Intelligent safety guarantee platform for crane

Abstract

The application relates to the technical field of data analysis, and discloses a crane intelligent safety guarantee platform. The platform comprises a data acquisition module, a state monitoring module, a basic operation and maintenance module, an operation guarantee module and a system management module. The data acquisition module collects crane operation parameters through a sensing network and performs feature extraction. The state monitoring module monitors and evaluates the states of key components such as steel wire rope wear and brake gap. The basic operation and maintenance module formulates a maintenance plan based on health indexes and historical data. The operation guarantee module performs safety evaluation on hoisting operations and generates a control scheme. The system management module monitors the platform operation process. The knowledge base module performs correlation analysis on various types of data and constructs an intelligent operation and maintenance knowledge base. The application is applicable to various types of cranes such as gantry cranes, bridge cranes and portal cranes specified in GB / T 20776, and realizes the intellectualization and precision of the crane maintenance process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, and particularly relates to a crane intelligent safety guarantee platform. BACKGROUND

[0002] At present, the safe operation and maintenance of large cranes mainly rely on manual inspection and regular maintenance. The traditional maintenance method mainly includes regular inspection of mechanical part wear, measurement of electrical system operating parameters, detection of safety protection device action state, etc. When the equipment fails or abnormity occurs, the maintenance personnel need to judge the fault reason according to experience and formulate a maintenance plan. At the same time, the existing crane maintenance management adopts a preventive maintenance strategy, which carries out maintenance according to fixed time intervals or running time, and records maintenance information through paper records or simple electronic form. This method ensures the safe operation of the equipment to a certain extent.

[0003] However, this traditional maintenance mode has obvious deficiencies. First, the manual inspection method is subjective, and the detection accuracy and reliability are difficult to guarantee, and potential faults are easy to be missed. Second, the fixed period preventive maintenance cannot dynamically adjust the maintenance plan according to the actual state of the equipment, resulting in waste of maintenance resources or untimely maintenance. Third, the record and management of maintenance information are relatively scattered, it is difficult to establish a full life cycle health record of the equipment, and it is difficult to fully utilize the historical maintenance experience to guide the subsequent work. Finally, there is a lack of intelligent control of the maintenance process, and it is difficult to guarantee the consistency and traceability of the maintenance quality. SUMMARY

[0004] The present application provides a crane intelligent safety guarantee platform for realizing the intelligentization and precision of the crane maintenance process, establishing an equipment health evaluation model through the fusion analysis of multi-source data, realizing the scientization of maintenance decision, and constructing a unified knowledge management platform to improve the maintenance efficiency and quality.

[0005] The application provides a crane intelligent safety guarantee platform, which comprises a data acquisition module, a state monitoring module, a basic operation and maintenance module, an operation guarantee module, a system management module and a knowledge base module.

[0006] In the technical scheme, the multi-dimensional sensing network is arranged on the important structural parts and mechanisms of the crane to realize real-time monitoring and data acquisition of the equipment operation state, and provide reliable data basis for health state evaluation; key parameters such as the steel wire rope wear degree, the brake gap, the reducer vibration and the motor temperature are analyzed and evaluated to realize accurate evaluation of the component state and quantitative expression of the health index; maintenance planning and resource scheduling are performed in combination with the historical load spectrum and working condition data of the crane to realize reasonable arrangement of the maintenance task and optimal allocation of the resources; real-time safety evaluation is performed on the hoisting space, the lifting height and the working radius to ensure the safety of the maintenance operation and effectively prevent collision and interference risks in the operation process; the maintenance process of the crane limiting protection device, the anti-collision system and the overload protection device is monitored and quality evaluated throughout the process to ensure the standardization and reliability of the maintenance operation; a multi-dimensional correlation analysis model of the equipment characteristic data set, the component health index, the maintenance task list, the dynamic control scheme and the quality evaluation result is established to build a complete crane intelligent operation and maintenance knowledge base, which provides effective knowledge support for subsequent maintenance decision, thereby realizing intelligent, accurate and standardized maintenance process of the crane, significantly improving the maintenance efficiency and reliability, reducing the maintenance cost and prolonging the service life of the equipment. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0008] Figure 1 An embodiment of the crane intelligent safety guarantee platform in the present application is shown in the figure.

[0009] Figure 2 Another embodiment of the crane intelligent safety guarantee platform in the present application is shown in the figure. DETAILED DESCRIPTION

[0010] The present application provides a crane intelligent safety guarantee platform. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "comprising" or "having" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device comprising a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0011] For the convenience of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 An embodiment of the crane intelligent safety guarantee platform in the present application includes:

[0012] The data acquisition module is used to collect equipment operation parameters through the multi-dimensional sensing network acquisition equipment arranged on the important structural parts, hoisting mechanism, luffing mechanism and slewing mechanism of the crane, and to obtain equipment feature data set by feature extraction and standardization processing on the equipment operation parameters;

[0013] The state monitoring module is used to analyze and evaluate the crane wire rope wear degree, brake gap, reducer vibration and motor temperature according to the equipment feature data set, and to generate component health index;

[0014] The basic operation and maintenance module is used to perform maintenance planning and resource scheduling based on the component health index in combination with the crane historical load spectrum and working condition data, and to form an intelligent maintenance task list;

[0015] The operation guarantee module is configured to perform safety evaluation on the crane hoisting space, lifting height and working radius according to the equipment characteristic data set and the maintenance task list, and generate a dynamic control scheme.

[0016] The system management module is configured to monitor the maintenance process of the crane position limiting protection device, anti-collision system and overload protection device according to the maintenance task list and the dynamic control scheme, and output a quality evaluation result.

[0017] The knowledge base module is configured to perform multi-dimensional correlation analysis on the equipment characteristic data set, component health index, maintenance task list, dynamic control scheme and quality evaluation result, and construct a crane intelligent operation and maintenance knowledge base.

[0018] It can be understood that the execution subject of the present application can be a crane intelligent safety guarantee system, and can also be a terminal or a server, and the specific implementation is not limited herein. The server is taken as an example for description of the embodiments of the present application.

