current load state of the lift, where Representative The pressure value of a pressure sensor, represents the normalized vehicle weight value, Represents the number of sensors; Sum the data of all pressure sensors and calculate the total pressure value on the lift fulcrum, that is, , then calculate the total pressure value and the standardized vehicle weight value The difference between the two values ​​is calculated and the absolute value is used to ensure that the error is always positive. Finally, in order to quantify the degree of error, the error value is divided by the standardized vehicle weight value. , and multiply by 100% to get the load error percentage A lift has 4 sensors, and the pressure data measured are 2300N, 2500N, 2400N and 2350N respectively. The standardized weight of the vehicle is 9500N. Calculate the total pressure , the load error percentage is calculated as follows: ; The calculation results show that the current load error of the lift is 0.526%. If the safety range set by the system is ±5%, the error value is within the acceptable range. Therefore, it can be judged that the current load status of the lift is normal.

[0022] The load data establishment submodule continuously monitors the load changes of the lift based on the current load status of the lift and combines the timestamp recorded data to establish a real-time load data set; Each time new data is collected, the current time information is obtained, such as using the Unix timestamp format to record data to ensure that the data can be arranged in chronological order. Then, the collected pressure sensor data, vehicle weight data, and the calculated load error percentage are stored in a database or real-time data stream to form a data sequence. At the same time, the data recording time interval is set, such as collecting data once per second. In subsequent data analysis, the changing trend of the load error percentage can be calculated, such as using a sliding window to calculate the average load error within each minute to monitor load changes. During the continuous data recording process, an abnormal monitoring threshold can also be set. For example, if the load error percentage exceeds ±5% for 10 consecutive times, an alarm is triggered. In this way, a real-time load data set is established.

[0023] See also Figure 3 , the load status assessment module includes: The load data comparison submodule compares the load data of each fulcrum based on the real-time load data set, filters out the load data that deviates from the normal range, and calculates the difference between the fulcrum load and the global load mean to obtain abnormal load data; Compare the load data of each fulcrum. First, obtain the load data of each fulcrum in different time periods, and summarize the load data of all fulcrums. Determine the benchmark load level by calculating the average load of each fulcrum in a specific time. To filter out load data that deviates from the normal range, it is necessary to first set the load fluctuation range. This range can be obtained based on historical data statistics. For example, by calculating the average load of all fulcrums and standard deviation To determine the normal range , filter out the load data outside this range as abnormal data. In actual application, suppose the load data of a lift fulcrum are 520kg, 510kg, 530kg, 600kg and 490kg respectively, while the mean of other fulcrums is 515kg and the standard deviation is 20kg. Then the data of 600kg for this fulcrum is abnormal load data. In order to further analyze the difference between the fulcrum load and the global load mean, it is necessary to calculate the load mean of each fulcrum. and the global mean For comparison, for example, if the load mean of a certain fulcrum is 490kg and the global mean is 515kg, then its deviation value is -25kg, and all abnormal load data are screened out.

[0024] The load center analysis submodule identifies the overall load center of the vehicle based on abnormal load data using the formula: ; and ; Get the horizontal coordinate of the load center and the longitudinal coordinate of the load center , and analyze the load differences between multiple support points to obtain the vehicle equilibrium state, where Representative Load data of each support point, and Representative The coordinate position of the pivot point, Represents the number of fulcrums; The vehicle has four pivot points, whose coordinates are 、 、 and , the load data are 500kg, 520kg, 480kg and 510kg respectively, then the load center coordinates are calculated as follows: Calculate the horizontal coordinate: ; ; Calculate the vertical coordinate: ; ; The load center coordinates are obtained as The balance state of the vehicle can be further analyzed by the position of the load center, for example, the center of symmetry of the vehicle design Perform a comparison to determine whether there is a shift.

[0025] The load offset calculation submodule calls the vehicle balance state, calculates the difference between the load center coordinates and the vehicle's theoretical symmetry center, analyzes the offset of each support point, and obtains load offset analysis information; Call the vehicle balance state, calculate the difference between the load center coordinates and the vehicle theoretical symmetry center, first set the vehicle theoretical symmetry center As a reference value, calculate the load center coordinate offset , , and then calculate the overall offset: , in actual calculation, it is assumed that the theoretical symmetry center of the vehicle is , the offset is calculated as follows: , , calculate the overall offset: ; This result indicates that the vehicle load center is offset by 0.028 m relative to the theoretical symmetry center. This value can be used to further determine whether the offset is within an acceptable range. For example, if the maximum allowable offset is set to 0.05 m, the current offset is within the acceptable range. Otherwise, the load distribution needs to be adjusted to reduce the imbalance and obtain load offset analysis information.

