An intelligent safety analysis and early warning system for cranes

Abstract

The present invention relates to the technical field of data analysis, and includes an intelligent safety analysis and warning system for a crane. The system includes a behavior pattern capture module, an operation anomaly analysis module, a load monitoring module, a dynamic warning adjustment module, a risk assessment module, and a real-time warning signal sending module. In the present invention, by analyzing the operation data and operation behavior of the crane in real time, the accuracy and response speed of the safety warning system are effectively enhanced. The strategy of dynamically adjusting the operation limit and speed allows the system to adapt to changes in different working environments, and timely adjusts the safety threshold to cope with immediate operation conditions and environmental factors. Further, by comparing with historical accident data, behavior patterns similar to historical accidents are accurately identified, and potential risks are warned in advance, which can significantly reduce safety accidents caused by operation errors or crane failures, ensure the safety of personnel at the construction site, and at the same time avoid possible property losses.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular, to a crane intelligent safety analysis and warning system. Background Art

[0002] Data analysis involves using algorithms, statistical models, and computer software to process data in order to extract useful information and insights from the data. The key is to transform a large amount of raw data into useful information, which in turn supports the decision-making process. The applications of data analysis are very extensive, including business intelligence, financial forecasting, health information management, logistics, and supply chain optimization, etc. With the development of machine learning and artificial intelligence technologies, the methods and tools of data analysis are also constantly evolving, enabling more accurate and effective processing of large-scale and complex data sets.

[0003] Among them, the theme of the crane intelligent safety analysis and warning system is to use data analysis technology to monitor and analyze the operation data of the crane in real time, identify possible safety risks, and issue early warnings in advance. Its main purpose is to enhance the safety of crane operation and prevent accidents from occurring. By integrating data collected by sensors (such as position, load, operating speed, etc.), the current state of the equipment and potential safety threats can be evaluated, and alarms can be sent to operators or maintenance teams in a timely manner, which is crucial for ensuring the safety of the construction site, especially in high-risk heavy industrial environments.

[0004] Although the existing technologies can process a large amount of data, they often show limitations in real-time response and environmental adaptability. For example, in traditional analysis models, they usually rely on fixed analysis cycles and preset models, and often cannot meet the requirements of real-time monitoring and immediate response in rapidly changing industrial environments. The lack of flexibility leads to significant omissions in dealing with emergencies, unable to capture subtle anomalies in crane operation in a timely manner, increasing the risk of accidents. In addition, the existing technologies fail to make full use of historical data for risk prediction and warning, and also fail to effectively match operation behaviors with accident patterns, weakening the pertinence and forward-looking of preventive measures and affecting the overall safety performance. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a crane intelligent safety analysis and warning system.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions: A crane intelligent safety analysis and warning system includes:

[0007] The behavior pattern capture module tracks the operation sequence of the operator through the crane operation interface, counts the time points and durations of each movement of the joystick to a new position, continuously updates the behavior data of the operator, and outputs an operation behavior feature set;

[0008] The operation anomaly analysis module receives the operation behavior feature set, analyzes the operator's behavior pattern, compares the operator's current operation data with the crane standard operation data, calculates the difference, identifies and marks potential risk points, and obtains the abnormal behavior analysis result;

[0009] The load monitoring module monitors the load condition of the crane in real time through sensors, records the vibration frequency and amplitude generated by the load, continuously tracks and analyzes the recorded data, detects abnormal vibration conditions beyond the normal range, evaluates the load instability risk of the crane, and generates a load status analysis report;

[0010] The dynamic warning adjustment module evaluates the current environmental conditions and the crane operation mode in real time according to the load status analysis report, dynamically adjusts the crane operation speed according to the real-time data, matches different working conditions, synchronously calculates the new load limit, and outputs the adjusted safety threshold;

[0011] The risk assessment module combines the adjusted safety threshold and the abnormal behavior analysis result, matches the current operation data of the crane with the crane accident database, identifies similar operation behaviors, synchronously calculates the risk value of the current operation, divides the risk level, and comprehensively outputs the risk assessment result;

[0012] The real-time warning sending module determines the corresponding warning level according to the risk assessment result, sends the warning information to the operator's control interface and the ground monitoring center, receives the immediate warning feedback from all relevant personnel, and executes the emergency operation corresponding to the warning level in the safety manual, and outputs the warning execution record.

[0013] As a further solution of the present invention, the operation behavior feature set includes operation sequence data, time point records, duration records, operation force data, and operation frequency data; the abnormal behavior analysis result includes behavior pattern deviation records and risk point marking results; the load status analysis report includes load vibration frequency, load amplitude, abnormal vibration detection results, and load stability evaluation results; the adjusted safety threshold includes operation speed adjustment values and new load limit parameters; the risk assessment result includes operation risk values, risk level division results, and accident mode matching results; the warning execution record includes warning levels, warning information transmission records, emergency operation execution situations, and warning feedback reception records.

[0014] As a further solution of the present invention, the behavior pattern capture module includes an operation data acquisition sub-module, a behavior feature extraction sub-module, and an operation feature integration sub-module;

[0015] The operation data acquisition sub-module obtains the operator's operation sequence based on the crane operation interface. By recording the time points and durations when the joystick moves to new positions, and successively counting the change processes and time spans of each operation, an operator operation behavior data set is generated.

[0016] The behavior feature extraction sub-module extracts the joystick movement time point and duration data based on the operator operation behavior data set, separates the action features, classifies and splits them according to the data type, and establishes an operation behavior feature set.

[0017] The operation feature integration sub-module integrates the data based on the operation behavior feature set. According to the separated joystick feature data, the data is integrated. By uniformly classifying the feature items and supplementing the missing information, all the data is merged into a complete operation feature structure, and an operation behavior feature set is generated.

[0018] As a further solution of the present invention, the operation anomaly analysis module includes a behavior pattern analysis sub-module, a difference calculation sub-module, and a risk point marking sub-module.