[0019] Specifically, a multi-dimensional sensor network is deployed at key positions of the crane. Specifically, a stress sensor for measuring stress change is installed at a key position of an important structural member, an acceleration sensor for monitoring abnormal vibration of a transmission system is installed at a lifting mechanism, an angle sensor for measuring a pitch angle is installed at an amplitude conversion mechanism, and a temperature sensor for measuring temperature is installed at a slewing mechanism. The data acquisition module collects original data at a sampling frequency of 1 Hz for the strain sensor, 1000 Hz for the acceleration sensor, 1 Hz for the angle sensor and 1 Hz for the temperature sensor. Then, zero drift correction is performed on the strain signal, low-pass filtering is performed on the angle signal, and mean value filtering is performed on the rotation speed signal. The processed data are synchronized under a unified time reference, and statistical characteristics such as peak value coefficient, standard deviation and root mean square value are extracted. The frequency domain characteristics are calculated through 512-point fast Fourier transform. The state monitoring module performs component state evaluation on the standardized equipment characteristic data set, analyzes the wire rope broken wire rate and surface wear condition, measures the brake pad clearance value and brake torque change, calculates the reducer amplitude and frequency characteristics, monitors the motor temperature rise rate and thermal load level. The time sequence variation law of the component state quantity is obtained, and the statistical characteristics are calculated in a sliding time window. The state parameters are compared with the threshold range, and abnormal points and abnormal intervals are marked. The missing data is repaired by using a cubic spline interpolation method, and statistical analysis with 95% confidence is performed. The health index of each component is obtained through weighted fusion.

[0020] The time sequence correlation analysis is performed on the component health index and the equipment historical load spectrum, the state distribution law under different load levels is counted, the time period is divided based on the working condition data, the demand degree and difficulty coefficient of each maintenance project are calculated, and the maintenance priority is determined. According to the priority, the maintenance projects are sorted, the maintenance time and operation space requirement are estimated, the required maintenance tool quantity and maintenance personnel configuration are counted, the matching is performed in combination with the tool inventory state and personnel skill level, and the maintenance task list containing detailed time arrangement is generated. In the maintenance process, the equipment feature data set is combined with the maintenance task requirements, the obstacle contour point of the lifting space, the limiting value of the lifting height and the boundary value of the working radius are extracted respectively, the safety boundary is established through three-dimensional coordinate mapping. The reachability of the operation area, the height margin and the radius redundancy are calculated by calculating the minimum envelope, the danger level is divided and the collision risk coefficient is calculated, the risk area map is generated, and the optimal maintenance path and obstacle points are planned accordingly, and a dynamic safety control scheme is formed.

[0021] The quality monitoring is performed on the maintenance execution process, the displacement sensor signal of the limiting protection device, the distance sensor signal of the anti-collision system and the weighing sensor signal of the overload protection device are collected respectively. The monitoring data is subjected to synchronous sampling and noise reduction processing, and the abnormal points and mutation intervals are counted. The performance indexes such as the action accuracy of the limiting protection, the anti-collision response time and the overload protection triggering accuracy are calculated through segmented analysis, and the normativity and completion quality of the maintenance operation are evaluated. The various data are subjected to multi-dimensional correlation analysis, the equipment feature data, the health index, the maintenance task, the control scheme and the quality evaluation results are aligned according to the time stamp, the numerical value type parameters are subjected to normalization processing in the interval [-1, 1], and the text description is subjected to structured coding. The correlation matrix between parameters is established, the correlation coefficient is calculated, the strong correlation parameter pairs with correlation degree higher than 0.8 are extracted, the fault-symptom-cause correlation link is analyzed, and the knowledge base of the system is formed.

[0022] For example, when the crane steel wire rope is maintained, the periodic fluctuation of the steel wire rope surface strain value is monitored through the strain sensor, the abnormal vibration in the 30Hz frequency band is found through filtering and spectrum analysis, and the local fatigue damage of the steel wire rope is determined in combination with the broken wire detection data. The system automatically generates a maintenance task list, arranges maintenance personnel with matching technical level to replace the damaged steel wire rope section in the specified time period. The obstacle distribution of the lifting space is dynamically monitored during the maintenance process, and a safe disassembly and assembly path is planned. The load capacity of the newly replaced steel wire rope section is tested by the weighing sensor during acceptance, and the maintenance quality is confirmed.

[0023] In the embodiments of the present application, the present application realizes real-time monitoring and data acquisition of the running state of the equipment by arranging a multi-dimensional sensing network on important structural parts and mechanisms of the crane, thereby providing a reliable data basis for health state evaluation; key parameters such as the wear degree of the steel wire rope, the brake gap, the reducer vibration and the motor temperature are analyzed and evaluated, thereby realizing accurate evaluation of the component state and quantitative expression of the health index; the basic operation and maintenance module combines the historical load spectrum and working condition data of the crane to perform maintenance planning and resource scheduling, thereby realizing reasonable arrangement of the maintenance tasks and optimal allocation of resources; the operation guarantee module performs real-time safety evaluation on the lifting space, the lifting height and the working radius, thereby ensuring the safety of the maintenance operation and effectively preventing collision and interference risks in the operation process; the system management module performs whole-process monitoring and quality evaluation on the maintenance process of the crane limiting protection device, the anti-collision system and the overload protection device, thereby ensuring the standardization and reliability of the maintenance operation; the knowledge base module establishes a multi-dimensional correlation analysis model of the equipment characteristic data set, the component health index, the maintenance task list, the dynamic management and control scheme and the quality evaluation results, thereby constructing a complete crane intelligent operation and maintenance knowledge base, providing effective knowledge support for subsequent maintenance decisions, and thereby realizing intelligent, accurate and standardized crane maintenance process, significantly improving the maintenance efficiency and reliability, reducing the maintenance cost and prolonging the service life of the equipment.

