CN118550234B - Aerial work platform safety control method and system based on luffing angle - Google Patents
Aerial work platform safety control method and system based on luffing angle Download PDFInfo
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Abstract
The invention discloses a method and a system for safely controlling an aerial work platform based on a luffing angle, which relate to the field of platform control, and the method for safely controlling the aerial work platform based on the luffing angle comprises the following steps: s1, acquiring parameters; s2, acquiring an original load parameter of the aerial working platform and an original angle value of the aerial working platform; s3, extracting platform construction characteristic data and calculating construction load data; s4, calculating an amplitude variation angle value of the aerial work platform generated during construction; s5, analyzing the amplitude angle value of the aerial work platform, and controlling and optimizing the aerial work platform based on the analysis result. The invention ensures the consistency and comparison effectiveness of the data during analysis through standardized processing, weights and comprehensively analyzes each factor, provides more accurate and scientific decision basis, and reduces operation risk and improves operation safety through accurately calculating and analyzing the load and angle change of the aerial work platform.
Description
Technical Field
The invention relates to the field of platform control, in particular to a method and a system for safely controlling an aerial work platform based on a luffing angle.
Background
The aerial working platform is a generic name of various devices specially used for executing working tasks in the air, is widely applied to buildings, maintenance, installation, cleaning and other occasions requiring personnel or devices to arrive at a high place for working, and is mainly used for providing a safe and stable working environment, reducing or eliminating safety risks brought by using traditional climbing devices such as ladders, scaffolds and the like, and along with the development of technology, more intelligent elements such as a remote control system, an automatic stabilizing system and the like are added into the modern aerial working platform, so that the accuracy and the safety of operation are further improved.
The amplitude variation angle refers to the angle of rotation of the working arm of the aerial working platform in the horizontal plane, the amplitude variation angle determines the working range of the aerial working platform, the larger amplitude variation angle means that the platform can cover a wider horizontal area, so that the aerial working platform can work in a complex environment, meanwhile, the stability of the platform can be influenced by the variation of the amplitude variation angle, and under the extreme amplitude variation angle, the platform can become unstable due to the deviation of the gravity center, so that the operation safety is ensured by accurate control and structural design.
However, the existing aerial work platform safety control method based on the luffing angle does not comprehensively analyze the load condition of the aerial work platform when in use, so that the luffing angle of the aerial work platform cannot be accurately calculated when in use, the precision of the aerial work platform safety control method based on the luffing angle is not ideal when in use, and the safety of the aerial work platform when in use is greatly influenced.
For the problems in the related art, no effective solution has been proposed at present.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a method and a system for safely controlling an aerial work platform based on an amplitude variation angle, so as to overcome the technical problems in the prior art.
For this purpose, the invention adopts the following specific technical scheme:
according to one aspect of the invention, a method and a system for safely controlling an aerial work platform based on a luffing angle are provided, and the method comprises the following steps:
s1, acquiring parameters of an aerial working platform, weather conditions of construction operation and construction information of the aerial working platform;
s2, acquiring an original load parameter of the aerial working platform and an original angle value of the aerial working platform based on the aerial working platform parameters;
S3, extracting platform construction characteristic data according to the construction information of the aerial working platform, and calculating construction load data based on the platform construction characteristic data, the construction weather condition and the original load parameters of the aerial working platform;
S4, calculating an aerial work platform variable angle value generated during construction based on construction load data and an aerial work platform original angle value;
s5, analyzing the amplitude angle value of the aerial work platform, and controlling and optimizing the aerial work platform based on the analysis result.
As a preferred scheme, extracting platform construction characteristic data according to construction information of an aerial work platform, and calculating construction load data based on the platform construction characteristic data, construction operation weather conditions and original load parameters of the aerial work platform, wherein the method comprises the following steps:
s31, setting construction data extraction rules, and extracting platform construction characteristic data in the construction information of the aerial working platform based on the construction data extraction rules;
s32, classifying the platform construction feature data, and weighting the classification result of the platform construction feature data to obtain a construction feature influence set;
S33, substituting the construction characteristic influence set into the original load parameters of the aerial working platform to calculate operation load data;
S34, analyzing the influence of the weather conditions of the construction operation, and calculating construction load data based on the analysis of the influence value of the weather conditions of the construction operation and the operation load data.
As a preferred scheme, classifying the platform construction feature data, and weighting the classification result of the platform construction feature data, the construction feature influence set is obtained, which comprises the following steps:
S321, presetting a platform construction characteristic data classification standard, and classifying the platform construction characteristic data based on the platform construction characteristic data classification standard;
S322, setting a characteristic weighting rule, and weighting the platform construction characteristic data in the classification result of the platform construction characteristic data based on the characteristic weighting rule;
s323, integrating the weighting result of the platform construction characteristic data to obtain a construction characteristic influence set.
As a preferred scheme, substituting the construction characteristic influence set into the original load parameter calculation operation load data of the aerial work platform comprises the following steps:
s331, carrying out data cleaning on the construction characteristic influence set, and merging the construction characteristic influence set subjected to data cleaning with original load parameters of an aerial working platform;
S332, calculating a platform operation comprehensive load value based on a combination result of the construction characteristic influence set and the original load parameters of the aerial work platform;
S333, simulating and verifying the comprehensive load value of the platform operation, and optimizing and adjusting the comprehensive load value of the high platform operation based on a simulation verification result;
S334, taking the optimized and adjusted platform operation integrated load value as operation load data.