[0026] See also Figure 4 , the load balancing adjustment module includes: The fulcrum pressure adjustment submodule adjusts the force of the lifting fulcrum based on the load offset analysis information, calculates the current pressure value of each fulcrum, and compares it with the current balance threshold of the lift. It filters out the fulcrum pressure data that exceeds the normal range and obtains the over-limit fulcrum pressure data; Obtain real-time load offset analysis information of the lift fulcrum, which is calculated from the pressure data collected by the sensor. For example, when four fulcrums lift a device with a mass of 2000kg, each fulcrum should bear a load of about 500kg. However, due to the uneven distribution of the equipment mass, there is a deviation in the pressure of each fulcrum. Assume that the pressure value of a certain fulcrum is 550kg, while the other three fulcrums are 490kg, 505kg and 455kg respectively. At this time, the pressure distribution is uneven and needs to be adjusted. Then, call the force data of the lifting fulcrum, analyze the pressure applied by each fulcrum, calculate the current actual pressure value of the fulcrum, use a pressure sensor to measure the force condition of each fulcrum, and obtain the corresponding pressure value of the fulcrum. For example, at fulcrum A, the pressure detected by the sensor is 550kg, that is, , the pressures at fulcrums B, C, and D are 490kg, 505kg, and 455kg respectively, that is, , , Then, calculate the current balance threshold of the lift. This threshold is usually set as the theoretical balanced load that each support point should bear. For example, in this example, the pressure of each support point should be 500kg under ideal conditions, that is, Next, compare the pressure value of each fulcrum with the balance threshold to determine whether it exceeds the normal range. Assuming that the deviation of the normal range is set to ±5%, that is, the pressure value is allowed to be between [475kg, 525kg], then It exceeds the threshold and needs further adjustment. Finally, all the fulcrum pressure data that exceeds the range are filtered out. For example, in this case, fulcrum A (550kg) and fulcrum D (455kg) are out of the range, generating over-limit fulcrum pressure data.

[0027] The pressure deviation calculation submodule recalculates the pressure adjustment value and corrects the deviation based on the over-limit fulcrum pressure data using the formula: ; Get the Pressure adjustment value of each pivot point , get the pivot pressure adjustment parameter, where, Representative The current pressure of the pivot point, Represents the current balance threshold of the lift. Representative The pressure of other fulcrums, Represents the number of fulcrums, It means that in the summation process, except for the The pressure on all other fulcrums except include in; If the current pressure of fulcrum A is , the current pressure of fulcrum D is , the balance threshold is set to , the pressure adjustment value needs to be calculated. The pressure adjustment value of pivot A is calculated as follows: ; ; Calculate the sum of the pressures at support points B, C, and D: , , ; ; Calculate the mean term: ; ; Calculation results: ; ; Therefore, the pressure adjustment value of fulcrum A is -116.67 kg, which means that the pressure of this fulcrum needs to be reduced by 116.67 kg.

[0028] Calculation of the pressure adjustment value of pivot point D: ; ; Calculate the sum of the pressures at fulcrums A, B, and C: , , ; ; Calculate the mean term: ; ; Calculation results: ; ; Therefore, the pressure adjustment value of fulcrum D is 105kg, which means that the pressure of fulcrum needs to be increased by 105kg. The pressure adjustment value of fulcrum A is -116.67kg, which means that the pressure of fulcrum A needs to be reduced by 116.67kg. The pressure adjustment value of fulcrum D is 105kg, which means that the pressure of fulcrum D needs to be increased by 105kg. The adjustment value will be used for subsequent servo motor control to perform actual pressure adjustment operations.

