An intelligent detection method for large crane boom load

Abstract

The present invention relates to the field of crane boom weight detection, and specifically discloses an intelligent detection method for the weight of a large crane boom. The present invention performs detection and calibration on weight-related sensors before weighing, thereby ensuring the accuracy of the measuring instrument; based on the historical weighing data and operating environment data of the crane, a temperature-weighing deviation correlation model and a wind-weighing deviation correlation model are constructed, the operating environment data when the crane extracts the weight are obtained, the compensation amount of the crane weight based on environmental factors is analyzed, and then the preliminary calculated crane weight is corrected, thereby reducing the influence of environmental factors on the weighing result and improving the reliability of the weighing result; by real-time monitoring of the lifting torque of each section of the crane boom and comparing it with the maximum allowable lifting torque, it is determined whether the crane is overloaded and an early warning is issued, thereby improving the crane overload assessment system and effectively preventing serious accidents such as overturning of the crane due to overloading.

Description

Technical Field

[0001] The invention relates to the field of crane arm hoisting weight detection, and in particular to an intelligent detection method for the hoisting weight of a large crane arm. Background Art

[0002] In modern engineering construction and many industrial fields, large cranes play an extremely critical role. With their powerful lifting capabilities, large cranes have become indispensable mechanical equipment, greatly improving the efficiency and convenience of engineering operations.

[0003] However, once a large crane is overloaded, a series of extremely serious consequences will occur. Overloading will cause the boom structure to bear pressure beyond the design limit, causing catastrophic damage such as deformation, cracking or even breaking of the boom, which will not only cause the heavy objects being hoisted to fall, causing major safety accidents, but also pose a serious threat to the safety of life and property of surrounding personnel.

[0004] Therefore, accurate detection of the load of large crane booms is an important link to ensure the safe operation of cranes and avoid overloading accidents, and it has extremely important practical significance.

[0005] However, the existing crane boom load detection methods still have some limitations and shortcomings in practical applications.

[0006] For example, an existing Chinese patent with publication number CN116698174A discloses a light load calibration method for high-altitude equipment based on a weighing and pressure sensor, including: obtaining the current value corresponding to the zero load of the weighing sensor; obtaining the current value corresponding to the rated load, and obtaining a standard calibration curve of the weighing sensor according to the current value corresponding to the zero load and the current value corresponding to the rated load; obtaining the maximum range limiting current corresponding to the maximum range of the weighing sensor, and obtaining the zero load calibration curve of the weighing sensor according to the current value corresponding to the zero load and the maximum range limiting current corresponding to the maximum range of the weighing sensor; and performing load warning according to the zero load calibration curve and the standard calibration curve of the weighing sensor.

[0007] For example, the existing Chinese patent with publication number CN113124972A discloses a method and system for weighing materials in an excavator, including obtaining the first supporting force of the boom cylinder in each posture when the bucket is empty and obtaining the second supporting force of the boom cylinder in each posture when the bucket is loaded; obtaining the angle parameters of each posture of the boom; and determining the actual weight of the material in the bucket according to the obtained angle parameters of each posture of the boom and the first supporting force and the second supporting force. This invention is used to improve the function of weighing materials in an excavator and meet people's requirements for accurate weighing of excavator materials.

[0008] The above patent has the following deficiencies: First, when the above patent performs weighing analysis based on the data detected by the sensor, the sensor is not detected and calibrated before use. If the sensor is in poor condition or fails, resulting in a large deviation between the actual output and the ideal output, it will directly affect the subsequent weighing results.

[0009] Secondly, the above patent does not take into account the possible impact of environmental factors on the weighing results and compensate for the weighing results, such as ambient temperature and wind force, which makes the weighing analysis insufficiently reliable.

[0010] Thirdly, the above patent judges whether there is overload based on the weighing result, i.e. the lifting weight, without considering the load or force conditions of each arm section of the lifting machinery to judge whether there is overload, which makes the overload assessment system incomplete and poses a safety hazard. Summary of the invention

[0011] In response to the above problems, the present invention proposes an intelligent detection method for load on a large crane boom. The specific technical solution is as follows: A method for intelligent detection of load on a large crane boom, comprising the following steps: Step 1, layout of distributed weighing-related sensors: Weighing-related sensors are respectively laid out at the connection points of each boom section of the large crane, wherein the weighing-related sensors include angle sensors, length sensors and torque sensors.

[0012] Step 2: Instrument testing and calibration before weighing: When the large crane is in an unloaded state, start the crane, obtain the output of the weighing-related sensors of each boom section of the crane in various postures, determine whether the weighing-related sensors of each boom section of the crane need to be calibrated, and provide feedback.