As a preferred scheme, based on the combination result of the construction characteristic influence set and the original load parameters of the aerial working platform, the calculation platform operation comprehensive load value comprises the following steps:
S3321, normalizing the construction characteristic influence set and the original load parameters of the aerial working platform;
s3322, calculating a platform load total value through a regression algorithm based on the normalized construction characteristic influence set and the original load parameters of the aerial work platform;
S3323, extracting aerial work platform node information in aerial work platform parameters, and presetting a node load weight duty ratio;
S3324, calculating a node load value of the aerial work platform node information based on the total platform load value and the load weight ratio;
S3325, verifying the total platform load value and the node load value, and summarizing the verified total platform load value and the node load value to generate a comprehensive platform operation load value.
As a preferable scheme, based on the normalized construction characteristic influence set and the original load parameter of the aerial working platform, a calculation formula for calculating the total load value of the platform through a regression algorithm is as follows:
；
Wherein W is the total load value of the platform;
Feature influence intercept values are concentrated for construction feature influence;
the ith construction characteristic influence value in the construction characteristic influence set is used for carrying out construction on the construction characteristic influence set;
the original load parameters of the aerial working platform are obtained;
and N is the total number of construction characteristic influence sets.
Preferably, analyzing the construction work weather condition influence and calculating the construction load data based on the analysis construction work weather condition influence value and the operation load data comprises the steps of:
s341, preprocessing the influence of the weather conditions of the construction operation, and evaluating the influence value of the weather conditions of the construction operation after preprocessing;
S342, unifying the construction operation weather condition influence value and the operation load data, merging the construction operation weather condition influence value and the operation load data after unifying the data, and obtaining operation influence data;
s343, constructing a construction load model through a multiple linear regression algorithm, substituting operation influence data into the construction load model, and calculating construction load data;
and S344, performing simulation verification on the construction load data, and optimizing and adjusting the construction load data based on a simulation verification result.
As a preferred scheme, based on construction load data and an original angle value of an aerial work platform, calculating an aerial work platform luffing angle value generated during construction comprises the following steps:
s41, extracting node load values and overhead working platform node information in construction load data, and calculating construction node moment;
S42, calculating a luffing angle value of the aerial working platform based on the moment of the construction node and the original angle value of the aerial working platform;
s43, verifying the luffing angle value of the aerial work platform, and optimally adjusting the luffing angle value of the aerial work platform after verification.
As a preferred scheme, based on the construction node moment and the original angle value of the aerial work platform, calculating the aerial work platform luffing angle value comprises the following steps:
S421, summarizing the moment of the construction node to obtain the total moment of the construction node of the aerial working platform;
s422, setting a platform moment of inertia rule, and calculating an aerial work platform angle change value based on the platform moment of inertia rule and the total moment of construction nodes of the aerial work platform;
S423, combining the original angle value of the aerial work platform with the angle change value of the aerial work platform to obtain the amplitude angle value of the aerial work platform.
According to another aspect of the present invention, there is provided an aerial work platform safety control system based on a luffing angle, the system comprising:
the parameter acquisition module is used for acquiring parameters of the aerial work platform, the weather conditions of construction operation and construction information of the aerial work platform;
the original parameter module is used for acquiring an original load parameter of the aerial working platform and an original angle value of the aerial working platform based on the aerial working platform parameters;
The platform load module is used for extracting platform construction characteristic data according to the construction information of the aerial work platform and calculating construction load data based on the platform construction characteristic data, construction operation weather conditions and original load parameters of the aerial work platform;
The amplitude variation angle module is used for calculating the amplitude variation angle value of the aerial working platform generated during construction based on construction load data and the original angle value of the aerial working platform;
the safety control module is used for analyzing the amplitude angle value of the aerial work platform and controlling and optimizing the aerial work platform based on the analysis result;
the parameter acquisition module, the original parameter module, the platform load module, the amplitude angle module and the safety control module are sequentially connected.
The beneficial effects of the invention are as follows:
1. the invention ensures the consistency and comparison effectiveness of the data during analysis through standardized processing, weights and comprehensively analyzes each factor, provides more accurate and scientific decision basis, and reduces operation risk and improves operation safety through accurately calculating and analyzing the load and angle change of the aerial work platform.
2. The invention reduces the operation time, improves the working efficiency, reduces the error operation, improves the working efficiency, reduces the long-term operation cost, predicts the potential maintenance requirement to reduce the unexpected downtime by analyzing the load data and the angle change, and reduces the dependence on the professional skill by optimizing the control flow.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a method flow diagram of a method for aerial work platform safety control based on luffing angles according to an embodiment of the present invention;
FIG. 2 is a system block diagram of an aerial work platform safety control system based on luffing angles according to an embodiment of the present invention.
In the figure:
1. A parameter acquisition module; 2. an original parameter module; 3. a platform load module; 4. a luffing angle module; 5. and a safety control module.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to the embodiment of the invention, a method and a system for safely controlling an aerial work platform based on a luffing angle are provided.