[0024] In a specific embodiment, the data acquisition module is specifically used for:

[0025] (1) collecting force data of the structural parts through strain sensors of important structural parts of the crane, collecting vibration data, angle data and temperature data of the transmission system through acceleration sensors, angle sensors and temperature sensors of the hoisting mechanism, the luffing mechanism and the slewing mechanism;

[0026] (2) performing zero drift correction on the force data of the structural parts, 1000HZ high-order filtering on the vibration data, low-pass filtering on the temperature data and mean value filtering on the angle data, to obtain filtered parameters;

[0027] (3) extracting peak coefficients, standard deviations and root mean square values of the time domain characteristics of the filtered parameters, to form time sequence characteristic data;

[0028] (4) performing 512-point fast Fourier transform on the time sequence characteristic data, calculating power spectral density and main frequency components, to obtain frequency domain characteristic data;

[0029] (5) performing 95% confidence interval test on the frequency domain characteristic data, eliminating abnormal values beyond the limit, and performing linear interpolation on the missing data segments, to obtain effective operation data;

[0030] (6) The effective operation data is normalized according to the maximum and minimum value method, converted to the interval [-1, 1], and a device feature data set is generated, wherein the device feature data set includes steel wire rope wear feature data, brake gap feature data, reducer vibration feature data, and motor temperature feature data.

[0031] Specifically, the data acquisition module acquires data through a multi-dimensional sensing network arranged at key positions. A resistance surface strain gauge is installed at the position of an important structural member, the range of the sensor is ±3000με, and it is mainly used for real-time acquisition of deformation data of the important structural member. A general piezoelectric acceleration sensor is installed at the hoisting mechanism, the range is determined according to the mechanism transmission scene, and the range is ±5g, which is used to monitor the abnormal vibration of the transmission system during the lifting process. An angle sensor is installed on the boom to measure the pitch angle, the range is 0°-360°, which is used to acquire the angle change of the boom during the lifting process. A temperature sensor is installed on the slewing mechanism, the measurement range is -50℃-200℃, which is used to obtain the temperature data of the slewing motor.

[0032] For important components, the sampling frequency is 1 Hz for the resistance surface strain gauge, 1000 Hz for the general piezoelectric acceleration sensor, 1 Hz for the angle sensor, and 1 Hz for the temperature sensor. The deformation data is decomposed by a db4 wavelet basis function to the fourth order, and four scale detail coefficients and one approximation coefficient are obtained. After reconstruction, the denoised deformation signal is obtained. The boom pitch data has a zero drift problem, and the mean value of the signal in the static state is calculated and used as the zero offset for correction. The preprocessed signals of various types are uniformly sampled and processed, and in a 60-second time window, the time domain statistical features are calculated: the peak coefficient reflects the impact of the signal, which is obtained by dividing the maximum value of the signal by the root mean square value; the standard deviation represents the fluctuation degree of the signal; and the root mean square value reflects the effective value of the signal. The step size of each time window is 10 seconds, that is, the feature values are updated every 10 seconds to form continuous time series feature data.

[0033] Before performing fast Fourier transform on the time series feature data, windowing is performed to reduce spectral leakage, and the Hanning window is selected. 512 points are selected for FFT transformation to obtain the frequency spectrum in the range of 0-50 Hz. The power spectral density is calculated to reflect the distribution of signal energy in the frequency domain. At the same time, the main frequency component is extracted, that is, the frequency point with the largest amplitude and its amplitude, which can reflect the vibration characteristics of the equipment. Based on the normal distribution assumption, the 95% confidence interval is calculated for the obtained frequency domain feature data. Data points outside the confidence interval are marked as abnormal values, which are often related to equipment failure or abnormal operation. For data missing due to communication interruption, sensor failure, etc., if the missing time is less than 1 second, linear interpolation method is used for data repair to ensure the continuity of the data.

[0034] The last step is data normalization, using the maximum and minimum value normalization method, so that all data are mapped to the interval [-1, 1]. In this way, data of different dimensions and different orders of magnitude can be compared and analyzed uniformly.

[0035] For example, take a gantry crane as an example, such as the strain data of the main beam of a 600t gantry crane, the vibration data of the hoisting mechanism reducer, and the inclination data of the rigid and flexible legs. The gantry crane performs a 600-ton lifting operation, which lasts about 30 minutes. The strain sensor at the center of the main beam records a maximum value of 400 micro-strain, and there is no power frequency interference. The strain value appears periodic fluctuations, and through wavelet decomposition, it is found that there are abnormal fluctuations in the third layer of detail coefficients, indicating that there may be local deformation. Time domain feature extraction is performed on these data, and the peak coefficient calculated is 3.2, and the standard deviation is 0.85. FFT analysis found that there was an interference frequency at 28Hz, which corresponds to the natural vibration frequency of the equipment. Through confidence interval analysis, it is found that there are 3 abnormal points in the force value data, and after interpolation processing, a continuous data curve is generated. Normalize all features to form a standardized feature data set.

[0036] In a specific embodiment, the state monitoring module is used for:

[0037] (1) Calculate the wire breakage rate and surface wear degree according to the wire wear feature data, calculate the brake pad clearance value and braking torque according to the brake clearance feature data, calculate the amplitude and frequency characteristics according to the reducer vibration feature data, and calculate the temperature rise rate and thermal load according to the motor temperature feature data. Get component state quantity;

[0038] (2) High-frequency acquisition of component state quantity, calculation of statistical characteristics in sliding time window, formation of state time series data;

[0039] (3) Compare the state time series data with the pre-set threshold range, mark the out-of-limit points and abnormal intervals, and obtain abnormal marking data;

[0040] (4) Data repair of missing segments in abnormal marking data through cubic spline interpolation, 95% confidence statistical analysis, and obtain of credible state value;

[0041] (5) Weighted fusion and normalization calculation of credible state value, and generation of component health index.

[0042] Specifically, the state monitoring module processes data classification after obtaining the device feature data set. The original data is divided into four categories according to the detection object: the steel wire rope wear feature data includes surface photoelectric signals and broken wire electromagnetic signals; the brake gap feature data includes displacement sensing data and pressure sensing data; the reducer vibration feature data includes acceleration and speed signals; and the motor temperature feature data includes stator temperature and rotor temperature data.