[0013] Step 3: Preliminary calculation and analysis of weighing results: When a large crane extracts a heavy object, obtain the resultant moment of the crane. And the moment generated by the gravity of each boom section , Indicates The number of the boom section, , analyze the moment generated by the lifting weight , and obtain the horizontal distance from the crane rotation center to the hanging point of the load , further analysis of the hoisting gravity , and then preliminarily calculate the crane's lifting weight .

[0014] Step 4: Compensate the weighing results based on environmental factors: Obtain the historical weighing data and operating environment data of the large crane, build the temperature-weighing deviation correlation model and wind-weighing deviation correlation model of the large crane, obtain the operating environment data when the crane extracts the heavy objects, and analyze the compensation amount for the crane lifting weight based on environmental factors. .

[0015] Step 5: Obtain and feedback the weighing results: Preliminary calculation of the crane's lifting weight and the amount of compensation for crane load based on environmental factors Substitute into the calculation formula Get the hoisting weight of the crane detection , and provide feedback.

[0016] Step 6. Crane overload monitoring and early warning: Real-time monitoring of the lifting torque of each section of the crane boom, determine whether the crane is overloaded, and issue an early warning.

[0017] Based on the above embodiment, the specific analysis process of step 2 includes: when the large crane is in an unloaded state, starting the crane, adjusting each section of the crane's boom according to preset principles so that it performs various set actions to obtain various postures of the crane.

[0018] The outputs of the weighing related sensors of each boom section of the crane in various postures are obtained, and the outputs of the angle sensor, length sensor and torque sensor of each boom section of the crane in various postures are obtained.

[0019] The expected outputs of the weighing-related sensors of each boom section of the crane in various postures stored in the database are extracted to obtain the expected outputs of the angle sensor, length sensor and torque sensor of each boom section of the crane in various postures.

[0020] On the basis of the above embodiment, the specific analysis process of step two also includes: S1: comparing the output of the angle sensor of each boom section under various postures of the crane with the expected output of its boom angle sensor, obtaining the absolute value of the difference between the output of the angle sensor of each boom section under various postures of the crane and the expected output of its boom angle sensor, and recording it as the angle output deviation of each boom section under various postures of the crane.

[0021] The threshold values ​​of the angle output deviation of each boom section in various postures of the crane stored in the database are extracted.

[0022] The angle output deviation of each boom section in various postures of the crane is compared with the threshold value of the angle output deviation of each boom section in various postures of the crane. If the angle output deviation of a boom section in a certain posture of the crane is greater than the threshold value of its boom angle output deviation, the angle sensor of the boom section of the crane needs to be calibrated, and the boom sections in the crane whose angle sensors need to be calibrated are counted.

[0023] S2: Similarly, according to the analysis method of S1, the output of the length sensor of each boom section of the crane in various postures is compared with the expected output of its boom length sensor to determine whether the length sensor of each boom section of the crane needs to be calibrated, and the boom sections of the crane whose length sensors need to be calibrated are counted.

[0024] S3: Similarly, according to the analysis method of S1, the output of the torque sensor of each boom section of the crane in various postures is compared with the expected output of its boom torque sensor to determine whether the torque sensor of each boom section of the crane needs to be calibrated, and the number of boom sections in the crane whose torque sensors need to be calibrated is counted.

[0025] S4: Provide feedback to each boom section of the crane that needs to be calibrated for the angle sensor, length sensor and torque sensor.

[0026] Based on the above embodiment, the specific analysis process of step four includes: extracting the historical weighing data and operating environment data of the large crane stored in the database, obtaining the actual lifting weight and detected lifting weight of each historical weighing of the large crane, and obtaining the ambient temperature, wind speed and wind direction of each historical weighing of the large crane.

[0027] Subtract the actual lifting weight from the detected lifting weight of each historical weighing of the large crane to obtain the weighing deviation of each historical weighing of the large crane.

[0028] According to the ambient temperature and weighing deviation of each historical weighing of the large crane, based on the single variable principle, a coordinate system is established with ambient temperature as the independent variable and weighing deviation as the dependent variable. According to the ambient temperature and weighing deviation of each historical weighing of the large crane, the corresponding data points are marked in the coordinate system. The trend curve of the weighing deviation of the large crane changing with the ambient temperature is drawn by using the method of establishing a mathematical model. The function corresponding to the trend curve of the weighing deviation of the large crane changing with the ambient temperature is obtained, and the temperature-weighing deviation correlation model of the large crane is constructed.

[0029] According to the wind speed, wind direction and weighing deviation of each historical weighing of the large crane, a data set of the model is established. The machine learning algorithm is used to analyze the weighing deviation of the large crane corresponding to each wind speed range under various wind directions, and a correlation model of the wind force-weighing deviation of the large crane is constructed.