[0019] The behavior pattern analysis sub-module extracts the current behavior features of the operator based on the operation behavior feature set, decomposes the behavior feature data, analyzes the regularity and change trend in the action parameters and operation sequence, matches the data patterns in the behavior features that match the operation standards, and generates the current behavior pattern analysis result.

[0020] The difference calculation sub-module compares the current behavior feature data of the operator with the crane standard operation data based on the current behavior pattern analysis result. By comparing the difference values in the action parameters one by one, it detects and records the data deviations that do not meet the standards, and generates the difference data result.

[0021] The risk point marking sub-module identifies the risk deviation points in the operator's behavior pattern based on the difference data result. By marking the data items in the deviation values that exceed the allowable range and dividing the risk levels according to the deviation intensity, an abnormal behavior analysis result is generated.

[0022] As a further solution of the present invention, the load monitoring module in the load monitoring module includes a load data acquisition sub-module, a vibration analysis and calculation sub-module, and a risk assessment generation sub-module.

[0023] The load data acquisition sub-module uses sensors to monitor the load condition of the crane in real time. By collecting the vibration frequency and amplitude data of the sensors, recording the vibration change values corresponding to the load point by point, and segmentally extracting the vibration time series data, combined with the load parameters, load vibration monitoring data is generated.

[0024] The vibration analysis and calculation sub-module calculates point by point the vibration frequency and amplitude data based on the load vibration monitoring data, determines the frequency change trend by grouping the time series data of the vibration parameters and comparing the amplitude difference values, extracts the time period range of the outliers, and generates abnormal vibration characteristic data;

[0025] The risk assessment and generation sub-module integrates the distribution of all abnormal points based on the abnormal vibration characteristic data, analyzes point by point the impact of the abnormal vibration data on the load stability, and generates a load status analysis report in combination with the corresponding time period range.

[0026] As a further solution of the present invention, the dynamic warning and adjustment module in the dynamic warning and adjustment module includes an environmental condition assessment sub-module, an operation dynamic adjustment sub-module, and a safety threshold calculation sub-module;

[0027] The environmental condition assessment sub-module extracts the temperature, humidity, wind speed and ground stability parameters in the environmental data based on the load status analysis report, obtains item by item the running speed and load range parameters of the crane operation mode, and analyzes the time change trend of each parameter to generate environmental operation condition data;

[0028] The operation dynamic adjustment sub-module compares the current load range with the environmental limit conditions based on the environmental operation condition data, reallocates the operation parameters according to the environmental conditions, adjusts item by item the running speed, operation radius and hoisting rhythm of the crane, and generates operation dynamic adjustment data by matching the differential conditions in real time with the adjustment results;

[0029] The safety threshold calculation sub-module extracts the adjusted running parameters and load pressure values based on the operation dynamic adjustment data, re-analyzes the maximum load parameter range in the environmental limit conditions, integrates the load limit value with the dynamic adjustment data, calculates the adjusted safety load limit value, and outputs the adjusted safety threshold.

[0030] As a further solution of the present invention, the adjusted safety load limit value is calculated according to the formula:

[0031] ;

[0032] wherein, represents the adjusted safety load limit value, represents the load pressure value after dynamic adjustment, represents the adjusted running parameter value, represents the reference running parameter value under the environmental limit conditions, represents the maximum load parameter range of the environmental limit.

[0033] As a further solution of the present invention, the risk assessment module in the risk assessment module includes a data matching and analysis sub-module, a risk value calculation sub-module, and a risk level division sub-module;

[0034] Based on the adjusted safety threshold and the abnormal behavior analysis result, the data matching and analysis sub-module extracts dynamic parameters in the current operation data of the crane. By comparing the key behavior patterns in the operation data with the historical operation behaviors stored in the crane accident database, it identifies similar operation data and corresponding behavior characteristics, and generates similar operation behavior data;

[0035] Based on the similar operation behavior data, the risk value calculation sub-module extracts accident parameters in the historical operation behavior that match the current operation, calculates the risk value corresponding to the current operation behavior, and evaluates the potential risk factors in the current operation behavior one by one, generating operation risk value data;

[0036] Based on the operation risk value data, the risk level division sub-module classifies the risk value of the current operation data according to the range of risk parameters, synchronously associates and matches the risk level with the operation data, and comprehensively outputs the risk assessment result.

[0037] As a further solution of the present invention, the risk value corresponding to the current operation behavior is calculated according to the formula:

[0038] ;

[0039] wherein, represents the risk value of the current operation behavior, represents the specific value of the historical accident parameter, represents the corresponding parameter value of the current operation behavior, represents the standard deviation of the potential risk factor in the historical data, represents the time parameter of the current operation behavior, represents the mean value of the time parameter in the historical data, represents the parameter value, represents the number of risk factors.

[0040] As a further solution of the present invention, the real-time warning sending module in the real-time warning sending module includes a warning level determination sub-module, a warning information sending sub-module, and an emergency operation execution sub-module;

[0041] Based on the risk assessment result, the warning level determination sub-module calls the data of the current operation state and the risk level matching rules in the safety manual, determines the warning level corresponding to the risk of the current operation, and according to the warning level, matches the emergency operation process and execution steps in the safety manual, generating warning level information;

[0042] Based on the warning level information, the warning message sending sub-module extracts the key information in the warning content, sends the warning messages item by item to the operator control panel and the monitoring center interface through wireless communication, and receives the feedback signals from the ground monitoring center and the operator, records the feedback content and response confirmation situation item by item, and generates a warning sending record;

[0043] Based on the warning sending record, the emergency operation execution sub-module extracts the confirmed emergency operation content of the feedback, calls the operation interface of the crane equipment, gradually executes the emergency operation instructions corresponding to the warning level in the safety manual, records the operation completion time and feedback status, and generates a warning execution record.