The invention will be further described with reference to the accompanying drawings and the specific embodiments, according to an embodiment of the invention, as shown in fig. 1, an aerial work platform safety control method based on an amplitude variation angle according to an embodiment of the invention includes the following steps:
s1, acquiring parameters of an aerial working platform, weather conditions of construction operation and construction information of the aerial working platform;
Specifically, a technical manual is obtained from a manufacturer of the aerial work platform, including detailed parameters of the platform, such as a maximum working height, a maximum platform height, a maximum working amplitude, a rated load, a model, key parameters and the like, if an enterprise has an equipment management system, the detailed parameters and history maintenance records of each aerial work platform are queried in the equipment management system, and the manufacturer or the supplier is directly contacted to obtain the latest and most accurate aerial work platform parameter information.
The method comprises the steps of acquiring real-time and predicted weather conditions through an official website of a weather bureau, a mobile phone application or a professional weather service provider, setting a weather station on a construction site, monitoring weather parameters such as wind speed, temperature, humidity, air pressure and the like in real time, checking a construction log, knowing operation records of an aerial work platform, including operation time, place, operation content and the like, communicating with operators, acquiring feedback and advice of the use conditions of the aerial work platform by the operators, knowing the actual use conditions of the platform through monitoring video playback if the aerial work platform is provided with a monitoring system, and checking an operation plan, and knowing the construction project and time schedule of the aerial work platform to be performed.
S2, acquiring an original load parameter of the aerial working platform and an original angle value of the aerial working platform based on the aerial working platform parameters;
Specifically, the platform is weighed by using a wagon balance or a professional weighing device to obtain the empty weight of the platform, the original load parameters such as the maximum work load, the maximum work radius and the like are calculated according to the rated load, the working height and the like of the platform through a physical formula and engineering experience, the parameters such as the rated load, the maximum work radius and the like of the platform are obtained by referring to a technical manual or a user guide provided by a manufacturer, if the platform is provided with a sensor and a monitoring system, the load condition of the platform is monitored in real time through the devices, the angles of a working arm and the working platform of the platform are directly measured by using angle measuring instruments such as a level meter, an inclinometer and the like, and if the platform is provided with a modern control system, the current working arm angle and the working platform angle are read through the control system, and the parameters such as the maximum working angle and the operating range of the platform are obtained by referring to the technical manual or the user guide provided by the manufacturer.
S3, extracting platform construction characteristic data according to the construction information of the aerial working platform, and calculating construction load data based on the platform construction characteristic data, the construction weather condition and the original load parameters of the aerial working platform;
specifically, according to the construction information of the aerial work platform, platform construction characteristic data are extracted, and construction load data are calculated based on the platform construction characteristic data, construction operation weather conditions and original load parameters of the aerial work platform, and the method comprises the following steps:
s31, setting construction data extraction rules, and extracting platform construction characteristic data in the construction information of the aerial working platform based on the construction data extraction rules;
The method comprises the steps of determining which parameters are key for constructing platform construction characteristics, such as working height, working radius, platform load, working environment and the like, determining data sources, including equipment records, operator reports, monitoring system data and the like, formulating standard formats for data records and storage, ensuring accuracy and consistency of the data, setting data quality standards, such as accuracy, integrity and timeliness of the data, determining the frequency of data updating, ensuring real-time performance and effectiveness of the data, extracting platform construction characteristic data in high-altitude operation platform construction information, and collecting relevant data from various data sources according to set data extraction rules.
Cleaning and preprocessing the collected data, including removing abnormal values, filling missing values, unifying data formats, etc., extracting platform construction feature data from the preprocessed data by using data extraction rules, verifying the extracted feature data to ensure accuracy and reliability, and storing the extracted and verified platform construction feature data in a proper data management system or database
Specifically, the platform construction feature data includes environmental classifications, classifying the working environment into indoor, outdoor, urban center, suburban area, mountain area, etc., defining relevant parameters such as wind speed, temperature range, humidity, visibility, etc., for each environment type, and determining where to obtain these environmental parameters, such as weather station data, field monitoring devices, etc., while formulating how to extract these parameters from the raw data, such as through API interface calls, database queries, or file importation.
S32, classifying the platform construction feature data, and weighting the classification result of the platform construction feature data to obtain a construction feature influence set;
Specifically, classifying the platform construction feature data, and weighting the classification result of the platform construction feature data, and acquiring the construction feature influence set comprises the following steps:
S321, presetting a platform construction characteristic data classification standard, and classifying the platform construction characteristic data based on the platform construction characteristic data classification standard;
Specifically, the service requirements of the construction of the aerial working platform are understood, including the working environment, the working tasks, the safety requirements and the like, which features are critical are determined based on the service requirements, such as the working height, the working radius, the platform load, the wind speed, the temperature and the like, and classification standards are formulated for each critical feature. For example, as for the work height, it is classified into several categories such as a low-altitude work, a hollow work, and a high-altitude work, for example, the work height is 5 meters or less, the hollow work, the work height is between 5 meters and 20 meters, the high-altitude work, the work height is over 20 meters, and the validity of the classification standard is tested in practical application and adjusted according to feedback.