[0030] Based on the above embodiment, the specific analysis process of step four also includes: obtaining the operating environment data of the crane when it extracts heavy objects through the environmental detection sensors installed on the large crane, and obtaining the ambient temperature, wind speed and wind direction when the crane extracts heavy objects.

[0031] Substitute the ambient temperature when the crane extracts the heavy object into the temperature-weighing deviation correlation model of the large crane to obtain the weighing deviation corresponding to the ambient temperature when the crane extracts the heavy object, which is recorded as .

[0032] Substitute the wind speed and wind direction when the crane is lifting heavy objects into the correlation model of wind force-weighing deviation of large cranes to obtain the weighing deviation corresponding to the wind speed and wind direction when the crane is lifting heavy objects, which is recorded as .

[0033] The weighing deviation corresponding to the ambient temperature when the crane is lifting heavy objects Weighing deviation corresponding to wind speed and wind direction when crane extracts heavy objects Substitute into the analysis formula Get the compensation amount for crane hoisting weight based on environmental factors ,in They represent the weight factors corresponding to the preset ambient temperature and ambient wind force, .

[0034] On the basis of the above embodiment, the specific analysis process of step six is: through the torque sensors arranged on each section of the crane boom, the lifting torque of each section of the crane boom is monitored in real time, the maximum lifting torque allowed by each section of the crane boom stored in the database is extracted, and the lifting torque of each section of the crane boom is compared with the maximum lifting torque allowed by each section of the crane boom. If the lifting torque of a certain section of the crane boom is greater than the maximum lifting torque allowed by its boom, the crane is overloaded and an early warning is issued.

[0035] Compared with the prior art, the intelligent detection method for the load of a large crane boom described in the present invention has the following beneficial effects: 1. The present invention determines whether the weighing-related sensors of each boom section of the crane need to be calibrated by testing the outputs of the weighing-related sensors of each boom section in various postures of the crane in the no-load state before weighing and comparing them with the expected outputs, and provides feedback to realize the detection and verification of the sensors before weighing, thereby ensuring the measurement accuracy of the weighing-related sensors, thereby improving the accuracy of the weighing results.

[0036] 2. Based on the historical weighing data and operating environment data of large cranes, the present invention constructs a temperature-weighing deviation correlation model and a wind force-weighing deviation correlation model for large cranes, obtains the operating environment data when the crane extracts heavy objects, analyzes the compensation amount for the crane's lifting weight based on environmental factors, and then corrects the preliminary calculated crane's lifting weight, thereby reducing the impact of environmental factors on the weighing results and improving the reliability of the weighing results.

[0037] 3. The present invention monitors the lifting torque of each boom section of the crane in real time and compares it with the maximum lifting torque allowed by each boom section to determine whether the crane is overloaded and issue an early warning, thereby improving the crane overload assessment system, effectively preventing serious accidents such as overturning of the crane due to overloading, and ensuring operational safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0039] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0040] Figure 2 It is a schematic diagram of the crane operation scene of the present invention.

[0041] Figure 3 It is a schematic diagram of the angle from the crane rotation center through the equivalent end points of the first two boom sections to the hanging point of the load according to the present invention.

[0042] Figure numerals: 1. First boom section; 2. Second boom section; 3. Third boom section; 4. Crane rotation center; 5. Equivalent end points of the first two boom sections; 6. Hanging point of the load; 7. Angle from the crane rotation center through the equivalent end points of the first two boom sections to the hanging point of the load. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 As shown, the present invention provides an intelligent detection method for load on a large crane boom, comprising the following steps: Step 1, deployment of distributed weighing-related sensors: Weighing-related sensors are respectively deployed at the connection points of each boom section of the large crane, wherein the weighing-related sensors include angle sensors, length sensors and torque sensors.

[0045] It should be noted that the large crane is a foldable crane with three-section booms.

[0046] It should be noted that the weighing-related sensor refers to a sensor used to obtain and calculate crane weight-related data.

[0047] In a specific embodiment, the torque sensor is a torque limiter.

[0048] Step 2: Instrument testing and calibration before weighing: When the large crane is in an unloaded state, start the crane, obtain the output of the weighing-related sensors of each boom section of the crane in various postures, determine whether the weighing-related sensors of each boom section of the crane need to be calibrated, and provide feedback.

[0049] As a preferred solution, the specific analysis process of step 2 includes: when the large crane is in an unloaded state, starting the crane, adjusting each section of the crane's boom according to preset principles so that it performs various set actions to obtain various postures of the crane.