[0029] The servo motor control submodule sends control instructions to the lift servo motor based on the fulcrum pressure adjustment parameters, adjusts the pressure of each fulcrum to match the expected load balance state, and obtains the balance state parameters; Call the fulcrum pressure adjustment parameters. In the above calculation, fulcrum A needs to reduce the pressure by 250kg, and fulcrum D needs to increase the pressure by 408.33kg. The adjustment range of the lift servo motor is usually between ±500kg. Ensure that the adjustment parameters are within the executable range. Then, send an adjustment signal to the servo motor to control the oil pressure regulator of each fulcrum to perform the action. For example, for fulcrum A, by reducing the oil pressure flow, its pressure is reduced by 250kg, while fulcrum D increases the hydraulic support to increase its pressure by 408.33kg. Assuming that the current hydraulic system adjustment accuracy is ±5kg, after execution, the pressure of fulcrum A should be adjusted to about 500±5kg, and the pressure of fulcrum D should be adjusted to 500±5kg. Finally, the sensor re-measures the pressure of each fulcrum to confirm whether the adjusted state matches the target value. If the error exceeds the allowable range, the pressure adjustment process is repeated until the balance state parameters are obtained.

[0030] See also Figure 5 , the lift monitoring module includes: The balance state monitoring submodule monitors the balance state parameters, collects the adjusted pressure sensor data and real-time pressure data, and calls the original state pressure data for comparison to obtain the pressure change value; The adjusted pressure data is collected by the pressure sensor, and the real-time pressure data is obtained at the same time, and the original pressure data is called for comparison. The adjusted pressure data is provided by the pressure sensors of each fulcrum of the lift. The real-time pressure data is continuously updated during the operation of the lift, and the original pressure data is stored in the system database. After the data is acquired, the adjusted pressure data is compared with the original pressure data point by point to calculate the pressure change of each fulcrum. For example, a lift has 4 fulcrums. Assuming that the fulcrum pressures in the original state are 120kPa, 130kPa, 125kPa and 128kPa respectively, and the adjusted pressure data are 118kPa, 135kPa, 123kPa and 130kPa respectively, the pressure change values ​​are -2kPa, +5kPa, -2kPa and +2kPa respectively. By comparing and analyzing the pressure change data, the system can identify the trend of pressure adjustment, determine whether abnormal fluctuations occur, and obtain the pressure change value.

[0031] The regulation error analysis submodule analyzes the regulation error and regulation deviation based on the pressure change value, using the formula: ; Calculate the adjustment error value , analyze whether it exceeds the allowable range of the lift design and obtain the error judgment result, among which, Representative The pressure after the fulcrum is adjusted, Representative The pressure of the fulcrum in its original state, Represents the number of fulcrums; If the adjusted pressures of the four supporting points are 118kPa, 135kPa, 123kPa and 130kPa respectively, and the original pressures are 120kPa, 130kPa, 125kPa and 128kPa respectively, then the calculation results are: ; ; ; Calculated adjustment error value The system compares the error value with the design allowable range of the lift. If the maximum allowable error of the lift is 0.05, the error is within the allowable range. If the calculated result is greater than this value, the system will mark the adjustment as abnormal and generate an error judgment result.

[0032] The safety warning trigger submodule calls the error judgment result. If the adjustment error exceeds the design allowable range, it will automatically trigger a safety warning, record the related data, and obtain the load adjustment feedback log; The error judgment result is called. If the adjustment error exceeds the design allowable range, a safety warning will be automatically triggered. The safety warning can be triggered by sound alarm, light signal or system notification. At the same time, the system will record the related data of abnormal pressure adjustment and store it in the load adjustment log. For example, if the error judgment result exceeds the threshold of 0.05, the system will send an alarm to the maintenance personnel and record the abnormal data, including the pressure values ​​before and after adjustment of each fulcrum, error value, adjustment time and other information, to obtain the load adjustment feedback log.

[0033] See also Figure 6 , the security protection control module includes: The pressure change analysis submodule analyzes the pressure change data of the lifting fulcrum based on the load regulation feedback log, calculates the pressure change trend, and obtains the fulcrum pressure change value; Extract the pressure data of all lifting fulcrums at different time points, including the pressure change values ​​before, during and after lifting. The pressure data of the lifting fulcrum can be collected by pressure sensors. The sensors are usually installed at the bottom or connection parts of the lifting fulcrum. The collected data will be transmitted to the data processing unit and combined with the feedback log for time series analysis to ensure the accuracy of the pressure data. Next, the data at different time points are sorted and stored in time series. The pressure change rate of each fulcrum in different time intervals is calculated using the pressure change rate calculation formula: ,in, Indicates the The pressure change rate of each fulcrum, Indicates the pressure value at the current moment. Indicates the pressure value at the previous moment, Represents the time interval. Assuming that the pressure at a certain point changes from 120kPa to 140kPa and the time interval is 10s, the pressure change rate is calculated as follows: , perform the same calculation on all supports to obtain the global pressure change trend. Then, normalize the pressure change rate data to facilitate the subsequent calculation of load stability. The normalization method uses minimum-maximum normalization: ,in, represents the normalized pressure change rate, and They represent the minimum and maximum pressure change rates in the batch of data respectively, and the fulcrum pressure change values ​​of all fulcrums are obtained.