Collecting construction data of an aerial working platform, including working records, environment monitoring data and the like, cleaning the collected data, removing errors and abnormal values, extracting preset classification features from the cleaned data, classifying the extracted feature data into corresponding categories according to preset classification standards, verifying classification results, ensuring classification accuracy, and optimizing classification standards and classification methods according to verification results.
S322, setting a characteristic weighting rule, and weighting the platform construction characteristic data in the classification result of the platform construction characteristic data based on the characteristic weighting rule;
Specifically, through historical data analysis, which features have stronger correlation with construction quality and efficiency is identified, a proper weight distribution method such as a hierarchical analysis method, a data driving method and the like is selected, then a specific weighting rule such as an operation height, a weight of 0.5, an operation radius, a weight of 0.3, a platform load and a weight of 0.2 is formulated according to the selected weight distribution method, and the rationality of the weighting rule is tested in practical application and is adjusted according to feedback.
The method comprises the steps of ensuring clear and accurate classification results of feature data, calculating the weight of each feature according to a set weighting rule, for example, distributing the calculated weight to corresponding feature data, verifying the rationality of a weight distribution result, ensuring that the weight distribution reflects the importance and actual influence of the feature, and using the weighted feature data in subsequent data analysis and decision process.
S323, integrating the weighting result of the platform construction characteristic data to obtain a construction characteristic influence set.
Specifically, the consistency of the formats of all feature data is ensured, the feature weights are classified and weighted according to the previous steps, the normalization processing is carried out to ensure that the sum of the feature weights is 1, the data of each feature and the corresponding weight are combined, and the data are realized by creating a data structure, wherein each column represents a feature, each row represents an observation value and simultaneously contains weight information.
For each feature, calculating the average influence degree of the feature in all observed values, multiplying the data of each feature by the weight of the feature, then averaging the features, sequencing the features according to the calculated average influence degree, identifying which features have the largest influence on the construction activity, setting a threshold according to service requirements, distinguishing which features have the influence regarded as obvious, integrating the features with the influence degree exceeding the threshold and the data thereof into an influence set, wherein the influence set is used as the key point of subsequent analysis and decision.
Specifically, the data of the working height, working radius and platform load are ensured to be complete, and the processing has been performed according to the classification and weighting steps, and the sum of the weights is ensured to be 1, for example, if other features are provided, the weights of all the features are ensured to be added to be equal to 1, a data structure is created, wherein the working height, working radius and platform load data of each observed value and the corresponding weights are contained, and for each feature, the influence degree thereof is calculated, for example, the influence degree of the working height = working height data is calculated, the features are ordered according to the calculated influence degree, the influence of which features have the largest influence on the construction activity is determined, and a threshold is set, for example, the features with the influence degree exceeding the threshold and the data thereof are integrated into an influence set, only when the influence degree of the features exceeds 0.1.
S33, substituting the construction characteristic influence set into the original load parameters of the aerial working platform to calculate operation load data;
specifically, substituting the construction characteristic influence set into the original load parameter calculation operation load data of the aerial work platform comprises the following steps:
s331, carrying out data cleaning on the construction characteristic influence set, and merging the construction characteristic influence set subjected to data cleaning with original load parameters of an aerial working platform;
specifically, checking the data in the construction feature influence set, identifying any abnormal value, error or repeated record, selecting a method for filling the missing value according to the actual situation, such as using average value, median, mode or regression analysis, and the like, judging whether the abnormal value is the input error or the actual data, determining whether to keep, correct or delete the abnormal value according to the situation, and carrying out standardized processing on the data to make the abnormal value, such as converting the data of different units into the same unit, and ensuring that the cleaned data is accurate and reliable through cross verification or other methods.
The method comprises the steps of ensuring that a construction characteristic influence set is consistent with the data format of original load parameters of an aerial work platform, such as column names, data types and the like, creating a new data structure for storing combined data, combining the cleaned construction characteristic influence set with the original load parameters of the aerial work platform according to a certain key such as a timestamp, an operation number and the like, checking the combined data, ensuring that no data is lost or repeated, carrying out necessary cleaning and arrangement, verifying the integrity and consistency of the combined data, and ensuring the accuracy and reliability of the data.
Specifically, checking the data in the two data sets, ensuring that no error or abnormal value exists, filling according to actual conditions, performing standardized processing on the data, such as converting the operation height from meter to foot, ensuring that the column names and units of the two data sets are consistent, merging the two data sets into a new data structure according to keys such as an operation number or a time stamp, checking the merged data, ensuring that no data is lost or repeated, and verifying the integrity and consistency of the merged data.
S332, calculating a platform operation comprehensive load value based on a combination result of the construction characteristic influence set and the original load parameters of the aerial work platform;
specifically, based on the combination result of the construction characteristic influence set and the original load parameters of the aerial working platform, the calculation platform operation comprehensive load value comprises the following steps:
S3321, normalizing the construction characteristic influence set and the original load parameters of the aerial working platform;
Specifically, for each feature, the minimum and maximum values among all observed values are calculated, for example, for the influence data of the working height, if the minimum value is 0.2 and the maximum value is 0.8, the normalized influence degree is (influence degree-0.2)/(0.8-0.2), the normalized data is checked to ensure that all values are within the range of [0,1], and the normalization method used and the calculated parameters are recorded.