[0043] For the evaluation of the steel wire rope wear state, the broken wire rate and surface wear degree calculation formula are introduced:

[0044]

[0045] wherein, W r represents the comprehensive wear rate of the steel wire rope, α i is the number of broken wires at the i-th position, β i is the surface wear depth (mm) at the i-th position, L t is the total length of detection (m), P s is the pitch of the steel wire rope (mm), and k1 and k2 are the weight coefficients of broken wires and wear, and n is the number of detection points.

[0046] The brake state evaluation uses the following formula:

[0047] M b =γ p F n (1-δ g / δ0)R e

[0048] wherein, M b is the braking torque (N·m), γ p is the friction coefficient, F n is the brake normal pressure (N), δ g is the current gap value (mm), δ0 is the initial gap value (mm), and R e is the equivalent friction radius (m).

[0049] The reducer vibration feature calculation formula is:

[0050]

[0051] wherein, V c is the comprehensive vibration feature value, H k is the weighting coefficient of the k-th frequency band, A k is the amplitude of the k-th frequency band (mm), ω k is the angular frequency of the k-th frequency band (rad / s), and m is the number of frequency bands.

[0052] The motor temperature feature calculation formula is:

[0053] T l = ξ s (dT / dt0+ η r Q h

[0054] wherein T l is a thermal load indicator, ξ s is a temperature rise rate weight, dT / dt is a temperature change rate (°C / s), η r is a thermal resistance coefficient, and Q h is a heat generation power (W).

[0055] A 60-second sliding time window is adopted for these state quantities, with a window sliding step of 10 seconds. In each time window, statistical characteristics such as mean, standard deviation, peak value, etc. are calculated to form a continuous state time series data stream. For the steel wire rope, the focus is on the sudden change and cumulative trend of the number of broken wires; for the brake, the gap change rate and torque fluctuation are monitored; for the reducer, the vibration amplitude variation law is analyzed; for the motor, the temperature rise speed and thermal equilibrium state are tracked. The threshold range of each parameter is set: the steel wire rope broken wire rate threshold is 0.15, the surface wear depth threshold is 2 mm; the brake gap change amount threshold is 0.5 mm, the torque fluctuation threshold is 5%; the reducer vibration speed threshold is 5 mm / s, the acceleration threshold is 10 m / s 2 ; the motor temperature rise rate threshold is 2°C / min, and the maximum temperature threshold is 120°C. Data points exceeding the threshold range are marked as abnormal points, and consecutive abnormal points constitute an abnormal interval.

[0056] The marked abnormal data segment is processed, and if the data missing time is less than 1 second, a cubic spline interpolation method is used for repair. The end point value and derivative value are used as boundary conditions for interpolation to ensure that the repaired data curve is smooth and continuous. Statistical analysis with 95% confidence is performed on the repaired data to eliminate incidental abnormalities and retain the true state change characteristics. Finally, data fusion is performed, with the steel wire rope state weight set to 0.4, the brake state weight set to 0.3, the reducer state weight set to 0.2, and the motor state weight set to 0.1. The comprehensive health indicator is calculated. The maximum and minimum value normalization method is used to map the health indicator to the 0-1 interval, with a value closer to 1 indicating a better state.

[0057] For example, by scanning the surface of the steel wire rope, multiple different degrees of wear are found. The surface wear depth is detected by a photoelectric sensor to be between 1.2-1.8 mm, and the electromagnetic sensor detects that there are 2-3 broken wires locally. Substitute these data into the wear rate calculation formula to get the comprehensive wear rate 0.12. At the same time, brake gap detection finds that brake pad wear causes the gap to increase by 0.3 mm, and the braking torque decreases by 3%. The reducer vibration monitoring shows abnormalities at 28 Hz and 56 Hz frequency bands, with a vibration acceleration of 8 m / s2 The motor temperature rises to 95°C at full load, with a temperature rise rate of 1.5°C / min. These data are analyzed by time series analysis and threshold comparison, marking the abnormal interval, confirming the state change trend by interpolation and statistical analysis, and calculating the component health index 0.85, indicating that the equipment is in a normal but attention-required state.

[0058] In a specific embodiment, the basic operation and maintenance module is used for:

[0059] (1) Time series correlation of component health index and crane historical load spectrum, statistical analysis of equipment state distribution under different load levels, and obtaining historical state data;

[0060] (2) Time period division of historical state data and working condition data, analysis of maintenance demand degree and maintenance difficulty coefficient in each period, and generation of maintenance priority data;

[0061] (3) Hierarchical sorting of maintenance projects according to maintenance priority data, calculation of required repair time and operation space of each project, and formation of maintenance operation list;

[0062] (4) Maintenance resource statistics according to the maintenance operation list, calculation of the required number of repair tools and maintenance personnel configuration, and obtaining of resource demand data;

[0063] (5) Matching and screening of repair tool inventory status and maintenance personnel skill level according to resource demand data, and generation of resource allocation scheme;

[0064] (6) Combination and arrangement of maintenance operation list and resource allocation scheme according to time sequence, forming intelligent maintenance task list.

[0065] Specifically, the basic operation and maintenance module performs time series alignment and correlation analysis on the component health index and the historical load spectrum. The load spectrum records the load conditions of the crane at different time points, including lifting weight, lifting frequency, working time, etc. The component health index is matched with the load spectrum data according to the time stamp, and a mapping relationship between health status and load level is established. Through statistical analysis, the health state distribution characteristics of each component under different load intervals (such as 0-20%, 20%-40%, 40%-60%, 60%-80%, 80%-100% rated load) are obtained.

[0066] The historical state data and working condition data are time segmented, and each 8 hours is divided into a working shift. In each time period, the component state deterioration speed, failure frequency and maintenance record are counted, and the maintenance demand degree is calculated. At the same time, the maintenance difficulty coefficient is considered, including the operation environment complexity, the maintenance space limitation degree, the special tool demand and other factors. Through the weighted calculation of these indexes, the maintenance priority score of different time periods is obtained. Based on the maintenance priority data, each maintenance project is graded and sorted. For each maintenance project, the maintenance operation steps are recorded in detail, the standard working hours required by each step are evaluated, and the total maintenance time is calculated. At the same time, the maintenance operation space requirement is measured, including the tool placing space, the maintenance personnel operation space and the spare part storage space. These information is sorted into the maintenance operation list, which contains specific parameters such as project priority, estimated working hours, space requirement, etc.