[0050] It should be noted that the various actions set to be performed by each boom section of the crane include but are not limited to telescoping, pitching, etc.

[0051] The outputs of the weighing related sensors of each boom section of the crane in various postures are obtained, and the outputs of the angle sensor, length sensor and torque sensor of each boom section of the crane in various postures are obtained.

[0052] The expected outputs of the weighing-related sensors of each boom section of the crane in various postures stored in the database are extracted to obtain the expected outputs of the angle sensor, length sensor and torque sensor of each boom section of the crane in various postures.

[0053] As a preferred solution, the specific analysis process of step 2 also includes: S1: comparing the output of the angle sensor of each boom section under various postures of the crane with the expected output of its boom angle sensor, obtaining the absolute value of the difference between the output of the angle sensor of each boom section under various postures of the crane and the expected output of its boom angle sensor, and recording it as the angle output deviation of each boom section under various postures of the crane.

[0054] The threshold values ​​of the angle output deviation of each boom section in various postures of the crane stored in the database are extracted.

[0055] The angle output deviation of each boom section in various postures of the crane is compared with the threshold value of the angle output deviation of each boom section in various postures of the crane. If the angle output deviation of a boom section in a certain posture of the crane is greater than the threshold value of its boom angle output deviation, the angle sensor of the boom section of the crane needs to be calibrated, and the boom sections in the crane whose angle sensors need to be calibrated are counted.

[0056] S2: Similarly, according to the analysis method of S1, the output of the length sensor of each boom section of the crane in various postures is compared with the expected output of its boom length sensor to determine whether the length sensor of each boom section of the crane needs to be calibrated, and the boom sections of the crane whose length sensors need to be calibrated are counted.

[0057] S3: Similarly, according to the analysis method of S1, the output of the torque sensor of each boom section of the crane in various postures is compared with the expected output of its boom torque sensor to determine whether the torque sensor of each boom section of the crane needs to be calibrated, and the number of boom sections in the crane whose torque sensors need to be calibrated is counted.

[0058] S4: Provide feedback to each boom section of the crane that needs to be calibrated for the angle sensor, length sensor and torque sensor.

[0059] It should be noted that when the output of the weighing-related sensors of each boom section of the crane deviates greatly from the expected output, it is necessary to check whether the zero point setting and signal processing unit of the weighing-related sensors of each boom section of the crane have faults or deviations.

[0060] In this embodiment, the present invention determines whether the weighing-related sensors of each boom section of the crane need to be calibrated by testing the outputs of the weighing-related sensors of each boom section of the crane in various postures in the no-load state before weighing and comparing them with the expected outputs, and provides feedback to achieve detection and verification of the sensors before weighing, thereby ensuring the measurement accuracy of the weighing-related sensors and improving the accuracy of the weighing results.

[0061] Step 3: Preliminary calculation and analysis of weighing results: When a large crane extracts a heavy object, obtain the resultant moment of the crane. And the moment generated by the gravity of each boom section , Indicates The number of the boom section, , analyze the moment generated by the lifting weight , and obtain the horizontal distance from the crane rotation center to the hanging point of the load , further analysis of the hoisting gravity , and then preliminarily calculate the crane's lifting weight .

[0062] As a preferred solution, the specific analysis process of step three includes: when a large crane lifts a heavy object, the angle, length and lifting torque of each boom section of the crane are obtained through the angle sensors, length sensors and torque sensors arranged on each boom section of the crane, which are recorded as the angle, length and torque of each boom section of the crane, and the relationship between the angle, length and torque of each boom section of the crane and the combined torque of the crane stored in the database is substituted to obtain the combined torque of the crane, which is recorded as .

[0063] It should be noted that the relationship between the angle, length and torque of each section of the crane boom stored in the database and the total torque of the crane is obtained based on physical principles combined with force analysis, and is an existing relatively mature calculation method, which will not be elaborated here.

[0064] It should be noted that the angles between the boom sections of the crane and the horizontal direction can also be obtained by collecting images using a visual sensor and performing image processing and analysis.

[0065] It should be noted that the length of each boom section of the crane can also be obtained by a laser ranging sensor or image analysis.

[0066] As a preferred solution, the specific analysis process of step 3 further includes: extracting the weight of each section of the crane boom stored in the database and recording it as , Indicates crane The number of the boom section, , by analyzing the formula Get the gravity of each section of the crane boom ,in Represents the acceleration due to gravity.

[0067] Get the center of gravity of each section of the crane boom, and further get the horizontal distance between the center of gravity of each section of the crane boom and the crane rotation center, which is recorded as .

[0068] It should be noted that the center of gravity position of each boom section of the crane can be obtained through the design parameters of the crane or experimental measurements.