S3322, calculating a platform load total value through a regression algorithm based on the normalized construction characteristic influence set and the original load parameters of the aerial work platform;
Specifically, based on the normalized construction characteristic influence set and the original load parameter of the aerial working platform, a calculation formula for calculating the total load value of the platform through a regression algorithm is as follows:
；
Wherein W is the total load value of the platform;
Feature influence intercept values are concentrated for construction feature influence;
the ith construction characteristic influence value in the construction characteristic influence set is used for carrying out construction on the construction characteristic influence set;
the original load parameters of the aerial working platform are obtained;
and N is the total number of construction characteristic influence sets.
S3323, extracting aerial work platform node information in aerial work platform parameters, and presetting a node load weight duty ratio;
Specifically, identifying which nodes in the parameters of the aerial working platform are node information, such as working height, working radius, load condition and the like, collecting data related to the nodes, including monitoring data, historical records, equipment logs and the like in real time, cleaning and preprocessing the collected data to ensure the accuracy and consistency of the data, extracting key node information from the preprocessed data, analyzing the historical data to identify which nodes have stronger correlation with the performance and the safety of the platform, selecting a proper weight distribution method, such as a hierarchical analysis method, a data driving method and the like, formulating a specific node load weight ratio, such as working height, weight ratio of 0.4, working radius, weight ratio of 0.3, load condition, weight ratio of 0.3, verifying and adjusting, testing the rationality of the weight ratio in practical application, and adjusting according to feedback.
S3324, calculating a node load value of the aerial work platform node information based on the total platform load value and the load weight ratio;
Specifically, the total load value of the aerial working platform is determined, the total load value comprises data of multiple aspects such as working height, working radius, load condition and the like, the load of each node is multiplied by the weight of each node according to the preset node load weight duty ratio, for example, if the weight duty ratio of the working height is 0.4, the load value of the working height is the actual value of the working height multiplied by 0.4, for each node, the load value of each node is calculated by repeating the steps, and the load values of all the nodes are added to obtain the total load value of the platform.
Specifically, assuming that the total load value of the platform is 100, the load value of the working height=the actual value of the working height 0.4, the load value of the working radius=the actual value of the working radius 0.3, the load value of the load condition=the actual value of the load condition 0.2, assuming that the actual value of the working height is 30, the actual value of the working radius is 25, and the actual value of the load condition is 20, the load value of the working height=30×0.4=12, the load value of the working radius=25×0.3=7.5, and the load value of the load condition=20×0.2=4, the node load values obtained by the calculation are added to obtain the total load value of the platform, and the total load value=12+7.5+4=23.5.
S3325, verifying the total platform load value and the node load value, and summarizing the verified total platform load value and the node load value to generate a comprehensive platform operation load value.
Specifically, the calculation process and the data source of the total platform load value and the node load value are checked, so that no calculation error or data entry error is ensured, the load value is verified by using different methods or data sources, such as actual measurement, historical data comparison or expert evaluation, any abnormal or unexpected load value is identified and investigated, whether the load value is real or error data is determined, and consistency between the total platform load value and the node load value is ensured, namely, the total platform load value is equal to the sum of the load values of all nodes.
Integrating the verified platform load total value and the node load value into a unified data structure, carrying out further statistical analysis on the node load value, such as calculating an average value, a standard deviation and the like to generate a comprehensive load index, and generating a platform operation comprehensive load value according to analysis requirements, wherein the comprehensive load value can be a single numerical value or a comprehensive index system comprising a plurality of indexes, verifying the generated comprehensive load value, ensuring that the generated comprehensive load value reflects the actual operation state of the platform, recording the generation process and result of the comprehensive load value, and providing basis for subsequent analysis and decision.
Specifically, the working height load value, the working radius load value and the load condition load value are integrated into a data structure, the average load value of the three nodes is calculated to generate a comprehensive load index, the working radius load value is 7.5 under the assumption that the working height load value is 12, the load condition load value is 4, the comprehensive load value= (12+7.5+4)/3=8.17 is ensured, the comprehensive load value 8.17 reflects the actual operation state of the platform, and the generation process and the result of the comprehensive load value are recorded.
S333, simulating and verifying the comprehensive load value of the platform operation, and optimizing and adjusting the comprehensive load value of the high platform operation based on a simulation verification result;
Specifically, according to the actual operation environment and conditions of the platform, one or more simulation models are established to simulate the working state of the platform, the platform operation comprehensive load value and other related parameters and variables are input into the simulation models, the simulation models are operated to simulate the operation conditions of the platform under different working states, the simulation operation results are analyzed, the performance and accuracy of the comprehensive load value under the simulation environment are evaluated, the simulation results are compared with the actual operation data of the platform, and the rationality and the effectiveness of the comprehensive load value are verified.
According to the simulation verification result, identifying any problem or deficiency of the comprehensive load value in the simulation, deeply analyzing the cause of the problem, and if the cause is due to the fact that the calculation method of the load value is improper in weight distribution or the limitation of the simulation model, providing a targeted adjustment scheme, such as weight distribution adjustment, calculation method improvement or simulation model optimization, and then according to the adjustment scheme, carrying out simulation verification again, testing the performance of the adjusted comprehensive load value, and according to the re-simulation verification result, continuing to optimize and adjust the comprehensive load value until the expected accuracy and practicability requirements are met.