[0067] According to the maintenance operation list, the resource demand analysis is carried out, and the use quantity and use time length of various maintenance tools are counted. The maintenance tools include general tools (wrench, screwdriver, etc.) and special tools (steel wire flaw detector, brake gap gauge, etc.). At the same time, the required number of maintenance personnel is calculated, including mechanical maintenance workers, electrical maintenance workers, detection technicians and other different types of workers. The detailed resource demand list is formed by summarizing. Based on the resource demand data, the inventory tools and maintenance personnel are screened and matched. The tool inventory state is checked, and the available quantity and soundness of various tools are confirmed. Then the skill level of maintenance personnel is evaluated, and different levels (senior worker, intermediate worker, primary worker) of personnel are matched with the technical requirements of maintenance tasks. Through optimization configuration, a reasonable resource scheduling scheme is formed.

[0068] The maintenance operation list and resource allocation scheme are combined and arranged in time sequence. Considering the priority of maintenance projects, the availability of personnel and tools, the use conflict of operation site and other constraint conditions, the execution time and sequence of each maintenance task are reasonably arranged. An intelligent maintenance task list containing detailed time nodes is compiled.

[0069] For example, the analysis of recent operating data found that the crane mainly works in the 40%-60% rated load range, with a cumulative working time of 180 hours. Combined with the component health indicators, the steel wire rope wear is aggravated, the brake gap is increased, and the reducer vibration is increased, among which the steel wire rope state deteriorates the fastest. The segmented analysis of historical data found that the morning shift operation intensity is larger, and the light in the maintenance environment is darker, which increases the maintenance difficulty coefficient. Therefore, the replacement of the steel wire rope is listed as the highest priority maintenance project, which is expected to require 4 mechanical maintenance workers to operate simultaneously, with a working time of 6 hours, and requires special tools including a steel wire rope flaw detector, a hydraulic cutting tool, etc. Inspection and maintenance resources found that the current inventory tools meet the requirements, but there are only 2 senior workers skilled in steel wire rope replacement operation, and technical personnel from other teams need to be allocated for support. On the basis of having tools and personnel in place, the replacement of the steel wire rope is arranged on the next Tuesday maintenance day, and the relevant information is entered into the maintenance task list.

[0070] In an embodiment, the operation guarantee module is used for:

[0071] (1) decomposing the equipment feature data set into lifting space feature data, lifting height feature data, and working radius feature data, and performing operation area analysis on the maintenance task list to obtain a space parameter group;

[0072] (2) extracting lifting space obstacle contour points, lifting height limiting values, and working radius boundary values from the space parameter group, performing three-dimensional coordinate mapping, and forming safety boundary data;

[0073] (3) performing minimum envelope body calculation on the safety boundary data, analyzing lifting space accessibility, lifting height margin, and working radius redundancy, and obtaining safety margin data;

[0074] (4) dividing the maintenance operation area into risk levels according to the safety margin data, calculating the collision risk coefficient and interference probability of each area, and generating a risk area map;

[0075] (5) calculating the optimal path and obstacle points of the maintenance operation according to the risk area map, setting lifting space access conditions, lifting height limiting values, and working radius constraint values, and obtaining a control parameter set;

[0076] (6) performing time sequence combination and state association on the control parameter set to form a dynamic control scheme.

[0077] Specifically, the operation guarantee module classifies the equipment feature dataset. The dataset is divided into three subsets: hoisting space feature data, including spatial point cloud data scanned by the laser radar; lifting height feature data, including measurement values of the height sensor and the angle sensor; and working radius feature data, including data of the arm length sensor and the horizontal distance sensor. Meanwhile, the work area information in the maintenance task list is analyzed, and parameters such as work point position, work range, and required space size are extracted.

[0078] In the extraction of the space parameters, the following three-dimensional space safety boundary mapping formula is used:

[0079]

[0080] wherein S b represents the space safety boundary index, x p , y p , and z p are three-dimensional coordinate values of the obstacle points, λ1, λ2, and λ3 are weight coefficients in each direction, u i , v i , and w i are direction cosines of the obstacle points, N c is the total number of feature points, and D s is a safety distance reference value.

[0081] The extracted space parameters are subjected to minimum envelope body calculation, and the discrete space point set is constructed into a closed geometric body through the convex hull algorithm. In the calculation of the hoisting space accessibility, the positional relationship between the crane movement range and the obstacle is considered. The lifting height margin is determined by comparing the rated height with the actual demand height, and the working radius redundancy is obtained by calculating the difference between the actual working radius and the maximum allowable radius. Based on the safety margin data, the work area is divided into three levels of safety zone, warning zone, and forbidden zone. Through the calculation of the shortest distance between the hoisting equipment and the obstacle, combined with the movement speed and direction of the equipment, the collision risk is evaluated. Meanwhile, the relative motion between multiple moving parts is considered, and the interference probability is calculated.

[0082] According to the risk area map, the maintenance work path is planned, the A* algorithm is used to calculate the optimal path from the starting point to the target point, and key obstacle avoidance points are set on the path. Access conditions are set for different regions, including personnel qualification requirements, work time limits, etc. Dynamic constraint values are set for the lifting height and the working radius, which are adjusted in real time according to the working state of the equipment. All control parameters are organized in time sequence, and the correlation between the parameters is established. For example, the lifting height limit value and the working radius are coupled and need to be adjusted together. Meanwhile, the sequence of maintenance work is considered to ensure that the constraint conditions of various parameters match each other.