[0044] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0045] In the present invention, by analyzing the operation data and operation behaviors of the crane in real time, the accuracy and response speed of the safety warning system are effectively enhanced. The strategy of dynamically adjusting the operation limit and speed allows the system to adapt to the changes in different working environments, timely adjusts the safety threshold to cope with the immediate operation conditions and environmental factors. Further, by comparing with the historical accident data, the behavior patterns similar to historical accidents are accurately identified, potential risks are pre-warned, and safety accidents caused by operation errors or crane failures can be significantly reduced, ensuring the safety of the personnel at the construction site, and at the same time avoiding possible property losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is the system flow chart of the present invention;

[0047] Figure 2 is the system module diagram of the present invention;

[0048] Figure 3 is the flow chart of the behavior pattern capture module of the present invention;

[0049] Figure 4 is the flow chart of the operation anomaly analysis module of the present invention;

[0050] Figure 5 is the flow chart of the load monitoring module of the present invention;

[0051] Figure 6 is the flow chart of the dynamic warning adjustment module of the present invention;

[0052] Figure 7 is the flow chart of the risk assessment module of the present invention;

[0053] Figure 8 is the flow chart of the real-time warning sending module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0054] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0055] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality" is two or more, unless otherwise specifically defined.

[0056] Please refer to Figure 1 , a crane intelligent safety analysis and early warning system includes:

[0057] The behavior pattern capture module tracks the operator's operation sequence through the crane operation interface, counts the time points and durations of each movement of the joystick to a new position, continuously updates the operator's behavior data, and outputs an operation behavior feature set;

[0058] The operation anomaly analysis module receives the operation behavior feature set, analyzes the operator's behavior pattern, compares the operator's current operation data with the crane standard operation data, calculates the difference, identifies and marks potential risk points, and obtains an abnormal behavior analysis result;

[0059] The load monitoring module monitors the load condition of the crane in real time through sensors, records the vibration frequency and amplitude generated by the load, continuously tracks and analyzes the recorded data, detects abnormal vibration conditions beyond the normal range, evaluates the load instability risk of the crane, and generates a load status analysis report;

[0060] The dynamic early warning adjustment module evaluates the current environmental conditions and crane operation mode in real time according to the load status analysis report, dynamically adjusts the crane operation speed according to the real-time data, matches the differential working conditions, synchronously calculates the new load limit, and outputs the adjusted safety threshold;

[0061] The risk assessment module combines the adjusted safety threshold and the abnormal behavior analysis result, matches the current operation data of the crane with the crane accident database, identifies similar operation behaviors, synchronously calculates the risk value of the current operation, divides the risk level, and comprehensively outputs the risk assessment result;

[0062] The real-time warning sending module determines the corresponding warning level according to the risk assessment result, sends the warning information to the operator's control interface and the ground monitoring center, receives the instant warning feedback from all relevant personnel, and executes the emergency operations corresponding to the warning level in the safety manual, and outputs the warning execution record.

[0063] The operation behavior feature set includes operation sequence data, time point records, duration records, operation force data, and operation frequency data; the abnormal behavior analysis result includes behavior pattern deviation records and risk point marking results; the load status analysis report includes load vibration frequency, load amplitude, abnormal vibration detection results, and load stability evaluation results; the adjusted safety thresholds include operation speed adjustment values and new load limit parameters; the risk assessment result includes operation risk values, risk level classification results, and accident mode matching results; the warning execution record includes warning levels, warning information transmission records, emergency operation execution conditions, and warning feedback reception records.

[0064] Please refer to Figure 2 and Figure 3 , the behavior pattern capturing module includes an operation data acquisition sub-module, a behavior feature extraction sub-module, and an operation feature integration sub-module;

[0065] Based on the crane operation interface, the operation data acquisition sub-module obtains the operator's operation sequence, records the time point and duration when the joystick moves to a new position, and sequentially counts the change process and time span of each operation to generate the operator's operation behavior data set.

[0066] Based on the crane operation interface, the operator's operation sequence is obtained. During the acquisition process, the movement trajectory of the joystick is recorded through sensors and control modules, including the starting position, target position, movement time point, and movement duration of each movement of the joystick. The noise data is removed using a filtering algorithm, and the outliers are filtered and replaced by setting upper and lower limit ranges. At the same time, all the recorded data is sorted by time axis into segments, and the complete operation behavior data set of the operator is generated in the time order of operations, providing standardized input data for subsequent analysis.

[0067] Based on the operator's operation behavior data set, the behavior feature extraction sub-module extracts the joystick movement time point and duration data, separates the action features, classifies and splits them according to the data type, and establishes an operation behavior feature set.

[0068] Based on the operator's operation behavior dataset, extract the data of the joystick movement time points and durations. Segment all the data according to the time series, screen the key time nodes of the joystick movement and the corresponding durations. Use distribution fitting to analyze the variation rules of different force characteristics. Combine the frequency characteristics with the force characteristics to generate a multi-dimensional feature matrix that conforms to the operation behavior feature description. At the same time, split the data according to the joystick feature dimension to ensure the integrity and separability of all data at specific time points and dimensions. Finally, form an operation behavior feature set classified by category.

[0069] Based on the operation behavior feature set, the operation feature integration sub-module integrates the data according to the separated joystick feature data. By unifying the classified feature items and supplementing the missing information, all the data is merged into a complete operation feature structure to generate an operation behavior feature set.

[0070] Based on the operation behavior feature set, integrate and process the joystick feature data through the time series feature analysis method. First, use the time axis to synchronize and align the joystick movement time points. The interpolation algorithm is used to make up for the missing data in the time series generated after alignment. Further, through normalization processing, map the feature values of different dimensions to a unified numerical range for subsequent analysis and feature processing. By combining the duration and frequency characteristics of the joystick, form a complete operation feature set, and finally construct a standardized operation behavior feature set for analyzing the patterns and characteristics of complete operation behaviors.

[0071] Please refer to Figure 2 and Figure 4 , the operation anomaly analysis module includes a behavior pattern analysis sub-module, a difference calculation sub-module, and a risk point marking sub-module.

[0072] Based on the operation behavior feature set, the behavior pattern analysis sub-module extracts the current behavior characteristics of the operator, decomposes the behavior feature data, analyzes the regularity and variation trend in the action parameters and operation sequences, and matches the data patterns that match the operation standards in the behavior characteristics to generate the analysis results of the current behavior pattern.