Specifically, when the simulation result of the comprehensive load value under certain working heights and load conditions has larger deviation from actual data, the analysis reason is that the weight distribution of the working heights and load conditions is not accurate enough or the simulation model fails to fully consider certain actual working conditions, the weight distribution of the working heights and load conditions is determined and adjusted, the simulation model is improved to better reflect the actual working conditions, the simulation model is rerun according to an adjustment scheme, the adjusted comprehensive load value is tested, and the comprehensive load value is continuously adjusted according to the result of the reemulation verification until the simulation result is more consistent with the actual data.
S334, taking the optimized and adjusted platform operation integrated load value as operation load data.
Specifically, before the optimized and adjusted comprehensive load value is applied to operation, all data are ensured to be verified, the data are accurate and reliable, the comprehensive load value is converted into a data format suitable for an operation management system or a decision support system, and then the comprehensive load value is integrated into the operation management system of the aerial working platform, so that the system can receive and process the data.
In the operation process of the platform, the comprehensive load value is monitored in real time, any abnormal or load condition exceeding the safety range is found in time, the comprehensive load value is combined with the early warning system, when the load value exceeds the preset safety threshold, the system can automatically give out a warning, provide operation guidance according to the comprehensive load value, help operators to make safer operation decisions, use the comprehensive load value to make and adjust a maintenance plan of the platform, ensure necessary maintenance and inspection in a period with higher load, periodically evaluate the relation between the comprehensive load value and the performance of the platform, and perform continuous optimization and improvement.
The accuracy of the comprehensive load value is verified, all data are guaranteed to be up-to-date and reliable, the comprehensive load value is converted into a format which can be identified by an operation management system, the comprehensive load value is integrated into the operation management system of the aerial work platform through database records, so that an operator can check the load value on an operation interface in real time, the operator can monitor the comprehensive load value in real time through the system and receive early warning when the load value approaches or exceeds a safety threshold, the operator adjusts an operation strategy according to the comprehensive load value, such as reducing working height or keeping safe operation of the load, and a maintenance team adjusts a maintenance plan according to the comprehensive load value, so that necessary maintenance and inspection after a load peak period are guaranteed.
S34, analyzing the influence of the weather conditions of the construction operation, and calculating construction load data based on the analysis of the influence value of the weather conditions of the construction operation and the operation load data.
Specifically, analyzing the construction work weather condition influence, and calculating construction load data based on the analysis construction work weather condition influence value and the operation load data includes the steps of:
s341, preprocessing the influence of the weather conditions of the construction operation, and evaluating the influence value of the weather conditions of the construction operation after preprocessing;
Specifically, historical weather data related to a construction operation is collected, including temperature, humidity, wind force, precipitation, visibility, and the like, the collected weather data is checked, erroneous, abnormal, or missing values are removed or corrected, the original weather data is converted into a format suitable for analysis, such as converting text description into numerical codes, characteristics conducive to analysis are extracted or constructed from the original data, such as combining the temperature and the humidity to generate a thermal index, the weather data is subjected to standardized processing so as to perform comparison across different dimensions and magnitudes, a model is selected or developed to evaluate the influence of weather on the construction operation, such as a linear regression, decision tree, or machine learning model, the model is trained using the historical data, the accuracy of the model is verified by a cross-validation method or the like, the construction operation influence values under different weather conditions are calculated using the trained model, the evaluation result is analyzed, weather factors having the greatest influence on the construction operation are identified, the influence degree thereof is determined, and the evaluation result is used for decision support of the construction operation, such as adjusting the operation plan to adapt to the weather condition.
Specifically, data such as temperature, humidity, wind speed, precipitation and visibility are collected, some obvious error records are removed, such as summer data with negative temperature, weather description such as cloudiness is converted into numerical codes, a thermal index is calculated, comprehensive influences of temperature and humidity are considered, all weather data are standardized to be within a range of 0-1, the preprocessed data are used for evaluating the influence value of construction operation weather conditions, a random forest model is selected for evaluating the influence of weather on construction operation, the historical data is used for training the model, the accuracy of the model is verified through cross verification, the construction operation influence value under different weather conditions is calculated through the model, the analysis result shows that the wind speed and precipitation are factors with the largest influence on construction operation, and according to the evaluation result, a construction team adjusts an operation plan under weather conditions with high wind speed or high precipitation.
S342, unifying the construction operation weather condition influence value and the operation load data, merging the construction operation weather condition influence value and the operation load data after unifying the data, and obtaining operation influence data;
specifically, it is ensured that the construction operation weather condition influence value and the operation load data have the same data format and unit, two groups of data are aligned according to the time stamp or date, corresponding data points are ensured to reflect the same time period, the dimension influence is eliminated by carrying out standardized processing on all data variables, so that the data can be directly compared, the integrity and the accuracy of the data are checked, any abnormal value or missing data is removed, and interpolation or other data cleaning methods are carried out if necessary.