[0083] For example, by acquiring the three-dimensional point cloud data of the work area through laser radar, the positions of fixed obstacles such as overhead lines and pipelines are identified. At the same time, the spatial coordinates of the inspection points and the required work space are extracted from the maintenance task list. After three-dimensional coordinate mapping, the spatial distribution characteristics of the obstacles are obtained. Through the minimum envelope body algorithm, the discrete point cloud is converted into a regular geometric body, and the minimum distance between the obstacles and the boom during rotation is calculated. When classifying the danger level, the area within 2 meters from the obstacle is marked as a forbidden zone, and the area within 2-5 meters is marked as a warning zone. According to the spatial distribution characteristics, an optimal maintenance path is planned to avoid high-voltage lines, and 5 obstacle avoidance points are set at key positions. In the generated dynamic control scheme, it is stipulated that the maximum lifting height of the boom in this area does not exceed 80% of the rated height, the working radius does not exceed 60% of the maximum radius, and the rotation speed needs to be reduced to half of the normal speed.

[0084] In a specific embodiment, the system management module is used for:

[0085] (1) Task analysis of the maintenance task list and the dynamic control scheme, extracting the maintenance requirements of the crane limit protection device, the anti-collision system and the overload protection device, and obtaining the monitoring parameter table;

[0086] (2) According to the monitoring parameter table, collecting the displacement sensor signal of the limit protection device, the distance sensor signal of the anti-collision system and the weighing sensor signal of the overload protection device, forming the original monitoring data;

[0087] (3) The original monitoring data is processed by noise reduction and data synchronization, and the signal mutation points and abnormal intervals are counted to obtain the monitoring feature data;

[0088] (4) The monitoring feature data is analyzed by section statistics, the action accuracy of the limit protection device, the response time of the anti-collision system and the trigger accuracy of the overload protection device are calculated, and the performance index data is generated;

[0089] (5) According to the performance index data, the maintenance operation specification is evaluated, the completion state and the operation quality of the key maintenance nodes are marked, and the evaluation statistical data is formed;

[0090] (6) The evaluation statistical data is calculated by weighting and comprehensive scoring, and the quality evaluation result is output.

[0091] Specifically, the system management module extracts specific monitoring requirements from the maintenance task list and dynamic control scheme. The maintenance requirements of the limit protection device include the action position, action time, and reset state of the limit switch in each direction; the maintenance requirements of the anti-collision system include the obstacle recognition distance, early warning trigger time, and braking response time; the maintenance requirements of the overload protection device include the weighing accuracy, alarm threshold, and action reliability. These requirements are organized into a structured monitoring parameter table, which clearly specifies the detection method and judgment standard for each parameter. According to the monitoring parameter table, a data acquisition scheme is set up to synchronously collect sensor signals of the three protection devices. The limit protection device uses an LVDT displacement sensor with a measurement range of 0-500mm and a resolution of 0.1mm; the anti-collision system uses an ultrasonic distance sensor with a measurement range of 0-10m and a response time of 20ms; the overload protection device uses a strain-type weighing sensor with a measurement range of 0-50t and an accuracy level of 0.1 level. The raw data collected by these sensors includes signal values and timestamps.

[0092] The collected raw data is uniformly converted to a sampling frequency of 100Hz, and a Butterworth low-pass filter is used for noise reduction processing with a cutoff frequency of 40Hz. The three signals are time-aligned, with the earliest timestamp as the reference point, and linear interpolation is used to ensure data synchronization. The first derivative of the signal is calculated, and a dynamic threshold is set to identify signal mutation points. When the signal change rate exceeds the set threshold, it is marked as a mutation point. Continuous mutation points form an abnormal interval. The monitoring feature data after synchronization processing is analyzed by segmenting. The action accuracy of the limit protection device is obtained by calculating the deviation between the actual action position and the set position; the response time of the anti-collision system is determined from the time interval between detecting the obstacle and triggering the brake; the trigger accuracy of the overload protection device is calculated by multiple loading tests, and the ratio of the number of accurate triggers to the total number of tests is calculated. These performance indicators reflect the working state of each protection device.

[0093] According to the performance indicators, the standardization of maintenance operations is evaluated. Key maintenance nodes are marked, including device debugging, function testing, parameter setting, etc. The completion of each node is recorded, including whether the operation process is standardized, whether the test data meets the standards, whether the fault is eliminated, etc. For unqualified items, specific problems and treatment methods are recorded in detail. All evaluation data form a structured statistical table. The evaluation statistical data is comprehensively processed, and weight coefficients are set for different evaluation items. The weights of the limit protection, anti-collision, and overload protection systems are 0.4, 0.3, and 0.3, respectively. Each system is further divided into different evaluation items with different weights. The quality evaluation score is calculated by weighted average.

[0094] For example, check the maintenance requirements of each protection device: the position error of the lifting limit action should be less than 5mm, the time of big arm amplitude limit action should be less than 0.5s; the anti-collision system must trigger the alarm within 1s after detecting the obstacle, and complete the braking within 2s; the overload protection must accurately alarm when the load exceeds 110% of the rated value. Through sensor data collection, it is found that the position deviation of the lifting limit at the upper limit is 7mm, which is reduced to 4mm by adjusting the installation position of the limit switch; the detection distance of the anti-collision system is normal, but the alarm delay is 1.3s, which is shortened to 0.8s after controller parameter setting; the display error of the overload protection weighing system is 0.5%, and the trigger reliability test is 10 times of normal action. These data are weighted and calculated to obtain a maintenance quality score of 92 points, which meets the acceptance standard.

[0095] In an embodiment, the knowledge base module is configured to:

[0096] (1) align the equipment feature data set, component health index, maintenance task list, dynamic management and control scheme, and quality evaluation result according to the time stamp to form time sequence correlation data;

[0097] (2) normalize the numerical value type parameters in the time sequence correlation data, and structure the text type parameters to obtain standardized correlation data;

[0098] (3) establish the correlation matrix between parameters according to the standardized correlation data, calculate the correlation coefficient between each pair of parameters, and generate the correlation degree index;

[0099] (4) arrange the correlation degree index in descending order according to the numerical value, extract the parameter pairs with a correlation degree higher than 0.8 to form a strong correlation parameter set;

[0100] (5) perform causal link analysis based on the strong correlation parameter set, establish a fault-symptom-cause correlation mapping table, and obtain a knowledge rule set;

[0101] (6) verify and classify the knowledge rule set to form a crane intelligent operation and maintenance knowledge base.