[0073] Based on the specific data content of the operation behavior feature set, first group all the operation behaviors according to the joystick movement parameters, and sequentially extract the feature data related to the joystick, including the movement time point, duration, and movement direction. Then, analyze the variation trend of each feature value in a frame-by-frame manner according to the time sequence. In the specific execution process, use each movement of the joystick as the basic unit, calculate its start time and end time, and record the complete movement process. By uniformly organizing these data, form a basic dataset of the behavior pattern including the joystick movement changes, providing key behavior feature basis support for subsequent analysis.

[0074] Based on the current behavior pattern analysis result, the difference calculation sub-module compares the current behavior feature data of the operator with the standard operation data of the crane. By comparing the difference values in the action parameters one by one, it detects and records the data deviation that does not meet the standard, and generates a difference data result;

[0075] On the basis of obtaining the current operator's behavior pattern, each of its behavior parameters is compared with the set standard operation parameters one by one. By comparing the moving time point, duration, and direction data of the joystick, it calculates whether there are differences beyond the preset standard value range in each operation parameter; for example, by calculating the difference between the start time and the end time of the joystick item by item and comparing it with the time range in the standard operation parameters, the deviation value is marked. Finally, all the analyzed difference data are classified and sorted out, including which parameters have deviations and the specific values of each deviation value, forming a comprehensive difference analysis data set, providing direct data support for marking and evaluating non-standard behavior characteristics.

[0076] Based on the difference data result, the risk point marking sub-module identifies the risk deviation points in the operator's behavior pattern. By marking the data items that exceed the allowable range in the deviation value, it divides the risk levels according to the deviation intensity and generates an abnormal behavior analysis result;

[0077] Based on the difference data result existing in the operator's behavior, a detailed risk assessment is carried out for each deviation data item. First, the deviation points that exceed the allowable range are extracted one by one, and the position of each in the set risk level is calculated according to the size of the deviation value. For example, for the deviation of the joystick movement parameter, first record the numerical difference that exceeds the time range and compare it with the preset grading standard. The deviation close to the standard range is marked as a low-risk point, while the deviation that exceeds the allowable range by a large margin is marked as a high-risk point. After integrating all the risk points, the specific deviation values and corresponding data items are listed according to the risk level. For example, the specific difference value and its data characteristics of each high-risk point are listed. All the risk point data are refined into a standardized risk grading result, forming the final abnormal behavior analysis result, marking the key risk points and their deviation characteristics in the operator's behavior pattern, providing a clear goal for subsequent improvement and optimization.

[0078] Please refer to Figure 2 and Figure 5 As shown in, the load monitoring module includes a load data acquisition sub-module, a vibration analysis and calculation sub-module, and a risk assessment and generation sub-module;

[0079] The load data acquisition sub-module uses sensors to monitor the load condition of the crane in real time. By collecting the vibration frequency and amplitude data of the sensors, it records the vibration change values corresponding to the load point by point, extracts the vibration time series data in segments, and combines the load parameters to generate load vibration monitoring data;

[0080] The load status of the crane is monitored in real time using sensors. Specifically, vibration monitoring sensors are installed at key parts of the crane. The sensors record the vibration parameter data corresponding to the load during the operation in real time, including the numerical changes in vibration frequency and vibration amplitude. During the acquisition process, the vibration data is sampled point by point at fixed intervals in chronological order, and the vibration characteristic values at each time point are recorded. At the same time, according to the acquired vibration frequency and amplitude data, combined with load parameters such as the load weight and load position of the crane, the data is sorted into a complete time series file. The time series data is divided into multiple sub-time periods, and the vibration change values for each period are extracted and sorted. Detailed load vibration monitoring data is generated through the above method, which is used to describe the vibration characteristics during the load change process and provide basic data support for subsequent analysis.

[0081] Based on the load vibration monitoring data, the vibration analysis and calculation sub-module performs point-by-point calculations on the vibration frequency and amplitude data. By grouping the time series data of the vibration parameters and comparing the amplitude difference values, the trend of frequency change is judged, the time period range of the outliers is extracted, and the abnormal vibration characteristic data is generated.

[0082] Based on the load vibration monitoring data, the changes in the recorded vibration frequency and amplitude are analyzed point by point. During the execution process, by slicing the vibration time series data, the data for each time period is classified into independent groups for calculation. The difference in vibration amplitude change for each time period is calculated, and the maximum value, minimum value, and average value in the change difference are extracted. Subsequently, a trend analysis is performed on the vibration frequency within each time period. By comparing the frequency value at each time point with the frequency change of its adjacent time point point by point, it is judged whether the vibration frequency shows abnormal fluctuations. Outliers are screened for points where the amplitude change value exceeds the standard range, and the specific time period range and amplitude characteristics are recorded. Finally, the time periods where all outliers are located and the corresponding vibration parameter data are sorted out, and the abnormal vibration characteristic data describing the abnormal vibration law is generated, providing a basic basis for subsequent risk analysis.

[0083] Based on the abnormal vibration characteristic data, the risk assessment and generation sub-module integrates the distribution of all abnormal points, analyzes the impact of the abnormal vibration data on the load stability point by point, and generates a load status analysis report in combination with the corresponding time period range.

[0084] Based on the abnormal vibration characteristic data, the distribution positions of each abnormal point and their corresponding vibration characteristics are detailedly integrated, and the influence of abnormal vibration on the load stability of the crane is analyzed point by point. In the specific implementation process, first, the vibration amplitude and frequency values corresponding to each abnormal point are statistically analyzed, and at the same time, the time period range where the point is located and the corresponding load parameter data are recorded. The distribution ranges of multiple abnormal points are compared and sorted to determine whether the abnormal points have concentrated characteristics or random distribution characteristics. Subsequently, the influence of all abnormal vibration points on the load stability is analyzed hierarchically according to the vibration frequency and amplitude characteristics. For example, the degree of damage to the load center of gravity stability caused by vibration is evaluated according to the proportion of the vibration amplitude exceeding the normal range, and the instability of the load state is judged according to the situation of frequency fluctuation exceeding the standard. Finally, combining the statistical results of all abnormal points, a detailed load state analysis report is generated according to the time period range and load parameters. The report content includes the distribution law of abnormal points, vibration parameter characteristics, and comprehensive evaluation of load stability.