Creating a new data frame, combining weather condition influence values and operation load data into the same data structure, combining the weather condition influence values and the operation load values of each row of data containing the weather influence values and the operation load values of the same time point according to time stamps or identifiers by using a database management tool or data processing software, re-verifying the consistency and accuracy of the data after combining the data, ensuring that errors are not introduced in the combining process, comprehensively analyzing the combined data, identifying the correlation between the weather condition and the operation load and the comprehensive influence of the weather condition and the operation load on construction operation, further analyzing the data by using a statistical model or a machine learning method, such as utilizing multiple regression to analyze the influence of the weather condition and the operation load on the construction efficiency, generating a detailed report according to the analysis result, utilizing the comprehensive data to provide operation guidance to help a project management team to make operation decisions under different weather conditions, analyzing how the variables commonly influence the construction efficiency by using a multiple linear regression model, optimizing the construction plan, such as optimizing the weather operation plan at high temperature or high wind speed prediction according to the model result.
S343, constructing a construction load model through a multiple linear regression algorithm, substituting operation influence data into the construction load model, and calculating construction load data;
Specifically, variables related to construction loads, such as the number of workers, equipment utilization rate, material consumption, construction progress and the like, are selected, historical construction data including variables and corresponding construction load data are collected, preprocessing work such as cleaning, filling missing values, converting data formats and the like is performed on the collected data, feature selection and feature conversion are performed, a construction load model is established by using a multiple linear regression algorithm, for example, construction loads = beta 0+ beta 1 (the number of workers) +beta 2 (the equipment utilization rate) +beta 3 (the material consumption) +beta 4 (the construction progress) +epsilon, a regression coefficient is estimated by using a historical data training model, accuracy of the model is verified through methods such as cross verification and fitting goodness test, new operation influence data including variables selected during model construction are collected, preprocessing is performed on the new data, consistency of format and model input requirements is ensured, the preprocessed operation influence data are substituted into the established construction load model, and the construction load data, namely the predicted construction load value is calculated according to model output.
And S344, performing simulation verification on the construction load data, and optimizing and adjusting the construction load data based on a simulation verification result.
Specifically, according to the characteristics and requirements of construction projects, one or more simulation models are constructed, the models can simulate actual construction environments and conditions, such as simulation software or professional construction management software, construction load data are input into the simulation models as input parameters, including but not limited to the number of workers, equipment use rate, material consumption and the like, the simulation models are operated, different construction scenes and conditions are simulated, and the performance of the construction load data under different conditions is observed, detailed analysis is performed on simulation results, whether the performance of the construction load data in the simulation accords with expectations or not is identified, whether any abnormal or non-logical data performance exists is checked, the simulation results are compared with the actual construction data, and the simulation accuracy and the effectiveness of the construction load data are verified.
According to the simulation result, any significant problem or deviation from the expected, such as over-high or under-low construction load estimation, and deep analysis of possible causes of the problem, including errors of input data, inaccuracy of a model or influence of external factors, is recognized, and according to the cause of the problem, a specific adjustment strategy is formulated, including adjustment of input data, modification of model parameters or improvement of data collection and processing methods, after necessary adjustment of the construction load data, the effect of simulation verification test adjustment is carried out again, and optimization adjustment is continued according to the new simulation result, which requires multiple iterations, so that the accuracy and practicability of the construction load data are ensured to be optimal, and the whole simulation verification and optimization adjustment process including the used model, encountered problems, measures taken and final results is recorded.
S4, calculating an aerial work platform variable angle value generated during construction based on construction load data and an aerial work platform original angle value;
specifically, based on construction load data and an original angle value of an aerial work platform, calculating an aerial work platform luffing angle value generated during construction comprises the following steps:
s41, extracting node load values and overhead working platform node information in construction load data, and calculating construction node moment;
Specifically, it is ensured that access rights are available to obtain construction load data including node load values and aerial work platform node information, including node positions, load sizes, action directions and the like, the data are arranged and formatted so as to facilitate processing, required node load values and relevant platform node information are identified, specifically, position coordinates (such as x, y and z axis coordinates) of each node, load amounts on the nodes and action directions thereof need to be identified, a rotation axis for moment calculation in construction operation is determined, the length of a moment arm is calculated according to the position coordinates of each node and the position of the rotation axis, the moment arm is the shortest distance from the node to the rotation axis, the moment of each node is obtained through geometric calculation, and the moment of each node is calculated by using the formula.
S42, calculating a luffing angle value of the aerial working platform based on the moment of the construction node and the original angle value of the aerial working platform;
specifically, based on the construction node moment and the original angle value of the aerial work platform, calculating the aerial work platform luffing angle value comprises the following steps:
S421, summarizing the moment of the construction node to obtain the total moment of the construction node of the aerial working platform;
Specifically, it is ensured that the moment of each node, including the magnitude and direction of the force, has been calculated, and for each node, its moment value is recorded or extracted, and the influence of the direction of the moment on the total moment is noted, before the total moment is calculated, the direction of each moment needs to be confirmed, the moment can be clockwise or anticlockwise, and is determined according to the direction of the rotation axis and the direction of the force, so as to ensure that the directions of all the moments have been correctly identified and considered before the combination, the moment vectors of all the nodes are vector-combined, i.e. all the moments are summed with respect to the same rotation axis, wherein the anticlockwise moment is a positive value, and the clockwise moment is a negative value.