[0102] Specifically, the knowledge base module performs time alignment processing on various types of data. The device feature data set contains real-time collected sensor data, the component health index records device state evaluation results, the maintenance task list contains specific maintenance operation records, the dynamic management and control scheme records safety management and control parameters, and the quality evaluation results contain maintenance effect verification data. These data use a unified timestamp format, accurate to the millisecond level. Through timestamp comparison, data from different sources are one-to-one corresponding according to time sequence, establishing a time index relationship. After aligning the data, standardization processing is performed. For numerical value type parameters such as sensor measurement values, health index values, and management and control parameter values, the maximum and minimum value normalization method is used to map them to the [-1, 1] interval. For text type parameters such as fault description, maintenance measures, and evaluation opinions, a professional term dictionary is established to convert text information into a fixed format code. For example, "steel wire rope broken wire" is coded as "F001", and "brake gap too large" is coded as "F002", realizing the structured expression of text information.

[0103] Based on the standardized data, the correlation between parameters is calculated. The Pearson correlation coefficient is used to calculate the linear correlation degree between numerical value parameters, and the mutual information entropy is used to calculate the correlation degree between classification parameters. An N x N correlation matrix is constructed, where N is the total number of parameters. Each element in the matrix represents the correlation coefficient between the corresponding parameter pair, with a value range of [-1, 1], and the closer the absolute value is to 1, the stronger the correlation. The elements in the correlation matrix are sorted in descending order according to the absolute value, and parameter pairs with a correlation coefficient absolute value greater than 0.8 are extracted. These parameter pairs have significant correlation relationships. For example, the correlation degree of steel wire rope vibration frequency and broken wire rate, the correlation degree of brake temperature and braking torque, and the correlation degree of motor current and bearing temperature. These strongly correlated parameters form the skeleton structure of the parameter network.

[0104] Based on the strongly correlated parameter set, the causal relationship between parameters is analyzed, and the fault evolution link is established. The order of parameter changes is determined through time series analysis, and the causality between parameters is judged combined with professional knowledge. The analysis results are organized into a three-layer structure: the fault phenomenon layer records the abnormal performance of the device, the symptom feature layer records the early warning signals before the fault occurs, and the root cause layer records the essential cause of the fault. In this way, a fault knowledge graph is constructed. The constructed knowledge rules are verified and classified. According to the component type, including mechanical transmission, electrical control, safety protection, etc. Then according to the fault level classification, including critical fault, major fault, general fault, etc. Finally, according to the maintenance type classification, including daily maintenance, regular maintenance, fault repair, etc. The organized knowledge base is convenient for quick retrieval and application.

[0105] For example, all data related to the brake is collected, including brake pad temperature, braking torque, friction coefficient, action time and other parameters, and these data are aligned according to time. Numerical parameters such as temperature and torque are normalized, and text descriptions such as "brake slip" and "brake overheating" are encoded. Through correlation analysis, it is found that the correlation between brake pad temperature and braking torque is 0.85, indicating that the two are highly correlated. Further analysis shows that the braking torque decreases before the temperature rises, and the temperature rise leads to a decrease in the friction coefficient, forming a fault knowledge rule: abnormal braking torque (root cause) leads to brake pad temperature rise (fault symptom), and further leads to brake performance decline (fault phenomenon).

[0106] In a specific embodiment, as shown in FIG. 1, it is another schematic diagram of the crane intelligent safety guarantee platform in the present application. The crane intelligent safety guarantee platform further comprises: Figure 2

[0107] A network communication module for encrypting and compressing the transmission of the equipment characteristic data set, establishing a data transmission channel, and realizing the multi-network protocol conversion of industrial real-time Ethernet, wireless communication network and mobile communication network;

[0108] A message center module for classifying and prioritizing the component health indicators, the maintenance task list, the dynamic management and control scheme and the quality evaluation results, and realizing the data interaction and information sharing between the functional modules through a message subscription distribution mechanism;

[0109] An interface module for providing a unified data access interface and an application programming interface, and supporting the calling and integration of the equipment characteristic data set, the maintenance task list and the quality evaluation results by a third-party system.

[0110] The above-described embodiments are merely used to illustrate the technical solutions of the present application, but not to limit the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalent replacements; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.​

Claims

1. A crane intelligent safety assurance platform, characterized in that, The crane intelligent safety guarantee platform comprises: A data acquisition module, configured to acquire equipment operation parameters through a multi-dimensional sensing network acquisition device arranged on important structural parts, hoisting mechanisms, luffing mechanisms and slewing mechanisms of the crane, and to obtain equipment feature data sets by performing feature extraction and standardization processing on the equipment operation parameters; A state monitoring module, configured to analyze and evaluate crane steel wire rope wear degree, brake clearance, reducer vibration and motor temperature according to the equipment feature data sets, and to generate component health indicators; A basic operation and maintenance module, configured to perform maintenance planning and resource scheduling based on the component health indicators in combination with crane historical load spectrum and working condition data, and to form an intelligent maintenance task list; An operation guarantee module, configured to perform safety evaluation on crane hoisting space, hoisting height and working radius according to the equipment feature data sets and the maintenance task list, and to generate a dynamic control scheme; A system management module, configured to monitor maintenance processes of crane limit protection devices, anti-collision systems and overload protection devices according to the maintenance task list and the dynamic control scheme, and to output quality evaluation results; A knowledge base module, configured to perform multi-dimensional correlation analysis on the equipment feature data sets, the component health indicators, the maintenance task list, the dynamic control scheme and the quality evaluation results, and to construct a crane intelligent operation and maintenance knowledge base; The data acquisition module is specifically configured to: acquire structural part stress data through strain sensors of the important structural parts of the crane, and acquire vibration data, angle data and temperature data of a transmission system through acceleration sensors, angle sensors and temperature sensors of the hoisting mechanisms, luffing mechanisms and slewing mechanisms of the crane; perform zero drift correction on the structural part stress data, 1000HZ high-order filtering on the vibration data, low-pass filtering on the temperature data and mean value filtering on the angle data to obtain filtered parameters; extract peak coefficient, standard deviation and root mean square value time domain features from the filtered parameters to form time series feature data; perform 512-point fast Fourier transform on the time series feature data to calculate power spectral density and main frequency components and obtain frequency domain feature data; perform 95% confidence interval test on the frequency domain feature data to eliminate abnormal values beyond the limit and perform linear interpolation on missing data segments to obtain effective operation data; perform normalization processing on the effective operation data according to the maximum and minimum value method to convert to the [-1, 1] interval to generate the equipment feature data sets, wherein the equipment feature data sets comprise steel wire rope wear feature data, brake clearance feature data, reducer vibration feature data and motor temperature feature data.