[0034] The load stability calculation submodule uses the fulcrum pressure change value to analyze the stability of the load distribution using the formula: ; Calculate load stability factor , determine whether it is an abnormal state and obtain the stability judgment result, where, Representative The pressure after the fulcrum is adjusted, Represents the average value of the fulcrum pressure, represents the standard deviation of the pivot pressure, Represents the total number of pivot points; There are four supporting points with pressure values ​​of 140kPa, 135kPa, 145kPa, and 138kPa respectively. The mean is calculated as follows: ; The pressure deviation of the above four supporting points is calculated as follows: ; ; Then, calculate the load stability coefficient and substitute it into the numerical calculation: ; ; ; Calculated load stability factor , indicating that the current load distribution of the lift is close to the equilibrium state and has not reached the abnormal state threshold, so there is no need to adjust the lift working state. When the lifting height is limited, it is necessary to adjust the hydraulic system or limit the lifting height according to the calculation results. The calculation results directly determine the system adjustment measures. The numerical results are related to the set threshold of the lifting safety state, further indicating the necessity of safety adjustment.

[0035] The lifting safety adjustment submodule calls the stability judgment result. If it is judged to be an abnormal state, it adjusts the working state of the lift and limits the lifting height to obtain a lifting safety state; Call the stability judgment result. If the abnormal state is established, adjust the working state of the lift. The adjustment methods include reducing the hydraulic system pressure, reducing the lifting speed or directly limiting the maximum lifting height of the lift. The choice of adjustment method is based on the load stability coefficient The value range of If the value is between 1.0 and 1.2, adjust the hydraulic system pressure to 90% of the original setting value. If the load stability coefficient is calculated as 1.2, the maximum lifting height of the lift is limited to 80% of the initial setting height. Assuming that the initial maximum lifting height of the lift is 2.5m, if the load stability coefficient is calculated as , then the adjusted height is: ,like If it exceeds 1.5, the lifting operation will be completely stopped and a warning signal will be issued. In addition, the adjusted lifting data will be recorded and new lifting safety status data will be formed for subsequent stability monitoring to obtain the lifting safety status.

[0036] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent load balancing control system for a car lift, characterized in that: The system comprises: The data acquisition and analysis module obtains data from the pressure sensors installed at the lift fulcrum and the vehicle weight data, receives the data stream through the lift control system, analyzes the pressure sensor and vehicle weight data, and establishes a real-time load data set; The load state assessment module compares the load data of each support point based on the real-time load data set, identifies the load distribution that deviates from the normal range, and analyzes the load difference between the overall load center of the vehicle and each support point to obtain load offset analysis information; The load balancing adjustment module adjusts the force of the lifting fulcrum based on the load offset analysis information and compares it with the current balance threshold of the lift. If a deviation outside the normal range is detected, the pressure adjustment value is automatically recalculated and the lift servo motor is controlled to adjust the fulcrum pressure to obtain the balance state parameters. The lift monitoring module monitors the balance state parameters, collects the adjusted pressure sensor data, and compares it with the original state in real time. If the error exceeds the design allowable range, it automatically triggers a safety warning, records the relevant data, and obtains a load adjustment feedback log.

2. The intelligent load balancing control system for a car lift according to claim 1, characterized in that: The real-time load data set includes pressure value, total weight, and collection time; the load offset analysis information includes offset value, balance assessment result, and difference index; the balance state parameters include adjustment pressure, threshold comparison information, and adjustment index; and the load adjustment feedback log includes error results, deviation amount, and warning mark.

3. The intelligent load balancing control system for a car lift according to claim 1, characterized in that: The data acquisition and analysis module includes: The data acquisition submodule acquires data from the pressure sensor installed at the lift fulcrum and the vehicle weight data, parses and extracts the measured values, removes abnormal data through unit conversion and data correction, and generates accurate sensor and vehicle data; The load state calculation submodule analyzes the deviation between the sensor pressure data and the vehicle weight based on the accurate sensor and vehicle data, using the formula: ； Calculating Load Error Percentage , get the current load state of the lift, where Representative The pressure value of a pressure sensor, represents the normalized vehicle weight value, Represents the number of sensors; The load data establishment submodule continuously monitors the load changes of the lift based on the current load status of the lift and combines the timestamp recorded data to establish a real-time load data set.