[0085] Please refer to Figure 2 and Figure 6 , the dynamic warning and adjustment module includes an environmental condition evaluation sub-module, an operation dynamic adjustment sub-module, and a safety threshold calculation sub-module;

[0086] Based on the load state analysis report, the environmental condition evaluation sub-module extracts the temperature, humidity, wind speed, and ground stability parameters in the environmental data, obtains the operating speed and load range parameters of the crane operation mode item by item, and analyzes the time variation trend of each parameter to generate environmental operation condition data.

[0087] Based on the load state analysis report, key data parameters related to the environment are extracted item by item, including environmental data such as temperature, humidity, wind speed, and ground stability. The time series values of the above data are obtained through sensors and monitoring devices, and they are integrated into a complete set of environmental data sets in chronological order. At the same time, parameters such as the operating speed and load range under the crane operation mode are extracted, and these parameters are synchronously compared and analyzed with the environmental data to determine the variation trend of each parameter in different time periods. For example, the influence of the increase or decrease of wind speed over time on the operating speed of the crane, and the influence of ground stability changes on load parameters. By analyzing, the correlation and variation law between each parameter are obtained, and finally, environmental operation condition data describing the mutual relationship between the environment and operation conditions is generated, providing a basis for dynamically adjusting operation parameters.

[0088] Based on the environmental operation condition data, the operation dynamic adjustment sub-module compares the current load range with the environmental limit conditions, reallocates the operation parameters according to the environmental conditions, adjusts the operating speed, operation radius, and hoisting rhythm of the crane item by item, and generates operation dynamic adjustment data by matching the differential conditions through real-time update of the adjustment results.

[0089] Based on the environmental operating condition data, the current load range parameters are compared with the environmental limit conditions one by one. By calculating whether the load range parameters exceed the limit range defined in the environmental data, the adaptability of the operating conditions is judged. Subsequently, the operating parameters are carefully adjusted. For example, by analyzing the relationship between the wind speed parameter and the crane operating speed, the operating speed is appropriately reduced to match the current wind speed condition; for the operating radius, the lifting range of the crane is adjusted in combination with the ground stability data; in terms of the lifting rhythm, the interval time for each lifting is re-planned according to the changes in environmental temperature and humidity to avoid equipment stability problems caused by too fast operation. By adjusting the operating parameters item by item and updating all adjustment results in real time, it is ensured that each operation matches the environmental conditions, and finally complete operating dynamic adjustment data is generated to provide real-time support for safety and efficiency.

[0090] Based on the operating dynamic adjustment data, the safety threshold calculation sub-module extracts the adjusted operating parameters and load pressure values, re-analyzes the maximum load parameter range in the environmental limit conditions, integrates the load limit value with the dynamic adjustment data, calculates the adjusted safe load limit value, and outputs the adjusted safety threshold.

[0091] The adjusted safe load limit value is calculated according to the formula:

[0092] ;

[0093] where, represents the adjusted safe load limit value, which is used to evaluate the maximum load that the system can safely bear in a dynamic environment, represents the load pressure value after dynamic adjustment, represents the adjusted operating parameter value, which is the optimized adjustment of the system operating conditions in combination with the dynamic adjustment data, represents the reference operating parameter value under the environmental limit conditions, which is used as a reference value for comparison and adjustment, represents the maximum load parameter range of the environmental limit, which is the upper limit value of the allowable load extracted from the current environmental conditions.

[0094] Parameter : The load pressure value after dynamic adjustment is obtained by real-time monitoring of the load operation data of the system. Taking the load pressure data recorded by a certain device during operation as an example, it is monitored that the load values of the device within one minute are 1000 N, 1050 N, 1100 N, 1070 N, 1150 N, and 1120 N respectively. The load pressure value after dynamic adjustment is obtained by calculating the average value of these load values. The calculation process is as follows:

[0095] ;

[0096] Substitute the data:

[0097]

[0098] ;

[0099] Parameter : The adjusted operating parameter value is a dynamically optimized value of a set of actual operating data obtained through operating status monitoring. For example, during the operation of the system, the monitored operating parameters include key factors such as equipment rotation speed, temperature, and vibration amplitude. These factors are quantified into a single value through the weighted average method: the rotation speed is 3000 revolutions per minute, the temperature is 75 degrees Celsius, and the vibration amplitude is 0.8 millimeters, with weights of 0.5, 0.3, and 0.2 respectively. According to the weighted average formula:

[0100]

[0101] Substitute the data:

[0102]

[0103]

[0104] Parameter : The reference operating parameter value is the design standard value for the equipment operation, usually directly obtained from the equipment manual or design specifications. For example, the designed operating parameters of the equipment are 3200 revolutions per minute for rotation speed, 70 degrees Celsius for temperature, and 0.5 millimeters for vibration amplitude. According to the same weighted average calculation:

[0105]

[0106] Substitute the data:

[0107]

[0108]

[0109] Parameter : The maximum load parameter range restricted by the environment The maximum load parameter range restricted by the environment is calculated from the allowable load data of the system operating environment. Assume that the maximum allowable load range of the environment where the equipment operates is 5000 N. The influences of the actually measured temperature fluctuation range, humidity range, and vibration frequency of the environment where the equipment is located on the maximum allowable load range are 5%, 3%, and 2% respectively. The calculation formula for the maximum load parameter range is:

[0110]

[0111] Substitute the data:

[0112]

[0113]

[0114] Substitute the calculation results of the above parameters into the formula respectively:

[0115]

[0116]

[0117] The calculation result shows that the safety load limit value is 20.6. It indicates that under the current dynamic load pressure, adjusted operating parameters and environmental limit conditions, the maximum safety load limit for the equipment operation is 20.6 units, which can be used to evaluate the safety range of the equipment during actual operation and avoid operation failures caused by excessive load.