[0069] In a specific embodiment, the center of gravity of each boom section of the crane is the geometric center of each boom section of the crane.

[0070] In a specific embodiment, the rotation center of the crane is the center position of the crane chassis.

[0071] By analyzing the formula Get the moment generated by the gravity of each boom section .

[0072] The resultant moment of the crane And the moment generated by the gravity of each boom section Substitute into the analysis formula Get the moment generated by the load .

[0073] As a preferred option, see Figure 2 As shown, the specific analysis process of step three also includes: E1: Record the various boom sections of the crane as the first boom section, the second boom section, and the third boom section in the order of connection, obtain the lengths of the first boom section, the second boom section, and the third boom section of the crane, and record them as , and obtain the angle between each section of the crane boom.

[0074] E2: The angle between the first boom and the second boom of the crane is recorded as , by analyzing the formula Get the equivalent length of the first and second booms of the crane combined .

[0075] E3: See Figure 3As shown, the terminal end point of the second boom of the crane is taken as the equivalent end point of the first boom and the second boom of the crane, and is recorded as the equivalent end point of the first two booms of the crane, the connection line between the rotation center of the crane and the equivalent end points of the first two booms and the connection line between the rotation center of the crane and the hanging point of the load are obtained, and they are recorded as the first reference line and the second reference line respectively, the angle between the first reference line and the second reference line is obtained, and it is recorded as the angle from the rotation center of the crane through the equivalent end points of the first two booms to the hanging point of the load, and is expressed as .

[0076] It should be noted that the angle from the crane rotation center through the equivalent end points of the first two boom sections to the hanging point of the load can be obtained by using mathematical methods of trigonometric functions and vector operations.

[0077] In a specific embodiment, the angle from the crane rotation center through the first two boom equivalent endpoints to the weight suspension point is obtained. The specific method is: take the crane rotation center as the origin, establish a plane rectangular coordinate system, obtain the vector from the crane rotation center to the first two boom equivalent endpoints and the vector from the crane rotation center to the weight suspension point, and record them as , using the vector angle formula Calculate the angle from the crane's rotation center through the equivalent end points of the first two boom sections to the hanging point of the load .

[0078] E4: By analyzing the formula Get the horizontal distance from the crane's rotation center to the hanging point of the load .

[0079] As a preferred solution, the specific analysis process of step 3 further includes: The horizontal distance from the crane's slewing center to the hanging point of the load Substitute into the analysis formula Get the weight of the hoist .

[0080] The weight of the hoist Substitute into the analysis formula Get a preliminary calculation of the crane's lifting weight ,in Represents the acceleration due to gravity.

[0081] It should be noted that the present invention calculates and analyzes the hoisting weight of the crane based on the moment balance principle.

[0082] Step 4: Compensate the weighing results based on environmental factors: Obtain the historical weighing data and operating environment data of the large crane, build the temperature-weighing deviation correlation model and wind-weighing deviation correlation model of the large crane, obtain the operating environment data when the crane extracts the heavy objects, and analyze the compensation amount for the crane lifting weight based on environmental factors. .

[0083] As a preferred solution, the specific analysis process of step four includes: extracting the historical weighing data and operating environment data of the large crane stored in the database, obtaining the actual lifting weight and detected lifting weight of each historical weighing of the large crane, and obtaining the ambient temperature, wind speed and wind direction of each historical weighing of the large crane.

[0084] Subtract the actual lifting weight from the detected lifting weight of each historical weighing of the large crane to obtain the weighing deviation of each historical weighing of the large crane.

[0085] It should be noted that the weighing deviation of each historical weighing of the large crane may be a positive number, a negative number or zero.

[0086] According to the ambient temperature and weighing deviation of each historical weighing of the large crane, based on the single variable principle, a coordinate system is established with ambient temperature as the independent variable and weighing deviation as the dependent variable. According to the ambient temperature and weighing deviation of each historical weighing of the large crane, the corresponding data points are marked in the coordinate system. The trend curve of the weighing deviation of the large crane changing with the ambient temperature is drawn by using the method of establishing a mathematical model. The function corresponding to the trend curve of the weighing deviation of the large crane changing with the ambient temperature is obtained, and the temperature-weighing deviation correlation model of the large crane is constructed.

[0087] According to the wind speed, wind direction and weighing deviation of each historical weighing of the large crane, a data set of the model is established. The machine learning algorithm is used to analyze the weighing deviation of the large crane corresponding to each wind speed range under various wind directions, and a correlation model of the wind force-weighing deviation of the large crane is constructed.

[0088] It should be noted that the weighing deviations of the large crane corresponding to each wind speed range under various wind directions can be presented in the form of a table.