4. The intelligent load balancing control system for a car lift according to claim 1, characterized in that: The load status assessment module includes: The load data comparison submodule compares the load data of each fulcrum based on the real-time load data set, filters out the load data that deviates from the normal range, and calculates the difference between the fulcrum load and the global load mean to obtain abnormal load data; The load center analysis submodule identifies the overall load center of the vehicle based on the abnormal load data using the formula: ； and ； Get the horizontal coordinate of the load center and the longitudinal coordinate of the load center , and analyze the load differences between multiple support points to obtain the vehicle equilibrium state, where Representative Load data of each support point, and Representative The coordinate positions of the pivot points, Represents the number of fulcrums; The load offset calculation submodule calls the vehicle balance state, calculates the difference between the load center coordinates and the vehicle theoretical symmetry center, analyzes the offset of each support point, and obtains load offset analysis information.

5. The intelligent load balancing control system for a car lift according to claim 1, characterized in that: The load balancing adjustment module includes: The fulcrum pressure adjustment submodule adjusts the force of the lifting fulcrum based on the load offset analysis information, calculates the current pressure value of each fulcrum, compares it with the current balance threshold of the lift, filters out the fulcrum pressure data that exceeds the normal range, and obtains the over-limit fulcrum pressure data; The pressure deviation calculation submodule recalculates the pressure adjustment value and corrects the deviation based on the over-limit support pressure data, using the formula: ； Get the Pressure adjustment value of each pivot point , get the pivot pressure adjustment parameter, where, Representative The current pressure of the pivot point, Represents the current balance threshold of the lift. Representative The pressure of other fulcrums, Represents the number of fulcrums, It means that in the summation process, all The pressure on all other fulcrums except include in; The servo motor control submodule sends a control instruction to the lift servo motor according to the fulcrum pressure adjustment parameter, adjusts the pressure of each fulcrum to match the expected load balance state, and obtains the balance state parameter.

6. The intelligent load balancing control system for a car lift according to claim 1, characterized in that: The lift monitoring module includes: The equilibrium state monitoring submodule monitors the equilibrium state parameters, collects the adjusted pressure sensor data and real-time pressure data, and calls the original state pressure data for comparison to obtain the pressure change value; The regulation error analysis submodule analyzes the regulation error and regulation deviation based on the pressure change value, using the formula: ； Calculate the adjustment error value , analyze whether it exceeds the allowable range of the lift design and obtain the error judgment result, among which, Representative The pressure after the fulcrum is adjusted, Representative The pressure of the fulcrum in its original state, Represents the number of fulcrums; The safety warning triggering submodule calls the error determination result. If the adjustment error exceeds the design allowable range, a safety warning is automatically triggered, and related data is recorded to obtain a load adjustment feedback log.

7. The intelligent load balancing control system for a car lift according to claim 1, characterized in that: The system further comprises: The safety protection control module analyzes the pressure change data of the lifting fulcrum based on the load adjustment feedback log, calculates the stability of the load distribution, and determines whether it is an abnormal state. If abnormal, it adjusts the working state of the lift and limits its lifting height to obtain a lifting safety state; The lifting safety status includes operation restrictions, status adjustments, and height limit indicators.

8. The intelligent load balancing control system for a car lift according to claim 7, characterized in that: The security protection control module includes: The pressure change analysis submodule analyzes the pressure change data of the lifting fulcrum based on the load adjustment feedback log, calculates the pressure change trend, and obtains the fulcrum pressure change value; The load stability calculation submodule calls the fulcrum pressure change value to analyze the stability of the load distribution using the formula: ； Calculate load stability factor , determine whether it is an abnormal state and obtain the stability judgment result, where, Representative The pressure after the fulcrum is adjusted, Represents the average value of the fulcrum pressure, represents the standard deviation of the pivot pressure, Represents the total number of pivot points; The lifting safety adjustment submodule calls the stability determination result, and if it is determined to be an abnormal state, adjusts the working state of the lift, limits the lifting height, and obtains a lifting safety state.

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