[0118] Please refer to Figure 2 and Figure 7 , the risk assessment module includes a data matching and analysis sub-module, a risk value calculation sub-module, and a risk level division sub-module;

[0119] Based on the adjusted safety threshold and the abnormal behavior analysis result, the data matching and analysis sub-module extracts the dynamic parameters from the current operation data of the crane. By comparing the key behavior patterns in the operation data with the historical operation behaviors stored in the crane accident database, it identifies the similar operation data and corresponding behavior characteristics, and generates the similar operation behavior data.

[0120] Based on the adjusted safety threshold and the abnormal behavior analysis result, extract the dynamic parameters from the current operation data of the crane, including key indicators such as operating speed, operating radius, load weight, and hoisting time. Compare these dynamic parameters one by one, and match the current operation behavior pattern with the historical operation behaviors recorded in the crane accident database. During the matching process, by comparing the key behavior parameters, such as whether there are similarities between the change trend of the operating speed and the accident trend in the historical data, and whether the load weight distribution is related to the historical abnormal data, identify the historical data similar to the current operation behavior and its corresponding behavior characteristics through point-by-point comparison of parameter values and behavior characteristics, and finally generate a set of similar operation behavior data to provide a historical reference basis for further risk assessment.

[0121] Based on the similar operation behavior data, the risk value calculation sub-module extracts the accident parameters in the historical operation behavior that match the current operation, calculates the risk value corresponding to the current operation behavior, evaluates the potential risk factors in the current operation behavior one by one, and generates the operation risk value data.

[0122] The risk value corresponding to the current operation behavior, according to the formula:

[0123] ;

[0124] Perform calculations, where represents the risk value of the current operation behavior, represents the specific value of the historical accident parameter, which is used as the reference data for evaluating the risk that may be caused by the current operation behavior, represents the corresponding parameter value of the current operation behavior, indicating the actually measured data value in the current operation behavior, represents the standard deviation of the potential risk factor in the historical data, which is used to measure the volatility of this risk factor, represents the time parameter of the current operation behavior, indicating the time range when the current operation behavior occurs, represents the mean value of the time parameter in the historical data, which is used to compare and correct the potential risk with the current time parameter, represents the parameter value, represents the number of risk factors.

[0125] : The specific value of the historical accident parameter, which represents the accident characteristic parameter corresponding to the current operation behavior extracted from the historical operation data. It is extracted after normalizing the historical operation behavior data. The specific value of the historical accident parameter is derived from the recorded industrial equipment operation logs, such as the temperature overlimit value and pressure fluctuation value of the equipment. Assume that the parameter values obtained from the monitoring data are: , , .

[0126] : The corresponding parameter value of the current operation behavior, which represents the parameter value extracted from the current real-time monitoring of the equipment operation behavior. It is obtained through the real-time data acquisition module in the monitoring system. The parameter values monitored in the current operation behavior are: , , .

[0127] : The standard deviation of the potential risk factor in the historical data, which represents the fluctuation range of a certain risk factor in historical accidents. It is obtained by calculating the standard deviation of the parameters of similar accidents in the historical data. The standard deviation values of the three risk factors calculated from the historical data are respectively: , , .

[0128] : The time parameter of the current operation behavior, indicating the time range when the current operation occurs. For example, by monitoring the operation time of the current equipment as 2 pm, that is, the time is quantified as 14 (hours).

[0129] : The mean value of the time parameter in historical data, representing the time distribution center of historical operation behaviors. The time mean value is extracted by analyzing the time when the corresponding operation behaviors occur in all historical data. For example, the mean value of the operation time in historical data is 12 (hours).

[0130] Substitute the above parameters into the formula for calculation step by step, which is divided into the following steps:

[0131] Calculate the sum of the absolute value differences between the historical accident parameters and the current operation behavior parameters:

[0132]

[0133]

[0134] Calculate the square root of the sum of the squares of the standard deviations of the potential risk factors in historical data:

[0135]

[0136]

[0137] Calculate the square root of the difference of the time parameter:

[0138]

[0139] Substitute all the calculation results into the formula:

[0140]

[0141]

[0142] The final calculated result of the risk value is 2.78. The result shows that compared with the historical accident parameters, the risk level of the current operation behavior belongs to the upper-middle range. The calculation process comprehensively considers the historical accident parameters, the current operation behavior parameters, the potential risk standard deviation, and the time parameter, realizing a comprehensive assessment of the risk value. The value can be used as a basis for further analyzing the sources of potential risks and formulating countermeasures, providing support for optimizing operation behaviors and avoiding risks.

[0143] The risk level classification sub-module grades the risk value of the current operation data according to the range of risk parameters based on the operation risk value data, synchronously associates and matches the risk level with the operation data, and comprehensively outputs the risk assessment result;

[0144] Based on the operational risk value data, the risk values of the current operational data are classified according to a preset range of risk parameters. For example, the risk values are divided into three levels: low risk, medium risk, and high risk. Through the division rules of the risk value range, the risk levels of each operational parameter are clearly marked, and at the same time, the risk levels are associated with the corresponding operational data to ensure that each risk value and its corresponding dynamic parameters can be matched one by one. For example, the high-risk label of the running speed is bound to specific time points and operational data, which is convenient for subsequent evaluation and adjustment. Finally, a complete risk assessment result including the risk level division and the corresponding operational parameters is comprehensively output, which is used to guide the safe operation of the crane and optimize the operation behavior.

[0145] Please refer to Figure 2 and Figure 8 For real-time warning signal sending module, it includes a warning level determination sub-module, a warning information sending sub-module, and an emergency operation execution sub-module;

[0146] Based on the risk assessment result, the warning level determination sub-module calls the data of the current operation status and the risk level matching rules in the safety manual to determine the warning level corresponding to the risk of the current operation, and generates warning level information according to the warning level by matching the emergency operation procedures and execution steps in the safety manual.