[0089] In another specific embodiment, a temperature-weighing deviation association model and a wind force-weighing deviation association model of a large crane are obtained. The specific method is: the large crane is placed under different ambient temperature conditions and different wind force conditions for multiple test experiments, and the relationship between the crane's weighing deviation and the ambient temperature and the relationship between the crane's weighing deviation and the wind speed and wind direction are obtained respectively, and then the temperature-weighing deviation association model and the wind force-weighing deviation association model of the large crane are constructed.

[0090] As a preferred solution, the specific analysis process of step four also includes: obtaining the operating environment data of the crane when it extracts heavy objects through the environmental detection sensors installed on the large crane, and obtaining the ambient temperature, wind speed and wind direction when the crane extracts heavy objects.

[0091] Substitute the ambient temperature when the crane extracts the heavy object into the temperature-weighing deviation correlation model of the large crane to obtain the weighing deviation corresponding to the ambient temperature when the crane extracts the heavy object, which is recorded as .

[0092] Substitute the wind speed and wind direction when the crane is lifting heavy objects into the correlation model of wind force-weighing deviation of large cranes to obtain the weighing deviation corresponding to the wind speed and wind direction when the crane is lifting heavy objects, which is recorded as .

[0093] The weighing deviation corresponding to the ambient temperature when the crane is lifting heavy objects Weighing deviation corresponding to wind speed and wind direction when crane extracts heavy objects Substitute into the analysis formula Get the compensation amount for crane hoisting weight based on environmental factors ,in They represent the weight factors corresponding to the preset ambient temperature and ambient wind force, .

[0094] It should be noted that the weighing deviation corresponding to the ambient temperature when the crane extracts the heavy object Weighing deviation corresponding to wind speed and wind direction when crane extracts heavy objects Can be positive, negative or zero.

[0095] It should be noted that the weight factors corresponding to the ambient temperature and ambient wind force are set according to the influence of the ambient temperature and ambient wind force on the crane weighing. In a specific embodiment, the weight factors corresponding to the ambient temperature and ambient wind force are 0.4 and 0.6 respectively.

[0096] Step 5: Obtain and feedback the weighing results: Preliminary calculation of the crane's lifting weight and the amount of compensation for crane load based on environmental factors Substitute into the calculation formula Get the hoisting weight of the crane detection , and provide feedback.

[0097] In this embodiment, the present invention constructs a temperature-weighing deviation correlation model and a wind force-weighing deviation correlation model for the large crane based on the historical weighing data and operating environment data of the large crane, obtains the operating environment data when the crane extracts heavy objects, analyzes the compensation amount for the crane lifting weight based on environmental factors, and then corrects the preliminary calculated crane lifting weight, thereby reducing the impact of environmental factors on the weighing results and improving the reliability of the weighing results.

[0098] Step 6. Crane overload monitoring and early warning: Real-time monitoring of the lifting torque of each section of the crane boom, determine whether the crane is overloaded, and issue an early warning.

[0099] As a preferred solution, the specific analysis process of step six is: through the torque sensors arranged on each section of the crane boom, the lifting torque of each section of the crane boom is monitored in real time, the maximum lifting torque allowed for each section of the crane boom stored in the database is extracted, and the lifting torque of each section of the crane boom is compared with the maximum lifting torque allowed for each section of the crane boom. If the lifting torque of a certain section of the crane boom is greater than the maximum lifting torque allowed for its boom, the crane is overloaded and an early warning is issued.

[0100] It should be noted that the maximum lifting moment allowed for each boom section of the crane is the safety moment of each boom section of the crane, and the maximum lifting moment allowed for each boom section of the crane can be obtained through the design parameters of the crane or experimental measurements.

[0101] In a specific embodiment, the maximum lifting moment allowed for each boom section of the crane is ninety percent of the rated lifting moment of each boom section of the crane.

[0102] In a specific embodiment, when the crane is overloaded, the crane will take corresponding safety measures, such as stopping the lifting action, etc., to avoid safety accidents.

[0103] In this embodiment, the present invention monitors the lifting torque of each boom section of the crane in real time and compares it with the maximum lifting torque allowed by each boom section to determine whether the crane is overloaded and issue an early warning, thereby improving the crane overload assessment system, effectively preventing serious accidents such as overturning of the crane due to overloading, and ensuring operational safety.