S422, setting a platform moment of inertia rule, and calculating an aerial work platform angle change value based on the platform moment of inertia rule and the total moment of construction nodes of the aerial work platform;
Specifically, it is ensured that the moment of each node, including the magnitude and direction of the force, has been calculated, and for each node, its moment value is recorded or extracted, and the influence of the direction of the moment on the total moment is noted, before the total moment is calculated, the direction of each moment needs to be confirmed, the moment is clockwise or anticlockwise, and is determined according to the rotation axis and the direction of the force, it is ensured that the directions of all the moments have been correctly identified and considered before the combination, and the moment vectors of all the nodes are vector-combined, i.e. all the moments are summed with respect to the same rotation axis, wherein the anticlockwise moment is a positive value, and the clockwise moment is a negative value.
S423, combining the original angle value of the aerial work platform with the angle change value of the aerial work platform to obtain the amplitude angle value of the aerial work platform.
Specifically, in order to ensure that there are original angle values and angle variation values of the aerial work platform. The original angle is the angle position of the platform at a certain reference moment, the angle change value is the variation of the angle of the platform from that moment, and the original angle value and the angle change value are added, and the angle can overflow, namely more than 360 degrees or less than 0 degree, due to angle calculation, the final angle value needs to be adjusted, so that the angle can be ensured to be in the standard range of 0 degrees to 360 degrees.
S43, verifying the luffing angle value of the aerial work platform, and optimally adjusting the luffing angle value of the aerial work platform after verification.
Specifically, the actual angles of the platform are obtained by means of an actual measuring tool (such as an angle meter or an inclination sensor), the measured values are compared with the calculated amplitude angle values, the errors between the calculated values and the measured values are analyzed to determine whether the errors are within an acceptable range, if the errors exceed a preset threshold value, the sources of the errors, which may be errors in the calculation method, the data input or the inaccuracy of the measuring equipment, are further investigated, a feedback mechanism is provided to ensure that any error information received can be used for correcting and improving the angle calculation process, if systematic errors are found, a model or algorithm for adjusting the angle calculation may be needed, including adjusting the relevant angle conversion factors or taking into account additional environmental factors, checking and updating the accuracy of the input data, for example, to ensure that the angle change values are accurately recorded and transmitted, and all sensors are calibrated, after any adjustment is performed, a verification process is repeatedly performed to ensure that the adjustment made effectively reduces the errors, and a series of operation tests are performed under the control environment to verify the performance of the angle values after adjustment in the actual operation.
S5, analyzing the amplitude angle value of the aerial work platform, and controlling and optimizing the aerial work platform based on the analysis result.
Specifically, sufficient luffing angle data is collected, which should include angle values under different operating conditions, such as different loads, angle records under different weather conditions, statistical analysis is performed on the collected angle data, including calculation of average values, standard deviations, error ranges, etc., conventional behavior and any abnormal fluctuations of the angle values are identified, and time series analysis or other advanced analysis techniques are used to identify trends and patterns of angle changes, such as whether systematic deviations, periodic changes, etc., and analyze factors that may affect luffing angle accuracy, such as mechanical wear, control system delays, environmental influence wind speeds, temperatures, etc., and based on the analysis results, a mathematical model or control algorithm for angle control is optimized, such as, if the angle deviation is found to be related to load size, the control algorithm is adjusted to automatically compensate for load changes, control system parameters, such as proportional, integral and derivative parameters of the controller, improve response speed and reduce overshoot, and implement updates in control system software, including new control strategies and algorithms, ensure that all updates are subjected to rigorous tests, establish a real-time feedback system, automatically monitor control parameters and continuously monitor the control parameters to implement actual performance and verify that the actual environmental conditions are suitable for actual environmental conditions, such as appropriate after the actual performance of the actual conditions are verified.
According to another embodiment of the present invention, there is provided a safety control system for an aerial work platform based on a luffing angle as shown in fig. 2, the system comprising:
the parameter acquisition module 1 is used for acquiring parameters of the aerial work platform, the weather conditions of construction operation and construction information of the aerial work platform;
The original parameter module 2 is used for acquiring an original load parameter of the aerial working platform and an original angle value of the aerial working platform based on the aerial working platform parameters;
The platform load module 3 is used for extracting platform construction characteristic data according to the construction information of the aerial working platform and calculating construction load data based on the platform construction characteristic data, construction operation weather conditions and original load parameters of the aerial working platform;
the luffing angle module 4 is used for calculating the luffing angle value of the aerial working platform generated during construction based on construction load data and the original angle value of the aerial working platform;
the safety control module 5 is used for analyzing the amplitude angle value of the aerial work platform and controlling and optimizing the aerial work platform based on the analysis result;
the parameter acquisition module 1, the original parameter module 2, the platform load module 3, the amplitude angle module 4 and the safety control module 5 are sequentially connected.
In summary, by means of the technical scheme, the consistency and comparison effectiveness of data in analysis are ensured through standardized processing, each factor is weighted and comprehensively analyzed, a more accurate and scientific decision basis is provided, and meanwhile, through accurate calculation and analysis of load and angle change of an aerial working platform, operation risks are reduced, and operation safety is improved.
In addition, the invention reduces the operation time, improves the working efficiency, reduces the error operation, improves the working efficiency, reduces the long-term operation cost, predicts the potential maintenance requirement to reduce the unexpected downtime by analyzing the load data and the angle change, and reduces the dependence on the professional skill by optimizing the control flow.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
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