[0147] Based on the risk assessment result, the current operation status data is compared one by one with the risk level matching rules in the safety manual. By identifying the risk levels of each operational parameter, it is judged whether the current operation is within different risk ranges such as low, medium, and high risks. According to the risk assessment result, the corresponding warning level is determined. For example, for operational parameters with a lower risk level, it is matched with a low-level warning, while for operations with a higher risk level and parameters exceeding the safety threshold range, it is matched with a high-level warning. At the same time, the corresponding emergency operation procedures in the safety manual are combined according to the warning level, including specific steps such as clearly stopping urgently, reducing the operation speed, or reducing the load. Finally, warning level information including the warning level and emergency measures is generated, which is used to guide subsequent operation processing.

[0148] Based on the warning level information, the warning information sending sub-module extracts the key information in the warning content, and sends the warning information item by item to the operator control panel and the monitoring center interface through wireless communication, and receives the feedback signals from the ground monitoring center and the operator, records the feedback content and response confirmation situation item by item, and generates a warning sending record.

[0149] Based on the early warning level information, extract the key information in the early warning content item by item, such as the risk level, the abnormal range of operating parameters, and the recommended emergency operation procedures, and send them item by item to the operator control panel and the ground monitoring center interface through the wireless communication module. Ensure the accuracy and integrity of each early warning message during the information sending process, and at the same time monitor whether there are problems such as information delay or loss during the sending process. The early warning information includes recommended emergency handling measures and operation instructions, and receive the feedback signals from the operator and the monitoring center in real time. Generate an early warning sending record item by item by recording the content of the feedback signal and confirming the received status, including the time of early warning sending, the feedback reception situation, and the operation response confirmation status, to ensure that all early warning information has been correctly transmitted and recorded.

[0150] Based on the early warning sending record, the emergency operation execution sub-module extracts the confirmed emergency operation content in the feedback, calls the operation interface of the crane equipment, and gradually executes the emergency operation instructions corresponding to the early warning level in the safety manual, records the time of operation completion and the feedback status, and generates an early warning execution record;

[0151] Based on the early warning sending record, extract the confirmed emergency operation content from the feedback of the operator and the monitoring center, including the recommended operation steps and execution requirements, call the control interface of the crane equipment to gradually execute the corresponding emergency operation instructions, such as steps of reducing the running speed, reducing the lifting load, and shortening the operation radius, etc. During the execution process, record the time point of completion of each operation and the response status of the equipment in real time to ensure that each instruction is executed in sequence and correct feedback is obtained. At the same time, organize the real-time data collected during the operation execution process into an operation record file, such as recording the start time of the emergency operation, the completion time of execution, and the status change of the equipment. Finally, generate a complete early warning execution record for subsequent analysis and operation log archiving.

[0152] The above is only the preferred embodiment of the present invention, and it is not intended to limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An intelligent safety analysis and warning system for a crane, characterized in that: The system includes: The behavior pattern capture module tracks the operator's operation sequence through the crane operation interface, counts the time points and durations of each movement of the joystick to a new position, continuously updates the operator's behavior data, and outputs an operation behavior feature set; The operation anomaly analysis module receives the operation behavior feature set, analyzes the operator's behavior pattern, compares the operator's current operation data with the crane standard operation data, calculates the difference, identifies and marks potential risk points, and obtains an abnormal behavior analysis result; The load monitoring module monitors the load condition of the crane in real time through sensors, records the vibration frequency and amplitude generated by the load, continuously tracks and analyzes the recorded data, detects abnormal vibration conditions beyond the normal range, evaluates the load instability risk of the crane, and generates a load status analysis report; The dynamic warning adjustment module evaluates the current environmental conditions and crane operation mode in real time according to the load status analysis report, dynamically adjusts the crane operation speed according to real-time data, matches different working conditions, synchronously calculates a new load limit, and outputs an adjusted safety threshold; The risk assessment module combines the adjusted safety threshold and the abnormal behavior analysis result, matches the crane's current operation data with the crane accident database, identifies similar operation behaviors, synchronously calculates the risk value of the current operation, classifies the risk level, and comprehensively outputs a risk assessment result; The real-time warning sending module determines the corresponding warning level according to the risk assessment result, sends warning information to the operator's control interface and the ground monitoring center, receives the immediate warning feedback from all relevant personnel, and executes the emergency operations corresponding to the warning level in the safety manual, and outputs a warning execution record; 2. The crane intelligent safety analysis and warning system according to claim 1, wherein: The operation behavior feature set includes operation sequence data, time point records, duration records, operation force data, and operation frequency data; the abnormal behavior analysis result includes behavior pattern deviation records and risk point marking results; the load status analysis report includes load vibration frequency, load amplitude, abnormal vibration detection results, and load stability evaluation results; the adjusted safety threshold includes operation speed adjustment values and new load limit parameters; the risk assessment result includes operation risk values, risk level classification results, and accident mode matching results; The warning execution record includes warning levels, warning information transmission records, emergency operation execution conditions, and warning feedback reception records; 3. The crane intelligent safety analysis and warning system according to claim 1, characterized in that: The behavior pattern capture module includes an operation data acquisition sub-module, a behavior feature extraction sub-module, and an operation feature integration sub-module; The operation data acquisition sub-module obtains the operator's operation sequence based on the crane operation interface, records the time points and durations of the joystick moving to new positions, and sequentially counts the change process and time span of each operation to generate an operator operation behavior data set; The behavior feature extraction sub-module extracts the joystick movement time point and duration data based on the operator operation behavior data set, separates the action features, classifies and splits them according to the data type, and establishes an operation behavior feature set; Based on the set of operation behavior characteristics and according to the separated joystick characteristic data, the operation feature integration sub-module integrates the data. By uniformly classifying the feature items and supplementing the missing information, all the data is merged into a complete operation feature structure, generating an operation behavior feature set.