[0104] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for intelligently detecting the load of a large crane arm, characterized in that: The steps include: Step 1: Distributed weighing related sensor layout: Weighing related sensors are respectively laid out at the connection points of each boom section of the large crane, wherein the weighing related sensors include angle sensors, length sensors and torque sensors; Step 2: Instrument testing and calibration before weighing: Start the large crane when it is unloaded, obtain the output of the weighing-related sensors of each boom section of the crane in various postures, determine whether the weighing-related sensors of each boom section of the crane need to be calibrated, and provide feedback; Step 3: Preliminary calculation and analysis of weighing results: When a large crane extracts a heavy object, obtain the resultant moment of the crane. And the moment generated by the gravity of each boom section , Indicates The number of the boom section, , analyze the moment generated by the lifting weight , and obtain the horizontal distance from the crane rotation center to the hanging point of the load , further analysis of the hoisting gravity , and then preliminarily calculate the crane's lifting weight ; Step 4: Compensate the weighing results based on environmental factors: Obtain the historical weighing data and operating environment data of the large crane, build the temperature-weighing deviation correlation model and wind-weighing deviation correlation model of the large crane, obtain the operating environment data when the crane extracts the heavy objects, and analyze the compensation amount for the crane lifting weight based on environmental factors. ; According to the ambient temperature and weighing deviation of each historical weighing of the large crane, a coordinate system is established based on the single variable principle with ambient temperature as the independent variable and weighing deviation as the dependent variable. According to the ambient temperature and weighing deviation of each historical weighing of the large crane, the corresponding data points are marked in the coordinate system. The trend curve of the weighing deviation of the large crane changing with the ambient temperature is drawn by using the method of establishing a mathematical model. The function corresponding to the trend curve of the weighing deviation of the large crane changing with the ambient temperature is obtained, and the temperature-weighing deviation correlation model of the large crane is constructed. According to the wind speed, wind direction and weighing deviation of each historical weighing of large cranes, a data set of the model is established. The machine learning algorithm is used to analyze the weighing deviation of large cranes corresponding to various wind speed ranges under various wind directions, and a correlation model of wind force-weighing deviation of large cranes is constructed. Step 5: Obtain and feedback the weighing results: Preliminary calculation of the crane's lifting weight and the amount of compensation for crane load based on environmental factors Substitute into the calculation formula Get the hoisting weight of the crane detection , and provide feedback; Step 6. Crane overload monitoring and early warning: Real-time monitoring of the lifting torque of each section of the crane boom, determine whether the crane is overloaded, and issue an early warning.

2. According to claim 1, a large crane arm load intelligent detection method is characterized by: The specific analysis process of step 2 includes: When the large crane is unloaded, start the crane and adjust the crane boom sections according to the preset principles to make it perform various preset actions to obtain various postures of the crane; Obtain the output of the weighing related sensors of each boom section of the crane in various postures, and obtain the output of the angle sensor, length sensor and torque sensor of each boom section of the crane in various postures; The expected outputs of the weighing-related sensors of each boom section of the crane in various postures stored in the database are extracted to obtain the expected outputs of the angle sensor, length sensor and torque sensor of each boom section of the crane in various postures.

3. According to claim 2, a large crane arm load intelligent detection method is characterized by: The specific analysis process of step 2 also includes: S1: Compare the output of the angle sensor of each boom section under various postures of the crane with the expected output of its boom angle sensor, and obtain the absolute value of the difference between the output of the angle sensor of each boom section under various postures of the crane and the expected output of its boom angle sensor, which is recorded as the angle output deviation of each boom section under various postures of the crane; Extracting the threshold of the angle output deviation of each boom section in various postures of the crane stored in the database; Compare the angle output deviation of each boom section under various postures of the crane with the threshold value of the angle output deviation of each boom section under various postures of the crane. If the angle output deviation of a boom section under a certain posture of the crane is greater than the threshold value of the angle output deviation of the boom section, the angle sensor of the boom section of the crane needs to be calibrated, and count the boom sections in the crane whose angle sensors need to be calibrated; S2: Similarly, according to the analysis method of S1, the output of the length sensor of each boom section of the crane in various postures is compared with the expected output of its boom length sensor, to determine whether the length sensor of each boom section of the crane needs to be calibrated, and the boom sections of the crane whose length sensors need to be calibrated are counted; S3: Similarly, according to the analysis method of S1, the output of the torque sensor of each boom section of the crane in various postures is compared with the expected output of its boom torque sensor, to determine whether the torque sensor of each boom section of the crane needs to be calibrated, and the boom sections of the crane whose torque sensors need to be calibrated are counted; S4: Provide feedback to each boom section of the crane that needs to be calibrated for the angle sensor, length sensor and torque sensor.

4. The method for intelligently detecting the load of a large crane arm according to claim 1, characterized in that: The specific analysis process of step three includes: When a large crane lifts a heavy object, the angle, length and lifting torque of each boom section of the crane are obtained through the angle sensors, length sensors and torque sensors arranged on each boom section of the crane. These are recorded as the angle, length and torque of each boom section of the crane. These are substituted into the relationship between the angle, length and torque of each boom section of the crane and the resultant torque of the crane stored in the database to obtain the resultant torque of the crane, which is recorded as .