2. The intelligent safety assurance platform for cranes of claim 1, wherein, The state monitoring module is configured to: calculate wire breakage rate and surface wear degree according to the steel wire rope wear feature data, calculate brake pad clearance value and braking torque according to the brake clearance feature data, calculate amplitude and frequency features according to the reducer vibration feature data, and calculate temperature rise rate and thermal load according to the motor temperature feature data to obtain component state quantities; perform high-frequency acquisition on the component state quantities, calculate statistical features in a sliding time window to form state time series data; The state time series data is compared with a preset threshold range, an over-limit point and an abnormal interval are marked, and abnormal marking data is obtained; The missing segments in the abnormal marking data are repaired by cubic spline interpolation, 95% confidence statistical analysis is performed, and a reliable state value is obtained; The reliable state value is weighted and normalized to generate the component health index.

3. The intelligent safety assurance platform for cranes of claim 2, wherein, The basic operation and maintenance module is configured to: The component health index is time-series associated with the crane historical load spectrum, the equipment state distribution under different load levels is counted, and historical state data is obtained; The historical state data and the working condition data are divided into time periods, the maintenance demand degree and the maintenance difficulty coefficient in each period are analyzed, and maintenance priority data is generated; Maintenance projects are ranked according to the maintenance priority data, the required maintenance time and operation space of each project are calculated, and a maintenance operation list is formed; The maintenance resources are counted according to the maintenance operation list, the required number of maintenance tools and the configuration of maintenance personnel are calculated, and resource demand data is obtained; The resource demand data is used to match and filter the inventory status of maintenance tools and the skill level of maintenance personnel, and a resource allocation scheme is generated; The maintenance operation list and the resource allocation scheme are combined and arranged in time sequence to form the intelligent maintenance task list.

4. The intelligent safety assurance platform for cranes of claim 3, wherein, The operation guarantee module is configured to: The equipment feature data set is decomposed into lifting space feature data, lifting height feature data, and working radius feature data, and the maintenance task list is analyzed for operation area to obtain a space parameter group; Lifting space obstacle contour points, lifting height limit values, and working radius boundary values are extracted from the space parameter group, three-dimensional coordinate mapping is performed, and safety boundary data is formed; The safety boundary data is calculated for the minimum envelope, the lifting space accessibility, the lifting height margin, and the working radius redundancy are analyzed, and safety margin data is obtained; The maintenance operation area is divided into risk levels according to the safety margin data, the collision risk coefficient and the interference probability of each region are calculated, and a risk region map is generated; The optimal path and obstacle points of the maintenance operation are calculated according to the risk region map, the lifting space access conditions, the lifting height limit values, and the working radius constraint values are set, and a control parameter set is obtained; The control parameter set is time-series combined and state-associated to form the dynamic control scheme.

5. The intelligent safety assurance platform for cranes of claim 4, wherein, The system management module is configured to: The maintenance task list and the dynamic control scheme are analyzed for tasks, the crane limit protection device maintenance requirements, the anti-collision system maintenance requirements, and the overload protection device maintenance requirements are extracted, and a monitoring parameter table is obtained; The displacement sensor signal of the limit protection device, the distance sensor signal of the anti-collision system, and the weighing sensor signal of the overload protection device are collected according to the monitoring parameter table, and raw monitoring data is formed; The raw monitoring data is denoised and synchronized, signal mutation points and abnormal intervals are counted, and monitoring feature data is obtained; Segmented statistical analysis is performed on the monitoring feature data to calculate performance index data of action accuracy of the position limiting protection device, response time of the anti-collision system, and trigger accuracy of the overload protection device; According to the performance index data, the maintenance operation specification is evaluated, the completion state and operation quality of the key maintenance node are marked, and evaluation statistical data are formed; The evaluation statistical data are weighted and scored to output the quality evaluation result.

6. The intelligent safety assurance platform for cranes of claim 5, wherein, The knowledge base module is configured to: Align the equipment feature data set, the component health index, the maintenance task list, the dynamic management and control scheme, and the quality evaluation result according to a time stamp to form time sequence correlation data; Normalize numerical parameters in the time sequence correlation data and structure text parameters to obtain standardized correlation data; According to the standardized correlation data, a correlation matrix between parameters is established, a correlation coefficient between parameters is calculated, and a correlation degree index is generated; The correlation degree index is arranged in descending order according to the numerical value, and parameter pairs with a correlation degree higher than 0.8 are extracted to form a strong correlation parameter set; Based on the strong correlation parameter set, a causal link analysis is performed to establish a fault-symptom-cause correlation mapping table and obtain a knowledge rule set; The knowledge rule set is verified and classified to form the crane intelligent operation and maintenance knowledge base.

7. The intelligent safety assurance platform for cranes of claim 6, wherein, The crane intelligent safety guarantee platform further comprises: A network communication module configured to encrypt and compress the equipment feature data set for transmission, establish a data transmission channel, and realize multi-network protocol conversion of industrial real-time Ethernet, wireless communication network, and mobile communication network; A message center module configured to classify and prioritize the component health index, the maintenance task list, the dynamic management and control scheme, and the quality evaluation result, and realize data interaction and information sharing between functional modules through a message subscription distribution mechanism; An interface module configured to provide a unified data access interface and application programming interface, and support calling and integration of the equipment feature data set, the maintenance task list, and the quality evaluation result by a third-party system.

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