4. The crane intelligent safety analysis and early warning system according to claim 1, characterized in that: The operation anomaly analysis module includes a behavior pattern analysis sub-module, a difference calculation sub-module, and a risk point marking sub-module; Based on the set of operation behavior characteristics, the behavior pattern analysis sub-module extracts the current behavior characteristics of the operator, decomposes the behavior characteristic data, analyzes the regularity and change trend in the action parameters and operation sequence, and matches the data patterns in the behavior characteristics that match the operation standards, generating the analysis result of the current behavior pattern; Based on the analysis result of the current behavior pattern, the difference calculation sub-module compares the current behavior characteristic data of the operator with the standard operation data of the crane. By comparing the difference values in the action parameters one by one, it detects and records the data deviation that does not meet the standards, generating a difference data result; Based on the difference data result, the risk point marking sub-module identifies the risk deviation points in the operator's behavior pattern. By marking the data items that exceed the allowable range in the deviation values and classifying the risk levels according to the deviation intensity, it generates an analysis result of abnormal behavior.

5. The intelligent safety analysis and warning system for a crane according to claim 1, characterized in that: In the load monitoring module, the load monitoring module includes a load data acquisition sub-module, a vibration analysis and calculation sub-module, and a risk assessment generation sub-module; The load data acquisition sub-module uses sensors to monitor the load condition of the crane in real time. By collecting the vibration frequency and amplitude data of the sensors, it records the vibration change values corresponding to the load point by point, extracts the vibration time series data in segments, and combines the load parameters to generate load vibration monitoring data; Based on the load vibration monitoring data, the vibration analysis and calculation sub-module calculates the vibration frequency and amplitude data point by point. By grouping the time series data of the vibration parameters and comparing the amplitude difference values, it judges the frequency change trend and extracts the time range of the abnormal values, generating abnormal vibration characteristic data; Based on the abnormal vibration characteristic data, the risk assessment generation sub-module integrates the distribution of all abnormal points, analyzes the impact of the abnormal vibration data on the load stability point by point, and combines the corresponding time range to generate a load status analysis report.

6. The crane intelligent safety analysis and warning system according to claim 1, wherein: In the dynamic warning and adjustment module, the dynamic warning and adjustment module includes an environmental condition assessment sub-module, an operation dynamic adjustment sub-module, and a safety threshold calculation sub-module; Based on the load status analysis report, the environmental condition assessment sub-module extracts the temperature, humidity, wind speed, and ground stability parameters in the environmental data, obtains the running speed and load range parameters of the crane operation mode item by item, and analyzes the time change trend of each parameter, generating environmental operation condition data; Based on the environmental operation condition data, the operation dynamic adjustment sub-module compares the current load range with the environmental limit conditions, reallocates the operation parameters according to the environmental conditions, adjusts the running speed, operation radius, and hoisting rhythm of the crane item by item, and matches the differential conditions by real-time updating the adjustment results, generating operation dynamic adjustment data; The safety threshold calculation sub-module dynamically adjusts the data based on the operation, extracts the adjusted operating parameters and load pressure values, re-analyzes the maximum load parameter range in the environmental limit conditions, integrates the load limit value with the dynamically adjusted data, calculates the adjusted safety load limit value, and outputs the adjusted safety threshold.

7. The crane intelligent safety analysis and warning system according to claim 6, characterized in that: The adjusted safety load limit value is calculated according to the formula: ； Perform calculations, where, represents the adjusted safe load limit value, represents the dynamically adjusted load pressure value, represents the adjusted operating parameter value, represents the reference operating parameter value under environmental limit conditions, represents the maximum load parameter range under environmental limits.

8. The crane intelligent safety analysis and early warning system according to claim 1, characterized in that: In the risk assessment module, the risk assessment module includes a data matching and analysis sub-module, a risk value calculation sub-module, and a risk level classification sub-module; Based on the adjusted safety threshold and the abnormal behavior analysis result, the data matching and analysis sub-module extracts the dynamic parameters in the current operation data of the crane. By comparing the key behavior patterns in the operation data with the historical operation behaviors stored in the crane accident database, it identifies similar operation data and corresponding behavior characteristics, and generates similar operation behavior data; Based on the similar operation behavior data, the risk value calculation sub-module extracts the accident parameters in the historical operation behavior that match the current operation, calculates the risk value corresponding to the current operation behavior, evaluates the potential risk factors in the current operation behavior one by one, and generates operation risk value data; Based on the operation risk value data, the risk level classification sub-module classifies the risk value of the current operation data according to the range of risk parameters, synchronously associates and matches the risk level with the operation data, and comprehensively outputs the risk assessment result.

9. The crane intelligent safety analysis and warning system according to claim 8, characterized in that: The risk value corresponding to the current operation behavior is calculated according to the formula: ； Perform calculations, where, represents the risk value of the current operation behavior, represents the specific value of the historical accident parameter, represents the corresponding parameter value of the current operation behavior, represents the standard deviation of the potential risk factor in the historical data, represents the time parameter of the current operation behavior, represents the mean value of the time parameter in the historical data, represents the parameter value, represents the number of risk factors.

10. The crane intelligent safety analysis and early warning system according to claim 1, characterized in that: In the real-time warning sending module, the real-time warning sending module includes a warning level determination sub-module, a warning information sending sub-module, and an emergency operation execution sub-module; Based on the risk assessment result, the warning level determination sub-module calls the data of the current operation status and the risk level matching rules in the safety manual, determines the warning level corresponding to the risk of the current operation, and generates warning level information according to the warning level by matching the emergency operation procedures and execution steps in the safety manual; Based on the warning level information, the warning information sending sub-module extracts the key information in the warning content, sends the warning information item by item to the operator control panel and the monitoring center interface through wireless communication, and receives the feedback signals from the ground monitoring center and the operator, records the feedback content and response confirmation situation item by item, and generates a warning sending record; Based on the warning sending record, the emergency operation execution sub-module extracts the confirmed emergency operation content, calls the operation interface of the crane equipment, gradually executes the emergency operation instructions corresponding to the warning level in the safety manual, records the operation completion time and feedback status, and generates a warning execution record.

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