5. The method for intelligently detecting the load of a large crane arm according to claim 4, characterized in that: The specific analysis process of step three also includes: Extract the weight of each section of the crane boom stored in the database and record it as , Indicates crane The number of the boom section, , by analyzing the formula Get the gravity of each section of the crane boom ,in represents the acceleration due to gravity; Get the center of gravity of each section of the crane boom, and further get the horizontal distance between the center of gravity of each section of the crane boom and the crane rotation center, which is recorded as ; By analyzing the formula Get the moment generated by the gravity of each boom section ; The resultant moment of the crane And the moment generated by the gravity of each boom section Substitute into the analysis formula Get the moment generated by the load .

6. The method for intelligently detecting the load of a large crane arm according to claim 5, characterized in that: The specific analysis process of step three also includes: E1: Record the crane boom sections as the first boom, the second boom, and the third boom in the order of connection, obtain the lengths of the first boom, the second boom, and the third boom of the crane, and record them as , and obtain the angle between each section of the crane boom; E2: The angle between the first boom and the second boom of the crane is recorded as , by analyzing the formula Get the equivalent length of the first and second booms of the crane combined ; E3: The terminal endpoint of the second boom of the crane is taken as the equivalent endpoint of the first boom and the second boom of the crane, and recorded as the equivalent endpoints of the first two booms of the crane, and the connecting line between the crane rotation center and the equivalent endpoints of the first two booms and the connecting line between the crane rotation center and the hanging point of the load are obtained, and recorded as the first reference line and the second reference line respectively, and the angle between the first reference line and the second reference line is obtained, and recorded as the angle from the crane rotation center through the equivalent endpoints of the first two booms to the hanging point of the load, and expressed as ; E4: By analyzing the formula Get the horizontal distance from the crane's rotation center to the hanging point of the load .

7. The method for intelligently detecting the load of a large crane arm according to claim 6, characterized in that: The specific analysis process of step three also includes: The moment generated by the load The horizontal distance from the crane's slewing center to the hanging point of the load Substitute into the analysis formula Get the weight of the hoist ; The weight of the hoist Substitute into the analysis formula Get a preliminary calculation of the crane's lifting weight ,in Represents the acceleration due to gravity.

8. The method for intelligently detecting the load of a large crane arm according to claim 1, characterized in that: The steps for obtaining historical weighing data and operating environment data of large cranes include: Extract the historical weighing data and operating environment data of the large crane stored in the database, obtain the actual lifting weight and the detected lifting weight of each historical weighing of the large crane, and obtain the ambient temperature, wind speed and wind direction of each historical weighing of the large crane; Subtract the actual lifting weight from the detected lifting weight of each historical weighing of the large crane to obtain the weighing deviation of each historical weighing of the large crane.

9. The method for intelligently detecting the load of a large crane arm according to claim 8, characterized in that: The specific analysis process of step 4 also includes: Through the environmental detection sensors installed on large cranes, the operating environment data when the crane is lifting heavy objects is obtained, and the ambient temperature, wind speed and wind direction when the crane is lifting heavy objects are obtained; Substitute the ambient temperature when the crane extracts the heavy object into the temperature-weighing deviation correlation model of the large crane to obtain the weighing deviation corresponding to the ambient temperature when the crane extracts the heavy object, which is recorded as ; Substitute the wind speed and wind direction when the crane is lifting heavy objects into the correlation model of wind force-weighing deviation of large cranes to obtain the weighing deviation corresponding to the wind speed and wind direction when the crane is lifting heavy objects, which is recorded as ; The weighing deviation corresponding to the ambient temperature when the crane is lifting heavy objects Weighing deviation corresponding to wind speed and wind direction when crane extracts heavy objects Substitute into the analysis formula Get the compensation amount for crane hoisting weight based on environmental factors ,in They represent the weight factors corresponding to the preset ambient temperature and ambient wind force, .

10. The method for intelligently detecting the load of a large crane arm according to claim 1, characterized in that: The specific analysis process of step six is ​​as follows: Through the torque sensors arranged on each section of the crane boom, the lifting torque of each section of the crane boom is monitored in real time, the maximum lifting torque allowed for each section of the crane boom stored in the database is extracted, and the lifting torque of each section of the crane boom is compared with the maximum lifting torque allowed for each section of the crane boom. If the lifting torque of a certain section of the crane boom is greater than the maximum lifting torque allowed for its boom, the crane is overloaded and an early warning is issued.

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